docs(failures): consolidated 5-mode failure taxonomy

Consolidates failure modes scattered across V2_DEEP_ANALYSIS,
E1_E2_RESULTS, E3_FINDINGS, KVC_EVICTION_GRANULARITY,
REAL_ALI_KVC_EXPERIMENT into a single lookup table with
five fields per mode: symptom → root cause → trigger →
current mitigation → real fix.

Five modes covered:
  A. Mooncake "instance not alive" cascade
     — E2 80%-failure pathology; admission no-space →
       seed burst → heartbeat drop → batch abort
  B. Cold-D / overlap-pinning
     — shared boilerplate hash pins all sessions to a
       subset of D's; load_floor_bonus is a patch, the
       real fix is exclusive_overlap redefinition
  C. Evict storm (session-level eviction)
     — release_session frees 38–88K tokens in one shot;
       fix is BLOCK_LEVEL_EVICTION_DESIGN
  C'. Reseed storm (turn-1 concurrent seeds)
     — startup-phase mooncake burst; fix is per-D
       pending-seed budget, frequency drops after C
  D. Streaming-session correction invariant crash (E3)
     — schedule_batch.py:1646 landmine, hotfixed by
       986f351, root-fix is removing the correction
       path entirely (BLOCK_LEVEL_EVICTION §3.7)

Each mode has a forensic link back to the original
experiment doc that surfaced it.

§6 adds a diagnostic cheat sheet: "if you see X, look at Y."
§7 wires every mode to a roadmap item — Milestone 1 should
graduate §1–§4 to "mitigated" and eliminate §5.

INDEX_ZH gets a new §1.6 section linking this and the
SGLang patch inventory.

No code change. Reading dependency for anyone debugging
a sweep or writing paper §Limitations.

AGENTS.md

@@ -1,9 +1,33 @@
 # AGENTS.md
 
+## For new collaborators / agents
+
+Before doing anything else, read [docs/INDEX_ZH.md](docs/INDEX_ZH.md). It points to the
+3 must-read docs and a role-based reading path (new SWE, paper reviewer,
+reproducing student, control-plane reader).
+
+Cross-branch progress, weaknesses, and roadmap live in
+[docs/AUDIT_AND_ROADMAP_ZH.md](docs/AUDIT_AND_ROADMAP_ZH.md). It is the single source of truth
+for "what's done, what's broken, what to do next."
+
+Two engineering work items are pre-specced and ready to pick up:
+- block-level eviction refactor — [docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md)
+- D→P incremental KV sync — [docs/D_TO_P_SYNC_CONTRACT_ZH.md](docs/D_TO_P_SYNC_CONTRACT_ZH.md)
+
+Evaluation protocol (paper-quality N, paired CI, stratification,
+baselines) is in [docs/EVALUATION_PROTOCOL_ZH.md](docs/EVALUATION_PROTOCOL_ZH.md).
+
 ## Environment
 
 Use `uv` to manage all python environment. `uv add` to manage deps so that we can `uv sync` to get exactly same runnable environment each time.
 
+Algorithm-layer unit tests (no GPU, no SGLang):
+
+```bash
+uv sync --group test
+uv run pytest
+```
+
 ## Goal
 
 Build a minimal prototype on top of **SGLang xPyD** to test whether **session-aware / KV-cache-aware P/D routing** can improve **end-to-end latency** for agentic coding workloads.

README.md

@@ -6,6 +6,9 @@
 
 更完整但仍然简洁的说明见 [docs/PROJECT_OVERVIEW.md](docs/PROJECT_OVERVIEW.md)。
 
+新加入的合作者：先看 [docs/INDEX_ZH.md](docs/INDEX_ZH.md)，按"我是谁"选 3 篇必读文档。
+项目当前进度、薄弱点、路线图总览见 [docs/AUDIT_AND_ROADMAP_ZH.md](docs/AUDIT_AND_ROADMAP_ZH.md)。
+
 ## 当前做了什么
 
 - 启动单机 SGLang P/D 栈。
@@ -99,3 +102,28 @@ uv run agentic-pd-hybrid replay \
 - SGLang 改动：`feat(sglang): ...` / `fix(sglang): ...`。
 - `third_party/sglang` 的基线是 clean SGLang `v0.5.10` snapshot。
 - 不提交 `outputs/`、日志、`__pycache__`、虚拟环境。
+
+## 单元测试（无 GPU）
+
+算法层（policies、Algorithm 1 / Theorem 1）有 pure-Python 单测，跑测试不需要 GPU、不需要 SGLang：
+
+```bash
+uv sync --group test
+uv run pytest
+```
+
+详见 [tests/README.md](tests/README.md)。
+
+## 评测脚本
+
+按 [docs/EVALUATION_PROTOCOL_ZH.md](docs/EVALUATION_PROTOCOL_ZH.md) 跑数据后：
+
+```bash
+# M3: 按 turn_id / input_length / overlap_ratio / append_tokens 分桶
+scripts/analysis/stratified.py outputs/<run>/request-metrics.jsonl
+
+# M2: paired-on-same-trial bootstrap 95% CI
+scripts/analysis/paired_compare.py \
+    --baseline outputs/run-dp/request-metrics.jsonl \
+    --candidate outputs/run-kvc/request-metrics.jsonl
+```

docs/AUDIT_AND_ROADMAP_ZH.md

@@ -0,0 +1,140 @@
+# 项目整体审阅与下一阶段路线图
+
+**日期**：2026-05-12
+**分支起点**：`improve/audit-and-foundations`（基于 `h200-cu130`）
+**性质**：跨分支整合 + 路线图，供合作者判断每个 commit 是否值得 merge
+**对象**：项目下一个 SWE / research agent + 论文 reviewer 预读
+
+本文把 `main` / `kvc-debug-journey-v1-to-v4` / `feat/d-to-p-sync` / `h200-cu130` / `kvc-real-ali-iter-v1` 五个分支的进度、已成立的贡献、薄弱点、走到 SOSP/OSDI + 工业级的路线图集中到一处，方便快速对齐。
+
+---
+
+## 0. TL;DR
+
+1. **已经成立**：v1 → v2 算法（reset-on-success、字典序 Route、worker-mode Admit RPC）有形式化定义 + 两条 theorem + SWE-Bench 50 sess ts=1 上 6/8 指标击败 4DP CA 的实测。
+2. **核心薄弱点**：(a) session-level eviction 与 KVC 设计意图冲突；(b) D→P 增量 KV 同步不存在，TTFT p99 长尾来自此；(c) mooncake "instance not alive" 级联是控制层根本可用性问题；(d) 评测仍缺多 baseline 多 trace 强统计。
+3. **不需要 GPU 也能推进**的事：算法层 unit test、形式化设计文档（block-level evict、D→P sync 接口契约）、评测协议、分层分析工具、文档体系收口。本路线图的 Milestone 1 大部分都属于此类。
+4. **进 OSDI/SOSP 必须做的**：执行 §S1（block-level evict）+ §S2（D→P sync POC）+ §M2/M3/M4（多 baseline / 全 Ali / paired 协议）。预计 3–4 个月单/双人。
+
+---
+
+## 1. 五个分支的状态总览
+
+| 分支 | 角色 | 当前状态 | 最关键产出 |
+|---|---|---|---|
+| `main` | "已发布" 基线 | 落后 origin 18 commit；2P4D + worker-admission + seed-min2 报出 vs default PD 的 9% mean / 19% p90 改善 | `KVCACHE_CENTRIC_PROGRESS_ZH.md` 的两档策略：latency-best vs stable |
+| `kvc-debug-journey-v1-to-v4` | 主工作分支 | v1→v5 完整算法演化；`KVC_ROUTER_ALGORITHM.md` 三段算法 + 两条 theorem | SWE-Bench 50 sess ts=1：v2 6/8 指标击败 4DP CA；**TTFT p99 仍输 3×**（1.28s vs 0.43s），诊断为 8.3% reseed 慢路径 |
+| `feat/d-to-p-sync` | 占位分支 | 代码空，仅 `RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md` | 已排除"capacity-backup 是 D→P sync"的误解；列出 4 项工程子任务 |
+| `h200-cu130` | 真硬件 + RDMA 验证 | 4×H200 + mlx5_60 NDR 400 Gb/s 上跑 E1/E2/E3 | **E2 80% failure**（mooncake 死链级联）；**E3 16min 触发 SGLang patch invariant crash**；最新 `KVC_EVICTION_GRANULARITY_DESIGN_ZH.md` 把 root cause 上升到"session-level 是错的 eviction granularity" |
+| `kvc-real-ali-iter-v1` | 真 Ali trace 验证 | 8×H20，179-req KVC-fit slice + 600-req/15min cold-window | KVC vs DP：KVC-fit p50 −46% ✅；real 15min p90 +19s ❌，53 errors vs DP 1；KVC 默认 mem-fraction OOM，必须降到 0.82 |
+
+---
+
+## 2. 已经"硬"成立的贡献
+
+按"reviewer 能不能反驳"为标尺：
+
+1. **Reset-on-success 修复 v1 thrashing**：v1 永久 blacklist → migration 死循环 failure mode 有实测 + Algorithm 3 形式化 + Theorem 1 的不饿死证明（`KVC_ROUTER_ALGORITHM.md` §3.4 / §4.1）。
+2. **三段算法分工清晰**：Algorithm 1（字典序 Route）+ Algorithm 2（D 自治 Admit RPC）+ Algorithm 3（Dispatch + reset-on-success）。v5 把 admission 从 router 估算改成 D RPC（Option D）是把 capacity ground truth 与 routing score 解耦的正确分层。
+3. **Direct-to-D 快路径的确定性命中**（Theorem 2）：只要 residency ⊇ prefix ∧ append ≤ τ_append ∧ cap_ok 三条件同时成立必走快路径；SWE-Bench 91.6% 命中、TTFT p50 = 0.43s 是结构性结果。
+4. **每一个 negative result 都有 forensic 级解释**：mooncake death、cold-D、reseed 慢路径、session-level evict 都有代码定位 + 时间线 + 反例。这条对 paper 是真正加分项。
+
+---
+
+## 3. 让 reviewer 一击致命的薄弱点
+
+### 3.1 评测方法层
+
+- **M1 N 不足**：SWE-Bench v2 baseline N=3 确认 categorical，v2 自身 N 不足；缺 bootstrap CI。
+- **M2 比较口径不对等**：E2 80% 失败时用 "successful only" 算 latency 与 E1 全集比；paper 必须 paired-on-same-trial。
+- **M3 trace 偏 KVC-friendly**：KVC-fit slice 按 small-append + high overlap 筛过；full Ali（turn2+ ratio 26%、single-turn 极多）的 dilution 后结果没跑过。
+- **M4 baseline 不够强**：缺 vLLM + prefix-cache、DistServe、SplitWise、Mooncake-Master 任何一个。
+- **M5 trace 单一性**：缺 ShareGPT/Mooncake trace、缺 long-context tool-use agent benchmark、缺合成 adversarial trace。
+- **M6 硬件覆盖**：只 single-node ≤ 8 GPU；没有跨节点、没有 ≥ 32 GPU 集群实测。
+
+### 3.2 系统设计层
+
+- **S1 Session-level eviction 与 KVC 设计意图冲突**：90 次 evict、平均一次 free 67K tokens、25/50 session 必须 50–90K 重 prefill。`KVC_EVICTION_GRANULARITY_DESIGN_ZH.md` 已识别但未实现修复。
+- **S2 D→P 增量同步不存在**：TTFT p99 长尾 50% 来自 P 重 prefill。`capacity-backup` 是 seed-time 静态快照，不是 D→P sync。修复需改 SGLang radix 的单生产者假设。
+- **S3 Mooncake 级联 death**：admission no-space → 持续重试 seed → 心跳掉线 → SGLang 整批 abort（E2 1054/1285 失败）。控制层根本可用性 bug。
+- **S4 Admission RPC 同步阻塞**：缺 backoff / hedging / staleness budget。D scheduler GIL 抖动即把 router 卡死。
+- **S5 Cold-D / overlap-pinning**：boilerplate 24-token block hash 让所有 session 与 D0/D1 重叠 → D2/D3 0 binding。load-floor bonus 是补丁，不是 first-principles 修复。
+- **S6 SGLang 本地 patch 已 785 行 / 10 文件**，含 `schedule_batch.py:1646` 这种 hot-path 不变量改动；E3 crash 就是 vendored patch 引入的 latent landmine。
+- **S7 失败恢复 / 幂等性**：streaming session 在 chunked-prefill retry 下幂等性靠 `SessionSlot.restore_to_req`；缺 worker crash / mooncake 重连 / partial KV 损坏的恢复 protocol。
+- **S8 没有 multi-tenant / SLO-aware scheduling**：算法目标隐式 w_ttft=w_lat=1。生产里 interactive / batch / background 必须分级。
+- **S9 Topology fixed at boot**：P/D 比例是启动参数。生产负载需要 elastic。
+- **S10 Backpressure pause hint 信号未闭环**：触发 20 次但因 no-BP 无人响应；control-plane 没接通。
+
+### 3.3 工程基础设施层
+
+- **可观测性**：metrics 是 jsonl + 离线 `recompute_summary.py`；生产需要 Prometheus + Grafana + OpenTelemetry trace。
+- **形式化测试**：算法层与状态层缺 unit test；`SessionSlot.restore_to_req` 幂等性是作者自己 flag 的 invariant。
+- **混沌注入**：mooncake death 这种 control-plane failure 必须有 fault injection harness。
+- **代码体量**：`replay.py` 2460 行，集 orchestration / policy hook / control plane / metrics 于一身——prototype OK，paper-quality artifact 偏弱。
+
+---
+
+## 4. 路线图
+
+分三个 milestone。每个 milestone 可独立交付（paper 章节或工程 release）。
+
+### Milestone 1 — Defensible SOSP/OSDI submission（3–4 个月，单 / 双人）
+
+**目标**：把现有算法 + 失败诊断收口成能扛 PC 第一轮的稿子。
+
+1. **执行 §S1（block-level eviction refactor）** — 见 `docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md`。
+   - Streaming-session decode 输出在每个 turn finish 时通过 `cache_finished_req` 增量提交进 radix tree。
+   - `SessionSlot` 退化为纯 metadata（仅持 `last_node` + lock_ref）。
+   - `release_session` 改为 `dec_lock_ref` + 删 slot；evict 完全交给 SGLang radix LRU。
+   - 预期：evict 粒度从 67K tokens/次降到 24 tokens/次；reseed 频率降一个数量级。
+2. **执行 §S2（D→P 增量同步 POC）** — 见 `docs/D_TO_P_SYNC_CONTRACT_ZH.md`。
+   - microbench 证明：D append 完成后异步推 KV block 回 P 端 radix → 下次 reseed 跳过 re-prefill。
+3. **修 §S3（mooncake death 级联）**：admission RPC backoff + jitter；per-D pending-seed budget；mooncake heartbeat 与 admission 解耦。
+4. **修 §S5 的 first-principles 解法**：把 `overlap` 重定义为 "session 在 D 上独占 prefix 的 hash 数"（去掉 boilerplate 共享 hash 贡献），让 score 自然分散。
+5. **重做评测**：见 `docs/EVALUATION_PROTOCOL_ZH.md`。N≥3 + bootstrap CI + 多 baseline + 全 Ali + 分层报告。
+6. **形式化扩充**：加 Theorem 3（block-level evict 下重 prefill cost 上界）+ Theorem 4（D→P sync 的 staleness budget β 与 reseed cost 关系）。
+7. **Artifact**：一键脚本 + Dockerfile + 4×A100 一小时复现核心 table/figure。
+
+### Milestone 2 — Production-quality serving substrate（再 3–6 个月，2–3 人）
+
+8. **控制平面分层**：把 `replay.py` 拆成 `router/` / `control/` / `obs/` / `orch/`。
+9. **Elastic topology**：autoscaling controller，输入 (P queue, D transfer queue, D KV usage)。
+10. **Multi-tenant + SLO classes**：interactive / batch / background 三档独立 admission budget。
+11. **Failure injection harness**：mooncake link flap / D OOM kill / router GC pause / partial KV corruption；每个 case 有恢复 SLA。
+12. **Persistent KV tier**：CPU DRAM + NVMe + RDMA-attached pool；evict 改为 demote。
+13. **Cross-node + heterogeneous**：H100 + H200 + L40S 混合，topology-aware routing。
+14. **Observability**：per-request OpenTelemetry + Prometheus per-D + Grafana 主面板。
+
+### Milestone 3 — 真正能进 OSDI'27 的科研增量（6–12 个月）
+
+15. **Learning-based admission / migration**：multi-armed bandit / RL 控制 τ_reject 与 K；用 trace 训 session-aliveness predictor。
+16. **跨 router residency consensus**：轻量 gossip 共享 `Σ.resident[d]`。
+17. **可证明 competitive ratio**：在 oracle KV-residency 模型下证明 KVC expected TTFT 与 offline optimal 比值有界。
+18. **分布式 prefix tree**：逻辑 prefix 映射到多 D 物理副本，支持 multi-tenant prefix 共享（system prompt / tool schema）。
+19. **Energy-aware variant**：GPU SM 利用率 + PCIe/RDMA 能耗进目标函数。
+20. **End-to-end agent serving framing**：从 request-level latency 上升到 agent task completion time（coding agent 一个 task 30+ turn）。
+
+---
+
+## 5. 不需要 GPU 也能推进的工作清单
+
+按 ROI 排：
+
+- [x] 本路线图（`AUDIT_AND_ROADMAP_ZH.md`）。
+- [x] 合作者入口（`docs/INDEX_ZH.md`）。
+- [x] Block-level eviction 具体设计（`docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md`）。
+- [x] D→P sync 接口契约（`docs/D_TO_P_SYNC_CONTRACT_ZH.md`）。
+- [x] 评测协议（`docs/EVALUATION_PROTOCOL_ZH.md`）。
+- [x] `KvAwarePolicy` 纯函数 score 抽取 + unit test（Algorithm 1）。
+- [x] 不饿死性质测试（Theorem 1）。
+- [x] 分层分析脚本（按 turn-index / append-size / overlap 三维分桶）。
+- [x] Paired-comparison 协议 helper。
+- [ ] Mooncake death 的可重现 mock harness（无 GPU 也能跑）。
+- [ ] SGLang patch surface 的归类清单（每个 patch 标"必须" / "实验性" / "可下线"）。
+- [ ] Failure-mode taxonomy 文档（cold-D、overlap-pin、mooncake death、reseed storm、evict storm）。
+
+---
+
+## 6. 单句结论
+
+> 这个项目已经具备了 SOSP/OSDI workshop / poster 的素材；要进 main track，需要把 §S1（block-level evict）和 §S2（D→P sync）做实、把 §M3（full Ali）和 §M4（两个强 baseline）补齐、把 §S3（mooncake 级联 death）的 control-plane fix 写进可重复 artifact。如果只能做一件事，先做 block-level eviction refactor —— 它同时解决"reseed 太频繁"和"P 端 radix 多生产者扩展的前置条件"。

docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md

@@ -0,0 +1,309 @@
+# Block-level Eviction Refactor — 设计文档
+
+**日期**：2026-05-12
+**前置**：[KVC_EVICTION_GRANULARITY_DESIGN_ZH.md](KVC_EVICTION_GRANULARITY_DESIGN_ZH.md)（架构层 manifesto）
+**性质**：实现层设计 + API 草案 + 测试计划，供下一个合作者直接据此编码
+**Status**：草案，未实现。代码全部 quoted from `third_party/sglang/python/sglang/srt/mem_cache/session_aware_cache.py @ origin/h200-cu130`
+
+---
+
+## 0. TL;DR
+
+把 `SessionAwareCache` 当前对 streaming-session **整段 KV 一次性 free** 的语义改成：
+
+1. Streaming-session decode 输出在 turn finish 时 **增量 commit 进 radix tree**。
+2. `SessionSlot` 退化为**纯 metadata**（仅持 `last_node` + lock_ref 状态），不再独占 KV 区间。
+3. `release_session` 改为只 dec_lock_ref + 删 slot，**让 SGLang 标准 radix LRU 按 block 粒度蚕食**。
+
+预期收益：evict 粒度从一次 ~67K tokens 降到 ~24 tokens（page_size 个 token），reseed 频率降一个数量级；同时把 P 端 radix tree 改造成可被外部喂数据（为 [D_TO_P_SYNC_CONTRACT_ZH.md](D_TO_P_SYNC_CONTRACT_ZH.md) 铺路）。
+
+---
+
+## 1. 现状代码梳理
+
+### 1.1 关键文件与函数
+
+`third_party/sglang/python/sglang/srt/mem_cache/session_aware_cache.py`
+
+| 函数 / 字段 | 当前语义 |
+|---|---|
+| `SessionSlot.req_pool_idx` | streaming-session 独占的 req_pool 槽位 |
+| `SessionSlot.kv_committed_len` | 上一 turn 完成时已 commit 的 KV 长度（已计入 cache_protected_len 部分进入 radix） |
+| `SessionSlot.kv_allocated_len` | 当前已分配但**未进 radix** 的 KV 长度（"session-exclusive 尾部"） |
+| `SessionSlot.cache_protected_len` | 首 turn 提交 radix 时的 protected 边界 |
+| `match_prefix(streaming req)` | 命中 slot → 返回 `req_to_token[req_pool_idx, :prefix_len]`，bypass radix |
+| `cache_unfinished_req(streaming req)` | subsequent turns → **完全 skip inner**（不进 radix） |
+| `cache_finished_req(streaming req)` | 调 `slot.save_from_req`，**不调 inner.cache_finished_req** |
+| `release_session(sid)` | `dec_lock_ref(slot.last_node)` + `free(req_to_token[req_pool_idx, cache_protected_len:kv_allocated_len])` + 回收 req_pool 槽位 |
+
+### 1.2 当前为什么是错的（重述）
+
+`[cache_protected_len, kv_allocated_len)` 是首轮入 radix 之后所有累积的 decode 输出 + 后续 turn 的 extend。在 Inferact / SWE-Bench 实测：
+
+- `cache_protected_len` ≈ 首 turn boilerplate ~12K
+- `kv_allocated_len` 累积 50–100K
+- 每次 `release_session` 一次性释放 38–88K，这部分**从未进 radix**，无法享受 leaf-by-leaf 渐进 evict
+
+→ session 被 evict 后必须从 client 原 prompt 重 prefill 全长 + mooncake transfer 全长，跟 naive PD-disagg 等价（详见 manifesto §1）。
+
+---
+
+## 2. 目标行为表
+
+| 场景 | 现状 | 目标 |
+|---|---|---|
+| Session 累积 50K KV，D 满了 | `release_session` 一次释放 38K | radix LRU 从最老 leaf 开始 evict，单次 ~24 tokens |
+| Session 被 evict 后再到来 | 必须 reseed 50K | 仅 re-prefill 被 evict 的 leaf 部分（典型 ≤ 5K） |
+| Evicted session TTFT | 50–90K reseed ≈ 3–7s | 5K append-prefill ≈ 200ms |
+| 不被 evict 的 session | 同 session 内 turns append-only | 同样 append-only（不变） |
+| Direct-to-D fast path 命中率 | 91.6% (SWE-Bench) / 38% (E3 Inferact) | 应 ≥ 85% 即使 saturation |
+
+---
+
+## 3. 设计
+
+### 3.1 SessionSlot 字段精简
+
+**after refactor**：
+
+```python
+@dataclass
+class SessionSlot:
+    virtual_node: _VirtualNode = field(default_factory=_VirtualNode)
+
+    # Pointer into the radix tree — the deepest node owned by this session's
+    # committed prefix. Held under inc_lock_ref so radix LRU never evicts this
+    # *active* leaf out from under a turn-in-progress. Released by
+    # release_session.
+    last_node: Any = None
+    swa_uuid_for_lock: Optional[str] = None
+
+    # Bookkeeping fields (no longer authoritative ownership of KV indices).
+    last_access_time: float = field(default_factory=time.monotonic)
+
+    # Mamba state stays slot-owned (mamba doesn't fit the radix model).
+    mamba_pool_idx: Any = None
+    mamba_ping_pong_track_buffer: Any = None
+    mamba_next_track_idx: Any = None
+    mamba_last_track_seqlen: Any = None
+    mamba_branching_seqlen: Any = None
+```
+
+**删除**：`req_pool_idx`、`kv_committed_len`、`kv_allocated_len`、`cache_protected_len`、`swa_evicted_seqlen`。这些字段的真值改由 radix tree + req_to_token_pool 共同维护。
+
+### 3.2 `cache_finished_req` 改造
+
+**after refactor**：
+
+```python
+def cache_finished_req(self, req: Req, is_insert: bool = True, **kwargs):
+    if not _is_streaming(req):
+        return self.inner.cache_finished_req(req, is_insert=is_insert, **kwargs)
+
+    session_id = req.session.session_id
+    slot = self.slots.setdefault(session_id, SessionSlot())
+
+    # KEY CHANGE: always delegate to inner — this inserts the new tokens
+    # (kv_committed_len .. fill_ids end) as radix-tree blocks. Subsequent
+    # match_prefix calls for this session will hit the radix tree directly.
+    result = self.inner.cache_finished_req(req, is_insert=is_insert, **kwargs)
+
+    # Update slot bookkeeping only (no KV ownership).
+    slot.last_node = req.last_node
+    slot.swa_uuid_for_lock = req.swa_uuid_for_lock
+    slot.last_access_time = time.monotonic()
+
+    # Mamba state still goes through slot.
+    slot.mamba_pool_idx = req.mamba_pool_idx
+    ...
+    return result
+```
+
+**不变量**：
+- `inner.cache_finished_req` 会把 `[kv_committed_len_old, kv_committed_len_new)` 范围内对齐到 page_size 的 KV 插入 radix。这个语义来自 SGLang 标准实现，无需改 inner。
+- `slot.last_node` 现在指向**当前 session 已 commit prefix 的尾节点**，每个 turn 后向前推进。
+- `dec_lock_ref(old_last_node)` + `inc_lock_ref(new_last_node)` 必须在 turn 切换时执行。
+
+### 3.3 `cache_unfinished_req` 改造
+
+streaming session 的 subsequent turn **不再 skip inner**。原因：现在 `match_prefix` 走 radix，chunked-prefill 中间状态也需要 inner 维护：
+
+```python
+def cache_unfinished_req(self, req: Req, **kwargs):
+    if _is_streaming(req) and kwargs.get("chunked", False):
+        # Chunked prefill: forward to inner so the per-chunk extend gets
+        # tracked in the radix LRU access timestamps.
+        ...
+    self.inner.cache_unfinished_req(req, **kwargs)
+```
+
+具体的 chunked 处理细节需要保留对 `prefix_indices` 重建的逻辑（参考当前实现 lines 215–225），但调用 `inner.cache_unfinished_req` 不能 skip。
+
+### 3.4 `match_prefix` 改造
+
+退化为**纯 inner 转发**——SessionSlot 不再持 KV 指针：
+
+```python
+def match_prefix(self, params: MatchPrefixParams) -> MatchResult:
+    # No more slot-fast-path. Streaming sessions reuse KV via radix tree
+    # match like every other request.
+    return self.inner.match_prefix(params)
+```
+
+调用方需要的 "这个 session 的 committed prefix 长度" 信息改为通过 `inner.match_prefix(...).device_indices.shape[0]` 推导。
+
+### 3.5 `release_session` 改造
+
+**after refactor**：
+
+```python
+def release_session(self, session_id: str) -> int:
+    slot = self.slots.pop(session_id, None)
+    if slot is None:
+        return 0
+
+    # Just release our radix lock — radix LRU can now reclaim our prefix
+    # leaves at its own pace. NO direct token_to_kv_pool free.
+    if slot.last_node is not None:
+        if slot.swa_uuid_for_lock is not None:
+            self.inner.dec_lock_ref(
+                slot.last_node,
+                DecLockRefParams(swa_uuid_for_lock=slot.swa_uuid_for_lock),
+            )
+        else:
+            self.inner.dec_lock_ref(slot.last_node)
+
+    # Mamba state still needs explicit cleanup if present.
+    if slot.mamba_pool_idx is not None:
+        ...
+
+    return 0  # "freed_tokens" no longer meaningful; radix LRU shed lazily
+```
+
+### 3.6 `get_session_status` / `list_session_statuses` 改造
+
+`resident_tokens` 现在的真值来自 radix tree。需要在 inner 暴露一个 helper：
+
+```python
+# In BasePrefixCache / RadixCache:
+def tokens_under(self, node) -> int:
+    """Count tokens in the path from root to `node` (inclusive)."""
+    ...
+
+# In SessionAwareCache:
+def get_session_status(self, session_id: str) -> Optional[Dict[str, Any]]:
+    slot = self.slots.get(session_id)
+    if slot is None:
+        return None
+    resident_tokens = self.inner.tokens_under(slot.last_node) if slot.last_node else 0
+    return {
+        "session_id": session_id,
+        "resident": resident_tokens > 0,
+        "resident_tokens": int(resident_tokens),
+        "last_access_time": float(slot.last_access_time),
+    }
+```
+
+`admit_direct_append` 的容量检查改用 `resident_tokens` 的 radix 真值（去掉 `kv_committed_len / kv_allocated_len` 双值不一致的可能）。
+
+### 3.7 SGLang 调度路径配套改动
+
+参考 `schedule_batch.py:1572-1646`，当前 streaming-session correction（commit b8e6f13 / 986f351 引入）建立在 SessionSlot 拥有独立 KV 范围之上。block-level refactor 后这条 correction 路径**完全无需存在**——req 的 fill_ids / prefix_indices 由 inner radix `match_prefix` 直接给出一致值。
+
+**移除项**：
+- `schedule_batch.py:1572-1585` 的 `actual_extend_len = max(0, len(fill_ids) - len(prefix_indices))` correction 块。
+- `schedule_batch.py:1646` 的 `assert seq_len - pre_len == req.extend_input_len`（refactor 后该不变量结构上必然成立）。
+- E3 触发的 latent landmine ([E3_FINDINGS_ZH.md](E3_FINDINGS_ZH.md) §2)随之消失。
+
+---
+
+## 4. 不变量（必须在 PR 自测中覆盖）
+
+| Inv | 内容 |
+|---|---|
+| I1 | `release_session(sid)` 后，下一次同 session 请求的 `match_prefix` 行为只取决于 radix tree 的常驻状态——不依赖 `slots` dict。 |
+| I2 | 任意 (session_id, turn_id) 的 `cache_finished_req` 调用后，radix tree 上必然存在一条 root→leaf 路径覆盖该 turn 的全部 committed token（即 `tokens_under(slot.last_node)` 严格不降）。 |
+| I3 | `restore_to_req` 必须**幂等**：在 chunked-prefill 重试场景下，对同一 req 可被调用多次而最终 req 状态等价。当前实现靠"不清 slot 字段"实现 → refactor 后改由 radix `match_prefix` 的纯函数性质保证。 |
+| I4 | 无 streaming-session 的请求（`req.session is None`）行为 **不变**：所有路径 short-circuit 到 inner。 |
+| I5 | 任一 turn 结束后，对 `slot.last_node` 的 `inc_lock_ref` 必须有对应的 `dec_lock_ref`，且 `release_session` 是最终的释放点。 |
+
+---
+
+## 5. 测试计划（无 GPU 可跑）
+
+### 5.1 单元测试（mock inner cache）
+
+写一个 `MockRadixCache(BasePrefixCache)`，记录所有 `cache_finished_req / cache_unfinished_req / match_prefix / evict / dec_lock_ref` 调用序列。然后：
+
+| Test | 断言 |
+|---|---|
+| `test_release_session_no_direct_free` | 调 `release_session` 后，Mock 上 **没有** 直接 `free(kv_indices)` 调用，只有 `dec_lock_ref` |
+| `test_subsequent_turn_inserts_radix` | 模拟 turn 0 → 1 → 2 三次 `cache_finished_req`，断言每次都触发 `inner.cache_finished_req` |
+| `test_match_prefix_uses_inner` | streaming 与 non-streaming 都仅走 `inner.match_prefix` |
+| `test_restore_idempotent` | 模拟 chunked-prefill 重试，连续两次 `match_prefix` 返回的 `device_indices` 一致 |
+| `test_eviction_under_pressure_is_block_level` | inject 一个 "pool 满，必须 evict 24 tokens" 的状态，断言 `release_session` 不被触发，inner 的 LRU 单步走 |
+
+### 5.2 Property-based 测试
+
+```python
+@given(turns=lists(integers(min_value=24, max_value=2048), min_size=1, max_size=50))
+def test_committed_tokens_monotone(turns):
+    """tokens_under(slot.last_node) is monotonically non-decreasing across turns."""
+    ...
+```
+
+### 5.3 Integration smoke（需要 GPU，但放在 sweep 脚本里）
+
+执行 `sweep_e2_kvc_v2_rdma.sh` 同 trace 同配置，对比指标：
+- evict 总次数（期望从 90 → < 10）
+- 单次平均 evict tokens（期望从 67K → < 500）
+- TTFT p99（期望从 1.28s → < 0.7s）
+- direct-to-D 命中率（期望 ≥ 85%）
+
+---
+
+## 6. 工程量与风险
+
+### 6.1 工程量
+
+| 工作 | 估时 | 风险 |
+|---|---|---|
+| §3.1–§3.6 SessionAwareCache 改造 | 2–3 天 | 中：需要熟悉 radix 内部 lock_ref / evict 协议 |
+| §3.7 schedule_batch 清理 | 0.5 天 | 低：是删代码 |
+| §4 不变量单元测试 | 2 天 | 低 |
+| §5.3 GPU smoke + 数据对比 | 2 天 | 中：mooncake 仍可能触发 E2 级联 death，需要 §S3 修复一并跑 |
+| **总计** | **~1 周** | |
+
+### 6.2 关键风险
+
+1. **`inner.cache_finished_req` 对 streaming-session req 的兼容性**：当前 SGLang 标准 radix 假设 req 在 cache_finished_req 时是 "完整 prefill+decode 完成"。streaming-session 的 req 在每个 turn 结束时还会留下"未完成的 conversation"，要确保 inner 在插入时不会把 decode-only tokens 当成可丢弃尾巴。需要 audit `radix_cache.py:cache_finished_req` 的实现。
+
+2. **lock_ref 顺序**：turn N+1 开始的 `match_prefix` → inc_lock_ref(new_node)，turn N 结束的 dec_lock_ref(old_node)，时序若反了会在并发下让 LRU 把刚 commit 的 leaf 误 evict。建议加 assertion：`dec_lock_ref` 之前 `inc_lock_ref` 必须先到。
+
+3. **chunked-prefill retry**：见 I3。SGLang 当前 `restore_to_req` 不清 slot 字段就是为此 retry。refactor 后必须确认 inner radix `match_prefix` 在 retry 下也幂等（标准 radix tree 是的，但要写测试明确锁住这个性质）。
+
+---
+
+## 7. 与 D→P sync 工作的关系
+
+block-level evict 是 [D_TO_P_SYNC_CONTRACT_ZH.md](D_TO_P_SYNC_CONTRACT_ZH.md) 的**前置条件**：
+
+- D→P sync 需要 P 端 radix tree **可接收外部喂入的 KV block**。
+- 当前 P 端 radix 假设单生产者（本 worker 模型输出）。
+- block-level refactor 完成后，streaming-session 的 KV 已经走标准 radix 路径——再让 radix tree 接受"外部喂入"的额外生产者就只是扩展 insert API，而不是发明新的存储路径。
+
+→ 两件事可顺序做：先 block-level evict，再 D→P sync。
+
+---
+
+## 8. 接班 agent 的最小动作
+
+1. fork 一个 `feat/block-level-evict` 分支（从 `improve/audit-and-foundations` 或 `h200-cu130`）。
+2. 实现 §3.1–§3.6。
+3. 写 §5.1 + §5.2 单元测试。
+4. 在 8×H100 / H200 上跑 §5.3 smoke，对比 evict 频次和 TTFT p99。
+5. 若 §6.2 风险 1 成立，进 SGLang `radix_cache.py` 看是否需要给 streaming-session req 加 `is_session_active=True` flag 阻止"丢弃 decode 尾"。
+
+---
+
+**核心句**：把 session 当 lifecycle 边界（保留），但**不要**让它做 eviction 边界（移交给 radix LRU）。这次 refactor 同时解决"reseed 太频繁"和"P 端 radix 不可外部喂入"两个 blocker。

docs/D_TO_P_SYNC_CONTRACT_ZH.md

@@ -0,0 +1,247 @@
+# D→P 增量 KV 同步 — 接口契约与 rollout 计划
+
+**日期**：2026-05-12
+**前置**：[RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md](RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md)（缺口定位）+ [BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md)（前置条件）
+**性质**：跨层接口契约 + staleness budget 形式化 + 分阶段 rollout
+**Status**：草案。`feat/d-to-p-sync` 分支当前为空，本文是该分支应当首先 land 的设计文档
+
+---
+
+## 0. TL;DR
+
+reseed 慢路径的 50% 时间在 P 重 prefill，**修复 transfer 段（启 RDMA）只能解一半**。彻底消除长尾的唯一办法是让 P 端 backup 增量跟上 D 端的 append：
+
+> D 在 direct-to-D 路径上完成一个 turn → 异步把新 commit 的 KV block 推回 P 端 radix → 下次 reseed 时 P 端 radix 命中完整 prefix，无需 re-prefill，仅一次 P→D transfer。
+
+本文给出三层（mooncake / SGLang / agentic-pd-hybrid）的接口契约、一个 **staleness budget β** 的形式化定义，以及四阶段 rollout 计划，让该工作可以与 block-level eviction 解耦推进。
+
+---
+
+## 1. Staleness Budget β —— 形式化定义
+
+设 D 上 session `s` 的 committed prefix 长度为 `L_D(s, t)`（time `t` 的瞬时值），P 上同 session 的 backup prefix 长度为 `L_P(s, t)`。
+
+```
+staleness(s, t) := L_D(s, t) - L_P(s, t)   ≥ 0
+```
+
+**Staleness budget β** 是系统承诺维持的上界：
+
+```
+∀ s, ∀ t :  staleness(s, t) ≤ β
+```
+
+直观：β 越小 → reseed 命中 P 端 backup 的可能越高 → reseed 退化为单次 P→D transfer + ≤ β tokens 的 re-prefill。
+
+- **β = 0**：完全同步（D 每 commit 一块就阻塞等 P ack）。延迟成本高，不推荐。
+- **β = ∞**：当前状态（P 端 backup 永远 seed-time 静态快照）。
+- **β = 一个 page（24 tokens）**：单 block sync。理论最优粒度，但 D 端每次 append 都触发一次 D→P RPC。
+- **β = O(append_len)（典型 1K–4K）**：批量 sync。推荐起点，把同 turn 的 decode 输出聚合后整批推送。
+- **β = O(turn_size)（典型 ~50K）**：粗粒度 sync。失效 reseed bypass，仅减少 transfer。不可取。
+
+→ rollout 推荐 β = `max(page_size, min(committed_in_turn, β_max))`，`β_max` 默认 4096。
+
+---
+
+## 2. 三层接口契约
+
+### 2.1 Mooncake 层：双角色化
+
+**当前状态**（详见 [RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md](RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md) §3）：
+
+- `MooncakeKVManager` 在初始化时按 `disaggregation_mode ∈ {PREFILL, DECODE}` 强角色化。
+- `MooncakeKVSender` 仅在 PREFILL 模式实例化，`MooncakeKVReceiver` 仅在 DECODE 模式实例化。
+- `add_transfer_request` 含硬约束 `assert disaggregation_mode == PREFILL`。
+
+**目标接口**：
+
+```python
+# third_party/sglang/python/sglang/srt/disaggregation/base/conn.py
+class BaseKVManager:
+    roles: set[KVRole]   # 替换原单值字段，允许 {PREFILL, DECODE}
+
+class KVRole(Enum):
+    PREFILL = "prefill"
+    DECODE = "decode"
+    PREFILL_BACKUP_RECEIVER = "prefill_backup_receiver"   # 新：P 端接收 D→P sync
+    DECODE_BACKUP_SENDER = "decode_backup_sender"         # 新：D 端发送 D→P sync
+```
+
+**新增类**（实现层 ~400 LOC）：
+
+| 类 | 角色 | 关键方法 |
+|---|---|---|
+| `DecodeKVSender` | D 端把 append 后的新 KV block 推回 P | `enqueue_sync(session_id, kv_blocks, target_p)` 异步入队，返回 `sync_id` |
+| `PrefillKVReceiver` | P 端接收 D→P sync 包 | `recv_loop()` 后台线程；每个包触发 callback 注入 radix tree |
+
+**Bootstrap channel**：需要独立于现有 P→D 通道的第二个 bootstrap socket（避免 buffer pointer 协商冲突）。配置：
+- 默认 disable，由 ServerArgs flag `--enable-d2p-sync` 开启
+- 新增 port range `BOOTSTRAP_D2P_PORT_BASE = 22000`
+
+### 2.2 SGLang 层：Radix 多生产者扩展
+
+**当前状态**：P 端 radix 假设单生产者（本 worker 模型输出）。`RadixCache.cache_finished_req` 内部直接从 `req_to_token_pool[req_pool_idx, :]` 取 KV indices 插入树。
+
+**目标接口**（在 [BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md) 完成之后）：
+
+```python
+class RadixCache(BasePrefixCache):
+    def insert_external(
+        self,
+        token_ids: Sequence[int],
+        kv_tensor: torch.Tensor,
+        *,
+        source_worker_id: str,
+        session_id: str,
+    ) -> InsertExternalResult:
+        """
+        Insert KV blocks supplied by an external worker (D→P sync).
+
+        Allocates fresh slots in token_to_kv_pool, copies kv_tensor into them,
+        and threads the resulting indices through the radix tree exactly like
+        cache_finished_req would for a local prefill.
+
+        Invariants:
+            - Same model layout (verified at handshake time, not per-call).
+            - On collision with existing radix path, no-op for the shared prefix
+              and only insert the diverging suffix.
+            - Inserted nodes get lock_ref += 1 if `pin=True`, default False.
+              D→P sync is best-effort; LRU is allowed to evict the inserted leaves.
+        """
+```
+
+**关键设计点**：
+
+| 决策 | 选项 | 推荐 |
+|---|---|---|
+| KV index 重映射 | A) D 发原 indices, P 重映射；B) D 发紧密打包的 tensor，P 重新分配 | **B**：避免跨 worker 索引泄漏 |
+| 失败处理 | A) D→P 失败 → 退化为重 prefill；B) 重试 N 次 | **A** + 后续 reseed 时若 P 未命中走旧路径 |
+| Reference counting | sync 进 P 的 KV 是否被 pin？ | **不 pin**：P 端 LRU 自然管理，避免 backup 把生产 KV 挤出 |
+| 与 evict 协调 | sync 来到时 P 满怎么办？ | 让 sync insert 触发 inner.evict → 与本地生产 KV 公平 LRU 竞争 |
+| 同 session 多 P 实例 | router round-robin 把 turn 派到不同 P 怎么办？ | **接受 multi-source**：每个 P 维护自己的 backup；reseed 时挑 staleness 最小者 |
+
+### 2.3 agentic-pd-hybrid 层：Hooks 与状态机
+
+**新增 CLI flag**：
+
+```bash
+--enable-d2p-sync                     # off by default
+--d2p-staleness-budget-tokens 4096    # β_max
+--d2p-sync-batch-min-tokens 24        # 至少 ≥ 1 page 才触发
+--d2p-sync-target-policy {last_p, round_robin, broadcast}
+                                      # last_p: 推回该 session 上次 seed 的 P
+                                      # broadcast: 推到所有 P（reseed 时灵活但带宽大）
+```
+
+**新增 state 字段**（`replay.py` 的 `DirectSessionState`）：
+
+```python
+@dataclass
+class DirectSessionState:
+    ...
+    # NEW: per-P backup view, populated by D->P sync callbacks.
+    prefill_resident_tokens_by_p: dict[str, int] = field(default_factory=dict)
+    last_d2p_sync_at: float | None = None
+```
+
+**Hook 在 `_invoke_session_direct` 完成后**：
+
+```python
+async def _invoke_session_direct(...):
+    ...
+    response = await self._stream_direct_to_d(...)
+    if response.ok and self.config.enable_d2p_sync:
+        new_committed = response.kv_committed_len
+        prev_p_resident = max(session.prefill_resident_tokens_by_p.values(), default=0)
+        staleness = new_committed - prev_p_resident
+        if staleness >= self.config.d2p_sync_batch_min_tokens:
+            target_p = self._choose_d2p_target(session)
+            asyncio.create_task(
+                self._issue_d2p_sync(session, target_p, prev_p_resident, new_committed)
+            )
+```
+
+**Hook 在 reseed 路径**（`_invoke_kvcache_seeded_router`）：
+
+```python
+async def _invoke_kvcache_seeded_router(..., request):
+    ...
+    if self.config.enable_d2p_sync:
+        # Probe P-side residency before issuing full re-prefill.
+        probe = await self._probe_prefill_residency(session_id)
+        if probe.resident_tokens >= request.prefix_len - β_max:
+            # Use the up-to-date backup: skip re-prefill, just trigger P→D transfer.
+            return await self._invoke_p_to_d_transfer_only(...)
+    # Fall back to existing path.
+    return await self._invoke_kvcache_seeded_router_legacy(...)
+```
+
+---
+
+## 3. 性质（待证明）
+
+### 3.1 Theorem 4 候选（论文形式）
+
+*设 staleness budget β 维持成立。对一个 session `s` 在 D 上累积长度 L、被 evict 后 reseed 触发：*
+
+```
+reseed_cost(s) ≤ T_p2d(L) + T_prefill(min(β, L))
+```
+
+*其中 T_p2d 是 P→D transfer 时间（在 RDMA 下 ~L · 4 ns/token），T_prefill 是 prefill 时间（在 H100 TP1 Qwen3-30B 下 ~50K tokens/s）。当 β ≪ L 时退化为 single P→D transfer 主导。*
+
+**对比 baseline**（无 D→P sync）：`reseed_cost = T_p2d(L) + T_prefill(L − seed_size)`，re-prefill 占主导。
+
+### 3.2 与 Theorem 2 的关系
+
+Theorem 2 只保证 direct-to-D 路径的快速命中。Theorem 4 把"fast path miss 时的 fallback cost"也压低到次秒级，使 KVC 在**全分位数**击败 DP 成为可能。
+
+---
+
+## 4. 四阶段 Rollout
+
+| Phase | 范围 | GPU 需求 | 验收指标 |
+|---|---|---|---|
+| **P1** | block-level eviction refactor（[BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md)） | 4×H100 smoke | evict 单次平均 ≤ 500 tokens |
+| **P2** | mooncake 双角色化 + microbench（D→P 单包 RTT、带宽利用） | 单机 + RDMA | P→D RTT < 50ms（local），单 16K-token block 带宽 ≥ 50% 理论上限 |
+| **P3** | SGLang `insert_external` + agentic-pd-hybrid hook（仅 best-effort，无 reseed probe） | 4×H100 + RDMA | sync 触发率 > 80% 同 turn 内完成；不引入新 failure mode |
+| **P4** | reseed probe 接通 + 端到端 evaluation | 4×H100 + RDMA | reseed 单次 < 0.5s（vs 当前 3–7s），TTFT p99 < 0.5s |
+
+**关键决策点**：P1 → P2 之间需要走 audit，确认 SGLang radix `insert_external` 不会与 streaming-session decode 路径冲突。若发现严重冲突，引入 "P-only sync mode" 占位，等架构稳定再放开。
+
+---
+
+## 5. 风险与对策
+
+| 风险 | 影响 | 对策 |
+|---|---|---|
+| Mooncake 双角色化破坏现有 P→D 单向路径 | E2 已暴露 mooncake "instance not alive" 级联，再加一条通道可能放大 | P2 阶段先用独立 bootstrap channel + feature flag；保留 disable 路径 |
+| D→P sync 占用 D 出口带宽，影响 direct-to-D append-prefill 延迟 | 直接劣化主路径 | sync 用低优先级 QP（RDMA SL=0），且 batch 触发，单 turn 内最多 1 次 |
+| P 端 radix 被 backup 填满，反而挤出本地生产 KV | P 端 prefill 速度降 | sync 插入不 pin（§2.2），让 LRU 公平竞争 |
+| 多 P 多 backup view 协调复杂 | router 选择 target_p 时需考虑 staleness | 起点用 `last_p` policy（recency-biased），观察实测分布再决定是否上 `broadcast` |
+| 跨 SGLang patch 升级时 `insert_external` 与 upstream API 漂移 | 维护负担 | 把 API 限制在我方 vendor patch 边界（不污染 upstream radix），并写 contract test |
+
+---
+
+## 6. 与 block-level eviction 的解耦关系
+
+| 工作 | 是否依赖另一个 |
+|---|---|
+| block-level eviction | 不依赖 D→P sync，可独立交付。能单独降低 reseed 频次 |
+| D→P sync | **依赖** block-level eviction：需要 P 端 radix 是 streaming session KV 的真值源 |
+| 一起做 | 收益最大：reseed 频次降一个数量级 + 单次 reseed 时间降一个数量级 |
+
+→ rollout 顺序：block-level eviction 先 land，D→P sync 随后开 `feat/d-to-p-sync` 推进。两者**不应**合在一个 PR 里。
+
+---
+
+## 7. 接班 agent 的最小动作
+
+1. 在 `feat/d-to-p-sync` 分支上 land 本文。
+2. 等 block-level eviction 进 main 后，开 P2 阶段：mooncake 双角色化 + microbench（单测，无 SGLang 主路径耦合）。
+3. P3 阶段加 `insert_external` 与 hook；以 disabled-by-default 进 main。
+4. P4 端到端 evaluation 后再判断 reseed probe policy（`last_p` vs `broadcast`）。
+
+---
+
+**核心句**：D→P 增量同步不是"再加一条网络通道"那么简单，关键是把 P 端 radix 从单生产者扩展到允许 best-effort 外部喂入。Block-level eviction 是这件事的前置条件——所以两件工作可以一前一后，不能颠倒。

docs/E1_E2_FIX_DESIGN_ZH.md

@@ -0,0 +1,137 @@
+# E1 / E2 Failure Modes — Fix Design Space (no code changes)
+
+**Status**: design proposal for review.
+**Branch**: `h200-cu130`.
+**Companion**: `docs/E1_E2_RESULTS_ZH.md` §5b–§5d for the forensic findings this design responds to.
+
+This document evaluates candidate fixes for the two pathologies E1 / E2 exposed:
+- **Q1**: D scheduler thread starves the mooncake C++ control plane during LRU evictions, causing P-side `batch_transfer_sync` to time out (~30 s) and the hair-trigger in `conn.py:1270` to permanently blacklist the D's mooncake_session_id.
+- **Q2**: KvAwarePolicy's overlap-first lex score, combined with workloads where new sessions share boilerplate hash_ids with already-resident sessions on D0/D1, leaves D2 cold for the entire run.
+
+For each problem we list candidate fixes, the layer they touch, their assumptions, and what could go wrong. **No code is committed** until a path is chosen.
+
+---
+
+## Q1 — Eviction starves mooncake control plane
+
+### Mechanism recap
+
+Inside `decode-0.log` at the moment of P-side timeout (`Sync batch data transfer timeout after 37452515723ns`):
+
+```
+01:56:34  Decode batch ... gen 174 tok/s    ← serving fine
+01:56:42  session id 1000315 does not exist, cannot delete.
+01:56:42  Trimmed decode session cache via LRU. evicted=2, freed=77675, available 38574 → 116249
+01:56:42  Trimmed decode session cache via LRU. evicted=1, freed=36166, available 29038 → 65204
+01:56:42  Decode transfer failed ...        ← P-side timeout fires
+```
+
+`maybe_trim_decode_session_cache` (in vendored sglang scheduler) walks per-session resident bookkeeping, releases GPU KV slots via `kv_pool_allocator.free()`, and updates `session_aware_cache` under lock. While that runs, the scheduler main loop is busy and the mooncake control-plane callbacks scheduled into the same event loop don't get serviced. P sees no completion ack within 30 s → `batch_transfer_sync` returns nonzero → hair-trigger fires.
+
+### Design space
+
+| # | Fix | Layer | Mechanism | Assumes | Risks |
+|---|---|---|---|---|---|
+| **Q1.A** | Pre-emptive low-watermark eviction | vendored SGLang | Trigger LRU when `token_usage > 0.7` in idle scheduler ticks, so admission rarely needs to evict inline. SGLang already has `_decode_session_cache_low_watermark_tokens`; question is whether it currently runs proactively or only on-demand. | Idle ticks exist to absorb the work; the per-trim cost is bounded enough that doing it pre-emptively doesn't hurt the steady-state. | If proactive trims pick "warm" sessions (recently active), we lose direct-to-D fast-path hits. Need careful watermark + LRU-priority tuning. |
+| **Q1.B** | Async eviction thread | vendored SGLang | Move LRU trim off the scheduler main loop into a background worker. Scheduler main loop only calls `notify_evict_needed()`; mooncake control plane keeps running. | KV pool free / session_aware_cache mutations can be made thread-safe with reasonable lock granularity. | Largest blast radius. Concurrent in-flight transfers can race with eviction of the same KV slots; need explicit ref-counting. Harder to reason about correctness. |
+| **Q1.C** | Bump mooncake transfer timeout | mooncake env / wheel patch | Set `MC_TRANSFER_TIMEOUT_NS` (or equivalent) from 30 s default → 120 s+, giving D's eviction more headroom before P gives up. | A real broken link won't go unnoticed for ≥120 s. | Pure defense-in-depth. Doesn't fix LRU thrashing; under heavier load eviction could exceed 120 s too. Slows real-failure detection. |
+| **Q1.D** | Windowed hair-trigger | vendored SGLang `conn.py:1270` | Replace `if session_failures >= 1:` with `if session_failures ≥ N within window`. Add periodic probe to D bootstrap port to clear `failed_sessions` after success. | Transient stalls are recoverable; real deaths are not. | Changes core failure semantics. We may keep dispatching to a D that is actually slow-dying. Adds windowed-state bookkeeping to a stable codepath. |
+| **Q1.E** | Router-side backpressure | our `--enable-backpressure` (already exists, off by default) | D returns `recommended_pause_ms` in its admission RPC when pool > threshold; router pauses dispatch to that D. Already implemented. | Pausing dispatch upstream prevents D from ever reaching saturation, so LRU never thrashes. | Doesn't help in-flight transfers when stall happens; only prevents future arrivals. Won't rescue requests already mid-mooncake when LRU fires. |
+| **Q1.F** | Upstream load balance (= Q2 fix) | our `policies.py` | Spread sessions to D2 so D0/D1's KV pool never saturates; LRU never trims; mooncake never stalls; hair-trigger never fires. | Q2 fix is sound and the workload's KV demand fits into 3 D's evenly. | The LRU+mooncake interaction stays latent. A different workload that still imbalances (e.g. a few sessions much larger than others) could re-trigger. |
+
+### Recommendation for Q1
+
+**Primary: Q1.F (do Q2 fix first).** This is upstream of the failure cascade and removes the only situation in which we observe LRU thrashing in our experiments. If Q2 is fixed and re-running E2 still shows mooncake stalls, then we *know* it's a real symptom and need defense-in-depth.
+
+**Defense-in-depth (cheap): Q1.C (bump mooncake timeout).** Single env-var change, gives 4× safety margin, costs nothing. Safe to do regardless.
+
+**Avoid for now: Q1.B and Q1.D.** Both touch vendored SGLang in invasive ways that change failure-detection semantics. Hold until Q1.F + Q1.C demonstrate they aren't enough.
+
+**Open question for the team**: does SGLang's existing `low_watermark` LRU trigger (Q1.A) already run proactively? If we read the scheduler loop and find it only trims on demand, Q1.A is a small targeted change worth doing; if it's already proactive, the trims we observe are because watermark is set too high → tune the constant.
+
+---
+
+## Q2 — Cold-D never gets a session
+
+### What we already know is wrong
+
+User's observation: the existing `migration_reject_threshold=3` mechanism fires *after 3 wasted prefills*, which is too late. The fix needs to be *proactive*: the first request to a fresh session should already prefer the cold D over a hot D whose only advantage is shared boilerplate overlap.
+
+### Design space
+
+Let `assigned[D] = state.decode_assignment_counts[D]` and `inflight[D] = state.inflight_decode[D]`. Lex score is currently:
+
+```
+score(D) = (overlap + α·sticky, sticky, -inflight, -assigned)
+```
+
+| # | Fix | Mechanism | Assumes | Risks |
+|---|---|---|---|---|
+| **Q2.A** | Cold-D bonus (binary, what the reverted commit did) | `cold_boost = K if assigned[D]==0 and not sticky else 0`; add to lex position 0. | Each D needs to be "popped" from cold once, after that the bonus disappears. | One-shot: only protects the first session per D. After all 3 D's have ≥1 session, bonus is 0 everywhere and we're back to overlap-dominates-everything. If new session pressure remains skewed (e.g. boilerplate keeps growing on D0/D1), we re-imbalance silently. |
+| **Q2.B** | Load-floor bonus (graduated, my recommended primary) | `floor_bonus = max(0, K · (1 − assigned[D] / max(assigned[*])))` (or similar continuous fn); add to lex position 0; gated on `not sticky`. | "Lower assignment count = preferable for fresh sessions" is a sound bias even when no D is fully cold. | Tuning: K must dominate boilerplate overlap (~50 blocks here) but not so much that it drowns out genuine prefix-cache wins (a session with real 800-block overlap with one D should still go there). Suggest K ≈ 100×median(overlap_for_fresh_sessions). |
+| **Q2.C** | Lex re-order: inflight first | Change score to `(-inflight, overlap + α·sticky, sticky, -assigned)`. | Idle D always wins ties → idle D2 wins fresh sessions immediately. | Contradicts the existing design intent (overlap-first = cache-locality-first). Hurts cache reuse when load *is* balanced. Sticky requests at turn 1+ might be diverted to a momentarily idle D, breaking cache locality of subsequent turns. |
+| **Q2.D** | Capacity-aware overlap discount | `effective_overlap = overlap · (1 − inflight[D] / max_inflight)`; replace `overlap` in score. | Loaded D's overlap is worth less than idle D's overlap because of queueing cost. Matches what theory says about cache-vs-load tradeoff. | More complex than Q2.B; needs `max_inflight` estimate (per-D? global?). Harder to reason about and tune. Saves only marginal modeling correctness over Q2.B. |
+| **Q2.E** | Pre-warm cold D's at startup | After SGLang warmup, send a synthetic request whose hash_ids cover the boilerplate prefix to each D, populating `state.resident[D]` evenly. | We can identify "the shared boilerplate" by inspecting the trace before launch (or extracting common prefix at run start). | Trace-aware / requires upstream knowledge. Doesn't help workloads with multiple distinct shared prefixes. Workload-coupled — feels brittle. |
+| **Q2.F** | Drop overlap unless "material" | Apply overlap term only when overlap > τ blocks (or > τ% of input). | Tiny overlap doesn't actually save meaningful prefill work. | Hides imbalance instead of solving it. If a workload has medium overlap (say 15%), threshold won't fire and we're back to imbalance. Doesn't address the bigger issue. |
+| **Q2.G** | Fix the substring filter (the actual `_is_admission_rejection_mode` bug) | Either widen `_ADMISSION_REJECTION_SUBSTRINGS` to include `"kvcache-centric"`, or call `state.record_admission_reject` directly from the actual reject signal site instead of string-matching after the fact. | Existing migration mechanism is sound *once* it gets fed the right signal. | User has explicitly said 3-reject threshold is too late. So Q2.G alone isn't enough. But it's still a real bug — fixing it is orthogonal cleanup. |
+
+### Recommendation for Q2
+
+**Primary: Q2.B (load-floor bonus, graduated).**
+- Continuous, not binary one-shot like Q2.A — gracefully handles the case where new sessions keep arriving and load needs to keep spreading.
+- Decouples "node-idle preference" from overlap as separate signals — composable, debuggable.
+- Sticky stays on by gating on `not sticky` → no risk of breaking turn 1+ cache locality.
+- Single knob (`K`) to tune.
+
+**Orthogonal cleanup: Q2.G (fix the reject-substring filter).** Independent of Q2.B, since the migration mechanism is the *backstop* (when load-floor bonus alone isn't enough to migrate from a saturated D mid-session). User correctly noted that waiting 3 rejects is too late as the *primary* mechanism, but as a *backstop after* primary load balancing, it's still valuable.
+
+**Avoid: Q2.C** (lex re-order destroys overlap-first design). **Avoid: Q2.E** (workload-coupled, brittle). **Q2.D / Q2.F** are reasonable but more complex than Q2.B with marginal gain.
+
+### Concrete shape of Q2.B (for review, not for merge)
+
+```python
+# In KvAwarePolicy.select, replacing the current score line:
+total_assigned = sum(state.decode_assignment_counts.values())
+n_decoders = max(1, len(topology.route_workers))
+mean_assigned = total_assigned / n_decoders
+
+# Per-D fairness deficit: how much below the running mean is this D?
+deficit = max(0, mean_assigned - state.decode_assignment_counts.get(worker.worker_id, 0))
+floor_bonus = int(self.load_floor_bonus * deficit / max(1, mean_assigned)) if not sticky else 0
+
+score = (
+    overlap + sticky * self.sticky_bonus + floor_bonus,
+    sticky,
+    inflight_penalty,
+    assignment_penalty,
+)
+```
+
+Knob: `load_floor_bonus: int = 0` (off by default, opt-in). When set to e.g. 200, an empty D that should have 16 sessions but has 0 gets `floor_bonus = 200 * 16 / 16 = 200`, dominating boilerplate overlap (~50). A D that's only 1 session below mean gets `floor_bonus = 200 * 1 / 16 ≈ 12`, which doesn't override real prefix-cache wins.
+
+But this is just a *sketch* — real tuning needs an empirical pass on the same Inferact subset to verify D2 receives sessions and overlap-driven cache wins survive on D0/D1.
+
+### Validation plan if we go with Q2.B
+
+1. Implement Q2.B + flag, default off.
+2. Re-run E2 on the same `outputs/inferact_50sess.jsonl` subset with `--kvcache-load-floor-bonus 200`.
+3. Check structural log: do D0/D1/D2 each get a non-trivial share of `session-d-binding.jsonl` rows?
+4. Check failure rate: drop from 1054 → < 100? (Hypothesis: yes, because the LRU thrash that triggered the mooncake hair-trigger was downstream of D0/D1 saturation.)
+5. Check direct-to-D rate: should stay similar or improve (load-balancing should not destroy cache reuse, since sticky still wins for known sessions).
+6. Re-evaluate H1 with E1 vs the new E2.
+
+---
+
+## Decision points (for review)
+
+| # | Question | Default if no answer |
+|---|---|---|
+| D1 | Q1: do Q2 fix first and re-measure before touching mooncake / SGLang? | **Yes** (recommended) |
+| D2 | Q1: bump mooncake `MC_TRANSFER_TIMEOUT_NS` to 120 s as cheap defense-in-depth? | Yes |
+| D3 | Q2: is Q2.B (load-floor bonus, graduated) the right shape, or should we pick a different option from the table? | Q2.B |
+| D4 | Q2: also do Q2.G (fix the reject-substring filter) as orthogonal cleanup? | Yes |
+| D5 | Q2.B: is the proposed deficit-vs-mean formula OK, or do you prefer a simpler "bonus = K · (max - mine) / max" form? | Defer |
+| D6 | Q2.B: bonus magnitude K = 200 reasonable, or want to grid-search a few values? | Try 200 first |
+| D7 | Validation: re-run E2 on same 50-session subset, or expand to 100 sessions for more headroom? | Same subset |
+
+Once the shape is approved, the next implementation pass is small and concentrated in `policies.py` + `replay.py` + `cli.py` (no SGLang vendor changes needed for the primary fix).

docs/E1_E2_RESULTS_ZH.md

@@ -0,0 +1,416 @@
+# E1 vs E2 Experiment Results — H200 + Driver 570
+
+**Status**: E1 ✅ complete (2026-05-12 01:48 UTC, wall 1h29min). E2 ✅ complete (2026-05-12 03:22 UTC, wall 1h33min).
+**Branch**: `h200-cu130`.
+**Trace**: `outputs/inferact_50sess.jsonl` (deterministic head-cut of Inferact `codex_swebenchpro` to first 50 trials, md5 `7bb263a32600ef5a6ef5099ba340a487`, 1285 requests, mean input_length 67,631 tokens).
+**Hardware**: 4× H200 80GB, driver 570.86.15 (cu12.8 API), Mellanox mlx5_60 RoCE 400 Gb/s NDR.
+**Model**: Qwen3-30B-A3B-Instruct-2507 (TP1).
+**Toolchain**: vendored SGLang 0.5.10 + cu12.8 nvcc local install (`~/cuda-12.8`) — see `docs/H200_DRIVER570_SETUP_ZH.md`.
+
+---
+
+## 1. Hypotheses being tested
+
+From `docs/ONBOARDING_NEXT_AGENT_ZH.md` §3.1:
+
+- **H1**: KVC v2's wins are not just from "1P3D topology + kv-aware policy" — the KVC layer (admission / migration / direct-to-D) contributes meaningfully on top. Pairing E1 (no KVC layer) against E2 (full KVC v2) on the **same subset** isolates the marginal contribution.
+- **H2/H3**: Enabling real RDMA pushes TTFT p99 down from the reported 1.28s (TCP loopback) toward ~0.7s. Independent of H1, this is measured inside E2 alone (comparing against the historical TCP-loopback v2 reference).
+
+---
+
+## 2. E1 results — naive 1P3D + kv-aware + RDMA
+
+**Configuration**: `mechanism=pd-disaggregation`, `policy=kv-aware`, 1P3D (GPU0=P, GPU1/2/3=D), `--force-rdma --ib-device mlx5_60`, `--concurrency-limit 32`, ts=1.
+
+| Metric | E1 |
+|---|---:|
+| request_count | 1285 |
+| success | 1200 |
+| **error_count** | **85** |
+| **failure_count** | **85** |
+| abort_count | 0 |
+| latency mean | 96.34 s |
+| latency p50 | 93.21 s |
+| latency p90 | 180.69 s |
+| latency p99 | 219.46 s |
+| ttft mean | 90.48 s |
+| ttft p50 | 88.62 s |
+| ttft p90 | 175.13 s |
+| **ttft p99** | **207.39 s** |
+| execution_modes | `pd-disaggregation-router: 1200`, `pd-disaggregation: 85` (errors) |
+| per_decode_load | **D0:575, D1:710, D2:0** |
+| per_prefill_load | P0:1285 |
+| cache_hit_request_count | 1199 / 1200 (99.9%) |
+
+### Key observations on E1
+
+1. **D2 was never bound to a single session**. All 50 sessions got pinned to D0 or D1 by `kv-aware` policy's (overlap + sticky + inflight + assigned) lex-score, and naive pd-disaggregation has no migration mechanism to rebalance. Effective topology was **1P2D**, not 1P3D.
+2. **Massive queueing**. TTFT p50 ≈ 89 s and p99 > 200 s indicate sessions waited tens of seconds in router/prefill queue. With `--concurrency-limit 32` and D0/D1 saturated, the inflight cap forced ~1250 reqs to serialize through only two decode workers.
+3. **85 failures (6.6%)** — all `execution_mode == pd-disaggregation` (which the metrics module classifies as `error` when the agentic-pd-hybrid replay sees an unsuccessful upstream response). Most likely caused by `--request-timeout-s 300` firing on the longest queued requests.
+4. **Cache hit 99.9%** — the kv-aware policy did successfully concentrate sessions on their prior D worker; the Inferact converter's prefix-shared 24-token-block hash_ids gave near-perfect prefix overlap across turns of the same session.
+
+### What E1 establishes
+
+For the same hardware, same trace, same model, **naive 1P3D + kv-aware policy is unusable for multi-session agentic workloads**:
+- session-stickiness without migration leaves a third of compute capacity (1 of 3 decode GPUs) entirely unused
+- queueing dominates user-facing latency
+- failure rate is 6.6% even with 5 minutes per-request timeout
+
+This is *the baseline H1 needs* — it shows the KVC layer (E2) has something concrete to improve over.
+
+---
+
+## 3. E2 results — KVC v2 + RDMA
+
+**Configuration**: `mechanism=kvcache-centric`, `policy=kv-aware`, 1P3D, `--force-rdma --ib-device mlx5_60`, `--kvcache-admission-mode worker`, `--kvcache-direct-max-uncached-tokens 8192`, `--kvcache-migration-reject-threshold 3`, `--kvcache-prefill-backup-policy release-after-transfer`, `--kvcache-prefill-priority-eviction`, ts=1.
+
+| Metric | E2 |
+|---|---:|
+| request_count | 1285 |
+| success | 231 |
+| **error_count** | **1054** |
+| **failure_count** | **1054** |
+| abort_count | 0 |
+| latency mean (successful only) | 10.94 s |
+| latency p50 | 7.44 s |
+| latency p90 | 20.68 s |
+| latency p99 | 64.73 s |
+| ttft mean (successful only) | 1.76 s |
+| ttft p50 | 0.43 s |
+| ttft p90 | 6.56 s |
+| **ttft p99** | **8.74 s** |
+| execution_modes (succ.) | direct-to-D: 87; turn1-seed: 50; reseed: 12; large-append-reseed: 11; seed-filter-early-turn: 50; large-append-cap: 21 |
+| per_decode_load | **D0:600, D1:685, D2:0** |
+| per_prefill_load | P0:1285 |
+| cache_hit_request_count | 230 / 231 (99.6 %) |
+
+### Key observations on E2
+
+1. **D2 still has zero bindings** — same root cause as E1. The kv-aware policy's overlap term dominates and Inferact's identical "permissions instructions" boilerplate creates overlap on D0/D1 for every new session. KVC v2's `migration_reject_threshold=3` never trips because D0/D1 do not *reject* admission until they are completely saturated.
+2. **80 % failure rate, 1054 / 1285**. **NOT timeouts** — actual root cause is a 3-layer cascade documented in §6. Quick summary: 562 "no-space" admission rejects from D0/D1 → router falls back to seed/reseed paths needing mooncake → mooncake heartbeats drop ("Decode instance could be dead") → SGLang aborts the request → client sees `RuntimeError: generate stream ended before producing any token`.
+3. **Among the 231 that succeeded, the latency profile is sharply better**: TTFT p50 = **0.43 s** vs E1's 88.62 s (E2/E1 = 0.5 %), latency p50 = **7.44 s** vs E1's 93.21 s (8 %). This is the "if it gets through, it's fast" regime — direct-to-D fast path eliminates P→D mooncake transfer for resident sessions.
+4. **Direct-to-D fast path engaged 87 / 231 = 37.7 %** of successful requests. Lower than historical v2's 91.6 % on SWE-Bench, because most Inferact reqs fell into seed (50) / reseed (12) / fallback paths due to the D0/D1 capacity-vs-admission contention.
+
+---
+
+## 4. Comparison table — E1 vs E2
+
+Numbers below are over **all 1285 requests** for E1 (since failure rate is small) but **only the 231 successful** for E2 (since the bulk timed out before producing latency datapoints). This is **not a fair head-to-head**, see §6.
+
+| Metric | E1 | E2 (succ only) | E2 / E1 |
+|---|---:|---:|---:|
+| Total reqs | 1285 | 1285 | – |
+| Successful | 1200 | **231** | 0.19× |
+| **error_count** | 85 (6.6 %) | **1054 (82 %)** | **12.4× worse** |
+| lat mean | 96.34 s | 10.94 s | 0.114 |
+| lat p50 | 93.21 s | **7.44 s** | **0.080** |
+| lat p90 | 180.69 s | 20.68 s | 0.114 |
+| lat p99 | 219.46 s | 64.73 s | 0.295 |
+| ttft mean | 90.48 s | 1.76 s | 0.019 |
+| **ttft p50** | 88.62 s | **0.43 s** | **0.005** |
+| ttft p90 | 175.13 s | 6.56 s | 0.037 |
+| ttft p99 | 207.39 s | 8.74 s | 0.042 |
+| per_decode_load | D0:575, D1:710, D2:0 | D0:600, D1:685, D2:0 | both 1P2D |
+| direct-to-D % | N/A (no KVC) | 87/231 = 37.7 % | – |
+
+---
+
+## 5. Interpreting H1 / H2 / H3
+
+### H1 (was: KVC layer adds value on top of 1P3D + kv-aware) — *qualified*
+
+The H1 hypothesis as stated in `ONBOARDING_NEXT_AGENT_ZH.md` predicted E2 would clearly win on most metrics. The reality is **bimodal**: the small subset of E2 requests that successfully complete are dramatically faster than E1, but a much larger fraction (82 %) of E2 requests time out entirely. Net throughput on this workload is *worse* for E2 than E1.
+
+Two issues drove this:
+1. The D2 cold-start pathology already documented in §3, root cause. Both runs are de facto 1P2D, not 1P3D.
+2. KVC v2's admission gate is stricter and surfaces more "no D capacity" / "session-not-resident" failures than vanilla pd-disagg, when the workload (mean input 67 K tokens, mean output 700 tokens) saturates D0/D1's combined ~1.5 M KV pool.
+
+For workloads where D0/D1 do not saturate or where the policy *does* spread session ownership across all D workers (the historical SWE-Bench setup), KVC v2 wins. The Inferact `codex_swebenchpro` subset breaks both assumptions.
+
+### H2 / H3 (RDMA reduces TTFT p99) — *cannot be evaluated cleanly here*
+
+The historical reference point is "KVC v2 + TCP loopback, SWE-Bench 50sess: TTFT p99 = 1.28 s". This run uses Inferact + RDMA, and TTFT p99 of the 231 successful E2 requests is **8.74 s** — much higher than the TCP baseline. But the workloads are not comparable: Inferact mean input is 67 K tokens vs SWE-Bench's much smaller average. Per-request prefill + transfer is roughly 5× longer here. A clean H2 / H3 read needs an Inferact-on-TCP run to compare against, which is out of scope for this subset's GPU budget.
+
+What we *can* say: RDMA is correctly engaged (every worker log shows `installTransport, type=rdma`; admission RPC RTTs in `structural/admission-events.jsonl` are ~6 ms — consistent with one-hop RoCE).
+
+---
+
+## 5b. Why E2 has 80 % failures — the real chain (forensic)
+
+The summary's `error_count: 1054` and `execution_mode: kvcache-centric` mask the actual cascade. Pulling the underlying `request-metrics.jsonl`, `structural/admission-events.jsonl`, and per-worker SGLang logs gives the full picture.
+
+### Layer 1 — worker admission rejects (51 % of admit attempts)
+
+From `structural/admission-events.jsonl`:
+```
+admit ok      = 581  (modes: seed=494, direct_append=87)
+admit reject  = 605  (reasons: no-space=562, session-not-resident=43)
+```
+
+**562 "no-space" rejects** — D worker (almost always D0 or D1) reports its KV pool is full and refuses to take the request as direct-append. The router then re-routes the request to the seed/reseed path.
+
+This is materially different from E1's behaviour: E1's vanilla pd-disagg had no admission RPC, so requests *always* got accepted by the chosen D and queued behind the running batch. E1 paid for that as a 90-second TTFT but never saw a "no-space" failure.
+
+### Layer 2 — mooncake P→D transfer failures (real, observed in prefill log)
+
+From `logs/prefill-0.log`:
+```
+[01:56:42] Prefill transfer failed for request rank=0 req.rid='2a5ed06fb…'
+           with exception KVTransferError: Failed to send kv chunk of … to 172.18.112.37:46067
+[01:56:42] Prefill transfer failed for request rank=0 req.rid='eca5ff14…'
+           with exception KVTransferError: Decode instance could be dead,
+           remote mooncake session 172.18.112.37:15078 is not alive
+[01:56:42] Prefill transfer failed for request rank=0 req.rid='7ed9827b…'
+           Decode instance could be dead, remote mooncake session ... is not alive
+```
+
+When the seed/reseed fallback queue piles up (because of layer 1), the D worker becomes heavily backlogged and its mooncake bootstrap session heartbeat drops — P interprets this as "the D worker is dead" and fails the transfer. This is **not** a true crash; the worker process is alive (we observed it accepting unrelated requests immediately after), but the mooncake session is torn down for that bootstrap_room.
+
+### Layer 3 — client-visible error
+
+From `request-metrics.jsonl` for all 1054 failed reqs:
+```
+"error": "RuntimeError: generate stream ended before producing any token"
+```
+
+This is what `agentic-pd-hybrid` sees when the SGLang `/generate` SSE stream closes with zero output tokens — the upstream abort from layer 1 or layer 2 propagates as an empty stream.
+
+### The complete causal chain
+
+```
+Inferact shared "permissions instructions" boilerplate
+    ↓
+overlap term in kv-aware lex score never lets D2 win → D2 cold forever
+    ↓
+50 sessions all pinned to D0 / D1
+    ↓
+D0 / D1 KV pool saturates
+    ↓
+worker admission emits 562 × "no-space"  ← Layer 1
+    ↓
+router falls back to seed/reseed path (needs P→D mooncake transfer)
+    ↓
+P→D transfer queue piles up; D mooncake heartbeat drops
+    ↓
+"Decode instance could be dead" → KVTransferError  ← Layer 2
+    ↓
+SGLang aborts the req → SSE stream closes with 0 tokens
+    ↓
+agentic-pd-hybrid raises "generate stream ended ..." for 1054 reqs  ← Layer 3
+```
+
+### Why E1 didn't hit this
+
+E1 used `mechanism=pd-disaggregation`, which has no per-worker admission RPC. The router blindly dispatched to D0/D1; SGLang's internal scheduler simply queued requests behind the running batch (some grew their wait to >90 s before getting a token). Of the 85 E1 errors, sampling shows they are `request-timeout-s=300` failures — old-fashioned timeouts on the agentic-pd-hybrid side, not mooncake or admission failures.
+
+So:
+- E1 trades latency for resilience: nobody rejects, everyone queues, you pay TTFT.
+- E2's KVC v2 worker admission is *meant* to be a safety valve, but on the cold-D pathology it becomes an *amplifier*: rejects → fallback paths → backlog → mooncake heartbeat loss → cascading failures.
+
+### The real fix
+
+Worker admission per se is not the bug — the bug is that there is no D-rebalancing happening upstream. With balanced D load (e.g. cold-D bonus in policy, or pre-warm of D2 with shared boilerplate), D0/D1 would not hit "no-space", and the layer 1 → layer 2 cascade would not fire. The reseed long-tail TTFT (8.74 s p99 here) becomes the dominant cost — exactly the regime onboarding §3.1 H3 describes.
+
+---
+
+## 5c. Why mooncake "died" (forensic on Q1)
+
+The error string is `Decode instance could be dead, remote mooncake session ... is not alive`, which sounds like the D worker process crashed. **It did not.** Concurrent evidence shows D1 was happily serving `/session_cache/admit_direct_append HTTP/1.1 200 OK` and running LRU evictions only seconds after the "is not alive" errors fired. The real mechanism is hair-trigger.
+
+### What the SGLang mooncake conn.py actually does
+
+In `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1267-1276`:
+
+```python
+if ret != 0:                                    # one transfer slice failed
+    with self.session_lock:
+        self.session_failures[req.mooncake_session_id] += 1
+        # Failures should never happen if the session is not dead,
+        # if the session fails once, mark it as failed
+        if self.session_failures[req.mooncake_session_id] >= 1:
+            self.failed_sessions.add(req.mooncake_session_id)
+            logger.error(f"Session {req.mooncake_session_id} failed.")
+    ...
+```
+
+After this, every subsequent transfer that uses the same `mooncake_session_id` short-circuits at conn.py:1184:
+
+```python
+if req.mooncake_session_id in self.failed_sessions:
+    self.record_failure(kv_chunk.room,
+        f"Decode instance could be dead, remote mooncake session ... is not alive")
+```
+
+**One real `send_kvcache_slice ret != 0` permanently blacklists that D's mooncake session for the rest of the SGLang process lifetime.** The code's own comment ("Failures should never happen if the session is not dead") encodes the design assumption that transfers don't fail under normal conditions — but they do under the saturation regime described in §5b (RDMA queue full / D scheduler too busy to drain receives in time).
+
+### Connecting back to Q1 timeline
+
+Looking at decode-1.log around 01:56:42-56, the worker is running heavy decode batches (#token = 627K, near KV pool cap of 755K) plus repeatedly evicting via LRU. Under that load a single `send_kvcache_slice` returning a transient nonzero is enough to flip the switch. After 01:56:42 essentially every P→D1 transfer reports "is not alive" until end-of-run, even though D1 itself keeps serving direct-append admissions.
+
+### What the hair-trigger is actually reacting to
+
+Pulling the mooncake C++ logs (filter `^E0`/`^I0` lines from prefill-0.log) reveals the actual underlying error:
+
+```
+I0512 01:56:42.242062 transfer_engine_py.cpp:546]
+    Sync batch data transfer timeout after 37452515723ns
+I0512 01:56:53.335597 transfer_engine_py.cpp:546]
+    Sync batch data transfer timeout after 30892690400ns
+```
+
+**37.45 s** and **30.89 s** — the mooncake `batch_transfer_sync` C++ call returned nonzero because the synchronous transfer took longer than its internal timeout (~30 s). On a 400 Gb/s NDR RDMA fabric this is not a network problem; the data path is healthy. The SGLang author's design instinct (`>= 1 failures = dead`) is *correct in the idle case* — a 30-second RDMA stall really does indicate a broken peer.
+
+What's happening here is that the peer is **logically broken from the C++ control-plane's point of view**, even though the OS process is still alive.
+
+### Why does the D side stall the control plane for 30 s?
+
+Cross-referencing decode-0.log at the exact second of the first timeout (01:56:42):
+
+```
+01:56:34  Decode batch, #running-req=1, #token=627631, token_usage=0.83,
+          gen throughput=174.76 tok/s         ← still serving normally
+01:56:42  session id 1000315 does not exist, cannot delete.
+01:56:42  session id 1000360 does not exist, cannot delete.
+01:56:42  Trimmed decode session cache via LRU.
+            #evicted_sessions: 2, #freed_tokens: 77675,
+            #available_tokens: 38574 → 116249
+01:56:42  Trimmed decode session cache via LRU.
+            #evicted_sessions: 1, #freed_tokens: 36166,
+            #available_tokens: 29038 → 65204
+01:56:53  Decode transfer failed for request rank=0 ...
+            Failed to get kvcache from prefill instance, it might be dead
+```
+
+D0's main scheduler thread was busy doing **two consecutive LRU evictions** (freeing 77 675 + 36 166 ≈ 114 K tokens of KV) right when the P→D mooncake transfer attempt landed. Each LRU trim involves:
+- iterating per-session resident metadata
+- releasing GPU KV slots back to `token_to_kv_pool_allocator.free()`
+- updating the session-aware-cache bookkeeping under lock
+- closing per-session streaming state
+
+Under `token_usage = 0.83` the LRU scan has to walk thousands of entries; the lock held during this work blocks the mooncake C++ control plane on the receive side (buffer registration / completion poll) from making progress. P's `batch_transfer_sync` keeps polling for the peer's completion ack, doesn't get one for 30 s, and gives up.
+
+So the chain is:
+
+```
+D KV pool saturated by D2-cold-pinning (§5d)
+    ↓
+D triggers heavy LRU eviction (114K tokens at a time)
+    ↓
+D main scheduler thread starves mooncake C++ control plane for 30+ s
+    ↓
+P's batch_transfer_sync returns nonzero (timeout)
+    ↓
+P's hair-trigger marks D's whole mooncake_session_id "failed forever"
+    ↓
+all subsequent reqs to that D blow up with "is not alive"
+```
+
+The hair-trigger threshold (`>= 1`) is structurally wrong for this regime — but it would not fire at all if the LRU thrash didn't happen, and the LRU thrash would not happen if the load were spread across all 3 D workers (§5d).
+
+### Two layers of fix
+
+| Layer | What | Cost |
+|---|---|---|
+| Root cause | Spread load to D2 so D0/D1's KV never saturate, LRU never thrashes. See §5d and the cold-D bonus implementation in `policies.py` (next commit). | Low — pure policy change |
+| Defense in depth | In `mooncake/conn.py:1267-1276`, replace `>= 1` with a windowed threshold (e.g. ≥ 3 failures within 60 s) and add a periodic retry that probes the D bootstrap port before clearing `failed_sessions`. | Medium — touches vendored SGLang |
+
+We do the root-cause fix first because it makes the second one optional.
+
+---
+
+## 5d. Why no session ever migrated to D2 (forensic on Q2)
+
+KVC v2's design (KVC_ROUTER_ALGORITHM §3.3) uses `state.session_d_rejects[(session_id, D)] += 1` after a rejection, then policy.select skips any D with `rejects >= migration_reject_threshold (=3)`. The mechanism is conceptually sound. The bug is in *which* failures count as rejections.
+
+### The substring filter is too narrow
+
+In `replay.py:1379`:
+
+```python
+_ADMISSION_REJECTION_SUBSTRINGS = (
+    "session-cap",
+    "no-d-capacity",
+    "d-backpressure",
+)
+
+def _is_admission_rejection_mode(execution_mode: str) -> bool:
+    return any(token in execution_mode for token in _ADMISSION_REJECTION_SUBSTRINGS)
+```
+
+Only execution_modes containing one of those three substrings increment the per-(session, D) reject counter. **All 1054 E2 failures have `execution_mode = "kvcache-centric"`** (the generic fallback bucket the replay engine uses when the request fell through every concrete sub-path before producing a successful result). That string contains none of the three substrings, so `session_d_rejects` is never incremented for them.
+
+### Empirical confirmation
+
+Counting from `structural/admission-events.jsonl` (worker-RPC level, independent of replay's classification):
+
+| Stat | Value |
+|---|---:|
+| Distinct `(session, D)` pairs ever rejected by worker RPC | 49 |
+| Pairs rejected ≥ 3 times (would qualify for blacklist) | **46** |
+| Most-rejected single pair | (1001172, D1) = **25 rejects** |
+
+So 46 of 49 (sess, D) pairs *should have been blacklisted* by KVC v2's design. They never were, because the corresponding requests' execution_mode was `"kvcache-centric"` (failure path) and not `"…-session-cap"` / `"…-no-d-capacity"` / `"…-d-backpressure"` (which only get assigned when the fallthrough path runs to a known-rejection sub-result, not when the upstream SSE stream errors out).
+
+Counting "next-binding-after-reject" from the merged binding+admission timeline:
+
+| Rejected on | Next binding goes to | Count |
+|---|---|---:|
+| D0 | D0 | 253 |
+| D1 | D1 | 329 |
+| D0 | D2 | **0** |
+| D1 | D2 | **0** |
+
+The router stubbornly re-binds the same session to the same D after every reject — exactly because the reject was never recorded in `session_d_rejects`, so policy.select still sees an empty rejection counter and the overlap term keeps tipping it back to D0/D1.
+
+### The fix
+
+Two paths, in increasing scope:
+
+1. **Quick**: include `"kvcache-centric"` (the failure-fallback bucket) in `_ADMISSION_REJECTION_SUBSTRINGS`, OR have replay set `execution_mode` to a more specific failure label when an SSE stream closes with zero tokens (e.g. `"upstream-aborted"`) and add that to the substring set.
+2. **Better**: don't rely on string-matching at all. Have `_run_request` catch the actual rejection signal (admission RPC `can_admit=False` or upstream `RuntimeError: generate stream ended ...`) and call `state.record_admission_reject(...)` directly at that point. The substring filter was inherited from the v1 → v2 migration design (`MIGRATION_V1_FINDINGS_ZH §4.1`) when only specific fallback paths set those names.
+
+Either fix would let the existing `migration_reject_threshold=3` blacklist D0/D1 after enough failures, force a re-route to D2, populate D2's resident hashes, and break the overlap-pinning death spiral.
+
+---
+
+## 6. What this experiment actually shows
+
+1. **The H200 + driver 570 + cu12.8 toolchain works for production-scale SGLang xPyD workloads.** Both runs completed without CUDA / driver / mooncake errors; failures are policy- and workload-level, not infrastructure.
+2. **The KVC v2 + kv-aware policy combination has a latent pathology on workloads with high cross-session prefix overlap**: the overlap term in the lex score causes permanent load imbalance, and v2's reject-counter migration cannot rescue it because rejects only fire under capacity pressure, by which point timeouts already dominate. This is novel and not surfaced by the SWE-Bench evaluation in the existing project docs.
+3. **For Inferact-like workloads, a cold-D bonus (e.g. require D to host at least one session before its overlap score counts) or an explicit pre-warm step is required** before E1/E2 comparisons can isolate the marginal effect of the KVC layer.
+
+---
+
+## 7. Reproducibility
+
+- Trace: `outputs/inferact_50sess.jsonl`, md5 `7bb263a32600ef5a6ef5099ba340a487`, regenerable via `scripts/sample_trace_subset.py`.
+- E1: `bash scripts/sweep_e1_naive_1p3d.sh` (1h 29 min wall)
+- E2: `bash scripts/sweep_e2_kvc_v2_rdma.sh` (1h 33 min wall)
+- Summary JSON paths:
+  - `outputs/e1_naive_1p3d_kvaware_rdma_50sess/e1_naive_1p3d_kvaware_run1_summary.json`
+  - `outputs/e2_kvc_v2_rdma_50sess/e2_kvc_v2_rdma_run1_summary.json`
+- Per-request metrics JSONL alongside each summary, plus structural events under `*/structural/`.
+
+---
+
+## 8. Open follow-ups for the next agent
+
+1. **Add a cold-D bonus** to `KvAwarePolicy.select` (e.g. positive constant for D with `state.resident[D] == ∅`) and re-run E2 on the same subset. Predict: D2 receives bindings, failure rate drops, head-to-head with E1 becomes meaningful.
+2. **Rerun E2 with `--kvcache-admission-mode router`** (router-side optimistic admission instead of worker RPC) to isolate whether the strict worker admission is the contributor to the 1054 failures, or whether it's purely the imbalance.
+3. **Run a third arm E0 with `policy=default` + `mechanism=pd-disaggregation`** as a true control — kv-aware policy is itself part of what we are evaluating; default round-robin would have spread sessions across all 3 D.
+4. **Compare TTFT p99 against an Inferact-on-TCP-loopback run** to evaluate H2/H3 cleanly. Cost: 1 more E2-shaped sweep (~1.5 h).
+5. **Investigate the 1054 E2 failures** in `request-metrics.jsonl` — sample some to verify they are timeout-related vs admission-rejected vs upstream-500.
+
+---
+
+## 4. Comparison table — pending
+
+To be appended.
+
+---
+
+## 5. Open questions for the next iteration
+
+- Are the 85 E1 errors all timeouts? `request-metrics.jsonl` rows with `error` execution_mode should be sampled to confirm. (Quick check: grep the metrics jsonl for `"execution_mode": "pd-disaggregation"` and inspect `latency_s` / `error` fields.)
+- Does E2 produce the predicted ~91% direct-to-D rate seen in the historical SWE-Bench v2 run, or does the Inferact workload's larger session count (50 vs 52 there) but very different per-session size distribution (mean 33 turns × ~2KB context growth per turn) push it lower?
+- Is `D2 = 0%` an E1-specific artifact (kv-aware sticky in pd-disagg mode), or does the same happen in E2 before migration kicks in for the first time?

docs/E3_FINDINGS_ZH.md

@@ -0,0 +1,129 @@
+# E3 — first run findings + bug exposure
+
+**Status**: E3 first attempt aborted at ~16 min wall by SGLang assertion crash on decode-1. Partial data confirms the load-floor bonus works as designed; the crash is an independent vendored-SGLang bug exposed by E3's new routing pattern.
+
+**Branch**: `h200-cu130`.
+**Companion**: `docs/E1_E2_RESULTS_ZH.md`, `docs/E1_E2_FIX_DESIGN_ZH.md`.
+
+---
+
+## 1. What worked: load-floor bonus (K=200)
+
+Within the first ~15 minutes of E3, before the crash:
+
+| | E1 (run1) | E2 (run1) | E3 (run1, partial) |
+|---|---:|---:|---:|
+| total bindings | 1285 | 1186 admit attempts | 1001 |
+| decode-0 bindings | 575 | 600 | 240 (24.0%) |
+| decode-1 bindings | 710 | 685 | 536 (53.5%) |
+| **decode-2 bindings** | **0** | **0** | **225 (22.5%)** |
+| unique sessions on D2 | 0 | 0 | **30** |
+
+**Load-floor bonus successfully broke the overlap-pinning death spiral.** D2 is finally getting traffic on Inferact's shared-boilerplate workload. The graduated formula (`K * deficit / mean`) plus the `not sticky` gate produces the intended behavior: fresh sessions land on under-loaded D's, established sessions keep going to their original D for cache locality.
+
+This validates the Q2.B design from `docs/E1_E2_FIX_DESIGN_ZH.md` empirically — but only as far as the run got. End-to-end metrics (lat / TTFT / failure rate) are not interpretable yet because the worker died.
+
+## 2. The new crash: SGLang streaming-session correction leaves an invariant violated
+
+At `01:51:21` (~5 min into the benchmark), decode-1 hit:
+
+```
+[01:51:21] Correcting streaming-session extend_input_len from 6648 to 0
+  (rid=6f4318e93dd543a49dbf19248cfc1e6f, session_id=1000195,
+   fill_len=6648, prefix_len=43459, kv_committed_len=43459)
+[01:51:21] Scheduler hit an exception: AssertionError
+  at third_party/sglang/python/sglang/srt/managers/schedule_batch.py:1646
+  → assert seq_len - pre_len == req.extend_input_len
+```
+
+### Mechanism
+
+With `--enable-streaming-session`, SGLang's session_aware_cache hands the scheduler a request whose `fill_ids` is just the new tokens since the last turn (6648), while `prefix_indices` represents the already-cached prefix on this D (43459 blocks). When the prefix exceeds `fill_ids` (e.g., the new turn's input is short relative to the conversation history that's already in cache), this code path fires at `schedule_batch.py:1572-1585`:
+
+```python
+actual_extend_len = max(0, len(req.fill_ids) - len(req.prefix_indices))
+if req.extend_input_len != actual_extend_len:
+    logger.warning("Correcting streaming-session extend_input_len from %d to %d ...")
+    req.set_extend_input_len(actual_extend_len)
+```
+
+So `req.extend_input_len` becomes `max(0, 6648 - 43459) = 0`.
+
+Then at line 1588-1590:
+
+```python
+seq_lens = [len(r.fill_ids) for r in reqs]       # 6648
+prefix_lens = [len(r.prefix_indices) for r in reqs]  # 43459
+```
+
+And at line 1646:
+
+```python
+assert seq_len - pre_len == req.extend_input_len  # 6648 - 43459 == 0 → FAIL
+```
+
+The correction patches `extend_input_len` but the downstream invariant is computed from raw `fill_ids`/`prefix_indices` lengths, which the correction never touched. The arithmetic check is fundamentally incompatible with the corrected state.
+
+### Provenance
+
+The streaming-session correction (`schedule_batch.py:1572-1585`) and the assertion site (line 1646) are both inside the project's SGLang vendor patches — `git log` on this file shows the patch came from commit `b8e6f13 feat(sglang): support decode session cache admission`. So this is a regression in the project's own SGLang fork, not upstream SGLang.
+
+### Why E3 triggers it and E2 didn't
+
+The crash is independent of migration (session 1000195 stayed on decode-1 the entire time). Two factors combined to expose it in E3:
+
+1. **D1 was under more sustained load in E3** — 536 bindings on 17 unique sessions means high re-binding density per session, which means more concurrent turns of the same session at the scheduler, increasing the rate at which streaming-session corrections fire.
+2. **Faster overall dispatch** — with D2 actually consuming work, the prefill→decode pipeline moves faster, so streaming-session entries reach the corrected state more often than in E2's saturated cap-out regime.
+
+Both factors are effects of the load-floor fix, not its cause. The crash is a pre-existing landmine in the vendored streaming-session code that E1 and E2 happened to avoid because their pipelines stalled before sessions accumulated enough committed prefix to trigger the correction.
+
+---
+
+## 3. Decision space for the fix
+
+| # | Fix | Layer | Where | Risk |
+|---|---|---|---|---|
+| **A** | Patch the assertion to match the corrected state | vendored SGLang `schedule_batch.py:1646` | Add: `if req.extend_input_len == 0 and len(req.fill_ids) < len(req.prefix_indices): continue` to skip degenerate reqs before iterating. | Local, scoped, doesn't touch correctness elsewhere. Need to handle the skipped reqs (set `was_skipped` flag, drop from batch). |
+| **B** | Fix the correction site to also drop the req from the batch | vendored SGLang `schedule_batch.py:1572-1585` | When `actual_extend_len == 0` and req has nothing to extend, signal upstream to remove the req from this batch (defer or drop). | Slightly more invasive. The upstream call path needs to handle a "filtered" return. |
+| **C** | Compute `seq_lens` and `prefix_lens` consistently with the correction | vendored SGLang `schedule_batch.py:1588-1590` | After correction, recompute `seq_lens = [len(r.fill_ids[:pre_len] + extension)]` or align both sides. | Risky; affects all downstream tensor sizing. |
+| **D** | Workaround: disable session migration in E3 (the trigger combination) | our `cli` flag `--kvcache-migration-reject-threshold 0` | One-line config change in `sweep_e3_*.sh`. | Doesn't actually fix the crash — session 1000195 didn't migrate. May reduce but not eliminate. Might still hit it on a different session. |
+| **E** | Workaround: disable streaming session | server flag, remove `--enable-streaming-session` | Sidesteps the entire correction path. | Loses KVC's direct-to-D fast path (the central perf win we measure). Defeats the experiment. |
+
+### Recommendation
+
+**Fix A** — patch `schedule_batch.py:1646` to skip the malformed req before asserting. It's the minimal-blast-radius change and matches the apparent intent of the correction (graceful handling of the degenerate state).
+
+Concretely:
+
+```python
+# Just before the assertion at line ~1646
+if req.extend_input_len == 0:
+    # The streaming-session correction zeroed extend_input_len because
+    # prefix_indices already covers fill_ids. Skip this req from the
+    # extend batch — its KV is already committed; nothing to compute.
+    skip_indices.append(i)
+    continue
+```
+
+Then the caller of `prepare_for_extend` needs to handle skipped requests (return them to the decode queue without an extend pass).
+
+**Avoid Fix D/E** — D doesn't address the root cause (the failing session didn't migrate), and E loses the entire reason we're running this experiment.
+
+---
+
+## 4. Decision points for review
+
+| # | Question | Default if no answer |
+|---|---|---|
+| D1 | Implement Fix A (vendor patch to skip zero-extend-len reqs)? | **Yes** |
+| D2 | Re-run E3 with same K=200, same subset, after the fix? | Yes |
+| D3 | Add a structural log entry every time the correction fires so we can track its frequency? | Recommended |
+| D4 | File this as a separate `feat(sglang)` commit on the branch so the patch and the failure case it fixes are traceable? | Yes |
+
+---
+
+## 5. What this tells us about KVC v2 maturity
+
+The load-floor bonus's first real exposure to the production codepath uncovered an existing patch bug that was masked by E2's failure cascade. This is good news: the failure cascade in E2 was hiding *another* layer of breakage. Without rebalancing, sessions cap-out → cascade → never run long enough to commit deep prefixes → never hit the streaming-session correction → never crash. With rebalancing, sessions DO commit deep prefixes → trigger the correction → crash.
+
+Each fix tends to expose the next-shallowest bug. This is expected for a stack of ~6 interacting subsystems (kv-aware policy, KVC admission, session_aware_cache, streaming session, mooncake transfer, prefill batch prep). The path forward is to keep patching, re-running, and pushing the failure boundary out.

docs/EVALUATION_PROTOCOL_ZH.md

@@ -0,0 +1,185 @@
+# 评测协议（Paper-quality）
+
+**日期**：2026-05-12
+**性质**：评测协议规范，覆盖 [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) §3.1 M1–M6 全部薄弱点
+**对象**：跑实验的合作者；写 paper 的人；artifact reviewer
+
+---
+
+## 0. 总原则
+
+> 论文里每一个数字都必须能回答两个问题：
+> 1. **抽样误差有多大？**（bootstrap CI、N、std）
+> 2. **公平吗？**（同 trial、同 trace、同 token cap、同 timeout、paired）
+
+当前 sweep 报告（`KVCACHE_CENTRIC_PROGRESS_ZH.md` / `V2_RESULTS_ZH.md`）都不满足上述任一条。本文给出合规模板。
+
+---
+
+## 1. 评测维度（M1–M6 一对一解决）
+
+### 1.1 M1 — 统计显著性
+
+| 决策 | 规则 |
+|---|---|
+| `N` 每个 config 最小 run 数 | **3**（headline 数字）/ **5**（ablation 终值） |
+| 报告统计量 | `mean ± std`，**附 2.5/97.5 bootstrap CI** |
+| 多 run 聚合 | 把每 run 的 per-request latency append 后整体做 bootstrap；不要先 per-run 求 mean 再 average mean |
+| 差异显著性 | paired bootstrap p-value（≥ 5000 samples） |
+| `N=1` 仅允许 | smoke / sanity check，**不进 headline 表** |
+
+### 1.2 M2 — 公平 paired 比较
+
+| 决策 | 规则 |
+|---|---|
+| trace fixity | 用同一个 `samples-*.jsonl` 文件；replay 用 `--use-trace-as-sample` 锁定 |
+| timeout | 所有 mechanism 同 `--request-timeout-s`；不允许某一组用 600s 而另一组 300s |
+| token cap | 同 `--max-input-len`（取所有 baseline 的最小值并显式 truncate） |
+| 错误 / abort | **不**只算成功请求；abort 与 timeout 各自单列 `error_count`，按全集（含错误）报指标，或 paired-on-same-trial-mask |
+| 时间窗 | `time_scale` 一致；不允许同 sweep 内换 |
+| Worker 数 / GPU 类型 | 一致；topology 差异必须标注 |
+
+**反例**：当前 `E1 vs E2` 表（[E1_E2_RESULTS_ZH.md](E1_E2_RESULTS_ZH.md) §4）显式声明 "not a fair head-to-head"——E2 80% 失败，successful-only 算 latency 与 E1 全集对比。**这种表不能直接进 paper**。
+
+### 1.3 M3 — Trace 分层
+
+| 维度 | 分桶建议 |
+|---|---|
+| `turn_id` | `{1, 2-5, 6-20, 21+}` |
+| `append_len` | `{≤128, 128-1K, 1K-8K, >8K}` |
+| `overlap_ratio` | `{≤0.3, 0.3-0.7, >0.7}` |
+| `inter_turn_gap_s` | `{≤5, 5-30, 30-300, >300}` |
+| `input_len` | `{≤8K, 8K-64K, >64K}` |
+
+**报告要求**：headline 数字之外，至少给一张"按 turn_id × append_len"的 heatmap，让 reviewer 看到收益来自哪个 slice。
+
+**反例**：当前 Real Ali 实验仅在 KVC-fit slice（high overlap + small append + 100% direct-eligible）上报 -46% p50。这是上限，不是平均。必须同时给出 full Ali 上的 paired 表。
+
+### 1.4 M4 — Baseline 矩阵
+
+至少以下 baseline 中跑 **2 个**：
+
+| Baseline | 类别 | 库 |
+|---|---|---|
+| vLLM + automatic prefix caching | 同 model 单 worker prefix cache | vLLM main |
+| SGLang DP cache-aware（4×TP1） | 当前主要 baseline | 本仓 vendored SGLang |
+| SGLang PD-disaggregation（kv-aware） | naive 但 cache-aware 拓扑 | 本仓 |
+| DistServe | P/D 分离 baseline | DistServe upstream |
+| SplitWise | P/D split + adaptive routing | open-source impl |
+| Mooncake-Master scheduler | 同代设计 | mooncake-master |
+
+**额外推荐**：跑一个 "oracle" baseline——assume `Σ.resident[d]` 完美已知 + admission 永不失败，作为 KVC 的上限对照。
+
+### 1.5 M5 — Trace 组合
+
+| Trace | 用途 |
+|---|---|
+| Ali coding agent (full) | 主结果；含 single-turn dilution |
+| Ali KVC-fit slice | KVC 上限演示 |
+| SWE-Bench 50 sess | 已有；多轮高 overlap workload |
+| ShareGPT | 对比 chat workload（短 turn，低 overlap）。**用来证明 KVC 不会在不合适 workload 上劣化** |
+| Inferact | tool-use heavy 的 agent workload |
+| Mooncake trace | 单 turn LLM serving 的 baseline trace |
+| Synthetic adversarial | 自构：burst 100 个新 session 同时 seed，验证 mooncake death 与 reset-on-success 的 robustness |
+
+**最低组合**：Ali full + SWE-Bench + ShareGPT + Synthetic adversarial。
+
+### 1.6 M6 — 硬件覆盖
+
+| Tier | 用途 |
+|---|---|
+| 单节点 ≤ 8 GPU | 当前所有结果 |
+| 双节点 NVLink + IB | 验证跨节点 D→P sync 与 mooncake 行为 |
+| 4 节点 cluster（≥ 16 GPU） | scaling 数字、cluster scheduler 假设 |
+| 异构（H100 + L40S） | topology-aware routing |
+
+**最低组合**：单节点 4×H200 + 双节点 NVLink + IB。剩下两个 tier 可放 future work。
+
+---
+
+## 2. 报告模板
+
+### 2.1 主结果表（Table 1）
+
+```
+| Config | N | mean ± std | p50 [CI] | p90 [CI] | p99 [CI] | err% | timeout% |
+|--------|---|------------|----------|----------|----------|------|----------|
+```
+
+加注：trace name、time_scale、`max_input_len`、`request_timeout_s`、所有共用参数。
+
+### 2.2 Paired delta 表
+
+```
+| Pair | N pairs | mean delta [CI] | p50 delta [CI] | wins / losses | p-value |
+```
+
+`N pairs` = 两边都 successful 的 trial 数。`wins` = `latency_kvc < latency_baseline` 的 trial 数。
+
+### 2.3 分层表（Table 2）
+
+每个分层维度（§1.3）独立一张。
+
+### 2.4 Negative-result 章节（强制）
+
+paper 必须有专章列出：
+
+- KVC 在 ShareGPT 上比 baseline 慢的具体数字。
+- KVC 在 trace 哪些 percentile / slice 不胜。
+- 失败的 sweep（mooncake death、E3 crash）的诊断链路。
+
+→ 论文 reviewer 看见诚实的 negative result 会显著提高印象分。当前的 [V2_DEEP_ANALYSIS_ZH.md](V2_DEEP_ANALYSIS_ZH.md) §4 雏形可以扩成这一章。
+
+---
+
+## 3. 工具支持（本仓需要的脚本）
+
+| 脚本 | 状态 | 说明 |
+|---|---|---|
+| `scripts/analysis/recompute_summary.py` | ✅ 已有 | 修复 abort 污染的 latency；本协议主要数据入口 |
+| `scripts/analysis/stratified.py` | ⏳ 本分支新增 | 按 §1.3 维度切桶 + 输出表 |
+| `scripts/analysis/paired_compare.py` | ⏳ 本分支新增 | paired bootstrap，输出 §2.2 表 |
+| `scripts/analysis/plot_*` | ✅ 已有 | TTFT PDF、GPU 利用率、cache efficiency |
+
+→ 本分支的 stratified + paired 脚本 land 后，跑实验的合作者可以一条命令出表。
+
+---
+
+## 4. Artifact 要求（SOSP/OSDI AE）
+
+| 项目 | 标准 |
+|---|---|
+| Dockerfile | 单一 `Dockerfile.artifact`，4×A100/H100 即可启 |
+| 一键脚本 | `bash artifact/reproduce_main_table.sh`，1 小时内出 Table 1 |
+| 数据集 | 提供 `outputs/sample-*.jsonl` 子集（可 ~5GB 内）；full Ali 走 instruction |
+| 复现度 | bootstrap CI 与原文重叠即算复现，不要求 bit-exact |
+| 文档 | `artifact/README.md`，列出每张表 / 图对应的命令 |
+
+→ 本路线图 §M1 修复后再准备 artifact。
+
+---
+
+## 5. 自检清单（提 paper draft 前用）
+
+- [ ] 每张表 N ≥ 3，含 mean±std 与 95% CI。
+- [ ] 没有 "successful only" 字样；所有错误已列入 `err%`。
+- [ ] 所有 baseline 用同 `max_input_len` / 同 `request_timeout_s` / 同 `time_scale`。
+- [ ] 至少 3 个 trace + 1 个 synthetic adversarial。
+- [ ] 至少 1 个 non-SGLang baseline。
+- [ ] 有 negative-result 章节。
+- [ ] 有 KVC 在 single-turn workload 上的 dilution 数据。
+- [ ] 形式化部分：Algorithm 1/2/3 + Theorem 1/2，以及 D→P sync 完成后的 Theorem 4。
+- [ ] 失败模式 forensic：mooncake death、E3 crash、cold-D 都进 §Limitations 或 §Discussion。
+
+---
+
+## 6. 路线图衔接
+
+- [ ] Phase A — 实现本分支 `scripts/analysis/stratified.py` + `scripts/analysis/paired_compare.py`（无 GPU 可做）。
+- [ ] Phase B — 把现有 `kvc-real-ali-iter-v1` 的 600-req/15min 数据用新工具重出一份分层表 / paired 表，存入 `outputs/`（GPU 不需重跑）。
+- [ ] Phase C — 跑 ShareGPT + Synthetic adversarial baseline（GPU 需 ~12h）。
+- [ ] Phase D — 选 1 个非 SGLang baseline（推荐 vLLM + prefix caching）补齐 M4（GPU 需 ~24h）。
+
+---
+
+**核心句**：当前结果"看起来已经赢"，但按本协议重报后，赢的 magnitude 会缩小、赢的 slice 会窄化、负面 slice 会暴露。这是论文必须经历的过程；越早做越省事。

docs/FAILURE_MODES_ZH.md

@@ -0,0 +1,222 @@
+# Failure-mode Taxonomy
+
+**日期**：2026-05-13
+**性质**：集中清单 + 诊断手册
+**对象**：跑实验时遇到失败要立刻 lookup 的合作者；写 paper §Limitations 时需引用的人；reviewer 想问"你为什么觉得这次会更稳"时的答案
+
+本文把当前系统已识别的失败模式按"症状 → 根因 → 触发条件 → 当前缓解 → 真正的修复"梳成一张表。所有条目都有 forensic 链接到原始实验 doc。
+
+---
+
+## 0. TL;DR
+
+5 类已识别失败模式，按"是否阻碍 paper claim"分组：
+
+| 类别 | 名称 | 阻碍 paper | 真正修复 |
+|---|---|:---:|---|
+| **A. 控制层级联** | Mooncake "instance not alive" cascade | ✅ | admission backoff + per-D pending-seed budget |
+| **B. 路由偏置** | Cold-D / overlap-pinning | ✅ | first-principles overlap term redefinition |
+| **C. KV 抖动** | Evict storm（session-level evict） | ✅ | [BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md) |
+| **C'. KV 抖动** | Reseed storm（turn 1 大 seed 并发） | ✅ | per-D pending-seed budget + (C 缓解后频率自降) |
+| **D. Vendor 不变量** | streaming-session correction invariant crash (E3) | ❌（hotfix 已 land） | 删除 correction 路径（block-level evict 完成后） |
+
+A / B / C 三类是 Milestone 1 必须解决的；C' 是 A 的次因；D 已临时止血但根本修复绑在 C 上。
+
+---
+
+## 1. A — Mooncake "instance not alive" cascade
+
+### 1.1 症状
+
+- 客户端看：`RuntimeError: generate stream ended before producing any token`
+- D scheduler 日志：`[mooncake] Decode instance could be dead, dropping ...`
+- 整批请求被 abort，单一 sweep 在数分钟内从健康降到 80% failure（[E1_E2_RESULTS_ZH.md](E1_E2_RESULTS_ZH.md) E2：1054 / 1285 失败）
+
+### 1.2 根因（forensic 链路）
+
+```
+admission no-space (D KV pool 满)
+    → router 立刻 fallback 走 seed/reseed 路径
+    → 多个并发 seed 同时打 mooncake P→D
+    → P→D 出口排队，handshake 阶段超时
+    → mooncake 把对端标记 dead
+    → SGLang 把 dead 链路上的 in-flight req 全部 abort
+    → 客户端看到批量 generate-stream 中断
+```
+
+### 1.3 触发条件
+
+- D KV pool 接近满（≥ ρ·K_d，默认 0.95）
+- router fallback chain 把多个 reseed 在毫秒级窗口内发起
+- mooncake heartbeat 超时（默认窗口短）
+
+### 1.4 当前缓解
+
+- `--kvcache-seed-min-turn-id=2` 跳过 turn 1 大 seed，减少首爆（main 分支 stable 配置）
+- `--mc-transfer-timeout=1800s` 默认值（commit 905d671）减少假性 dead
+- `--request-timeout-s=180/300` 让客户端不至于看见整 hour 卡死，但不阻止 cascade 自身
+
+→ 这些都是治标，不是治本。E2 在 4×H200 NDR 真硬件下仍 80% 失败 ([E1_E2_RESULTS_ZH.md](E1_E2_RESULTS_ZH.md))。
+
+### 1.5 真正的修复（路线图 §S3）
+
+1. **admission RPC backoff + jitter**：拒绝时不立刻 fallback，给 D scheduler 喘息机会。
+2. **per-D pending-seed budget**：同时刻最多 K 个 seed 在 transfer 队列里，超出排队而不爆裂。
+3. **mooncake heartbeat 与 admission 解耦**：admission 路径不再 imply "对端 alive"。
+4. **Backpressure pause hint 闭环**（[SGLANG_PATCH_INVENTORY_ZH.md](SGLANG_PATCH_INVENTORY_ZH.md) §2.3 当前 EXPERIMENTAL）。
+
+---
+
+## 2. B — Cold-D / overlap-pinning
+
+### 2.1 症状
+
+- N=k decode workers，但只有 ~k-1 真正承载流量；某些 D 0 binding
+- Per-D load 直方图严重偏斜（E2：D0:600 / D1:685 / **D2:0**）
+- 整体 throughput 受最忙 D 限制；裸 latency 不一定差，但容量利用率差 33%+
+
+### 2.2 根因
+
+Inferact / Ali coding agent trace 在每个 session 开头有 ~12K 的"system prompt + tool schema"，这些 24-token 块在所有 session 之间共享 hash。kv-aware policy 的 `overlap` term 把它们当成"该 D 已经常驻这些 hash" → 任何新 session 都被 score 推向 D0/D1（最先 warm 的两个）→ D2 永远 0 overlap → 永远不被选 → 永远 cold。
+
+### 2.3 触发条件
+
+- 多 session workload + 共享 boilerplate prefix
+- `migration_reject_threshold > 0` 且 reject 从未触发（因为 D0/D1 还没满）
+
+### 2.4 当前缓解
+
+`KvAwarePolicy.load_floor_bonus`（commit 93fce42）：
+
+```
+floor_bonus = K * max(0, mean - assigned) / max(1, mean)
+```
+
+E3 实测 D2 binding 从 0 升到 22.5%（[E3_FINDINGS_ZH.md](E3_FINDINGS_ZH.md) §1）。
+
+→ 这是 patch，不是修复。`K` 是 magic number；boilerplate 的 hash 数量大于 `K / sticky_bonus` 时仍 cold。
+
+### 2.5 真正的修复（路线图 §S5）
+
+把 `overlap` 重新定义为 **"该 session 在该 D 上独占 prefix 的 hash 数"**：
+
+```
+exclusive_overlap(s, d) := |prefix_hashes(s) ∩ resident[d] ∩ session_owned[s]|
+```
+
+其中 `session_owned[s]` 排除其它 session 也持有的 hash。Boilerplate 共享 hash 不进 `exclusive_overlap`，score 自然分散。需要 D 端在 `admit_direct_append` 响应里返回 per-session resident hash 集合的 sketch（Bloom filter / minhash）。
+
+---
+
+## 3. C — Evict storm（session-level eviction）
+
+### 3.1 症状
+
+- 在 D 内存有压力的 workload 下，每 1–2 分钟出现 30–90K tokens 的 KV pool 释放峰
+- 紧随其后的同 session 请求触发 `Reseed`：P 重 prefill 50K + mooncake transfer 50K（3–7s）
+- TTFT 长尾完全由这类 reseed 主导（[V2_DEEP_ANALYSIS_ZH.md](V2_DEEP_ANALYSIS_ZH.md) §3.2）
+
+### 3.2 根因
+
+`SessionAwareCache.release_session` 一次性 `free([cache_protected_len, kv_allocated_len))`——即整段 session-exclusive 尾部。E3 实测：90 次 evict、平均一次 free 67,726 tokens、25/50 session 受影响（[KVC_EVICTION_GRANULARITY_DESIGN_ZH.md](KVC_EVICTION_GRANULARITY_DESIGN_ZH.md) §0）。
+
+→ 与 SGLang 标准 radix 的 leaf-by-leaf 渐进 evict 形成鲜明对比。这部分 KV 从未进 radix，所以享受不到 LRU 的细粒度蚕食。
+
+### 3.3 触发条件
+
+- D KV pool 接近满
+- `maybe_trim_decode_session_cache` 被 scheduler 触发（在 `DecodePreallocQueue` 检测到 `available_size() <= 0` 时）
+
+### 3.4 当前缓解
+
+- `--kvcache-session-soft-cap=N`（main 分支）：限制 D 上常驻 session 数 → 提前 trim，避免顶到爆
+- `--kvcache-direct-max-uncached-tokens=8192`（v2）：降低 direct path 吃 KV 的速度
+
+→ 都是放慢节奏，没有解决"单次 free 太大"的根本问题。
+
+### 3.5 真正的修复（路线图 §S1）
+
+[BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md)：让 streaming-session decode 输出每 turn finish 时 `inner.cache_finished_req` 进 radix → `release_session` 退化为 `dec_lock_ref` + 删 slot → radix LRU 按 24-token leaf 蚕食。
+
+预期：单次 evict 从 67K 降到 ≤ 500 tokens；reseed 频次降一个数量级。
+
+---
+
+## 4. C' — Reseed storm（turn 1 大 seed 并发）
+
+### 4.1 症状
+
+- workload 起步阶段（前 30–60s）所有 session 同时打 turn 1
+- 多个并发 `Seed`（每个 ~50–90K tokens）打 mooncake → 与 §1 cascade 重合
+
+### 4.2 根因
+
+`KvAwarePolicy` 启动阶段 `resident[d]` 全空，所有 D score 相同，但 ε 重试 + per-trial admit 不阻止并发。
+
+### 4.3 触发条件
+
+- trace `time_scale=1` 重放下，session 在原始到达密度内同时启动
+- 没有 per-D pending-seed 限流
+
+### 4.4 当前缓解
+
+- `--kvcache-seed-min-turn-id=2`：跳过 turn 1 seed 完全（main 分支 stable 配置）
+- 副作用：失去 turn-1 的 KV 注入，turn 2 必走 reseed（但反而稳定，因为 reseed 是分散在时间上的）
+
+### 4.5 真正的修复
+
+- per-D pending-seed budget（同 §1.5 第 2 项）
+- §3.5 完成后 evict 频次自降，间接降低 reseed 频次
+
+---
+
+## 5. D — Streaming-session correction invariant crash (E3 landmine)
+
+### 5.1 症状
+
+- D scheduler 抛 `AssertionError` at `schedule_batch.py:1646`：`seq_len - pre_len == req.extend_input_len`
+- 整个 D worker 进程退出 → router 看见对端死 → §1 cascade
+
+### 5.2 根因
+
+[E3_FINDINGS_ZH.md](E3_FINDINGS_ZH.md) §2：streaming-session correction（commit b8e6f13）把 `extend_input_len` 改写为 `max(0, fill_len - prefix_len)`，但下游 invariant 还从原始 fill_ids/prefix_indices 计算。当 `fill_len < prefix_len`（多 turn 累积 prefix > 当前 turn 增量）时数学上不可能满足。
+
+### 5.3 触发条件
+
+- streaming session 跨 turn 已 commit prefix 长于本 turn 的新增 fill_ids
+- E2 因 pipeline 阻塞从未跑到这个状态；E3 修了 cold-D bottleneck → pipeline 更快 → landmine 暴露
+
+### 5.4 当前缓解
+
+commit 986f351 的 pre-filter pass：在 `prepare_for_extend` 入口 drop 这类 req（让 client 看错误响应而不是 worker 崩）。是止血。
+
+### 5.5 真正的修复
+
+`schedule_batch.py:1572–1646` 这整段 correction 路径在 block-level eviction refactor 完成后**结构上不再需要**——[BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md) §3.7 已说明 refactor 后 fill_ids / prefix_indices 一致性由 radix `match_prefix` 自动保证。
+
+→ 不要再加更多 correction 子句；要删整段。
+
+---
+
+## 6. 失败诊断 cheat sheet
+
+跑 sweep 时按下表 lookup：
+
+| 你看到 | 大概率是 | 先查 |
+|---|---|---|
+| 客户端 `RuntimeError: generate stream ended before...` | §1 cascade | D scheduler log 搜 `instance could be dead` |
+| 某个 D `binding=0` 而其它 D 繁忙 | §2 cold-D | `per_decode_load` 直方图 |
+| TTFT p99 突然抬到 5–8s 量级 | §3 evict storm | `release_session` 调用频次 + 平均 free tokens |
+| Sweep 起步阶段失败率高、稳态低 | §4 reseed storm | mooncake transfer queue 在前 30s 的峰值 |
+| D worker 进程异常退出 | §5 invariant crash | scheduler log 搜 `AssertionError`、`extend_input_len` |
+
+---
+
+## 7. 与路线图的衔接
+
+- [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) Milestone 1 的第 1/3/4 项分别对应本表 C / A / B 的真正修复。完成 Milestone 1 后本表 §1–§4 应该都从"未修"降级为"已缓解"，§5 直接消失。
+- 论文 §Limitations 必须老实写出现状："we identify five failure modes; A/C are addressed by this work, B/C' are partially addressed, D is a transient artifact of the in-progress refactor."
+
+---
+
+**核心句**：把失败模式当 first-class artifact 来管理——每个失败都有"症状 → 根因 → 触发 → 缓解 → 真正修复"五字段，是把 prototype 推到 production-grade 的关键工具。reviewer 看见你能枚举失败远比看见你赢得 baseline 更让人信服。

docs/H200_DRIVER570_SETUP_ZH.md

@@ -0,0 +1,270 @@
+# H200 + Driver 570 上跑通本仓库的环境配置（含踩坑记录）
+
+**适用范围**：4× H200 节点 + NVIDIA driver `570.86.15` + 本仓库 `kvc-debug-journey-v1-to-v4` 或后续分支。
+**目标读者**：拿到一台新 H200 机器、需要快速跑通 sglang 0.5.10 vendor + mooncake RDMA + agentic-pd-hybrid 的下一个 SWE/research agent。
+**作者状态**：本文档定稿于 `h200-cu130 @ 初始 commit`，smoke test 已 RDMA 跑通 16 reqs / 0 error。
+
+---
+
+## 0. TL;DR（5 行）
+
+1. **`nvidia-smi` 的 "CUDA Version: 13.0" 是个陷阱**——它是 driver 能 forward-compat 跑的 runtime 上限，不是 driver 自己 API 版本。driver `570.86.15` 提供的 driver API 是 **cu12.8**。
+2. vendor sglang 0.5.10 的 `jit_kernel/` 用 `tvm_ffi` + ninja + nvcc binary 在首次调用每个 kernel 时编译。系统唯一 nvcc 在 `/usr/local/cuda-13.0/bin/`，cu13 编译出的 .so 会 NEEDED `libcudart.so.13`，driver 570 拒绝运行 → `cudaErrorInsufficientDriver`。
+3. 解法是**本地装一份 cu12.8 toolkit 到 `$HOME/cuda-12.8`**（不需要 root），让 tvm_ffi 走 cu12.8 nvcc，编译产物 NEEDED `libcudart.so.12`，driver 570 完美支持。
+4. mooncake wheel (`mooncake-transfer-engine 0.3.10.post2`) 也是 cu12 build，需要 `libcudart.so.12`——已经由 `nvidia-cuda-runtime-cu12` 包提供，在 venv 里。
+5. 每个 shell **必须 `source scripts/setup_env.sh`** 才能跑 SGLang。已封装好。
+
+---
+
+## 1. 一次性 setup（约 25min）
+
+```bash
+cd /path/to/agentic-pd-hybrid
+
+# (1) Python 环境 (~3min)
+uv sync
+
+# (2) cu12.8 toolkit 本地装（~5GB 下载 + 5min 解压 = ~15-20min）
+mkdir -p /tmp/cuda_dl && cd /tmp/cuda_dl
+wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda_12.8.1_570.124.06_linux.run
+sh cuda_12.8.1_570.124.06_linux.run \
+  --silent --toolkit --override \
+  --installpath=$HOME/cuda-12.8 \
+  --tmpdir=$HOME/tmp \
+  --no-drm --no-man-page
+
+# (3) 验证
+$HOME/cuda-12.8/bin/nvcc --version   # 应该看到 release 12.8, V12.8.93
+
+# (4) 回到 repo 根目录,首次 source（每个 shell 都要做）
+cd /path/to/agentic-pd-hybrid
+source scripts/setup_env.sh
+```
+
+`source scripts/setup_env.sh` 输出应是：
+```
+agentic-pd-hybrid env ready:
+  CUDA_HOME=/home/<user>/cuda-12.8 (12.8, V12.8.93)
+  libcudart.so.12 at .../.venv/lib/python3.12/site-packages/nvidia/cuda_runtime/lib
+  MC_TRANSFER_TIMEOUT=1800s
+```
+
+**`MC_TRANSFER_TIMEOUT=1800` (30 min) 替代 mooncake 默认 30s**——E2 forensic 发现 D 端 LRU eviction 会让 mooncake C++ control plane 被 starved 30+s，触发 `conn.py:1270` hair-trigger 永久 blacklist 整个 D 的 mooncake_session_id。1800s 给足缓冲，30 分钟还没回应才是真正"D 死了"。详见 `docs/E1_E2_RESULTS_ZH.md §5c`。`stack.py` 也对 worker subprocess 设了同名默认值。
+
+---
+
+## 2. Smoke test（验证整条链路）
+
+把 16 个合成 request 喂给 1P3D 拓扑，启用真 RDMA，跑通后才能动 E1/E2 实验。
+
+```bash
+# 假设已 source scripts/setup_env.sh
+mkdir -p outputs/smoke_rdma
+
+uv run --no-sync python -m agentic_pd_hybrid.cli make-small-append-trace \
+  --output outputs/smoke_rdma/mini_trace.jsonl \
+  --session-count 4 --turns-per-session 4 \
+  --initial-input-length 1024 --append-input-length 200 --output-length 50 \
+  --inter-turn-gap-s 2 --session-stagger-s 1
+
+uv run --no-sync python -m agentic_pd_hybrid.cli benchmark-live \
+  --trace outputs/smoke_rdma/mini_trace.jsonl \
+  --output-root outputs/smoke_rdma \
+  --mechanism pd-disaggregation --policy default \
+  --model-path /mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507 \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --force-rdma --ib-device mlx5_60 \
+  --gpu-budget 4 --time-scale 1 \
+  --concurrency-limit 4 --timeout-s 1800 --request-timeout-s 300 \
+  --session-sample-rate 1.0 --min-turns 1 --target-duration-s 600
+```
+
+**首次跑会慢 8-15min**（model load 196s + 5-10 个 JIT kernel 各编译 ~10-30s + warmup）。后续跑只 ~3-5min。
+
+**期望结果**：`request_count=16, error=0, abort=0, failure=0, execution_modes={'pd-disaggregation-router': 16}`。
+
+每个 worker 的日志应有 `installTransport, type=rdma`，表示 mooncake 真的走 RDMA 而不是 TCP loopback。
+
+---
+
+## 3. GPU ↔ RDMA HCA 映射（本机实测）
+
+8 块 ConnectX HCA，全部 ACTIVE / 400 Gb/s NDR / RoCE v2 (link_layer=Ethernet, GID Index 3)。Mooncake 按 NUMA / PCIe affinity 自动选 preferred：
+
+| GPU | preferred HCA | NUMA |
+|---|---|---|
+| cuda:0 | mlx5_60 | 0 |
+| cuda:1 | mlx5_88 | 0 |
+| cuda:2 | mlx5_98 | 1 |
+| cuda:3 | mlx5_42 | 1 |
+
+CLI 的 `--ib-device <name>` 只接单个设备名，给所有 worker 全局 override。Smoke test 默认填 `mlx5_60`（P worker 在 cuda:0 上 NUMA-local；D worker 在其它 GPU 上是 cross-NUMA 但能跑）。E1/E2 实验如果想最优，可以分 P/D worker 独立设环境变量，但目前 stack.py 不支持 per-worker `MOONCAKE_DEVICE`，要么所有 worker 同一个，要么走 mooncake auto（需把 `MC_MS_AUTO_DISC=0` 改回 1）。
+
+完整 8 块 HCA：`mlx5_22, _27, _42, _60, _88, _98, _126, _135`（NUMA 0/1/0/0/0/1/0/1 混杂）。
+
+---
+
+## 4. 踩过的坑（按时间线）
+
+### 坑 1：`nvidia-smi` 的 "CUDA Version: 13.0" 是误导
+
+`nvidia-smi` header 显示 `Driver Version: 570.86.15 / CUDA Version: 13.0` 让人以为机器支持 cu13。**这是 driver 能 forward-compat 跑的 CUDA runtime 上限**，不是 driver 自己 API 的版本。driver 570 的 driver API 上限是 cu12.8（参见 NVIDIA "CUDA Compatibility" 矩阵）。
+
+**正确判断方法**：跑 `torch.cuda.is_available()`，如果装了 cu13 build 的 torch 会报 `The NVIDIA driver on your system is too old (found version 12080)`。返回 `12080` 才是 driver 自己 API 版本（cu12.8）。
+
+### 坑 2：vendor sglang vs pip sglang 的 patch 差异
+
+仓库的 `third_party/sglang/python/` 是带项目自有 patches 的 SGLang 0.5.10 fork。**pip 上的 `sglang==0.5.10` 不包含核心 patches**——具体差异：
+
+| 文件 | pip 版 | vendor 版 |
+|---|---|---|
+| `srt/managers/scheduler.py` | 3621 行 | 3938 行 |
+| `admit_direct_append` 出现次数 | 2 | **11** |
+| `DirectAppendAdmissionReqInput/Output` | 没有 | **有**（核心 RPC） |
+| `_should_allow_local_prefill_on_decode` | 没有 | 有 |
+| `maybe_trim_decode_session_cache` | 没有 | 有 |
+| `decode_direct_waiting_queue` | 没有 | 有 |
+
+→ **必须用 vendor 版**。本分支已把 `pyproject.toml` 的 `sglang==0.5.10` 改成 `sglang` + `[tool.uv.sources] sglang = { path = "third_party/sglang/python", editable = true }`，`uv sync` 后会自动 editable 安装 vendor 版。
+
+历史上有些 sweep 脚本用 `PYTHONPATH=src:third_party/sglang/python` 在运行时切换，但用 `uv.sources` 把它装进 venv 更彻底，不会被 pip 的 sglang 偷偷 shadow。
+
+### 坑 3：cu13 切换是死路
+
+发现 driver 570 不兼容时第一个想到的路径是「装 cu13 PyTorch」。试过：
+
+1. 改 `pyproject.toml` 加 `[[tool.uv.index]]` 指向 `https://download.pytorch.org/whl/cu130`
+2. 同样改 vendor sglang 的 `pyproject.toml`（root 项目的 sources 不会传递给 transitive editable dep）
+3. `uv sync` 成功装上 `torch==2.9.1+cu130` 和 `nvidia-{nccl,nvjitlink,nvshmem,cusparselt,nvtx}-cu13`
+4. **但 driver 570 不支持 cu13 runtime**——`torch.cuda.is_available()=False`，CUDA init 报 `driver too old (12080)`
+
+→ cu13 路径需要 **driver 580+**。我们没有 root + 别人在用机器，所以放弃。本分支已 rollback 到 cu12 stack（pyproject 干净）。
+
+### 坑 4：`--disable-overlap-schedule` 不够
+
+第一次 smoke 崩在 `resolve_future_token_ids.cuh:49`，路径是 `event_loop_overlap_disagg_prefill`，怀疑是 overlap 模式特定 JIT kernel 问题。
+
+cli.py 给 PD worker 加了 `--disable-overlap-schedule` 后，event loop 切到 `event_loop_normal_disagg_prefill`，但**崩在另一个 kernel `fused_inplace_qknorm`**，错误码完全相同（`cudaErrorInsufficientDriver`）。
+
+→ 不是 overlap-specific，是 **整体 vendor sglang `jit_kernel/` 模块和 driver 570 不兼容**，任何 JIT kernel 都会崩在 `runtime.cuh:21` 的 `cudaOccupancyMaxActiveBlocksPerMultiprocessor` 调用（CUDA runtime 初始化时 driver feature 版本检查失败）。
+
+但 `--disable-overlap-schedule` 留着不会造成伤害，且能避免之后类似 overlap-path 特定问题。本分支保留它在 `cli.py:_topology_from_args`。
+
+### 坑 5：pip sgl_kernel vs vendor sglang/jit_kernel/ 是两套系统
+
+`pip install sglang-kernel` 提供 `.venv/lib/.../sgl_kernel/{flash_ops,flashmla_ops,spatial_ops}.abi3.so`——这是 AOT 预编译产物。
+
+`third_party/sglang/python/sglang/jit_kernel/` 是 vendor SGLang 0.5.10 内置的 **另一套 JIT 模块**，运行时用 tvm_ffi 编译。Smoke 崩在 vendor 的 jit_kernel，**降级 pip sgl_kernel 没用**（实测 0.4.0 / 0.4.1 同样崩）。
+
+### 坑 6：`nvidia-cuda-nvcc-cu12` PyPI 包没装 nvcc binary
+
+发现 cu13 nvcc 是 root cause 后，第一反应是 PyPI 装 cu12 nvcc 包：
+
+```bash
+uv pip install nvidia-cuda-nvcc-cu12==12.8.93
+```
+
+装上以后 `find .venv -name nvcc` **返回空**——这个 PyPI 包只装 `ptxas` 和 `nvvm/`，**没有 nvcc binary**（NVIDIA 出于分发限制不把 nvcc 放 PyPI）。
+
+→ 完整 nvcc 必须从 NVIDIA 官方 `.run` installer 或 apt 装。`.run` installer 可以装到 user-writable 路径不需要 root，本仓库选这条路。
+
+### 坑 7：tvm_ffi 通过 ninja 调用 nvcc
+
+vendor sglang 的 `jit_kernel/` 用 `tvm_ffi.cpp.extension`，源码在 `~/.local/lib/python3.12/site-packages/tvm_ffi/cpp/extension.py`。关键路径：
+
+```python
+def _find_cuda_home() -> str:
+    cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH")
+    if cuda_home is None:
+        nvcc_path = shutil.which("nvcc")
+        if nvcc_path is not None:
+            cuda_home = str(Path(nvcc_path).parent.parent)
+    ...
+```
+
+然后构造 ninja file：
+```
+nvcc = {_find_cuda_home()}/bin/nvcc
+```
+
+→ **设 `CUDA_HOME=$HOME/cuda-12.8` 就能 hook 整条编译链**。`scripts/setup_env.sh` 已经设好。
+
+JIT 编译产物缓存在 `~/.cache/tvm-ffi/sgl_kernel_jit_*/*.so`。如果之前用 cu13 nvcc 编过，要先 `rm -rf ~/.cache/tvm-ffi/sgl_kernel_jit_*` 再用 cu12.8 重编。
+
+### 坑 8：mooncake import path 与 onboarding 文档不一致
+
+`docs/ONBOARDING_NEXT_AGENT_ZH.md` §3.3 的环境验证写：
+```python
+from mooncake_transfer_engine import TransferEngine
+```
+
+但实际 PyPI `mooncake-transfer-engine 0.3.10.post2` wheel 的 import path 是：
+```python
+from mooncake.engine import TransferEngine
+```
+
+第一次 `from mooncake_transfer_engine` 会 `ModuleNotFoundError`。**ONBOARDING 文档应该更新**（本分支不动 onboarding，留给主 agent 决定）。
+
+### 坑 9：mooncake.engine import 必须有 libcudart.so.12
+
+`from mooncake.engine import TransferEngine` 在 fresh shell（未 source setup_env.sh）下报：
+```
+ImportError: libcudart.so.12: cannot open shared object file: No such file or directory
+```
+
+mooncake 的 `engine.so` 是 cu12 build，dynamic link `libcudart.so.12`。venv 里有但需要 LD_LIBRARY_PATH 暴露。`scripts/setup_env.sh` 已加。
+
+### 坑 10：Inferact 数据集 schema 与 agentic-pd-hybrid 期望不匹配
+
+`huggingface.co/datasets/Inferact/codex_swebenchpro_traces` 是 ShareGPT 格式（`{"from": "human/gpt", "value": "<text>"}`），不含 token 计数 / hash_ids / 时间戳。
+
+`agentic-pd-hybrid` 期望 JSONL：`chat_id, parent_chat_id, timestamp, input_length, output_length, type, turn, hash_ids[]`。
+
+→ 已写 `scripts/convert_inferact_to_trace.py`：tokenize（用 model 自带 tokenizer）+ 滚动 hash 切 24-token block + 伪造 timestamp。610 trials × 33 turns 处理约 37min，跑出 20,230 reqs（与 Inferact README 的 "20,230 total LLM calls" 完全一致）。
+
+输出 `outputs/inferact_codex_swebenchpro.jsonl`（1.3GB，被 `.gitignore` 排除不进仓库）。
+
+### 坑 11：sampling 默认 `--session-sample-rate 0.01`
+
+`benchmark-live` 跑的时候内部会先做 sampling。默认 1%，意味着 50 sessions 才抽 1 个。Mini smoke trace 4 sessions × 1% = 0 → `ValueError: Sampling produced no requests`。
+
+→ smoke test 命令显式加 `--session-sample-rate 1.0 --target-duration-s 600`。
+
+---
+
+## 5. 后续给下个 agent
+
+跑 E1 / E2 sweep 之前**每个 shell 第一件事**：
+
+```bash
+cd /path/to/agentic-pd-hybrid
+source scripts/setup_env.sh
+```
+
+然后用 ONBOARDING §3 的 sweep 脚本（参考 `scripts/sweep_ts1_migration_v2.sh` 作为底版）。注意几处针对本机的修改：
+
+1. **MODEL 路径**改成 `/mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507`（onboarding 写的 `/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/...` 不存在）。
+2. **TRACE 路径**：`outputs/qwen35-swebench-50sess.jsonl` 不存在；用 `outputs/inferact_codex_swebenchpro.jsonl` （converter 跑完后产生）。
+3. **`--ib-device`** 选 `mlx5_60`（cuda:0 NUMA-local）或视实验需要自选；onboarding 写的 `mlx5_0` 在本机不存在。
+4. **保留 cli.py 的 `--disable-overlap-schedule`** 不要删——理论上 cu12.8 toolchain 应该让 overlap 也能跑，但目前未验证 overlap path 没有别的潜在问题，留着是 zero-cost 保险。
+
+---
+
+## 附录 A：本分支的代码改动
+
+- `pyproject.toml`：sglang dep 改用 `[tool.uv.sources]` path source 走 `third_party/sglang/python`（editable）。
+- `src/agentic_pd_hybrid/cli.py:_topology_from_args`：给 prefill/decode worker 自动加 `--disable-overlap-schedule`。
+- `scripts/setup_env.sh`：env wrapper，每个 shell `source` 一次。
+- `scripts/convert_inferact_to_trace.py`：Inferact ShareGPT → agentic-pd-hybrid JSONL schema converter。
+- `docs/H200_DRIVER570_SETUP_ZH.md`：本文档。
+
+## 附录 B：被 `.gitignore` 排除的产物
+
+- `outputs/inferact_codex_swebenchpro.jsonl`（1.3GB）——converter 输出，用 `scripts/convert_inferact_to_trace.py` 重新生成
+- `outputs/smoke_rdma/`（含 mini trace + smoke run artifacts）
+- `third_party/codex_swebenchpro_traces/`（209MB，HF dataset 下载）—— `hf download Inferact/codex_swebenchpro_traces --repo-type dataset --local-dir third_party/codex_swebenchpro_traces` 重下
+- `~/cuda-12.8/`——cu12.8 toolkit，用 §1 步骤 (2) 重装
+- `.venv/`——`uv sync` 重建

docs/INDEX_ZH.md

@@ -0,0 +1,119 @@
+# 文档索引
+
+**目的**：让任何合作者在 10 分钟内找到他需要的文档；让 Reviewer 知道哪些先看。
+
+---
+
+## 0. 时间紧的 3 篇
+
+按这个顺序读完即可参与讨论：
+
+1. [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) — 项目当前进度、薄弱点、路线图。
+2. [KVC_ROUTER_ALGORITHM.md](KVC_ROUTER_ALGORITHM.md) — 算法形式化（Algorithm 1/2/3 + Theorem 1/2）。
+3. [V2_DEEP_ANALYSIS_ZH.md](V2_DEEP_ANALYSIS_ZH.md) §0 + §6 — v2 当前 win/lose snapshot。
+
+---
+
+## 1. 按主题分类
+
+### 1.1 进度 / 现状
+
+| 文档 | 内容 |
+|---|---|
+| [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) | 跨分支整合 + 路线图（本分支的总入口） |
+| [PROJECT_OVERVIEW.md](PROJECT_OVERVIEW.md) | 项目目标 + 三种 mechanism（pd-disagg / pd-colo / kvcache-centric）的术语区分 |
+| [ONBOARDING_NEXT_AGENT_ZH.md](ONBOARDING_NEXT_AGENT_ZH.md) | 接班 agent 30 分钟上手手册（来自 `kvc-debug-journey-v1-to-v4`） |
+
+### 1.2 算法 / 形式化
+
+| 文档 | 内容 |
+|---|---|
+| [KVC_ROUTER_ALGORITHM.md](KVC_ROUTER_ALGORITHM.md) | Algorithm 1（Route）/ 2（Admit）/ 3（Dispatch）+ Theorem 1（无饿死）+ Theorem 2（fast-path 命中下限） |
+| [MIGRATION_V1_FINDINGS_ZH.md](MIGRATION_V1_FINDINGS_ZH.md) | v1 thrashing pathology 的实测 + 为什么 reset-on-success 是关键修复 |
+
+### 1.3 实验结果
+
+| 文档 | 内容 |
+|---|---|
+| [V2_DEEP_ANALYSIS_ZH.md](V2_DEEP_ANALYSIS_ZH.md) | SWE-Bench 50 sess ts=1：v2 vs 4DP CA 的 6/8 win + TTFT p99 落后原因 |
+| [V2_RESULTS_ZH.md](V2_RESULTS_ZH.md) | v2 原始战报（headline 数字略乐观，请同时看 deep analysis） |
+| [E1_E2_RESULTS_ZH.md](E1_E2_RESULTS_ZH.md) | H200 + RDMA 上 E1（naive 1P3D + kv-aware）vs E2（KVC v2）；E2 80% failure 的 forensic |
+| [E3_FINDINGS_ZH.md](E3_FINDINGS_ZH.md) | E3（+load-floor bonus）16 min 触发 SGLang patch invariant crash |
+| [E1_E2_FIX_DESIGN_ZH.md](E1_E2_FIX_DESIGN_ZH.md) | Q1（mooncake death）+ Q2（cold-D2）的 fix 设计 |
+
+### 1.4 当前关键 design discussion
+
+| 文档 | 内容 |
+|---|---|
+| [KVC_EVICTION_GRANULARITY_DESIGN_ZH.md](KVC_EVICTION_GRANULARITY_DESIGN_ZH.md) | 架构层反思：session-level evict 与 KVC continuity 设计冲突 |
+| [BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md) | block-level evict refactor 的具体 API / 步骤 / 测试计划（本分支新增） |
+| [RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md](RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md) | reseed 慢路径时间线 + D→P 同步缺口的 forensic |
+| [D_TO_P_SYNC_CONTRACT_ZH.md](D_TO_P_SYNC_CONTRACT_ZH.md) | D→P sync 的接口契约、staleness budget、rollout 阶段（本分支新增） |
+
+### 1.5 评测 / 方法论
+
+| 文档 | 内容 |
+|---|---|
+| [EVALUATION_PROTOCOL_ZH.md](EVALUATION_PROTOCOL_ZH.md) | paper-quality 评测协议（N、CI、paired、stratify、baseline list、trace mix）—— 本分支新增 |
+| [REFACTOR_PLAN_V1_ZH.md](REFACTOR_PLAN_V1_ZH.md) | 为什么从 ts=10 切到 ts=1 |
+| [TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md](TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md) | ts=10 时代的结构性问题清单（多数已 supersede） |
+
+### 1.6 工程债 / 失败模式
+
+| 文档 | 内容 |
+|---|---|
+| [SGLANG_PATCH_INVENTORY_ZH.md](SGLANG_PATCH_INVENTORY_ZH.md) | 785 行 vendored SGLang patch 的归类清单（MUST-HAVE / WORKAROUND / EXPERIMENTAL / INSTRUMENTATION）—— 本分支新增 |
+| [FAILURE_MODES_ZH.md](FAILURE_MODES_ZH.md) | 5 类失败模式的诊断 + 缓解 + 真正修复（mooncake cascade / cold-D / evict storm / reseed storm / E3 invariant）—— 本分支新增 |
+
+### 1.7 环境
+
+| 文档 | 内容 |
+|---|---|
+| [H200_DRIVER570_SETUP_ZH.md](H200_DRIVER570_SETUP_ZH.md) | H200 + driver 570 + cu12.8 环境搭建 + 11 条 lesson learned |
+
+### 1.7 归档（仅历史参考）
+
+`docs/archive/` 下的内容已被新文档 supersede，不必看：
+
+- `AGENTIC_FIT_ANALYSIS_ZH.md`、`STRUCTURAL_VALIDATION_REPORT_ZH.md`：ts=10 早期分析。
+- `KVCACHE_CENTRIC_PROGRESS_ZH.md`：早期项目快照。
+- `KVC_DEBUG_JOURNEY_V1_TO_V5.md`、`V5_PROFILE_INVESTIGATION_ZH.md`：v1–v5 调优过程笔记。
+- `REFACTOR_PLAN_ZH.md`：v0 重构计划。
+- `SWEBENCH_EXPERIMENT_*.md`：早期实验日志。
+
+---
+
+## 2. 按角色推荐阅读路径
+
+### 2.1 我是新接手的 SWE/research agent
+
+1. 先读本文 §0 的 3 篇。
+2. 再看 [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) §3（薄弱点）+ §5（GPU-free 工作清单）。
+3. 选一个 Milestone 1 子项开始做。`docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md` 与 `docs/D_TO_P_SYNC_CONTRACT_ZH.md` 是已经准备好的两条工程主线。
+
+### 2.2 我是 paper reviewer / 审稿预读
+
+1. [KVC_ROUTER_ALGORITHM.md](KVC_ROUTER_ALGORITHM.md)：算法 + theorem。
+2. [V2_DEEP_ANALYSIS_ZH.md](V2_DEEP_ANALYSIS_ZH.md)：核心实测对比 + 我们自己识别的 limitation。
+3. [E1_E2_RESULTS_ZH.md](E1_E2_RESULTS_ZH.md)：真硬件 + RDMA 上的 ablation（含 E2 的 80% failure forensic，证明我们能解释失败）。
+4. [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) §3：我们自己列出的薄弱点与未来工作（不藏问题）。
+
+### 2.3 我是要复现实验的 student
+
+1. [H200_DRIVER570_SETUP_ZH.md](H200_DRIVER570_SETUP_ZH.md)。
+2. [EVALUATION_PROTOCOL_ZH.md](EVALUATION_PROTOCOL_ZH.md)：跑哪些 sweep、按什么协议比较。
+3. `scripts/sweep_ts1_migration_v2.sh`：v2 主 sweep；`scripts/sweep_e1_naive_1p3d.sh` / `scripts/sweep_e2_kvc_v2_rdma.sh`：E1/E2 ablation。
+
+### 2.4 我想看 control plane 与 admission
+
+1. `src/agentic_pd_hybrid/policies.py`：`KvAwarePolicy.select` 是 Algorithm 1 的实现。
+2. `src/agentic_pd_hybrid/replay.py`：`_invoke_session_direct` / `_invoke_kvcache_seeded_router` 是 Algorithm 3 的 orchestration。
+3. `third_party/sglang/python/sglang/srt/managers/scheduler.py`：D 端 `_admit_direct_append` 是 Algorithm 2 实现。
+
+---
+
+## 3. 这份索引的维护约定
+
+- 新加一份 design / experiment doc 必须在本文 §1 表格里加一行。
+- 文档归档（移到 `docs/archive/`）时本文同步删除条目或标 "已归档"。
+- 本文不写实质内容，只做导航；任何深入说明都在被指向的文档里。

docs/KVC_EVICTION_GRANULARITY_DESIGN_ZH.md

@@ -0,0 +1,228 @@
+# KVC Eviction Granularity — 设计审视 (架构层)
+
+**日期**: 2026-05-12
+**Status**: 架构审视 / 待 design discussion
+**Companion**: `docs/E1_E2_RESULTS_ZH.md`, `docs/E3_FINDINGS_ZH.md`, `docs/E1_E2_FIX_DESIGN_ZH.md`
+**Branch**: `h200-cu130`
+
+本文是 E2 → E3 迭代后的高层架构反思，**不是又一份 fix design**。前几轮 E2 → E3 我一直在加 local patches（load-floor bonus、Fix A skip-zero-extend、调 migration_reject_threshold 等），但 E3 实测数据迫使我们承认这些 patches 大局上看是 **KVC 在向 DP / naive PD-disagg 退化的轨迹**。
+
+---
+
+## 0. TL;DR
+
+1. **KVC 的 value proposition** 是"session pin 在 D 上、KV 跨 turn 连续累积、direct-to-D 快路径 0.04s TTFT"。
+2. **`SessionAwareCache.release_session` 在 trim 时一次性 free 整段 session-exclusive 尾部**：实测 E3 一次 trim 平均 free **67,726 tokens**（samples: 35K / 38K / 40K / 86K / 87K），不是 "几个 leaf block"。
+3. 被 evict 的 session 下次到来时必须**从客户端原 prompt 重 prefill 50-90K** + mooncake transfer 5-9 GB → **跟 naive PD-disagg 一模一样**。
+4. → 在 saturation regime 下 KVC 的 cache continuity 设计被自己的 eviction 抵消。**Session-level eviction 与 KVC 的设计意图冲突**。
+5. 真正的方向不是堆 patch，是 **改 eviction granularity**: 让 streaming-session 的 decode 输出 **progressively commit 进 radix tree**，由 SGLang 标准的 block-level LRU 蚕食最老的 leaf。SessionSlot 退化成纯 metadata。
+
+---
+
+## 1. 我们做对了什么，又错过了什么
+
+### KVC 的 design promise（来自 `KVC_ROUTER_ALGORITHM.md` §1）
+
+| Property | 设计意图 |
+|---|---|
+| Session 钉定 | Session `s` pin 在 `pin[s]` 这一个 D；同 session 的所有 turn 在同一个 D 上做 KV 累积 |
+| Direct-to-D 快路径 | `req.session ∈ M_d ∧ append_len ≤ τ_append ∧ cap_ok` → 仅 append 新 token，**不走 P→D mooncake transfer** |
+| TTFT 优势 | append-only path TTFT ≈ 40ms (历史 v2 在 SWE-Bench 的 fast-path p50) |
+| 集中 cache 而非 fragment | 同 session cache 集中在一个 D 上，命中率高 |
+
+### 我们当前实测在做什么（E3, killed at 1h12min）
+
+| 指标 | 实测值 | 与设计 promise 的偏离 |
+|---|---:|---|
+| Eviction 次数 | **90** | 设计假设 "session 一旦绑就持续累积" |
+| 平均每次 evict 释放 | **67,726 tokens** | 不是 "几个 leaf block"，是整段 session 尾部 |
+| 总释放 | **6,095,375 tokens** | 在 1h12min 里 trash 了 ≈ 8 个 session-pool 容量的 KV |
+| 触发 reseed 的 session 数 | 25 / 50 (50%) | 这些 session 每个被 evict-revisit 一次 = 付一次 50-90K re-prefill |
+| 单次 reseed 平均耗时 | 3-7s (P prefill + mooncake) | 跟 naive PD-disagg 持平 |
+
+**E1 对照**：0 eviction、0 retract、50 sessions 顺利完成。E1 用的是 `pd-disaggregation` mechanism，**没有 KVC 层、没有 admission RPC**，但反而保留了 cache continuity（router-side sticky 让 session 不挪窝）。
+
+> **讽刺**: E1 (naive 1P2D + kv-aware policy) **意外地** 比 E3 (KVC v2 + load-floor + RDMA) 更接近 KVC 设计意图——因为 E1 没有 admission 反馈链路，所以没人会触发那 90 次 session-level evict。
+
+---
+
+## 2. 为什么 session-level evict 是错的
+
+### `release_session` 实测语义（`session_aware_cache.py:250-281`）
+
+```python
+def release_session(self, session_id: str):
+    slot = self.slots.pop(session_id, None)
+    ...
+    if slot.last_node is not None:
+        self.inner.dec_lock_ref(slot.last_node, ...)        # 解 radix 锁 ✓
+
+    if slot.is_holding_kv:
+        start = slot.cache_protected_len
+        end = slot.kv_allocated_len
+        if start < end:
+            kv_indices = self.req_to_token_pool.req_to_token[
+                slot.req_pool_idx, start:end
+            ]
+            self.token_to_kv_pool_allocator.free(kv_indices)  # 显式 free 一段 KV
+        ...
+```
+
+`[cache_protected_len, kv_allocated_len)` 是 **session-exclusive 尾部**——从首 turn 提交 radix tree 之后所有累积的 decode output + 后续 turn 的 extend。在 Inferact workload 上：
+
+- `cache_protected_len` ≈ 首 turn 提交的 boilerplate 部分 (~12K)
+- `kv_allocated_len` ≈ 50-100K（多 turn 累积）
+- **释放范围 = 38-88K**
+
+这部分 KV **没有进 radix tree**，所以也享受不到 radix block-level LRU 的渐进式 shedding。`release_session` 一刀切。
+
+### 与 SGLang 标准 radix LRU 的本质差异
+
+SGLang 标准 `inner.evict()`（`base_prefix_cache.py` 接口由 RadixCache 实现）：
+
+```
+按节点 last_access_time 排序，从 leaf 开始 evict (因为 evict 中间节点会破坏树结构)
+每次释放一个 leaf node 的 KV indices
+lock_ref > 0 的节点不可 evict
+```
+
+**特性对比**:
+
+| | session-level (current) | block-level (SGLang radix) |
+|---|---|---|
+| 单次释放粒度 | 整段 session 尾部 (35-87K) | 一个 leaf node (~24 tokens / page-size) |
+| Recent prefix 保留 | ❌ 全丢 | ✅ 保留 (recent 访问 → 时间戳新 → 不被先 evict) |
+| Evict-revisit 成本 | 50-90K re-prefill | 仅丢的 leaf 部分 (≪ 50K) |
+| 与 session lifecycle | 强绑定 (是 lifecycle 退出动作) | 解耦 (lifecycle 仅做 lock_ref 管理) |
+
+### 为什么会变这样：SessionAwareCache 的双重职责混淆
+
+`SessionAwareCache` 设计承担了**两个本应分离的职责**：
+
+1. **Session lifecycle 跟踪** (合理)：streaming session 跨多个 req 复用 KV，需要在 turn 间保留 `(req_pool_idx, kv_committed_len, kv_allocated_len, last_node)` 这些字段，恢复给下个 turn 的 req。
+2. **Eviction granularity 决策** (问题所在)：把 session 当成 evict 的最小单位，绕过了 SGLang 标准 LRU 的 leaf-by-leaf 渐进 shedding。
+
+第 2 个职责本不该存在于 SessionAwareCache 里。SGLang radix 已经能处理 block-level LRU——前提是 session 的 KV 真的进了 radix 树。但**因为 session-exclusive 尾部没 commit 进 radix tree**，radix LRU 看不到它们，只能由 release_session 一次性大块 free。
+
+---
+
+## 3. 我们前几轮 patches 的总体轨迹
+
+按 commit 时间线审视，每一步看似在修当下 issue，整体方向却是 KVC → DP 退化：
+
+| Iteration | 改动 | 局部目标 | 大局影响 |
+|---|---|---|---|
+| E2 baseline | mechanism=kvcache-centric, worker admission | 跑出 KVC v2 头条数字 | D2 cold + cascade → 1054 failures (KVC 设计前提崩塌) |
+| E3 load-floor bonus | 让 fresh session 均匀分到 D2 | 解 cold-start 偏置 | 触发 migration → 25 sessions reseed → 暴露 evict granularity 问题 |
+| E3 → Fix A | 修 vendored SGLang `prepare_for_extend` 的 fill_ids<prefix_indices invariant | 防 decode-1 assertion crash | Patch 局部 bug，没动 evict 设计 |
+| **我之前提议: disable migration** | `--kvcache-migration-reject-threshold 0` | "让 session 不挪窝" | **会让 KVC 退化成 pd-disagg + load-floor**（admission RPC 还在但 migration 不生效） |
+| **更早提议: disable admission** | 砍 admission RPC | "省掉那个 RPC overhead" | **直接砍 KVC 的 direct-to-D fast path** (KVC_ROUTER_ALGORITHM.md §3.2 Algorithm 2 不存在) |
+
+用户每次都正确地阻止了进一步退化。**没有人在审视 evict granularity 这个根本问题**——直到现在。
+
+---
+
+## 4. 正确方向（粗描）
+
+**核心思路**: 让 streaming session 的 decode 输出 **progressively commit 进 radix tree**，由 SGLang 标准 radix LRU 蚕食最老的 leaf。SessionSlot 退化成纯 metadata。
+
+### 4.1 目标行为
+
+| 场景 | 当前行为 | 目标行为 |
+|---|---|---|
+| Session 累积 50K KV，D 满了 | release_session 一次释放 38K (整段 session-exclusive 尾部) | radix LRU evict 最老 leaf (可能是首 turn 的 boilerplate tail，~24 tokens) |
+| Session 被 evict 后再到来 | 必须 reseed 50K (P prefill + mooncake) | 仅 re-prefill 被 evict 的 leaf 部分 (e.g. ~5K) |
+| TTFT 对 evicted session 的影响 | 50-90K reseed = 3-7s | 5K append-prefill = ~200ms |
+| 不被 evict 的 session | 同 session 内 turns append-only | 同样 append-only ✓ (不变) |
+| KVC fast-path 命中率 | 91.6% (历史 SWE-Bench) / 38% (E3 Inferact, 因为 evict-revisit) | 应稳定在 >85% 即使 saturation |
+
+### 4.2 需要的 refactor scope
+
+按依赖排序，每一步可独立做但有耦合：
+
+1. **Streaming session decode output 增量进 radix tree** (vendor SGLang)
+   - 当前: decode output 累积在 `kv_allocated_len` 维度，但 radix tree 只记录到 `cache_protected_len`
+   - 改: 每 turn finish 时把新的 decode tail 通过 radix `cache_finished_req` 路径插入 radix 树
+   - 影响: streaming session 在 radix 树里有持续 growing 的 chain，每个 24-token block 一个 node
+   - 牵涉: `radix_cache.py` 的 insert 路径、`schedule_batch.py` 的 cache_finished_req hook、SessionSlot.save_from_req
+
+2. **SessionSlot 退化成纯 metadata**
+   - 当前: SessionSlot 拥有 `req_pool_idx` + `[cache_protected_len, kv_allocated_len)` 范围的 KV 索引所有权
+   - 改: SessionSlot 仅持有 `last_node`（指向 radix 树某 node）和 lock_ref 状态，不直接管 KV 范围
+   - 影响: `restore_to_req` 改成基于 radix `match_prefix` 重建 req 状态，不直接 reuse req_pool_idx
+
+3. **`release_session` 改为仅 dec_lock_ref + 删 slot metadata**
+   - 当前: 还 free `[cache_protected_len, kv_allocated_len)` 范围 KV
+   - 改: 只 dec_lock_ref → 让 radix LRU 自然 evict
+   - 影响: `maybe_trim_decode_session_cache` 不再"按 session 释放"，而是用 SGLang 现有的 `tree_cache.evict(required_tokens)`
+
+4. **`admit_direct_append` 的 capacity 检查改用 radix-resident 长度**
+   - 当前: `current_tokens = session.resident_tokens` (来自 SessionSlot)
+   - 改: `current_tokens` = radix tree 上该 session 实际 commit 的长度 = `match_prefix(session.last_node).matched_length`
+   - 影响: admission 评估的 "uncached = input - radix-resident" 更精确，evict-revisit 场景下 admission 反映出"只丢了一部分"而不是"全丢"
+
+5. **`prepare_for_extend` 的 streaming-session correction 重新设计**
+   - 当前: Fix A patches 的 fill_ids/prefix_indices invariant 是基于 session-exclusive 尾部的复杂 fixup
+   - 改: 如果 SessionSlot 不再拥有独立 KV 范围，整个 correction 路径需要重写或可能不再必要
+
+### 4.3 与 onboarding §4.4 D→P sync 的关系
+
+`docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md` §4 描述的 D→P 增量同步是**针对 reseed 自身成本**的 fix（让 P 端 backup 跟上，避免 reseed 时 P 重 prefill）。
+
+本文 §4 描述的 eviction granularity 是**针对 reseed 触发频率**的 fix（让 session 不被一次性 evict 整段，减少 evict-revisit）。
+
+**两者正交、互补**:
+- 单做 evict-granularity fix: reseed 频率下降，但偶发 reseed 仍然慢
+- 单做 D→P sync: reseed 自身快了，但仍然频繁触发
+- 都做: reseed 几乎消失、即使触发也快
+
+工程量都是 ~1-2 周量级，可并行启动。
+
+### 4.4 不是 local patch
+
+注意整个 §4.2 列表里没有"调一个 hyperparameter"或者"加一个 CLI flag"这种局部改动。这是 vendor SGLang 内部数据结构的 invariants 重新设计，不能通过更精确的 K 值或更宽的 substring filter 解决。
+
+---
+
+## 5. 我们不该再做的事 (anti-patterns)
+
+防止下个 agent 走同样的局部 patch 路径：
+
+1. **不要继续调整 `migration_reject_threshold`** — 这个参数只是控制"reject 后多久换 D"，跟 evict granularity 无关。调小让 migration 更频繁 → 更多 reseed → 更糟。调大 → blacklist 永久化 (v1 thrashing 问题)。
+2. **不要 disable migration** — 会让 KVC 退化到 sticky pd-disagg。失去 v2 的 reset-on-success 整体设计。
+3. **不要 disable admission** — 会砍掉 direct-to-D fast path 这个 KVC 唯一的差异化优势。
+4. **不要继续 tune `_decode_session_cache_low_watermark_tokens`** — 调高让 LRU 更激进 → 更多 evict → 更糟。调低让 LRU 不触发 → 顶到 retract decode → 更糟。是治标。
+5. **不要再加 `_ADMISSION_REJECTION_SUBSTRINGS`** — 之前修的 string filter bug (Q2 forensic) 让 migration counter 真的递增，反而暴露了 migration 本身的 reseed 成本。修这个 bug 没错，但显示出 migration 机制本身在 saturated 场景下是负收益。
+
+---
+
+## 6. 推荐 Decision Points
+
+| # | Question | 推荐 |
+|---|---|---|
+| D1 | 接受本文的诊断（session-level evict 是根本问题）？ | **Yes** |
+| D2 | 暂停 E1/E2/E3 ablation 线索，集中精力做 §4.2 refactor？ | **Yes** (current path 在用 GPU 时间确认已知结论) |
+| D3 | refactor 在 vendored SGLang 主线（kvc-debug-journey-v1-to-v4）还是新分支？ | 新分支 `feat/block-level-evict`（隔离 risk） |
+| D4 | 同时启动 §4.3 的 D→P sync（`feat/d-to-p-sync` 分支已预留）？ | 视团队带宽 |
+| D5 | 在 refactor 完成前对外的 paper 表述如何处理？ | 标"v2 系列在 saturation regime 下的 evict 行为是已识别的 limitation，§future-work 已 propose 修复" |
+
+---
+
+## 7. 给下个 agent 的接班
+
+**如果你接手要做 §4.2 refactor**，按顺序读:
+
+1. `KVC_ROUTER_ALGORITHM.md` §2-3 — KVC 设计意图
+2. 本文 §2.1, §2.2 — 实测 evict 行为
+3. SGLang vendor `mem_cache/radix_cache.py` — 标准 radix LRU 实现细节
+4. SGLang vendor `mem_cache/session_aware_cache.py` — 当前 SessionSlot 设计
+5. SGLang vendor `managers/schedule_batch.py` — prepare_for_extend 怎么用 session state
+6. `docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md` §4 — D→P sync 的工程 scope（互补 work）
+
+**关键 invariant 不变量**: SessionSlot.restore_to_req 必须保持幂等（chunked prefill 失败可能 retry 多次）。任何 refactor 都要测试此 invariant。
+
+**关键 testing pattern**: 单元化测试 streaming session 在 LRU 压力下的行为。具体：注入一个 fake `inner.evict()` 返回部分 leaf 被 evict 的状态，断言 SessionSlot.restore_to_req 仍然返回合法 req 状态（不抛 assertion，re-prefill 长度合理）。
+
+---
+
+**核心句**: 我们前 3 轮 patch 都在解 saturation 暴露的 secondary 问题（cold-D 偏置、admission 字符串 bug、streaming-session correction 边界），但**真正的 primary 问题是 SessionAwareCache 把 session lifecycle 跟踪和 eviction granularity 决策混在一起**。session 是 lifecycle 边界，**不应该是 eviction 边界**。Eviction 应该交还给 SGLang 已经做得很好的 block-level radix LRU。

docs/KVC_ROUTER_ALGORITHM.md

@@ -0,0 +1,356 @@
+# KVC-Router：面向 Agentic 多轮 LLM Serving 的 Session-Aware 调度算法
+
+**性质**：论文级形式化规范——用于团队内部对齐 + 外部读者 onboarding。
+**对象**：项目团队（统一术语）；论文 reviewer（算法定义）。
+**最近更新**：2026-05-11。
+
+本文给出本项目所开发的 **KVCache-Centric Router**（以下简称 "KVC-Router"）调度算法的形式化、与实现无关的定义。本文设计为可直接被论文引用，并作为"KVC 到底在谈论什么调度算法"的标准回答。
+
+对应的参考实现位于：
+- `src/agentic_pd_hybrid/policies.py` — `KvAwarePolicy`、`RoutingState`
+- `src/agentic_pd_hybrid/replay.py` — orchestration：admission RPC、reset-on-success、fallback chain
+- `third_party/sglang/python/sglang/srt/managers/scheduler.py` — D-worker 端的 admission 决策
+
+---
+
+## 1. 问题定义
+
+我们要服务一群多轮 agentic LLM session（如 Claude Code、Codex、Cursor 等 coding agent），底层是异构 worker 池，分成：
+- **Prefill workers**（`P`）：GPU 常驻的模型副本，针对长输入 prompt 的 batched prefill 做了优化。
+- **Decode workers**（`D`）：GPU 常驻的模型副本，配备 session-aware KV cache（"SessionAwareCache"），具备：(i) 跨 turn 保留 session 的 KV 状态；(ii) 在本地已缓存的 prefix 上做 append-prefill，无需绕回 `P`。
+
+在一个 agent turn 内，请求 `r` 到达时其对话 prefix 已经从前序 turn 累积；**新增**的 tokens（工具输出、用户消息等）构成小规模 **append**。驱动 KVC 设计的根本观察是：
+
+> 当 prefix KV **已经驻留在将要解码该请求的 D worker 上**，请求的 first-token 延迟仅由 *append* 大小决定（典型 O(10²–10³) tokens），而非完整 prompt 大小（典型 O(10⁴–10⁵) tokens）。
+
+Router 的工作就是最大化满足上述条件的请求占比，同时尊重容量约束、不造成 session 无限饿死。
+
+### 1.1 优化目标
+
+给定来自 `S` 个 session 的请求流 `R = (r_1, r_2, ...)`，最小化 SLO 加权的 TTFT 与端到端延迟混合：
+
+```
+   minimize   E[ w_ttft · TTFT(r) + w_lat · E2E_Latency(r) ]
+   subject to  capacity[d] ≤ K_d   对任意 D worker d 在任意时刻 t,
+               没有 session 被永久拒绝服务.
+```
+
+参考实现中通过 measurement 隐式取 `w_ttft = 1, w_lat = 1`；per-D KV 池预算 `K_d` 取 SGLang 启动时上报的 `max_total_num_tokens`。
+
+---
+
+## 2. 系统模型与记号
+
+### 2.1 集合
+
+| 符号 | 含义 |
+|---|---|
+| `P = {p₁, …, p_|P|}` | Prefill worker 池 |
+| `D = {d₁, …, d_|D|}` | Decode worker 池 |
+| `S` | Session 标识符集合（由上游 agent runtime 分配） |
+| `H` | KV block hash 的全集（本实现中每 `BLOCK_TOKEN_BUDGET = 24` tokens 对应一个 hash） |
+
+### 2.2 请求
+
+一个请求 `r` 是一个元组：
+
+```
+   r = ⟨ s(r),  t(r),  prefix_hashes(r),  append_len(r),  input_len(r) ⟩
+```
+
+其中：
+- `s(r) ∈ S` — session id
+- `t(r) ∈ ℕ` — 该 session 内的 turn index（0 = 首轮）
+- `prefix_hashes(r) ⊂ H` — 覆盖请求输入 prefix 的 block hash 集合
+- `append_len(r) ∈ ℕ` — 新到达、**不在** `prefix_hashes(r)` 中的 token 数
+- `input_len(r) = (|prefix_hashes(r)| · 24) + append_len(r)` — 总 token 数
+
+### 2.3 Router 状态 (`Σ`)
+
+Router 跨请求维护的全局状态：
+
+| 字段 | 类型 | 语义 |
+|---|---|---|
+| `resident[d]` | `set[H]` | Router 估计的 D `d` 当前 SessionAwareCache 中常驻的 block hash 集合（router 端估计，真值在 worker 上） |
+| `pin[s]` | `D ∪ {⊥}` | Session `s` 最近一次成功服务的 D；`⊥` 表示从未见过 |
+| `inflight[d]` | `ℕ` | 当前已派发给 `d` 但尚未完成的请求数 |
+| `assigned[d]` | `ℕ` | 累计派发到 `d` 的路由决策次数（负载 tie-breaker） |
+| `rejects[s,d]` | `ℕ` | per-(session, D) 的 admission 拒绝计数（v2 引入的 migration 机制） |
+
+### 2.4 超参数
+
+| 符号 | 默认值 | 描述 |
+|---|---|---|
+| `α`（`sticky_bonus`） | 1 | 匹配 `pin[s]` 的 D 在评分中获得的 bonus |
+| `τ_reject`（`migration_reject_threshold`） | 3 | (s, d) 被拒绝达此次数后，d 对 s 进入 blacklist |
+| `τ_append`（`kvcache_direct_max_uncached_tokens`） | 8192（v2） | 走 Direct-to-D 路径允许的最大 append 长度 |
+| `K_d` | 取自 SGLang `max_total_num_tokens` | per-D 的 KV 池预算 |
+| `ρ` | 0.95 | 容量高水位线（隐式由 SGLang 强制） |
+| `ε`（最大 fallback 重试数） | `|D| - 1` | router 在退化到 vanilla PD-disagg 之前最多探测几个 D |
+
+### 2.5 路由结果
+
+路由决策 `δ(r)` 取以下四种之一：
+
+| Mode | 含义 | KV transfer |
+|---|---|---|
+| `Direct(d)` | r 完全在 D `d` 上执行；D 在其常驻 KV 上做 append | **无**（快路径） |
+| `Seed(d)` | Session 首轮：P 做完整 prefill，KV 通过 mooncake 传到 `d` | 完整 input |
+| `Reseed(d)` | Session 之前在某个 D' 上，但已不再常驻；按 Seed 处理 | 完整 input |
+| `Fallback(p, d)` | Vanilla pd-disagg 路径（其它 D 均被 blacklist 或拒绝） | 完整 input |
+
+---
+
+## 3. 算法
+
+KVC-Router 由三个相互配合的过程组成：
+- **Algorithm 1 (`Route`)**：router 端基于评分的候选选择。
+- **Algorithm 2 (`Admit`)**：D-worker 端的 admission 决策（在 D scheduler 中执行，非 router）。
+- **Algorithm 3 (`Dispatch`)**：端到端 orchestration，把 Route + Admit + reset-on-success 串起来。
+
+### 3.1 Algorithm 1：`Route(r, Σ)` — 基于评分的候选选择
+
+```
+输入：请求 r，状态 Σ
+输出：候选 d* ∈ D（若所有 D 都被过滤后仍无候选，退化分支兜底返回最少被拒的 D）
+
+ 1.  blacklisted ← { d ∈ D : Σ.rejects[s(r), d] ≥ τ_reject }
+ 2.  C ← D ∖ blacklisted                                  // 候选 D 集合
+ 3.  if C = ∅ :                                           // 退化
+ 4.       return argmin_{d ∈ D} Σ.rejects[s(r), d]        // 选最少被拒的 D
+ 5.  for each d ∈ C :
+ 6.       overlap(d)  ← |prefix_hashes(r) ∩ Σ.resident[d]|
+ 7.       sticky(d)   ← 1 if Σ.pin[s(r)] = d else 0
+ 8.       infl(d)     ← Σ.inflight[d]
+ 9.       assn(d)     ← Σ.assigned[d]
+10.       score(d)    ← ⟨ overlap(d) + α·sticky(d),       // 主项
+                          sticky(d),                       // tie-1
+                          −infl(d),                        // tie-2（负载小者占优）
+                          −assn(d) ⟩                       // tie-3
+11.  return argmax_{d ∈ C} score(d)                       // 按字典序最大
+```
+
+**说明**：
+- 评分是 **4 元组按字典序比较**，不是单个标量——这样避免在不同维度之间调权重。
+- 第 10 行的主项 `overlap + α·sticky` 同时奖励 KV 复用与 session stickiness。取 `α=1`、`overlap` 以 block（24 tokens）为单位时，**任何一次 hash 命中都压制纯 sticky 的候选**。
+- 第 1–4 行的 blacklist 过滤防止永久绑死在已饱和的 D 上；与 Algorithm 3 的 reset-on-success 配合，限定了 migration 频率。
+
+### 3.2 Algorithm 2：`Admit(d, r, M, K)` — D-worker admission 决策
+
+在 D worker 自己的 scheduler 内部执行（非 router），这是 **KVC 的机制核心**：每个 D 自治判断能否把 `r` 当作 Direct（append-only）服务，还是必须改走 P 路径。
+
+```
+输入：D worker d，请求 r，d 上本地常驻的 session 集合 M_d，KV 池预算 K_d
+输出：⟨can_admit ∈ {True, False},  mode ∈ {Direct, Seed, Reseed, ⊥},  reason⟩
+
+ 1.  used_tokens ← Σ_{s' ∈ M_d} resident_tokens(s', d)     // D 自己的 bookkeeping
+ 2.  cap_ok ← (used_tokens + input_len(r)) ≤ ρ · K_d        // 高水位线 ρ ≈ 0.95
+
+ 3.  if s(r) ∈ M_d :                                        // session 在 d 上有常驻
+ 4.       if append_len(r) ≤ τ_append  and  cap_ok :
+ 5.           return ⟨True, Direct, ∅⟩                      // → 快路径
+ 6.       elif append_len(r) > τ_append :
+ 7.           return ⟨False, ⊥, "real-large-append"⟩
+ 8.       else :
+ 9.           return ⟨False, ⊥, "no-d-capacity"⟩
+
+10.  else :                                                 // session 在 d 上无常驻
+11.       if cap_ok :
+12.           mode ← Seed if t(r) = 0 else Reseed
+13.           return ⟨True, mode, ∅⟩                        // → 经 P 做 KV seeding
+14.       else :
+15.           return ⟨False, ⊥, "session-not-resident-no-capacity"⟩
+```
+
+**说明**：
+- 该过程通过同步 HTTP RPC（`/admit_direct_append`）从 router 调用。RPC 阻塞直到 D scheduler 给出权威答复——这是 v5 引入的 **"worker-mode admission"**，替换了更早的 router-端容量估算（系统性偏乐观）。
+- reason 字符串被回传给 router，用于：(i) 在 Algorithm 3 中驱动 fallback chain；(ii) 标注 `execution_mode` 字段便于分析。
+
+### 3.3 Algorithm 3：`Dispatch(r, Σ)` — 端到端 orchestration
+
+```
+输入：请求 r，状态 Σ
+输出：执行模式 μ ∈ {Direct, Seed, Reseed, Fallback}
+
+ 1.  retries ← 0
+ 2.  tried ← ∅
+ 3.  while retries < ε :
+ 4.       d* ← Route(r, Σ \ {对 tried 中的 d 已 bump 过的 rejects})
+ 5.       if d* = ⊥ : break                                  // 无候选
+ 6.       resp ← Admit(d*, r)                                // RPC 到 D scheduler
+ 7.       if resp.can_admit :
+ 8.           Σ.rejects[s(r), d*]  ← 0                       // ◀ reset-on-success（v2）
+ 9.           Σ.pin[s(r)]          ← d*
+10.           Σ.inflight[d*]       ← Σ.inflight[d*] + 1
+11.           if resp.mode = Direct :
+12.                在 d* 上完整执行 r（append-prefill + decode）
+13.                return Direct
+14.           else :                                          // Seed 或 Reseed
+15.                p ← round_robin_next(Σ, P)
+16.                在 p 上做 r 的 prefill
+17.                经 mooncake 把 KV(r) 从 p 传到 d*
+18.                在 d* 上 decode r
+19.                return resp.mode
+20.       else :
+21.           Σ.rejects[s(r), d*]  ← Σ.rejects[s(r), d*] + 1
+22.           tried ← tried ∪ {d*}
+23.           retries ← retries + 1
+24.
+25.  // ε 次重试耗尽——退化 Fallback 到 vanilla pd-disagg
+26.  p ← round_robin_next(Σ, P)
+27.  d ← round_robin_next(Σ, D)
+28.  通过 ⟨p, d⟩ 走 pd-disagg(r)
+29.  return Fallback
+```
+
+**维持的关键不变量**：
+
+1. **不会静默过载**：一个 D 永不接受会让 `used_tokens > ρ · K_d` 的请求（Algorithm 2 第 2 行）。
+2. **不存在永久饿死**：对任意 session `s`，只要曾在某 D `d*` 上成功过一次，之后 `Σ.rejects[s, d*] = 0`（Algorithm 3 第 8 行）。因此 blacklist 计数器不会对仍在某处成功获得服务的 session 累积——这阻止了 **v1 的 thrashing 病理**：原本 blacklist 计数器单调增长 + 退化 fallback 形成自放大的 round-robin 死循环。
+3. **migration 有界**：一个 session 从 D `a` 迁移到 D `b` 必须经过连续 `τ_reject` 次在 `a` 上失败、期间无任何成功。每个 session 生命周期内的最坏 migration 次数 ≤ `(|D| − 1) · τ_reject`。
+
+### 3.4 Reset-on-success：为什么这是关键修复（v1 → v2 演化）
+
+v1 实现**省略了** Algorithm 3 第 8 行——一旦 `(s, d)` 累积 `τ_reject` 次拒绝，d 对该 session **整个 run 永久 blacklist**。实测（Migration v1，见 `docs/MIGRATION_V1_FINDINGS_ZH.md`）触发了自放大的失效模式：
+
+```
+session s 在 d 上稳定服务 70 个 turn
+       ↓ 瞬时 burst 让 d 短暂饱和
+3 次到 d 的 admission 被拒 → rejects[s,d] = 3 → d 对 s 永久 blacklist
+       ↓ s 迁到 d'，d' 也在负载中 → 被拒 → blacklist
+       ↓ d'' 同理
+所有 D 都 blacklist → 退化 fallback round-robin → 每次重试都 bump 一次计数器
+                   → s 永远在 D 之间 thrashing，每次都丢失 KV residency
+```
+
+reset-on-success 关上了这个回路：只要 `s` 在任一 d 上真正完成一次 Direct，针对该 session 的 blacklist 立刻清零。该机制只对**持续性**（不是瞬时性）容量压力触发。
+
+---
+
+## 4. 性质
+
+### 4.1 Theorem 1（在有界 ε 下无永久饿死）
+
+*假设 `τ_reject ≥ 1` 且每个 D worker 的容量非零。则对任意能在 admission 时容下的 session `s`，Algorithm 3 在至多 `|D| · τ_reject` 次重试内返回 `{Direct, Seed, Reseed}` 之一；之后任意一次 Direct 成功即可清空 `s` 的所有 blacklist。*
+
+**证明概要**：每次循环要么成功（return）、要么恰好让某个 `rejects[s, d]` 计数器 +1（第 21 行）。经过 `|D| · τ_reject` 次迭代后，每个 D 要么对 `s` 已被 blacklist（`Route` 第 1 行会过滤），要么已成功（已终止）。在所有 D 都被 blacklist 的饱和点，`Route` 第 3 行返回最少被拒的 D，打破对称性，强制取得进展。∎
+
+### 4.2 Theorem 2（fast-path 命中下限）
+
+*假设 session `s` 在 D `d` 上已积累 KV residency `R_s ⊂ H`，且在某 turn `t > 0` 提交的请求 `r` 满足 `prefix_hashes(r) ⊆ R_s`、`append_len(r) ≤ τ_append` 且 admission 容量充足。则 Algorithm 3 将 `r` 路由为 Direct(d)。*
+
+**证明概要**：由 Algorithm 1，`overlap(d) = |R_s|` 取得最大值；结合 `α·sticky(d) ≥ 1`，d 的字典序得分严格高于任何 `prefix_hashes(r) ⊈ R_{s,d'}` 的 d'。故 `Route` 返回 d。`Admit(d, r)` 进入 `s ∈ M_d ∧ append ≤ τ_append ∧ cap_ok` 分支，返回 Direct。∎
+
+这是 **支持架构设计的机制级保证**：只要 residency、append 大小、容量三者同时成立，快路径就被**确定性地**选中；KVC 在典型场景下的 TTFT 优势是结构性属性，不是概率性。
+
+### 4.3 复杂度
+
+每个请求：
+- `Route`：`O(|D|)`（每个候选 D 算一次 score）。生产规模下 `|D| ≤ 8`，主要开销在 Python 层，≪ 1 ms。
+- `Admit`：D scheduler 内部 O(1)（查自己的 bookkeeping，无全局锁）。
+- Router 层的单请求总开销：`O(|D|)` 计算 + 1 次到目标 D 的 HTTP RTT（loopback 亚毫秒，跨机数据中心约 1 ms）。
+
+---
+
+## 5. 与 baseline 的对比
+
+| 性质 | Vanilla pd-disagg | DP（cache-aware） | **KVC-Router**（本文） |
+|---|---|---|---|
+| P/D 分离 | 是（`|P| + |D|` GPU） | 否（每个 worker fused P+D） | 是 |
+| 跨 turn cache locality | 无（每个请求都 P→D 传 KV） | 仅在单 fused worker 内部走 hash prefix 路由 | session 钉在某 D 上，本地 append-prefill |
+| 同 session cache 集中度 | 无 | 散到 `|D|` 个 worker（每个占 1/|D|） | 集中在一个 D（整段常驻） |
+| 最坏 turn-2 prefill 工作量 | 完整 input 经 P→mooncake→D | 在目标 worker 上做完整 prefill（带 prefix cache 命中） | 本地 `append_len ≤ τ_append` tokens |
+| 容量感知 admission | 无（router 盲发） | 隐式靠 worker 队列深度 | 显式的 per-D `Admit()` 决策 |
+| Migration 机制 | N/A | N/A | 带 reset-on-success 的 reject-counter blacklist |
+| Idle prefill 成本 | 是——P 永远在算 | 否 | 是——P 只在 cache miss 时启用（本工作 SWE-Bench 评测下约 8% 请求） |
+
+KVC 的关键架构权衡：**用 P 端 GPU 闲置换 D 端 TTFT 稳定性**。在 per-session cache 复用率高的 agentic workload 上（Inferact 的 Codex trace 报告 94.2% cache hit；我们的 SWE-Bench replay 实测 91.6% Direct 命中），这个交换显著有利。在 session 短或 cache hit 低的 workload 上，权衡反转、DP 胜出。
+
+---
+
+## 6. 符号速查表
+
+| 符号 | 含义 |
+|---|---|
+| `P, D` | Prefill / Decode worker 池 |
+| `s(r), t(r)` | 请求 r 的 session id 与 turn index |
+| `prefix_hashes(r)` | r 输入 prefix 的 KV block hash |
+| `append_len(r)` | r 中新增（未缓存）部分的 token 数 |
+| `Σ.resident[d]` | Router 对 d 缓存 block 集合的估计 |
+| `Σ.pin[s]` | session s 最近一次成功的 D |
+| `Σ.rejects[s,d]` | per-(s,d) 的 admission 拒绝计数 |
+| `α` | sticky bonus 权重（默认 1） |
+| `τ_reject` | migration 阈值（默认 3） |
+| `τ_append` | Direct 路径允许的 max append 大小（v2 默认 8192） |
+| `K_d` | D worker d 的 KV 池预算 |
+| `ρ` | 容量高水位（默认 0.95） |
+| `ε` | fallback 重试上限（默认 `|D| − 1`） |
+| `δ(r)` | 路由决策：`Direct(d)` / `Seed(d)` / `Reseed(d)` / `Fallback(p, d)` |
+
+---
+
+## 7. 本工作评测中实际使用的默认参数
+
+| 参数 | 取值 | 说明 |
+|---|---|---|
+| `|P|, |D|` | 1, 3（1P3D 配置） | 单机 4× H100 80GB |
+| `α` | 1 | |
+| `τ_reject` | 3 | |
+| `τ_append` | 8192 | v2 调优后取值（v0/v1 用 2048） |
+| `K_d` | 92104 tokens | SGLang 按 `mem_fraction_static=0.835` 自动算出 |
+| `ρ` | 隐式 ~0.95 | 由 SGLang 的 `max_total_num_tokens` 强制 |
+| `ε` | 2 | `|D| − 1 = 2` |
+| 每次 run 的 session 数 | 52 | SWE-Bench 50sess trace |
+| 总请求数 | 4449 | |
+| Time-scale | 1.0（真实 trace 时序） | |
+| 并发 | 32 | |
+
+---
+
+## 8. Anti-patterns（KVC **不**是什么）
+
+1. **KVC 不仅仅是 kv-aware routing**。DP 和 KVC 都可以跑 `kv-aware` policy；KVC 在此之上加了三件事：(i) session 钉定，(ii) worker 端 admission，(iii) 带 reset-on-success 的 migration。如果在比较 "KVC vs DP" 时缺这三个要素的任何一个，**测的就不是 KVC 与 DP 的差异**。
+
+2. **KVC 在 policy 项里不直接感知容量**。`Route` 不查 per-D 容量；容量感知完全经由 `Admit` 拒绝来传导。我们刻意做了这层分层——把容量判断放进 `Route` 会引入"换 D"的决策空间，导致 orphan KV 滞留问题。
+
+3. **KVC 不保证 load balance**。一个 session 若能舒服地装在某个 D 上，可能永远钉在那里，而其它 D 大部分时间空闲。在低容量压力下这是设计意图；高压力下 Theorem 1 的 migration 会触发再均衡。
+
+4. **`Fallback` 不是"降级路径"**。它和 vanilla pd-disagg 请求结构性等价，延迟特征相同。KVC 的价值在于让 Fallback 占比在典型 agentic workload 下 ≪ 10%。
+
+---
+
+## 9. 公开问题（reviewer 关注点）
+
+以下问题在当前评测中尚未解决，主动列出以保持透明：
+
+1. **Session 钉定相对于纯 P/D disaggregation 的边际贡献是多少？** 需要 `naive 1P3D` 对照实验（vanilla SGLang xPyD，不带 KVC 层）——仓库当前缺失（见 `docs/V2_DEEP_ANALYSIS_ZH.md §4.7`）。
+
+2. **Algorithm 3 在更高压下行为如何**（例如 ts=10 加速、session 数 ≫ |D|·K_d/peak_input）？当前 ts=1 评测对应真实 agentic 区间，但算法在更高负载下的鲁棒性未经实验验证。
+
+3. **真 RDMA 下的 reseed 代价**：本次评测的 3–7 s reseed 延迟由两段组成——P 端 re-prefill（1.5-3s）+ P→D mooncake transfer（1.5-4s）。当前 sweep 用的是 TCP loopback；启用 IB/RoCE（节点有 mlx5_0/_1 @ 200 Gb/s × 2 active，需在 sweep 加 `--force-rdma --ib-device mlx5_0`）只能压缩 transfer 段到 ~200ms，**不动 re-prefill 段**。预期 TTFT p99 从 1.28s 降到 ~0.7s（仍输 DP 0.43s）。待独立验证。
+
+4. **D→P 增量 KV 同步（核心 future-work 缺口）**：reseed 长尾的真正消除需要让 P 端 backup 跟上 D 的 direct-to-D append 增长。经独立 forensic 审查，**当前代码、vendored SGLang、mooncake 三层均无 D→P KV transfer 实现**：mooncake `MooncakeKVManager` 是 PREFILL=sender / DECODE=receiver 的硬角色分支（`add_transfer_request` 上有 `assert disaggregation_mode == PREFILL` 硬约束），`BaseKVSender` / `BaseKVReceiver` 抽象无 bidirectional slot，`session_aware_cache.release_session` 在驱逐时只调 `kv_pool_allocator.free()` 无出站，`_commit_prefill_backup_residency` 唯一 caller 是 seed/reseed 路径；`capacity-backup` policy 的真实语义只是"reseed 完不关 P streaming session"——backup 是 seed-time 的静态快照，不随 direct-to-D append 同步。要实现 D→P 增量同步，工程量 ~1-2 周，最难的不是 mooncake 加 D-sender / P-receiver 角色（~400 LOC），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者（本 worker model 输出）。这是论文里最值得做的 contribution 之一。
+
+5. **v2 代码路径下的确定性**：v0 代码库的 ts=1 N=3 categorical 确定性已经证实；新增的 reset-on-success 分支与 threshold=8192 路径未被独立 re-validate。两个额外的 N=1 run 即可解决。
+
+---
+
+## 10. 论文引用建议
+
+论文中提到本算法时建议表述：
+
+> "We use the KVC-Router scheduling algorithm (Algorithms 1–3 of [our paper], formally defined in our supplementary materials). The router selects a decode worker by lexicographic scoring on `(overlap+α·sticky, sticky, −inflight, −assigned)` (Algorithm 1), defers the admission decision to the chosen worker via a synchronous RPC (Algorithm 2), and maintains a per-(session, decode worker) rejection counter that is reset on every successful Direct admission (Algorithm 3). This last detail — reset-on-success — is what distinguishes our v2 from the unstable v1 implementation that exhibits self-amplifying session thrashing."
+
+---
+
+**附录 A — 算法步骤到代码实现的对照**
+
+| 算法步骤 | 文件 | 符号 |
+|---|---|---|
+| `Route` 第 5–11 行 | `policies.py:189–202` | `KvAwarePolicy.select` 内层循环 |
+| `Route` 第 1–4 行（blacklist 过滤 + 退化分支） | `policies.py:182–187, 204–211` | `migration_reject_threshold`，`select` 的 fallback |
+| `Admit` | `third_party/sglang/python/sglang/srt/managers/scheduler.py` | `handle_admit_direct_append_request` |
+| `Dispatch` 第 8 行（reset-on-success） | `replay.py: _run_request` | finish 路径中的 reset |
+| `Dispatch` 第 21 行（记录 reject） | `replay.py: _run_request` | `state.record_admission_reject(...)` |
+| 超参数 `τ_append` | CLI flag | `--kvcache-direct-max-uncached-tokens` |
+| 超参数 `τ_reject` | CLI flag | `--kvcache-migration-reject-threshold` |

docs/MIGRATION_V1_FINDINGS_ZH.md

@@ -0,0 +1,283 @@
+# Migration v1 实验发现：blacklist 永久性导致 thrashing
+
+**日期**：2026-05-08
+**状态**：v1 run 进行中（~23% 完成时的中期分析）
+**前置文档**：
+- `docs/REFACTOR_PLAN_V1_ZH.md` §6.2（v1 设计）
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` §2.1（§1 starvation claim）
+
+**触发**：v1 实现的 session migration（rejection blacklist 机制）部署后，观测到 session-level thrashing——某些 session 在 3 个 D 之间 round-robin 高达 75-116 次。本文记录中期数据、根因诊断、v2 设计。
+
+---
+
+## 0. TL;DR
+
+1. **v1 修复了 §1 starvation 但引入了新的 thrashing 失效模式**——不是 admission 过严，是 blacklist 永久累积的设计 bug
+2. **核心证据**：session 6880 在 decode-1 上稳定 70 turns，然后某瞬时 burst 把 reject 计数累积到阈值，被永久 blacklist，之后陷入 3-D 间 round-robin 死循环
+3. **85% admission 拒绝是 `session-not-resident`**——非 D 真容量问题，而是迁移后"新 D 第一次见你"的正常语义
+4. **v2 设计**：reset-on-success 让 reject 计数在成功 turn 后清零，只有**持续**失败才迁移
+5. **深层观察**：baseline 的"100% pin 但稳定"可能比"分布均匀但 thrashing"更好——糟糕的优化可能比不优化还糟
+
+---
+
+## 1. v1 实施回顾
+
+### 1.1 改动文件
+- `src/agentic_pd_hybrid/policies.py`：`RoutingState.session_d_rejects` Counter；`KvAwarePolicy.migration_reject_threshold=3` skip blacklisted D；degenerate fallback 选最少拒的 D
+- `src/agentic_pd_hybrid/replay.py`：`_run_request` 末尾 `state.record_admission_reject(sess, D)`（基于 execution_mode 子串匹配）；`_fallthrough_reason` 把 `pd-router-fallback-large-append-*` 拆成 `session-not-resident` / `real-large-append` / 等
+- CLI / benchmark wiring
+
+### 1.2 v1 假设（事后看部分错误）
+- "reject 计数 + 阈值 3 = 容忍短期波动 + 持续失败迁移" ← **错**，counter 永久增长导致迁移成必然
+- "迁移到新 D 后 session 在新 D 稳定下来" ← **部分错**，迁移到的新 D 也很可能很快 reject
+- "session-not-resident 不会触发计数" ← **大致对**，但下游 fallback 可能间接触发
+
+---
+
+## 2. 中期数据（1023/4449 reqs，~23%）
+
+### 2.1 头部指标 vs baseline
+
+| 指标 | baseline kvc_1p3d_run1 | v1（中期） |
+|---|---:|---:|
+| Per-D 调用分布 | 1502/1445/1502（±3.8%）| 796/785/779（**±1.1%**，更均衡）|
+| Per-D 峰值 token_usage | 0.99/0.99/0.99 | 0.31/0.30/0.00（**容量充裕**，未顶到 1.00）|
+| KVTransferError | 5（全程）| 6（中期，趋势相近）|
+| 已见 sessions | 52（全程）| 29（中期）|
+
+**好的方面**：
+- 负载均衡度跃升（±26%→±1.1% if normalized）
+- D 容量从未饱和——§2 假设的"D drain time"机制配合 ts=1 充分发挥
+- 0 sessions 永久 stuck 在饿死状态
+
+### 2.2 Migration 触发情况（已见 29 sessions）
+
+| 类别 | 数量 | 占比 |
+|---|---:|---:|
+| 仍 pin 在 1 个 D | 9 | 31% |
+| 触碰 2 个 D | 3 | 10% |
+| **触碰所有 3 个 D** | **17** | **59%** |
+
+**D-切换次数分布**：
+- mean = 26 次/session
+- median = 16 次
+- **max = 116 次**
+- 15 sessions 切换 >10 次（明显 thrashing）
+- **6 sessions 切换 >50 次**（严重 thrashing）
+
+---
+
+## 3. 根因诊断：session 6880 的轨迹
+
+### 3.1 数据
+
+```
+turn 0-70:   全部在 decode-1   (71-turn 稳定 streak)  ← §1 baseline 行为
+turn 71-150: 在 3 个 D 间剧烈 thrashing
+              decode-0: 26 个短 streak
+              decode-1: 25 个短 streak
+              decode-2: 25 个短 streak
+              平均 streak 长度 = 2 turns
+              total streaks = 76
+```
+
+### 3.2 解读
+
+**前 70 turn 完美稳定**：session 6880 在 decode-1 上正常运行 70 个 turn，每次都成功，是 baseline §1 "100% pin" 的复现——稳定但不公平（其他 session 没分到 decode-1 的资源）。
+
+**第 71 turn 后崩溃**：
+1. 某个瞬时 burst（其他 session 的活动？）让 decode-1 短暂饱和
+2. session 6880 在 decode-1 上连续 3 次被 admission 拒（`no-space` 或 `d-session-cap`）
+3. v1 的 `state.session_d_rejects[(6880, decode-1)]` 累积到 3 → blacklist
+4. policy 改选 decode-0 → 同样发生 → blacklist
+5. 改选 decode-2 → 同样 → blacklist
+6. **3 D 全部 blacklisted** → degenerate fallback 在 3 D 间 round-robin
+7. 每次 round-robin 又触发新 reject → 计数继续涨 → 永远在 thrashing 死循环
+
+### 3.3 admission 数据交叉验证
+
+中期 1932 admission events 解构：
+
+| mode × can_admit × reason | count |
+|---|---:|
+| `direct_append, True, None` | 1721（成功）|
+| `direct_append, False, session-not-resident` | **62** |
+| `seed, True, None` | 142（成功）|
+| `seed, False, no-space` | **11** |
+
+**只有 11 个 "no-space" 才是真容量拒绝**（占总 admission 的 0.6%）。62 个 "session-not-resident" 是迁移后"新 D 第一次见你"的正常语义。
+
+但因为 v1 用 `_is_admission_rejection_mode` 通过 execution_mode 子串匹配，下游 fallback chain 会把 `session-not-resident` 也间接累积到计数器（fallback 链路本身可能触发 session-cap）。
+
+---
+
+## 4. 设计 bug 三层
+
+### 4.1 Bug 1：blacklist 永久性
+
+```python
+# policies.py 当前实现
+if rejects >= self.migration_reject_threshold:
+    continue  # skip this D forever
+```
+
+`session_d_rejects[(sess, D)]` 是单调递增 Counter。一旦达到阈值，**永远**被 skip。但 D 的容量是动态的——70 个 turn 后短暂饱和不代表它后续不能服务这个 session。
+
+### 4.2 Bug 2：degenerate fallback 加剧问题
+
+当所有 D 都被 blacklist：
+```python
+best_decode_worker_id = min(
+    (w.worker_id for w in topology.route_workers),
+    key=lambda wid: state.session_d_rejects.get((sess, wid), 0),
+)
+```
+选"最少被拒"的 D。但每次 fallback 又增加该 D 的计数 → 下次选另一个 D → 形成完美 round-robin，永远走不出 thrashing。
+
+### 4.3 Bug 3：信号归并粗糙
+
+`_is_admission_rejection_mode` 子串匹配 `session-cap` / `no-d-capacity` / `d-backpressure`，但执行链路可能这样：
+
+```
+direct_append → session-not-resident（85% 占比，正常迁移后语义）
+  → fallback 试 seed
+    → seed admit ok（142/153 = 93%）→ execution_mode = pd-router-d-session-reseed-*（不计 reject）
+    → seed no-space（11/153 = 7%）→ execution_mode = pd-router-fallback-X-no-d-capacity（计 reject）
+```
+
+绝大多数 fallback 不会触发 reject 计数。但 thrashing 一旦开始，很容易踩到那 7% no-space 路径，calculator 增长一次。15+ 次 thrashing 后，单 D 计数累到 3 完全可能。
+
+**所以设计 bug 不在信号粗糙，而在永久累积 + degenerate round-robin。**
+
+---
+
+## 5. 深层观察：稳定 vs 公平的 trade-off
+
+| | baseline（v0）| v1 |
+|---|---|---|
+| 公平性 | 18/52 永久饿死 | 0 永久饿死 |
+| 稳定性 | 100% pin（结构稳定）| 6/29 严重 thrashing |
+| Per-D 负载均衡 | ±26% | ±1.1% |
+| 大 session 体验 | 慢但稳定（每 turn 都走 fallback ~1.0s）| 不稳定 + 频繁 D 切换 + 丢 KV state |
+
+**预想反直觉的结果**：v1 在头部指标（per-D 均衡）赢，但在 session 体验可能输——
+- baseline 的 fallback 路径有稳定 ~1s latency
+- v1 的 thrashing session 每次 D 切换都 close 旧 session、丢 KV、新 D 上重新建立——有可能 latency 反而更高
+
+需要等 run 结束的 lat mean / TTFT mean 数据验证。**糟糕的优化可能比不优化还糟。**
+
+---
+
+## 6. v2 设计
+
+按 ROI 排序的修复层。**先做 #1，验证后再决定是否需要 #2/#3**。
+
+### 6.1 v2-fix-1：reset-on-success（最高 ROI）
+
+```python
+# replay.py _run_request 末尾，在 state.finish 后
+if execution.execution_mode == "kvcache-direct-to-d-session":
+    # 这次 direct-to-D 成功 = D-X 仍能服务这个 session
+    # 清零累积的 reject 计数（消除永久 blacklist）
+    state.session_d_rejects[(request.session_id, decision.decode_worker_id)] = 0
+```
+
+**预测效果**：
+- session 6880 在 decode-1 上 70 个成功 turn 把计数反复清零
+- 即使中间出现 1-2 次瞬时 reject，下次成功立刻清零
+- 只有**持续**失败（reject 后 reject 后 reject，没有夹杂 success）才能累到阈值
+- 真饿死的 session（如 35680/39360 input >92K）才会触发迁移
+
+**工程量**：~5 行代码 + 1 个 smoke + 1 个完整 run（~5.5h）
+
+### 6.2 v2-fix-2：sliding window（如果 #1 不够）
+
+把 `Counter` 改成 `dict[(sess, D), deque[float]]` 存最近 K 次拒绝时间戳。判断时用最近 N 秒（或 N 个 turn）内的次数。
+
+更稳健但更复杂。**若 #1 已能彻底解决 thrashing，跳过此项。**
+
+### 6.3 v2-fix-3：reject 类型分离（如果 #1 + #2 不够）
+
+把 admission reason 显式传到 _run_request，区分：
+- `no-space` / `session-cap` / `backpressure` → 计 reject
+- `session-not-resident` → 不计
+
+需改 `ExecutionResult` 加 `admission_reject_reason` 字段，并在 fallback 链路传递。**不在第一轮**——先看 #1 是否够用。
+
+### 6.4 v2 应保留的 v1 设计
+
+- 阈值 3（不变）
+- `record_admission_reject` 的子串匹配（不变）
+- 新 fallback labels（`session-not-resident` 等）（不变）
+- degenerate fallback 选最少拒的 D（不变，但因为 reset-on-success 几乎不会触发到此分支）
+
+---
+
+## 7. 实验计划
+
+| 阶段 | 动作 | 时间 |
+|---|---|---|
+| 1 | 等 v1 run 完成（ETA ~16:30）| 自然 |
+| 2 | 跑 analyzer 量化 v1 thrashing 实际代价 | 5 min |
+| 3 | 实现 v2-fix-1（reset-on-success）| 30 min |
+| 4 | smoke test | 10 min |
+| 5 | 完整 v2 run（KVC 1P3D ts=1 N=1）| ~5.5h |
+| 6 | 三方对比：baseline / v1 / v2 | 30 min |
+| 7 | 决定是否需要 v2-fix-2 / v2-fix-3 | – |
+
+---
+
+## 8. 三方对比预测（待数据验证）
+
+| 指标 | baseline（v0）| v1（thrashing）| **v2（self-healing 预测）** |
+|---|---:|---:|---:|
+| Errors | 5 | ? | 2-5（仅 35680/39360 等真容量超限）|
+| Per-D 均衡 | ±26% | **±1.1%** | ±5-10%（部分 pin 仍 sticky）|
+| Direct-to-D rate | 42.8% | ?（可能因 thrash 反而下降）| **65-75%**（持续 affinity，转换 §1 fallback）|
+| Lat mean | 1.574s | ?（可能因 thrash 上升）| **1.30-1.45s**（达到 4DP 1.443s 水平）|
+| TTFT mean | 0.244s | ? | **0.10-0.15s** |
+| 最大 D-switches/session | 0 | 116 | <10（仅真饿死 session）|
+| Sessions 永久饿死 | 18 | 0 | 2-3（仅真容量超限）|
+
+预测核心：v2 应该结合 baseline 的稳定性（70-turn streak 应保留）+ v1 的公平性（无永久饿死），消除 v1 的 thrashing 副作用。
+
+---
+
+## 9. 局限与未验证
+
+1. **v1 中期数据 (23%) 推测**：完整数据可能改变 thrashing 严重性的判断
+2. **session 6880 trajectory 的崩溃机理是推断**：基于 admission events 数据 + streak 模式，但没有直接日志证明 reject 计数何时跨阈值（需要在 v2 加 instrument 输出）
+3. **reset-on-success 的预测效果未验证**：基于"70 turn 成功" + "1-2 次瞬时 reject" 的假设；如果 burst 持续多 turn，仍可能跨阈值
+4. **可能还有未发现的设计 bug**：v2 也许还会暴露新问题
+5. **三方对比需 same trace + same scale + same ts=1**：baseline 已有 N=3，v1/v2 各 N=1（ts=1 确定性 → N=1 可信）
+
+---
+
+## 10. 给 TEAM_REPORT 和 REFACTOR_PLAN_V1 的更新建议
+
+完成 v2 验证后：
+
+1. 在 `TEAM_REPORT` §3 ts=1 验证更新章节加入 §3.3 "Migration mechanism evolution: v0 → v1 → v2"
+2. 在 `REFACTOR_PLAN_V1` §6.2 标注实施反思——预设的 "rejection blacklist" 设计漏掉了 reset-on-success 这条
+3. 在新文档 `docs/POLICY_DESIGN_PRINCIPLES_ZH.md` 提炼出原则："任何会累积的代价机制必须配 healing/decay 机制，否则会陷入 self-amplifying 失效模式"
+
+---
+
+## 附录 A：本文数据来源
+
+| 章节 | 数据源 |
+|---|---|
+| §2 | `outputs/qwen3-30b-tp1-ts1-migration-v1/kvcache-centric-*/` 中期日志 |
+| §3.1 | `structural/session-d-binding.jsonl` 跨 turn 序列 |
+| §3.3 | `structural/admission-events.jsonl` mode/reason 交叉表 |
+
+## 附录 B：相关代码位置
+
+| 内容 | 位置 |
+|---|---|
+| RoutingState.session_d_rejects | `src/agentic_pd_hybrid/policies.py:46` |
+| KvAwarePolicy.select 跳过 blacklisted D | `src/agentic_pd_hybrid/policies.py:155-162` |
+| Degenerate fallback 选最少拒的 D | `src/agentic_pd_hybrid/policies.py:184-192` |
+| record_admission_reject 触发位置 | `src/agentic_pd_hybrid/replay.py:359-364`（_run_request） |
+| _is_admission_rejection_mode 子串集合 | `src/agentic_pd_hybrid/replay.py` `_ADMISSION_REJECTION_SUBSTRINGS` |
+| _fallthrough_reason 分类 | `src/agentic_pd_hybrid/replay.py` `_fallthrough_reason` |

docs/ONBOARDING_NEXT_AGENT_ZH.md

@@ -0,0 +1,364 @@
+# 接班 Agent 上手手册
+
+**对象**：接手本项目的下一个 SWE/research agent
+**目标**：30 分钟读完后达到当前主 agent 的认知水平，能独立跑对照实验、看懂数据、避开历史坑
+**作者状态**：本手册定稿于 `kvc-debug-journey-v1-to-v4 @ 506d360`，下一个工作分支是 `feat/d-to-p-sync`
+
+---
+
+## 0. 你是谁，你将要做什么（5 行 TL;DR）
+
+1. 你接手的是 **agentic-pd-hybrid**——SGLang xPyD 基础上加 session-aware KVCache 层的 LLM serving 框架，目标是在多轮长 context coding agent workload 上比 vanilla DP 快
+2. v2（迁移机制 + threshold tuning）已经在 SWE-Bench 50sess trace ts=1 上**击败 4DP CA** 6/8 个 latency/TTFT 指标，但 **TTFT p99 输 3×**（1.28s vs 0.43s）
+3. 上一个 agent 已诊断出 TTFT p99 长尾的根因——8.3% 请求走 reseed 慢路径，每次需要 P 重算 prefill + mooncake transfer = 3-7s
+4. **你的任务**：在有 GPU + IB RDMA 的环境上跑 2 组对照实验，验证 (a) naive 1P3D + kv-aware 相对 KVC 的边际贡献、(b) 启用真 RDMA 后 KVC v2 的 TTFT p99 是否能压到 ~0.7s 量级
+5. 跑完结果 push 到 `outputs/`，主 agent 会拉下来更新 paper draft 和 future-work 文档
+
+---
+
+## 1. 必读文档（按这个顺序读，**不要乱跳**）
+
+### Level 1：核心 30 分钟（**必读**，读完就能开始干活）
+
+| # | 文档 | 时长 | 为什么读它 |
+|---|---|---:|---|
+| 1 | `docs/PROJECT_OVERVIEW.md` | 5min | 项目目标 + 三种 mechanism（pd-disagg / pd-colo / kvcache-centric）的术语区分 |
+| 2 | `docs/V2_DEEP_ANALYSIS_ZH.md` §0 (TL;DR) + §6 (生产决策) | 10min | 当前状态最准确的 snapshot——v2 赢什么、输什么、为什么 |
+| 3 | `docs/KVC_ROUTER_ALGORITHM.md` §1-§3 + §9 | 10min | 形式化的算法（Algorithm 1/2/3）+ 4 个 open questions。**§9 OQ#4 就是你正在解决的问题** |
+| 4 | `docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md` §0-§2 | 5min | reseed 路径完整时间线（t=0 → t=4550ms），知道每段耗时分别来自哪里 |
+
+读完上面 4 篇就能跑实验了。如果你时间紧张，**就只读这 4 篇 + 本手册**。
+
+### Level 2：进阶（**遇到具体问题时再读**）
+
+| 文档 | 何时读 |
+|---|---|
+| `docs/REFACTOR_PLAN_V1_ZH.md` | 想理解为什么从 ts=10 切到 ts=1 |
+| `docs/MIGRATION_V1_FINDINGS_ZH.md` | 想理解 v1→v2 演化（v1 为何 thrashing，v2 reset-on-success 怎么修的） |
+| `docs/V2_RESULTS_ZH.md` | v2 原始战报（注意：headline 表略乐观，请优先看 `V2_DEEP_ANALYSIS_ZH.md` 的修订版） |
+| `docs/V2_DEEP_ANALYSIS_ZH.md` §4 全文 | 论文 reviewer 的对等性挑战 + 我们的辩驳；写 paper 时必读 |
+| `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` | 想理解 ts=10 时代的 §1-§9 结构性问题清单（很多问题在 ts=1 下消失，但底层机制仍在） |
+
+### Level 3：归档（**别读**，是历史包袱）
+
+- `docs/archive/AGENTIC_FIT_ANALYSIS_ZH.md`：ts=10 时代的早期分析，结论已被 ts=1 数据 supersede
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md`：ts=10 数据下的结构性验证，同上
+- `docs/archive/KVC_DEBUG_JOURNEY_V1_TO_V5.md`：v1-v5 调优 sweep 的过程笔记，知道有这个文件就行
+- `docs/archive/V5_PROFILE_INVESTIGATION_ZH.md`：profile 调查，已 supersede
+- `docs/archive/REFACTOR_PLAN_ZH.md`：v0 重构计划，已被 V1 supersede
+- `docs/archive/SWEBENCH_EXPERIMENT_*.md`：早期实验日志
+
+### Level 0：本手册的"姐妹"文档（**读这个之前你应该已经在看本文了**）
+
+- `docs/ONBOARDING_NEXT_AGENT_ZH.md`（就是本文）
+
+---
+
+## 2. 项目当前状态快照（用一张表说清）
+
+```
+Trace:        outputs/qwen35-swebench-50sess.jsonl  (4449 reqs / 52 sessions, time-scale=1.0)
+Hardware:     4× H100 80GB + Mellanox mlx5_0/_1 @ 200 Gb/s IB (active, but **未启用** in current sweep)
+Model:        Qwen3-30B-A3B-Instruct-2507 (TP1)
+Branch:       kvc-debug-journey-v1-to-v4 = 主分支（v2 已合入）
+              feat/d-to-p-sync           = 预留给 D→P 增量同步的开发，**当前空**
+              main                       = 旧 baseline，比主分支落后 18 commit
+```
+
+### 已得出的结论（高置信度）
+
+1. **v2 (reset-on-success + threshold 8192) 击败 4DP CA**：lat mean -1.4%、p50 -13%、TTFT mean -25%、TTFT p50 -55%、TTFT p90 -67%
+2. **TTFT p99 KVC 输 3×**：1.28s vs 0.43s。来自 8.3% reseed/fallback 慢路径
+3. **慢路径耗时五五开**：P 端 re-prefill ~1.5-3s + mooncake P→D transfer ~1.5-4s（**当前是 TCP loopback**，未启用真 RDMA）
+4. **capacity-backup 救不了 slow path**：直接 audit 过，P 端 backup 不会随 direct-to-D append 更新，是 seed-time 静态快照
+5. **D→P 增量同步代码不存在**：经 Opus agent forensic 审查 + 全分支 git 检索确认
+
+### 待验证的核心假设（**这是你的实验任务**）
+
+| # | 假设 | 验证方法 | 预期结果 |
+|---|---|---|---|
+| H1 | KVC v2 相对 4DP 的胜利不只是来自 1P3D 拓扑——KVC 层（admission / migration / direct-to-D）也有显著贡献 | 跑 naive 1P3D + policy=kv-aware ts=1 N=1（vanilla SGLang pd-disagg，无 KVC 层）作为中间对照 | naive 1P3D 应该处于 KVC v2 和 4DP 之间。如果它 ≈ KVC v2 → 胜利来自拓扑而非 KVC 层；如果 ≈ 4DP → 胜利来自 KVC 层 |
+| H2 | 启用真 RDMA 把 mooncake P→D transfer 从 1.5-4s 压到 200-400ms，TTFT p99 从 1.28s 降到 ~0.7s | 在 v2 sweep 加 `--force-rdma --ib-device mlx5_0`，跑同 trace 同 ts=1 | TTFT p99 应该 ~0.5-0.8s 区间。如果没改变 → mooncake 实际没用 RDMA / 配置错误；如果降到 ~0.3s → 我们对 transfer 段贡献的估计偏低 |
+| H3 | 即使启用 RDMA，TTFT p99 仍然输 DP（因为 re-prefill 段不动） | 同 H2 实验结果 | 应该看到 TTFT p99 ~0.7s > DP 0.43s。如果 ≤ DP → 我们对 re-prefill 段成本的估计错了，可能整个 slow path 理论需要重审 |
+
+---
+
+## 3. 你要跑的实验（the main task）
+
+### 3.1 实验矩阵（按 ROI 排序）
+
+GPU hour 珍贵，砍掉了原计划的 naive 1P3D + policy=default baseline（low-ROI——naive 1P3D 用 default policy 在多轮 cache 命中上几乎必败，没必要拿这个对比当 H1 的对照点）。最终保留 2 个 run：
+
+| # | 配置 | GPU | mechanism | policy | RDMA | 预期时长 | 目的 |
+|---|---|---:|---|---|---|---:|---|
+| **E1** | naive 1P3D kv-aware | 4 | pd-disaggregation | kv-aware | **on** | ~5.5h | H1：分离"1P3D + kv-aware policy"贡献 vs "KVC 层（admission/migration/direct-to-D）"贡献 |
+| **E2** | KVC v2 + RDMA | 4 | kvcache-centric | kv-aware | **on** | ~5.5h | H2/H3：验证 RDMA 能把 TTFT p99 从 1.28s 压到 ~0.7s |
+
+两个 run 串行约 11h，并行用两组 GPU 可压到 ~5.5h。
+
+### 3.2 启动配置：详细 flag 清单
+
+参考 `scripts/sweep_ts1_migration_v2.sh` 作为底版。两个新 sweep 脚本的关键 flag：
+
+#### E1: naive 1P3D kv-aware
+
+```bash
+python -m agentic_pd_hybrid \
+  --mechanism pd-disaggregation \
+  --policy kv-aware \
+  --topology-pd 1P3D \
+  --transfer-backend mooncake \
+  --force-rdma --ib-device mlx5_0 \   # ← 单独测拓扑+policy 而非 transport，必须开 RDMA 才能跟 E2 公平
+  --trace outputs/qwen35-swebench-50sess.jsonl \
+  --time-scale 1.0 \
+  --concurrency 32 \
+  --request-timeout-s 300 \
+  --max-input-len 87811 \              # ← 拉齐到 DP 限，消除 abort 数量不对等
+  --output-root outputs/qwen3-30b-tp1-ts1-naive-1p3d-kvaware
+```
+
+#### E2: KVC v2 + RDMA
+
+参考 `scripts/sweep_ts1_migration_v2.sh`，**只加两个 flag**：
+
+```diff
+  --transfer-backend mooncake \
++ --force-rdma --ib-device mlx5_0 \
++ --max-input-len 87811 \
+  --kvcache-direct-max-uncached-tokens 8192 \
+  --kvcache-migration-reject-threshold 3 \
+  --kvcache-prefill-backup-policy release-after-transfer \
+```
+
+**保留 v2 的其它所有配置**——这是 v2 + RDMA 的 ablation，**不要顺手改其它东西**。
+
+### 3.3 实验前的环境验证（**别跳**）
+
+```bash
+# 1. GPU
+nvidia-smi -L                # 应该看到 4 张 H100 80GB
+
+# 2. RDMA
+ibstat | grep -E "State|Rate|Port"
+# 期望：mlx5_0 / mlx5_1 都是 State=Active, Rate=200 Gb/s
+
+# 3. Mooncake 能识别 RDMA 设备
+python -c "from mooncake_transfer_engine import TransferEngine; e=TransferEngine(); print(e.get_local_topology())"
+# 期望：输出包含 mlx5_0 / mlx5_1
+
+# 4. 现有 v2 数据可读
+python3 scripts/analysis/recompute_summary.py outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl
+# 期望：打印出 failure_count=45, abort_count=40 等
+
+# 5. 算法实现 syntax check
+python3 -m py_compile src/agentic_pd_hybrid/{policies,replay,metrics,benchmark,cli}.py
+# 期望：全过
+```
+
+任何一步失败**立刻停下来排查**，不要硬上。
+
+---
+
+## 4. 已踩过的坑（避免重复）
+
+| # | 坑 | 症状 | 教训 |
+|---|---|---|---|
+| 1 | **abort 被计入 latency stats** | DP/KVC 都有 0.08s 的快速失败被算成"快请求"，拉低 mean/p50 | 已在 `metrics.py` 修复（commit `5eac9b4`）。新 run 出 summary 时会自动包含 `abort_count` / `failure_count` 字段 |
+| 2 | **max-input-len 双方不一致**（KVC=92098 vs DP=87811） | SGLang 按 mem_fraction_static 自动算 max_total_num_tokens，KVC decode-only worker GPU 内存多 2 GB | 跑新 run 时显式传 `--max-input-len 87811` 强制对齐 |
+| 3 | **mooncake 默认 TCP loopback** | sweep 脚本只传 `--transfer-backend mooncake` 不够，会落到 TCP，跑出来比 RDMA 慢 10× | 必须加 `--force-rdma --ib-device mlx5_0` |
+| 4 | **capacity-backup 不是 D→P 同步** | flag 名字误导，看代码就会发现它只是"reseed 完不关 P session"，KV 是 seed-time 静态快照 | 不要在 capacity-backup 上浪费时间；要真正消灭 reseed 长尾必须实现 D→P，去 `feat/d-to-p-sync` |
+| 5 | **N=1 在 ts=1 下"够用"是有条件的** | baseline N=3 确认 categorical 完全确定，但 v2 引入的 reset-on-success 等新代码路径未独立验证 | v2 + RDMA 的对照建议 N=2，对 RDMA-on/off 各一次 |
+| 6 | **ts=10 数据**别参考 | 当年的 372/912/396 errors 是 benchmark artifact，不代表真实生产 | 所有比较锁定 ts=1，不要尝试 ts=10 "复现"或验证 |
+| 7 | **critic agent 的 "MAJOR" 别盲信** | 上一轮 critic 把 cache fragmentation / prefill 闲置标为 MAJOR，其实是 KVC 的**设计意图** | 详见 `V2_DEEP_ANALYSIS_ZH §4.4 / §4.5`。Audit 视角和生产视角要分清 |
+| 8 | **GPU utilization 图布局有残留小问题** | 组标签 (KVC 1P3D / DP 4-way CA) 与 subplot title 视觉上仍有轻微挤压 | 已被用户接受为可发表状态。不要再花时间调这张图 |
+
+---
+
+## 5. CLI 速查表
+
+### 跑实验
+```bash
+# 完整 sweep（参考 v2）
+bash scripts/sweep_ts1_migration_v2.sh
+
+# 写自己的 sweep：复制 sweep_ts1_migration_v2.sh，改 mechanism/policy/output-root
+```
+
+### 看数据
+```bash
+# 修复版 summary（推荐用这个，旧的 summary.json 含 abort 污染）
+python3 scripts/analysis/recompute_summary.py outputs/<run>/*_metrics.jsonl
+
+# 跨配置对照
+python3 scripts/analysis/analyze_ts1_validation.py    # 比较 KVC vs DP ts=1 4-run
+```
+
+### 出图（参考 v2 流程）
+```bash
+# 4 张已有的图，对应不同 viz 问题
+python3 scripts/analysis/plot_v2_path_breakdown.py    # execution_mode 分布 + path-level latency
+python3 scripts/analysis/plot_ttft_pdf.py             # TTFT PDF (KVC vs DP)
+python3 scripts/analysis/plot_gpu_utilization.py      # GPU 利用率（请求计数 vs 工作量）
+python3 scripts/analysis/plot_cache_efficiency.py     # cache 效率（hit rate vs turn + uncached ECDF）
+
+# 数据更新后重新出图：直接 rerun，每个脚本都参数化了输入路径
+```
+
+### Git
+```bash
+# 主分支（实验）
+git checkout kvc-debug-journey-v1-to-v4
+
+# 新功能分支（D→P 同步，空）
+git checkout feat/d-to-p-sync
+
+# 远程
+origin = git@ipads.se.sjtu.edu.cn:wangjh/agentic-pd-hybrid.git
+
+# Push 用 (SSH known_hosts 第一次需要 accept)
+GIT_SSH_COMMAND='ssh -o StrictHostKeyChecking=accept-new -o UserKnownHostsFile=~/.ssh/known_hosts' git push
+
+# user.email 没设全局，建议 per-commit 传：
+git -c user.email=YOUR_EMAIL -c user.name=YOUR_NAME commit -m "..."
+```
+
+---
+
+## 6. 跑完结果后看什么数字（checklist）
+
+每个 run 跑完，**至少**收集以下几个数字（用 `recompute_summary.py`）：
+
+```
+☐ request_count                            (期望 4449)
+☐ error_count + abort_count + failure_count
+☐ latency_stats_s.{mean, p50, p90, p99}
+☐ ttft_stats_s.{mean, p50, p90, p99}      ← 别忘 p99！这是 KVC 的真实代价点
+☐ execution_modes 分布
+☐ per_decode_load 分布（看负载均衡）
+☐ per_prefill_load （注意：dispatcher 计数 ≠ GPU 工作量）
+☐ cache_hit_request_count + total_cached_tokens (推 cache hit rate)
+```
+
+### 两组对照实验跑完后看以下"决定性数字"
+
+| 比较 | 关键看点 | 决策 |
+|---|---|---|
+| E1 (naive 1P3D kv-aware) vs E2 (KVC v2 + RDMA) | TTFT p50/p99、direct-to-D 占比 | 量化"KVC 层（admission/migration/direct-to-D）在 kv-aware 之上的额外收益"（H1） |
+| KVC v2 (TCP, 历史 v2 run) vs E2 (KVC v2 + RDMA) | TTFT p99、reseed mode 的耗时（execution_mode == reseed 的 ttft_s p50） | 验证 H2/H3：RDMA 救多少 transfer 段 |
+| E1 (naive 1P3D kv-aware) vs DP 4w（历史 ts=1 baseline）| 全部 latency / TTFT 指标 | 间接锚定"拓扑差异 + kv-aware policy"的天花板 |
+
+### 期待的数字范围（如果实验顺利）
+
+| 配置 | lat p50 | lat p99 | TTFT p50 | TTFT p99 | direct-to-D % |
+|---|---:|---:|---:|---:|---:|
+| **E1** naive 1P3D kv-aware | ~0.75s | ~8-10s | ~0.20s | ~0.8-1.2s | N/A |
+| **E2** KVC v2 + RDMA | ~0.58s | ~7-8s | ~0.04s | **~0.5-0.8s** | ~91% |
+| (参考) KVC v2 + TCP（历史） | 0.58s | 8.7s | 0.04s | 1.29s | 91.6% |
+| (参考) DP 4w（历史 ts=1） | 0.67s | 8.4s | 0.09s | 0.43s | N/A |
+
+**如果你看到的数字偏离这个范围 ≥ 2×**，先停下来检查配置（环境验证 §3.3 那些项目），不是写报告。
+
+---
+
+## 7. 遇到 X 怎么办（FAQ）
+
+**Q: 跑出来 KVC v2 + RDMA 的 TTFT p99 比预期高很多（> 1s）。**
+
+A: 大概率 RDMA 没真用上。检查：
+1. `outputs/<run>/<subdir>/benchmark-config.json` 里 `force_rdma` 是不是 `True`、`ib_device` 是不是 `"mlx5_0"`
+2. 服务器 startup log（`outputs/<run>/<subdir>/logs/prefill-0.log`）有没有 "MOONCAKE_DEVICE=mlx5_0" / "using RDMA" 类信息
+3. `ibstat mlx5_0` 看 active 状态没掉
+
+**Q: KVC v2 + RDMA 跑出来 TTFT p99 ≤ DP（违反 H3）。**
+
+A: 这是个好消息。可能性：
+1. 我们对 re-prefill 段耗时估计偏高（实际 SGLang 的 prefix cache 把 P 端 re-prefill 救了一半）
+2. RDMA 直接快到把 transfer 段压到 ~50ms 量级，整个 reseed < 1.5s
+3. v2 的 reseed 触发频率被 RDMA 间接降低（某种 race condition 改善了 LRU 行为）
+
+任一情况都值得**深挖**，建议把 reseed mode 的 `ttft_s` 分布单独拉出来看（应该有清晰的双峰：fast reseed + 极少数 outlier）。
+
+**Q: naive 1P3D 跑不起来 / SGLang 报错。**
+
+A: 仓库里 `outputs/qwen3-30b-exps/pd-disaggregation-default-20260427T062616Z/` 有过历史的 1P1D 跑通配置可以参考。常见坑：
+1. `--mechanism pd-disaggregation` 和 `--topology` 必须配合，topology 不能用 KVC 的 1P3D 名字
+2. SGLang vendored 在 `third_party/sglang/`，**不要**`pip install sglang` 用外部版本——可能 API 不对齐
+3. `--policy default` 时不要传 `--kvcache-*` 系列 flag，会被 ignore 但会污染 config 输出
+
+**Q: 我想跑别的对照（更大 trace / 更多 GPU / 真实 RDMA 跨节点）。**
+
+A: 先把上面 2 个 E1-E2 跑完。这 2 个是论文核心 contribution 的 ablation，不能跳。其它对照（更长 trace、8 GPU 2P6D、真跨节点 RDMA、补 naive 1P3D + policy=default）见 `V2_DEEP_ANALYSIS_ZH §7.3`，作为 follow-up。
+
+**Q: 跑完后想自动出对比图。**
+
+A: 4 个现有 `plot_*.py` 脚本都是参数化的，把输入路径改成你的新 run 就能复用。如果对比维度变多（如三方对比 naive vs KVC vs DP），可以扩展现有脚本而不是新写——见 `plot_ttft_pdf.py` 的模板。
+
+**Q: 发现 metrics.jsonl 字段不一致 / 缺字段。**
+
+A: 看 `src/agentic_pd_hybrid/metrics.py` 里 `RequestMetrics` dataclass。所有新增字段必须在那里加，否则 `recompute_summary.py` 会报 KeyError。**注意**：dataclass 的 `field_names` 是按 `RequestMetrics.__dataclass_fields__` 取的，不是 jsonl 里所有 key。
+
+---
+
+## 8. 如果你完全卡住
+
+读这一段：
+
+1. **不要**尝试在没看本手册 §1 必读文档的情况下硬上代码
+2. **不要**在 main 分支或 `feat/d-to-p-sync` 上跑实验——用 `kvc-debug-journey-v1-to-v4`
+3. **不要**修 metrics.py 的统计字段，除非你能解释清楚为什么它当前的 abort 排除是对的
+4. **不要**信任 critic agent 的"MAJOR"标签，要看代码层证据
+5. **不要**跳过环境验证（§3.3）直接跑长 sweep——5h 跑出垃圾数据浪费的成本更高
+
+如果你卡住超过 30 分钟，把卡点写成一句话，去主 agent 留言（git commit message / branch 注释）。
+
+---
+
+## 9. 主 agent 留给你的两个具体期待
+
+1. **两组对照实验跑完后**，在新 commit message 里给我以下数字（用 `recompute_summary.py` 输出格式）：
+   ```
+   E1 naive 1P3D kv-aware:  lat={mean,p50,p90,p99}  ttft={mean,p50,p90,p99}  fail_count
+   E2 KVC v2 + RDMA:        同上 + reseed-mode 的 ttft p50/p99 分开
+   ```
+
+2. **跑 E2 时收集 reseed 路径的实测耗时分布**：
+   ```
+   pd-router-d-session-reseed 这个 execution_mode 的 ttft_s 分布
+   并把 P→D mooncake transfer 时长 vs P 端 re-prefill 时长 单独拉出
+   （需要在 structural/admission-events.jsonl 里找 timestamp diff）
+   ```
+
+   这两组数字直接决定 paper future-work 章节怎么写 D→P sync 的必要性。
+
+---
+
+## 附录 A：关键文件位置速查
+
+| 你在找什么 | 在哪 |
+|---|---|
+| 算法实现 | `src/agentic_pd_hybrid/policies.py` (KvAwarePolicy + RoutingState) |
+| 整个 replay orchestration | `src/agentic_pd_hybrid/replay.py` (~3000 行，**慢慢读**) |
+| 指标统计 | `src/agentic_pd_hybrid/metrics.py` |
+| CLI 入口 | `src/agentic_pd_hybrid/cli.py` |
+| Server 启动配置 | `src/agentic_pd_hybrid/stack.py` |
+| SGLang 改动 | `third_party/sglang/python/sglang/srt/{managers/scheduler.py, managers/io_struct.py, disaggregation/mooncake/...}` |
+| 历史 sweep 脚本 | `scripts/sweep_ts1_*.sh` |
+| 分析脚本 | `scripts/analysis/*.py` |
+| 实验输出 | `outputs/qwen3-30b-tp1-ts1-*/` |
+
+## 附录 B：关键 commit 速查（按"想理解什么改动看什么 commit"组织）
+
+| 想理解 | 看 commit |
+|---|---|
+| v2 的核心改动 | `2ec0deb feat(kvc): session migration with reset-on-success + direct-append threshold tuning` |
+| metrics.py 修复 | `5eac9b4 fix(metrics): exclude aborted requests from latency/ttft/tpot stats` |
+| 完整 analysis 文档（多版本叠加修订）| `c01d610` (latest) / `9ccd853` / `b5af195` / `c551906` / `517677d` |
+| 算法形式化定义 | `37e9caa docs(kvc): production-decision reframe + formal router algorithm spec` |
+| 各种 figure 脚本 | `c551906` (TTFT PDF) / `b5af195` (path breakdown) / `517677d` (GPU + cache) |
+| backpressure 代码 | `c47adaf feat(kvc): honor admission backpressure hints` 和 `ca4b64c feat(sglang): expose backpressure pause hint` |
+
+---
+
+**核心句**：先读 §1 Level 1 的 4 篇文档（30 min）+ 本手册（30 min），然后按 §3 跑 E1/E2/E3 三组实验，按 §6 收集决定性数字，遇到坑查 §4，结果 push 到 `outputs/` 下。**别瞎改不属于本任务的代码**——你的工作是验证 v2 的胜利在 ablation 中是否站得住，不是开发新机制（那是 `feat/d-to-p-sync` 分支的事，下一阶段才做）。
+
+跑完之后期待你的 commit！

docs/REAL_ALI_KVC_EXPERIMENT_LOG_ZH.md

@@ -1,514 +0,0 @@
-# Real Ali KVC 实验日志
-
-**分支**：`kvc-real-ali-iter-v1`，从 `kvc-debug-journey-v1-to-v4` checkout 出来。
-**日期**：2026-05-11/12。
-**环境**：单机 8x NVIDIA H20，SGLang xPyD，模型 `/home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct`。
-**真实 trace**：`/home/admin/cpfs/wjh/ali-trace/trace-qwen3-coder-formatted/041715-041717.jsonl`。
-
-本日志记录真实 Ali workload 上的 KVC pd-hybrid 迭代。结论只按当前证据成立；`time-scale=10` smoke 和 KVC-friendly slice 不作为 full workload headline。
-
-## 1. 当前最新进展
-
-已新增真实 Ali trace 的固定样本和 sweep 管线：
-
-- `scripts/prepare_real_ali_samples.py`：从真实 Ali trace 生成可复现实验样本，保留真实 input/output/hash_ids/timestamp，可选择 rebase timestamp。
-- `scripts/sweep_real_ali_kvc.sh`：对同一 prebuilt sample 依次跑 DP cache-aware、PD-disaggregation、KVC、KVC+backpressure。
-- `benchmark-live --use-trace-as-sample`：直接 replay 指定 trace，避免不同策略重新采样导致不可比。
-- `replay-progress.jsonl` heartbeat：后续长跑会每 30s 写客户端侧进度，不轮询 `/server_info`，避免扰动 scheduler。
-- `prepare_real_ali_samples.py --max-sampled-duration-s`：为快速 smoke 生成 capped sample；只用于迭代，不用于 headline。
-
-已经完成的真实 Ali KVC-fit smoke：
-
-- 样本：`outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl`
-- 179 requests，64 sessions，全部 multi-turn；turn2+ 共 115 个，direct-eligible ratio 100%。
-- `time-scale=10`，concurrency 32。
-- DP cache-aware、PD-disaggregation、KVC no-backpressure、KVC+backpressure 均已完成。
-
-## 2. 全量 Ali trace 画像
-
-`outputs/real-ali-kvc-iter/ali-full-profile.json` 显示：
-
-| 指标 | 数值 |
-|---|---:|
-| requests | 763,727 |
-| sessions | 555,905 |
-| multi-turn sessions | 39,247 |
-| turn2+ requests | 207,822 |
-| turn2+ direct-eligible ratio | 82.95% |
-| input p50 / p90 / p99 | 4,329 / 51,067 / 112,955 tokens |
-| output p50 / p90 / p99 | 93 / 826 / 5,616 tokens |
-| append p50 / p90 / p99 | 303 / 2,879 / 17,885 tokens |
-| inter-turn gap p50 / p90 / p99 | 4.65s / 38.68s / 1,133s |
-
-这个 profile 说明 KVC 有真实适用面：turn2+ 的 hash overlap 和小 append 很常见。但 full workload 里 single-turn session 极多，KVC 收益会被显著稀释；因此必须分 slice 报告，不能只报 KVC-fit 子集。
-
-## 3. 已跑样本
-
-### Continuous 15min cold-window session sample
-
-路径：`outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl`
-
-- 600 requests，439 sessions，32 multi-turn sessions。
-- rebased duration：886.544s，覆盖约 15min。
-- turn2+ requests：161，direct-eligible：143，ratio 88.8%。
-- input p50 / p90 / p99：3,871 / 68,234 / 98,131。
-- output p50 / p90 / p99：85 / 712 / 5,195。
-- append p50 / p90 / p99：274 / 2,202 / 16,120。
-- inter-turn gap p50 / p90 / p99：4.656s / 19.376s / 63.575s。
-
-这是对 179-request KVC-fit smoke 的替代验证样本。它按 900s 窗口分成 15 个时间桶，轮转选择窗口内从 root 开始的整 session，直到达到 600 requests。这样避免 parent 缺失导致 `load_trace()` 把真实 session 切碎，也让请求覆盖整个 15min，而不是只取窗口开头 600 条。
-
-重要边界：它是 **cold-window / new-session-only** sample，不是完整 raw production window；它排除了窗口开始前已经活跃的 ongoing sessions。因此可以用于“600+ 请求、15min、真实混合负载”的稳定性验证，但不能单独代表全量 Ali production window。
-
-### KVC-fit small append
-
-路径：`outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl`
-
-- 179 requests，64 sessions。
-- input p50 / p90：6,446 / 15,491。
-- output p50 / p90：112 / 1,159。
-- append p50 / p90：215 / 855。
-- overlap ratio p50 / p90：0.875 / 0.938。
-
-这是 KVC-friendly slice，用来验证机制上限和 microbenchmark 是否能迁移到真实 token/hash 序列。
-
-### Representative-mt / early multi-turn balanced
-
-路径：`outputs/real-ali-kvc-iter/samples-balanced/ali-representative-mt.jsonl`
-
-- 460 requests，64 sessions。
-- input p50 / p90：41,175 / 98,621。
-- append p50 / p90 / p99：272 / 1,979 / 13,900。
-
-这个样本更接近真实 multi-turn 压力，后续用于验证大上下文、大 resident KV 下是否仍能稳定。但它当前实现是“从 start_time 后取最早 64 个 multi-turn session”，不是严格随机或分层 representative；正式 headline 需要按 input/append/output/gap 分层抽样。
-
-### Capped smoke samples
-
-为避免少数真实长 gap 让 smoke 浪费大量 wall time，新增：
-
-- `outputs/real-ali-kvc-iter/samples-balanced-cap120s/ali-kvc-fit-smallappend.jsonl`：177 requests，64 sessions，duration 65.859s。
-- `outputs/real-ali-kvc-iter/samples-balanced-cap120s/ali-representative-mt.jsonl`：359 requests，64 sessions，duration 117.366s。
-
-这些样本去掉了 KVC-fit 原样本末尾 timestamp 3613s 和 5414s 的两个请求，因此只能用于快速工程迭代；正式对比仍应使用完整样本或真实连续窗口。
-
-## 4. 当前结果
-
-### 4.1 DP cache-aware vs KVC+backpressure, KVC-fit, time-scale=10
-
-| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| 8-way DP cache-aware | 179 | 0 | 0 | 6.603s | 3.126s | 17.639s | 34.582s | 1.112s | 1.052s |
-| KVC 2P6D + worker admission + backpressure | 179 | 0 | 0 | 4.443s | 2.076s | 13.288s | 21.202s | 0.700s | 0.154s |
-
-Paired comparison（KVC - DP）：
-
-- overall E2E mean delta：-2.161s；p50 delta：-1.427s；152/179 wins。
-- turn2+ direct 子集：mean delta -2.503s；p50 delta -1.508s；103/115 wins。
-- turn2+ TTFT mean delta：-0.930s；p50 delta -0.887s。
-
-执行路径：
-
-- KVC turn1 seed：64 requests。
-- `kvcache-direct-to-d-session`：115 requests。
-- session reused：115。
-- actual KV transfer blocks：623。
-
-结构日志：
-
-- admission probes：179，全为 `ok`。
-- transfer queue depth：p50=0，p90=2，max=3。
-- backpressure event：0。
-
-解释：这轮证明的是 **KVC direct-to-D/session reuse** 在真实 Ali KVC-fit slice 上有正信号；不是证明 backpressure 有效，因为没有触发 backpressure。
-
-### 4.2 PD-disaggregation baseline, KVC-fit, time-scale=10
-
-| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| PD-disaggregation 2P6D | 179 | 0 | 0 | 7.850s | 6.306s | 15.192s | 22.405s | 4.994s | 5.336s |
-
-Paired comparison（PD - DP）：
-
-- overall E2E mean delta：+1.247s。
-- p50 delta：+2.231s。
-- 46/179 faster，133/179 slower。
-
-解释：在这个 KVC-fit slice 上，普通 PD-disaggregation 明显弱于 8-way DP cache-aware。它付出了 P->D transfer 和拆分调度成本，却没有 KVC direct-to-D 的 bypass 收益。
-
-### 4.3 KVC no-backpressure 消融, KVC-fit, time-scale=10
-
-| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| KVC 2P6D worker admission, no backpressure | 179 | 0 | 0 | 4.404s | 1.936s | 13.200s | 21.326s | 0.604s | 0.139s |
-
-Paired comparison：
-
-- KVC no-BP vs DP：mean delta -2.200s，p50 delta -1.434s，153/179 wins。
-- KVC no-BP vs PD-disaggregation：mean delta -3.447s，p50 delta -3.514s，163/179 wins。
-- KVC no-BP vs KVC+BP：mean delta -0.039s，p50 delta -0.005s，92/179 wins。
-
-结构分析：
-
-- direct-to-D rate：64.25%。
-- admission probes：179，全为 `ok`。
-- transfer queue depth：p50=0，p90=2，max=3。
-- pause_ms 全 0，backpressure event 0。
-
-解释：no-backpressure 与 +backpressure 几乎等价，说明本 slice 没有 D 压力；本轮提升来自 direct-to-D，不来自反压。
-
-### 4.4 Continuous 15min / 600-request window, time-scale=1
-
-样本：`outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl`
-
-重要边界：这是 cold-window / new-session-only session sample，不是完整 raw window。它覆盖约 15min，且 `missing_parent_count=0`，但排除了窗口开始前已活跃的 ongoing sessions。
-
-运行结果：
-
-| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 | TTFT p90 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| DP cache-aware 8-way | 600 | 1 | 0 | 13.942s | 5.222s | 29.299s | 151.183s | 6.162s | 1.746s | 19.176s |
-| PD-disaggregation 2P6D | 600 | 1 | 0 | 40.886s | 40.018s | 84.681s | 113.460s | 38.545s | 37.782s | 81.852s |
-| KVC 2P6D mem_fraction_static=0.82 | 600 | 53 | 0 | 12.386s | 4.225s | 37.998s | 78.234s | 10.078s | 2.674s | 27.774s |
-
-KVC 默认启动失败：
-
-- 默认 KVC 2P6D 在 H20 上两次启动 OOM，均未进入 replay。
-- 日志显示 decode/prefill worker 启动时只剩约 526MB，模型加载阶段 OOM。
-- `--load-format layered` 不支持 Qwen3-Coder-30B-A3B。
-- 使用 `--mem-fraction-static 0.82` 后 KVC 能启动并完成 replay，但这降低了 KV pool 容量，因此这轮 KVC 是 memory-constrained rerun。
-- 尝试 `KVC_SEED_MIN_TURN_ID=2` + `mem_fraction_static=0.82` 时，启动阶段 scheduler 被 SIGKILL，疑似 OS OOM killer，未进入 replay。
-
-Paired comparison（只在两边都有 latency 的 547 个 paired request 上计算）：
-
-- KVC vs DP：mean delta -1.335s，p50 delta -0.055s，p90 delta +19.371s，284 wins / 263 losses。
-- KVC vs PD：mean delta -28.341s，p50 delta -25.687s，p90 delta +2.834s，465 wins / 82 losses。
-
-KVC 结构数据：
-
-- execution modes：388 `pd-router-turn1-seed`，90 `kvcache-direct-to-d-session`，67 `pd-router-fallback-large-append-session-cap`，1 `pd-router-large-append-reseed`，1 `pd-router-turn1-d-backpressure`，53 `kvcache-centric` error rows。
-- direct-to-D rate：15.0%。
-- direct-to-D session 分布：413/439 sessions 在 0-20% direct rate；只有 6 sessions 在 80-100%。
-- admission probes：533；reason `ok` 531，`no-space` 2；queue depth p50=0，p90=2，max=5。
-- pause hint 非零 20 次，但没有 backpressure event，因为本轮 no-BP。
-
-KVC error breakdown：
-
-- 50 `ReadTimeout`。
-- 2 `HTTPStatusError 400 Bad Request` on `open_session`。
-- 1 context length error：同 DP/PD 的 `input_length=310521 > 262144`。
-- 错误主要集中在 turn1：50 turn1，3 turn2+。
-
-解释：
-
-1. KVC 相对普通 PD 仍明显更好，说明普通 P->D disaggregation 在真实 600-request 窗口上成本很高。
-2. KVC 相对 DP 只在 clean request 的 mean/p50 上有小幅正信号，但 p90 变差，而且 error_count 从 DP 的 1 增到 53。
-3. 因此在这个 600-request / 15min window 上，**KVC 不能算稳定提升系统**。主要问题不是 direct-to-D 快路径无效，而是该快路径覆盖率只有 15%，并且 turn1 seed / session admission / memory-constrained KV pool 引入大量 timeout。
-4. 这直接修正 179-request KVC-fit smoke 的结论：小样本证明 KVC 适用 slice 存在；600-request mixed window 证明当前实现还不能稳定服务真实混合 workload。
-
-## 5. 是否已经相对 pd-colocation/pd-disaggregation 取得提升
-
-当前只能下这个限定结论：
-
-1. **相对 PD-disaggregation：已经取得清晰提升。**
-   PD-disaggregation p50 6.306s，KVC no-BP p50 1.936s，KVC+BP p50 2.076s；TTFT p50 5.336s vs 0.139s/0.154s。收益主要来自 turn2+ 直接打到已有 D session，避免每轮 P 全量 prefill 和 P->D KV transfer。
-
-2. **相对强 DP cache-aware：在 KVC-fit slice 上有提升。**
-   KVC no-BP 和 KVC+BP overall mean/p50/p90/p99 都优于 DP，并且 paired wins 分别是 153/179 和 152/179。但这是 KVC-friendly、全 multi-turn、turn2+ 100% direct-eligible 的 slice，不代表 full Ali workload。
-
-3. **相对 full workload：尚未证明。**
-   全量 Ali 里 single-turn 占多数，且长上下文和长尾 output 较多。KVC 的收益面会被 single-turn 稀释，D resident KV 容量和 tail 稳定性会成为更强约束。
-
-4. **相对 600-request / 15min mixed window：尚未取得稳定提升。**
-   KVC clean E2E mean/p50 有正信号，但 error_count=53/600，p90 paired delta 相对 DP 变差。按“E2E + error/truncation”标准，这不能算系统性胜出。
-
-## 6. 提升来自哪里
-
-主要收益链路：
-
-1. turn1 seed 在 D 上建立 session。
-2. turn2+ 若 append 小、hash overlap 高，直接走 `kvcache-direct-to-d-session`。
-3. direct-to-D 避免 P worker 参与，不走 P->D KV transfer。
-4. D 只对 append suffix 做少量 prefill，已有前缀 KV 直接复用。
-
-这带来两个可观测收益：
-
-- TTFT 大幅下降：turn2+ direct 子集 TTFT mean 从 DP 的约 1.04s 降到约 0.112s。
-- E2E 下降：direct 子集 mean E2E 降低约 2.50s。
-
-另外，KVC 的 cached_tokens 统计显著更高：KVC mean cached tokens 5,992，DP mean 228。这说明它确实复用了大段真实前缀 KV。
-
-## 7. 遇到的问题与修复
-
-### 问题 1：通用 sampler 会被单个长 session 主导
-
-现象：真实 Ali session 分布长尾明显，duration-oriented 采样容易选出不均衡样本，导致策略比较不可重复或不代表多 session 竞争。
-
-修复：新增 `scripts/prepare_real_ali_samples.py`，按 session 上限和每 session turn 上限生成 balanced sample，并保留真实 token/hash/timestamp。
-
-### 问题 2：不同策略重新采样导致不可比
-
-现象：`benchmark-live` 原本会按参数重新采样，不同策略可能 replay 不同请求。
-
-修复：新增 `--use-trace-as-sample`，所有策略 copy 并 replay 同一个 prebuilt sample；后续 paired comparison 才有意义。
-
-### 问题 3：长 trace replay 中途没有进度
-
-现象：`request-metrics.jsonl` 和 summary 只在 replay 结束后写出，跑真实 pacing 时很难判断是正常等待还是卡住。
-
-修复：新增 `replay-progress.jsonl` heartbeat，每 30s 写 submitted/completed/inflight/errors/execution_modes。它只使用客户端本地状态，不访问 `/server_info`。
-
-### 问题 4：`/server_info` polling 会扰动 scheduler
-
-现象：旧 profiling 里 1Hz polling 曾明显改变错误数。真实 performance run 如果持续 poll pool，会把测量工具变成干扰源。
-
-修复：`scripts/sweep_real_ali_kvc.sh` 默认关闭 pool polling。容量类问题依赖结构日志和必要时单独 profile run，不混入 headline performance run。
-
-### 问题 5：backpressure smoke 没有触发 backpressure
-
-现象：KVC-fit smoke 中 transfer queue max 只有 3，所有 admission reason 都是 `ok`，pause_ms 全 0。
-
-结论：这轮不能证明 backpressure 有效，只能证明 direct-to-D 有效。需要更高 session 数、更大 resident KV 或更强并发的压力样本专门验证 backpressure。
-
-### 问题 6：环境和旧报告不一致
-
-现象：旧文档写的是 H100，本轮真实环境是 H20；模型路径也在 `/home/admin/cpfs/wjh/models/...`。
-
-处理：本日志按 H20 记录；跨文档比较时只看机制趋势，不把 H100/H20 的绝对 latency 混为同一实验。
-
-### 问题 7：continuous window 可能截断 session ancestry
-
-现象：按 timestamp 直接截窗口可能留下 parent turn 在窗口外的请求。对 KVC 来说，这会让 session reuse/turn chain 与真实 workload 不一致。
-
-处理：当前 continuous window 只作为待改进候选，不作为正式 headline。正式窗口需要保留 warmup ancestors，或显式保留原始 session chain 信息。
-
-## 8. 如果后续 full workload 效果不好，当前假设
-
-可能不是实现小 bug，而是方案适用面和资源约束共同导致：
-
-1. **single-turn 稀释收益**：全量 Ali session 中 single-turn 占多数，KVC seed 只带来成本，没有 turn2+ reuse。
-2. **长上下文挤占 D KV 池**：input p90 51K、p99 113K，resident KV 长尾会限制 D 上可同时保留的 session。
-3. **direct 不是免费 lunch**：turn1 seed、admission probe、session lifecycle 都有额外成本；只有后续 turns 充分复用时才摊薄。
-4. **D 端容量和 eviction 仍是核心风险**：旧 SWE 实验已经显示 session pinning + D 容量盲选会造成 starvation；early multi-turn balanced 样本可能复现。
-5. **普通 PD-disaggregation 很弱**：如果 KVC fallback 频繁退回普通 PD 路径，整体会被 P->D transfer 和高 TTFT 拖垮。
-6. **H20 显存余量不足会改变 KVC 条件**：默认 KVC 2P6D 启动 OOM，必须降 `mem_fraction_static` 才能完成 600-request run；这会进一步降低 D KV pool，放大 session-cap 和 timeout。
-
-## 9. 下一步验证顺序
-
-1. 补 sticky/session-affinity baseline，拆出“粘到同一个 D”和“KVC direct bypass”的贡献。
-2. 补 KVC `seed-min-turn-id=2` 或 no-turn1-seed，验证 turn1 seed 成本是否值得。
-3. 在 early multi-turn balanced 样本上跑 DP / PD / KVC no-BP / KVC+BP，验证大上下文真实 multi-turn 压力。
-4. 选小固定样本跑 `time-scale=1`，避免只在压缩 replay 条件下成立。
-5. 做包含 single-turn 的 continuous window，并处理窗口内 parent turn 缺失问题，再按 full Ali 分布加权报告。
-6. 对最终候选配置做 N>=3 rerun，报告方差；N=1 只作为 smoke。
-7. 针对 600-request window 优先跑 `seed-min-turn-id=2`，减少 single-turn turn1 seed；目标是先把 53/600 errors 降到接近 DP 的 1/600，再讨论 latency。
-   - 当前第一次尝试未进入 replay，启动阶段疑似 OS OOM；需要先解决 H20 启动显存/系统内存稳定性，或者降低 worker 数/模型内存占用。
-
-## 10. KVC error 根因与 multi-turn-only 验证准备
-
-用户指出 179-request run 不够，并要求至少 15min / 600+ 请求；当前正式问题定位基于
-`outputs/real-ali-kvc-iter/runs/window900s-600req-ts1-kvc-mem082/kvcache-centric-kv-aware-worker-admission-20260511T093601Z`。
-
-### 10.1 为什么 KVC 有大量 error
-
-该 run 为 600 requests，KVC mem0.82 有 53 errors：
-
-- 50 个 `ReadTimeout`。
-- 2 个 `/open_session` HTTP 400。
-- 1 个真实超上下文错误：input 310,521 > model context 262,144。
-
-按 turn 看，50/53 errors 在 turn1。按 structural admission 看，绝大多数失败请求在
-`structural/admission-events.jsonl` 中已经被 D 端 admission 判定 `can_admit=true`，所以这不是单纯的
-`d-session-cap` 或 `no-space`。主要失败点是 turn1 seed 进入 KVC seeded path 后，在
-P/D streaming session bootstrap、P->D transfer 或 router streaming 过程中超时；而混合真实窗口中 single-turn session 很多，
-这些 turn1 seed 对大多数 session 没有后续复用收益。
-
-结论：当前 KVC error 的主因是 **对 single-turn / 未知是否多轮的 session 做了过多 turn1 seed**，它把大量新 session 推进
-KVC control-plane 和 seeded router 路径，增加超时和 session lifecycle 残留；不是 direct-to-D fast path 本身出错。
-
-### 10.2 已做修复/消融开关
-
-代码与脚本修复：
-
-- `scripts/sweep_real_ali_kvc.sh` 新增 `KVC_SEED_ONLY_MULTITURN=1`，会传入
-  `--kvcache-seed-only-multiturn-sessions`。这是 oracle 消融，用来验证“只 seed 会有后续 turn 的 session”能否消除 turn1 seed 错误。
-- `src/agentic_pd_hybrid/replay.py` 对 `/open_session` 400 增加 close+retry 一次，并写
-  `structural/session-lifecycle.jsonl`。这是 lifecycle 健壮性修复，目标是处理 timeout 后服务端残留 session 导致的
-  “already exists” 400，不改变 routing policy。
-- `scripts/prepare_real_ali_samples.py` 新增 `--window-min-turns` 和 `--window-output-name`，用于生成可复现的 multi-turn-only window 样本。
-
-验证：
-
-- `uv run python -m py_compile scripts/prepare_real_ali_samples.py src/agentic_pd_hybrid/replay.py src/agentic_pd_hybrid/benchmark.py src/agentic_pd_hybrid/cli.py`
-- `bash -n scripts/sweep_real_ali_kvc.sh`
-
-### 10.3 已生成 multi-turn-only 样本
-
-样本路径：
-
-`outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl`
-
-生成命令：
-
-```bash
-uv run python scripts/prepare_real_ali_samples.py \
-  --trace /home/admin/cpfs/wjh/ali-trace/trace-qwen3-coder-formatted/041715-041717.jsonl \
-  --output-root outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn \
-  --window-duration-s 900 \
-  --window-target-requests 600 \
-  --window-buckets 15 \
-  --window-min-turns 2 \
-  --window-output-name ali-window-multiturn.jsonl \
-  --profiles representative-mt \
-  --max-sessions 64 \
-  --max-turns-per-session 12
-```
-
-样本 profile：
-
-- 626 requests，107 sessions，107 个都是 multi-turn sessions。
-- sampled duration 889.341s。
-- turn2+ = 519。
-- direct-eligible turn2+ = 473 / 519 = 91.1%。
-- missing parent = 0。
-- input p50/p90/p99 = 26,846 / 91,596 / 123,898 tokens。
-
-这个 case 是“过滤掉 single-turn 的多轮压力切片”，不能替代 full mixed workload，但可以回答：
-如果 workload 确实以多轮 coding agent session 为主，KVC 的 direct-to-D 覆盖率和稳定性是否接近 microbenchmark。
-
-### 10.4 GPU 资源阻塞
-
-截至本次记录，8 张 GPU 均被另一组 `vllm serve` 进程占用，每张约 82GB / 98GB，端口为 51000-51007。
-这些不是本 repo 的 SGLang/benchmark 进程，因此未启动新的性能 run，避免把资源冲突误判为 KVC 策略失败。
-
-GPU 释放后，优先跑两组：
-
-```bash
-# 混合真实窗口：验证 seed-only-multiturn 是否把 53/600 errors 降下来
-TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req/ali-window.jsonl \
-OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-ts1-kvc-seedonly-mt-mem082 \
-RUNS="kvc" \
-TIME_SCALE=1 \
-CONCURRENCY=32 \
-REQUEST_TIMEOUT_S=600 \
-STACK_TIMEOUT_S=1800 \
-EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
-KVC_SEED_ONLY_MULTITURN=1 \
-bash scripts/sweep_real_ali_kvc.sh
-
-# 多轮-only workload：DP vs KVC，对照过滤 workload 是否能复现 microbenchmark 收益
-TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
-OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-mem082 \
-RUNS="dp kvc" \
-TIME_SCALE=1 \
-CONCURRENCY=32 \
-REQUEST_TIMEOUT_S=600 \
-STACK_TIMEOUT_S=1800 \
-EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
-KVC_SEED_ONLY_MULTITURN=1 \
-bash scripts/sweep_real_ali_kvc.sh
-```
-
-### 10.5 multi-turn-only 启动尝试被 GPU 占用阻塞
-
-用户要求启动 multi-turn-only 的 `pd-disaggregation` vs `kvcache-centric` 对比。启动前检查发现 8 张 GPU 均被外部
-`vllm serve` 进程占用，每张约 84GB / 98GB，端口为 51000-51007。该进程不属于本 repo 的 SGLang/benchmark run。
-
-因此本次没有强行启动 SGLang。原因是剩余显存不足以启动 2P6D 或 8-worker 对照，强行运行只会得到初始化 OOM 或不稳定超时，
-不能用于判断 KVC pd-hybrid 是否优于 pd-disaggregation。
-
-资源释放后要运行的 multi-turn-only 对比命令：
-
-```bash
-TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
-OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082 \
-RUNS="pd kvc" \
-TIME_SCALE=1 \
-CONCURRENCY=32 \
-REQUEST_TIMEOUT_S=600 \
-STACK_TIMEOUT_S=1800 \
-EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
-KVC_SEED_ONLY_MULTITURN=1 \
-bash scripts/sweep_real_ali_kvc.sh
-```
-
-### 10.6 multi-turn-only PD vs KVC 正式结果
-
-资源释放后已启动并完成 multi-turn-only 对比。运行命令：
-
-```bash
-TRACE=outputs/real-ali-kvc-iter/samples-window-900s-600req-multiturn/ali-window-multiturn.jsonl \
-OUT_ROOT=outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082 \
-RUNS="pd kvc" \
-TIME_SCALE=1 \
-CONCURRENCY=32 \
-REQUEST_TIMEOUT_S=600 \
-STACK_TIMEOUT_S=1800 \
-EXTRA_SERVER_ARGS="--mem-fraction-static 0.82" \
-KVC_SEED_ONLY_MULTITURN=1 \
-bash scripts/sweep_real_ali_kvc.sh
-```
-
-Run 目录：
-
-- PD：`outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082/pd-disaggregation-kv-aware-20260512T030433Z`
-- KVC：`outputs/real-ali-kvc-iter/runs/window900s-600req-multiturn-ts1-pd-vs-kvc-mem082/kvcache-centric-kv-aware-worker-admission-20260512T040444Z`
-
-样本仍是 626 requests、107 sessions、889.341s，全部为 multi-turn session。
-
-| 策略 | Requests | Errors | Trunc | E2E mean | E2E p50 | E2E p90 | E2E p99 | TTFT mean | TTFT p50 | TTFT p90 |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| PD-disaggregation 2P6D | 626 | 0 | 0 | 97.013s | 70.243s | 214.309s | 308.406s | 94.506s | 69.048s | 212.528s |
-| KVC 2P6D worker admission, no BP, seed-only-multiturn | 626 | 39 | 0 | 43.362s | 8.239s | 135.289s | 236.475s | 40.578s | 1.442s | 132.233s |
-
-Paired comparison 只在 KVC 成功且 PD 也有 latency 的 587 个 request 上计算：
-
-- PD same-request E2E mean/p50/p90/p99：97.457s / 70.514s / 214.095s / 309.362s。
-- KVC same-request E2E mean/p50/p90/p99：43.362s / 8.239s / 135.930s / 237.283s。
-- mean E2E reduction：55.5%。
-- absolute mean improvement：54.095s。
-- wins/losses：472 / 115。
-
-按 KVC execution mode 拆分：
-
-| KVC mode | Count | KVC mean | PD same mean | Reduction |
-|---|---:|---:|---:|---:|
-| `kvcache-direct-to-d-session` | 286 | 2.255s | 92.944s | 97.6% |
-| `pd-router-fallback-large-append-session-cap` | 169 | 88.869s | 113.614s | 21.8% |
-| `pd-router-d-session-reseed` | 25 | 143.456s | 106.501s | -34.7% |
-| `pd-router-large-append-reseed` | 19 | 47.631s | 88.981s | 46.5% |
-| `pd-router-turn1-seed` | 78 | 55.974s | 73.050s | 23.4% |
-
-按 turn 深度拆分：
-
-- turn2+：504 successful paired requests，KVC mean 40.791s vs PD mean 101.055s，reduction 59.6%。
-- turn>=5：299 successful paired requests，KVC mean 34.121s vs PD mean 104.697s，reduction 67.4%。
-- turn>=10：161 successful paired requests，KVC mean 39.027s vs PD mean 86.548s，reduction 54.9%。
-
-KVC execution modes：
-
-- `kvcache-direct-to-d-session`：286。
-- `pd-router-fallback-large-append-session-cap`：169。
-- `pd-router-turn1-seed`：78。
-- `pd-router-d-session-reseed`：25。
-- `pd-router-large-append-reseed`：19。
-- `pd-router-fallback-no-d-capacity`：4。
-- `pd-router-turn1-d-backpressure`：5。
-- `pd-router-d-session-reseed-after-eviction`：1。
-- error rows：39，记录为 `kvcache-centric`。
-
-KVC 的收益来源非常清楚：286 个 direct-to-D request 的 same-request mean 从 PD 的 92.944s 降到 2.255s，基本复现了 microbenchmark 的核心机制收益。它跳过 P worker 和 P->D KV transfer，只在已有 D session 上处理 append suffix。总体 actual KV transfer blocks 从 PD same-success 的 4436 降到 KVC success 的 3827；summary 口径下 KVC total actual KV transfer blocks 为 3827，低于 PD 的 5276。
-
-但这轮仍不能作为“稳定生产级胜出”结论：
-
-1. KVC 仍有 39/626 errors，error rate 6.23%，PD 为 0。
-2. 39 个错误全部是客户端 `ReadTimeout`，不是服务端 OOM/Traceback；服务端日志未发现对应崩溃关键字。
-3. 错误分布：24 个 turn1，15 个 turn2+；按 decode 节点分布为 decode-0 15、decode-1 9、decode-3 7、decode-4 5、decode-5 3。
-4. 8 次 `/open_session` 400 已被 close+retry 兜住，并写入 `structural/session-lifecycle.jsonl`，没有形成 HTTP 400 error row。
-5. 长尾 drain 明显：PD 约 60min 完成，KVC 约 40min 完成；二者都远超 889s trace duration。KVC 在 900s 时已完成 490/626，而 PD 只完成 283/626，说明 KVC 中段吞吐更好，但最后几十个 large-append fallback 仍然拖尾。
-6. direct-to-D 覆盖率为 286/626 = 45.7%，低于样本静态 direct-eligible turn2+ ratio 91.1%。缺口主要来自 D session/residency capacity、large append session cap、reseed/fallback。
-
-当前判断：
-
-- 如果只看 successful paired request，multi-turn-only workload 上 KVC 相对 PD-disaggregation 已经有很强 E2E 提升，且提升主要来自 direct-to-D session reuse。
-- 如果按系统可靠性看，当前实现还不合格，因为 6.23% timeout 会抵消“稳定系统”的结论。
-- 真实 workload 与 microbenchmark 差距的主要原因不是 KVC fast path 无效，而是 fast path 覆盖率不足、D 侧 resident KV/session admission 压力、large append fallback、以及 seeded/reseed path 的 timeout 稳定性。

docs/REFACTOR_PLAN_V1_ZH.md

@@ -0,0 +1,385 @@
+# Refactor Plan v1：基于 ts=1 验证后的重构方向
+
+**日期**：2026-05-08
+**前置文档**：
+- `docs/archive/REFACTOR_PLAN_ZH.md`（v0，已被本文 supersede——v0 的 backpressure 切入点结论已撤回）
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（包含 §1-§7 结构性问题清单）
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md`（ts=10 数据下的早期验证）
+
+**触发**：`outputs/qwen3-30b-tp1-ts1-validation/` 4 个 run 完成（KVC 1P3D × N=3 + 4DP CA × 1，全部 ts=1）
+
+**目的**：把 ts=1 验证结果落到具体的重构决策——哪些事必须做、哪些事不要再做、KVC 项目本身是否需要重新定义价值主张
+
+---
+
+## 0. TL;DR
+
+1. **ts=10 失真是真的，影响 5-10×**——KVC 在 ts=10 灾难性输 DP 是 benchmark artifact，不是机制本身有问题
+2. **ts=1 同 scale 下 KVC ≈ DP**：lat mean 差 9%，TTFT 差 47%，errors 双 0
+3. **TEAM_REPORT 的 §1（session pin 不公平）是真问题，但代价从 6× 降到 ~2×**——仍是唯一值得做的 KVC 优化
+4. **TEAM_REPORT 的 §2/§3/§4/§5 大多是 ts=10 高压 artifact**——ts=1 下要么不显著、要么自然吸收
+5. **N=1 不可信是 ts=10 现象**——ts=1 下系统在 categorical 层面完全确定（routing/admission/errors 三次 run 完全相同）
+
+**项目落到情景 B（KVC ≈ DP）**——三种 forward 路径任团队决策（见 §6）。
+
+---
+
+## 1. ts=1 验证数据
+
+### 1.1 实验配置
+
+| 项 | 值 |
+|---|---|
+| Trace | `outputs/qwen35-swebench-50sess.jsonl`（4449 reqs / 52 sessions） |
+| 模型 | Qwen3-30B-A3B-Instruct-2507（TP1） |
+| 硬件 | 单机 4× H100 80GB（注：原始 ts=10 实验是 8 GPU；本次缩配） |
+| Time-scale | 1（真实 trace 时序，inter-turn gap p50 = 2.5s） |
+| Concurrency | 32 |
+| KVC 配置 | 1P3D，policy=kv-aware，admission=worker，seed-min-turn=1，prefill-priority-eviction |
+| DP 配置 | 4-way colo，policy=kv-aware（cache-aware） |
+| 输出根 | `outputs/qwen3-30b-tp1-ts1-validation/` |
+
+### 1.2 Headline 对比
+
+| Metric | KVC 1P3D ts=1（N=3 均值）| 4DP ts=1 | Delta |
+|---|---:|---:|---:|
+| **真实 mechanism errors** | **0** | **0** | 平 |
+| 报告 errors（口径不一致，见 §1.3） | 5 | 0 | – |
+| Lat mean | 1.574s | **1.443s** | DP 优 9% |
+| Lat p50 | 0.810s | **0.659s** | DP 优 19% |
+| Lat p90 | 3.796s | **3.641s** | DP 优 4% |
+| Lat p99 | 8.722s | **8.433s** | DP 优 3% |
+| TTFT mean | 0.244s | **0.129s** | DP 优 47% |
+| TTFT p50 | 0.122s | **0.090s** | DP 优 26% |
+| TTFT p90 | 0.572s | **0.252s** | DP 优 56% |
+| Per-worker spread | ±3.8% (3D) | ±3.1% (4 direct) | 接近 |
+
+### 1.3 KVC 5 errors 的真实身份
+
+DP 的同 5 个 (sess, turn) 也"失败"——但 metrics 口径不同：
+
+```
+KVC: 计入 error_count
+DP:  metrics 记 error=OK + finish_reason={'type':'abort', 'message':'Input length (X) exceeds the maximum allowed length (87811)'}
+```
+
+| sess | turn | input_len | KVC max | DP max |
+|---|---:|---:|---:|---:|
+| 35680 | 132 | 91600 | 92098 (✓) | 87811 (✗) |
+| 35680 | 133 | 92335 | 92098 (✗) | 87811 (✗) |
+| 39360 | 137 | 91700 | 92098 (✓) | 87811 (✗) |
+| 39360 | 138 | 92003 | 92098 (✓) | 87811 (✗) |
+| 39360 | 139 | 92135 | 92098 (✗) | 87811 (✗) |
+
+**两边都拒同样的请求**——区别只在于 KVC 在 P 端拒（KV 池满）、DP 在 prefill 端拒（max-input limit）。**真实 mechanism 错误率：KVC 0 / DP 0**。
+
+### 1.4 ts=1 的确定性
+
+KVC N=3 三次 run 跨 4449 records：
+
+| 维度 | 跨 run 差异 |
+|---|---|
+| `execution_mode` | **0 / 4449** records 不同 |
+| `assigned_decode_node` | **0 / 4449** records 不同 |
+| Errors（5 个 sess/turn 对） | **完全相同** |
+| 18 starved + 16 lucky session | **完全相同** |
+| Per-D load (1502/1445/1502) | **完全相同** |
+| Lat mean | 1.574 / 1.573 / 1.574（**0.06%** 漂移）|
+| Lat p50 | 0.811 / 0.809 / 0.812（**0.4%** 漂移）|
+| 单 request lat | abs p90 diff = 25ms |
+
+**结论**：低压 / ts=1 区间下 KVC 系统在 categorical 层面（路由 / admission / 失败位置）**完全确定**，仅低层数值有 model 计算微抖动。
+
+---
+
+## 2. 对 TEAM_REPORT §1-§7 的修订
+
+| § | TEAM_REPORT 原 claim | TEAM_REPORT 原优先级 | ts=1 验证后状态 | **修订优先级** |
+|---|---|---|---|---|
+| §2.1 | session pin + 容量盲选 → 25% 饿死 | **P0** | ✅ 结构性问题仍在（18/52 session 永久 pin），但代价从 6× 慢降到 ~2× | **P0**（唯一值得做的 KVC 优化）|
+| §2.2 | D-side LRU 跟不上 → 8% errors | **P0** | ⚠️ D 仍瞬时顶到 token_usage=1.00，但**ts=1 下 drain time 自然吸收**——0 KVTransferError 雪崩（vs ts=10 369 次） | **降级 P3**（drain time 已解决症状）|
+| §2.3 | 无 backpressure 通道 | P1（已实现）| ❌ ts=1 下 transfer cascade 不存在，backpressure 无作用对象 | **冷藏**（代码留着，但默认 off）|
+| §2.4 | P-side round-robin 不感知 D 健康 → prefill-0/-1 错误差 180× | P1 | ⚠️ 1P 配置不可测；ts=10 现象**高度怀疑也是 artifact**（错误本身在 ts=1 消失） | **存疑 / 重测后再说** |
+| §2.5 | admission RPC 进 scheduler 主循环 → 1Hz polling 让 errors ↑46× | P2 | ❌ 是 ts=10 高压时的现象，ts=1 下不显著 | **冷藏** |
+| §2.6 | time-scale=10 失真 → 所有 KVC vs DP 结论可能被放大 | **P0** | ✅ **完全证实**（74× errors↓, 8.7× TTFT↓, 7× per-D spread↓） | **DONE，作为前置条件锁定** |
+| §2.7 | execution_mode 标签命名错位 | P1 | ✅ 仍存在；本次 ts=1 又发现 `error_count` 在 KVC vs DP 口径不一致 | **P1**（纯 labeling 修复，~半天）|
+| §2.8 | N=1 不可信 → 实验必 N≥3 | P2 | ⚠️ **是 ts=10 高压现象**——ts=1 下 N=1 categorical 完全确定 | **改写规则**：高压 N≥3 / 常规 N=1 |
+| §2.9 | microbench 把 KVC 失效条件全规避 | – | 仍成立 | **保留观察**（实验设计原则）|
+
+---
+
+## 3. v0 REFACTOR_PLAN 回顾
+
+### 3.1 v0 做对的
+
+- **唯一代码改动选 backpressure**：作为对 §2.3 的最小验证手段是合理的
+- **预算 KISS**：用 8h GPU 验证 §1-§7，思路正确
+- **明确"P0 是 time-scale=1 baseline"**：v0 的 §1 末尾就指出 "time-scale=1 验证为 P0 待办"——本次实验正是把这条做了
+
+### 3.2 v0 的核心误判
+
+| v0 假设 | 实际 |
+|---|---|
+| backpressure 是 §3 的最小验证 → 也是修复 | ts=1 下 §3 的症状（transfer cascade）不存在，backpressure 无效 |
+| 8h 预算够跑 ts=1 baseline + backpressure smoke | ts=1 单 run 5.5h，4 run 全跑要 22h（实际跑了 22h） |
+| §1 / §2 的修复"超出 KISS 边界"，先验证不修 | 验证后发现 §1 是**唯一**值得做的真问题，应该早点把它纳入 |
+
+### 3.3 v0 的 backpressure 代码命运
+
+代码保留（`--enable-backpressure` 默认 off），原因：
+- 不删除是因为如果未来跑高压 / 大 trace / 真 RDMA 失败回归到类 ts=10 区间，可能仍有用
+- 但**不部署、不启用、不文档化为推荐配置**——避免给以后看到代码的人误导
+
+---
+
+## 4. 修订后的优先级矩阵
+
+```
+                    必做                   建议做                  不做
+                  ────────              ────────              ────────
+ts=1 必修        §1 capacity-aware   (空)                   §2 / §3 / §4 / §5
+                 policy + migration                          的 ts=10 fix
+
+ts=1 nice        §2.7 metrics 标签   (空)                   §2.8 N≥3 严苛规则
+to have         统一口径                                    （改成"高压 N≥3"）
+
+文档              §3 写入 TEAM      v0 标记 superseded     ts=10 数据归档
+                  REPORT 更新                               （但保留可追溯性）
+```
+
+**唯一进入"必做工程"列表的是 §1**。其他全是文档或冷藏。
+
+---
+
+## 5. KVC vs DP 拆分到 path-level 看真实差距
+
+理解 §1 的 ROI 必须先看 path-level（不是整体均值）：
+
+### 5.1 KVC 内部 path 性能（来自 ts=1 N=3 一致数据）
+
+| Path | n | 占比 | Lat p50 | TTFT p50 |
+|---|---:|---:|---:|---:|
+| `kvcache-direct-to-d-session`（快路径）| 1903 | **42.8%** | **0.475s** | **0.042s** |
+| `pd-router-fallback-large-append-session-cap`（慢路径）| 2409 | **54.2%** | 1.04s | 0.32s |
+| `pd-router-turn1-seed`（每 session 一次）| 52 | 1.2% | 0.375s | 0.057s |
+| 其余 | 85 | 1.8% | 多种 | 多种 |
+
+### 5.2 DP 全部 path（单一）
+
+| Path | n | 占比 | Lat p50 | TTFT p50 |
+|---|---:|---:|---:|---:|
+| `dp-colo-router` | 4449 | 100% | 0.659s | **0.090s** |
+
+### 5.3 路径级对比
+
+| | KVC direct | KVC fallback | DP |
+|---|---|---|---|
+| Lat p50 | **0.475s**（赢 DP 28%）| 1.04s（输 DP 58%）| 0.659s |
+| TTFT p50 | **0.042s**（赢 DP 53%）| 0.317s（输 DP 252%）| 0.090s |
+
+**事实陈述**：
+- KVC 快路径 **明显快于** DP（无 P 介入、无 mooncake transfer）
+- KVC 慢路径 **明显慢于** DP（P→D transfer 开销没法摊到 turn 内）
+- 当前 quick:slow = 42.8% : 54.2%——慢路径多 → 整体输 DP 9-47%
+- 如果能把比例反过来到 70:25 或更好，KVC 整体会赢 DP
+
+**§1 的本质就是"为什么有 54% 进了慢路径"**——因为 18/52 session 被 pin 在容量紧张的 D 上，每次 admission 都拒。
+
+---
+
+## 6. 三种 forward 路径
+
+> **更新（2026-05-09）**：情景 C **已实现**——见 `docs/V2_RESULTS_ZH.md`。下面三个分支保留作历史记录。
+>
+> | 情景 | 描述 | 状态 |
+> |---|---|---|
+> | A | KVC < DP，接受现状转维护 | 不适用 |
+> | B | KVC ≈ DP，重新定义价值主张 | 不适用 |
+> | **C** | **KVC > DP，优化拉大差距** | **✓ 实现：v2 在 7/8 头部指标击败 4DP（TTFT mean -24%, p50 -54%, p90 -64%；lat mean -0.8%, p50 -12.6%）** |
+>
+> 关键修复：(1) reset-on-success blacklist decay（消除 v1 thrashing），(2) `--kvcache-direct-max-uncached-tokens` 2048→8192（让 41% 大 append 走 direct-to-D 快路径）。direct-to-D rate 从 baseline 42.8% 升到 v2 91.7%。
+
+### 6.1 选项 A：接受现状，项目转维护
+
+**判断**：KVC 在 ts=1 + 同 scale 下 ≈ DP（9% 慢、47% TTFT 慢），但**也没灾难性输**。如果项目目标是"验证 KV-aware routing 在 agentic 上是否可行"，答案是 **可行但收益不显著**。
+
+**操作**：
+- 写 TEAM_REPORT §3 总结 ts=1 实验
+- 把 ts=1 数据 + 4 个 run 归档到 `RESULTS_FROZEN_TS1.md`
+- KVC 代码保留但标记 "experimental, not recommended for production"
+- 团队转下一个项目方向（不是本文范围）
+
+**成本**：1 周文档收尾。
+**风险**：放弃了 §1 修复后可能的 KVC > DP 上限。
+
+### 6.2 选项 B：做 §1，目标让 KVC > DP
+
+**判断**：5.3 节的路径分析表明 KVC 快路径已经赢 DP；如果把饿死 session 救回快路径，KVC 整体可能赢 DP。
+
+**具体改动**：
+
+#### 6.2.1 capacity-aware policy（`policies.py:166-172`）
+
+当前评分（无容量项）：
+```python
+score = (
+    overlap + sticky * self.sticky_bonus,
+    sticky,
+    inflight_penalty,
+    assignment_penalty,
+)
+```
+
+提议改为：
+```python
+# 新增：D 当前容量利用率（从 worker-mode admission 已能查到）
+capacity_used = worker_capacity_used_ratio.get(worker.worker_id, 0.0)
+
+# Hard cap：容量 > X 时禁止该 D 进入候选
+if capacity_used > HARD_CAP_THRESHOLD:  # e.g. 0.85
+    continue
+
+score = (
+    overlap_capped,           # 原 overlap，但限幅避免单个 D 永远赢
+    -capacity_used,           # 新增二级排序项：偏好空闲 D
+    sticky,
+    inflight_penalty,
+)
+```
+
+#### 6.2.2 session migration（`replay.py` 或 policy 层）
+
+当 session X 在 D-A 上连续被 admission 拒 N 次（如 N=3）：
+- 主动 release X 在 D-A 上的 session state
+- 允许下次 turn 把 X 路由到另一个 D
+- 代价：丢失 D-A 上已积累的 KV——但 fallback 路径本来也丢了，**净收益正**
+
+#### 6.2.3 metric 修复（`replay.py`）
+
+把"`pd-router-fallback-large-append-*`" 标签按真实原因细分：
+- `session-not-resident-on-pinned-D`（§1 主因）
+- `real-large-append`（>2048 阈值，§2.7）
+- `session-was-evicted`（被 LRU 踢过）
+- `session-cap-rejected`（worker admission 拒）
+
+让以后看 metrics 的人不再被名字误导。
+
+#### 6.2.4 验证
+
+- 每改动跑 KVC 1P3D ts=1 N=1（categorical 确定，不需要 N=3）
+- 对比 baseline run1（已有数据）
+- 关键指标：`kvcache-direct-to-d-session` 占比、整体 lat mean、TTFT mean
+- 目标：direct-to-D rate 从 42.8% 升到 > 70%、整体 lat 追平或赢 DP
+
+**成本**：3 天编码 + 5 天测试 + 2 天文档 ≈ 2 周。
+**风险**：
+- session migration 可能导致 thrash（A→B→A→B），需要冷却时间机制
+- capacity HARD_CAP 阈值需要 sweep 找最优
+- 改完仍可能不赢 DP（理论上限不知道）
+
+### 6.3 选项 C：保留 KVC，但寻找 KVC 真正赢的工作点
+
+**判断**：当前 SWE-Bench 50 sessions × 30B 模型 × 4 GPU 是一个特定工作点。KVC 的设计初衷是"长 multi-turn session 的 KV 复用"——可能在某些其他工作点有显著优势。
+
+**候选工作点**：
+- **更长 session（>200 turns）**：复用收益更大
+- **更小模型（如 7B / 14B）**：mooncake transfer 占比更大，KVC 节省更明显
+- **更大 trace（>200 sessions）**：DP 的 prefix cache 命中率会下降，KVC 的 session-aware 优势放大
+- **真实 RDMA（非 mooncake TCP loopback）**：transfer 更快，KVC 的 P→D 开销更小
+
+**操作**：
+- 设计 1-2 个新 micro/macro benchmark
+- 跑 KVC vs DP 对比
+- 找到差距 > 30% 的工作点（KVC 赢 / 输都是数据）
+
+**成本**：~1 个月（trace 设计 + benchmark + 分析）。
+**风险**：可能找不到 KVC 显著赢的工作点。
+
+---
+
+## 7. 推荐组合
+
+按风险 / 收益排序：
+
+1. **必做**（无论选 A/B/C）：
+   - 写 `TEAM_REPORT §3 ts=1 验证更新`
+   - 修 `metrics 标签口径`（§2.7 + KVC/DP error_count 一致化）
+   - **冷藏 backpressure 代码**（不删但默认 off）
+   - 把 v0 REFACTOR_PLAN 标 superseded
+
+2. **强烈推荐**：选项 B 的 §6.2.1（capacity-aware policy hard cap）
+   - 工程量小（~1 天编码 + 1 天测试）
+   - 验证 §1 修复的真实收益是否如预测
+   - 如果 direct-to-D rate 不显著提升 → 把 §6.2.2 也加上
+   - 如果还不行 → 接受现状走选项 A
+
+3. **看团队带宽**：选项 C 的工作点探索
+   - 不与 §6.2 冲突，可以并行
+   - 找到一个 KVC 真正赢的工作点会极大改变项目价值主张
+
+---
+
+## 8. 应该砍掉的事（明确列表）
+
+| 事 | 砍的理由 |
+|---|---|
+| backpressure smoke sweep（v0 计划的 4 run） | ts=1 下 backpressure 无作用对象 |
+| §2.5 admission API probe/commit 拆分 | 高压才显著，等找到 KVC 高压 workload 再说 |
+| §2.2 D-side 分层 LRU eviction（hot retract） | drain time 自然吸收 |
+| §2.4 P-side D-health-aware routing | 1P 测不出，ts=10 现象高度存疑 |
+| 大量 instrument（admission-events / pool timeseries） | 已经够了，先用现有数据 |
+| 任何 ts=10 区间的优化 | 那是 benchmark artifact 主导的区间，不代表真实部署 |
+| N≥3 实验作为硬规则 | 改写为"高压 N≥3，常规 N=1 即可" |
+
+---
+
+## 9. 风险与未验证的假设
+
+1. **4DP ts=1 是 N=1**：虽然 KVC ts=1 是确定性的，DP 是新机制 N=1，理论上需要 N≥3 验证。但 DP 在 ts=10 也是 0 errors / 1.43s mean，行为相对 KVC 更稳定，N=1 风险较小。**如选项 B 推进，建议补 N=2**。
+2. **2 个 input-too-long session 是 trace 数据问题**：这两个 session（35680、39360）在 turn 132+ / 137+ 才超过 input limit。可能是 trace 生成时没控制好 max input。**应该独立把这两个 session 从 trace 移除或截断后重跑作为对照**。
+3. **4 GPU 缩配 vs 8 GPU 原始**：本次 1P3D / 4DP 数据无法跨 8 GPU 原始数据直接比，需要在结论中明确。但 ts=1 + 同 scale 内部对比是干净的。
+4. **mooncake TCP loopback**：所有 transfer 在单机 TCP 模拟下进行。生产 RDMA 下 KVC 的 transfer 开销可能显著降低，KVC 优势可能扩大——这是 **选项 C 的一个候选维度**。
+5. **§1 修复是否真能让 direct-to-D 上升到 70%+ 是预测**：实际可能受 hash overlap 限制（即使 D 容量充裕，没有 prefix overlap 就走不了 direct-to-D）。**需要 §6.2 验证后才知道天花板**。
+6. **input-limit error 的 metrics 口径修复影响以后所有比较**：注意修改后 ts=10 历史数据的 error_count 也需要重算（或在分析时显式补偿）。
+
+---
+
+## 10. 决策点（需要团队确认）
+
+请审阅后回答：
+
+| # | 决策 | 选项 |
+|---|---|---|
+| D1 | 选哪条 forward 路径？ | A（维护）/ B（修 §1）/ C（探索 workload）/ B+C |
+| D2 | 写 TEAM_REPORT §3 ts=1 验证更新章节？ | Yes / No |
+| D3 | 把 v0 REFACTOR_PLAN 标 superseded？ | Yes / No |
+| D4 | 删除 backpressure 代码 vs 冷藏？ | 删 / 冷藏（默认 off）|
+| D5 | 修 metrics 标签口径（§2.7 + error_count 一致化）？ | Yes / No |
+| D6 | 是否补 4DP ts=1 N=2 / N=3 做更稳的 baseline？ | Yes / No |
+| D7 | 是否把 sess 35680 / 39360 从 trace 移除做"干净" baseline？ | Yes / No |
+
+---
+
+## 附录 A：本文数据来源
+
+| 章节 | 数据源 |
+|---|---|
+| §1.2-§1.4 | `outputs/qwen3-30b-tp1-ts1-validation/{kvc_1p3d_run{1,2,3},dp4}_{summary.json,metrics.jsonl}` |
+| §1.4 跨 run 一致性 | per-record diff via `scripts/analysis/analyze_ts1_validation.py` + 临时 diff 脚本 |
+| §5 path-level | metrics.jsonl 按 `execution_mode` 分组 |
+| §2 §1-§7 修订 | `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` 原数据 + ts=1 新数据交叉对比 |
+
+## 附录 B：相关文档
+
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — §1-§7 原结构性问题清单
+- `docs/archive/REFACTOR_PLAN_ZH.md` — v0 重构计划（本文 supersede）
+- `docs/archive/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析（§1-§7 来源）
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
+- `docs/archive/KVC_DEBUG_JOURNEY_V1_TO_V5.md` — v1→v5 演进
+- `docs/archive/V5_PROFILE_INVESTIGATION_ZH.md` — v5+profile 调查（已 critic 修订）
+- `scripts/sweep_ts1_kvc_n3_plus_dp.sh` — 本次 4 run sweep 脚本
+- `scripts/analysis/analyze_ts1_validation.py` — 本次分析脚本
+
+---
+
+**作者注**：本文偏决策导向。如果要写更技术的 §1 capacity-aware policy 实现细节，应该在 D1 决策为 B 之后单独出一份 `IMPL_CAPACITY_AWARE_POLICY.md`。

docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md

@@ -0,0 +1,368 @@
+# Reseed 慢路径现状与 D→P KV 同步缺口
+
+**日期**：2026-05-11
+**对象**：项目团队 + 后续 paper reviewer
+**性质**：基线现状落盘 + future-work 缺口定位
+**前置文档**：
+- `docs/V2_DEEP_ANALYSIS_ZH.md` §3.2 §4.2（reseed 路径在 v2 数据中的表现）
+- `docs/KVC_ROUTER_ALGORITHM.md` §3 §9（算法形式化 + open questions）
+
+**目的**：把"v2 的 reseed slow path 为什么慢、能不能用现有机制治、还差什么"三个问题落盘成单一参考文档，让团队不必再口头反复对齐，让论文 future-work 章节有可引用的基础。
+
+---
+
+## 0. TL;DR
+
+1. KVC v2 在 SWE-Bench 测试中 8.3% 请求走非 direct-to-D 的 reseed/fallback 路径，**单次 reseed 实测 3-7s**（TTFT p99 = 1.28s 全部来自这条路径）。
+2. 启用真 RDMA（节点有 mlx5_0/_1 @ 200 Gb/s × 2 active）能把 reseed 的 transfer 段（~1.5-4s）压到 ~200-400ms，但**对 re-prefill 段（~1.5-3s）无效**。预期 reseed 总时间从 3-7s 降到 1.7-3.2s，TTFT p99 ~0.7s，**仍输 DP（0.43s）**。
+3. 真正消除 reseed 长尾必须实现 **D→P 增量 KV 同步**——让 P 端 backup 跟上 D 在 direct-to-D append 路径上累积的 KV，避免 reseed 时重新跑 prefill kernel。
+4. 经 Opus agent 独立 forensic 审查（commit `9ccd853`）+ 全分支 git 检索：**当前代码、vendored SGLang、mooncake 三层均无 D→P 实现**，作者也没有在其它分支偷偷开发——仓库总共只有 main（旧 baseline）+ kvc-debug-journey-v1-to-v4（本工作分支）两个分支，main 还落后我们 18 个 commit。
+5. `--kvcache-prefill-backup-policy capacity-backup` 这个 flag 看起来像 D→P 同步但**不是**——它的真实语义只是"reseed 完不关 P streaming session"，P 端 KV 仍是 seed-time 的**静态快照**，不随 direct-to-D append 而增长。
+6. 实现 D→P 增量同步的工程量 ~1-2 周，最难的不是网络层（mooncake 加 D-sender / P-receiver 角色 ~400 LOC），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者。
+
+---
+
+## 1. 团队成员的三个质疑（关键框架，paper 引用建议保留原话）
+
+这三条质疑出自 v2 完成后的对话审查，**直接戳穿了"启用 capacity-backup 就能消除 slow path"的一厢情愿**。每条都有代码层证据支持，**全部成立**。
+
+### 质疑一：P 节点的 pool 塞得下所有 backup 的 KV cache 吗？
+
+**回答：塞不下，max 同时 backup ~1-2 个大 session。**
+
+代码证据（`src/agentic_pd_hybrid/replay.py:1618-1620`）：
+
+```python
+max_backup_sessions = max(1, capacity_tokens // max(1, target_tokens * 2))
+max_backup_sessions = min(max_backup_sessions, 4)
+```
+
+按 SWE workload 实测代入：
+- P 池 `capacity_tokens` ≈ 92,104 tokens（SGLang 启动时按 mem_fraction_static 自动分配）
+- 典型 session peak input `target_tokens` ≈ 50,000-80,000 tokens
+- 计算：`92K // (50K × 2) = 0` → `max(1, 0) = 1`
+- → **P 最多同时 backup 1 个大 session**
+
+对照小 session：
+- target 20K：`92K // 40K = 2` → backup 上限 2 个
+- target 10K：`92K // 20K = 4` → backup 上限 4 个（达到代码硬上限）
+
+→ **capacity-backup 在真实 agentic 长 context workload 下只能救少数 session，不是全员保险。**
+
+### 质疑二：P 上的 backup 是陈旧快照——49K 的 append 内容根本没经过 P
+
+**回答：完全正确，这是 capacity-backup 设计上的致命缺陷。**
+
+**用户提供的反例场景**（已成为 paper 中描述 slow path 的标准例子）：
+
+```
+turn 0:   P 做 prefill 1K tokens → 经 mooncake 传到 D → P 留 1K backup
+turn 1-50: 全部走 direct-to-D，D 上做 append-prefill，KV 在 D 上从 1K 增长到 50K
+           ↑↑↑ 关键：这 49K 的 append 内容（tool 输出、user 消息、模型生成）
+              **从未流经 P 节点**。P 端 backup 锁在 1K 状态。
+turn 51:  D 出于某种原因（容量、迁移、显式驱逐）拒绝 → 触发 reseed
+          → 即使 P 上有 backup，也只是 turn-0 的 1K
+          → 实际需要 D 上重建的是 50K（当前完整 context）
+          → P 必须从 prompt 重新 prefill 49K 的差额
+          → capacity-backup 节省的 compute 仅 ~2%
+```
+
+**代码证据**（独立 Opus agent forensic 审查，commit `9ccd853`）：
+
+1. 唯一更新 `session.prefill_resident_tokens` 的函数是 `_commit_prefill_backup_residency`（`replay.py:1483`）
+2. 这个函数的唯一 caller 是 `_invoke_kvcache_seeded_router`（`replay.py:2208`）—— 即 seed/reseed 路径
+3. `_invoke_session_direct`（`replay.py:2719`，direct-to-D 路径）只更新 `session.opened` / `resident_tokens` / `last_trace_request`，**从不触碰任何 P 端字段**
+4. `_commit_prefill_backup_residency` 内部用 `_estimate_session_resident_tokens(request)` 取的是**完整 request 的预估**，不是 append delta——所以连 bookkeeping 层面都不假设有增量更新
+
+→ **`capacity-backup` 的真实语义只是"reseed 完之后跳过 `_close_prefill_session`"**（`replay.py:2221`），P 端 streaming session 保持 open 状态、KV 留在 P 的 radix tree 中。但**不存在任何机制让这份 KV 跟上 D 端的 append 增长**。
+
+### 质疑三：D 触发 reseed 后，本机旧 session 的 KV cache 是不是清空了？P 做完 re-prefill，KV 推到哪里？
+
+**回答：是的，旧 KV 直接 free 掉；P 重新 prefill 完之后推到 router 选的新 target D（可能同 D，可能换 D）。中间没有"先 dump 到 P 再清"的快捷方式。**
+
+#### D 端驱逐时的 KV 处理
+
+代码证据（`replay.py:_close_decode_session`，1539-1569 行；`session_aware_cache.py:release_session`，250-276 行）：
+
+```python
+# replay.py 端
+async def _close_decode_session(..., evicting_for_capacity=False):
+    if not session.opened:
+        return
+    await _close_streaming_session(...)         # 给 D 发关闭信号
+    # 从 D 的 resident bookkeeping 里删掉这个 session
+    session.opened = False
+    session.resident_tokens = 0
+    if evicting_for_capacity and not session.prefill_opened:
+        residency.decode_evictions_without_prefill_backup += 1
+
+# SGLang 端（session_aware_cache.py）
+def release_session(self, session_id):
+    # 解锁引用 + 直接 free KV slots
+    self.token_to_kv_pool_allocator.free(kv_indices)
+    # ↑ 没有序列化、没有外发、没有 D→P 通道
+```
+
+**D 驱逐 = 把 KV slot 直接归还给 token pool 分配器。完全没有任何 outbound 网络调用。**
+
+#### Reseed 时 P→D 的目标选择
+
+驱逐之后的 reseed 路径（`_invoke_kvcache_seeded_router`，`replay.py:2101`）走的是与 turn 0 完全一样的 P-mediated seeding：
+
+```
+1. KvAwarePolicy.select() 选择一个 target D'（可能是同一个 D，也可能因 migration 换 D）
+2. _invoke_kvcache_seeded_router 在 D' 上 open 一个 streaming session
+3. 给 P 发完整 prompt → SGLang pd-router 让 P 做完整 prefill
+4. P 的 prefill 完成后通过 mooncake 把 KV 一次性推到 D'
+5. D' 上接收完毕，session 重建完成；decode 继续
+```
+
+**所以 P 做完 re-prefill 的 KV 推到 KvAwarePolicy 选的 target D'**——可能是：
+- 同一个 D（驱逐后重新接受）
+- 另一个 D（如果 reject 计数累积触发 migration，详见 KVC_ROUTER_ALGORITHM §3.3）
+
+无论哪种，**旧 D 的旧 KV 在新 KV 到达之前就已经被 free**。没有 D→D 的直接迁移路径，没有"先 dump 到 P 再推回"的快捷路径。
+
+---
+
+## 2. Reseed 路径的完整 step-by-step 现状
+
+把上面三个质疑串成端到端流程，以下是 v2 当前 reseed 路径的**完整**操作序列。每一步都标注实测耗时与代码位置。
+
+### 触发条件
+
+下列任一发生时 router 走 reseed 路径（详见 `KVC_ROUTER_ALGORITHM.md §3.3`）：
+- D 端 `Admit()` 返回 `can_admit=False`，原因为 `no-d-capacity` / `session-not-resident` / 等
+- KvAwarePolicy.select 返回的 D 不再持有该 session（migration 触发）
+- v1/v2 的 reject counter 累积让所有 D 都被 blacklist（极少触发，由 reset-on-success 保护）
+
+### 端到端时间线
+
+```
+t=0      上游 agent 发出 turn N 请求（input ~50K，append ~2K）
+            ↓
+t=~5ms   Router 的 KvAwarePolicy.select() 选 target D'（O(|D|) Python 评分）
+            ↓
+t=~10ms  Router → D' 发 admit_direct_append RPC
+            ↓
+t=~30ms  D' 返回 can_admit=False, reason="session-not-resident"
+         或 "no-d-capacity"，Algorithm 3 bump rejects[s, D']++
+            ↓ （fallback chain 最多再试 ε-1 个 D，对应 ε ~30ms 总额）
+t=~100ms 所有 D 都被拒 / 选不到适合 D，路径退化到 seeded router
+            ↓
+t=~110ms Router 转 _invoke_kvcache_seeded_router
+            ↓
+t=~120ms [可选] capacity-backup policy 下：_reserve_prefill_backup_capacity()
+         检查 P 池容量，若不够先 LRU 驱逐别的 P backup session
+            ↓
+t=~150ms P 上 open streaming session（HTTP /session/open）
+            ↓
+t=~200ms 发完整 prompt 到 SGLang pd-router → 路由到 P
+            ↓
+t=~250ms  P 开始 prefill
+         ↓
+         ↓ ←←← 大头 1：P-side re-prefill 段
+         ↓     P 必须 prefill 完整 ~50K tokens
+         ↓     即使 capacity-backup 开着，P 的 backup 只有 turn-0 的 ~1K
+         ↓     radix prefix cache 命中前 1K，剩余 49K 重算
+         ↓     实测耗时：~1.5-3s @ Qwen3-30B TP1
+         ↓
+t=~2000ms P 完成 prefill，KV 进入 mooncake transfer 队列
+            ↓
+t=~2050ms mooncake 开始 P→D' transfer
+         ↓
+         ↓ ←←← 大头 2：P→D mooncake transfer 段
+         ↓     KV 张量 ~5-9 GB（50K tokens × 2 bytes/token × layers × heads...）
+         ↓     **TCP loopback** 实测耗时：~1.5-4s
+         ↓     ↑↑↑ 当前 sweep 未启用 RDMA，走的是单机 lo 设备
+         ↓     若启用 IB RDMA @ 200 Gb/s，理论 200-400ms
+         ↓
+t=~4500ms transfer 完成，D' 上 session 重建好
+            ↓
+t=~4510ms D' 开始 decode（小幅度 append-prefill 余下的 ~2K append + 生成）
+            ↓
+t=~4550ms 首个 token 出来 → TTFT 测点
+```
+
+**单次 reseed 总耗时：3-7s**（中位 ~2.5s 来自较小 session，p99 ~7.7s 来自最大 session）。**re-prefill 段与 transfer 段大致五五开**，受 session 大小影响。
+
+### 这就是为什么 v2 的 TTFT p99 = 1.28s
+
+8.3% slow path 走的是上面这条流水线，其中 reseed 路径（`pd-router-d-session-reseed`）单独占 3.4%（150/4449 请求），构成 KVC TTFT p99 长尾的主要贡献。
+
+---
+
+## 3. 已审查的所有"看起来像 D→P 但其实不是"的代码
+
+下面这些在搜索时容易误判成 D→P 实现，**全部经独立 audit 排除**：
+
+| 文件:行 | 看起来像 | 实际是 |
+|---|---|---|
+| `replay.py:1483 _commit_prefill_backup_residency` | "把 backup 提交到 P" | bookkeeping 函数，更新 `session.prefill_resident_tokens` 计数字段。不传输任何 KV 数据，只在 seed/reseed 完成后被调用。 |
+| `replay.py:1572 _reserve_prefill_backup_capacity` | "预留 backup 空间" | 检查 P 池可用空间并按 LRU 驱逐别的 backup session 腾位置。不传 KV，只调整 reservation 计数。 |
+| `cli.py:182 --kvcache-prefill-backup-policy` | "backup 策略" | 只决定 reseed 完成后是否 `_close_prefill_session`。capacity-backup = 保留 P 端 streaming session 不关；release-after-transfer = 立刻关闭。**两种策略下 P 的 KV 都是 seed-time 的静态快照**。 |
+| `session_aware_cache.py:release_session` | "释放 session（可能含外发）" | 仅调 `kv_pool_allocator.free(kv_indices)`。零网络调用。 |
+| `disaggregation/decode.py: start_decode_thread` | "decode 端线程，可能有出站" | 纯 receiver loop。处理入站 `AUX_DATA / CHUNK_READY / STAGING_REQ / KVPoll.Success`，**没有出站 KV 传输分支**。 |
+| `disaggregation/mooncake/conn.py:1563` | "传输请求添加" | `assert disaggregation_mode == PREFILL`——硬约束，只有 P 端能调。 |
+| `mooncake.MooncakeKVSender` / `MooncakeKVReceiver` | "双向 sender / receiver" | 强角色化：Sender 只在 PREFILL 模式实例化，Receiver 只在 DECODE 模式。`BaseKVManager` 抽象无 bidirectional slot。 |
+| `pd-router-d-session-reseed-after-eviction` execution_mode | "走 backup 的快路径" | 实际还是走完整 `_invoke_kvcache_seeded_router`（P 完整 prefill + 完整 mooncake transfer），只是 `_eviction_suffix()` 在 execution_mode 字符串末尾加了 "-after-prefill-backed-eviction" 标签。**没有任何 fast-path 优化**。v2 中仅 2/4449 请求走到这个标签。 |
+
+---
+
+## 4. D→P 增量同步：要做的是什么
+
+完整 D→P 增量同步的设计目标：**让 P 端的 backup KV 在 direct-to-D append 完成后异步追上 D 端的 KV，让 reseed 退化为单次 P→D transfer（无需 P re-prefill）**。
+
+### 抽象数据流
+
+```
+当前：
+  direct-to-D append: D 本地 append-prefill，P 端 backup 锁住不变
+  reseed:             P re-prefill 完整 50K + P→D transfer 完整 50K
+
+目标：
+  direct-to-D append: D 本地 append-prefill，**同时**异步把新增的 KV 块推回 P
+  reseed:             P→D' transfer 完整 50K (already up-to-date)
+                      无需 P re-prefill
+```
+
+### 实现层面要改的事
+
+按工程难度排序：
+
+#### 4.1 Mooncake 双角色化（中等难度，~400 LOC）
+
+- `BaseKVSender` / `BaseKVReceiver` 抽象保留，但允许同一 worker 同时实例化两种角色
+- `MooncakeKVManager.__init__` 把 PREFILL / DECODE 分支改成"role set"，允许 worker 同时持有 sender 和 receiver
+- 新增 `DecodeKVSender` 类（D 端用于把 append KV 推回 P）
+- 新增 `PrefillKVReceiver` 类（P 端用于接收 D 的 append KV）
+- 引入第二个 bootstrap channel（避免与原 P→D 通道在 buffer pointer 协商上冲突）
+
+#### 4.2 D 端 append commit hook（容易）
+
+- 每次 `direct-to-D-session` 完成后，识别新写入的 KV 块（D scheduler 在 commit 时知道）
+- 入队 D→P 传输（异步，不阻塞 next request）
+- 标记 backup 是否成功送达 P（用于后续 reseed 决策）
+
+#### 4.3 P 端 radix tree 多生产者扩展（**最难，工程量主体**）
+
+**这是真正的架构 blocker**。SGLang 的 P 端 radix cache 当前假设：
+- 单一生产者（本 worker 的 model 输出）
+- 树插入只在 prefill / decode 完成时发生
+- KV 索引由本 worker 的 token_to_kv_pool_allocator 分配
+
+要让 P 接收 D 喂来的 KV 块，需要：
+- 扩展 radix tree 节点的写入路径，允许"外部供给的 KV + token 序列"被插入
+- 处理 KV 索引重映射（D 的 slot 号在 P 上无意义）
+- 处理 reference counting（同一 session 可能既被本 worker 用、又被 D 喂回更新）
+- 处理 eviction policy 协调（P 端 radix LRU 不应让"被 D 喂入的 backup"先被驱逐）
+- 处理 KV 数据格式的跨 worker 兼容（同样的 model layout，应该是 trivial，但需要测试）
+
+#### 4.4 agentic-pd-hybrid 端 hook（容易）
+
+- `_invoke_session_direct` 完成后，新增一步：触发 D→P 同步 RPC（异步）
+- `_invoke_kvcache_seeded_router` 在 reseed 触发前先 probe P 是否有 up-to-date backup；若有，跳过 re-prefill，只做 P→D transfer
+- 新增 CLI flag `--enable-d-to-p-sync`，默认 off，保留 baseline 行为
+- 新增 structural log channel 记录 D→P 同步事件 / 失败 / 延迟
+
+### 实现完毕后的预期收益
+
+| 指标 | 当前 (v2) | RDMA only | RDMA + D→P sync |
+|---|---:|---:|---:|
+| reseed re-prefill 段 | 1.5-3s | 1.5-3s（不变） | **~0**（已有 up-to-date backup） |
+| reseed transfer 段 | 1.5-4s | 0.2-0.4s | 0.2-0.4s |
+| reseed 总耗时 | 3-7s | 1.7-3.4s | **0.2-0.4s** |
+| TTFT p99 | 1.285s | ~0.7s | **~0.4-0.5s**（与 DP 接近或胜过） |
+| 8.4% slow path 占比 | 不变 | 不变 | 可能保持但单次代价大幅下降 |
+
+→ 这就是 paper 里 future-work 应当声明的**"完整版 KVC 才能真正在 TTFT 全分位数上击败 DP"** 的路径。
+
+---
+
+## 5. 仓库分支审查（确认无作者私下实现）
+
+`git ls-remote origin --refs` 完整结果：
+
+```
+9ccd853...  refs/heads/kvc-debug-journey-v1-to-v4   ← 本工作分支（含本文档）
+e9062b1...  refs/heads/main                          ← baseline，落后我们 18 commit
+```
+
+- **服务器只有 2 个分支**，**0 个 tag**，**0 个隐藏 ref**
+- main 是更老的 baseline；含 `_commit_prefill_backup_residency` 等同名函数，但语义与本工作分支一致——都是静态 backup，无 D→P 同步
+- 全 git 历史搜索 `D->P / d-to-p / decode.*prefill.*transfer / kv.*pushback / kv.*sync / incremental / mirror` 关键词，**唯一命中是 commit `9ccd853`**（本文档相关的 doc 改动）
+- 唯一 remote 是 `origin`（`git@ipads.se.sjtu.edu.cn:wangjh/agentic-pd-hybrid.git`），无 upstream / fork
+
+→ **作者没有在其它分支偷偷实现 D→P**。这块工作是真空。
+
+---
+
+## 6. 下一步
+
+按 ROI 排序：
+
+### 必做（落地下一阶段）
+
+1. **新开 `feat/d-to-p-sync` 分支** 从当前 `kvc-debug-journey-v1-to-v4` 起步
+2. 写设计文档 `docs/D_TO_P_SYNC_DESIGN_ZH.md`：
+   - 包括上面 §4 的实现细节
+   - 添加 sequence diagram（P/D 通信时序）
+   - 评估 SGLang radix tree 多生产者扩展的具体 API 改动
+   - 评估 D→P 同步对 direct-to-D fast path 自身延迟的影响（理想是异步零开销）
+3. POC 阶段 1：mooncake 双角色化 + 一个能跑通的 D→P transfer 单测
+4. POC 阶段 2：P 端 radix tree 多生产者扩展（重点工程量）
+5. POC 阶段 3：agentic-pd-hybrid 端的 hook + flag
+6. 端到端验证：跑同 trace 同 ts=1 配置，目标 TTFT p99 < 0.5s
+
+### 推荐
+
+7. **同时启用真 RDMA**（独立于 D→P 工作，只需改 sweep 脚本加 `--force-rdma --ib-device mlx5_0`），先把现有 transfer 段加速作为 baseline
+8. **跑 RDMA-only 对照**：先证明单 RDMA 启用能把 TTFT p99 从 1.28s 压到 ~0.7s，再用 D→P sync 把剩下的 re-prefill 段也吃掉。这样 paper 里能写两条独立的 ablation
+
+### 不要做的事
+
+- 在 main / 工作分支上做 D→P 实验（隔离开），主分支应该保持 v2 稳定
+- 试图通过 capacity-backup 现有 flag "调出"D→P 效果——它结构上做不到
+
+---
+
+## 附录 A：本文档涉及的代码位置
+
+| 函数 / 字段 | 位置 |
+|---|---|
+| `_commit_prefill_backup_residency` | `src/agentic_pd_hybrid/replay.py:1483` |
+| `_reserve_prefill_backup_capacity` | `src/agentic_pd_hybrid/replay.py:1572` |
+| `_close_prefill_session` | `src/agentic_pd_hybrid/replay.py:1507` |
+| `_close_decode_session` | `src/agentic_pd_hybrid/replay.py:1539` |
+| `_invoke_session_direct` (direct-to-D 路径) | `src/agentic_pd_hybrid/replay.py:2719` |
+| `_invoke_decode_session_direct` | `src/agentic_pd_hybrid/replay.py:2826` |
+| `_invoke_kvcache_seeded_router` (reseed 路径) | `src/agentic_pd_hybrid/replay.py:2101` |
+| `DirectSessionState.prefill_resident_tokens` | `src/agentic_pd_hybrid/replay.py:128` |
+| `_eviction_suffix` | `src/agentic_pd_hybrid/replay.py:1220` |
+| `--kvcache-prefill-backup-policy` CLI flag | `src/agentic_pd_hybrid/cli.py:182-189, 436-441` |
+| `MooncakeKVManager.__init__` | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:187-256` |
+| `start_decode_thread` (decode 端 receive loop) | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1425-1496` |
+| `add_transfer_request` (assert PREFILL) | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1563` |
+| `MooncakeKVSender` / `MooncakeKVReceiver` | `third_party/sglang/python/sglang/srt/disaggregation/mooncake/conn.py:1648, 1740` |
+| `BaseKVSender` / `BaseKVReceiver` 抽象 | `third_party/sglang/python/sglang/srt/disaggregation/base/conn.py` |
+| `session_aware_cache.release_session` | `third_party/sglang/python/sglang/srt/mem_cache/session_aware_cache.py:250-276` |
+| `session_controller._close` | `third_party/sglang/python/sglang/srt/managers/session_controller.py:293-316` |
+
+## 附录 B：相关 commit
+
+| Commit | 内容 |
+|---|---|
+| `9ccd853` | docs: D→P 缺口的 Opus forensic audit 写入 V2_DEEP_ANALYSIS §4.2 + KVC_ROUTER_ALGORITHM §9 |
+| `2ec0deb` | v2 实现（reset-on-success + threshold 2048→8192）—— 直接 trigger 了对 reseed 慢路径的关注 |
+| `c47adaf` | feat: backpressure pause hint（与 reseed 不直接相关，但展示了"D 端可主动告知 router"的通信通道存在，是未来 D→P sync 控制平面的潜在基础） |
+
+## 附录 C：相关 paper 章节建议
+
+- **§Background**：把 §1-§2 的 reseed 现状作为 motivation 摆出
+- **§Algorithm**：参考 `KVC_ROUTER_ALGORITHM.md` Algorithm 1-3
+- **§Evaluation §Slow Path Cost**：把 §2 的端到端时间线作为 Figure（sequence diagram）
+- **§Future Work / Limitations**：把本文 §4 作为 KVC 真正实现"完整 fast path 替代"的 roadmap，引用 D→P 工作的设计文档（后续 `feat/d-to-p-sync` 分支产物）
+
+---
+
+**核心句**：v2 实现的 KVC 在 91.6% 请求上证明了 session-affinity 路由的价值，但 8.3% 的 reseed 慢路径让 TTFT p99 比 DP 差 3×。这条慢路径的 50% 时间在 P 端 re-prefill、50% 在 mooncake transfer——RDMA 只能救后者，**D→P 增量 KV 同步是唯一能消除 re-prefill 的机制**，且当前在框架、SGLang、mooncake 三层都没有实现，需要新建 `feat/d-to-p-sync` 分支从设计文档开始。

docs/SGLANG_PATCH_INVENTORY_ZH.md

@@ -0,0 +1,165 @@
+# Vendored SGLang Patch — 归类清单
+
+**日期**：2026-05-13
+**基线**：clean SGLang v0.5.10 snapshot @ `bded083`
+**当前 HEAD**：`origin/h200-cu130` + 本分支 (785 行新增 / 17 行删除 / 10 文件)
+**目的**：让 reviewer 与下一个合作者一眼看清"哪些 patch 是核心机制、哪些是 workaround、哪些可以在 refactor 后下线"。对应 [AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) §3.2 / §S6 的工程债项。
+
+---
+
+## 0. TL;DR
+
+| 分类 | 文件数 | 行数（估） | 命运 |
+|---|---:|---:|---|
+| MUST-HAVE — 核心机制（Algorithm 1/2/3、streaming session lifecycle、admit RPC） | 6 | ~450 | 长期保留，是 paper claim 的核心 |
+| WORKAROUND — 已识别的 latent 问题修补，应在 refactor 后下线 | 2 | ~150 | block-level eviction refactor 完成后大量删除 |
+| EXPERIMENTAL — 未闭环的特性，论文不依赖 | 1 | ~60 | 可下线或保留为 future-work hook |
+| INSTRUMENTATION — 诊断 / 日志 | 1 | ~50 | 保留但应隔离到 debug build |
+| MINOR — 杂项 | 1 | ~3 | 不影响决策 |
+
+**关键指引**：当 block-level eviction refactor（[BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md)）完成时，WORKAROUND 类的 ~150 行应同步删除。E3 触发的 `schedule_batch.py` invariant landmine 是这条路径上的产物，不修引擎而是修 evict 粒度才是正解。
+
+---
+
+## 1. 文件粒度清单
+
+### 1.1 `mem_cache/session_aware_cache.py` — MUST-HAVE *（待 refactor）*
+
+| 项目 | 内容 | 引入 | 分类 |
+|---|---|---|---|
+| `SessionSlot` dataclass | streaming session 跨 turn 复用 KV 的 metadata | b8e6f13 | MUST-HAVE |
+| `last_access_time` 字段 | LRU 决策需要 | 6e5ed8d | MUST-HAVE |
+| `match_prefix` / `cache_finished_req` / `cache_unfinished_req` 的 streaming 分支 | session 复用快路径 | b8e6f13 | **MUST-HAVE → 待 refactor**（block-level evict 后语义大改） |
+| `release_session` 直接 `free(kv_indices)` | session 退出时一次性归还 KV | b8e6f13 | **WORKAROUND → 替换**（refactor 后改为只 `dec_lock_ref`） |
+| `slot_held_tokens` / `get_session_status` / `list_session_statuses` | 状态查询 | 6e5ed8d | MUST-HAVE |
+
+**说明**：本文件是 KVC 设计的中枢。block-level eviction refactor（[BLOCK_LEVEL_EVICTION_DESIGN_ZH.md](BLOCK_LEVEL_EVICTION_DESIGN_ZH.md) §3.1–§3.6）改造的就是这里。`SessionSlot` 的 5 个 KV-ownership 字段（`req_pool_idx` / `kv_committed_len` / `kv_allocated_len` / `cache_protected_len` / `swa_evicted_seqlen`）应在 refactor 后删除；这部分**将由 commit message 单独标记**，方便回滚。
+
+### 1.2 `managers/scheduler.py` — 混合类别
+
+D worker 端的 Algorithm 2 实现，含多个独立 patch。按行级归类：
+
+| 函数 / 行段 | 内容 | 分类 | 何时可下线 |
+|---|---|---|---|
+| `admit_direct_append(...)` | Algorithm 2 的 D 端 admission RPC handler | **MUST-HAVE** | 不下线（论文核心） |
+| `_should_allow_local_prefill_on_decode(req)` | 决定 decode worker 是否接受无 bootstrap 的本地 append-prefill | **MUST-HAVE** | 不下线 |
+| `_decode_session_cache_low_watermark_tokens()` | 水位线参数读取 | **WORKAROUND** | block-level evict 后由 radix LRU 取代 |
+| `_decode_session_cache_target_available_tokens()` | 目标可用 token 数计算 | **WORKAROUND** | 同上 |
+| `maybe_trim_decode_session_cache(...)` | 主动 trim session（触发 `release_session`） | **WORKAROUND** | 同上：refactor 后 radix LRU 自然蚕食，trim 不再必要 |
+| `_compute_backpressure_pause_hint(...)` | 给 router 的 pause 提示 | **EXPERIMENTAL** | 信号未闭环（[REAL_ALI_KVC_EXPERIMENT_LOG_ZH.md](../docs/archive/) §4.3），路线图 §S10；可保留为 future work hook |
+| `_compute_pool_breakdown_for_diagnostics()` | 池状态快照供 `/server_info` | **INSTRUMENTATION** | 长期保留但建议门 flag 化 |
+
+### 1.3 `managers/schedule_batch.py` — WORKAROUND（待删除）
+
+| 项目 | 内容 | 引入 | 分类 |
+|---|---|---|---|
+| streaming-session `extend_input_len` correction (lines ~1572–1585) | 在 fill_ids < prefix_indices 时把 extend_input_len 改为 0 | b8e6f13 | **WORKAROUND** |
+| pre-filter pass dropping `fill_ids < prefix_indices` reqs | E3 触发 assertion 后的 hotfix（commit 986f351） | 986f351 | **WORKAROUND** |
+| invariant assert `seq_len - pre_len == req.extend_input_len` 的容忍逻辑 | 与 correction 配套 | b8e6f13 | **WORKAROUND** |
+
+**全部** ~85 行在 block-level eviction refactor 完成后**应整体删除**——`BLOCK_LEVEL_EVICTION_DESIGN_ZH §3.7` 已说明 refactor 后该不变量结构上必然成立，correction 路径无需存在。E3 的 landmine ([E3_FINDINGS_ZH.md](E3_FINDINGS_ZH.md) §2) 是该 workaround 的产物。
+
+### 1.4 `managers/session_controller.py` — MUST-HAVE
+
+| 项目 | 内容 | 分类 |
+|---|---|---|
+| streaming session lifecycle hooks（open / close / admit signal） | 让 P/D worker 知道何时开始 / 结束一个 streaming session | MUST-HAVE |
+| session ID 路由 | 让 admission RPC 找到正确的 SessionSlot | MUST-HAVE |
+
+不下线。
+
+### 1.5 `managers/io_struct.py` — MUST-HAVE
+
+| 项目 | 内容 | 分类 |
+|---|---|---|
+| `AdmitDirectAppendReqInput` / `AdmitDirectAppendReqOutput` | admit RPC 的请求 / 响应消息类型 | MUST-HAVE |
+| backpressure pause hint 字段 | 同上消息的 optional 字段 | EXPERIMENTAL |
+
+可以把 EXPERIMENTAL 字段折叠到 MUST-HAVE 消息里保持兼容；本身不构成下线压力。
+
+### 1.6 `managers/tokenizer_communicator_mixin.py` — MUST-HAVE
+
+admit RPC 的 communicator-side glue。19 行，不下线。
+
+### 1.7 `entrypoints/http_server.py` — MUST-HAVE
+
+`/admit_direct_append` HTTP endpoint 注册。6 行。
+
+### 1.8 `disaggregation/decode.py` — 混合类别
+
+| 项目 | 内容 | 分类 |
+|---|---|---|
+| `DecodeReqToTokenPool`: `assert len(reusing) <= 1` 放宽 | 让 local append-prefill 在一个 batch 里复用多个 req_pool_idx | **MUST-HAVE** |
+| `DecodePreallocQueue` 引入 `refresh_allocatable_tokens` + `maybe_trim_decode_session_cache` 触发 | pool 满时主动 trim session | **WORKAROUND**（refactor 后改由 radix LRU 自然 shed） |
+| `--disaggregation-decode-allow-local-prefill` flag | 服务端 opt-in 本地 append-prefill | **MUST-HAVE** |
+
+trim 触发逻辑 ~30 行在 refactor 后应删除。
+
+### 1.9 `server_args.py` — MUST-HAVE
+
+| 项目 | 内容 | 分类 |
+|---|---|---|
+| `--radix-eviction-policy priority` 选项 | E1/E2 实验需要 | MUST-HAVE |
+| `--disaggregation-decode-allow-local-prefill` flag | 见 §1.8 | MUST-HAVE |
+
+13 行，全部是 CLI 接口扩展，不下线。
+
+### 1.10 `disaggregation/mooncake_transfer_engine.py` — MINOR
+
+3 行小调整。不构成决策点。
+
+---
+
+## 2. 按分类汇总
+
+### 2.1 MUST-HAVE（保留）
+
+约 6 个文件、450 行：
+- `admit_direct_append` 主链路（Algorithm 2）：scheduler + io_struct + tokenizer_communicator_mixin + http_server + session_controller
+- `SessionSlot` 主链路（streaming session lifecycle）：session_aware_cache 多数字段、session_controller
+- CLI / server interface：server_args、decode.py 的 `allow_local_prefill`
+
+### 2.2 WORKAROUND（block-level evict refactor 后删除）
+
+约 2.5 个文件、150 行：
+- `session_aware_cache.release_session` 的 token-free 路径
+- `scheduler.py` 的 `_decode_session_cache_*_watermark_tokens` + `maybe_trim_decode_session_cache`
+- `schedule_batch.py` streaming-session correction + drop-pre-filter（含 E3 landmine 的 hotfix）
+- `decode.py` `DecodePreallocQueue` 中的 trim 触发
+
+→ 这些 patch 的存在是当前架构的产物；refactor 后应整段删除而不是修小 bug。
+
+### 2.3 EXPERIMENTAL（未闭环）
+
+约 60 行：
+- backpressure pause hint（`_compute_backpressure_pause_hint` + io_struct 字段）：可作为未来 control-plane 反馈机制的 hook 保留；若 1 个月后仍未接通，下线
+
+### 2.4 INSTRUMENTATION（长期保留但门 flag 化）
+
+约 50 行：
+- `_compute_pool_breakdown_for_diagnostics` + 相关 `/server_info` 字段：建议加 `--enable-diagnostic-pool-snapshot` flag，避免 prod 路径背诊断开销
+
+### 2.5 MINOR
+
+约 3 行：忽略。
+
+---
+
+## 3. 维护约定
+
+1. **新加 SGLang 改动必须落到本表**：在 commit message 用 `feat(sglang): ...` / `fix(sglang): ...` 前缀，并在 PR 描述声明落到 §2 哪一类。
+2. **不直接覆盖 upstream 文件**：所有 patch 必须可在 v0.5.10 上 git apply（保留 hunk header 整洁）。
+3. **删除 WORKAROUND 时同步删 doc**：refactor 完成的同一个 PR 应把本文表中对应行划掉。
+4. **不下放 EXPERIMENTAL 到主路径**：未闭环的 patch 必须默认 disabled。
+
+---
+
+## 4. 与路线图的衔接
+
+- Milestone 1（[AUDIT_AND_ROADMAP_ZH.md](AUDIT_AND_ROADMAP_ZH.md) §4）执行 block-level eviction refactor 时，**整段 §2.2 应该消失**——这是衡量 refactor 完成度的客观指标。
+- Milestone 2 把 control plane 拆层（§4.8）时，§2.3 backpressure pause hint 应或被启用、或被下线，不允许悬挂。
+- Milestone 3 引入 learning-based admission（§4.15）时，§2.1 的 `admit_direct_append` 接口应保持稳定，policy 替换在 router 侧而非 D 侧。
+
+---
+
+**核心句**：vendored SGLang 的 785 行不是 monolithic 黑箱——三分之二是核心机制（论文必备），三分之一是当前架构的 workaround（refactor 后可整段删）。reviewer 看到本表能立刻判断"哪些是 paper 的真贡献、哪些是 prototype 当前的临时支撑"。

docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md

@@ -0,0 +1,641 @@
+# agentic-pd-hybrid 现框架性能与结构性问题报告
+
+**对象**：项目团队同学
+**前置假设**：读者**没看过** v3-v6 KVC 实验日志
+**数据范围**：项目仓库 `outputs/` 下截止 2026-05-06 的全部实验产物
+**目的**：把"现状"和"问题"分别交代清楚，给后续改造提供共同事实基础
+
+---
+
+## 0. 给没看过实验的读者：基础概念速览
+
+### 0.1 项目目标
+验证 **session-aware / KV-cache-aware P/D routing** 在 **agentic coding workload**（多轮 session、长 context、增量 append）上能否降低端到端延迟。基线对比对象是 vanilla SGLang xPyD。
+
+### 0.2 三种部署机制（**这三个名词全程会用**）
+
+| 机制 | 形态 | KV 流向 |
+|---|---|---|
+| **pd-disaggregation**（"PD disagg"） | P 和 D 是独立进程、分占不同 GPU | 每个请求 P 算 prefill → mooncake 推 KV → D 解码 |
+| **pd-colo**（"DP"，data-parallel） | 没有 PD 拆分，N 个独立完整 worker（每个自己 prefill+decode） | 没有 KV transfer；router 按 hash 分配请求 |
+| **kvcache-centric**（"KVC"） | 部署形态同 PD disagg；**D 上多了 SessionAwareCache**，能跨 turn 保留 session KV | 运行时决策：可走 direct-to-D（无 P）、可走 P→D disagg、可走带 reseed 的混合 |
+
+**Direct-to-D**（"D-direct"）：KVC 的快路径——D 上已有该 session 的 KV，新 turn 在 D 本地做 append-prefill，零 P 介入、零 mooncake transfer。这是 KVC 理论上能省时间的核心。
+
+**Fallback**：KVC admission 拒了 / 阈值不满足 / D 不健康时，退化到普通 PD disagg 路径。
+
+**Routing policy**（与机制正交）：
+- `default`：纯 round-robin
+- `sticky`：turn 2+ 黏到 session 的 last D
+- `kv-aware`：按 hash overlap + sticky 评分选 D（**KVC 必须配它**才能正确工作）
+
+### 0.3 数据来源
+- Trace：`outputs/qwen35-swebench-50sess.jsonl`（SWE-Bench 抽样，4449 reqs / **52 sessions** / 每 session 8-150 turns / time-scale=10 / concurrency=32）
+- 模型：Qwen3.5-35B-A3B (TP4) 和 Qwen3-30B-A3B (TP1) 两组
+- 硬件：单机 8×H100 80GB，mooncake TCP loopback 模拟 P→D 传输
+
+---
+
+# 第一部份：性能数据现象
+
+## 1.1 三种机制在 Qwen3.5-35B (TP4) SWE 50sess 上的表现
+
+来源：`outputs/swebench-exps/`。
+
+| Run | Mechanism | Policy | Errors | Lat mean | Lat P50 | Lat P99 | TTFT mean | TTFT P50 |
+|---|---|---|---:|---:|---:|---:|---:|---:|
+| `pd-disaggregation-default-20260426T202540Z` | pd-disagg | default | **0/4449** | 1.66s | 0.97s | 7.68s | 0.45s | 0.34s |
+| `pd-colo-default-20260426T210129Z` | pd-colo | default | **4447/4449** | – | – | – | – | – |
+| `pd-colo-default-20260427T033519Z` | pd-colo | default | **0/4449** | 1.77s | 0.86s | 9.67s | 0.29s | 0.25s |
+| `pd-colo-kv-aware-20260427T042034Z` | pd-colo | kv-aware | 469/4449 | 1.52s | 0.82s | 8.27s | 0.26s | 0.23s |
+| `pd-colo-kv-aware-20260427T044944Z` | pd-colo | kv-aware | **0/4449** | **1.57s** | 0.81s | 8.48s | **0.22s** | **0.17s** |
+| `kvcache-centric-default-worker-admission-20260426T210800Z` | KVC | default | **4390/4449** | – | – | – | – | – |
+
+### 现象解读
+
+**(1) pd-disagg 是稳定基线**：1.66s mean / 0 errors / 4199 cache hits（94.4%）。可以正常服务。
+
+**(2) pd-colo（DP）有两次 run，第一次几乎全 crash，第二次稳定**：
+- 04-26 的 4447/4449 errors 来自 SGLang `--disaggregation-mode null` + Qwen3.5-35B-A3B（Mamba/GDN hybrid）的 `token_to_kv_pool_allocator memory leak` bug，crash 了
+- 04-27 的两次 pd-colo run 都跑通了。**`pd-colo-kv-aware-20260427T044944Z` 是这一组实验里跑分最好的配置**——0 errors / TTFT P50 = 0.171s（pd-disagg 的 50%）
+
+**(3) KVC 在 SWE 35B 上的唯一一次 run 几乎全 crash**：4390/4449 = 98.7% errors。但**那 56 个跑通的 direct-to-D 请求性能优异**——Lat mean 1.24s，TTFT P50 0.081s，KV transfer 196 块（vs PD disagg 的 105K 块，**−99.8%**）。说明 KVC 机制本身有效，但 admission control 把绝大多数请求过滤掉了。
+
+### 一句话：在 Qwen3.5-35B 上，**pd-colo + kv-aware 是头名**，KVC 机制配置不当几乎不可用。
+
+---
+
+## 1.2 同 trace 切到 Qwen3-30B (TP1)：v1→v6 演进
+
+为绕开 Mamba 模型的 SGLang bug，团队后续切到 Qwen3-30B-A3B (TP1) 跑 KVC 调优 sweep。**所有结果用同一份 SWE 50sess trace**，可以横向比较。来源：`outputs/qwen3-30b-tp1-*` 各目录。
+
+### 1.2.1 各版本配置概览
+
+| 版本 | 关键改动（一句话） |
+|---|---|
+| v2 | KVC + `--policy default`（这个 policy 选择 **是 bug**，下文 §2.5） |
+| v3 | KVC + `--policy kv-aware` |
+| v4 | v3 + replay 端 session soft_cap 从 4 抬到 16 |
+| v5 (Option D) | 把 admission 决策从 replay 估算改成 D worker 真实容量回答（`worker-mode admission`） |
+| v5+profile | v5 + 1Hz `/server_info` polling 做时序 instrument |
+| v6 P0 | v5 baseline 同配置 rerun ×3 验证可复现性 |
+
+### 1.2.2 各版本同 trace 结果总表
+
+| 版本 | Errors | Lat mean | Lat P50 | Lat P90 | Lat P99 | TTFT P50 | direct-to-D% |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **8-way DP cache-aware** | **0** | **1.43s** | **0.65s** | **3.61s** | **8.37s** | **0.093s** | – |
+| v3 1P7D KVC | 363 (8.2%) | 4.88s | 1.75s | 12.67s | 28.72s | 0.363s | 39% |
+| v3 2P6D KVC | 9 (0.2%) | 3.58s | 1.52s | 9.23s | 18.70s | 0.328s | 31% |
+| v4 1P7D cap=16 | 435 (10%) | 4.21s | 1.08s | 13.38s | 24.45s | 0.056s | 49% |
+| v4 2P6D cap=16 | 403 (9%) | 2.51s | 0.84s | 6.51s | 18.34s | 0.051s | 53% |
+| v5 1P7D Option D | 9 (0.2%) | 5.18s | 1.59s | 14.67s | 26.09s | 0.207s | 45% |
+| v5 2P6D Option D | 9 (0.2%) | 3.49s | 1.31s | 9.09s | 24.92s | 0.244s | 41% |
+| v5+profile 1P7D | 6 (0.1%) | 4.21s | 1.18s | 11.33s | 28.83s | 0.060s | 55% |
+| v5+profile 2P6D | **415 (9.3%)** | 3.23s | 1.11s | 8.36s | 20.26s | 0.168s | 41% |
+| v5 rerun ×3（无 profile） | **372 / 912 / 396** | 3.00–3.50s | 0.94–1.22s | 7.68–8.65s | 18.97–20.37s | 0.07–0.18s | 40-42% |
+
+**8DP CA 在每一项指标都是头名**：
+- Latency mean **比所有 KVC 配置好 +43%~+260%**
+- TTFT P50 **0.093s**（KVC 最佳 v4 2P6D 是 0.051s——TTFT 单项 KVC 是有优势的，但被整体 P99 灾难抵消）
+- 0 errors（KVC 任一配置 errors 在 9-912 之间漂移）
+
+### 1.2.3 v5+profile 的诡异：加 1Hz polling 让 errors 从 9 涨到 415
+
+这条单独看：v5 baseline 跑出来 9 errors，加上 1Hz `/server_info` polling 之后 415 errors（**46×**）。原因机理见 §2.5。
+
+### 1.2.4 v6 P0 用 ×3 rerun 验证可复现性，结果是不能复现
+
+**关键事实**：v5 baseline 完全相同配置跑 3 次：
+
+| Run | Errors | Lat mean | Lat P50 | TTFT P50 |
+|---|---:|---:|---:|---:|
+| rerun1 | **372** | 3.50s | 1.11s | 0.147s |
+| rerun2 | **912** | 3.00s | 0.94s | 0.071s |
+| rerun3 | **396** | 3.42s | 1.22s | 0.183s |
+
+errors 漂移 **2.5×**（372→912）。Latency mean / P50 也漂移 ~30%。**这意味着 v3-v6 之前所有"single-run"对比的差异 < 30% 的都不可信。**
+
+但要注意：**3 次 v5 中最优的 P50（0.94s）仍然比 8DP CA（0.65s）慢 1.45×**——这个差距大于 single-run variance，所以"DP 全胜 KVC"的头条结论不受 variance 影响。
+
+### 1.2.5 一个有趣的反差：v4 vs v5
+
+- v4：errors 多（~10%）、direct-to-D 占比高（53-58%）、整体 P50 较好（0.84s）
+- v5：errors 少（0.2%）、direct-to-D 占比降低（41-45%）、整体 P50 反而退步（1.31s）
+
+**v5 没有让性能变好，只是把"硬错误"转成了"诚实拒绝"——v4 的 admission 是乐观估算，admit 进来后 D 装不下变成 mooncake 32s timeout（统计成 errors）；v5 让 D 自己拍板，admit 拒得早，请求改走 fallback（统计成低 direct-to-D 率）。容量本身没变。**
+
+---
+
+## 1.3 microbench 上 KVC 击败 PD disagg —— 但本仓库没保留实际 run
+
+`docs/PROJECT_OVERVIEW.md` 写明：
+
+> micro-benchmark 上，`kvcache-centric` 可以比 `pd-disaggregation` 好。原因很简单：**session 少、D KV 放得下**，turn2+ 可以直接走 D session。
+
+但 `outputs/` 里**没有** microbench 实际 run（只有 microbench trace 生成器 `microbench.py` 和它的几个示例 trace 文件）。所以 microbench 的"KVC 赢"是基于设计预期 + 历史口口相传，**没有可重现的产物**。
+
+**这本身是个问题**——下文 §2.6 会解释 microbench 的默认参数（4 sessions × 30K input × 1K append）正好把所有 KVC 失效条件都规避掉了。
+
+---
+
+## 1.4 头条结论（Part 1 总结）
+
+| 工作负载 / 模型 | 头名机制 | KVC 表现 |
+|---|---|---|
+| Microbench（8 session × 30K × 1K append） | KVC > PD disagg（无落地数据，按设计） | 设计上必然赢 |
+| SWE 35B (TP4) | **pd-colo + kv-aware**（1.57s mean, 0 errors） | KVC 唯一 run 中 98.7% errors |
+| SWE 30B (TP1) | **8-way DP cache-aware**（1.43s mean, 0 errors） | KVC 6 个配置全输；最佳的 v4 2P6D 慢 75%、errors 9% |
+
+**真实 agentic 工作负载（SWE-Bench）上，KVC 机制目前没有任何配置能跑赢 naive DP cache-aware。**
+
+---
+
+# 第二部份：结构性问题分析
+
+每条按 (1) 现象（实锤数据）、(2) 根因（代码位置）、(3) 影响量化 三段交代。
+
+## 2.1 KvAwarePolicy 不感知 D 容量 + Session 永久 pin 在初始 D 上 ★ 最严重
+
+### 2.1.1 现象（实锤）
+
+**(a) 每个 session 整 run 中只访问 1 个 D**——基于 v5 rerun1/2/3 全部 4449×3 = 13347 条 metrics：
+
+| Run | sessions | avg distinct-D-per-session |
+|---|---:|---:|
+| rerun1 | 52 | **1.00** |
+| rerun2 | 52 | **1.00** |
+| rerun3 | 52 | **1.00** |
+
+3 次独立 run、156 次 session 实例，**没有一个** session 跨 D 迁移过。
+
+**(b) Direct-to-D 命中率呈极端双峰**——以 rerun1 为例（其他两次形态相同）：
+
+| direct-to-D rate | session 数 |
+|---|---:|
+| 0–20%（"饿死"） | **15** |
+| 20–40% | 7 |
+| 40–60% | 11 |
+| 60–80% | 5 |
+| 80–100%（"顺利"） | **14** |
+
+中间档稀少，两端拥挤。
+
+**(c) 跨 3 次 run 一致饿死的 session = 13/52，且这些 session 的 input 是顺利 session 的 1.98×**：
+
+```
+13 sessions starved (<20% direct-to-D) in ALL 3 runs
+  avg peak input of consistently-starved sessions: 62043 tokens
+  avg peak input of consistently-lucky sessions:   31344 tokens
+```
+
+**结构性、可复现、与 session 大小强相关。** 排除"运气"假说。
+
+### 2.1.2 根因（代码）
+
+`policies.py:166-172` `KvAwarePolicy.select()` 评分函数：
+
+```python
+score = (
+    overlap + sticky * self.sticky_bonus,    # 主项：历史 KV overlap
+    sticky,                                   # 二级
+    inflight_penalty,                         # 三级
+    assignment_penalty,                       # 四级
+)
+```
+
+**评分中完全没有 D 当前容量项**。
+
+session X 第一次落到 D-2 → 在 D-2 上积累 hash_id → 之后不管 D-2 多满，X 的 turn N+1 的 overlap 在 D-2 上仍是最大 → 永远选 D-2。即使 D-5 全空也轮不到。
+
+`RoutingState.decode_resident_blocks` (`policies.py:46`) 还从不缩减——但因为 SWE trace 的 hash_ids 是 session-unique，**不缩减并不影响"选对 D"，只影响内存**——真正问题在评分函数无容量项。
+
+### 2.1.3 影响量化
+
+- 25%（13/52）的 session 几乎每个 turn 走 fallback 路径
+- fallback 路径 mean lat 约 3.5s vs direct-to-D ~0.5s——**饿死 session 每 turn 慢 6×**
+- 这 13 个 session 还容易撞 mooncake 32s timeout（见 §2.2、§2.3），P99 完全由它们决定
+- **SLO 视角下：25% 的用户体验是系统性糟糕**
+
+---
+
+## 2.2 D 端 LRU 只能 evict idle session → 跟不上压力
+
+### 2.2.1 现象（实锤）
+
+来源：`outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/decode-{0..5}.log`，全 run 计数：
+
+| D worker | "Trimmed decode session cache" 事件 | KVTransferError | 峰值 token_usage |
+|---|---:|---:|---:|
+| decode-0 | 9 | 0 | 0.99 |
+| decode-1 | 43 | 4 | 0.99 |
+| decode-2 | 16 | **153** | 0.97 |
+| decode-3 | 37 | 29 | 0.99 |
+| decode-4 | 28 | **90** | **1.00** |
+| decode-5 | 30 | **93** | **1.00** |
+
+**所有 6 个 D 都顶到 token_usage ≥ 0.97，2 个顶到 1.00（KV 池完全耗尽）。LRU 触发 9-43 次，远不够——transfer 错误是 LRU 触发量的 5-10×。**
+
+decode-2 极端：trim 16 次 vs error 153 次 = LRU 跑得比错误慢 9.5×。
+
+### 2.2.2 根因（代码）
+
+`scheduler.py:2040` 的 `evict_idle_streaming_sessions_lru` 实际只能 evict：
+
+> 所有 req 都 finished + streaming 模式 + 该 session 没有 inflight transfer
+
+但 SWE 高并发（concurrency=32 + time-scale=10 → effective inter-turn gap p50=0.25s）下，每个 session 几乎一直有 inflight req。**hot session 永远不 idle，LRU 永远找不到东西可踢。**
+
+### 2.2.3 影响量化
+
+- 单 run 累计 KVTransferError：6 个 D 之和 = **369 次**
+- 对应 ~8% 请求失败率（v5 errors 9/372/912 三次平均 ~430/4449 = 9.7%）
+- **每次 mooncake timeout = 32s**——直接构成 P99 18-26s 的尾巴
+
+修复需要 SGLang 内部分层 eviction：除 idle session 外，按访问频率 / 时序加权强制 retract——**不在当前 KISS 边界**。
+
+---
+
+## 2.3 没有 D → Replay backpressure 通道
+
+### 2.3.1 现象
+
+§2.2 数据显示 D 顶到 token_usage=1.00 时仍在持续接收新请求，最终撞 mooncake 32s timeout。**整个错误链路里没有"D 过载，请慢点发"的反向信号**。
+
+定量证据：rerun1 的 KVTransferError 时间分布——**98% 集中在 run 后半段**（参考 `KVC_DEBUG_JOURNEY_V1_TO_V5.md` §v4）。前期 D 容量充裕时正常，达到上限后**所有后续请求集中失败**——典型的"无 backpressure 系统在过载点雪崩"模式。
+
+### 2.3.2 根因（代码）
+
+链路：
+
+```
+replay 端按 trace 时序 + concurrency=32 持续发请求
+  ↓
+PD Router 裸 round-robin (pd_router.py:43-49)
+  ↓
+P 收到请求做 prefill → mooncake 推 KV → D 端
+  ↓
+D 端 transfer queue 堆积 → 32s timeout
+  ↓
+errno 抛回 replay → fallback 路径，但 concurrency 不降
+```
+
+D 端的 `admit_direct_append` 响应里**只有 can_admit/reason 等过去时字段，没有任何"建议节流"的指示**。
+
+### 2.3.3 修复（本次代码改动已实现）
+
+代码已加 `recommended_pause_ms` 字段：
+- `third_party/sglang/.../io_struct.py:DirectAppendAdmissionReqOutput` 增加 `recommended_pause_ms: int = 0`
+- `scheduler.py:_compute_backpressure_pause_hint`：按 `transfer_queue_depth`、`retracted_queue_depth`、`token_usage_after` 计算
+- `replay.py`：admission 响应里读到 hint → 更新 `DecodeResidencyState.pause_until_s[D]` → 下次发到该 D 之前 sleep
+- CLI flag：`--enable-backpressure`（默认 off，保留 baseline 行为）
+- 同时新增 3 个结构性日志（`structural/admission-events.jsonl` / `backpressure-events.jsonl` / `session-d-binding.jsonl`）
+
+**待 GPU smoke 验证。预期 errors 从 ~370 降到 < 50；P99 改善（消除 32s timeout 尾巴）；mean latency 可能略升（被强制 sleep）。**
+
+修复脚本：`scripts/sweep_backpressure_smoke.sh`（4 个 run × 30-60 min）；分析器：`scripts/analysis/analyze_backpressure_smoke.py`。
+
+### 2.3.4 注意
+
+backpressure 是**降级机制**，不是性能优化——它把"硬错误（32s timeout）"换成"主动等待"。整体 throughput 不会因此提升，但 P99 应大幅改善。
+
+---
+
+## 2.4 P-side round-robin 不感知 D 健康
+
+### 2.4.1 现象（实锤）
+
+来源：v5 rerun1 `prefill-{0,1}.log`，全 run 计数：
+
+| Worker | KVTransferError | "Decode instance could be dead" | 请求量 |
+|---|---:|---:|---:|
+| prefill-0 | **367** | 361 | 2225 |
+| prefill-1 | **2** | 0 | 2224 |
+
+**两 P 请求量完全均衡（round-robin），错误率差 180×**。日志里 prefill-0 的失败反复指向某个特定 D 的 IP（`to 10.45.80.47:XXXXX`）。
+
+### 2.4.2 根因（代码）
+
+`pd_router.py:43-49`：
+
+```python
+prefill_url, bootstrap_port = self.config.prefill_urls[
+    self.prefill_cursor % len(self.config.prefill_urls)
+]
+self.prefill_cursor += 1
+```
+
+裸 round-robin。不感知：
+- P 当前 inflight transfer 数
+- 目标 D 的健康状态 / 容量
+
+后果：当某个 D 进入 hot 状态时，被 round-robin 派去给它推 KV 的 P **持续失败**；另一个 P 接到的请求恰好命中健康 D，完全没事。**单 P 故障不会被路由层避开。**
+
+### 2.4.3 影响量化
+
+- prefill-0 几乎独自承担了**全部 KVTransferError 的 99%**（367/(367+2)）
+- 如果 router P 选择能避开"正在和 hot D 死磕"的链路，这部分 ~8% 的整体错误率应可降到 < 1%
+
+### 2.4.4 备注
+
+这条结论目前来自单次 run 的 N=1 数据。需要跨 N≥3 次 rerun 验证一致性才能完全确信——加上 §2.1.1 (b/c) 也证明 P-D 链路绑定结构性强相关，"prefill-0 死磕某 D"很可能在每次 run 都重复（由初始 session 落点决定）。
+
+---
+
+## 2.5 Admission RPC 进 scheduler 主循环 → 自我干扰
+
+### 2.5.1 现象（实锤）
+
+v5 baseline 配置不开 polling：errors = 9
+完全相同配置 + 1Hz `/server_info` polling：errors = **415**（**46×**）
+
+来源：`outputs/qwen3-30b-tp1-v5-optD/exp2_2p6d_kvc_optD_summary.json`（baseline 9 errors）vs `qwen3-30b-tp1-v5-optD-profile/exp2_2p6d_kvc_optD_profile_summary.json`（415 errors）。
+
+### 2.5.2 根因（代码）
+
+`/server_info`（被 polling 调用）和 `admit_direct_append` 都进 SGLang scheduler 主循环：
+
+- `/server_info` → `scheduler.py:get_streaming_session_cache_status` → 遍历每个 session slot 计算 `is_idle`
+- `admit_direct_append` → 读 `token_to_kv_pool_allocator.available_size()` + 触发 `maybe_trim_decode_session_cache`
+
+scheduler 主循环本身在跑 decode/prefill 的 forward。这些 RPC 进队列就和 forward 抢调度。
+
+### 2.5.3 真实负载下 admission RPC 频率远高于 1Hz
+
+- 4449 reqs / ~2700s ≈ **1.6 reqs/s**
+- 每个 turn 做 1-3 次 admission probe（direct-append + 可能的 seed retry）
+- × 8 worker = **每秒 ~16-40 次 admission RPC**
+
+也就是 admission 流量本身比 1Hz polling 高一个量级。如果 1Hz polling 都能让 errors 涨 46×，admission 自己的扰动至少同等。
+
+### 2.5.4 修复
+
+不在本轮 KISS 内。设计方向是把 admission 拆成两个端点：
+- `POST /probe` → lock-free 读 snapshot（轻），90% 流量走这条
+- `POST /commit_evict` → 进 scheduler 队列，做实际 LRU（重），仅 probe 不够时调
+
+这部分需要 SGLang 内部 atomic publish snapshot 到共享内存——**结构性改动**。
+
+### 2.5.5 注意
+
+v6 P0 的 ×3 baseline rerun（不开 polling）errors 也是 372/912/396——**polling 不是 415 唯一原因**。本身 v5 admission 设计就敏感，polling 是放大器。
+
+---
+
+## 2.6 Replay 时间被 time-scale=10 压缩 → 测量学失真
+
+### 2.6.1 现象（实锤）
+
+v5 rerun1 metrics 解出的真实 inter-turn gap 分布：
+
+```
+原始 trace inter-turn gap (n=4397):
+  p10=1.6s   p50=2.5s   p90=7.8s   p99=25.1s   max=261s
+
+time-scale=10 实际 replay gap (= 原始 / 10):
+  p10=0.16s  p50=0.25s  p90=0.78s  p99=2.5s    max=26s
+```
+
+### 2.6.2 这意味着什么
+
+真实 agentic 用户/agent 在每个 turn 之间停 **2-8 秒**——思考、打字、tool call 异步返回、agent reasoning。
+
+`microbench.py:20-21` 的默认 `inter_turn_gap_s=1.0` + `session_stagger_s=0.1` 也大致符合这个量级（1 秒左右）。
+
+但 SWE replay 设的 time-scale=10 把这个间隔**人为压到 0.25 秒**——D 还没消化完 turn N，turn N+1 就来了。
+
+### 2.6.3 为什么这么设计
+
+纯粹**节省测试时间**：
+- 原始 trace 跨度 ~6000s（≈100 分钟）
+- time-scale=10 → ~600s（≈10 分钟）
+- sweep 5 版本 × 3 重复 = 25h vs 2.5h
+
+### 2.6.4 它扭曲了什么
+
+1. **抹掉 D 的自然 idle 时间**：真实部署里每个 session 在 turn 间有几秒空窗，正好让 D 端 LRU 把它 evict 出去给其他 session 让位（§2.2 idle 判定）。time-scale=10 下几乎所有 session 一直忙——LRU 永远找不到 idle session。
+2. **人为提升并发压力**：concurrency=32 在 time-scale=10 下意味着 D 端持续承受 320 effective concurrent agents 的压力——远超真实部署。
+3. **掩盖 backpressure 等慢节奏机制的价值**：如果 inter-turn gap 是 2.5s，backpressure 让 replay 等 0.5s 几乎不影响吞吐；time-scale=10 下 0.5s 的 sleep 等于直接跳过下一个 turn。
+
+### 2.6.5 严重性：所有 KVC vs DP 结论都带这个失真
+
+**v3-v6 全部数据基于 time-scale=10**。所以"KVC 在 SWE 上输给 DP"的程度可能被 benchmark 放大。**真实部署里 inter-turn gap 是 2.5s 的话，KVC 可能根本不会撞到当前看到的容量瓶颈**。
+
+这是项目当前**最严重但还没修的测量学问题**。修复成本极小（只是去掉 `--time-scale 10`），但意义重大——**P0 应该立刻跑一组 time-scale=1 baseline**（KVC + DP 各 N=3）。
+
+---
+
+## 2.7 direct-to-D append 阈值 = 2048 是个 magic number
+
+### 2.7.1 现象（实锤）
+
+`replay.py:51` 默认值：
+
+```python
+kvcache_direct_max_uncached_tokens: int = 2048
+```
+
+判定（`replay.py:2177`）：当新 turn 的 uncached append > 2048 token 时，**禁止 direct-to-D**，请求改走 P→D reseed 路径。
+
+实测 v5 rerun1 的 uncached append 分布（`input_length - cached_tokens`）：
+
+```
+所有 4449 请求:
+  p10=50  p25=181  p50=610  p75=2907  p90=36495  p99=91600  max=103971
+
+> 2048: 1222/4449 = 27.5%
+```
+
+**双峰分布**：median 只有 610，但 p90 已经 36K。
+
+### 2.7.2 根因（代码）
+
+阈值是个 magic number——**没有任何代码注释解释为什么是 2048**，git log 里也没人调过它。
+
+合理推测它存在的理由（按可信度）：
+
+| 理由 | 是否成立 |
+|---|---|
+| D 是 decode-tuned，max-prefill-tokens 通常 4-8K，append > 2K 会触发 D 内部多 chunk prefill 拖慢 decode | 强 |
+| 大 append 在 D 上 prefill 会阻塞当前正在 decoding 的其他 session 的 TPOT | 强 |
+| P 有更优化的 prefill kernel 和 batch | 弱（D 的 prefill kernel 同源） |
+| 工程上的"安全默认值"，没认真测过 | 强（git log 印证） |
+
+### 2.7.3 但更严重的 bug：execution_mode 标签命名错位
+
+`execution_mode` 名字里带 "large-append" 的请求一共 **2060 个**，其中：
+
+- **1222 个（59.3%）实际 uncached append ≤ 2048**
+
+也就是说，**"large-append" 这个标签名对超过一半的实例是错的**。看 `replay.py:2168-2178` 的判断：
+
+```python
+if (
+    _should_bypass_prefill(...)              # 要求 overlap > 0
+    and direct_append_length is not None
+    and direct_session_reused                 # 要求 session 在本 D 上 opened 过
+    and not direct_session_reset
+    and direct_append_length <= config.kvcache_direct_max_uncached_tokens
+):
+    # direct-to-D
+else:
+    # 进入 "large-append" 分支
+```
+
+**这个 else 分支的 5 个进入条件里，"append > 2048" 只是其中一个。** session 不在本 D 上、被 evict 过、overlap=0 都会进这个分支，但 `execution_mode` 仍然写 `pd-router-fallback-large-append-*`——导致看 metrics 的人误以为问题是 append 太大。
+
+### 2.7.4 实际：阈值不是主要瓶颈，session 不在 D 上才是
+
+把 turn≥2 的请求按"append 是否 > 2048"和"实际 execution mode"交叉：
+
+```
+Turn≥2 小 append (≤2048), n=3129:
+   1854 (59%)  kvcache-direct-to-d-session            ← 走通了
+   1141 (37%)  pd-router-fallback-large-append-session-cap  ← 标签骗人
+   ...
+
+Turn≥2 大 append (>2048), n=1216:
+    813 (67%)  pd-router-fallback-large-append-session-cap
+    365 (30%)  kvcache-centric (失败)
+     22         pd-router-large-append-reseed                ← 真正受阈值影响的
+    ...
+```
+
+**真正因 append > 2048 而失败的请求**：约 50 个（large-append-reseed + 部分 large-append fallback），仅占总数 1-2%。
+
+**绝大多数 fallback 实际是 §2.1 的 session 不在 D 上**——名字里带 "large-append" 是误导。
+
+### 2.7.5 修复
+
+两件事：
+1. 把 `execution_mode` 标签按真实原因细分——把 "large-append" 拆成 "session-not-resident" / "real-large-append" / "session-reset" 等
+2. 阈值本身可以做 sweep（2048 / 4096 / 8192 / 16384）找最优——但收益空间有限（最多改善那 1-2% 的请求）
+
+---
+
+## 2.8 跨 run variance 巨大：N=1 不可信
+
+### 2.8.1 现象（实锤）
+
+v5 baseline 完全相同配置跑 3 次（`qwen3-30b-tp1-v5-optD-baseline-rerun/`）：
+
+| Run | Errors | Lat mean | Lat P50 | TTFT P50 |
+|---|---:|---:|---:|---:|
+| rerun1 | 372 | 3.50s | 1.11s | 0.147s |
+| rerun2 | **912** | 3.00s | 0.94s | 0.071s |
+| rerun3 | 396 | 3.42s | 1.22s | 0.183s |
+
+errors 漂移 **2.5×**（372→912），P50 latency 漂移 ~30%，TTFT P50 漂移 **2.6×**。
+
+### 2.8.2 根因（推测）
+
+源头不止一个，至少包含：
+
+1. **§2.1 + §2.2 的复合**：D 容量过载是临界点附近的非线性系统——initial session-to-D assignment 的随机性决定了哪个 D 先饱和。
+2. **mooncake TCP loopback 的随机性**：单机 loopback 的 32s timeout 触发概率受当前 GPU 内存碎片、PCIe 状态影响。
+3. **scheduler 主循环里 admission RPC 与 decode 抢资源的随机性**（§2.5）。
+
+### 2.8.3 影响
+
+**所有 single-run 比较 < 30% 差异都不可信**。这意味着：
+- v3 vs v4 的 P50 差异（1.75s vs 1.08s）勉强有意义（差异 38%）
+- v4 vs v5 的 P50 差异（0.84s vs 1.31s）勉强有意义（差异 56%）
+- v5+profile 的 1P7D vs baseline（mean 4.21s vs 5.18s）→ 差异 18%，**不可信**
+- 所有 `direct-to-D 占比 ±5%` 的差异都是噪声
+
+### 2.8.4 这条规则要求所有后续实验
+
+**要任何 KVC 配置间或 KVC vs DP 的对比，最少跑 N=3，最好 N=5。** 不跑 N≥3 的实验在做"碰运气科研"。
+
+8h 一次 sweep 装不下 N=3 + 多版本对比，所以必须**牺牲版本数量保 N≥3**。
+
+---
+
+## 2.9 microbench 的 KVC 优势不能外推到真实 agentic
+
+`microbench.py:13-22` 默认参数：
+
+| 维度 | 默认值 |
+|---|---|
+| `session_count` | 8 |
+| `turns_per_session` | 3 |
+| `initial_input_length` | 10000 |
+| `append_input_length` | **1000** ← 低于 §2.7 的 2048 阈值 |
+| `output_length` | 1000 |
+| `inter_turn_gap_s` | **1.0** ← 接近真实 agentic |
+| `session_stagger_s` | 0.1 |
+
+**与 SWE workload 的关键维度对比**：
+
+| 维度 | microbench | SWE 50sess |
+|---|---|---|
+| Session 数 | 4-8 | 52 |
+| Per-session peak input | ~31K | median 49K, max 104K |
+| 总 working-set / 7D 容量（92K each） | 0.19×（5× 冗余） | **3.95×（4× 过载）** |
+| Append size 是否过 2048 | 几乎 100% 过不到 | 28% 超过 |
+| Session 数是否过 cap | 4 ≤ 28（v3 cap×7D） | 52 远超 |
+
+**Microbench 把 KVC 的所有失效条件都规避了**：容量充裕、append 卡阈值之下、session 数远低于 cap、inter-turn gap 接近真实——这一组参数让 KVC 五项判断（路由 / admission / 没被 evict / append ≤ 阈值 / 无 backpressure）全部通过 → 100% 走 direct-to-D 快路径。
+
+**而 SWE workload 在每一项上都把 KVC 推过临界点。**
+
+所以"KVC 在 microbench 赢 PD disagg"是个**弱命题**——它只证明了机制能跑，没有证明在真实 agentic 下能赢。
+
+---
+
+# 第三部份：一句话总结与下一步
+
+## 现状一句话
+
+> 在所有可比的真实 agentic workload（SWE 35B / 30B）上，**naive DP cache-aware 全胜 KVC 任何配置**，且差距 > 30%（远超 single-run variance）。Microbench 上 KVC 赢 PD disagg 的设计前提（容量富余、append 小、session 少）在真实 workload 下不成立。
+
+## 排序后的结构性问题（按修复 ROI）
+
+| 排名 | 问题 | 影响 | 修复成本 |
+|---|---|---|---|
+| **P0** | §2.6 time-scale=10 失真 → 所有 KVC vs DP 结论可能被 benchmark 放大 | 颠覆性 | 极低（改 flag） |
+| **P0** | §2.1 session 永久 pin + 容量盲选 | 25% session 永远饿死 | 中（改 policy） |
+| **P0** | §2.2 D-side LRU 跟不上 | ~8% errors 来自此 | 中（改 SGLang） |
+| P1 | §2.3 没 backpressure | 把 timeout 雪崩变可控 | **已实现**（待 GPU smoke） |
+| P1 | §2.4 P-side 不感知 D 健康 | 单 P 出错率差 180× | 中 |
+| P1 | §2.7 / 2.8 metrics 标签命名错位 | 数据解读经常出错 | 低（改字符串） |
+| P2 | §2.5 admission RPC 进 scheduler 主循环 | 自我干扰 | 高（结构改动） |
+| P2 | §2.8 N=1 不可信 | 实验方法学 | 0（团队约定） |
+
+## 立刻能做的三件事
+
+1. **跑 time-scale=1 baseline**（KVC v5 + 8DP CA 各 N=3，~6h GPU）—— 不修代码、单变量、决定后续路线。
+2. **跑 backpressure smoke**（已实现，4 run × ~30-60 min，~3-4h GPU）—— 验证 §2.3 修复的端到端效果。
+3. **修 metrics 标签命名**（`pd-router-fallback-large-append-*` → 按真实原因分类）—— 让以后看数据的人不会再被误导。
+
+## 不立刻做但要重新讨论的
+
+- **§2.1 capacity-aware policy**：之前考虑过的"评分加 capacity 项"会引入"换 D"的副作用（孤儿 KV、新 D 上仍可能饿死），需要跟 §2.2 的 D 端 hot retract 一起设计。
+- **§2.5 admission API 拆 probe / commit**：是结构性正确方向，但要动 SGLang 内部 + atomic publish 机制，不是 KISS。
+- **是否保留 KVC 这条线**：如果 P0 跑完 time-scale=1 baseline 后 KVC 仍系统性输 DP，应该认真讨论 KVC 项目目标是否需要重新定义（比如只做"中等容量 + 长 session"工作点的方案，而不是替代 vanilla DP）。
+
+---
+
+## 附录 A：本报告所有数据的来源
+
+| 章节 | 数据源 |
+|---|---|
+| 1.1 SWE 35B | `outputs/swebench-exps/{pd-disagg,pd-colo,kvcache-centric}-*` |
+| 1.2 TP1 series | `outputs/qwen3-30b-tp1-{exps,v3-kvaware,v4-cap16,v5-optD,v5-optD-profile,v5-optD-baseline-rerun}/` |
+| 2.1 session pinning | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/exp2_2p6d_run{1,2,3}_metrics.jsonl` |
+| 2.2 D LRU 计数 | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/decode-{0..5}.log` |
+| 2.4 P imbalance | `outputs/qwen3-30b-tp1-v5-optD-baseline-rerun/.../logs/prefill-{0,1}.log` |
+| 2.5 polling 影响 | v5 baseline summary vs v5+profile summary |
+| 2.6 inter-turn gap | rerun1 metrics 的 `trace_timestamp_s` 字段 |
+| 2.7 append 分布 | rerun1 metrics 的 `input_length - cached_tokens` |
+| 2.8 variance | rerun1/2/3 三组 summary |
+
+## 附录 B：相关已有文档
+
+- `docs/PROJECT_OVERVIEW.md` — 项目目标、microbench 结论
+- `docs/archive/AGENTIC_FIT_ANALYSIS_ZH.md` — 结构性缺陷的早期分析（本报告 §2 的来源）
+- `docs/archive/KVC_DEBUG_JOURNEY_V1_TO_V5.md` — v1→v5 详细演进日记
+- `docs/archive/V5_PROFILE_INVESTIGATION_ZH.md` — v5+profile 调查（含 critic 修订）
+- `docs/archive/SWEBENCH_EXPERIMENT_RESULTS.md` — SWE 35B 早期实验
+- `docs/archive/REFACTOR_PLAN_ZH.md` — 当前重构计划
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md` — 结构性 claim 验证（本报告的精简版）

docs/V2_DEEP_ANALYSIS_ZH.md

@@ -0,0 +1,624 @@
+# KVC v2 深度分析：相对 TEAM_REPORT 基线的改进、性能、新暴露的问题
+
+**日期**：2026-05-11
+**对象**：项目团队同学
+**基线**：`docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（v3-v6 ts=10 调优 sweep 的状态报告）
+**新数据**：
+- `docs/REFACTOR_PLAN_V1_ZH.md`（ts=1 4-run validation 结果）
+- `docs/MIGRATION_V1_FINDINGS_ZH.md`（v1 thrashing 诊断）
+- `docs/V2_RESULTS_ZH.md`（v2 reset-on-success + threshold tuning 结果）
+- Critic agent 的对等性审查（本文 §4）
+
+**目的**：把"TEAM_REPORT 之后的实验产物"按改进 / 性能 / 新问题三段重新审视，明确哪些原结构性问题被消解、哪些被掩盖、哪些是新引入的。
+
+---
+
+## 0. TL;DR
+
+1. **TEAM_REPORT 头条结论"真实 agentic workload 上 KVC 无配置能赢 naive DP"在 ts=1 下被推翻**——KVC v2 在 lat mean / p50 / p90、TTFT mean / p50 / p90 上全面优于 4DP CA。
+2. **生产决策结论：online coding agent serving 应选 KVC 1P3D**。KVC 的设计 motif（session affinity + 集中 cache + direct-to-D 快路径）正是 multi-turn 长上下文 agent workload 的 sweet spot；fast path 减少 prefill 工作量 6.9× 是机制目标实现，不是 measurement artifact。
+3. **真实代价只有一项：TTFT p99 = 1.29s vs DP 0.43s（KVC 3× 差）**——来自 8.3% 非 direct-to-D 路径的 mooncake reseed 长尾。生产部署要么用真 RDMA 把这条压下来，要么靠容量规划让 reseed 极少发生。
+4. **TEAM_REPORT §1（session pin 饿死）已被 v2 修好**——direct-to-D 从 42.8% 涨到 91.6%，severe thrashing 清零。但 reset-on-success 是事后补的——v1 直接加 migration 制造了更严重的 thrashing 失效模式，记入设计经验。
+5. **TEAM_REPORT §2/§3/§4/§5（LRU / backpressure / P-side imbalance / admission RPC 干扰）在 ts=1 下消失**，但是被 ts=1 的"低压自然 drain time"吸收，不是机制层面修好。一旦回到 ts=10 / 更长 trace / 更紧容量，会全部复现——属于潜在的，不是消除的。
+6. **方法学待办**（不影响产品决策）：(a) 补 naive 1P3D 对照分离"KVC 层贡献"vs"1P3D 拓扑贡献"；(b) 补 v2 N=2/3 验证 ts=1 确定性；(c) 拉齐两个 server 的 `max-input-len`（当前 KVC=92098 vs DP=87811 是 SGLang 自动算的差异，详见 §4.3）。
+
+---
+
+## 1. 三组新实验与 TEAM_REPORT 的关系
+
+### 1.1 时间线和因果链
+
+```
+TEAM_REPORT (2026-05-06)
+  ├─ §1-§7 列出 ts=10 数据下的 7 类结构性问题
+  ├─ 头条结论：KVC 全配置输 DP，需要重构
+  └─ 提出 backpressure 作为最小代码修复点
+
+       ↓ 2 天
+
+ts=1 validation (2026-05-07)
+  4 个 run：KVC 1P3D N=3 + 4DP CA × 1，全部 ts=1
+  ├─ 发现 1：ts=1 下 errors 从 372-912 跌到 5（DP 也 5 个，是 trace input-超限 artifact）
+  ├─ 发现 2：ts=1 下 KVC 在 categorical 层面完全确定（0/4449 records 跨 run 不同）
+  ├─ 发现 3：KVC 整体仍然慢 DP 9% / TTFT 慢 47%
+  └─ 结论：TEAM_REPORT §2/§3/§4/§5 是 ts=10 高压 artifact；§1 仍然是真问题（被 ts=1 衰减但不消失）
+
+       ↓ 1 天
+
+v1 migration (2026-05-08)
+  KVC 1P3D + rejection blacklist（policies.py 加 session_d_rejects Counter）
+  ├─ 修复 §1（session pin）——18/52 starved 降到 0
+  ├─ 但引入新失效模式：6 个 session 跨 3 D 严重 thrash（max 116 次切换）
+  ├─ Lat mean 反退化到 1.758s，TTFT mean 涨到 0.419s
+  └─ 中期诊断：blacklist 永久累积 + degenerate fallback 形成 self-amplifying 死循环
+
+       ↓ 1 天
+
+v2 migration (2026-05-09)
+  v1 + reset-on-success + --kvcache-direct-max-uncached-tokens 2048→8192
+  ├─ Thrashing 消除（max D-changes 116→45，severe thrashing 0）
+  ├─ direct-to-D 53.3%→91.6%（threshold 拉高让大 append 也走快路径）
+  ├─ Lat / TTFT 全面赢 baseline，且 7/8 头部指标赢 4DP
+  └─ 但 N=1 + critic 发现的对等性问题（见 §4）
+
+       ↓ 2 天
+
+本文 (2026-05-11)
+  把上述 5 天的数据放回 TEAM_REPORT 的结构性问题清单上做审计
+```
+
+### 1.2 同 trace 全部数字总表（按时间）
+
+来源：`outputs/qwen3-30b-tp1-*` 系列各 summary.json。**4449 reqs / 52 sessions / Qwen3-30B-A3B (TP1) / 4×H100 80GB**。
+
+| 阶段 | 时间尺度 | 配置 | Errors | Lat mean | Lat P50 | Lat P99 | TTFT mean | TTFT P50 | direct-to-D% |
+|---|---|---|---:|---:|---:|---:|---:|---:|---:|
+| **TEAM_REPORT baseline 区间（全部 ts=10）** | | | | | | | | | |
+| v5 1P7D Option D | 10 | KVC | 9 | 5.18s | 1.59s | 26.09s | 0.207s | – | 45% |
+| v5 2P6D Option D | 10 | KVC | 9 | 3.49s | 1.31s | 24.92s | 0.244s | – | 41% |
+| v5 rerun1 (重测) | 10 | KVC | **372** | 3.50s | 1.11s | 19.49s | 0.147s | – | ~40% |
+| v5 rerun2 | 10 | KVC | **912** | 3.00s | 0.94s | 20.37s | 0.071s | – | ~40% |
+| v5 rerun3 | 10 | KVC | **396** | 3.42s | 1.22s | 18.97s | 0.183s | – | ~40% |
+| 8-way DP CA | 10 | DP-colo | **0** | **1.43s** | **0.65s** | **8.37s** | **–** | **0.093s** | – |
+| **ts=1 validation 区间** | | | | | | | | | |
+| v0 baseline run1 | 1 | KVC 1P3D | 5 | 1.574s | 0.811s | 8.70s | 0.245s | 0.124s | **42.8%** |
+| v0 baseline run2 | 1 | KVC 1P3D | 5 | 1.573s | 0.809s | 8.74s | 0.243s | 0.120s | 42.8% |
+| v0 baseline run3 | 1 | KVC 1P3D | 5 | 1.574s | 0.812s | 8.76s | 0.243s | 0.123s | 42.8% |
+| 4-way DP CA | 1 | DP-colo | 0 | 1.443s | 0.659s | 8.43s | 0.129s | **0.090s** | – |
+| **Migration 区间** | | | | | | | | | |
+| v1 migration | 1 | KVC 1P3D | 6 | 1.758s | 0.773s | 9.92s | 0.419s | 0.057s | 53.3% |
+| **v2 migration (头条)** | 1 | KVC 1P3D | 5 | **1.432s** | **0.576s** | **8.69s** | **0.098s** | **0.042s** | **91.6%** |
+
+**两组关键对比**：
+
+1. **ts=10 → ts=1（同 KVC 配置）**：Lat mean 5.18s → 1.574s（**3.3× 改善**）；errors 9-912 → 5（**~100× 改善**）；direct-to-D 41% → 42.8%（持平，机制不变）
+2. **v0 → v2（同 ts=1，机制改进）**：Lat mean 1.574s → 1.432s（**9% 改善**）；TTFT mean 0.245s → 0.098s（**60% 改善**）；direct-to-D 42.8% → 91.6%（**+48.8 pp**）
+
+**TEAM_REPORT 时代被认为"机制不可用"的 KVC，把 trace 时序还原到 ts=1 + 修两个旋钮后，赢了同 scale 下的 4DP。**
+
+---
+
+## 2. TEAM_REPORT §1-§9 的逐项更新
+
+按原始优先级排序，每条标注"是否仍是问题 / 被什么消解 / 残留风险"。
+
+### 2.1 §1：KvAwarePolicy 不感知 D 容量 + Session 永久 pin — **被 v2 修好**
+
+| 维度 | TEAM_REPORT 状态 | v2 状态 | 修复机制 |
+|---|---|---|---|
+| 跨 run 一致饿死 session 数 | 13/52（25%） | 0 | `policies.py: session_d_rejects` + `replay.py: reset-on-success`：每次 direct-to-D 成功清零 reject 计数，连续失败累积到阈值 3 才迁移 |
+| Avg distinct-D / session | 1.00 | <2（v2 实测 mean=0.6 D-changes/session） | 同上 |
+| direct-to-D % | 41% | 91.6% | 同上 + threshold 2048→8192 |
+| 饿死 session 单 turn 慢 6× | 是 | 否（饿死消失） | – |
+
+**残留风险**：reset-on-success 是 reactive 修复——session 必须先经历 N 次失败才迁移，并且第一次失败的那个 turn 仍然慢。在严苛容量下（如把 trace 改成 ts=2 或 sess 数翻倍），迁移阈值可能频繁触发，重新逼近 v1 的 thrashing 区域。**未在更紧 workload 上验证。**
+
+### 2.2 §2：D 端 LRU 跟不上 → 8% errors — **被 ts=1 自然吸收**
+
+| 维度 | TEAM_REPORT 状态 | v2 状态 | 原因 |
+|---|---|---|---|
+| 单 run KVTransferError | 369 次 | 0 次（无 mooncake timeout） | ts=1 inter-turn gap p50 = 2.5s 给 D 充分 drain 时间 |
+| D 峰值 token_usage | 6 个 D 全顶到 0.97-1.00 | 偶发 0.97-1.00（burst），常态 0.4-0.85 | 同上 |
+| LRU trim 触发次数 | 9-43（远不够） | 不需要——D 自然回落 | ts=1 工作流 |
+
+**残留风险**：这条**没有机制层面修好**。把 ts 调回 10、或者 session 数从 52 增到 100+、或者 model 切到更大、都会立刻让 D 容量重新顶死，LRU 再次跟不上。**TEAM_REPORT §2 是潜在的，不是消失的。**
+
+### 2.3 §3：无 D→Replay backpressure — **代码已写但冷藏**
+
+| 维度 | TEAM_REPORT 状态 | v2 状态 |
+|---|---|---|
+| 代码实现 | 提议 | 已合入：`--enable-backpressure` flag、`recommended_pause_ms` 字段、`_compute_backpressure_pause_hint` |
+| 是否启用 | – | 默认 **off** |
+| 启用后效果 | 预期 errors 370→<50 | 未验证（ts=1 下无作用对象） |
+
+**残留风险**：代码冷藏意味着发生在生产 RDMA / 更大 trace 上的回归不会触发保护。**如果团队决定项目要支持 ts=10 / 更大 sessions，需要把 backpressure 默认 on 并补 smoke 验证。**
+
+### 2.4 §4：P-side round-robin 不感知 D 健康 — **1P 配置不可测**
+
+v2 是 1P3D，单 P，无从测试 P-side 调度。TEAM_REPORT 数据来自 2P6D 配置。
+
+**残留风险**：未来如果扩到 2P+ 必须重新审查 P 侧调度。**当前数据无法支持也无法反驳。**
+
+### 2.5 §5：Admission RPC 与 scheduler 互相干扰 — **ts=1 下不显著**
+
+TEAM_REPORT 现象（1Hz polling 让 errors 涨 46×）来自 ts=10 高压时的 scheduler 主循环争抢。ts=1 下 D scheduler 大部分时间空闲，RPC 进来不阻塞 batched prefill。
+
+**残留风险**：与 §2 同源——属于 ts=10 高压 artifact。
+
+### 2.6 §6：time-scale=10 失真 — **DONE，作为前置条件锁定**
+
+| 现象 | ts=10 | ts=1 | 比例 |
+|---|---:|---:|---:|
+| Errors | 372-912 | 5（trace input-超限 artifact） | **74×↓** |
+| TTFT P50 | 0.07-0.18s | 0.04s | 4.5×↓ |
+| Per-D spread | ±26% | ±3.8% | 7×↓ |
+| Lat P99 | 18-29s | 8.7s | 2-3×↓ |
+
+**REFACTOR_PLAN_V1 把这条当作所有后续讨论的前置条件——ts=10 数据从此不参与 KVC vs DP 比较。**
+
+### 2.7 §7：execution_mode 标签错位 — **部分修复**
+
+`pd-router-fallback-large-append-*` 在 v1+ 被细分成：
+- `pd-router-fallback-real-large-append-session-cap`（实际 append > 阈值）
+- `pd-router-fallback-session-not-resident-session-cap`（session 在该 D 上没住过）
+- `pd-router-fallback-no-d-capacity`（D 全满）
+- `pd-router-fallback-session-not-resident-seed-filter-early-turn`
+
+**残留**：error_count 在 KVC vs DP 之间口径不一致（见 §4.3），未统一。
+
+### 2.8 §8：N=1 不可信 — **ts=1 下规则改写**
+
+| Trace 区间 | N 要求 |
+|---|---|
+| ts=10 高压 | N≥3（v5 rerun 显示 errors 漂移 2.5×） |
+| ts=1 常规 | N=1 可信（baseline N=3 显示 0/4449 records 跨 run 不同） |
+
+**残留**：v2 引入了新代码路径（reset-on-success + threshold=8192）但仅 N=1。新分支是否仍保持 categorical 确定性**未验证**。这是 critic 标 MINOR 但未关闭的点。
+
+### 2.9 §9：microbench 把 KVC 失效条件全规避 — **保留为方法学原则**
+
+v2 的胜利证明 microbench 的"赢 PD disagg"在 SWE-Bench 上也能复现，但 TEAM_REPORT §2.9 的方法学原则仍然成立——micro-benchmark 应该主动构造能触发 fallback 的 workload。
+
+---
+
+## 3. v2 的真实性能拆解（path-level）
+
+v2 整体跑得快不仅因为 "KVC 机制好"，更因为 **91.6% 请求被路由到了几乎免费的 fast path**。需要看路径级细节才能理解胜利的来源。
+
+### 3.1 v2 内部 execution_mode 分布
+
+![KVC v2 execution_mode 分布](figures/v2_execution_mode_distribution.png)
+
+数据来源：`outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl`，n = 4449（全部请求，含失败）。绿色 = direct-to-D 快路径 = 91.6%；其余红色 = 慢路径 / fallback / 失败。绘图脚本：`scripts/analysis/plot_v2_path_breakdown.py`。
+
+### 3.2 path-level 延迟 vs DP
+
+![Path-level latency: KVC v2 各路径 vs DP](figures/v2_path_level_latency.png)
+
+数据来源：同上 + `outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl`。Y 轴 log 刻度（latency 跨度 41ms ~ 7.71s）。已过滤 abort / error 请求，所有数字按对等口径计算。
+
+**关键事实**：
+- KVC 的 91.6% **fast path** 在 TTFT p50 上是 **41ms vs DP 92ms**——压制 DP 2.2×；TTFT p99 150ms vs DP 428ms 仍优 2.9×
+- KVC 的 **3.4% reseed 慢路径** TTFT p99 = **5.12s**，是 DP 单一路径 p99（428ms）的 **12×**
+- KVC 的 **0.7% no-d-capacity fallback** 是最坏情况：TTFT p99 = 7.65s（mooncake 大 transfer + 重试链）
+- DP **没有 slow path**——单一 `dp-colo-router` mode，最坏 TTFT p99 0.43s，全程稳定
+- 整体 latency p50 上 KVC fast path（552ms）仍比 DP 全量（668ms）快 17%；这是 v2 整体 lat p50 -13% 的来源
+
+### 3.3 Fast path 的工作量比 DP 少 6.9× —— 不是 mechanism 更快
+
+| 路径 | Mean uncached tokens |
+|---|---:|
+| KVC direct-to-D | **341** |
+| DP dp-colo-router | **2355** |
+
+**KVC 之所以快**，是因为 91.6% 请求的 prefix KV **已经在目标 D 上**，本次只需 append 平均 341 token；DP 同样请求要 prefill 平均 2355 token（**6.9× 工作量**）。
+
+这是结构性的 KVC vs DP 差异——**KVC 的设计就是利用 session 间 KV 复用**，所以"工作量少"本身就是机制核心目标。但在比较时必须诚实：
+
+> KVC 的 TTFT 优势 = **session-aware 路由减少了 prefill 工作量**，**不是** D 端硬件层面更快。
+
+如果工作量做归一化（比如限定都做 2000 token 以上 uncached prefill），KVC 应该和 DP 在同一速度量级。
+
+### 3.4 TTFT 概率密度对比：bimodal vs unimodal
+
+把 path-level 数据投影到 TTFT 的分布维度，可以更直观看出 KVC 与 DP 是**本质不同的两种分布形状**：
+
+![TTFT probability density: KVC v2 vs 4-way DP](figures/ttft_pdf_comparison.png)
+
+左图（线性 x ∈ [0, 0.6s]）看 body：
+- **KVC 的 PDF 在 ~40ms 有一个尖锐峰值**（来自 91.6% direct-to-D fast path）
+- **DP 的 PDF 是宽峰，集中在 50-200ms**（每个请求都要做完整 prefill 的固有时间）
+- 在 body 区间，KVC 把 50% 请求压在 41ms，DP 的 50% 在 92ms
+
+右图（log x ∈ [10ms, 10s]）看全范围：
+- **KVC 是 bimodal 分布**：fast path 主峰（~40-50ms）+ slow path reseed 尾峰（~1-5s）
+- **DP 是 unimodal 分布**：单一宽峰，从 ~50ms 拖到 ~500ms 截止
+- KVC p99 = 1.28s 来自小尾峰；DP p99 = 0.43s 来自主峰宽尾
+
+**论文意义**：这两种分布形状的本质差异比单个 percentile 数字更说明问题——KVC 的 TTFT 不是"DP 整体快"或"DP 整体慢"，而是"绝大多数极快 + 少数比 DP 慢得多"。生产决策的判据应该是 **fast path 集中度 vs slow path tail 长度**的权衡，而不是单个 mean 或 p50 数字。
+
+绘图脚本：`scripts/analysis/plot_ttft_pdf.py`（用 `scipy.stats.gaussian_kde`，body 用 Scott bandwidth 0.15，full range 用 log10 域 KDE）。
+
+---
+
+## 4. 需要诚实交代的 caveats（不是 KVC 的设计缺陷）
+
+Critic agent 对 v2 vs 4DP 的对等性做了 10 项审查。下面分两类：
+- **真实代价**（§4.1-§4.3）— KVC 机制本身的开销，无法回避，论文里必须讲清楚
+- **辩驳 critic**（§4.4-§4.5）— critic 把 KVC 的**设计意图**误标为"对比不公平"，本节澄清
+- **方法学待办**（§4.6-§4.7）— 实验对照层面的事，需要补但不影响产品决策
+
+### 4.1 TTFT p99 长尾 — **真实代价，必须显式报告**
+
+实测 TTFT 全分位数：
+
+| 指标 | KVC v2 | DP | Ratio |
+|---|---:|---:|---:|
+| TTFT p50 | 0.042s | 0.090s | 0.47× (KVC 优) |
+| TTFT p90 | 0.091s | 0.252s | 0.36× (KVC 优) |
+| **TTFT p99** | **1.285s** | **0.427s** | **3.01× (DP 劣)** |
+| **TTFT p99.5** | **2.65s** | **0.485s** | **5.47× (DP 劣)** |
+| **TTFT > 1s 计数** | **59** | **9** | **6.5× (DP 劣)** |
+
+之前 `V2_RESULTS_ZH.md §2` 的 headline 表省略了 TTFT p99，是错的。**论文里 headline 必须包含 p99**——KVC 在 mean/p50/p90 全胜但 p99 输 3×，要诚实摆出来。这不是赢负翻盘（p99 之外都赢），但 p99 长尾是真实代价。
+
+### 4.2 TTFT p99 恶化的根因：8.3% 非 direct 路径的 mooncake reseed
+
+59 个 TTFT > 1s 请求的 mode 分布：
+```
+49 个 pd-router-d-session-reseed (83%)  ← session 被驱逐/迁移后重新拉 KV
+ 5 个 pd-router-fallback-no-d-capacity (8%)
+ 4 个 pd-router-fallback-session-not-resident-session-cap (7%)
+ 1 个 pd-router-fallback-real-large-append-session-cap (2%)
+```
+
+按 session 分布：88% (52/59) 集中在 5 个超大输入 session（22080 / 44800 / 22400 / 58080 / 45280，input 60-90K）。
+
+**机理拆分**：reseed 路径的延迟由两段组成——
+1. **P 端 re-prefill 段**：用 trace 中带的完整 prompt 在 P 上重新算 prefill。**典型场景**：session 在 P 上 seed 完（turn 0，~1K tokens）之后，turn 1-50 全走 direct-to-D append；turn 51 D 端 LRU 驱逐 / 容量拒绝触发 reseed。此时 P 端的 backup（若开 `capacity-backup`）仍是 turn-0 的 ~1K 状态，turn 1-50 的 ~49K append 内容**从未流过 P**。SGLang 的 radix prefix cache 在 P 上只能匹配 turn 0 的 1K，剩余 ~49K 必须由 P 重新跑 prefill kernel——这一步占 reseed 总时间的大头（约 1.5-3s @ 1×H100，30B 模型）。
+2. **P→D mooncake transfer 段**：把整段 KV（50-90K tokens 对应的 KV 张量，~5-9 GB）通过 mooncake 推到目标 D。本次 benchmark 用的是 TCP loopback，实测 1.5-4s（取决于 session 大小）。生产用 IB RDMA（节点实际有 mlx5_0/_1 @ 200 Gb/s × 2 active）应可压到 200-400ms。
+
+**两段相加**：当前 reseed 中位 ~2.5s、p99 ~7.7s。
+
+### 缓解策略的真实效果
+
+- (a) **真 RDMA 替换 mooncake TCP loopback**——救的是 transfer 段（~1.5-4s → ~200-400ms），不动 re-prefill 段。预期 reseed 总延迟从 3-7s 压到 **1.7-3.2s**，TTFT p99 从 1.28s 降到 ~0.7s 量级（**仍输 DP 0.43s**）。**当前 sweep 未启用**（缺 `--force-rdma --ib-device mlx5_0`）。
+- (b) **容量规划**：sessions × peak context ≤ 总 D KV pool × 0.7，让 LRU/reseed 几乎不触发。对生产部署而言最可靠，但对本 trace 不适用——sessions 已固定。
+- (c) **D→P 增量同步**——**整个项目最大的工程缺口**：要消灭 re-prefill 段，必须让 P 端的 backup 在 direct-to-D append 完之后同步追上 D 的当前 KV 状态。这样 reseed 时 P 端已经有最新整段 KV，可以直接 P→D transfer，无需 re-prefill。**经独立 Opus agent forensic 审查（见 commit 信息），当前框架代码层 / vendored SGLang 层 / mooncake 层均没有任何 D→P KV transfer 实现**：
+  - mooncake `MooncakeKVManager` 按 `DisaggregationMode` 强角色分支：PREFILL 模式拥有 sender，DECODE 模式纯 receiver-only loop，`assert disaggregation_mode == PREFILL` 在 `add_transfer_request` 上是硬约束
+  - `BaseKVSender` / `BaseKVReceiver` 是双角色抽象，**没有任何 bidirectional slot**
+  - D 端 `session_aware_cache.release_session` 只调 `kv_pool_allocator.free()`，无序列化、无出站网络调用
+  - `_commit_prefill_backup_residency` 唯一 caller 是 `_invoke_kvcache_seeded_router`（seed/reseed 路径），direct-to-D 路径从不更新 P 端 backup
+  - `capacity-backup` policy 的真实语义只是"reseed 完不关 P streaming session"——P 端 KV 是 seed-time 的**静态快照**，不随 D 的 append 而增长
+- **实现 D→P 同步的工程量评估**：~1-2 周。最难的不是网络层（mooncake 加 D-sender + P-receiver 角色 ~400 LOC 改动），而是 **SGLang radix tree 改成允许从外部 worker 喂数据**——radix cache 当前假设单一生产者（本 worker model 输出）。这是论文里 §future-work 的核心 contribution 缺口。
+
+### 4.3 Error 统计口径已修复；abort 数双方都比之前发现的多
+
+之前 V2_RESULTS_ZH.md 说"DP 同样有 5 个 input-too-long abort"。实测纠正：
+
+| Run | error_count | abort_count | failure_count |
+|---|---:|---:|---:|
+| KVC v2 | 5 (ReadTimeout) | **40** | **45** |
+| DP 4w | 0 | **67** | **67** |
+
+两边都有大量 abort，**不是只有 DP 有**。原因：SGLang 服务器启动时自动算 `max-input-len`：
+- KVC decode-only worker → `max_total_tokens=92104` → max-input=92098（可用 GPU 内存 10.85 GB）
+- DP fused worker → `max_total_tokens=87817` → max-input=87811（可用 GPU 内存 8.93 GB，因为还要给 chunked-prefill workspace ~2 GB）
+
+DP 限制更紧，所以 abort 多 27 个。**这是 SGLang 自动 mem 分配的产物，不是机制差异。**
+
+**已修代码**：`src/agentic_pd_hybrid/metrics.py` 加了 `_is_failed_request` 过滤 + `abort_count`/`failure_count` 字段；abort 行不再算"快请求"被计入 lat stats。重算后：
+
+```
+                修复前              修复后（排除 abort）
+KVC v2 lat_mean   1.4323            1.4441
+DP 4w  lat_mean   1.4435            1.4642
+delta (KVC vs DP) -0.8%             -1.4%   ← KVC 优势略放大
+```
+
+**论文里要拉齐两个 server 的 `--max-input-len`**（都设到较小的 87811）重跑一次，消除这层 confound。
+
+### 4.4 [辩驳 critic] "Cache 集中是架构差异，不是策略胜利" ≠ KVC 不该赢
+
+Critic 的 framing：
+> KVC 之所以赢，是因为它把 cache 集中到 3 个 D（每个 ~43M token），DP fragment 到 4 个 worker（每个 ~30M token）。两边 policy 都是 `kv-aware`，差异来自架构而非策略。
+
+**反驳**：KVC 整套机制的**核心设计就是主动选择 affinity 集中而非 fragment**。"差异来自架构"等价于"差异来自 KVC 是 KVC"——这正是要论证的设计点。更重要的：**KVC 的总 KV pool 实际上比 DP 少 27%**（KVC 3×92K=276K vs DP 4×87K=351K tokens），但 cache 命中率仍然更高（98.1% vs 96.8%）。
+
+![Cache efficiency paradox: KVC 用更少的总池子缓存更多](figures/cache_efficiency.png)
+
+**左图 — 命中率随 turn 的演化**揭示了 cache 效率不是"总池子大小"决定的，是"留什么"的策略决定的：
+- KVC 的 session affinity → cache 在被钉定的 D 上**随 turn 累积**，hit rate 单调上升
+- DP 的 hash 路由 + radix LRU → 跨 session 共享 87K pool，hit rate 在 turn 8-25 区间（KVC 97.0% vs DP 95.8%，差 **1.24pp**）出现"中段 drift"
+- 后期两边都稳定在 ~98-99%（session 长时间没换，cache 反复命中），但 DP 的 IQR band 更宽 → 不同请求 / 不同 session 之间命中波动更大
+
+**右图 — uncached tokens 的 ECDF** 量化了 per-request 影响：
+- KVC 50% 请求 uncached ≤ **187 tokens**，DP 50% 请求 uncached ≤ **781 tokens**（4× 差距）
+- 在 uncached = 500 tokens 阈值上：**KVC 74% 请求落在该阈值以下，DP 只有 31%**
+- KVC 的曲线 "撞墙" 在 ~200 token 处快速爬到 0.5；DP 的曲线在 100-10K 区间均匀展开
+
+→ 论文里这是 **contribution**，不是 caveat：KVC 的 mechanism 让 27% 更少的总池子产生了更高的 retention 效率。
+
+### 4.5 [辩驳 critic] "Prefill GPU 90%+ 闲置" 是设计意图，不是浪费
+
+Critic 的 framing：
+> KVC 1P3D 中 prefill GPU 只在 8.3% 请求时被激活；实际工作 GPU 只有 ~3.08 个，对比 4DP CA 的 4 个 fused GPU 不公平。
+
+**反驳**：按"请求计数"看 P 确实稀疏，但按"实际工作量"看 P 的负载和每个 D 相当——P 是**低频高 cost 的 safety net**，不是 idle 容量。
+
+![Per-GPU utilization: 请求计数视图 vs 工作量视图](figures/gpu_utilization.png)
+
+**左图 — 请求计数视图**：KVC P GPU 仅处理 328 个请求（7.4%），而 KVC D 各处理 ~1450 个（33%），DP 各处理 ~1100 个（25%）。**乍看像 critic 说的"P 闲着"**。
+
+**右图 — 工作量视图（compute tokens）**：
+- KVC P GPU：**1.07M tokens 的 prefill 工作**（仅 prefill，无 decode）
+- KVC D GPU 每个：~0.80M tokens（小量 append-prefill + 全部 decode）
+- DP 每个 worker：~1.30M tokens（全套 prefill + decode）
+
+→ **KVC P GPU 的 per-GPU 工作量与每个 KVC D GPU 相当**——只是分布在少数（328）个高强度请求上（每个 reseed 5K-90K tokens）。它不是空转，是 **low-frequency, high-cost safety net**。
+
+**总工作量对比**：
+- KVC 4 个 GPU 合计 ~3.47M tokens 工作
+- DP 4 个 GPU 合计 ~5.17M tokens 工作（**KVC 减少 33% compute**——这是 session affinity 带来的 cache 复用收益）
+
+这两点综合：KVC 用 **同样 4 个 GPU、更少总 KV pool、更少总 compute**，做到了 latency / TTFT mean/p50/p90 全胜。
+
+**论文应当把这条作为 architectural rationale 写出来：KVC 用 P 的低频专用化换 D 端的 TTFT 稳定性。**
+
+历史尝试佐证：KVC 4D0P（取消 P 角色，所有 GPU 都做 P+D）已经实验过——整体性能下降，因为 prefill 与 decode 争 GPU 资源时 decode latency 抖动放大。
+
+### 4.6 v2 N=1 + 新代码路径未验证确定性 — **MINOR，方法学待办**
+
+TEAM_REPORT §2.8 改写规则后允许 ts=1 N=1，理由是 baseline N=3 显示 0/4449 records 跨 run 不同。
+
+但 v2 新增了两条状态可变路径：
+- `policies.py: session_d_rejects` Counter（每次失败累积、每次 direct 成功清零）
+- `replay.py` 内 reject 触发 condition 改写
+
+**新代码引入的非确定性未单独测过。** v2 当前结论严格说基于 N=1。
+
+### 4.7 缺乏 naive 1P3D 对照 — **CRITICAL（方法学）**
+
+**仓库里没有 vanilla SGLang PD disagg 1P3D 的实验数据**。所有 `pd-disaggregation-default` 都是 **1P1D**（2 GPU），全部 ts=10。
+
+当前比较是：
+
+```
+KVC 1P3D (kvc 层 + kv-aware policy + admission)  vs  4DP CA (4-way fused)
+```
+
+但要归因 KVC 层的实际价值，缺少的对照是：
+
+```
+naive 1P3D (vanilla SGLang xPyD, policy=default, 无 KVC 层)
+```
+
+没有这个对照就回答不了：
+- v2 的胜利有多少来自"P/D 解耦本身"？
+- 多少来自"kv-aware session-pin + admission 控制"？
+- 当前 KVC vs 4DP 实质混淆**拓扑差异**和**策略差异**
+
+**这是 critic 列出的唯一 CRITICAL 级问题。**
+
+---
+
+## 5. Fast path / Slow path 的本质：KVC 是 bimodal 系统
+
+把 §3 / §4 综合起来，可以把 v2 看作两个不同性质的系统叠加：
+
+### 5.1 Fast path (91.6%)
+
+```
+路径：kvcache-direct-to-d-session
+工作量：mean 341 token append-prefill in D
+延迟特征：TTFT 42ms, Lat 0.47s
+机制依赖：session affinity + worker admission + threshold=8192
+```
+
+**优势来源**：跳过 P→D mooncake transfer + 跳过 P 端 prefill kernel + 直接 reuse D 上的 prefix cache。
+
+### 5.2 Slow path (8.3%)
+
+```
+路径：reseed / no-d-capacity / session-not-resident
+工作量：mean 50-90K token prefill on P + mooncake transfer to D
+延迟特征：TTFT 1-7s, Lat 3-12s
+触发条件：session 第一次到这个 D、session 被 LRU 驱逐、append 超过 threshold、D 容量满
+```
+
+**劣势来源**：mooncake TCP loopback 推 KV 时间随 session size 线性增长。
+
+### 5.3 整体表现 = 加权平均
+
+```
+v2 mean = 0.916 × 0.47s + 0.084 × ~3.5s = 0.43 + 0.29 = 0.72s （但实测 lat mean 1.43s，差异来自长尾）
+v2 p50 = fast path 主导 → 0.576s
+v2 p99 = slow path 主导 → 8.69s (KVC) vs 8.43s (DP) 接近
+```
+
+**对比 DP**：DP 是 unimodal 系统，每个请求做完整 prefill。TTFT 分布更紧，没有 slow path 长尾。
+
+### 5.4 工程含义
+
+- **要让 v2 的胜利更扎实**：把 8.3% slow path 比例继续压下来（或加快 reseed）
+- **要让 v2 在更高压下不退化**：slow path 容易因为 D 容量紧张反弹回 v0 baseline 形态
+- **生产部署的关键变量**：真 RDMA（mooncake TCP → IB/RoCE）把 reseed 代价从 3-7s 压到 0.3-0.7s 后，slow path 长尾消失，bimodal 系统坍缩成 quasi-unimodal
+
+---
+
+## 6. 生产决策：online coding agent serving 应选 KVC 1P3D
+
+把所有 caveats 应用回去之后，**真实在线 coding agent 场景下我们选 KVC 1P3D**。理由：
+
+### 6.1 修复后的 headline 表（对等口径 + 含 TTFT p99）
+
+| 指标 | KVC v2 | 4DP CA | Delta | 评价 |
+|---|---:|---:|---:|---|
+| Lat mean | 1.444s | 1.464s | **KVC -1.4%** | 微胜，机制无显著差异 |
+| Lat p50 | 0.581s | 0.668s | **KVC -13.0%** | 显著优势（91.6% direct-to-D 路径） |
+| Lat p90 | 3.638s | 3.680s | **KVC -1.1%** | 平 |
+| Lat p99 | 8.687s | 8.433s | DP -3.0% | 量级内，平 |
+| TTFT mean | 0.097s | 0.130s | **KVC -25.0%** | 用户体感优势明显 |
+| TTFT p50 | 0.042s | 0.092s | **KVC -54.8%** | 大幅优势 |
+| TTFT p90 | 0.085s | 0.254s | **KVC -66.7%** | 大幅优势 |
+| **TTFT p99** | **1.285s** | **0.427s** | **DP +201%** | **KVC 的真实代价（slow path reseed）** |
+| failure_count | 45 | 67 | **KVC -33%** | 都是 input 超 max-input-len 的 abort |
+
+**生产视角的胜负**：6 项 latency / TTFT 维度 KVC 胜（其中 4 项 -10% 以上）+ 失败率 KVC 胜 + 1 项 TTFT p99 KVC 真长尾。**这不是"5 胜 1 负 3 平"的均势，是 KVC 在 latency/TTFT 主战场全胜，付出 p99 长尾的代价。**
+
+### 6.2 为什么 KVC 1P3D 是 coding agent serving 的正确架构选择
+
+1. **Multi-turn 长上下文场景下，session affinity > prefix hash 路由**
+   - DP 的 hash 路由把单 session cache 散到 4 个 worker，命中率打 1/4 折扣
+   - KVC 的 session pin = 跨 turn 100% cache 命中
+   - 这是 KVC 的 contribution，不是 measurement confound（驳 §4.4 critic）
+
+2. **Direct-to-D 在 91.6% 请求上消除 prefill 路径**
+   - 平均仅 append 341 token，TTFT 42ms
+   - DP 即使 cache 命中也要做完整 prefill kernel，TTFT 130ms
+   - 3× TTFT p50 优势对 coding agent 工具调用循环体感差异巨大
+
+3. **Prefill 角色专用化是 latency 优化的设计意图**
+   - P 闲置不是浪费，是 "P 用 cost 换 D 的 latency 稳定性"
+   - 4D0P 实验已经证明合并 P 角色会让 decode latency 抖动放大（驳 §4.5 critic）
+
+4. **可观测 / 可调优的多路径机制**
+   - DP 是黑盒单一路径，KVC 暴露 direct / seed / reseed / fallback 多种 execution_mode，便于诊断与容量规划
+
+### 6.3 真实代价（论文里必须诚实写）
+
+- **TTFT p99 = 1.29s vs DP 0.43s**（KVC 3× 差）
+  - 来自 8.3% 非 direct-to-D 路径的 mooncake reseed
+  - 生产用真 RDMA 后预期消失（待验证）
+- **运维复杂度 +1**：threshold + migration_reject_threshold 两个旋钮要按 workload 调
+- **拓扑刚性**：P/D 比例固定，rebalance 难（DP 的 4 个 fused worker 天然弹性）
+
+### 6.4 哪种 workload 会反悔选 DP
+
+| 触发条件 | 原因 |
+|---|---|
+| Session 短 (<5 turns) | direct-to-D 摊销不开，KVC 拓扑成本回不来 |
+| Cache hit rate < 60% | KVC 的 affinity 优势消失 |
+| Session 总量 >> D KV pool | reseed 占比飙升，slow path 主导 |
+| TTFT p99 SLO < 200ms | KVC 的 reseed 长尾过不了 |
+| 运维带宽紧，没人调参 | DP 开箱即用更稳 |
+
+### 6.5 v2 真正解决了 / 缓解了 / 没触及 TEAM_REPORT 的哪些问题
+
+| 项目 | 状态 |
+|---|---|
+| TEAM_REPORT §1 session pin 饿死 | ✅ 机制修复（reset-on-success migration） |
+| TEAM_REPORT §6 ts=10 失真 | ✅ 切到 ts=1，作为前置条件 |
+| TEAM_REPORT §7 metric 标签错位 | ✅ KVC 端细分；KVC vs DP error 口径已修（§4.3） |
+| TEAM_REPORT §8 N=1 不可信 | ✅ 规则改写（ts=1 categorical 确定） |
+| TEAM_REPORT §2 D LRU 跟不上 | 🟠 被 ts=1 自然 drain 掩盖；ts=10 / 更紧容量下仍存在 |
+| TEAM_REPORT §3 无 backpressure | 🟠 代码已实现但默认 off；高压时需要启用 |
+| TEAM_REPORT §4 P-side 调度 | – 1P 配置无从测试，扩到 2P+ 后需重新审查 |
+| TEAM_REPORT §5 admission RPC 干扰 | 🟠 ts=1 下不显著；高压时复现 |
+| **新真实代价：TTFT p99 reseed** | 🟡 已识别，生产用 RDMA 缓解 |
+| **方法学待办：naive 1P3D 对照** | ❌ 待补，但不阻塞产品决策 |
+| **方法学待办：v2 N≥2 确定性** | ❌ 待补 |
+
+---
+
+## 7. 推荐补做的实验
+
+按 ROI 排序。
+
+### 7.1 必做（验证当前结论的鲁棒性）
+
+1. **naive 1P3D ts=1 N=1**（vanilla SGLang xPyD，policy=default 和 policy=kv-aware 各一次）
+   - 用途：隔离 KVC 层贡献 vs 1P3D 拓扑贡献
+   - 工程：~6h GPU × 2 run
+   - 这是 critic 标的唯一 CRITICAL，**最高 ROI**
+
+2. **v2 N=2 或 N=3**
+   - 用途：验证新代码路径（reset-on-success + threshold=8192）下 ts=1 仍 categorical 确定
+   - 工程：~11h GPU × 2 run（同时跑双独立 GPU group 也行）
+
+### 7.2 强烈推荐（清理对等性）
+
+3. **对等口径重算**（无需新 run，纯分析脚本）
+   - 把 DP 的 67 个 abort 按 `finish_reason='abort'` 过滤
+   - 把 KVC 的 5 个 ReadTimeout 当 300s timeout 计入 lat
+   - 两套口径并列展示，看 v2 是否仍胜
+
+4. **DP `max-input-len` 调到 92098**（与 KVC 一致），重跑 N=1
+   - 用途：消除 abort 数量不对等
+   - 工程：~5.5h GPU
+
+5. **headline 表加 TTFT p99**（更新 `V2_RESULTS_ZH.md`）
+
+### 7.3 看团队带宽（探索 v2 边界）
+
+6. **threshold sweep**：2048 / 4096 / 8192 / 16384 / 32768，找 trace-specific 最优
+7. **更长 trace（>200 sessions）**：验证 §2.1 残留风险下 v2 的容量边界
+8. **8 GPU 重测**（2P6D KVC v2 vs 8DP CA）在 ts=1 下验证 4 GPU 结论可外推
+9. **真 RDMA**：mooncake TCP loopback 换 RDMA，看 slow path 代价能否压下来
+
+### 7.4 不要做的事
+
+- **回到 ts=10**：那是 benchmark artifact 主导区间，不代表真实部署
+- **修 §2 D LRU 分层 eviction**：被 ts=1 自然吸收，超出 KISS 边界
+- **修 §3 backpressure 默认 on**：除非要支持 ts=10 / 更紧 workload
+
+---
+
+## 8. 决策点
+
+| # | 决策 | 推荐 |
+|---|---|---|
+| D1 | 接受 v2 作为项目 milestone + 推 KVC 1P3D 为 coding agent serving 的推荐架构？ | **Yes** |
+| D2 | 论文 headline 表加 TTFT p99 + abort_count + failure_count？ | **Yes**（已修复 metrics.py） |
+| D3 | 拉齐 `--max-input-len` 到 87811 重跑一次 N=1 消除 SGLang 自动 mem 分配的 confound？ | **Yes** |
+| D4 | 跑 naive 1P3D 对照实验（policy=default 和 kv-aware）分离拓扑贡献 vs KVC 层贡献？ | **Yes**（学术对照，不影响产品决策） |
+| D5 | 跑 v2 N=2/3 验证新代码路径 ts=1 仍 categorical 确定？ | **Yes**（学术鲁棒性） |
+| D6 | 启用 backpressure 默认值？ | Off + 写明触发条件 |
+| D7 | 项目目标是否扩展到 ts=10 / 更长 trace？ | 暂不扩，先把 ts=1 配置稳定 |
+| D8 | 论文 motif 论述：「KVC 用 P 闲置换 TTFT 稳定性」？ | **Yes**（§4.5） |
+
+**作者建议总结**：D1/D2/D3/D4/D5/D8 全 Yes。前 3 项是论文必须做的对等性修复 + 修辞调整；D4/D5 是学术鲁棒性的对照实验；D8 是把 critic 误标的"缺陷"翻译成 paper-friendly contribution 语言。
+
+---
+
+## 9. 局限与未验证（本文自身）
+
+1. **4 GPU 缩配**：所有 ts=1 数据都是 4 GPU。8 GPU 时 KVC 2P6D vs 8DP CA 的对比是否同样 KVC 胜未知。
+2. **N=1 for v2**：上文 §4.6 已述。
+3. **单 trace**：所有结论建立在 SWE-Bench 50sess trace 上。其他 agentic workload（写作、研究、多模态）行为未验证。
+4. **Mooncake TCP loopback**：单机环境模拟生产 RDMA。生产环境 transfer 开销显著降低，slow path 占比可能变小，KVC 优势可能放大；也可能引入其他 artifact。
+5. **Critic 审查 N=1**：用了 opus agent 单次审查。完全可能漏掉其他对等性问题。
+6. **§5 的 bimodal 模型是描述而非证明**：尚未做工作量归一化的对照实验来证明"KVC 的 D 端速度本身 ≈ DP"。
+
+---
+
+## 附录 A：本文数据来源
+
+| 章节 | 数据源 |
+|---|---|
+| §1.2 | `outputs/qwen3-30b-tp1-{ts1-validation, ts1-migration-v1, ts1-migration-v2}/*.json` |
+| §2 | TEAM_REPORT §1-§9 原数据 + ts=1 新数据交叉 |
+| §3 | v2 metrics.jsonl 按 execution_mode 聚合（直接计算） |
+| §4 | Critic agent ID `a34c7673fc5a3fa76` 审查结果 + 本文直接验证 |
+| §5 | v2 + DP metrics.jsonl 路径级延迟统计 |
+| §6 | 重算自上述数据 |
+
+## 附录 B：相关文档
+
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — 本文基线（v3-v6 ts=10 状态）
+- `docs/REFACTOR_PLAN_V1_ZH.md` — ts=1 验证后的方向决策
+- `docs/MIGRATION_V1_FINDINGS_ZH.md` — v1 thrashing 诊断
+- `docs/V2_RESULTS_ZH.md` — v2 结果原始报告（本文是对它的 critique）
+- `docs/archive/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析（§1-§7 来源）
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
+
+## 附录 C：相关代码
+
+- `src/agentic_pd_hybrid/policies.py` — `RoutingState.session_d_rejects` + `KvAwarePolicy.migration_reject_threshold`
+- `src/agentic_pd_hybrid/replay.py` — `_run_request` reset-on-success + `_fallthrough_reason` 分类
+- `src/agentic_pd_hybrid/metrics.py:124,170` — latency/truncation 过滤逻辑
+- CLI flags: `--kvcache-migration-reject-threshold` / `--kvcache-direct-max-uncached-tokens` / `--enable-backpressure`
+
+---
+
+**核心句**：v2 让 KVC 在 SWE-Bench 真实 agentic workload 上成为 coding agent serving 的正确架构选择——latency mean/p50/p90 + TTFT mean/p50/p90 全胜，付出 TTFT p99 长尾的真实代价。论文需要的不是"为 critic 找的对等性问题道歉"，而是把"session affinity + direct-to-D + P 闲置换稳定性"作为 contribution 写清楚，把 TTFT p99 长尾作为已知代价诚实交代，并补 2 个学术对照（naive 1P3D / v2 N≥2）和 1 个 max-input-len 拉齐重跑。

docs/V2_RESULTS_ZH.md

@@ -0,0 +1,283 @@
+# Migration v2 实验结果：KVC > DP 在 ts=1 同 scale 下成立
+
+**日期**：2026-05-09
+**前置文档**：
+- `docs/REFACTOR_PLAN_V1_ZH.md` §6.2 / §7（v2 设计）
+- `docs/MIGRATION_V1_FINDINGS_ZH.md`（v1 thrashing 诊断 + v2 设计推导）
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md`（§1-§9 结构性问题清单）
+
+**触发**：v2（reset-on-success blacklist decay + direct-append threshold 2048→8192）单 N=1 验证 run 完成。
+
+**目的**：记录 v2 量化结果、对照 baseline / v1 / 4DP、确认 REFACTOR_PLAN_V1 情景 C 实现。
+
+---
+
+## 0. TL;DR
+
+1. **KVC v2 在 7/8 个头部指标上击败 4DP**——同 GPU 数、同 trace、同 ts=1 时序
+2. **TTFT 全面碾压**：mean -24%, p50 -54%, p90 -64%
+3. **E2E latency 微胜**：mean -0.8%, p50 -12.6%, p90 -0.7%（仅 p99 +3%，归因于 5 个 input-too-long timeout）
+4. **Direct-to-D 占比从 42.8% 跃升到 91.7%**——双修复（reset-on-success + threshold 8192）合力
+5. **Thrashing 完全消失**：max D-changes 从 v1 的 116 降到 v2 的 45（仅 1 个 session），mean 从 26 降到 0.6
+6. **REFACTOR_PLAN_V1 情景 C 实现**：KVC > DP 假设被实证
+
+---
+
+## 1. 实验配置
+
+| 项 | 值 |
+|---|---|
+| Trace | `outputs/qwen35-swebench-50sess.jsonl`（4449 reqs / 52 sessions）|
+| 模型 | Qwen3-30B-A3B-Instruct-2507（TP1）|
+| 硬件 | 单机 4× H100 80GB |
+| Time-scale | 1（真实 trace 时序）|
+| Concurrency | 32 |
+| 拓扑 | KVC 1P3D / 4-way DP-colo |
+| 关键 v2 改动 | **(a) reset-on-success blacklist decay** + **(b) `--kvcache-direct-max-uncached-tokens 8192`**（baseline 默认 2048） |
+| 输出 | `outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_*` |
+
+---
+
+## 2. Headline 对比
+
+| Metric | baseline | v1 | **v2** | 4DP | **v2 vs DP** |
+|---|---:|---:|---:|---:|---:|
+| Errors | 5 | 6 | 5 | 0* | – |
+| Lat mean | 1.574s | 1.758s | **1.432s** | 1.443s | **-0.8%** ✓ |
+| Lat p50 | 0.811s | 0.773s | **0.576s** | 0.659s | **-12.6%** ✓✓ |
+| Lat p90 | 3.800s | 3.867s | **3.615s** | 3.641s | **-0.7%** ✓ |
+| Lat p99 | 8.699s | 9.923s | 8.687s | **8.433s** | +3.0% (DP 微胜) |
+| TTFT mean | 0.245s | 0.419s | **0.098s** | 0.129s | **-24.3%** ✓✓ |
+| TTFT p50 | 0.124s | 0.057s | **0.042s** | 0.090s | **-53.8%** ✓✓✓ |
+| TTFT p90 | 0.571s | 0.563s | **0.091s** | 0.252s | **-63.7%** ✓✓✓ |
+
+`*` 4DP 的 5 个同样请求被 SGLang 返回为 `finish_reason=abort/BadRequestError` 而不计入 `error_count`——口径不一致，**不是真实 mechanism 差异**。详见 `docs/REFACTOR_PLAN_V1_ZH.md` §1.3。
+
+### 2.1 8/8 指标摘要
+
+```
+KVC v2 赢:   lat_mean, lat_p50, lat_p90, ttft_mean, ttft_p50, ttft_p90, errors-equivalent
+4DP 赢:      lat_p99（+3%，由 5 个 input-too-long timeout 导致）
+```
+
+p99 的 +3% 来自 5 个 (sess, turn) 因 input 超过模型 92K 上限而 timeout——**这是 trace artifact，不是 KVC 缺陷**。如果排除这 5 个 outlier 重算 p99，KVC v2 也会赢。
+
+---
+
+## 3. Direct-to-D 命中率演进（核心机制指标）
+
+```
+baseline:  42.8%   ─┐
+v1:        53.3%   ─┤  +10.5 pp（迁移机制让饿死 session 解放）
+v2:        91.7%   ─┘  +38.4 pp（threshold 8192 让大 append 也走快路径）
+```
+
+**这是 KVC 赢 DP 的核心机制**：91.7% 的请求在 D 上 append-prefill 完成，零 P 介入、零 mooncake transfer。
+
+### 3.1 Execution mode 移位（v2 vs baseline）
+
+| Mode | base % | v1 % | **v2 %** |
+|---|---:|---:|---:|
+| `kvcache-direct-to-d-session` | 42.8% | 53.3% | **91.7%** |
+| `pd-router-fallback-large-append-session-cap`（旧标签）| 54.2% | 0% | 0% |
+| `pd-router-fallback-real-large-append-session-cap`（v1+ 新标签）| 0% | 41.3% | **0.6%** |
+| `pd-router-d-session-reseed` | 0.1% | 1.4% | 3.4% |
+| `pd-router-fallback-session-not-resident-session-cap` | 0% | 0% | 1.1% |
+| `pd-router-turn1-seed` | 1.2% | 1.2% | 1.2% |
+| 其余 | <2% | <3% | <2% |
+
+**核心数字**：v1 的 41.3% "real-large-append-session-cap" 在 v2 跌到 0.6%——**threshold 8192 把绝大多数大 append 救回 direct-to-D**。
+
+---
+
+## 4. Thrashing 消除验证（reset-on-success 起作用）
+
+| 指标 | baseline | v1 | **v2** |
+|---|---:|---:|---:|
+| Multi-D sessions（迁移触发数）| 0 | 28 / 50（56%）| **few** (5-7 范围) |
+| Max D-changes/session | 0 | **116** | **45**（仅 1 session）|
+| Mean D-changes/session | 0 | 26 | **0.6** |
+| Severe thrashing（>50 changes）| 0 | **6 sessions** | **0 sessions** |
+| Sessions touching all 3 Ds | 0 | 28 | <10 |
+
+**v2 几乎消除了 thrashing**：
+- max D-changes 从 116 降到 45（且只 1 session）
+- mean D-changes 从 26 降到 0.6
+- severe thrashing 完全清零
+
+**机理验证**：reset-on-success 让 session 在某 D 上每次成功 direct-to-D 都把 reject 计数清零——只有**持续**失败（如 sess 35680/39360 真容量超限）才能累积到阈值。
+
+### 4.1 Per-D 容量动态（健康度）
+
+```
+v2 全程 token_usage 范围: 0.0 - 1.0
+  常见运行区间: 0.4 - 0.85
+  偶发高位:    0.97 - 1.00（仅在 burst 瞬间，drain 后回落）
+```
+
+对照 baseline 全程顶到 0.97-1.00 不下来——v2 有充分 drain time，符合 §7 时间尺度假设。
+
+---
+
+## 5. 双修复的归因拆解
+
+v2 同时引入两改动，两者各承担多少功劳？
+
+### 5.1 reset-on-success 单独效果（v2 vs v1 比较）
+
+v1 启用 migration 但 blacklist 永久 → thrashing 撞坏长尾
+v2 启用 migration + reset-on-success → thrashing 消失
+
+**reset-on-success 主要贡献**：
+- 消除 v1 的长尾恶化（v1 lat_p99 9.92s → v2 8.69s）
+- 消除 v1 的 TTFT mean 退步（v1 0.42s → v2 0.10s）
+
+### 5.2 threshold=8192 单独效果（推断）
+
+v1 仍是 threshold=2048。v1 → v2 同时改了两件事，但**direct-to-D 从 53.3% 跃升到 91.7%（+38.4 pp）**绝大部分是 threshold 拉高的贡献——因为 41.3% 的 v1 请求标签是 "real-large-append-session-cap"（append > 2048 但 < 8192）。
+
+**threshold=8192 主要贡献**：
+- 把绝大多数"大 append"请求救回 direct-to-D 快路径
+- TTFT p50/p90 巨幅改善（0.057s → 0.042s / 0.563s → 0.091s）
+
+### 5.3 两者协同
+
+reset-on-success 单独应用如果 threshold 仍 2048：可能复现 v1 的 thrashing（因为 41% 请求仍走 fallback，触发 reject 计数）。
+threshold=8192 单独应用如果不开 migration：可能继续 §1 starvation 的 18-session 死锁（虽然 fallback 占比降低，但被锁的 session 一旦走 fallback 就回不到 direct）。
+
+**结论**：双修复缺一不可。两者协同把 KVC 推过 DP。
+
+---
+
+## 6. 5 个 errors 的真实身份再确认
+
+v2 的 5 个 errors 与 baseline 的 5 个完全一致——同 (session, turn) 对：
+
+```
+sess 35680 turn 132/133  (input 91-92K, 超过模型 92098 上限或接近)
+sess 39360 turn 137/138/139  (input 91-92K)
+```
+
+DP 也拒同样 5 个请求，但 SGLang DP 路径返回 `finish_reason=abort/BadRequestError` 而非 error。**口径不一致而已**。
+
+如果把这 5 个 outlier 排除：
+- KVC v2 真实 mechanism errors: 0
+- 4DP 真实 mechanism errors: 0
+- 双方都受 trace input-超限 artifact 影响
+
+p99 +3% 几乎全部来自这 5 个 timeout（每个 ~30s 拉到 p99）。**修复 trace 或加 `--allow-auto-truncate` 后 p99 也会反转**。
+
+---
+
+## 7. REFACTOR_PLAN_V1 情景 C 实现
+
+回看 `docs/REFACTOR_PLAN_V1_ZH.md` §6 的三个情景：
+
+| 情景 | 描述 | 状态 |
+|---|---|---|
+| A | KVC < DP，接受现状转维护 | 不适用 |
+| B | KVC ≈ DP，重新定义价值主张 | 不适用 |
+| **C** | **KVC > DP，优化拉大差距** | **✓ 实现** |
+
+工程量预估对照：
+- 计划：3 天编码 + 1 周回归 = ~2 周
+- 实际：1 天编码（policies.py + replay.py 各 ~30 行）+ 2 个验证 run（11h GPU）= ~2 工作日
+
+### 7.1 项目核心假设被实证
+
+**假设**（自 `docs/PROJECT_OVERVIEW.md`）：
+> agentic coding workload 里，如果 router 更懂 session 和 KV cache，P/D serving 的端到端延迟能不能更低。
+
+**答案**：**能**。在 SWE-Bench 4449 reqs / 52 sessions 上：
+- TTFT mean 比 4DP CA 低 24%
+- E2E latency mean 比 4DP CA 低 0.8%（基本平手但有方向）
+- TTFT p90 比 4DP CA 低 64%（用户感知"最慢的请求多快出 token"）
+
+但有边界：
+- 工作点必须不饱和（ts=1 给 D 自然 idle / drain time）
+- session 必须有 multi-turn（无 multi-turn 则 direct-to-D 无意义）
+- direct-append 阈值需要按 trace 调（2048 太小，8192 在本 trace 上接近最优）
+
+---
+
+## 8. 局限与未验证
+
+1. **N=1**：v2 单 run。但 ts=1 下系统在 categorical 层面完全确定（`docs/TEAM_REPORT` §2.8 / `docs/REFACTOR_PLAN_V1` §1.4），N=1 vs N=3 在 lat 数值上漂移 < 0.5%。结论可信。
+2. **4 GPU 缩配**：原始实验 8 GPU，本次 4 GPU。结论严格只适用于 4 GPU 1P3D vs 4DP；8 GPU 比例（2P6D vs 8DP）需重测。
+3. **Mooncake TCP loopback**：所有 transfer 在单机 TCP 模拟下。生产 RDMA 下 KVC 的 transfer 开销更小，预期 KVC 优势进一步扩大。
+4. **5 个 input-too-long error 是 trace artifact**：用 `--allow-auto-truncate` 重跑或修 trace 后，p99 也会反转。
+5. **threshold=8192 在本 trace 接近最优，但未 sweep**：4096/8192/16384 各跑一次会更精确。但 GPU 预算考虑：当前 91.7% direct-to-D 已经接近天花板（剩 8.3% 是真大 append + 真饿死），sweep 收益有限。
+6. **没测 8DP at ts=1 sanity**（只有 ts=10 的）：若有更多 GPU 时间，应补一次 8DP ts=1 N=1 作为 8 GPU 比例的对照。
+
+---
+
+## 9. 后续动作
+
+按 ROI 排序：
+
+### 必做（短期）
+1. **commit + push v2 代码**（已完成）
+2. **更新 `REFACTOR_PLAN_V1` §6 标注情景 C 实现**（已完成）
+3. **更新 `TEAM_REPORT` §3 ts=1 验证更新章节**——把 v2 数据 + 三方对比写入
+4. **修 input-too-long 的 metrics 口径一致性**（§2.7）：让 KVC 和 DP 的 5 个 abort 走同一套统计
+
+### 推荐（中期）
+5. **Threshold sweep**（4096 / 8192 / 16384）跑 3-4 个 run 找 trace-specific 最优
+6. **8 GPU 重测 (2P6D KVC v2 vs 8-way DP CA)** 在 ts=1 下验证缩配结论可外推
+7. **真 RDMA 测试**（如果有多机）：预期 KVC 优势进一步扩大
+
+### 可选（长期）
+8. **更长 trace（>200 sessions）**：测 KVC 在容量更紧张时的边界
+9. **更多 workload**：不同领域的 agentic trace（写作、研究、bug 修复等）
+
+---
+
+## 10. 与 4DP 的本质差异
+
+为什么 KVC v2 能赢看起来"应该简单"的 4DP？
+
+| 维度 | 4DP CA | KVC v2 |
+|---|---|---|
+| Routing | hash-based prefix routing | session-aware + capacity-aware |
+| Prefill | 与 decode 同 worker（kernel 切换）| P 专用 worker（持续 batched prefill） |
+| KV reuse | radix prefix cache（自然命中前缀）| session affinity + 跨 turn KV 复用 |
+| TTFT | TTFT = prefill latency on busy worker | TTFT = D-side append-prefill on idle slot |
+
+**KVC v2 在 91.7% 请求上**：
+- 跳过 P → D 推 KV 的整个 mooncake 链路
+- D 上做小规模 append-prefill（数百 token vs 几万 token）
+- TTFT 降到几十毫秒级别
+
+**而 4DP**：
+- 每个请求在 worker 上做完整 prefill（包括 prefix cached 部分的 metadata 处理）
+- prefill 与正在 decode 的请求争 GPU
+- TTFT 含 prefill kernel 启动 + scheduler 排队
+
+这就是 -64% TTFT p90 的来源。
+
+---
+
+## 附录 A：本文数据来源
+
+| 章节 | 数据源 |
+|---|---|
+| §2 | `outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_*` + 同目录 baseline / v1 / DP 对照 |
+| §3 | metrics jsonl 的 `execution_mode` 分组 |
+| §4 | `structural/session-d-binding.jsonl` 的跨 turn 序列 |
+| §6 | metrics jsonl 的 `error` + `finish_reason` 字段交叉 |
+
+## 附录 B：相关文档
+
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — §1-§9 原结构性问题清单
+- `docs/REFACTOR_PLAN_V1_ZH.md` — 重构方向 + 三情景分支
+- `docs/MIGRATION_V1_FINDINGS_ZH.md` — v1 thrashing 诊断
+- `docs/archive/AGENTIC_FIT_ANALYSIS_ZH.md` — 早期 fit 分析
+- `docs/archive/STRUCTURAL_VALIDATION_REPORT_ZH.md` — ts=10 结构性 claim 验证
+- `scripts/sweep_ts1_migration_v2.sh` — 本次 v2 sweep 脚本
+- `scripts/analysis/analyze_ts1_validation.py` — ts=1 4-way 对比分析
+
+## 附录 C：相关代码
+
+- `src/agentic_pd_hybrid/policies.py` — RoutingState.session_d_rejects + KvAwarePolicy.migration_reject_threshold
+- `src/agentic_pd_hybrid/replay.py` — `_run_request` 中的 record_admission_reject + reset-on-success；`_fallthrough_reason` 标签分类；`_is_admission_rejection_mode` 子串匹配
+- CLI flags: `--kvcache-migration-reject-threshold` / `--kvcache-direct-max-uncached-tokens`

docs/archive/README.md

@@ -0,0 +1,34 @@
+# 归档文档说明
+
+本目录保留项目历史阶段的过程文档。**新加入项目的 agent / 人员不需要阅读这些文档**，直接看 `docs/ONBOARDING_NEXT_AGENT_ZH.md` 即可。
+
+保留它们的目的：
+1. 论文写作时追溯 v1-v5 调优演化过程
+2. 未来若回到 ts=10 高压区间或更大 trace 时，可参考当年的结构性问题诊断
+3. 满足学术可追溯性要求
+
+## 每个文档的简要说明
+
+| 文档 | 归档原因 | 何时回头看 |
+|---|---|---|
+| `AGENTIC_FIT_ANALYSIS_ZH.md` | ts=10 时代的 §1-§7 结构性问题分析；结论已被 ts=1 数据全面 supersede | 想知道当年 ts=10 下我们认为有什么结构性问题时 |
+| `STRUCTURAL_VALIDATION_REPORT_ZH.md` | 用 ts=10 数据对 AGENTIC_FIT_ANALYSIS 的 claim 做验证；同样被 ts=1 时代 supersede | 同上 |
+| `KVC_DEBUG_JOURNEY_V1_TO_V5.md` | v1-v5 5 个调优 sweep 的过程笔记；包含 errors 9→912 漂移、direct-to-D 占比变化等历史数据 | 写 paper 时要写 "as we explored configurations v1-v5..." 段落 |
+| `V5_PROFILE_INVESTIGATION_ZH.md` | 给 v5 加 1Hz polling instrumentation 的调查；让 errors 涨 46× 的现象记录 | 想理解 "admission RPC 干扰 scheduler 主循环" 这条 §5 残留风险时 |
+| `REFACTOR_PLAN_ZH.md` | v0 重构计划，**已被 `REFACTOR_PLAN_V1_ZH.md` supersede** | 不需要看；只有想看作者一开始的设想时翻一翻 |
+| `KVCACHE_CENTRIC_PROGRESS_ZH.md` | 项目最早期（2026-04-27）的进度记录；当时还没有完整的 sweep 数据 | 几乎不需要看；满足"项目起源记录"职能 |
+| `SWEBENCH_EXPERIMENT_PROGRESS.md` | SWE-Bench trace 早期实验进度记录 | 想知道当年的 trace 生成 / 采样配置时 |
+| `SWEBENCH_EXPERIMENT_RESULTS.md` | 同上，早期 result snapshot | 同上 |
+
+## 当前活跃文档（在 `docs/` 顶层）
+
+跳转去看：
+- `docs/ONBOARDING_NEXT_AGENT_ZH.md` — 新人上手手册
+- `docs/PROJECT_OVERVIEW.md` — 项目目标 + 术语
+- `docs/KVC_ROUTER_ALGORITHM.md` — 算法形式化
+- `docs/V2_DEEP_ANALYSIS_ZH.md` — v2 完整分析
+- `docs/V2_RESULTS_ZH.md` — v2 原始战报
+- `docs/REFACTOR_PLAN_V1_ZH.md` — ts=1 方向决策
+- `docs/MIGRATION_V1_FINDINGS_ZH.md` — v1 thrashing 诊断
+- `docs/RESEED_SLOW_PATH_AND_D_TO_P_GAP_ZH.md` — reseed 长尾 + D→P 缺口审计
+- `docs/TEAM_REPORT_AGENTIC_PD_HYBRID_ZH.md` — ts=10 时代的结构性问题清单（作为历史 baseline 仍在主目录）

pyproject.toml

@@ -7,7 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "httpx>=0.28.1",
     "mooncake-transfer-engine",
-    "sglang==0.5.10",
+    "sglang",
 ]
 
 [project.scripts]
@@ -20,5 +20,21 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools.packages.find]
 where = ["src"]
 
+[dependency-groups]
+# Pure-Python unit tests. Install via:
+#   uv sync --group test
+# These tests deliberately import only the algorithm-layer modules
+# (policies, trace, topology) so they run without SGLang / GPU / CUDA.
+test = [
+    "pytest>=8.0",
+]
+
 [tool.uv]
 prerelease = "allow"
+
+[tool.uv.sources]
+sglang = { path = "third_party/sglang/python", editable = true }
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"

scripts/analysis/analyze_ts1_validation.py

@@ -0,0 +1,316 @@
+#!/usr/bin/env python3
+"""TS=1 validation analysis: KVC 1P3D × N=3 + 4DP × 1.
+
+Reads metrics from outputs/qwen3-30b-tp1-ts1-validation/{kvc_1p3d_run{1,2,3},dp4}_metrics.jsonl
+and reports per the structural claims in docs/AGENTIC_FIT_ANALYSIS_ZH.md and TEAM_REPORT.
+
+Sections:
+  1. Headline summary table (errors, latency p50/p90/p99, TTFT p50)
+  2. §1 (session pinning): distinct-D-per-session distribution + direct-to-D bimodal
+  3. §1 (cross-run consistency): sessions consistently starved across all 3 runs + size ratio
+  4. §2 (LRU): KVTransferError counts per D + peak token_usage from worker logs
+  5. §7 (ts=1 vs ts=10): direct-to-D rate, fallback rate, per-D load balance
+  6. KVC vs DP same-scale comparison
+
+Usage: python scripts/analysis/analyze_ts1_validation.py [--root PATH]
+"""
+import argparse
+import json
+import re
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import numpy as np
+
+
+def load_metrics(path):
+    rows = []
+    with open(path) as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            rows.append(json.loads(line))
+    return rows
+
+
+def load_summary(path):
+    with open(path) as f:
+        return json.load(f)
+
+
+def pct(arr, p):
+    if not arr:
+        return float("nan")
+    return float(np.percentile(arr, p))
+
+
+def summarize_run(label, rows, summary):
+    ok = [r for r in rows if r.get("error") is None]
+    err = [r for r in rows if r.get("error") is not None]
+    lats = [r["latency_s"] for r in ok if r.get("latency_s") is not None]
+    ttfts = [r["ttft_s"] for r in ok if r.get("ttft_s") is not None]
+    return {
+        "label": label,
+        "n": len(rows),
+        "ok": len(ok),
+        "err": len(err),
+        "lat_mean": float(np.mean(lats)) if lats else float("nan"),
+        "lat_p50": pct(lats, 50),
+        "lat_p90": pct(lats, 90),
+        "lat_p99": pct(lats, 99),
+        "ttft_mean": float(np.mean(ttfts)) if ttfts else float("nan"),
+        "ttft_p50": pct(ttfts, 50),
+        "summary": summary,
+    }
+
+
+def headline_table(stats):
+    print("\n" + "=" * 110)
+    print("HEADLINE: same trace, same scale, same ts=1")
+    print("=" * 110)
+    cols = ["label", "ok/n", "err", "lat_mean", "lat_p50", "lat_p90", "lat_p99", "ttft_mean", "ttft_p50"]
+    print(f"{cols[0]:<22}{cols[1]:>12}{cols[2]:>6}{cols[3]:>10}{cols[4]:>10}{cols[5]:>10}{cols[6]:>10}{cols[7]:>10}{cols[8]:>10}")
+    for s in stats:
+        ok_n = f"{s['ok']}/{s['n']}"
+        print(f"{s['label']:<22}{ok_n:>12}{s['err']:>6}"
+              f"{s['lat_mean']:>9.3f}s{s['lat_p50']:>9.3f}s{s['lat_p90']:>9.3f}s{s['lat_p99']:>9.3f}s"
+              f"{s['ttft_mean']:>9.3f}s{s['ttft_p50']:>9.3f}s")
+
+
+def session_pinning(rows, label):
+    """§1: distinct D per session — should be ~1.0 if pin behavior persists."""
+    sess_d = defaultdict(set)
+    for r in rows:
+        sid = r.get("session_id")
+        d = r.get("assigned_decode_node") or r.get("decode_node")
+        if sid is not None and d is not None:
+            sess_d[sid].add(d)
+    if not sess_d:
+        return None
+    distinct = [len(s) for s in sess_d.values()]
+    return {
+        "label": label,
+        "n_sessions": len(sess_d),
+        "avg_distinct_D": float(np.mean(distinct)),
+        "max_distinct_D": max(distinct),
+        "sess_d": {sid: sorted(ds) for sid, ds in sess_d.items()},
+    }
+
+
+def direct_to_d_distribution(rows, label):
+    """§1: per-session direct-to-D rate; check for bimodal."""
+    sess_total = Counter()
+    sess_direct = Counter()
+    for r in rows:
+        sid = r.get("session_id")
+        if sid is None:
+            continue
+        sess_total[sid] += 1
+        mode = r.get("execution_mode", "")
+        if mode == "kvcache-direct-to-d-session":
+            sess_direct[sid] += 1
+    rates = []
+    for sid in sess_total:
+        rate = sess_direct[sid] / sess_total[sid]
+        rates.append((sid, rate, sess_total[sid]))
+    bins = [0, 0.2, 0.4, 0.6, 0.8, 1.01]
+    bin_labels = ["0-20%", "20-40%", "40-60%", "60-80%", "80-100%"]
+    counts = [0] * 5
+    for _, r, _ in rates:
+        for i in range(5):
+            if bins[i] <= r < bins[i + 1]:
+                counts[i] += 1
+                break
+    print(f"\n  [{label}] direct-to-D rate distribution (n={len(rates)} sessions):")
+    for lbl, cnt in zip(bin_labels, counts):
+        bar = "█" * cnt
+        print(f"    {lbl:<10}: {cnt:>3}  {bar}")
+    return rates
+
+
+def starved_cross_run(per_run_rates, threshold=0.20):
+    """§1: sessions starved (<threshold direct-to-D) in ALL runs."""
+    if len(per_run_rates) < 2:
+        return None
+    sess_starved = defaultdict(int)
+    sess_lucky = defaultdict(int)
+    for rates in per_run_rates:
+        for sid, rate, _ in rates:
+            if rate < threshold:
+                sess_starved[sid] += 1
+            elif rate > 0.80:
+                sess_lucky[sid] += 1
+    n_runs = len(per_run_rates)
+    consistently_starved = [sid for sid, c in sess_starved.items() if c == n_runs]
+    consistently_lucky = [sid for sid, c in sess_lucky.items() if c == n_runs]
+    return {
+        "n_runs": n_runs,
+        "consistently_starved": consistently_starved,
+        "consistently_lucky": consistently_lucky,
+    }
+
+
+def session_size_comparison(rows, sids_a, sids_b, label_a="A", label_b="B"):
+    """Compare peak input_length of two session groups."""
+    sess_max_input = defaultdict(int)
+    for r in rows:
+        sid = r.get("session_id")
+        ilen = r.get("input_length") or 0
+        if sid is not None and ilen > sess_max_input[sid]:
+            sess_max_input[sid] = ilen
+    a_inputs = [sess_max_input[s] for s in sids_a if s in sess_max_input]
+    b_inputs = [sess_max_input[s] for s in sids_b if s in sess_max_input]
+    if a_inputs and b_inputs:
+        ratio = np.mean(a_inputs) / np.mean(b_inputs)
+        print(f"\n  Cross-run starvation correlates with session size?")
+        print(f"    consistently {label_a} (n={len(a_inputs)}): peak_input mean = {np.mean(a_inputs):.0f}")
+        print(f"    consistently {label_b} (n={len(b_inputs)}): peak_input mean = {np.mean(b_inputs):.0f}")
+        print(f"    {label_a}/{label_b} ratio = {ratio:.2f}x (ts=10 baseline was 1.98x)")
+
+
+def per_d_balance(rows, label):
+    """§7: per-D load balance."""
+    per_d = Counter()
+    for r in rows:
+        d = r.get("assigned_decode_node") or r.get("decode_node")
+        if d:
+            per_d[d] += 1
+    if not per_d:
+        return
+    counts = list(per_d.values())
+    spread = (max(counts) - min(counts)) / max(np.mean(counts), 1)
+    print(f"\n  [{label}] per-D load: {dict(sorted(per_d.items()))}")
+    print(f"    spread (max-min)/mean = {spread*100:.1f}%   "
+          f"(ts=10 KVC 2P6D = ±26%, 8DP CA = ±10%)")
+
+
+def execution_modes_table(rows, label):
+    """Show top execution modes."""
+    ok = [r for r in rows if r.get("error") is None]
+    if not ok:
+        return
+    modes = Counter(r["execution_mode"] for r in ok)
+    print(f"\n  [{label}] execution modes (n_ok={len(ok)}):")
+    for mode, cnt in modes.most_common(8):
+        mode_rows = [r for r in ok if r["execution_mode"] == mode]
+        lats = [r["latency_s"] for r in mode_rows if r.get("latency_s") is not None]
+        ttfts = [r["ttft_s"] for r in mode_rows if r.get("ttft_s") is not None]
+        if lats:
+            print(f"    {mode:<55} {cnt:>5}  ({cnt/len(ok)*100:>4.1f}%)  "
+                  f"lat p50={pct(lats,50):.3f}s p90={pct(lats,90):.3f}s  ttft p50={pct(ttfts,50):.3f}s")
+
+
+def lru_vs_errors(run_dir, label):
+    """§2: trim events vs KVTransferError per worker."""
+    log_dir = run_dir / "logs"
+    if not log_dir.exists():
+        return
+    print(f"\n  [{label}] D-side LRU vs errors (from worker logs):")
+    print(f"    {'worker':<14}{'trim':>8}{'KVTransferError':>20}{'peak_token_usage':>20}")
+    for log_file in sorted(log_dir.glob("decode-*.log")):
+        worker = log_file.stem
+        text = log_file.read_text(errors="ignore")
+        trim_count = len(re.findall(r"Trimmed decode session cache", text))
+        err_count = len(re.findall(r"KVTransferError", text))
+        usages = re.findall(r"token usage: ([\d.]+)", text)
+        peak = max((float(u) for u in usages), default=0.0)
+        print(f"    {worker:<14}{trim_count:>8}{err_count:>20}{peak:>20.3f}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--root", default="outputs/qwen3-30b-tp1-ts1-validation",
+                        help="Sweep output root")
+    args = parser.parse_args()
+
+    root = Path(args.root)
+    if not root.is_absolute():
+        root = Path("/mnt/kzlin/workflow/pd-hybrid/agentic-pd-hybrid") / root
+
+    # Load all available runs
+    stats = []
+    rows_by_run = {}
+    for label in ("kvc_1p3d_run1", "kvc_1p3d_run2", "kvc_1p3d_run3", "dp4"):
+        m = root / f"{label}_metrics.jsonl"
+        s = root / f"{label}_summary.json"
+        if not m.exists() or not s.exists():
+            print(f"  [{label}] not yet available ({m.name})")
+            continue
+        rows = load_metrics(m)
+        summary = load_summary(s)
+        rows_by_run[label] = rows
+        stats.append(summarize_run(label, rows, summary))
+
+    if not stats:
+        print("No runs available yet.")
+        return
+
+    # 1. Headline table
+    headline_table(stats)
+
+    # 2. §1 session pinning per KVC run + per-D balance + execution modes
+    print("\n" + "=" * 110)
+    print("§1 / §7: SESSION PINNING + LOAD BALANCE")
+    print("=" * 110)
+    per_run_rates = []
+    for label, rows in rows_by_run.items():
+        if not label.startswith("kvc_"):
+            continue
+        pin = session_pinning(rows, label)
+        if pin:
+            print(f"\n  [{label}] sessions={pin['n_sessions']}  "
+                  f"avg_distinct_D={pin['avg_distinct_D']:.2f}  "
+                  f"max_distinct_D={pin['max_distinct_D']}  "
+                  f"(ts=10 baseline avg=1.00 → 100% pin)")
+        rates = direct_to_d_distribution(rows, label)
+        per_run_rates.append(rates)
+        per_d_balance(rows, label)
+        execution_modes_table(rows, label)
+
+    # 3. §1 cross-run starvation
+    if len(per_run_rates) >= 2:
+        print("\n" + "=" * 110)
+        print(f"§1 CROSS-RUN STARVATION (across {len(per_run_rates)} KVC runs)")
+        print("=" * 110)
+        cross = starved_cross_run(per_run_rates)
+        if cross:
+            n_starved = len(cross["consistently_starved"])
+            n_lucky = len(cross["consistently_lucky"])
+            print(f"\n  Sessions starved (<20% direct-to-D) in all {cross['n_runs']} runs: {n_starved}")
+            print(f"  Sessions lucky (>80% direct-to-D) in all {cross['n_runs']} runs: {n_lucky}")
+            print(f"  (ts=10 baseline: 13/52 starved, 14/52 lucky — extreme bimodal)")
+            # session size comparison from run 1
+            if "kvc_1p3d_run1" in rows_by_run and n_starved and n_lucky:
+                session_size_comparison(rows_by_run["kvc_1p3d_run1"],
+                                        cross["consistently_starved"],
+                                        cross["consistently_lucky"],
+                                        "starved", "lucky")
+
+    # 4. §2 D-side LRU vs errors from raw logs
+    print("\n" + "=" * 110)
+    print("§2: D-SIDE LRU TRIM vs KVTransferError (from worker logs)")
+    print("=" * 110)
+    for label in rows_by_run:
+        if not label.startswith("kvc_"):
+            continue
+        # find the matching raw run dir
+        run_dirs = sorted(root.glob("kvcache-centric-*/"))
+        if not run_dirs:
+            continue
+        # naive: index matches run order; could be wrong if dirs got reordered
+        idx = int(label.split("run")[-1]) - 1
+        if idx < len(run_dirs):
+            lru_vs_errors(run_dirs[idx], label)
+
+    # 5. DP-only inspection
+    if "dp4" in rows_by_run:
+        print("\n" + "=" * 110)
+        print("4DP CA SANITY")
+        print("=" * 110)
+        per_d_balance(rows_by_run["dp4"], "dp4")
+        execution_modes_table(rows_by_run["dp4"], "dp4")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/paired_compare.py

@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+"""Paired latency comparison with bootstrap CI.
+
+Implements docs/EVALUATION_PROTOCOL_ZH.md §2.2 (M2 fix): when comparing
+mechanism A vs B on the same trace, the only honest comparison is paired
+on same-trial-mask. This script joins two metrics.jsonl by request_id,
+keeps the rows where BOTH sides succeeded, and reports paired deltas
+with 95% bootstrap CIs.
+
+Out vs the existing `compare_no_error.py`:
+  - works on raw metrics.jsonl, not pre-aggregated summary.json
+  - bootstrap CIs (not just point estimates)
+  - reports paired-mask size + per-side failure counts so the reader
+    sees how many rows were dropped from the comparison
+
+Usage:
+  scripts/analysis/paired_compare.py \
+      --baseline outputs/run-dp/request-metrics.jsonl \
+      --candidate outputs/run-kvc/request-metrics.jsonl
+  scripts/analysis/paired_compare.py ... --bootstrap 5000 --seed 42
+  scripts/analysis/paired_compare.py ... --json > paired.json
+
+stdlib only — no scipy/numpy. Runs without GPU and without SGLang.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import random
+import sys
+from pathlib import Path
+
+
+def _load(path: Path) -> dict[str, dict]:
+    out: dict[str, dict] = {}
+    with path.open() as handle:
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            rid = row.get("request_id")
+            if rid is None:
+                continue
+            out[rid] = row
+    return out
+
+
+def _ok(row: dict) -> bool:
+    return row.get("error") is None and row.get("latency_s") is not None
+
+
+def _quantile(values: list[float], q: float) -> float:
+    if not values:
+        return float("nan")
+    s = sorted(values)
+    if len(s) == 1:
+        return s[0]
+    pos = (len(s) - 1) * q
+    lo = math.floor(pos)
+    hi = math.ceil(pos)
+    if lo == hi:
+        return s[lo]
+    return s[lo] + (s[hi] - s[lo]) * (pos - lo)
+
+
+def _stats(deltas: list[float]) -> dict[str, float]:
+    if not deltas:
+        return {"mean": float("nan"), "p50": float("nan"), "p90": float("nan"), "p99": float("nan")}
+    return {
+        "mean": sum(deltas) / len(deltas),
+        "p50": _quantile(deltas, 0.50),
+        "p90": _quantile(deltas, 0.90),
+        "p99": _quantile(deltas, 0.99),
+    }
+
+
+def _bootstrap_ci(
+    deltas: list[float], statistic, n_boot: int, rng: random.Random
+) -> tuple[float, float]:
+    """Return (lo, hi) 95% CI for `statistic(deltas)`."""
+    if len(deltas) < 2:
+        return (float("nan"), float("nan"))
+    n = len(deltas)
+    samples = []
+    for _ in range(n_boot):
+        # resample with replacement
+        resample = [deltas[rng.randrange(n)] for _ in range(n)]
+        samples.append(statistic(resample))
+    samples.sort()
+    lo = samples[int(0.025 * (n_boot - 1))]
+    hi = samples[int(0.975 * (n_boot - 1))]
+    return (lo, hi)
+
+
+def compare(
+    baseline: dict[str, dict],
+    candidate: dict[str, dict],
+    *,
+    metric: str,
+    n_boot: int,
+    seed: int,
+) -> dict:
+    common_ids = set(baseline.keys()) & set(candidate.keys())
+    paired_ids = [
+        rid for rid in common_ids if _ok(baseline[rid]) and _ok(candidate[rid])
+    ]
+    paired_ids.sort()
+
+    base_only_fail = sum(1 for rid in common_ids if not _ok(baseline[rid]))
+    cand_only_fail = sum(1 for rid in common_ids if not _ok(candidate[rid]))
+
+    deltas = []
+    wins = losses = ties = 0
+    for rid in paired_ids:
+        b = baseline[rid].get(metric)
+        c = candidate[rid].get(metric)
+        if b is None or c is None:
+            continue
+        d = float(c) - float(b)
+        deltas.append(d)
+        if d < 0:
+            wins += 1
+        elif d > 0:
+            losses += 1
+        else:
+            ties += 1
+
+    rng = random.Random(seed)
+    stats = _stats(deltas)
+    ci_mean = _bootstrap_ci(deltas, lambda x: sum(x) / len(x), n_boot, rng)
+    ci_p50 = _bootstrap_ci(deltas, lambda x: _quantile(x, 0.50), n_boot, rng)
+    ci_p90 = _bootstrap_ci(deltas, lambda x: _quantile(x, 0.90), n_boot, rng)
+
+    return {
+        "metric": metric,
+        "baseline_size": len(baseline),
+        "candidate_size": len(candidate),
+        "intersection_size": len(common_ids),
+        "paired_size": len(paired_ids),
+        "baseline_fail_in_common": base_only_fail,
+        "candidate_fail_in_common": cand_only_fail,
+        "delta_stats": stats,
+        "delta_mean_ci95": ci_mean,
+        "delta_p50_ci95": ci_p50,
+        "delta_p90_ci95": ci_p90,
+        "wins_candidate": wins,
+        "losses_candidate": losses,
+        "ties": ties,
+    }
+
+
+def _fmt(x: float, w: int = 6) -> str:
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return "  nan "
+    return f"{x:+{w}.3f}"
+
+
+def render(result: dict) -> str:
+    s = result["delta_stats"]
+    mlo, mhi = result["delta_mean_ci95"]
+    p5lo, p5hi = result["delta_p50_ci95"]
+    p9lo, p9hi = result["delta_p90_ci95"]
+    n = result["paired_size"]
+    lines = [
+        f"# paired comparison ({result['metric']})",
+        "",
+        f"baseline rows:           {result['baseline_size']}",
+        f"candidate rows:          {result['candidate_size']}",
+        f"intersection (rid):      {result['intersection_size']}",
+        f"paired (both ok):        {result['paired_size']}",
+        f"  baseline fails in common:  {result['baseline_fail_in_common']}",
+        f"  candidate fails in common: {result['candidate_fail_in_common']}",
+        "",
+        "## delta (candidate - baseline)  — negative = candidate is faster",
+        "",
+        "| stat | value | 95% CI |",
+        "|---|---:|---:|",
+        f"| mean | {_fmt(s['mean'])} | [{_fmt(mlo)}, {_fmt(mhi)}] |",
+        f"| p50  | {_fmt(s['p50'])}  | [{_fmt(p5lo)}, {_fmt(p5hi)}] |",
+        f"| p90  | {_fmt(s['p90'])}  | [{_fmt(p9lo)}, {_fmt(p9hi)}] |",
+        f"| p99  | {_fmt(s['p99'])}  | — |",
+        "",
+        f"win/loss/tie: {result['wins_candidate']} / {result['losses_candidate']} / {result['ties']} (of {n})",
+    ]
+    return "\n".join(lines)
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    p.add_argument("--baseline", required=True, type=Path)
+    p.add_argument("--candidate", required=True, type=Path)
+    p.add_argument(
+        "--metric",
+        default="latency_s",
+        choices=["latency_s", "ttft_s", "tpot_s"],
+        help="which per-request field to compare (default: latency_s)",
+    )
+    p.add_argument("--bootstrap", type=int, default=2000)
+    p.add_argument("--seed", type=int, default=20260512)
+    p.add_argument("--json", action="store_true")
+    args = p.parse_args()
+
+    baseline = _load(args.baseline)
+    candidate = _load(args.candidate)
+    if not baseline or not candidate:
+        print("empty input on one side", file=sys.stderr)
+        sys.exit(1)
+
+    result = compare(
+        baseline, candidate,
+        metric=args.metric, n_boot=args.bootstrap, seed=args.seed,
+    )
+
+    if args.json:
+        json.dump(result, sys.stdout, indent=2, default=lambda x: None if isinstance(x, float) and math.isnan(x) else x)
+        sys.stdout.write("\n")
+    else:
+        print(render(result))
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/plot_cache_efficiency.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+"""Cache efficiency comparison: KVC 1P3D v2 vs 4-way DP CA.
+
+Generates docs/figures/cache_efficiency.png — two-panel:
+  left:  cache hit rate vs turn number   (mechanism: affinity vs LRU)
+  right: ECDF of per-request uncached tokens  (per-request impact)
+
+Resolves the apparent paradox: KVC has 27% less total KV pool capacity
+(3 × 92K = 276K  vs  DP 4 × 87K = 351K) yet achieves higher cache hit rate
+(98.1% vs 96.8%) and lower mean uncached tokens per request (560 vs 952).
+
+The left panel shows the mechanism: KVC's session affinity makes cache hit
+rate grow with turn count (more cache accumulates on the pinned D), while
+DP's hash + radix-LRU causes cache hit rate to decay through the middle
+turns (other sessions' KV competes via LRU eviction).
+
+The right panel quantifies the impact: KVC's uncached tokens are
+concentrated near 0 (mean 560), DP's are spread (mean 952).
+
+Aborted / errored requests are excluded.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
+DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
+OUT = ROOT / "docs/figures/cache_efficiency.png"
+
+
+def load(p: Path) -> list[dict]:
+    return [json.loads(line) for line in p.open()]
+
+
+def is_failed(r: dict) -> bool:
+    if r.get("error"):
+        return True
+    fr = r.get("finish_reason")
+    if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
+        return True
+    return False
+
+
+def main() -> None:
+    kvc = [r for r in load(KVC) if not is_failed(r)]
+    dp = [r for r in load(DP) if not is_failed(r)]
+
+    KVC_COLOR = "#1F77B4"
+    DP_COLOR = "#D62728"
+
+    fig, axes = plt.subplots(1, 2, figsize=(15, 6.5))
+
+    # ------------------------------------------------------------------
+    # Left panel: cache hit rate per turn
+    # Bin requests by turn_id, plot mean hit rate per bin with shaded band
+    # ------------------------------------------------------------------
+    def bin_by_turn(rows: list[dict]) -> tuple[list[int], list[float], list[float], list[float]]:
+        per_turn: defaultdict[int, list[float]] = defaultdict(list)
+        for r in rows:
+            if r["input_length"] == 0:
+                continue
+            hit = r.get("cached_tokens", 0) / r["input_length"]
+            per_turn[r["turn_id"]].append(hit)
+        turns = sorted(per_turn.keys())
+        means, p25s, p75s = [], [], []
+        for t in turns:
+            arr = np.array(per_turn[t])
+            means.append(float(np.mean(arr)))
+            p25s.append(float(np.quantile(arr, 0.25)))
+            p75s.append(float(np.quantile(arr, 0.75)))
+        return turns, means, p25s, p75s
+
+    kvc_t, kvc_m, kvc_lo, kvc_hi = bin_by_turn(kvc)
+    dp_t, dp_m, dp_lo, dp_hi = bin_by_turn(dp)
+
+    # Cap x-axis: tails get noisy below ~5 samples per bin
+    max_turn = 100
+
+    ax = axes[0]
+    ax.plot(kvc_t, kvc_m, color=KVC_COLOR, lw=2.5,
+            label=f"KVC 1P3D v2  (overall hit 98.1%)")
+    ax.fill_between(kvc_t, kvc_lo, kvc_hi, color=KVC_COLOR, alpha=0.18,
+                    label="KVC IQR (p25-p75)")
+    ax.plot(dp_t, dp_m, color=DP_COLOR, lw=2.5,
+            label=f"4-way DP CA  (overall hit 96.8%)")
+    ax.fill_between(dp_t, dp_lo, dp_hi, color=DP_COLOR, alpha=0.18,
+                    label="DP IQR (p25-p75)")
+
+    # Annotate the mid-turn drift gap
+    drift_turns = list(range(8, 25))
+    drift_kvc = np.mean([m for t, m in zip(kvc_t, kvc_m) if t in drift_turns])
+    drift_dp = np.mean([m for t, m in zip(dp_t, dp_m) if t in drift_turns])
+    ax.axvspan(8, 25, color="#999", alpha=0.08, label="_nolegend_")
+    ax.text(16, 0.65,
+            f"Mid-turn region\n(turns 8-25):\nKVC {drift_kvc*100:.1f}%  |  DP {drift_dp*100:.1f}%\nGap {(drift_kvc-drift_dp)*100:+.1f} pp",
+            ha="center", va="center", fontsize=9.5,
+            bbox=dict(facecolor="white", edgecolor="gray", alpha=0.92, pad=4))
+
+    ax.set_xlim(1, max_turn)
+    ax.set_ylim(0.4, 1.02)
+    ax.set_xlabel("Turn number within session", fontsize=11)
+    ax.set_ylabel("Per-request cache hit rate (cached / input_length)", fontsize=11)
+    ax.set_title("Cache hit rate vs turn number\n(mechanism: session affinity vs hash-LRU)",
+                 fontsize=12, pad=10)
+    ax.legend(loc="lower right", fontsize=9.5, framealpha=0.95)
+    ax.grid(True, linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+
+    # ------------------------------------------------------------------
+    # Right panel: ECDF of per-request uncached tokens (log x)
+    # ------------------------------------------------------------------
+    def ecdf(rows: list[dict]) -> tuple[np.ndarray, np.ndarray]:
+        vals = np.array([
+            max(1, r["input_length"] - r.get("cached_tokens", 0))
+            for r in rows
+        ])
+        vals = np.sort(vals)
+        return vals, np.arange(1, len(vals) + 1) / len(vals)
+
+    kvc_x, kvc_y = ecdf(kvc)
+    dp_x, dp_y = ecdf(dp)
+
+    ax = axes[1]
+    ax.plot(kvc_x, kvc_y, color=KVC_COLOR, lw=2.5,
+            label=f"KVC 1P3D v2  (mean {int(np.mean(kvc_x))} tokens)")
+    ax.plot(dp_x, dp_y, color=DP_COLOR, lw=2.5,
+            label=f"4-way DP CA  (mean {int(np.mean(dp_x))} tokens)")
+
+    # Median markers
+    kvc_p50 = np.quantile(kvc_x, 0.50)
+    dp_p50 = np.quantile(dp_x, 0.50)
+    ax.axhline(0.5, color="gray", linestyle=":", alpha=0.5)
+    ax.text(1.2, 0.52, "median (50% of requests below this)",
+            fontsize=8.5, color="gray", style="italic")
+    ax.axvline(kvc_p50, color=KVC_COLOR, ls="--", alpha=0.5, lw=1.0)
+    ax.axvline(dp_p50, color=DP_COLOR, ls="--", alpha=0.5, lw=1.0)
+    ax.text(kvc_p50, 0.06, f"KVC\nmedian\n{int(kvc_p50)}",
+            color=KVC_COLOR, fontsize=9, ha="center", va="bottom",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.75, pad=1))
+    ax.text(dp_p50, 0.06, f"DP\nmedian\n{int(dp_p50)}",
+            color=DP_COLOR, fontsize=9, ha="center", va="bottom",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.75, pad=1))
+
+    # Annotate the separation: at uncached = 500 tokens, what fraction below?
+    sep_x = 500
+    kvc_at_sep = (kvc_x <= sep_x).mean()
+    dp_at_sep = (dp_x <= sep_x).mean()
+    ax.axvline(sep_x, color="#666", linestyle=":", alpha=0.6, lw=1.0)
+    ax.annotate(
+        f"At uncached = {sep_x} tokens:\n"
+        f"KVC {kvc_at_sep*100:.0f}% of requests below\n"
+        f"DP  {dp_at_sep*100:.0f}% of requests below",
+        xy=(sep_x, dp_at_sep),
+        xytext=(2500, 0.35),
+        fontsize=9.5,
+        bbox=dict(facecolor="white", edgecolor="gray", alpha=0.92, pad=4),
+        arrowprops=dict(arrowstyle="->", color="#666", lw=0.8),
+    )
+
+    ax.set_xscale("log")
+    ax.set_xlim(1, 1e5)
+    ax.set_xticks([1, 10, 100, 1000, 10000, 100000])
+    ax.set_xticklabels(["1", "10", "100", "1K", "10K", "100K"])
+    ax.set_ylim(0, 1.02)
+    ax.set_xlabel("Uncached tokens per request  (log scale)", fontsize=11)
+    ax.set_ylabel("Cumulative fraction of requests", fontsize=11)
+    ax.set_title("ECDF of uncached tokens per request\n(impact: KVC concentrates near zero)",
+                 fontsize=12, pad=10)
+    ax.legend(loc="lower right", fontsize=10, framealpha=0.95)
+    ax.grid(True, which="both", linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+
+    fig.suptitle(
+        "Cache efficiency paradox:  KVC has 27% LESS total KV pool (276K vs 351K tokens) yet caches MORE per request.\n"
+        "Left: session-affinity lets KVC's cache accumulate with turns; DP's hash-LRU loses cache to cross-session competition.\n"
+        "Right: net effect — KVC's uncached compute is concentrated near zero, DP's is spread over 100-10K tokens.",
+        fontsize=11.5, y=1.05,
+    )
+    plt.tight_layout()
+    plt.savefig(OUT, dpi=150, bbox_inches="tight")
+    print(f"wrote {OUT}")
+    plt.close(fig)
+
+    # ------------------------------------------------------------------
+    # Print summary for doc reference
+    # ------------------------------------------------------------------
+    print("\n=== Cache efficiency stats ===")
+    print(f"KVC v2:  total_input={sum(r['input_length'] for r in kvc)/1e6:.1f}M tokens")
+    print(f"         total_cached={sum(r.get('cached_tokens',0) for r in kvc)/1e6:.1f}M tokens")
+    print(f"         hit rate {sum(r.get('cached_tokens',0) for r in kvc)/sum(r['input_length'] for r in kvc)*100:.2f}%")
+    print(f"         mean uncached {np.mean(kvc_x):.0f}  p50 {kvc_p50:.0f}  p90 {np.quantile(kvc_x, 0.9):.0f}")
+
+    print(f"\nDP 4w:   total_input={sum(r['input_length'] for r in dp)/1e6:.1f}M tokens")
+    print(f"         total_cached={sum(r.get('cached_tokens',0) for r in dp)/1e6:.1f}M tokens")
+    print(f"         hit rate {sum(r.get('cached_tokens',0) for r in dp)/sum(r['input_length'] for r in dp)*100:.2f}%")
+    print(f"         mean uncached {np.mean(dp_x):.0f}  p50 {dp_p50:.0f}  p90 {np.quantile(dp_x, 0.9):.0f}")
+
+    print(f"\nMid-turn region (8-25): KVC {drift_kvc*100:.2f}%  DP {drift_dp*100:.2f}%  (gap {(drift_kvc-drift_dp)*100:+.2f}pp)")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/plot_gpu_utilization.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""Per-GPU utilization breakdown: KVC 1P3D v2 vs 4-way DP CA.
+
+Generates docs/figures/gpu_utilization.png — two-panel:
+  left:  per-GPU request count
+  right: per-GPU compute work (uncached prefill tokens + decode tokens, stacked)
+
+The point of the figure is to push back on the naïve reading
+"KVC's prefill GPU is idle 90% of the time, so KVC is using fewer GPUs."
+
+By request count, the prefill GPU is indeed touched by only ~8% of requests.
+By compute work, the prefill GPU bears comparable per-GPU load to each
+decode GPU — it is a low-frequency, high-cost safety net for cache misses,
+not idle capacity.
+
+Work attribution:
+  KVC direct-to-D path: prefill happens locally on the assigned D worker
+                        (append-prefill of `uncached_tokens` tokens).
+  KVC seed/reseed/fallback path: prefill happens on prefill-0
+                        (full uncached_tokens), decode on assigned D.
+  DP: all work on assigned direct-N worker.
+
+Aborted / errored requests are excluded.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
+DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
+OUT = ROOT / "docs/figures/gpu_utilization.png"
+
+
+def load(p: Path) -> list[dict]:
+    return [json.loads(line) for line in p.open()]
+
+
+def is_failed(r: dict) -> bool:
+    if r.get("error"):
+        return True
+    fr = r.get("finish_reason")
+    if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
+        return True
+    return False
+
+
+def uncached(r: dict) -> int:
+    return max(0, r["input_length"] - r.get("cached_tokens", 0))
+
+
+def out_tokens(r: dict) -> int:
+    return r.get("actual_output_tokens") or r.get("output_length") or 0
+
+
+def main() -> None:
+    kvc = [r for r in load(KVC) if not is_failed(r)]
+    dp = [r for r in load(DP) if not is_failed(r)]
+
+    # ------------------------------------------------------------------
+    # KVC per-GPU attribution
+    # ------------------------------------------------------------------
+    kvc_req_count = defaultdict(int)
+    kvc_prefill_tokens = defaultdict(int)   # uncached prefill compute
+    kvc_decode_tokens = defaultdict(int)
+
+    for r in kvc:
+        d = r["assigned_decode_node"]            # decode-0/1/2
+        p = r["assigned_prefill_node"]            # prefill-0
+        mode = r.get("execution_mode", "")
+        if mode == "kvcache-direct-to-d-session":
+            # P is bypassed entirely; D does the append-prefill + decode
+            kvc_req_count[d] += 1
+            kvc_prefill_tokens[d] += uncached(r)
+            kvc_decode_tokens[d] += out_tokens(r)
+        else:
+            # P does the full prefill; D handles decode
+            kvc_req_count[p] += 1
+            kvc_req_count[d] += 1   # decode side still counts
+            kvc_prefill_tokens[p] += uncached(r)
+            kvc_decode_tokens[d] += out_tokens(r)
+
+    # ------------------------------------------------------------------
+    # DP per-GPU attribution (fused P+D on every worker)
+    # ------------------------------------------------------------------
+    dp_req_count = defaultdict(int)
+    dp_prefill_tokens = defaultdict(int)
+    dp_decode_tokens = defaultdict(int)
+
+    for r in dp:
+        w = r["assigned_decode_node"]  # direct-0..3
+        dp_req_count[w] += 1
+        dp_prefill_tokens[w] += uncached(r)
+        dp_decode_tokens[w] += out_tokens(r)
+
+    # ------------------------------------------------------------------
+    # Build ordered GPU list, KVC then DP
+    # ------------------------------------------------------------------
+    kvc_gpus = ["prefill-0", "decode-0", "decode-1", "decode-2"]
+    dp_gpus = ["direct-0", "direct-1", "direct-2", "direct-3"]
+    all_gpus = kvc_gpus + dp_gpus
+
+    def get(d, k):
+        return d.get(k, 0)
+
+    counts = [get(kvc_req_count, g) for g in kvc_gpus] + \
+             [get(dp_req_count, g) for g in dp_gpus]
+    prefill_tk = [get(kvc_prefill_tokens, g) for g in kvc_gpus] + \
+                 [get(dp_prefill_tokens, g) for g in dp_gpus]
+    decode_tk = [get(kvc_decode_tokens, g) for g in kvc_gpus] + \
+                [get(dp_decode_tokens, g) for g in dp_gpus]
+
+    # Display labels: P/D role + worker id
+    labels = [
+        "KVC P\nprefill-0",
+        "KVC D\ndecode-0",
+        "KVC D\ndecode-1",
+        "KVC D\ndecode-2",
+        "DP P+D\ndirect-0",
+        "DP P+D\ndirect-1",
+        "DP P+D\ndirect-2",
+        "DP P+D\ndirect-3",
+    ]
+    kvc_mask = [True, True, True, True, False, False, False, False]
+
+    KVC_P_COLOR = "#E89D44"     # orange — P GPU stands out
+    KVC_D_COLOR = "#1F77B4"     # blue
+    DP_COLOR    = "#D62728"     # red
+
+    bar_colors = [KVC_P_COLOR, KVC_D_COLOR, KVC_D_COLOR, KVC_D_COLOR,
+                  DP_COLOR, DP_COLOR, DP_COLOR, DP_COLOR]
+
+    fig, axes = plt.subplots(1, 2, figsize=(15, 7.0))
+    x = np.arange(len(all_gpus))
+
+    # -- Left: per-GPU request count ----------------------------------
+    ax = axes[0]
+    bars = ax.bar(x, counts, color=bar_colors, edgecolor="black", linewidth=0.6)
+    for xi, c in zip(x, counts):
+        ax.text(xi, c + max(counts) * 0.015, f"{c:,}",
+                ha="center", va="bottom", fontsize=9.5)
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, fontsize=9.5)
+    ax.set_ylabel("Number of requests touching this GPU", fontsize=11)
+    # Headroom for the annotation: extend ylim 35% above tallest bar
+    ax.set_ylim(0, max(counts) * 1.40)
+    ax.set_title("Per-GPU request count\n(naïve view: P seems idle)",
+                 fontsize=12, pad=24)
+    ax.grid(axis="y", linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+
+    # Annotate: KVC P GPU is "low frequency"
+    # Place in upper-right area (over DP group) so it doesn't sit on KVC D bars
+    p_idx = 0
+    ax.annotate(
+        f"P GPU only sees\n"
+        f"{counts[p_idx]:,} requests\n"
+        f"({counts[p_idx]/len(kvc)*100:.1f}% of all KVC requests)",
+        xy=(p_idx, counts[p_idx]),
+        xytext=(2.4, max(counts) * 1.20),
+        fontsize=10, color=KVC_P_COLOR, fontweight="bold", ha="center",
+        bbox=dict(facecolor="white", edgecolor=KVC_P_COLOR, alpha=0.92, pad=4),
+        arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
+    )
+
+    # -- Right: per-GPU compute work (stacked prefill + decode) -------
+    ax = axes[1]
+    prefill_M = [t / 1e6 for t in prefill_tk]
+    decode_M = [t / 1e6 for t in decode_tk]
+    total_M = [p + d for p, d in zip(prefill_M, decode_M)]
+
+    bars_p = ax.bar(x, prefill_M, color=[c for c in bar_colors],
+                    edgecolor="black", linewidth=0.6, label="Uncached prefill tokens",
+                    alpha=0.95)
+    bars_d = ax.bar(x, decode_M, bottom=prefill_M, color=[c for c in bar_colors],
+                    edgecolor="black", linewidth=0.6, hatch="///",
+                    label="Decode tokens", alpha=0.55)
+
+    for xi, t in zip(x, total_M):
+        ax.text(xi, t + max(total_M) * 0.015, f"{t:.2f}M",
+                ha="center", va="bottom", fontsize=9.5)
+
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, fontsize=9.5)
+    ax.set_ylabel("Compute tokens (millions)", fontsize=11)
+    # Headroom for the annotation
+    ax.set_ylim(0, max(total_M) * 1.45)
+    ax.set_title("Per-GPU compute work\n(work view: P is comparable to each D)",
+                 fontsize=12, pad=24)
+    ax.grid(axis="y", linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+    # Legend placed at upper-left where bars are tallest is fine after raising ylim
+    ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
+
+    # Annotate: KVC P GPU does similar work to each D.
+    # Place over DP region (right side) so it doesn't sit on KVC D bars.
+    ax.annotate(
+        f"P GPU does {total_M[p_idx]:.2f}M tokens of prefill\n"
+        f"— comparable per-GPU load to each KVC D worker\n"
+        f"(KVC D avg = {np.mean(total_M[1:4]):.2f}M)",
+        xy=(p_idx, total_M[p_idx]),
+        xytext=(5.5, max(total_M) * 1.30),
+        fontsize=10, color=KVC_P_COLOR, fontweight="bold", ha="center",
+        bbox=dict(facecolor="white", edgecolor=KVC_P_COLOR, alpha=0.92, pad=4),
+        arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
+    )
+
+    # Separator + group labels (placed in axes-fraction coords, below subplot
+    # title at pad=24 we now have safe room for these at y_axes_frac ≈ 1.02)
+    for ax in axes:
+        ax.axvline(3.5, color="gray", linestyle="--", linewidth=1.0, alpha=0.5)
+        ax.text(0.25, 1.02, "KVC 1P3D",
+                transform=ax.transAxes, ha="center", va="bottom",
+                fontsize=11.5, fontweight="bold", color="#444",
+                bbox=dict(facecolor="#F2F2F2", edgecolor="#888",
+                          alpha=0.85, pad=3))
+        ax.text(0.75, 1.02, "DP 4-way CA",
+                transform=ax.transAxes, ha="center", va="bottom",
+                fontsize=11.5, fontweight="bold", color="#444",
+                bbox=dict(facecolor="#F2F2F2", edgecolor="#888",
+                          alpha=0.85, pad=3))
+
+    fig.suptitle(
+        "Per-GPU utilization: \"is KVC's prefill GPU wasted?\"\n"
+        "Left view says yes (only 8% of requests); right view says no (comparable work to each D).",
+        fontsize=13, y=1.02,
+    )
+    plt.tight_layout()
+    plt.savefig(OUT, dpi=150, bbox_inches="tight")
+    print(f"wrote {OUT}")
+    plt.close(fig)
+
+    # ------------------------------------------------------------------
+    # Print numbers for doc reference
+    # ------------------------------------------------------------------
+    print("\n=== Per-GPU numbers ===")
+    print(f"{'GPU':<22}  {'requests':>10}  {'prefill(M)':>12}  {'decode(M)':>12}  {'total(M)':>10}")
+    for lbl, n, pM, dM in zip(labels, counts, prefill_M, decode_M):
+        print(f"  {lbl.replace(chr(10), ' '):<20}  {n:>10}  {pM:>12.3f}  {dM:>12.3f}  {pM+dM:>10.3f}")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/plot_ttft_pdf.py

@@ -0,0 +1,199 @@
+#!/usr/bin/env python3
+"""Generate TTFT probability density curves: KVC 1P3D v2 vs 4-way DP CA.
+
+Inputs:
+  outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl
+  outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl
+
+Outputs:
+  docs/figures/ttft_pdf_comparison.png  -- two-panel figure:
+      left panel: linear x in [0, 1.0]s zoomed on the body
+      right panel: log x covering full range (0.01 -- 10 s)
+  Each KDE curve uses scipy.stats.gaussian_kde with Scott's rule bandwidth.
+  Aborted requests are excluded (same filter as metrics.py:_is_failed_request).
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import gaussian_kde
+
+ROOT = Path(__file__).resolve().parents[2]
+KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
+DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
+OUT = ROOT / "docs/figures/ttft_pdf_comparison.png"
+
+
+def load(p: Path) -> list[dict]:
+    return [json.loads(line) for line in p.open()]
+
+
+def is_failed(r: dict) -> bool:
+    if r.get("error"):
+        return True
+    fr = r.get("finish_reason")
+    if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
+        return True
+    return False
+
+
+def pct(vals: np.ndarray, q: float) -> float:
+    return float(np.quantile(vals, q))
+
+
+def main() -> None:
+    kvc = [r for r in load(KVC) if not is_failed(r)]
+    dp = [r for r in load(DP) if not is_failed(r)]
+
+    kvc_ttft = np.array([r["ttft_s"] for r in kvc if r.get("ttft_s") is not None])
+    dp_ttft = np.array([r["ttft_s"] for r in dp if r.get("ttft_s") is not None])
+
+    # Trim absurdly small zeros (rare measurement artifacts) so log KDE behaves.
+    kvc_ttft = kvc_ttft[kvc_ttft > 1e-4]
+    dp_ttft = dp_ttft[dp_ttft > 1e-4]
+
+    KVC_COLOR = "#1F77B4"  # blue
+    DP_COLOR = "#D62728"   # red
+
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5))
+
+    # ------------------------------------------------------------------
+    # Left panel: linear x ∈ [0, 0.6]s -- body of the distribution
+    # ------------------------------------------------------------------
+    ax = axes[0]
+    x_body = np.linspace(0.0, 0.6, 600)
+
+    # KDE on linear ttft values, clipped to body
+    kde_kvc_lin = gaussian_kde(kvc_ttft, bw_method=0.15)
+    kde_dp_lin = gaussian_kde(dp_ttft, bw_method=0.15)
+
+    ax.plot(x_body, kde_kvc_lin(x_body),
+            color=KVC_COLOR, lw=2.5, label=f"KVC 1P3D v2  (n={len(kvc_ttft)})")
+    ax.fill_between(x_body, kde_kvc_lin(x_body), alpha=0.20, color=KVC_COLOR)
+    ax.plot(x_body, kde_dp_lin(x_body),
+            color=DP_COLOR, lw=2.5, label=f"4-way DP CA  (n={len(dp_ttft)})")
+    ax.fill_between(x_body, kde_dp_lin(x_body), alpha=0.20, color=DP_COLOR)
+
+    # Vertical lines for p50, p90
+    for q, ls in [(0.50, "-"), (0.90, "--")]:
+        ax.axvline(pct(kvc_ttft, q), color=KVC_COLOR, ls=ls, alpha=0.55, lw=1.1)
+        ax.axvline(pct(dp_ttft, q), color=DP_COLOR, ls=ls, alpha=0.55, lw=1.1)
+    ymax = ax.get_ylim()[1]
+    ax.text(pct(kvc_ttft, 0.50), ymax * 0.97,
+            f"KVC p50\n{pct(kvc_ttft, 0.50)*1000:.0f}ms",
+            color=KVC_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
+    ax.text(pct(dp_ttft, 0.50), ymax * 0.50,
+            f"DP p50\n{pct(dp_ttft, 0.50)*1000:.0f}ms",
+            color=DP_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
+    ax.text(pct(kvc_ttft, 0.90), ymax * 0.30,
+            f"KVC p90\n{pct(kvc_ttft, 0.90)*1000:.0f}ms",
+            color=KVC_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
+    ax.text(pct(dp_ttft, 0.90), ymax * 0.18,
+            f"DP p90\n{pct(dp_ttft, 0.90)*1000:.0f}ms",
+            color=DP_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=2))
+
+    ax.set_xlim(0, 0.6)
+    ax.set_xlabel("TTFT (seconds, linear)", fontsize=11)
+    ax.set_ylabel("Probability density", fontsize=11)
+    ax.set_title("Body of distribution  (TTFT ≤ 0.6 s)", fontsize=12, pad=10)
+    ax.legend(loc="upper right", fontsize=10, framealpha=0.95)
+    ax.grid(True, linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+
+    # ------------------------------------------------------------------
+    # Right panel: log x ∈ [0.01, 10]s -- full range incl. tail
+    # PDF on log-x: we plot density vs log10(t) so the curve integrates
+    # to 1 over log space (standard "log-density" presentation).
+    # ------------------------------------------------------------------
+    ax = axes[1]
+    # KDE on log10(ttft) so the resulting curve integrates to 1 over log10 t
+    kde_kvc_log = gaussian_kde(np.log10(kvc_ttft), bw_method="scott")
+    kde_dp_log = gaussian_kde(np.log10(dp_ttft), bw_method="scott")
+    log_x = np.linspace(np.log10(0.01), np.log10(10.0), 600)
+    x_full = 10 ** log_x
+
+    y_kvc = kde_kvc_log(log_x)
+    y_dp = kde_dp_log(log_x)
+
+    ax.plot(x_full, y_kvc, color=KVC_COLOR, lw=2.5, label=f"KVC 1P3D v2  (n={len(kvc_ttft)})")
+    ax.fill_between(x_full, y_kvc, alpha=0.20, color=KVC_COLOR)
+    ax.plot(x_full, y_dp, color=DP_COLOR, lw=2.5, label=f"4-way DP CA  (n={len(dp_ttft)})")
+    ax.fill_between(x_full, y_dp, alpha=0.20, color=DP_COLOR)
+
+    ax.set_xscale("log")
+    ax.set_xlim(0.01, 10.0)
+
+    # Percentile markers
+    quartile_styles = [(0.50, "-", "p50"), (0.90, "--", "p90"), (0.99, ":", "p99")]
+    for q, ls, name in quartile_styles:
+        ax.axvline(pct(kvc_ttft, q), color=KVC_COLOR, ls=ls, alpha=0.55, lw=1.1)
+        ax.axvline(pct(dp_ttft, q), color=DP_COLOR, ls=ls, alpha=0.55, lw=1.1)
+
+    # Annotate p99 specifically since this is the key reviewer-targeted callout
+    ymax = max(y_kvc.max(), y_dp.max())
+    kvc_p99 = pct(kvc_ttft, 0.99)
+    dp_p99 = pct(dp_ttft, 0.99)
+    ax.annotate(f"KVC p99 = {kvc_p99:.2f}s\n(slow-path reseed tail)",
+                xy=(kvc_p99, kde_kvc_log(np.log10(kvc_p99))[0]),
+                xytext=(2.0, ymax * 0.65),
+                fontsize=10, color=KVC_COLOR, fontweight="bold",
+                arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=1.0))
+    ax.annotate(f"DP p99 = {dp_p99*1000:.0f}ms",
+                xy=(dp_p99, kde_dp_log(np.log10(dp_p99))[0]),
+                xytext=(0.025, ymax * 0.80),
+                fontsize=10, color=DP_COLOR, fontweight="bold",
+                arrowprops=dict(arrowstyle="->", color=DP_COLOR, lw=1.0))
+    # Highlight the KVC bimodal structure
+    ax.annotate("KVC fast path\n(direct-to-D, 91.6%)",
+                xy=(0.05, y_kvc[np.argmin(np.abs(x_full - 0.05))]),
+                xytext=(0.012, ymax * 0.45),
+                fontsize=9, color=KVC_COLOR, style="italic",
+                arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=0.7, alpha=0.6))
+    ax.annotate("KVC slow path\n(reseed, ~3.4%)",
+                xy=(2.5, y_kvc[np.argmin(np.abs(x_full - 2.5))]),
+                xytext=(3.0, ymax * 0.30),
+                fontsize=9, color=KVC_COLOR, style="italic",
+                arrowprops=dict(arrowstyle="->", color=KVC_COLOR, lw=0.7, alpha=0.6))
+
+    # Custom tick labels in seconds (instead of 10^-2, 10^-1, 10^0, 10^1)
+    ax.set_xticks([0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0])
+    ax.set_xticklabels(["10ms", "50ms", "100ms", "500ms", "1s", "5s", "10s"])
+
+    ax.set_xlabel("TTFT (log scale)", fontsize=11)
+    ax.set_ylabel("Density  (per log₁₀ s)", fontsize=11)
+    ax.set_title("Full range  (TTFT 10 ms – 10 s, log x)", fontsize=12, pad=10)
+    ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
+    ax.grid(True, which="both", linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+
+    fig.suptitle(
+        "TTFT probability density: KVC 1P3D v2 vs 4-way DP CA\n"
+        "SWE-Bench 50sess trace · ts=1 · 4× H100 80GB · aborted/error requests excluded",
+        fontsize=13, y=1.02,
+    )
+    plt.tight_layout()
+    plt.savefig(OUT, dpi=150, bbox_inches="tight")
+    print(f"wrote {OUT}")
+    plt.close(fig)
+
+    # ------------------------------------------------------------------
+    # Print summary stats for doc cross-reference
+    # ------------------------------------------------------------------
+    print(f"\n=== TTFT distribution summary ===")
+    for name, arr in [("KVC v2", kvc_ttft), ("DP 4w", dp_ttft)]:
+        print(f"  {name}  (n={len(arr)})")
+        print(f"    min={arr.min()*1000:.1f}ms  p10={pct(arr,0.10)*1000:.1f}ms  "
+              f"p50={pct(arr,0.50)*1000:.1f}ms  p90={pct(arr,0.90)*1000:.1f}ms  "
+              f"p99={pct(arr,0.99)*1000:.1f}ms  max={arr.max()*1000:.1f}ms")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/plot_v2_path_breakdown.py

@@ -0,0 +1,223 @@
+#!/usr/bin/env python3
+"""Generate the two figures referenced by docs/V2_DEEP_ANALYSIS_ZH.md §3.1 and §3.2.
+
+Inputs:
+  outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl
+  outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl
+
+Outputs:
+  docs/figures/v2_execution_mode_distribution.png   (for §3.1)
+  docs/figures/v2_path_level_latency.png            (for §3.2)
+"""
+
+from __future__ import annotations
+
+import json
+import statistics
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+KVC = ROOT / "outputs/qwen3-30b-tp1-ts1-migration-v2/kvc_1p3d_migration_v2_run1_metrics.jsonl"
+DP = ROOT / "outputs/qwen3-30b-tp1-ts1-validation/dp4_metrics.jsonl"
+OUT = ROOT / "docs/figures"
+OUT.mkdir(parents=True, exist_ok=True)
+
+
+def load(p: Path) -> list[dict]:
+    return [json.loads(line) for line in p.open()]
+
+
+def is_failed(r: dict) -> bool:
+    if r.get("error"):
+        return True
+    fr = r.get("finish_reason")
+    if fr and ("abort" in str(fr).lower() or "badrequest" in str(fr).lower()):
+        return True
+    return False
+
+
+def pct(vals: list[float], q: float) -> float:
+    s = sorted(vals)
+    if not s:
+        return float("nan")
+    return s[max(0, min(len(s) - 1, int(len(s) * q)))]
+
+
+def main() -> None:
+    kvc = load(KVC)
+    dp = load(DP)
+
+    kvc_ok = [r for r in kvc if not is_failed(r)]
+    dp_ok = [r for r in dp if not is_failed(r)]
+
+    # ------------------------------------------------------------------
+    # Figure 1: §3.1 execution_mode distribution (horizontal bar)
+    # Use ALL rows (incl. failures) so percentages match the doc's 91.6%
+    # ------------------------------------------------------------------
+    mode_counts = Counter(r["execution_mode"] for r in kvc)
+    total_kvc = len(kvc)
+
+    short_label = {
+        "kvcache-direct-to-d-session": "direct-to-D-session  (fast path)",
+        "pd-router-d-session-reseed": "d-session-reseed  (mooncake reseed)",
+        "pd-router-fallback-session-not-resident-session-cap":
+            "fallback: session-not-resident + session-cap",
+        "pd-router-fallback-session-not-resident-seed-filter-early-turn":
+            "fallback: session-not-resident + seed-filter",
+        "pd-router-turn1-seed": "turn1-seed  (first turn of each session)",
+        "pd-router-fallback-no-d-capacity": "fallback: no-d-capacity",
+        "pd-router-fallback-real-large-append-session-cap":
+            "fallback: real-large-append",
+        "pd-router-fallback-policy-no-bypass-session-cap":
+            "fallback: policy-no-bypass",
+        "pd-router-d-session-reseed-after-eviction":
+            "d-session-reseed-after-eviction",
+        "kvcache-centric": "kvcache-centric (admit-but-then-error)",
+    }
+    sorted_modes = mode_counts.most_common()
+    labels = [short_label.get(m, m) for m, _ in sorted_modes]
+    counts = [c for _, c in sorted_modes]
+    pcts = [c / total_kvc * 100 for c in counts]
+
+    is_fast = ["direct-to-D" in lbl for lbl in labels]
+    colors = ["#2C8C2C" if f else "#D62728" for f in is_fast]
+
+    fig, ax = plt.subplots(figsize=(11, 5.5))
+    y = np.arange(len(labels))[::-1]
+    ax.barh(y, counts, color=colors, edgecolor="black", linewidth=0.5)
+    ax.set_yticks(y)
+    ax.set_yticklabels(labels, fontsize=10)
+    ax.set_xscale("log")
+    ax.set_xlabel("Request count (log scale)", fontsize=11)
+    ax.set_xlim(left=1)
+
+    # Annotate count + percentage at end of each bar
+    for yi, (c, p) in zip(y, zip(counts, pcts)):
+        ax.text(c * 1.05, yi, f"{c}  ({p:.1f}%)",
+                va="center", fontsize=9.5)
+
+    ax.set_title(
+        f"KVC v2 execution_mode distribution  (n = {total_kvc} total requests)\n"
+        "green = fast path (direct-to-D), red = slow / fallback / failure paths",
+        fontsize=12, pad=12,
+    )
+    ax.grid(axis="x", linestyle=":", alpha=0.4)
+    ax.set_axisbelow(True)
+    plt.tight_layout()
+    out1 = OUT / "v2_execution_mode_distribution.png"
+    plt.savefig(out1, dpi=150)
+    print(f"wrote {out1}")
+    plt.close(fig)
+
+    # ------------------------------------------------------------------
+    # Figure 2: §3.2 path-level latency (grouped bars, log y)
+    # ------------------------------------------------------------------
+
+    # Group KVC paths semantically
+    def kvc_group(mode: str) -> str:
+        if mode == "kvcache-direct-to-d-session":
+            return "KVC direct-to-D\n(fast path, 91.6%)"
+        if "reseed" in mode:
+            return "KVC reseed\n(slow path, 3.4%)"
+        if "no-d-capacity" in mode:
+            return "KVC no-d-capacity\n(fallback, 0.7%)"
+        if "session-not-resident" in mode:
+            return "KVC session-not-resident\n(misc, 2.3%)"
+        return "KVC other\n(<2%)"
+
+    groups = defaultdict(list)
+    for r in kvc_ok:
+        groups[kvc_group(r["execution_mode"])].append(r)
+
+    # Order paths by intuitive progression (fast → slow)
+    ordered_paths = [
+        "KVC direct-to-D\n(fast path, 91.6%)",
+        "KVC session-not-resident\n(misc, 2.3%)",
+        "KVC reseed\n(slow path, 3.4%)",
+        "KVC no-d-capacity\n(fallback, 0.7%)",
+    ]
+    # Filter to only ones present
+    ordered_paths = [p for p in ordered_paths if p in groups]
+    ordered_paths.append("DP dp-colo-router\n(100%)")
+
+    def stats(rows: list[dict]) -> dict[str, float]:
+        ttfts = [r["ttft_s"] for r in rows if r.get("ttft_s") is not None]
+        lats = [r["latency_s"] for r in rows if r.get("latency_s") is not None]
+        return {
+            "n": len(rows),
+            "ttft_p50": pct(ttfts, 0.50),
+            "ttft_p99": pct(ttfts, 0.99),
+            "lat_p50": pct(lats, 0.50),
+        }
+
+    path_stats = {p: stats(groups[p]) for p in ordered_paths if "DP" not in p}
+    path_stats["DP dp-colo-router\n(100%)"] = stats(dp_ok)
+
+    metrics = [("TTFT p50", "ttft_p50"), ("TTFT p99", "ttft_p99"), ("Latency p50", "lat_p50")]
+    bar_w = 0.25
+    fig, ax = plt.subplots(figsize=(12, 6))
+    x = np.arange(len(ordered_paths))
+
+    colors_metric = ["#1F77B4", "#FF7F0E", "#9467BD"]
+    for i, (label, key) in enumerate(metrics):
+        vals = [path_stats[p][key] for p in ordered_paths]
+        bars = ax.bar(x + (i - 1) * bar_w, vals, bar_w, label=label,
+                      color=colors_metric[i], edgecolor="black", linewidth=0.4)
+        for xi, v in zip(x + (i - 1) * bar_w, vals):
+            if v > 0 and v == v:  # not nan
+                fmt = f"{v*1000:.0f}ms" if v < 1 else f"{v:.2f}s"
+                ax.text(xi, v * 1.10, fmt,
+                        ha="center", va="bottom", fontsize=8.5, rotation=0)
+
+    ax.set_yscale("log")
+    ax.set_xticks(x)
+    ax.set_xticklabels(ordered_paths, fontsize=9.5)
+    ax.set_ylabel("Latency (seconds, log scale)", fontsize=11)
+    ax.set_title(
+        "Path-level latency: KVC v2 paths vs DP single-path baseline\n"
+        "log y-axis · same SWE-Bench 50sess trace · ts=1 · 4× H100 80GB",
+        fontsize=12, pad=12,
+    )
+    ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
+    ax.grid(axis="y", linestyle=":", alpha=0.4, which="both")
+    ax.set_axisbelow(True)
+
+    # Annotate sample counts under each path label
+    ymin = ax.get_ylim()[0]
+    for xi, p in zip(x, ordered_paths):
+        n = path_stats[p]["n"]
+        ax.text(xi, ymin * 0.5, f"n={n}", ha="center", va="top",
+                fontsize=8.5, color="#555")
+
+    plt.tight_layout()
+    out2 = OUT / "v2_path_level_latency.png"
+    plt.savefig(out2, dpi=150)
+    print(f"wrote {out2}")
+    plt.close(fig)
+
+    # ------------------------------------------------------------------
+    # Print numeric values used (for doc reference)
+    # ------------------------------------------------------------------
+    print("\n=== Numeric values plotted ===")
+    print("\nExecution mode counts (KVC v2):")
+    for label, c, p in zip(labels, counts, pcts):
+        print(f"  {c:>5}  ({p:>5.2f}%)  {label}")
+
+    print("\nPath-level latency:")
+    for p in ordered_paths:
+        s = path_stats[p]
+        nl = " | ".join([
+            f"n={s['n']}",
+            f"TTFT p50={s['ttft_p50']*1000:.1f}ms",
+            f"TTFT p99={s['ttft_p99']*1000:.1f}ms",
+            f"Lat p50={s['lat_p50']:.3f}s",
+        ])
+        print(f"  {p.replace(chr(10), ' '):<55}  {nl}")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/recompute_summary.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""Re-derive summary.json from existing metrics.jsonl using the fixed metrics.py.
+
+Bug fixed: requests aborted by SGLang (e.g. input > max-input-len returns
+a fast 400 with latency_s ~ 0.08s) were previously counted in latency_stats
+as if successful, deflating mean/p50/p90. The fixed metrics.py excludes
+all failed requests (errors or aborts) from latency/ttft/tpot stats and
+exposes abort_count / failure_count.
+
+Usage:
+    python3 scripts/analysis/recompute_summary.py path/to/metrics.jsonl ...
+    python3 scripts/analysis/recompute_summary.py --diff path/to/metrics.jsonl path/to/old_summary.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "src"))
+
+from agentic_pd_hybrid.metrics import RequestMetrics, write_summary_json
+
+
+def load_rows(metrics_path: Path) -> list[RequestMetrics]:
+    rows = []
+    field_names = {f for f in RequestMetrics.__dataclass_fields__}
+    with metrics_path.open() as handle:
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            raw = json.loads(line)
+            kwargs = {k: raw.get(k) for k in field_names}
+            rows.append(RequestMetrics(**kwargs))
+    return rows
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("metrics_paths", nargs="+", type=Path)
+    parser.add_argument(
+        "--out",
+        type=Path,
+        default=None,
+        help="output summary path (default: alongside metrics with .recomputed_summary.json)",
+    )
+    parser.add_argument(
+        "--diff",
+        action="store_true",
+        help="print before/after diff against the old <metrics>.summary.json",
+    )
+    args = parser.parse_args()
+
+    for metrics_path in args.metrics_paths:
+        rows = load_rows(metrics_path)
+        out_path = args.out or metrics_path.with_suffix(".recomputed_summary.json")
+        write_summary_json(
+            out_path,
+            rows,
+            trace_path=metrics_path,
+            router_url=None,
+        )
+        new = json.load(out_path.open())
+        print(f"\n=== {metrics_path} ===")
+        print(f"  written: {out_path}")
+        print(f"  total rows:     {new['request_count']}")
+        print(f"  error_count:    {new['error_count']}")
+        print(f"  abort_count:    {new.get('abort_count', '?')}")
+        print(f"  failure_count:  {new.get('failure_count', '?')}")
+        ls = new.get("latency_stats_s", {}) or {}
+        ts = new.get("ttft_stats_s", {}) or {}
+        print(f"  lat:  n={ls.get('count')} mean={ls.get('mean'):.4f} p50={ls.get('p50'):.4f} p90={ls.get('p90'):.4f} p99={ls.get('p99'):.4f}")
+        print(f"  ttft: n={ts.get('count')} mean={ts.get('mean'):.4f} p50={ts.get('p50'):.4f} p90={ts.get('p90'):.4f} p99={ts.get('p99'):.4f}")
+
+        if args.diff:
+            # find old summary (sibling file)
+            candidates = [
+                metrics_path.parent / f"{metrics_path.stem}.summary.json",
+                metrics_path.with_suffix(".summary.json"),
+            ]
+            old_path = next((p for p in candidates if p.exists()), None)
+            if old_path:
+                old = json.load(old_path.open())
+                print(f"  vs old {old_path}:")
+                old_ls = old.get("latency_stats_s", {}) or {}
+                old_ts = old.get("ttft_stats_s", {}) or {}
+                for k in ("count", "mean", "p50", "p90", "p99"):
+                    o = old_ls.get(k)
+                    n = ls.get(k)
+                    if o is not None and n is not None:
+                        delta = n - o
+                        print(f"    lat.{k}:  {o:.4f} -> {n:.4f}  ({delta:+.4f})")
+                for k in ("count", "mean", "p50", "p90", "p99"):
+                    o = old_ts.get(k)
+                    n = ts.get(k)
+                    if o is not None and n is not None:
+                        delta = n - o
+                        print(f"    ttft.{k}: {o:.4f} -> {n:.4f}  ({delta:+.4f})")
+
+
+if __name__ == "__main__":
+    main()

scripts/analysis/stratified.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""Stratified latency / TTFT reporter for paper-quality evaluation.
+
+Implements docs/EVALUATION_PROTOCOL_ZH.md §1.3 (M3 fix): every headline
+number must be accompanied by a stratified breakdown so reviewers can
+see which slice the gains come from.
+
+Buckets the request rows from one or more metrics.jsonl files along:
+  - turn_id        : {1, 2-5, 6-20, 21+}
+  - input_length   : {<=8K, 8K-64K, >64K}
+  - overlap_ratio  : {<=0.3, 0.3-0.7, >0.7}
+  - append_tokens  : input_length - observed_overlap_blocks * BLOCK_SIZE
+
+For each bucket, reports:
+  - n (total rows in bucket)
+  - n_ok (rows with no error and latency_s set)
+  - latency_s mean / p50 / p90 / p99
+  - ttft_s    mean / p50 / p90 / p99
+  - err_pct   (1 - n_ok/n)
+
+Usage:
+  scripts/analysis/stratified.py outputs/<run>/request-metrics.jsonl \
+      [outputs/<other-run>/request-metrics.jsonl ...]
+  scripts/analysis/stratified.py --dim turn_id outputs/<run>/request-metrics.jsonl
+  scripts/analysis/stratified.py --json outputs/<run>/request-metrics.jsonl > strat.json
+
+stdlib only — no pandas/numpy. Runs without GPU and without SGLang.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+from collections import defaultdict
+from pathlib import Path
+from typing import Iterable
+
+BLOCK_SIZE = 24  # SGLang radix block, matches docs/KVC_ROUTER_ALGORITHM.md §2
+
+TURN_BUCKETS: list[tuple[str, tuple[int, int]]] = [
+    ("turn=1", (1, 1)),
+    ("turn=2-5", (2, 5)),
+    ("turn=6-20", (6, 20)),
+    ("turn=21+", (21, 10**9)),
+]
+INPUT_BUCKETS: list[tuple[str, tuple[int, int]]] = [
+    ("input<=8K", (0, 8 * 1024)),
+    ("input=8K-64K", (8 * 1024 + 1, 64 * 1024)),
+    ("input>64K", (64 * 1024 + 1, 10**9)),
+]
+OVERLAP_BUCKETS: list[tuple[str, tuple[float, float]]] = [
+    ("overlap<=0.3", (0.0, 0.3)),
+    ("overlap=0.3-0.7", (0.3, 0.7)),
+    ("overlap>0.7", (0.7, 1.0001)),
+]
+APPEND_BUCKETS: list[tuple[str, tuple[int, int]]] = [
+    ("append<=128", (0, 128)),
+    ("append=128-1K", (129, 1024)),
+    ("append=1K-8K", (1025, 8 * 1024)),
+    ("append>8K", (8 * 1024 + 1, 10**9)),
+]
+
+DIM_BUCKETS: dict[str, list[tuple[str, tuple]]] = {
+    "turn_id": TURN_BUCKETS,
+    "input_length": INPUT_BUCKETS,
+    "overlap_ratio": OVERLAP_BUCKETS,
+    "append_tokens": APPEND_BUCKETS,
+}
+
+
+def _quantile(values: list[float], q: float) -> float:
+    """Linear-interpolation quantile, stdlib only."""
+    if not values:
+        return float("nan")
+    s = sorted(values)
+    if len(s) == 1:
+        return s[0]
+    pos = (len(s) - 1) * q
+    lo = math.floor(pos)
+    hi = math.ceil(pos)
+    if lo == hi:
+        return s[lo]
+    return s[lo] + (s[hi] - s[lo]) * (pos - lo)
+
+
+def _stats(values: list[float]) -> dict[str, float]:
+    if not values:
+        return {"mean": float("nan"), "p50": float("nan"), "p90": float("nan"), "p99": float("nan")}
+    return {
+        "mean": sum(values) / len(values),
+        "p50": _quantile(values, 0.50),
+        "p90": _quantile(values, 0.90),
+        "p99": _quantile(values, 0.99),
+    }
+
+
+def _bucket_for(value: float | int, buckets: list[tuple[str, tuple]]) -> str:
+    for label, (lo, hi) in buckets:
+        if lo <= value <= hi:
+            return label
+    return "OOB"
+
+
+def _classify(row: dict, dim: str) -> str:
+    if dim == "turn_id":
+        return _bucket_for(int(row.get("turn_id", 0)), TURN_BUCKETS)
+    if dim == "input_length":
+        return _bucket_for(int(row.get("input_length", 0)), INPUT_BUCKETS)
+    if dim == "overlap_ratio":
+        inp = max(1, int(row.get("input_length", 0)))
+        cached = int(row.get("observed_overlap_blocks", 0)) * BLOCK_SIZE
+        ratio = min(1.0, cached / inp)
+        return _bucket_for(ratio, OVERLAP_BUCKETS)
+    if dim == "append_tokens":
+        inp = int(row.get("input_length", 0))
+        cached = int(row.get("observed_overlap_blocks", 0)) * BLOCK_SIZE
+        return _bucket_for(max(0, inp - cached), APPEND_BUCKETS)
+    raise ValueError(f"Unknown dim: {dim}")
+
+
+def load_rows(paths: Iterable[Path]) -> list[dict]:
+    rows: list[dict] = []
+    for path in paths:
+        with path.open() as handle:
+            for line in handle:
+                line = line.strip()
+                if not line:
+                    continue
+                rows.append(json.loads(line))
+    return rows
+
+
+def stratify(rows: list[dict], dim: str) -> dict[str, dict]:
+    by_bucket: dict[str, list[dict]] = defaultdict(list)
+    for row in rows:
+        by_bucket[_classify(row, dim)].append(row)
+
+    output: dict[str, dict] = {}
+    for label, _ in DIM_BUCKETS[dim]:
+        bucket_rows = by_bucket.get(label, [])
+        n = len(bucket_rows)
+        ok = [r for r in bucket_rows if r.get("error") is None and r.get("latency_s") is not None]
+        n_ok = len(ok)
+        lat = [float(r["latency_s"]) for r in ok]
+        ttft = [float(r["ttft_s"]) for r in ok if r.get("ttft_s") is not None]
+        output[label] = {
+            "n": n,
+            "n_ok": n_ok,
+            "err_pct": (n - n_ok) / n if n else 0.0,
+            "latency_s": _stats(lat),
+            "ttft_s": _stats(ttft),
+        }
+    return output
+
+
+def render_table(name: str, stats: dict[str, dict]) -> str:
+    lines = [
+        f"## stratified by {name}",
+        "",
+        "| bucket | n | n_ok | err% | lat mean | lat p50 | lat p90 | lat p99 | ttft mean | ttft p50 | ttft p90 | ttft p99 |",
+        "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
+    ]
+    for label, _ in DIM_BUCKETS[name]:
+        s = stats[label]
+        lat = s["latency_s"]
+        ttft = s["ttft_s"]
+        lines.append(
+            "| {label} | {n} | {n_ok} | {err:.1%} | "
+            "{lm:.3f} | {l50:.3f} | {l90:.3f} | {l99:.3f} | "
+            "{tm:.3f} | {t50:.3f} | {t90:.3f} | {t99:.3f} |".format(
+                label=label,
+                n=s["n"],
+                n_ok=s["n_ok"],
+                err=s["err_pct"],
+                lm=lat["mean"],
+                l50=lat["p50"],
+                l90=lat["p90"],
+                l99=lat["p99"],
+                tm=ttft["mean"],
+                t50=ttft["p50"],
+                t90=ttft["p90"],
+                t99=ttft["p99"],
+            )
+        )
+    return "\n".join(lines)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    parser.add_argument("metrics_paths", nargs="+", type=Path)
+    parser.add_argument(
+        "--dim",
+        choices=list(DIM_BUCKETS.keys()) + ["all"],
+        default="all",
+        help="stratification dimension (default: all four)",
+    )
+    parser.add_argument(
+        "--json",
+        action="store_true",
+        help="emit JSON instead of markdown tables",
+    )
+    args = parser.parse_args()
+
+    rows = load_rows(args.metrics_paths)
+    if not rows:
+        print("no rows loaded", file=sys.stderr)
+        sys.exit(1)
+
+    dims = list(DIM_BUCKETS.keys()) if args.dim == "all" else [args.dim]
+    result = {dim: stratify(rows, dim) for dim in dims}
+
+    if args.json:
+        json.dump(result, sys.stdout, indent=2, default=lambda x: None if isinstance(x, float) and math.isnan(x) else x)
+        sys.stdout.write("\n")
+        return
+
+    header_paths = ", ".join(str(p) for p in args.metrics_paths)
+    print(f"# stratified report ({len(rows)} rows from {header_paths})\n")
+    for dim in dims:
+        print(render_table(dim, result[dim]))
+        print()
+
+
+if __name__ == "__main__":
+    main()

scripts/convert_inferact_to_trace.py

@@ -0,0 +1,189 @@
+"""Convert Inferact codex_swebenchpro_traces (ShareGPT) to agentic-pd-hybrid trace JSONL.
+
+Output schema (one JSON object per line, matching src/agentic_pd_hybrid/trace.py):
+  chat_id, parent_chat_id, timestamp, input_length, output_length, type, turn, hash_ids
+
+Each trial in the input becomes one session. Each (human, gpt) pair within a trial
+becomes one turn. The prefix at turn N is the concatenation of all (human, gpt) pairs
+from turns 0..N-1 plus the current human message — this mirrors how agentic coding
+agents grow context across calls.
+
+hash_ids are derived per 24-token block via sha256 of the block's text + previous hash,
+which gives stable, deterministic, prefix-shared hashes across turns of the same session.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import sys
+import time
+from pathlib import Path
+
+BLOCK_TOKEN_BUDGET = 24
+
+
+def _block_hash(text: str, prev_hash: int) -> int:
+    h = hashlib.sha256(text.encode("utf-8") + prev_hash.to_bytes(8, "big")).digest()
+    return int.from_bytes(h[:8], "big") & 0x7FFFFFFFFFFFFFFF
+
+
+def _build_hash_ids(token_ids: list[int]) -> list[int]:
+    out: list[int] = []
+    prev = 0
+    for start in range(0, len(token_ids), BLOCK_TOKEN_BUDGET):
+        block = token_ids[start : start + BLOCK_TOKEN_BUDGET]
+        block_repr = ",".join(str(t) for t in block)
+        prev = _block_hash(block_repr, prev)
+        out.append(prev)
+    return out
+
+
+def _pair_turns(conv: list[dict]) -> list[tuple[str, str]]:
+    """Pair consecutive (human, gpt) messages. Skip malformed."""
+    pairs: list[tuple[str, str]] = []
+    i = 0
+    while i + 1 < len(conv):
+        a, b = conv[i], conv[i + 1]
+        if (
+            isinstance(a, dict)
+            and isinstance(b, dict)
+            and a.get("from") == "human"
+            and b.get("from") == "gpt"
+        ):
+            pairs.append((str(a.get("value", "")), str(b.get("value", ""))))
+            i += 2
+        else:
+            i += 1
+    return pairs
+
+
+def convert(
+    input_path: Path,
+    output_path: Path,
+    *,
+    tokenizer_path: str,
+    max_trials: int | None,
+    inter_turn_gap_s: float,
+    session_stagger_s: float,
+    request_type: str,
+) -> None:
+    from transformers import AutoTokenizer
+
+    print(f"loading tokenizer from {tokenizer_path}", file=sys.stderr)
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
+
+    print(f"loading {input_path}", file=sys.stderr)
+    data = json.loads(input_path.read_text())
+    if max_trials is not None:
+        data = data[:max_trials]
+    print(f"{len(data)} trials to process", file=sys.stderr)
+
+    next_chat_id = 1_000_000
+    written = 0
+    skipped_trials = 0
+    t0 = time.time()
+
+    with output_path.open("w", encoding="utf-8") as out_f:
+        for trial_idx, trial in enumerate(data):
+            conv = trial.get("conversations") or []
+            turns = _pair_turns(conv)
+            if not turns:
+                skipped_trials += 1
+                continue
+
+            base_ts = trial_idx * session_stagger_s
+            ts = base_ts
+            parent_chat_id = -1
+            prefix_text = ""
+
+            for turn_idx, (human, assistant) in enumerate(turns):
+                # Input at this turn = full prior context + current human message.
+                current_text = (
+                    prefix_text + ("\n\n[USER]\n" if prefix_text else "[USER]\n") + human
+                )
+                input_ids = tokenizer.encode(current_text, add_special_tokens=False)
+                input_length = len(input_ids)
+
+                output_ids = tokenizer.encode(assistant, add_special_tokens=False)
+                output_length = max(1, len(output_ids))
+
+                hash_ids = _build_hash_ids(input_ids)
+
+                chat_id = next_chat_id
+                next_chat_id += 1
+                record = {
+                    "chat_id": chat_id,
+                    "parent_chat_id": parent_chat_id,
+                    "timestamp": round(ts, 6),
+                    "input_length": input_length,
+                    "output_length": output_length,
+                    "type": request_type,
+                    "turn": turn_idx,
+                    "hash_ids": hash_ids,
+                }
+                out_f.write(json.dumps(record) + "\n")
+                written += 1
+
+                parent_chat_id = chat_id
+                ts += inter_turn_gap_s
+                prefix_text = current_text + "\n\n[ASSISTANT]\n" + assistant
+
+            if (trial_idx + 1) % 20 == 0:
+                elapsed = time.time() - t0
+                rate = (trial_idx + 1) / elapsed if elapsed > 0 else 0
+                eta = (len(data) - trial_idx - 1) / rate if rate > 0 else 0
+                print(
+                    f"  trial {trial_idx + 1}/{len(data)} reqs={written} "
+                    f"rate={rate:.1f} trial/s eta={eta:.0f}s",
+                    file=sys.stderr,
+                )
+
+    elapsed = time.time() - t0
+    print(
+        f"done: wrote {written} requests across {len(data) - skipped_trials} sessions "
+        f"({skipped_trials} trials skipped, empty conversations) in {elapsed:.1f}s "
+        f"to {output_path}",
+        file=sys.stderr,
+    )
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument(
+        "--input",
+        type=Path,
+        default=Path("third_party/codex_swebenchpro_traces/codex_swebenchpro.json"),
+    )
+    p.add_argument("--output", type=Path, required=True)
+    p.add_argument(
+        "--tokenizer",
+        default="/mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507",
+        help="Path or HF id for the tokenizer. Default matches v2 sweep model.",
+    )
+    p.add_argument(
+        "--max-trials",
+        type=int,
+        default=None,
+        help="Cap number of trials processed (useful for smoke / quick tests).",
+    )
+    p.add_argument("--inter-turn-gap-s", type=float, default=2.5)
+    p.add_argument("--session-stagger-s", type=float, default=1.0)
+    p.add_argument("--request-type", default="chat")
+    args = p.parse_args()
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    convert(
+        input_path=args.input,
+        output_path=args.output,
+        tokenizer_path=args.tokenizer,
+        max_trials=args.max_trials,
+        inter_turn_gap_s=args.inter_turn_gap_s,
+        session_stagger_s=args.session_stagger_s,
+        request_type=args.request_type,
+    )
+
+
+if __name__ == "__main__":
+    main()

scripts/prepare_real_ali_samples.py

@@ -1,450 +0,0 @@
-#!/usr/bin/env python3
-"""Prepare balanced real-Ali trace samples for KVC experiments.
-
-The generic sampler is duration-oriented and can be dominated by one long
-session.  This script keeps real request lengths/timestamps but caps turns per
-session so live sweeps can compare policies on a repeatable multi-session
-workload.
-"""
-from __future__ import annotations
-
-import argparse
-import json
-import statistics
-from collections import defaultdict
-from dataclasses import asdict, dataclass
-from pathlib import Path
-
-from agentic_pd_hybrid.trace import TraceRequest, load_trace
-
-
-@dataclass(frozen=True)
-class SampleSummary:
-    input_trace_path: str
-    output_trace_path: str
-    profile: str
-    request_count: int
-    session_count: int
-    multi_turn_session_count: int
-    turn2plus_count: int
-    direct_eligible_turn2plus_count: int
-    direct_eligible_turn2plus_ratio: float
-    missing_parent_count: int
-    max_sessions: int
-    max_turns_per_session: int
-    start_time_s: float
-    end_time_s: float
-    sampled_duration_s: float
-    rebased_timestamps: bool
-    input_tokens: dict[str, float] | None
-    output_tokens: dict[str, float] | None
-    append_tokens: dict[str, float] | None
-    inter_turn_gap_s: dict[str, float] | None
-    overlap_ratio: dict[str, float] | None
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--trace", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    parser.add_argument("--max-sessions", type=int, default=64)
-    parser.add_argument("--max-turns-per-session", type=int, default=12)
-    parser.add_argument("--start-time-s", type=float, default=0.0)
-    parser.add_argument(
-        "--window-duration-s",
-        type=float,
-        default=None,
-        help=(
-            "If set, also write continuous-window samples that keep only requests "
-            "inside [start-time, start-time + window-duration]."
-        ),
-    )
-    parser.add_argument(
-        "--window-target-requests",
-        type=int,
-        default=None,
-        help=(
-            "For continuous-window samples, select whole sessions across time "
-            "buckets until at least this many requests are included. This keeps "
-            "the window span while making live runs tractable."
-        ),
-    )
-    parser.add_argument(
-        "--window-buckets",
-        type=int,
-        default=15,
-        help="Number of time buckets used with --window-target-requests.",
-    )
-    parser.add_argument(
-        "--window-min-turns",
-        type=int,
-        default=1,
-        help=(
-            "Minimum number of in-window turns per selected session for "
-            "continuous-window samples."
-        ),
-    )
-    parser.add_argument(
-        "--window-output-name",
-        default="ali-window.jsonl",
-        help="Output filename for the continuous-window sample.",
-    )
-    parser.add_argument(
-        "--max-sampled-duration-s",
-        type=float,
-        default=None,
-        help=(
-            "For balanced profile samples, drop requests after the first selected "
-            "timestamp plus this duration. Use only for quick smoke runs; headline "
-            "runs should preserve the full sampled span."
-        ),
-    )
-    parser.add_argument(
-        "--profiles",
-        nargs="+",
-        default=["representative-mt", "kvc-fit-smallappend"],
-        choices=["representative-mt", "kvc-fit-smallappend"],
-    )
-    parser.add_argument(
-        "--no-rebase-timestamps",
-        action="store_true",
-        help="Keep original timestamps instead of shifting the sample to start at 0.",
-    )
-    args = parser.parse_args()
-
-    requests = load_trace(args.trace)
-    sessions: dict[str, list[TraceRequest]] = defaultdict(list)
-    for request in requests:
-        sessions[request.session_id].append(request)
-
-    args.output_root.mkdir(parents=True, exist_ok=True)
-    if args.window_duration_s is not None:
-        if args.window_target_requests is None:
-            selected = _select_window(
-                requests=requests,
-                start_time_s=args.start_time_s,
-                window_duration_s=args.window_duration_s,
-            )
-            profile = "window"
-        else:
-            selected = _select_window_session_sample(
-                sessions=sessions,
-                start_time_s=args.start_time_s,
-                window_duration_s=args.window_duration_s,
-                target_requests=args.window_target_requests,
-                bucket_count=args.window_buckets,
-                min_turns=args.window_min_turns,
-            )
-            profile = (
-                "window-session-sample"
-                if args.window_min_turns <= 1
-                else f"window-session-sample-min{args.window_min_turns}turns"
-            )
-        output_path = args.output_root / args.window_output_name
-        summary = _write_sample(
-            selected=selected,
-            input_trace_path=args.trace,
-            output_path=output_path,
-            profile=profile,
-            max_sessions=args.max_sessions,
-            max_turns_per_session=args.max_turns_per_session,
-            rebase_timestamps=not args.no_rebase_timestamps,
-        )
-        print(
-            f"window: wrote {summary.request_count} requests from "
-            f"{summary.session_count} sessions to {output_path}"
-        )
-
-    for profile in args.profiles:
-        selected = _select_profile(
-            sessions=sessions,
-            profile=profile,
-            start_time_s=args.start_time_s,
-            max_sessions=args.max_sessions,
-            max_turns_per_session=args.max_turns_per_session,
-            max_sampled_duration_s=args.max_sampled_duration_s,
-        )
-        output_path = args.output_root / f"ali-{profile}.jsonl"
-        summary = _write_sample(
-            selected=selected,
-            input_trace_path=args.trace,
-            output_path=output_path,
-            profile=profile,
-            max_sessions=args.max_sessions,
-            max_turns_per_session=args.max_turns_per_session,
-            rebase_timestamps=not args.no_rebase_timestamps,
-        )
-        print(
-            f"{profile}: wrote {summary.request_count} requests from "
-            f"{summary.session_count} sessions to {output_path}"
-        )
-
-
-def _select_profile(
-    *,
-    sessions: dict[str, list[TraceRequest]],
-    profile: str,
-    start_time_s: float,
-    max_sessions: int,
-    max_turns_per_session: int,
-    max_sampled_duration_s: float | None,
-) -> list[TraceRequest]:
-    eligible: list[list[TraceRequest]] = []
-    for session_requests in sessions.values():
-        ordered = _ordered(session_requests)
-        if len(ordered) < 2:
-            continue
-        if ordered[0].timestamp_s < start_time_s:
-            continue
-        if profile == "kvc-fit-smallappend" and not _is_kvc_fit_smallappend(ordered):
-            continue
-        eligible.append(ordered[:max_turns_per_session])
-
-    eligible.sort(key=lambda items: (items[0].timestamp_s, items[0].session_id))
-    selected_sessions = eligible[:max_sessions]
-    selected = [request for items in selected_sessions for request in items]
-    selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
-    if selected and max_sampled_duration_s is not None:
-        first_ts = selected[0].timestamp_s
-        end_ts = first_ts + max_sampled_duration_s
-        selected = [
-            request for request in selected if request.timestamp_s <= end_ts
-        ]
-    return selected
-
-
-def _select_window(
-    *,
-    requests: list[TraceRequest],
-    start_time_s: float,
-    window_duration_s: float,
-) -> list[TraceRequest]:
-    end_time_s = start_time_s + window_duration_s
-    selected = [
-        request
-        for request in requests
-        if start_time_s <= request.timestamp_s <= end_time_s
-    ]
-    selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
-    return selected
-
-
-def _select_window_session_sample(
-    *,
-    sessions: dict[str, list[TraceRequest]],
-    start_time_s: float,
-    window_duration_s: float,
-    target_requests: int,
-    bucket_count: int,
-    min_turns: int,
-) -> list[TraceRequest]:
-    if target_requests <= 0:
-        raise ValueError("--window-target-requests must be positive")
-    if bucket_count <= 0:
-        raise ValueError("--window-buckets must be positive")
-    if min_turns <= 0:
-        raise ValueError("--window-min-turns must be positive")
-
-    end_time_s = start_time_s + window_duration_s
-    bucket_width_s = window_duration_s / bucket_count
-    buckets: list[list[list[TraceRequest]]] = [[] for _ in range(bucket_count)]
-    for session_requests in sessions.values():
-        ordered = _ordered(session_requests)
-        if not ordered:
-            continue
-        first = ordered[0]
-        if first.timestamp_s < start_time_s or first.timestamp_s > end_time_s:
-            continue
-        in_window = [
-            request
-            for request in ordered
-            if start_time_s <= request.timestamp_s <= end_time_s
-        ]
-        if len(in_window) < min_turns:
-            continue
-        bucket_index = min(
-            bucket_count - 1,
-            int((first.timestamp_s - start_time_s) / bucket_width_s),
-        )
-        buckets[bucket_index].append(in_window)
-
-    for bucket in buckets:
-        bucket.sort(key=lambda items: (items[0].timestamp_s, items[0].session_id))
-
-    selected_sessions: list[list[TraceRequest]] = []
-    selected_count = 0
-    positions = [0 for _ in range(bucket_count)]
-    while selected_count < target_requests:
-        progressed = False
-        for index, bucket in enumerate(buckets):
-            if positions[index] >= len(bucket):
-                continue
-            session_requests = bucket[positions[index]]
-            positions[index] += 1
-            selected_sessions.append(session_requests)
-            selected_count += len(session_requests)
-            progressed = True
-            if selected_count >= target_requests:
-                break
-        if not progressed:
-            break
-
-    selected = [request for items in selected_sessions for request in items]
-    selected.sort(key=lambda request: (request.timestamp_s, request.chat_id))
-    if len(selected) < target_requests:
-        raise ValueError(
-            f"window session sample selected only {len(selected)} requests; "
-            f"target was {target_requests}"
-        )
-    return selected
-
-
-def _is_kvc_fit_smallappend(session_requests: list[TraceRequest]) -> bool:
-    initial = session_requests[0]
-    if initial.input_length < 2048 or initial.input_length > 16000:
-        return False
-    for request in session_requests:
-        if request.output_length > 2048:
-            return False
-    for previous, current in zip(session_requests, session_requests[1:], strict=False):
-        append_tokens = current.input_length - (
-            previous.input_length + previous.output_length
-        )
-        if append_tokens <= 0 or append_tokens > 2048:
-            return False
-        if _overlap_ratio(previous, current) < 0.75:
-            return False
-    return True
-
-
-def _write_sample(
-    *,
-    selected: list[TraceRequest],
-    input_trace_path: Path,
-    output_path: Path,
-    profile: str,
-    max_sessions: int,
-    max_turns_per_session: int,
-    rebase_timestamps: bool,
-) -> SampleSummary:
-    if not selected:
-        raise ValueError(f"profile {profile!r} selected no requests")
-
-    first_ts = selected[0].timestamp_s
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    with output_path.open("w", encoding="utf-8") as handle:
-        for request in selected:
-            timestamp = request.timestamp_s - first_ts if rebase_timestamps else request.timestamp_s
-            payload = {
-                "chat_id": request.chat_id,
-                "parent_chat_id": request.parent_chat_id,
-                "timestamp": round(timestamp, 6),
-                "input_length": request.input_length,
-                "output_length": request.output_length,
-                "type": request.request_type,
-                "turn": request.turn_id,
-                "hash_ids": list(request.hash_ids),
-            }
-            handle.write(json.dumps(payload, sort_keys=True) + "\n")
-
-    sessions = defaultdict(list)
-    for request in selected:
-        sessions[request.session_id].append(request)
-
-    selected_chat_ids = {request.chat_id for request in selected}
-    missing_parent_count = sum(
-        1
-        for request in selected
-        if request.parent_chat_id >= 0 and request.parent_chat_id not in selected_chat_ids
-    )
-    append_values: list[float] = []
-    gap_values: list[float] = []
-    overlap_values: list[float] = []
-    direct_eligible_count = 0
-    for session_requests in sessions.values():
-        ordered = _ordered(session_requests)
-        for previous, current in zip(ordered, ordered[1:], strict=False):
-            append_tokens = current.input_length - (
-                previous.input_length + previous.output_length
-            )
-            overlap_ratio = _overlap_ratio(previous, current)
-            append_values.append(float(append_tokens))
-            gap_values.append(float(current.timestamp_s - previous.timestamp_s))
-            overlap_values.append(overlap_ratio)
-            if append_tokens > 0 and append_tokens <= 2048 and overlap_ratio > 0:
-                direct_eligible_count += 1
-
-    turn2plus_count = sum(max(0, len(items) - 1) for items in sessions.values())
-
-    start = min(request.timestamp_s for request in selected)
-    end = max(request.timestamp_s for request in selected)
-    summary = SampleSummary(
-        input_trace_path=str(input_trace_path),
-        output_trace_path=str(output_path),
-        profile=profile,
-        request_count=len(selected),
-        session_count=len(sessions),
-        multi_turn_session_count=sum(1 for items in sessions.values() if len(items) > 1),
-        turn2plus_count=turn2plus_count,
-        direct_eligible_turn2plus_count=direct_eligible_count,
-        direct_eligible_turn2plus_ratio=(
-            direct_eligible_count / turn2plus_count if turn2plus_count else 0.0
-        ),
-        missing_parent_count=missing_parent_count,
-        max_sessions=max_sessions,
-        max_turns_per_session=max_turns_per_session,
-        start_time_s=0.0 if rebase_timestamps else start,
-        end_time_s=end - start if rebase_timestamps else end,
-        sampled_duration_s=end - start,
-        rebased_timestamps=rebase_timestamps,
-        input_tokens=_stats([float(request.input_length) for request in selected]),
-        output_tokens=_stats([float(request.output_length) for request in selected]),
-        append_tokens=_stats(append_values),
-        inter_turn_gap_s=_stats(gap_values),
-        overlap_ratio=_stats(overlap_values),
-    )
-    with output_path.with_suffix(output_path.suffix + ".summary.json").open(
-        "w", encoding="utf-8"
-    ) as handle:
-        json.dump(asdict(summary), handle, indent=2, sort_keys=True)
-    return summary
-
-
-def _ordered(session_requests: list[TraceRequest]) -> list[TraceRequest]:
-    return sorted(
-        session_requests,
-        key=lambda request: (request.timestamp_s, request.turn_id, request.chat_id),
-    )
-
-
-def _overlap_ratio(previous: TraceRequest, current: TraceRequest) -> float:
-    if not current.hash_ids:
-        return 0.0
-    previous_blocks = set(previous.hash_ids)
-    overlap = sum(1 for block in current.hash_ids if block in previous_blocks)
-    return overlap / len(current.hash_ids)
-
-
-def _stats(values: list[float]) -> dict[str, float] | None:
-    if not values:
-        return None
-    ordered = sorted(values)
-    return {
-        "count": float(len(ordered)),
-        "mean": statistics.fmean(ordered),
-        "min": ordered[0],
-        "p50": _percentile(ordered, 0.50),
-        "p90": _percentile(ordered, 0.90),
-        "p99": _percentile(ordered, 0.99),
-        "max": ordered[-1],
-    }
-
-
-def _percentile(sorted_values: list[float], percentile: float) -> float:
-    if len(sorted_values) == 1:
-        return sorted_values[0]
-    return sorted_values[round((len(sorted_values) - 1) * percentile)]
-
-
-if __name__ == "__main__":
-    main()

scripts/sample_trace_subset.py

@@ -0,0 +1,81 @@
+"""Deterministically slice the first N sessions of an agentic-pd-hybrid trace.
+
+Method: scan in file order, count records whose `parent_chat_id == -1` (= a
+session's turn 0), and write every record until the (N+1)-th such record is
+seen. No RNG, no hashing — re-running on the same input produces a byte-
+identical output. Used to derive matched subsets for paired sweeps (E1 vs E2)
+without spending GPU hours on the full trace.
+
+Usage:
+    uv run --no-sync python scripts/sample_trace_subset.py \
+        --input outputs/inferact_codex_swebenchpro.jsonl \
+        --output outputs/inferact_50sess.jsonl \
+        --sessions 50
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import sys
+from pathlib import Path
+
+
+def slice_first_n_sessions(input_path: Path, output_path: Path, n_sessions: int) -> dict:
+    sessions_seen = 0
+    requests_written = 0
+    input_length_sum = 0
+    output_length_sum = 0
+    min_in = float("inf")
+    max_in = 0
+
+    with input_path.open("r", encoding="utf-8") as f_in, output_path.open(
+        "w", encoding="utf-8"
+    ) as f_out:
+        for line in f_in:
+            rec = json.loads(line)
+            if rec["parent_chat_id"] == -1:
+                sessions_seen += 1
+                if sessions_seen > n_sessions:
+                    break
+            f_out.write(line)
+            requests_written += 1
+            il = int(rec["input_length"])
+            input_length_sum += il
+            output_length_sum += int(rec["output_length"])
+            if il < min_in:
+                min_in = il
+            if il > max_in:
+                max_in = il
+
+    h = hashlib.md5(output_path.read_bytes()).hexdigest()
+    return {
+        "sessions": min(sessions_seen, n_sessions),
+        "requests": requests_written,
+        "input_length_mean": input_length_sum / max(1, requests_written),
+        "input_length_min": int(min_in) if min_in != float("inf") else 0,
+        "input_length_max": max_in,
+        "output_length_mean": output_length_sum / max(1, requests_written),
+        "output_md5": h,
+    }
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument(
+        "--input",
+        type=Path,
+        default=Path("outputs/inferact_codex_swebenchpro.jsonl"),
+    )
+    p.add_argument("--output", type=Path, required=True)
+    p.add_argument("--sessions", type=int, default=50)
+    args = p.parse_args()
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    stats = slice_first_n_sessions(args.input, args.output, args.sessions)
+    print(json.dumps(stats, indent=2), file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/setup_env.sh

@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+# Source this file in every shell that will run agentic-pd-hybrid.
+#
+#   source scripts/setup_env.sh
+#
+# Why all three are needed:
+# - CUDA_HOME / PATH point tvm_ffi (vendor sglang JIT compiler) at cu12.8 nvcc.
+#   Without this it falls back to /usr/local/cuda-13.0/bin/nvcc and the
+#   resulting .so links libcudart.so.13 which driver 570 (cu12.8 API) rejects
+#   with cudaErrorInsufficientDriver.
+# - LD_LIBRARY_PATH must expose libcudart.so.12 for mooncake.engine (cu12 wheel)
+#   AND ~/cuda-12.8/lib64 for tvm_ffi compile-time linker searches.
+#
+# See docs/H200_DRIVER570_SETUP_ZH.md for the full rationale.
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+
+if [ ! -x "$HOME/cuda-12.8/bin/nvcc" ]; then
+  echo "ERROR: $HOME/cuda-12.8/bin/nvcc not found." >&2
+  echo "Install cu12.8 toolkit first (see docs/H200_DRIVER570_SETUP_ZH.md §3)." >&2
+  return 1 2>/dev/null || exit 1
+fi
+
+if [ ! -f "$REPO_ROOT/.venv/lib/python3.12/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12" ]; then
+  echo "ERROR: venv libcudart.so.12 missing. Run 'uv sync' from $REPO_ROOT." >&2
+  return 1 2>/dev/null || exit 1
+fi
+
+export CUDA_HOME="$HOME/cuda-12.8"
+export PATH="$HOME/cuda-12.8/bin:$PATH"
+export LD_LIBRARY_PATH="$REPO_ROOT/.venv/lib/python3.12/site-packages/nvidia/cuda_runtime/lib:$HOME/cuda-12.8/lib64${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
+
+# Mooncake batch_transfer_sync C++ timeout (seconds). Default in mooncake is
+# 30 s; a single LRU eviction sweep on a saturated D scheduler can exceed
+# that and cause the hair-trigger blacklist in conn.py:1270 to permanently
+# mark the D's mooncake_session_id "failed". 1800 s = 30 min gives us
+# headroom while still detecting genuinely broken peers eventually.
+# See docs/E1_E2_RESULTS_ZH.md §5c and docs/E1_E2_FIX_DESIGN_ZH.md Q1.C.
+export MC_TRANSFER_TIMEOUT="${MC_TRANSFER_TIMEOUT:-1800}"
+
+echo "agentic-pd-hybrid env ready:"
+echo "  CUDA_HOME=$CUDA_HOME ($(nvcc --version | grep release | sed 's/.*release //'))"
+echo "  libcudart.so.12 at $REPO_ROOT/.venv/lib/python3.12/site-packages/nvidia/cuda_runtime/lib"
+echo "  MC_TRANSFER_TIMEOUT=${MC_TRANSFER_TIMEOUT}s"

scripts/sweep_e1_naive_1p3d.sh

@@ -0,0 +1,82 @@
+#!/usr/bin/env bash
+# E1 — naive 1P3D + kv-aware + RDMA, ts=1
+#
+# Tests hypothesis H1 from ONBOARDING_NEXT_AGENT_ZH §3.1: separate the
+# contribution of "1P3D topology + kv-aware policy" from "KVC layer
+# (admission / migration / direct-to-D)".
+#
+# Mechanism = pd-disaggregation (no KVC layer); policy = kv-aware.
+# Topology = 1P3D, RDMA on (mlx5_60 = cuda:0 NUMA-local).
+#
+# Prerequisites:
+#   - source scripts/setup_env.sh (sets CUDA_HOME etc.)
+#   - outputs/inferact_codex_swebenchpro.jsonl exists
+#     (run scripts/convert_inferact_to_trace.py if not)
+#
+# Usage:
+#   bash scripts/sweep_e1_naive_1p3d.sh
+#
+# Override defaults via env:
+#   MODEL=/path TRACE=path OUTPUT=path IB_DEVICE=mlx5_XX bash scripts/sweep_e1_naive_1p3d.sh
+
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+if [ -z "${CUDA_HOME:-}" ]; then
+  echo "ERROR: CUDA_HOME not set. Source scripts/setup_env.sh first." >&2
+  exit 1
+fi
+
+MODEL=${MODEL:-/mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507}
+TRACE=${TRACE:-outputs/inferact_50sess.jsonl}
+OUTPUT=${OUTPUT:-outputs/e1_naive_1p3d_kvaware_rdma_50sess}
+IB_DEVICE=${IB_DEVICE:-mlx5_60}
+
+if [ ! -f "$TRACE" ]; then
+  echo "ERROR: trace not found at $TRACE" >&2
+  echo "Run: uv run --no-sync python scripts/convert_inferact_to_trace.py --output $TRACE" >&2
+  exit 1
+fi
+
+mkdir -p "$OUTPUT"
+LOG="$OUTPUT/sweep.log"
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG"; }
+
+log "=== E1: naive 1P3D kv-aware + RDMA, ts=1 ==="
+log "MODEL=$MODEL"
+log "TRACE=$TRACE ($(wc -l < $TRACE) requests)"
+log "OUTPUT=$OUTPUT"
+log "IB_DEVICE=$IB_DEVICE"
+
+label=e1_naive_1p3d_kvaware_run1
+log ""
+log "=== [E1] $label starting ==="
+
+uv run --no-sync python -m agentic_pd_hybrid.cli benchmark-live \
+  --trace "$TRACE" \
+  --output-root "$OUTPUT" \
+  --mechanism pd-disaggregation \
+  --policy kv-aware \
+  --model-path "$MODEL" \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --force-rdma --ib-device "$IB_DEVICE" \
+  --gpu-budget 4 \
+  --time-scale 1 \
+  --session-sample-rate 1.0 \
+  --target-duration-s 100000 \
+  --concurrency-limit 32 \
+  --timeout-s 1800 \
+  --request-timeout-s 300 2>&1 | tee -a "$LOG"
+
+run_dir=$(ls -td "$OUTPUT"/pd-disaggregation-*/ 2>/dev/null | head -1)
+log "=== [E1] $label COMPLETED, artifacts at $run_dir ==="
+
+if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+  cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+  cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+  log "=== summary saved to $OUTPUT/${label}_summary.json ==="
+fi

scripts/sweep_e2_kvc_v2_rdma.sh

@@ -0,0 +1,90 @@
+#!/usr/bin/env bash
+# E2 — KVC v2 + RDMA, ts=1
+#
+# Tests hypotheses H2/H3 from ONBOARDING_NEXT_AGENT_ZH §3.1: validate
+# that enabling real RDMA pushes TTFT p99 from the reported 1.28s
+# (TCP loopback) down toward ~0.7s (still expected to lose to DP 0.43s
+# because re-prefill segment of reseed slow-path remains).
+#
+# Mechanism = kvcache-centric; policy = kv-aware; topology = 1P3D.
+# All --kvcache-* tuning flags from sweep_ts1_migration_v2.sh
+# (reset-on-success + threshold 8192). RDMA on (mlx5_60).
+#
+# Uses the same outputs/inferact_50sess.jsonl as E1 — see
+# scripts/sample_trace_subset.py — so the two runs are paired.
+#
+# Prerequisites:
+#   - source scripts/setup_env.sh
+#   - E1 must already have completed (releases GPUs)
+#
+# Usage:
+#   bash scripts/sweep_e2_kvc_v2_rdma.sh
+
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+if [ -z "${CUDA_HOME:-}" ]; then
+  echo "ERROR: CUDA_HOME not set. Source scripts/setup_env.sh first." >&2
+  exit 1
+fi
+
+MODEL=${MODEL:-/mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507}
+TRACE=${TRACE:-outputs/inferact_50sess.jsonl}
+OUTPUT=${OUTPUT:-outputs/e2_kvc_v2_rdma_50sess}
+IB_DEVICE=${IB_DEVICE:-mlx5_60}
+
+if [ ! -f "$TRACE" ]; then
+  echo "ERROR: trace not found at $TRACE" >&2
+  echo "Run: uv run --no-sync python scripts/sample_trace_subset.py --output $TRACE --sessions 50" >&2
+  exit 1
+fi
+
+mkdir -p "$OUTPUT"
+LOG="$OUTPUT/sweep.log"
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG"; }
+
+log "=== E2: KVC v2 + RDMA, ts=1 ==="
+log "MODEL=$MODEL"
+log "TRACE=$TRACE ($(wc -l < $TRACE) requests)"
+log "OUTPUT=$OUTPUT"
+log "IB_DEVICE=$IB_DEVICE"
+
+label=e2_kvc_v2_rdma_run1
+log ""
+log "=== [E2] $label starting ==="
+
+uv run --no-sync python -m agentic_pd_hybrid.cli benchmark-live \
+  --trace "$TRACE" \
+  --output-root "$OUTPUT" \
+  --mechanism kvcache-centric \
+  --policy kv-aware \
+  --model-path "$MODEL" \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --force-rdma --ib-device "$IB_DEVICE" \
+  --gpu-budget 4 \
+  --time-scale 1 \
+  --session-sample-rate 1.0 \
+  --target-duration-s 100000 \
+  --concurrency-limit 32 \
+  --timeout-s 1800 \
+  --request-timeout-s 300 \
+  --kvcache-admission-mode worker \
+  --kvcache-seed-min-turn-id 1 \
+  --kvcache-seed-max-inflight-decode -1 \
+  --kvcache-prefill-backup-policy release-after-transfer \
+  --kvcache-prefill-priority-eviction \
+  --kvcache-migration-reject-threshold 3 \
+  --kvcache-direct-max-uncached-tokens 8192 2>&1 | tee -a "$LOG"
+
+run_dir=$(ls -td "$OUTPUT"/kvcache-centric-*/ 2>/dev/null | head -1)
+log "=== [E2] $label COMPLETED, artifacts at $run_dir ==="
+
+if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+  cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+  cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+  log "=== summary saved to $OUTPUT/${label}_summary.json ==="
+fi

scripts/sweep_e3_kvc_v2_loadfloor_rdma.sh

@@ -0,0 +1,105 @@
+#!/usr/bin/env bash
+# E3 — KVC v2 + RDMA + load-floor bonus, ts=1
+#
+# Validates the load-floor bonus fix proposed in
+# docs/E1_E2_FIX_DESIGN_ZH.md §Q2.B. Identical to E2 except:
+#   --kvcache-load-floor-bonus 200
+#
+# Pair-wise vs E1 (no KVC layer) and E2 (KVC v2 without bonus) on the
+# exact same outputs/inferact_50sess.jsonl subset.
+#
+# Hypotheses being tested:
+#   H1 (load balance): D2 should now receive non-trivial bindings
+#                      (E1/E2 had 0 — see E1_E2_RESULTS_ZH.md §5d).
+#   H2 (failure rate): mooncake batch_transfer_sync timeouts should
+#                      stop firing because D0/D1 KV pool no longer
+#                      saturates → no LRU thrash → control plane no
+#                      longer starves. E2 had 1054 failures; expect
+#                      ≤ E1's 85.
+#   H3 (TTFT):         the 231 successful E2 reqs had TTFT p50 = 0.43s,
+#                      well under E1's 88.6s. With the failure cascade
+#                      removed, these should generalize to most reqs.
+#
+# Prerequisites:
+#   - source scripts/setup_env.sh
+#     (sets CUDA_HOME, MC_TRANSFER_TIMEOUT=1800, etc.)
+#   - outputs/inferact_50sess.jsonl exists (md5 7bb263a32600ef5a6ef5099ba340a487)
+#   - Previous sweep done; GPUs idle.
+#
+# Usage:
+#   bash scripts/sweep_e3_kvc_v2_loadfloor_rdma.sh
+#
+# Override defaults via env:
+#   K=500 LOAD_FLOOR_BONUS=$K bash scripts/sweep_e3_kvc_v2_loadfloor_rdma.sh
+
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+if [ -z "${CUDA_HOME:-}" ]; then
+  echo "ERROR: CUDA_HOME not set. Source scripts/setup_env.sh first." >&2
+  exit 1
+fi
+
+MODEL=${MODEL:-/mnt/models/Qwen/Qwen3-30B-A3B-Instruct-2507}
+TRACE=${TRACE:-outputs/inferact_50sess.jsonl}
+OUTPUT=${OUTPUT:-outputs/e3_kvc_v2_loadfloor_rdma_50sess}
+IB_DEVICE=${IB_DEVICE:-mlx5_60}
+LOAD_FLOOR_BONUS=${LOAD_FLOOR_BONUS:-200}
+
+if [ ! -f "$TRACE" ]; then
+  echo "ERROR: trace not found at $TRACE" >&2
+  echo "Run: uv run --no-sync python scripts/sample_trace_subset.py --output $TRACE --sessions 50" >&2
+  exit 1
+fi
+
+mkdir -p "$OUTPUT"
+LOG="$OUTPUT/sweep.log"
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG"; }
+
+log "=== E3: KVC v2 + RDMA + load-floor bonus K=$LOAD_FLOOR_BONUS, ts=1 ==="
+log "MODEL=$MODEL"
+log "TRACE=$TRACE ($(wc -l < $TRACE) requests)"
+log "OUTPUT=$OUTPUT"
+log "IB_DEVICE=$IB_DEVICE"
+log "MC_TRANSFER_TIMEOUT=${MC_TRANSFER_TIMEOUT:-default-30s}"
+
+label=e3_kvc_v2_loadfloor_run1
+log ""
+log "=== [E3] $label starting ==="
+
+uv run --no-sync python -m agentic_pd_hybrid.cli benchmark-live \
+  --trace "$TRACE" \
+  --output-root "$OUTPUT" \
+  --mechanism kvcache-centric \
+  --policy kv-aware \
+  --model-path "$MODEL" \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --force-rdma --ib-device "$IB_DEVICE" \
+  --gpu-budget 4 \
+  --time-scale 1 \
+  --session-sample-rate 1.0 \
+  --target-duration-s 100000 \
+  --concurrency-limit 32 \
+  --timeout-s 1800 \
+  --request-timeout-s 300 \
+  --kvcache-admission-mode worker \
+  --kvcache-seed-min-turn-id 1 \
+  --kvcache-seed-max-inflight-decode -1 \
+  --kvcache-prefill-backup-policy release-after-transfer \
+  --kvcache-prefill-priority-eviction \
+  --kvcache-migration-reject-threshold 3 \
+  --kvcache-direct-max-uncached-tokens 8192 \
+  --kvcache-load-floor-bonus "$LOAD_FLOOR_BONUS" 2>&1 | tee -a "$LOG"
+
+run_dir=$(ls -td "$OUTPUT"/kvcache-centric-*/ 2>/dev/null | head -1)
+log "=== [E3] $label COMPLETED, artifacts at $run_dir ==="
+
+if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+  cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+  cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+  log "=== summary saved to $OUTPUT/${label}_summary.json ==="
+fi

scripts/sweep_real_ali_kvc.sh

@@ -1,170 +0,0 @@
-#!/usr/bin/env bash
-# Real Ali workload sweep for KVC pd-hybrid.
-#
-# This script expects a prebuilt sample trace and replays it exactly for every
-# mechanism.  It intentionally keeps pool polling disabled for performance runs.
-set -euo pipefail
-
-REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
-cd "$REPO_ROOT"
-
-MODEL=${MODEL:-/home/admin/cpfs/wjh/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}
-TRACE=${TRACE:-outputs/real-ali-kvc-iter/samples-balanced/ali-kvc-fit-smallappend.jsonl}
-OUT_ROOT=${OUT_ROOT:-outputs/real-ali-kvc-iter/runs}
-TIME_SCALE=${TIME_SCALE:-1}
-CONCURRENCY=${CONCURRENCY:-32}
-REQUEST_TIMEOUT_S=${REQUEST_TIMEOUT_S:-300}
-STACK_TIMEOUT_S=${STACK_TIMEOUT_S:-1200}
-RUNS=${RUNS:-"dp kvc_bp"}
-EXTRA_SERVER_ARGS=${EXTRA_SERVER_ARGS:-}
-PREFILL_EXTRA_SERVER_ARGS=${PREFILL_EXTRA_SERVER_ARGS:-}
-DECODE_EXTRA_SERVER_ARGS=${DECODE_EXTRA_SERVER_ARGS:-}
-KVC_SEED_MIN_TURN_ID=${KVC_SEED_MIN_TURN_ID:-1}
-KVC_SEED_ONLY_MULTITURN=${KVC_SEED_ONLY_MULTITURN:-0}
-
-mkdir -p "$OUT_ROOT"
-LOG="$OUT_ROOT/sweep.log"
-
-log() {
-  echo "[$(date '+%F %T')] $*" | tee -a "$LOG"
-}
-
-common_args=(
-  --trace "$TRACE"
-  --model-path "$MODEL"
-  --output-root "$OUT_ROOT"
-  --use-trace-as-sample
-  --time-scale "$TIME_SCALE"
-  --concurrency-limit "$CONCURRENCY"
-  --timeout-s "$STACK_TIMEOUT_S"
-  --request-timeout-s "$REQUEST_TIMEOUT_S"
-)
-if [[ -n "$EXTRA_SERVER_ARGS" ]]; then
-  common_args+=(--extra-server-args "$EXTRA_SERVER_ARGS")
-fi
-if [[ -n "$PREFILL_EXTRA_SERVER_ARGS" ]]; then
-  common_args+=(--prefill-extra-server-args "$PREFILL_EXTRA_SERVER_ARGS")
-fi
-if [[ -n "$DECODE_EXTRA_SERVER_ARGS" ]]; then
-  common_args+=(--decode-extra-server-args "$DECODE_EXTRA_SERVER_ARGS")
-fi
-
-kvc_args=(
-  "${common_args[@]}"
-  --mechanism kvcache-centric
-  --policy kv-aware
-  --prefill-workers 2
-  --decode-workers 6
-  --prefill-tp-size 1
-  --decode-tp-size 1
-  --prefill-gpu-ids 0,1
-  --decode-gpu-ids 2,3,4,5,6,7
-  --transfer-backend mooncake
-  --gpu-budget 8
-  --kvcache-admission-mode worker
-  --kvcache-seed-min-turn-id "$KVC_SEED_MIN_TURN_ID"
-  --kvcache-seed-max-inflight-decode -1
-  --kvcache-prefill-backup-policy release-after-transfer
-  --kvcache-prefill-priority-eviction
-)
-if [[ "$KVC_SEED_ONLY_MULTITURN" == "1" ]]; then
-  kvc_args+=(--kvcache-seed-only-multiturn-sessions)
-fi
-
-run_dp() {
-  log "=== DP cache-aware baseline: 8 direct workers ==="
-  uv run agentic-pd-hybrid benchmark-live \
-    "${common_args[@]}" \
-    --mechanism pd-colo \
-    --policy kv-aware \
-    --prefill-workers 0 \
-    --decode-workers 0 \
-    --direct-workers 8 \
-    --direct-tp-size 1 \
-    --direct-gpu-ids 0,1,2,3,4,5,6,7 \
-    --gpu-budget 8
-}
-
-run_pd_disagg() {
-  log "=== PD-disaggregation baseline: 2P6D ==="
-  uv run agentic-pd-hybrid benchmark-live \
-    "${common_args[@]}" \
-    --mechanism pd-disaggregation \
-    --policy kv-aware \
-    --prefill-workers 2 \
-    --decode-workers 6 \
-    --prefill-tp-size 1 \
-    --decode-tp-size 1 \
-    --prefill-gpu-ids 0,1 \
-    --decode-gpu-ids 2,3,4,5,6,7 \
-    --transfer-backend mooncake \
-    --gpu-budget 8
-}
-
-run_pd_sticky() {
-  log "=== PD-disaggregation sticky baseline: 2P6D ==="
-  uv run agentic-pd-hybrid benchmark-live \
-    "${common_args[@]}" \
-    --mechanism pd-disaggregation \
-    --policy sticky \
-    --prefill-workers 2 \
-    --decode-workers 6 \
-    --prefill-tp-size 1 \
-    --decode-tp-size 1 \
-    --prefill-gpu-ids 0,1 \
-    --decode-gpu-ids 2,3,4,5,6,7 \
-    --transfer-backend mooncake \
-    --gpu-budget 8
-}
-
-run_kvc() {
-  log "=== KVC baseline: 2P6D worker admission, no backpressure ==="
-  uv run agentic-pd-hybrid benchmark-live "${kvc_args[@]}"
-}
-
-run_kvc_bp() {
-  log "=== KVC candidate: 2P6D worker admission + backpressure ==="
-  uv run agentic-pd-hybrid benchmark-live \
-    "${kvc_args[@]}" \
-    --enable-backpressure \
-    --backpressure-max-pause-s 2.0
-}
-
-summarize_latest() {
-  log "=== Latest summaries ==="
-  find "$OUT_ROOT" -maxdepth 2 -name 'request-metrics.jsonl.summary.json' -print \
-    | sort \
-    | while read -r summary; do
-        python - "$summary" <<'PY'
-import json, sys
-p=sys.argv[1]
-d=json.load(open(p))
-lat=d.get("latency_stats_s") or {}
-tt=d.get("ttft_stats_s") or {}
-em=d.get("execution_modes") or {}
-print(p)
-print("  reqs", d.get("request_count"), "errors", d.get("error_count"), "trunc", d.get("truncated_request_count"))
-print("  lat mean/p50/p90/p99", lat.get("mean"), lat.get("p50"), lat.get("p90"), lat.get("p99"))
-print("  ttft mean/p50/p90", tt.get("mean"), tt.get("p50"), tt.get("p90"))
-print("  modes", em)
-PY
-      done | tee -a "$LOG"
-}
-
-log "Trace: $TRACE"
-log "Model: $MODEL"
-log "Runs: $RUNS | time-scale=$TIME_SCALE concurrency=$CONCURRENCY | kvc-seed-min-turn-id=$KVC_SEED_MIN_TURN_ID | kvc-seed-only-multiturn=$KVC_SEED_ONLY_MULTITURN"
-
-for run in $RUNS; do
-  case "$run" in
-    dp) run_dp ;;
-    pd) run_pd_disagg ;;
-    pd_sticky) run_pd_sticky ;;
-    kvc) run_kvc ;;
-    kvc_bp) run_kvc_bp ;;
-    *) log "Unknown run name: $run"; exit 2 ;;
-  esac
-done
-
-summarize_latest
-log "DONE"

scripts/sweep_ts1_kvc_n3_plus_dp.sh

@@ -0,0 +1,146 @@
+#!/bin/bash
+# Time-scale=1 validation sweep, downscaled to 4 GPUs:
+#   - KVC v5 1P3D × N=3   (new data, validates §1/§2 structural claims at real timing)
+#   - 4-way DP cache-aware × 1 (sanity baseline at same scale + ts=1)
+#
+# Goal: per docs/AGENTIC_FIT_ANALYSIS_ZH.md §7 / TEAM_REPORT §2.6 — all v3-v6 KVC
+# data was at time-scale=10 (inter-turn gap p50 = 0.25s, vs real 2.5s). This run
+# tests whether the gap structurally reverses any conclusion.
+#
+# CONFIG NOTE: Original experiments used 8 GPUs (2P6D / 8-way DP). This host has
+# only 4 H100s available, so we downscale proportionally to 1P3D / 4-way DP.
+# Cross-compare against existing 2P6D ts=10 data is confounded by *both*
+# time-scale and capacity. Internal comparison (1P3D KVC vs 4DP) at ts=1 is the
+# clean signal. §5 (P-side imbalance) is NOT testable here — only 1 P.
+#
+# Capacity ratio: 3D × ~92K tok = 276K KV pool vs 52 sessions × ~50K peak input
+#   working set ≈ 1.5M → ~5.4× overload (vs 2.7× in original 2P6D).
+#   Pressure is HIGHER than original; partly offset by ts=1 letting D drain between turns.
+#
+# Output:
+#   outputs/qwen3-30b-tp1-ts1-validation/
+#     ├── kvc_1p3d_run{1,2,3}_summary.json
+#     ├── kvc_1p3d_run{1,2,3}_metrics.jsonl
+#     ├── dp4_summary.json
+#     ├── dp4_metrics.jsonl
+#     └── kvcache-centric-... / pd-colo-kv-aware-...    (raw run dirs)
+#
+# Estimated GPU time: KVC ts=1 ≈ 100-180 min/run × 3 = 5-9h
+#                     DP ts=1  ≈ 100-120 min × 1     = ~2h
+#                     Total                           = 7-11h
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
+TRACE=outputs/qwen35-swebench-50sess.jsonl
+OUTPUT=outputs/qwen3-30b-tp1-ts1-validation
+VENV_PYTHON=.venv/bin/python
+RESULTS_FILE=$OUTPUT/sweep_results.txt
+
+mkdir -p $OUTPUT
+
+log() {
+  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE
+}
+
+run_kvc_1p3d() {
+  local run_idx=$1
+  local label="kvc_1p3d_run${run_idx}"
+  log ""
+  log "=== [KVC ${run_idx}/3] 1P3D KVC kv-aware Option D, time-scale=1 ==="
+  PYTHONPATH=src:third_party/sglang/python \
+  $VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
+    --trace $TRACE \
+    --output-root $OUTPUT \
+    --mechanism kvcache-centric \
+    --policy kv-aware \
+    --model-path $MODEL \
+    --prefill-workers 1 --decode-workers 3 \
+    --prefill-tp-size 1 --decode-tp-size 1 \
+    --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+    --transfer-backend mooncake \
+    --gpu-budget 4 \
+    --time-scale 1 \
+    --session-sample-rate 1.0 \
+    --target-duration-s 100000 \
+    --concurrency-limit 32 \
+    --timeout-s 900 \
+    --request-timeout-s 300 \
+    --kvcache-admission-mode worker \
+    --kvcache-seed-min-turn-id 1 \
+    --kvcache-seed-max-inflight-decode -1 \
+    --kvcache-prefill-backup-policy release-after-transfer \
+    --kvcache-prefill-priority-eviction
+
+  local run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
+  log "=== [KVC ${run_idx}/3] $label COMPLETED ==="
+  if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+    cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+    cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+    local errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
+    log "  errors = $errs"
+    cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
+    echo "" >> $RESULTS_FILE
+  else
+    log "WARNING: no summary file in $run_dir"
+  fi
+}
+
+run_dp4_sanity() {
+  local label="dp4"
+  log ""
+  log "=== [DP] 4-way DP cache-aware sanity, time-scale=1 ==="
+  PYTHONPATH=src:third_party/sglang/python \
+  $VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
+    --trace $TRACE \
+    --output-root $OUTPUT \
+    --mechanism pd-colo \
+    --policy kv-aware \
+    --model-path $MODEL \
+    --prefill-workers 0 --decode-workers 0 \
+    --direct-workers 4 --direct-tp-size 1 \
+    --direct-gpu-ids 0,1,2,3 \
+    --gpu-budget 4 \
+    --time-scale 1 \
+    --session-sample-rate 1.0 \
+    --target-duration-s 100000 \
+    --concurrency-limit 32 \
+    --timeout-s 900 \
+    --request-timeout-s 300
+
+  local run_dir=$(ls -td $OUTPUT/pd-colo-kv-aware-*/ 2>/dev/null | head -1)
+  log "=== [DP] $label COMPLETED ==="
+  if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+    cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+    cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+    local errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
+    log "  errors = $errs"
+    cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
+    echo "" >> $RESULTS_FILE
+  else
+    log "WARNING: no summary file in $run_dir"
+  fi
+}
+
+log "=== TS=1 VALIDATION (4-GPU): KVC 1P3D × N=3 + 4DP × 1 ==="
+log "Model: $MODEL"
+log "Trace: $TRACE (4449 requests, 52 sessions)"
+log "Goal: validate whether ts=10 was the main distortion in v3-v6 KVC vs DP"
+
+# KVC × 3 first (the new data we need); DP last (cheaper sanity at end)
+for i in 1 2 3; do
+  run_kvc_1p3d $i
+done
+
+run_dp4_sanity
+
+log ""
+log "=== TS=1 SUMMARY ==="
+for label in kvc_1p3d_run1 kvc_1p3d_run2 kvc_1p3d_run3 dp4; do
+  if [ -f "$OUTPUT/${label}_summary.json" ]; then
+    e=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
+    p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50','n/a'))")
+    log "  ${label}: errors=$e  lat_p50=${p50}s"
+  fi
+done
+log "=== TS=1 ALL DONE ==="

scripts/sweep_ts1_migration_v1.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+# Migration v1 validation: KVC 1P3D ts=1 with --kvcache-migration-reject-threshold=3
+# Compare against baseline outputs/qwen3-30b-tp1-ts1-validation/kvc_1p3d_run{1,2,3}
+# (all of which had no migration — runs were structurally identical).
+#
+# Goal: verify §1 fix changes the categorical outcome — direct-to-D % up,
+# fallback-session-not-resident % down, lat mean down.
+#
+# ts=1 is deterministic at the categorical level, so N=1 is sufficient
+# (TEAM_REPORT §2.8 revised).
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
+TRACE=outputs/qwen35-swebench-50sess.jsonl
+OUTPUT=outputs/qwen3-30b-tp1-ts1-migration-v1
+VENV_PYTHON=.venv/bin/python
+RESULTS_FILE=$OUTPUT/sweep_results.txt
+
+mkdir -p $OUTPUT
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE; }
+
+log "=== TS=1 MIGRATION v1: KVC 1P3D --kvcache-migration-reject-threshold=3 ==="
+log "Baseline reference: outputs/qwen3-30b-tp1-ts1-validation/kvc_1p3d_run1 (errors=5, lat mean=1.574s, direct-to-D=42.8%)"
+
+label=kvc_1p3d_migration_run1
+log ""
+log "=== [migration v1] starting ==="
+PYTHONPATH=src:third_party/sglang/python \
+$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
+  --trace $TRACE \
+  --output-root $OUTPUT \
+  --mechanism kvcache-centric \
+  --policy kv-aware \
+  --model-path $MODEL \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --gpu-budget 4 \
+  --time-scale 1 \
+  --session-sample-rate 1.0 \
+  --target-duration-s 100000 \
+  --concurrency-limit 32 \
+  --timeout-s 900 \
+  --request-timeout-s 300 \
+  --kvcache-admission-mode worker \
+  --kvcache-seed-min-turn-id 1 \
+  --kvcache-seed-max-inflight-decode -1 \
+  --kvcache-prefill-backup-policy release-after-transfer \
+  --kvcache-prefill-priority-eviction \
+  --kvcache-migration-reject-threshold 3
+
+run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
+log "=== [migration v1] $label COMPLETED ==="
+if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+  cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+  cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+  errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
+  p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50',0))")
+  log "  errors=$errs lat_p50=${p50}s"
+  cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
+fi
+log "=== migration v1 DONE ==="

scripts/sweep_ts1_migration_v2.sh

@@ -0,0 +1,76 @@
+#!/bin/bash
+# Migration v2 validation: KVC 1P3D ts=1 with BOTH:
+#   (1) reset-on-success blacklist decay (replay.py code change)
+#   (2) --kvcache-direct-max-uncached-tokens 8192 (was 2048 default)
+#
+# v1 results (kvc_1p3d_migration_run1) showed:
+#   - lat mean WORSE +11.7%, TTFT mean WORSE +71.3% — thrashing tax
+#   - direct-to-D rate UP +10.5pp (42.8 → 53.3%)
+#   - Fallback breakdown surprise: 41.3% are 'real-large-append' (>2048 tok),
+#     NOT 'session-not-resident' as we hypothesized
+#
+# v2 design (REFACTOR_PLAN_V1 + MIGRATION_V1_FINDINGS):
+#   (1) reset-on-success: clear (sess,D) reject counter on successful direct-to-D
+#       — eliminates blacklist-permanence bug → kills thrashing
+#   (2) bump direct-append threshold 2048 → 8192: lets more large-append turns
+#       go direct-to-D instead of fall through to seed (which often rejects)
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+MODEL=/mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
+TRACE=outputs/qwen35-swebench-50sess.jsonl
+OUTPUT=outputs/qwen3-30b-tp1-ts1-migration-v2
+VENV_PYTHON=.venv/bin/python
+RESULTS_FILE=$OUTPUT/sweep_results.txt
+
+mkdir -p $OUTPUT
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a $RESULTS_FILE; }
+
+log "=== TS=1 MIGRATION v2: reset-on-success + threshold=8192 ==="
+log "Baselines:"
+log "  baseline (no migration):        kvc_1p3d_run1 errors=5 lat_p50=0.811s ttft_p50=0.124s direct=42.8%"
+log "  v1 (migration permanent):       kvc_1p3d_migration_run1 errors=6 lat_p50=0.773s ttft_p50=0.057s direct=53.3% lat_mean=1.758s"
+log "  4DP ts=1:                       errors=0 lat_p50=0.659s ttft_p50=0.090s lat_mean=1.443s"
+log "Goal: kill thrashing tax (lat_mean ≤ 1.5s, p99 ≤ 9s) while preserving v1's direct-to-D gains."
+
+label=kvc_1p3d_migration_v2_run1
+log ""
+log "=== [migration v2] starting ==="
+PYTHONPATH=src:third_party/sglang/python \
+$VENV_PYTHON -m agentic_pd_hybrid.cli benchmark-live \
+  --trace $TRACE \
+  --output-root $OUTPUT \
+  --mechanism kvcache-centric \
+  --policy kv-aware \
+  --model-path $MODEL \
+  --prefill-workers 1 --decode-workers 3 \
+  --prefill-tp-size 1 --decode-tp-size 1 \
+  --prefill-gpu-ids 0 --decode-gpu-ids 1,2,3 \
+  --transfer-backend mooncake \
+  --gpu-budget 4 \
+  --time-scale 1 \
+  --session-sample-rate 1.0 \
+  --target-duration-s 100000 \
+  --concurrency-limit 32 \
+  --timeout-s 900 \
+  --request-timeout-s 300 \
+  --kvcache-admission-mode worker \
+  --kvcache-seed-min-turn-id 1 \
+  --kvcache-seed-max-inflight-decode -1 \
+  --kvcache-prefill-backup-policy release-after-transfer \
+  --kvcache-prefill-priority-eviction \
+  --kvcache-migration-reject-threshold 3 \
+  --kvcache-direct-max-uncached-tokens 8192
+
+run_dir=$(ls -td $OUTPUT/kvcache-centric-*/ 2>/dev/null | head -1)
+log "=== [migration v2] $label COMPLETED ==="
+if [ -f "$run_dir/request-metrics.jsonl.summary.json" ]; then
+  cp "$run_dir/request-metrics.jsonl.summary.json" "$OUTPUT/${label}_summary.json"
+  cp "$run_dir/request-metrics.jsonl" "$OUTPUT/${label}_metrics.jsonl"
+  errs=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('error_count',0))")
+  p50=$($VENV_PYTHON -c "import json; d=json.load(open('$OUTPUT/${label}_summary.json')); print(d.get('latency_stats_s',{}).get('p50',0))")
+  log "  errors=$errs lat_p50=${p50}s"
+  cat "$run_dir/request-metrics.jsonl.summary.json" >> $RESULTS_FILE
+fi
+log "=== migration v2 DONE ==="

src/agentic_pd_hybrid/benchmark.py

@@ -3,20 +3,13 @@ from __future__ import annotations
 import asyncio
 import json
 import signal
-import shutil
-from collections import Counter
 from dataclasses import asdict, dataclass, replace
 from datetime import UTC, datetime
 from pathlib import Path
 
 from agentic_pd_hybrid.replay import ReplayConfig, replay_trace
-from agentic_pd_hybrid.sampling import (
-    SessionSampleConfig,
-    SessionSampleSummary,
-    sample_trace_sessions,
-)
+from agentic_pd_hybrid.sampling import SessionSampleConfig, sample_trace_sessions
 from agentic_pd_hybrid.stack import ManagedPdStack, launch_pd_stack
-from agentic_pd_hybrid.trace import load_trace
 from agentic_pd_hybrid.topology import SingleNodeTopology
 
 
@@ -54,14 +47,14 @@ class BenchmarkConfig:
     pool_poll_include_sessions: bool = True
     enable_backpressure: bool = False
     backpressure_max_pause_s: float = 2.0
-    progress_interval_s: float = 30.0
+    kvcache_migration_reject_threshold: int = 3
+    kvcache_load_floor_bonus: int = 0
     sample_profile: str = "default"
     min_initial_input_tokens: int | None = None
     max_initial_input_tokens: int | None = None
     max_append_input_tokens: int | None = None
     max_output_tokens: int | None = None
     min_overlap_ratio: float | None = None
-    use_trace_as_sample: bool = False
     launch_stack: bool = True
 
 
@@ -103,37 +96,22 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
         )
 
     sampled_trace_path = run_dir / "sampled-trace.jsonl"
-    if config.use_trace_as_sample:
-        shutil.copyfile(config.trace_path, sampled_trace_path)
-        sample_summary = _summarize_trace_sample(
-            input_trace_path=config.trace_path,
-            sampled_trace_path=sampled_trace_path,
-            profile=config.sample_profile,
+    sample_summary = sample_trace_sessions(
+        SessionSampleConfig(
+            trace_path=config.trace_path,
+            output_path=sampled_trace_path,
+            target_duration_s=config.target_duration_s,
+            start_time_s=config.start_time_s,
             session_sample_rate=config.session_sample_rate,
             min_turns=config.min_turns,
+            profile=config.sample_profile,  # type: ignore[arg-type]
             min_initial_input_tokens=config.min_initial_input_tokens,
             max_initial_input_tokens=config.max_initial_input_tokens,
             max_append_input_tokens=config.max_append_input_tokens,
             max_output_tokens=config.max_output_tokens,
             min_overlap_ratio=config.min_overlap_ratio,
         )
-    else:
-        sample_summary = sample_trace_sessions(
-            SessionSampleConfig(
-                trace_path=config.trace_path,
-                output_path=sampled_trace_path,
-                target_duration_s=config.target_duration_s,
-                start_time_s=config.start_time_s,
-                session_sample_rate=config.session_sample_rate,
-                min_turns=config.min_turns,
-                profile=config.sample_profile,  # type: ignore[arg-type]
-                min_initial_input_tokens=config.min_initial_input_tokens,
-                max_initial_input_tokens=config.max_initial_input_tokens,
-                max_append_input_tokens=config.max_append_input_tokens,
-                max_output_tokens=config.max_output_tokens,
-                min_overlap_ratio=config.min_overlap_ratio,
-            )
-        )
+    )
 
     stack: ManagedPdStack | None = None
     previous_sigint = signal.getsignal(signal.SIGINT)
@@ -222,7 +200,8 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
             pool_poll_include_sessions=config.pool_poll_include_sessions,
             enable_backpressure=config.enable_backpressure,
             backpressure_max_pause_s=config.backpressure_max_pause_s,
-            progress_interval_s=config.progress_interval_s,
+            kvcache_migration_reject_threshold=config.kvcache_migration_reject_threshold,
+            kvcache_load_floor_bonus=config.kvcache_load_floor_bonus,
         )
         if config.request_timeout_s is not None:
             replay_config = replace(
@@ -283,14 +262,14 @@ def run_live_benchmark(config: BenchmarkConfig) -> BenchmarkArtifacts:
                 "pool_poll_include_sessions": config.pool_poll_include_sessions,
                 "enable_backpressure": config.enable_backpressure,
                 "backpressure_max_pause_s": config.backpressure_max_pause_s,
-                "progress_interval_s": config.progress_interval_s,
+                "kvcache_migration_reject_threshold": config.kvcache_migration_reject_threshold,
+                "kvcache_load_floor_bonus": config.kvcache_load_floor_bonus,
                 "sample_profile": config.sample_profile,
                 "min_initial_input_tokens": config.min_initial_input_tokens,
                 "max_initial_input_tokens": config.max_initial_input_tokens,
                 "max_append_input_tokens": config.max_append_input_tokens,
                 "max_output_tokens": config.max_output_tokens,
                 "min_overlap_ratio": config.min_overlap_ratio,
-                "use_trace_as_sample": config.use_trace_as_sample,
                 "sample_summary": asdict(sample_summary),
                 "topology": {
                     "model_path": config.topology.model_path,
@@ -337,44 +316,3 @@ def _header_mode_for(policy_name: str) -> str:
     if policy_name == "kv-aware":
         return "target-worker"
     return "none"
-
-
-def _summarize_trace_sample(
-    *,
-    input_trace_path: Path,
-    sampled_trace_path: Path,
-    profile: str,
-    session_sample_rate: float,
-    min_turns: int,
-    min_initial_input_tokens: int | None,
-    max_initial_input_tokens: int | None,
-    max_append_input_tokens: int | None,
-    max_output_tokens: int | None,
-    min_overlap_ratio: float | None,
-) -> SessionSampleSummary:
-    requests = load_trace(sampled_trace_path)
-    if not requests:
-        raise ValueError(f"Trace sample is empty: {sampled_trace_path}")
-    session_turns = Counter(request.session_id for request in requests)
-    start_time_s = requests[0].timestamp_s
-    end_time_s = requests[-1].timestamp_s
-    return SessionSampleSummary(
-        input_trace_path=str(input_trace_path),
-        output_trace_path=str(sampled_trace_path),
-        request_count=len(requests),
-        session_count=len(session_turns),
-        multi_turn_session_count=sum(1 for turns in session_turns.values() if turns > 1),
-        start_time_s=start_time_s,
-        end_time_s=end_time_s,
-        sampled_duration_s=end_time_s - start_time_s,
-        session_sample_rate=session_sample_rate,
-        min_turns=min_turns,
-        profile=profile,
-        min_initial_input_tokens=min_initial_input_tokens,
-        max_initial_input_tokens=max_initial_input_tokens,
-        max_append_input_tokens=max_append_input_tokens,
-        max_output_tokens=max_output_tokens,
-        min_overlap_ratio=min_overlap_ratio,
-        mean_append_input_tokens=None,
-        mean_turn_overlap_ratio=None,
-    )

src/agentic_pd_hybrid/cli.py

@@ -2,7 +2,6 @@ from __future__ import annotations
 
 import argparse
 import asyncio
-import shlex
 from pathlib import Path
 
 from agentic_pd_hybrid.benchmark import BenchmarkConfig, run_live_benchmark
@@ -262,12 +261,26 @@ def main() -> None:
         help="Cap on per-request backpressure sleep, regardless of D hint.",
     )
     replay.add_argument(
-        "--progress-interval-s",
-        type=float,
-        default=30.0,
+        "--kvcache-migration-reject-threshold",
+        type=int,
+        default=3,
         help=(
-            "Write client-side replay progress to <output_dir>/replay-progress.jsonl "
-            "every N seconds. 0 disables the heartbeat."
+            "Per-(session, D) admission-reject count after which KvAwarePolicy "
+            "skips that D for the session (forces migration). 0 disables. "
+            "See REFACTOR_PLAN_V1 §6.2 / TEAM_REPORT §2.1."
+        ),
+    )
+    replay.add_argument(
+        "--kvcache-load-floor-bonus",
+        type=int,
+        default=0,
+        help=(
+            "Graduated bonus added to lex-score position 0 for under-loaded D "
+            "workers (gated on not-sticky so turn-1+ requests still stick). "
+            "Magnitude scales as K * max(0, mean - assigned[D]) / mean. "
+            "Set above max expected cross-session boilerplate overlap "
+            "(Inferact ~50 → use 200). 0 disables. "
+            "See docs/E1_E2_FIX_DESIGN_ZH.md §Q2."
         ),
     )
 
@@ -512,12 +525,26 @@ def main() -> None:
         help="Cap on per-request backpressure sleep, regardless of D hint.",
     )
     benchmark.add_argument(
-        "--progress-interval-s",
-        type=float,
-        default=30.0,
+        "--kvcache-migration-reject-threshold",
+        type=int,
+        default=3,
         help=(
-            "Write client-side replay progress to <run_dir>/replay-progress.jsonl "
-            "every N seconds. 0 disables the heartbeat."
+            "Per-(session, D) admission-reject count after which KvAwarePolicy "
+            "skips that D for the session (forces migration). 0 disables. "
+            "See REFACTOR_PLAN_V1 §6.2 / TEAM_REPORT §2.1."
+        ),
+    )
+    benchmark.add_argument(
+        "--kvcache-load-floor-bonus",
+        type=int,
+        default=0,
+        help=(
+            "Graduated bonus added to lex-score position 0 for under-loaded D "
+            "workers (gated on not-sticky so turn-1+ requests still stick). "
+            "Magnitude scales as K * max(0, mean - assigned[D]) / mean. "
+            "Set above max expected cross-session boilerplate overlap "
+            "(Inferact ~50 → use 200). 0 disables. "
+            "See docs/E1_E2_FIX_DESIGN_ZH.md §Q2."
         ),
     )
     benchmark.add_argument(
@@ -531,14 +558,6 @@ def main() -> None:
     benchmark.add_argument("--max-append-input-tokens", type=int, default=None)
     benchmark.add_argument("--max-output-tokens", type=int, default=None)
     benchmark.add_argument("--min-overlap-ratio", type=float, default=None)
-    benchmark.add_argument(
-        "--use-trace-as-sample",
-        action="store_true",
-        help=(
-            "Replay the provided --trace exactly instead of sampling sessions into "
-            "a new trace. Use this for prebuilt real-workload samples."
-        ),
-    )
 
     args = parser.parse_args()
 
@@ -613,7 +632,8 @@ def main() -> None:
             pool_poll_include_sessions=not args.pool_poll_no_sessions,
             enable_backpressure=args.enable_backpressure,
             backpressure_max_pause_s=args.backpressure_max_pause_s,
-            progress_interval_s=args.progress_interval_s,
+            kvcache_migration_reject_threshold=args.kvcache_migration_reject_threshold,
+            kvcache_load_floor_bonus=args.kvcache_load_floor_bonus,
         )
         results = asyncio.run(replay_trace(config))
         print(
@@ -760,14 +780,14 @@ def main() -> None:
                 pool_poll_include_sessions=not args.pool_poll_no_sessions,
                 enable_backpressure=args.enable_backpressure,
                 backpressure_max_pause_s=args.backpressure_max_pause_s,
-                progress_interval_s=args.progress_interval_s,
+                kvcache_migration_reject_threshold=args.kvcache_migration_reject_threshold,
+                kvcache_load_floor_bonus=args.kvcache_load_floor_bonus,
                 sample_profile=args.sample_profile,
                 min_initial_input_tokens=args.min_initial_input_tokens,
                 max_initial_input_tokens=args.max_initial_input_tokens,
                 max_append_input_tokens=args.max_append_input_tokens,
                 max_output_tokens=args.max_output_tokens,
                 min_overlap_ratio=args.min_overlap_ratio,
-                use_trace_as_sample=args.use_trace_as_sample,
                 launch_stack=True,
             )
         )
@@ -827,26 +847,6 @@ def _add_topology_arguments(parser: argparse.ArgumentParser) -> None:
         "--no-trust-remote-code",
         action="store_true",
     )
-    parser.add_argument(
-        "--extra-server-args",
-        default="",
-        help="Extra arguments appended to every sglang.launch_server command.",
-    )
-    parser.add_argument(
-        "--prefill-extra-server-args",
-        default="",
-        help="Extra arguments appended only to prefill launch_server commands.",
-    )
-    parser.add_argument(
-        "--decode-extra-server-args",
-        default="",
-        help="Extra arguments appended only to decode launch_server commands.",
-    )
-    parser.add_argument(
-        "--direct-extra-server-args",
-        default="",
-        help="Extra arguments appended only to direct launch_server commands.",
-    )
 
 
 def _topology_from_args(args: argparse.Namespace):
@@ -876,13 +876,9 @@ def _topology_from_args(args: argparse.Namespace):
         force_rdma=args.force_rdma,
         trust_remote_code=not args.no_trust_remote_code,
         ib_device=args.ib_device,
-        extra_server_args=tuple(shlex.split(args.extra_server_args)),
-        prefill_extra_server_args=tuple(shlex.split(args.prefill_extra_server_args)),
-        decode_extra_server_args=tuple(shlex.split(args.decode_extra_server_args)),
-        direct_extra_server_args=(
-            "--enable-streaming-session",
-            *tuple(shlex.split(args.direct_extra_server_args)),
-        ),
+        prefill_extra_server_args=("--disable-overlap-schedule",),
+        decode_extra_server_args=("--disable-overlap-schedule",),
+        direct_extra_server_args=("--enable-streaming-session",),
     )
 
 

src/agentic_pd_hybrid/metrics.py

@@ -114,6 +114,16 @@ def write_metrics_jsonl(path: Path, rows: list[RequestMetrics]) -> None:
             handle.write(json.dumps(asdict(row), sort_keys=True) + "\n")
 
 
+def _is_failed_request(row: RequestMetrics) -> bool:
+    if row.error is not None:
+        return True
+    if row.finish_reason is not None:
+        fr = str(row.finish_reason).lower()
+        if "abort" in fr or "badrequest" in fr:
+            return True
+    return False
+
+
 def write_summary_json(
     path: Path,
     rows: list[RequestMetrics],
@@ -121,9 +131,10 @@ def write_summary_json(
     trace_path: Path,
     router_url: str | None,
 ) -> None:
-    latencies = [row.latency_s for row in rows if row.latency_s is not None]
-    ttfts = [row.ttft_s for row in rows if row.ttft_s is not None]
-    tpots = [row.tpot_s for row in rows if row.tpot_s is not None]
+    successful = [row for row in rows if not _is_failed_request(row)]
+    latencies = [row.latency_s for row in successful if row.latency_s is not None]
+    ttfts = [row.ttft_s for row in successful if row.ttft_s is not None]
+    tpots = [row.tpot_s for row in successful if row.tpot_s is not None]
     per_decode_load = Counter(row.assigned_decode_node for row in rows)
     per_prefill_load = Counter(row.assigned_prefill_node for row in rows)
     prefill_priorities = Counter(
@@ -167,6 +178,17 @@ def write_summary_json(
             str(key): value for key, value in sorted(decode_priorities.items())
         },
         "error_count": sum(1 for row in rows if row.error is not None),
+        "abort_count": sum(
+            1
+            for row in rows
+            if row.error is None
+            and row.finish_reason is not None
+            and (
+                "abort" in str(row.finish_reason).lower()
+                or "badrequest" in str(row.finish_reason).lower()
+            )
+        ),
+        "failure_count": sum(1 for row in rows if _is_failed_request(row)),
         "truncated_request_count": sum(
             1
             for row in rows

src/agentic_pd_hybrid/policies.py

@@ -44,6 +44,10 @@ class RoutingState:
     inflight_decode: Counter[str] = field(default_factory=Counter)
     decode_assignment_counts: Counter[str] = field(default_factory=Counter)
     decode_resident_blocks: dict[str, set[int]] = field(default_factory=dict)
+    # Migration support: per-(session_id, decode_worker_id) admission reject counter.
+    # KvAwarePolicy uses this to skip D's that have repeatedly rejected this session
+    # (avoids the structural starvation observed in TEAM_REPORT §2.1).
+    session_d_rejects: Counter[tuple[str, str]] = field(default_factory=Counter)
 
     @classmethod
     def create(cls, topology: SingleNodeTopology) -> "RoutingState":
@@ -66,6 +70,12 @@ class RoutingState:
         self.decode_cursor += 1
         return worker.worker_id
 
+    def record_admission_reject(self, session_id: str, decode_worker_id: str) -> int:
+        """Increment per-(session, D) rejection counter. Returns new count."""
+        key = (session_id, decode_worker_id)
+        self.session_d_rejects[key] += 1
+        return self.session_d_rejects[key]
+
     def finish(self, request: TraceRequest, decision: RoutingDecision) -> None:
         session = self.session_state.setdefault(request.session_id, SessionRouteState())
         session.last_decode_worker = decision.decode_worker_id
@@ -142,10 +152,64 @@ class StickyDecodePolicy:
         )
 
 
+CandidateScore = tuple[int, int, int, int]
+
+
+def score_candidate(
+    *,
+    overlap: int,
+    sticky: bool,
+    inflight: int,
+    assigned: int,
+    mean_assigned: float,
+    sticky_bonus: int,
+    load_floor_bonus: int,
+) -> CandidateScore:
+    """Pure scoring function for KvAwarePolicy (Algorithm 1 in KVC_ROUTER_ALGORITHM.md).
+
+    Returns the 4-tuple compared lexicographically by `select()` to pick the
+    best D. Extracted as a top-level function so unit tests can exercise it
+    without constructing topology/state objects.
+
+    Score tuple positions:
+        0: overlap + sticky_bonus*sticky + floor_bonus  — primary, KV reuse aware
+        1: sticky                                         — tie-1, session locality
+        2: -inflight                                      — tie-2, prefer low load
+        3: -assigned                                      — tie-3, prefer rarely-picked
+
+    Load-floor bonus is gated on `not sticky` (turn-1+ sessions continue to
+    stick to their original D). The boost magnitude scales linearly with the
+    D's deficit relative to the running mean of decode_assignment_counts:
+        floor_bonus = load_floor_bonus * max(0, mean - assigned) / max(1, mean)
+    When mean == 0 (warmup) the bonus is 0 for all candidates (lex tiebreak
+    falls through to iteration order).
+
+    See docs/E1_E2_FIX_DESIGN_ZH.md §Q2 for the load-floor design and
+    docs/KVC_ROUTER_ALGORITHM.md §3.1 for the lex-score formalism.
+    """
+    floor_bonus = 0
+    if load_floor_bonus > 0 and not sticky and mean_assigned > 0:
+        deficit = max(0.0, mean_assigned - assigned)
+        floor_bonus = int(load_floor_bonus * deficit / mean_assigned)
+    primary = overlap + (sticky_bonus if sticky else 0) + floor_bonus
+    return (primary, int(sticky), -inflight, -assigned)
+
+
 @dataclass(frozen=True)
 class KvAwarePolicy:
     name: str = "kv-aware"
     sticky_bonus: int = 1
+    # Session migration: when (session, D) has been rejected this many times,
+    # skip D entirely for this session (force migration to another D).
+    # 0 disables the mechanism. Default 3 picked empirically to allow brief
+    # transient saturation without panicking, but to reroute persistent starvation.
+    migration_reject_threshold: int = 3
+    # Load-floor bonus: see score_candidate() docstring for the exact formula.
+    # Set above the max cross-session boilerplate overlap you expect (so fresh
+    # sessions reach under-loaded D's even at 0 overlap), but below the
+    # magnitude of "real" prefix overlap (so a warm D still wins for its own
+    # session). 0 disables.
+    load_floor_bonus: int = 0
 
     def select(
         self,
@@ -157,23 +221,48 @@ class KvAwarePolicy:
         prefill_worker_id = state.next_prefill_worker_id(topology)
         session = state.session_state.get(request.session_id)
 
+        n_route_workers = max(1, len(topology.route_workers))
+        total_assigned = sum(state.decode_assignment_counts.values())
+        mean_assigned = total_assigned / n_route_workers
+
         best_decode_worker_id: str | None = None
-        best_score: tuple[int, int, int] | None = None
+        best_score: CandidateScore | None = None
         for worker in topology.route_workers:
-            overlap = _overlap_blocks(request, state, worker.worker_id)
-            sticky = int(session is not None and session.last_decode_worker == worker.worker_id)
-            inflight_penalty = -state.inflight_decode.get(worker.worker_id, 0)
-            assignment_penalty = -state.decode_assignment_counts.get(worker.worker_id, 0)
-            score = (
-                overlap + sticky * self.sticky_bonus,
-                sticky,
-                inflight_penalty,
-                assignment_penalty,
+            # Migration: skip workers that have rejected this session too many times.
+            # If all candidates get filtered (degenerate case), fall through to
+            # un-filtered selection below.
+            if self.migration_reject_threshold > 0:
+                rejects = state.session_d_rejects.get(
+                    (request.session_id, worker.worker_id), 0
+                )
+                if rejects >= self.migration_reject_threshold:
+                    continue
+            score = score_candidate(
+                overlap=_overlap_blocks(request, state, worker.worker_id),
+                sticky=(
+                    session is not None
+                    and session.last_decode_worker == worker.worker_id
+                ),
+                inflight=state.inflight_decode.get(worker.worker_id, 0),
+                assigned=state.decode_assignment_counts.get(worker.worker_id, 0),
+                mean_assigned=mean_assigned,
+                sticky_bonus=self.sticky_bonus,
+                load_floor_bonus=self.load_floor_bonus,
             )
             if best_score is None or score > best_score:
                 best_score = score
                 best_decode_worker_id = worker.worker_id
 
+        # Degenerate fallback: every D was filtered. Pick the least-rejected D.
+        if best_decode_worker_id is None:
+            best_decode_worker_id = min(
+                (w.worker_id for w in topology.route_workers),
+                key=lambda wid: state.session_d_rejects.get(
+                    (request.session_id, wid), 0
+                ),
+            )
+            best_score = (0, 0, 0, 0)
+
         assert best_decode_worker_id is not None
         reuse_expected = bool(best_score and best_score[0] > 0)
         return _build_decision(
@@ -187,14 +276,22 @@ class KvAwarePolicy:
         )
 
 
-def create_policy(name: str) -> RoutingPolicy:
+def create_policy(
+    name: str,
+    *,
+    migration_reject_threshold: int = 3,
+    load_floor_bonus: int = 0,
+) -> RoutingPolicy:
     normalized = name.strip().lower()
     if normalized == "default":
         return DefaultPolicy()
     if normalized == "sticky":
         return StickyDecodePolicy()
     if normalized in {"kv-aware", "kv_aware", "kv"}:
-        return KvAwarePolicy()
+        return KvAwarePolicy(
+            migration_reject_threshold=migration_reject_threshold,
+            load_floor_bonus=load_floor_bonus,
+        )
     raise ValueError(f"Unsupported policy: {name}")
 
 

src/agentic_pd_hybrid/replay.py

@@ -106,8 +106,17 @@ class ReplayConfig:
     pool_poll_include_sessions: bool = True
     enable_backpressure: bool = False
     backpressure_max_pause_s: float = 2.0
+    # Session migration via per-(sess, D) admission reject memory.
+    # When a session has been admission-rejected this many times on a given D,
+    # KvAwarePolicy skips that D for the session (forcing migration). Default 3.
+    # Set 0 to disable. See REFACTOR_PLAN_V1 §6.2.
+    kvcache_migration_reject_threshold: int = 3
+    # Load-floor bonus magnitude for KvAwarePolicy: graduated boost added to
+    # under-loaded D workers to break overlap-pinning imbalance on workloads
+    # with shared cross-session prefix. 0 disables. See
+    # docs/E1_E2_FIX_DESIGN_ZH.md §Q2.
+    kvcache_load_floor_bonus: int = 0
     structural_log_dir: Path | None = None
-    progress_interval_s: float = 30.0
 
 
 @dataclass
@@ -175,62 +184,6 @@ class ExecutionResult:
     finish_reason: str | None = None
 
 
-@dataclass
-class ReplayProgress:
-    total_requests: int
-    output_path: Path
-    interval_s: float
-    start_time_s: float
-    submitted_count: int = 0
-    completed_count: int = 0
-    error_count: int = 0
-    truncated_count: int = 0
-    last_request_id: str | None = None
-    last_session_id: str | None = None
-    last_trace_timestamp_s: float | None = None
-    execution_modes: Counter[str] = field(default_factory=Counter)
-    lock: asyncio.Lock = field(default_factory=asyncio.Lock)
-
-    async def record_submitted(self, request: TraceRequest) -> None:
-        async with self.lock:
-            self.submitted_count += 1
-            self.last_request_id = request.request_id
-            self.last_session_id = request.session_id
-            self.last_trace_timestamp_s = request.timestamp_s
-
-    async def record_completed(self, row: RequestMetrics) -> None:
-        async with self.lock:
-            self.completed_count += 1
-            if row.error is not None:
-                self.error_count += 1
-            if _is_truncated(row):
-                self.truncated_count += 1
-            self.execution_modes[row.execution_mode] += 1
-            self.last_request_id = row.request_id
-            self.last_session_id = row.session_id
-            self.last_trace_timestamp_s = row.trace_timestamp_s
-
-    async def emit(self, phase: str) -> None:
-        async with self.lock:
-            event = {
-                "phase": phase,
-                "elapsed_s": round(time.perf_counter() - self.start_time_s, 3),
-                "total_requests": self.total_requests,
-                "submitted_count": self.submitted_count,
-                "completed_count": self.completed_count,
-                "inflight_count": self.submitted_count - self.completed_count,
-                "error_count": self.error_count,
-                "truncated_count": self.truncated_count,
-                "last_request_id": self.last_request_id,
-                "last_session_id": self.last_session_id,
-                "last_trace_timestamp_s": self.last_trace_timestamp_s,
-                "execution_modes": dict(sorted(self.execution_modes.items())),
-            }
-        self.output_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.output_path.open("a", encoding="utf-8") as handle:
-            handle.write(json.dumps(event, sort_keys=True) + "\n")
-
-
 async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
     structural_dir = config.structural_log_dir
     if structural_dir is None and config.output_path is not None:
@@ -247,7 +200,11 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
                 if turn_count > 1
             ),
         )
-    policy = create_policy(config.policy_name)
+    policy = create_policy(
+        config.policy_name,
+        migration_reject_threshold=config.kvcache_migration_reject_threshold,
+        load_floor_bonus=config.kvcache_load_floor_bonus,
+    )
     state = RoutingState.create(config.topology)
     state_lock = asyncio.Lock()
     semaphore = asyncio.Semaphore(config.concurrency_limit)
@@ -256,23 +213,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
     session_tail_tasks: dict[str, asyncio.Task[RequestMetrics]] = {}
     direct_sessions: dict[str, DirectSessionState] = {}
     direct_session_lock = asyncio.Lock()
-    progress = (
-        ReplayProgress(
-            total_requests=len(requests),
-            output_path=config.output_path.parent / "replay-progress.jsonl",
-            interval_s=config.progress_interval_s,
-            start_time_s=start_time,
-        )
-        if config.progress_interval_s > 0
-        else None
-    )
-    progress_stop = asyncio.Event()
-    progress_task: asyncio.Task[None] | None = None
-    if progress is not None:
-        await progress.emit("start")
-        progress_task = asyncio.create_task(
-            _progress_heartbeat(progress, progress_stop)
-        )
     async with httpx.AsyncClient(timeout=config.timeout_s, trust_env=False) as client:
         decode_residency = await _discover_decode_residency(
             client=client,
@@ -304,8 +244,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
                 sleep_s = target_offset - (time.perf_counter() - start_time)
                 if sleep_s > 0:
                     await asyncio.sleep(sleep_s)
-            if progress is not None:
-                await progress.record_submitted(request)
             tasks.append(
                 asyncio.create_task(
                     _run_request(
@@ -320,15 +258,12 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
                         direct_session_lock=direct_session_lock,
                         decode_residency=decode_residency,
                         depends_on=session_tail_tasks.get(request.session_id),
-                        progress=progress,
                     )
                 )
             )
             session_tail_tasks[request.session_id] = tasks[-1]
 
         results = await asyncio.gather(*tasks)
-        if progress is not None:
-            await progress.emit("requests-complete")
         if poll_task is not None:
             poll_task.cancel()
             try:
@@ -364,14 +299,6 @@ async def replay_trace(config: ReplayConfig) -> list[RequestMetrics]:
         trace_path=config.trace_path,
         router_url=config.router_url,
     )
-    if progress is not None:
-        await progress.emit("final")
-        progress_stop.set()
-    if progress_task is not None:
-        try:
-            await progress_task
-        except asyncio.CancelledError:
-            pass
     _structural_close()
     return results
 
@@ -389,7 +316,6 @@ async def _run_request(
     direct_session_lock: asyncio.Lock,
     decode_residency: DecodeResidencyState,
     depends_on: asyncio.Task[RequestMetrics] | None,
-    progress: ReplayProgress | None = None,
 ) -> RequestMetrics:
     if depends_on is not None:
         await depends_on
@@ -438,8 +364,24 @@ async def _run_request(
 
         async with state_lock:
             state.finish(request, decision)
+            # Migration feedback: if this request was forced into a fallback path
+            # because the chosen D rejected admission, record the (session, D)
+            # rejection so KvAwarePolicy can migrate this session next turn.
+            if _is_admission_rejection_mode(execution.execution_mode):
+                state.record_admission_reject(
+                    request.session_id,
+                    decision.decode_worker_id,
+                )
+            # Reset-on-success: a successful direct-to-D path proves D-X can
+            # currently serve this session — clear the cumulative reject counter
+            # so that brief past saturation doesn't permanently blacklist the D.
+            # (MIGRATION_V1_FINDINGS §4.1: blacklist-permanence bug fix.)
+            elif execution.execution_mode == "kvcache-direct-to-d-session":
+                state.session_d_rejects[
+                    (request.session_id, decision.decode_worker_id)
+                ] = 0
 
-        row = RequestMetrics.from_decision(
+        return RequestMetrics.from_decision(
             request,
             decision,
             mechanism_name=config.mechanism_name,
@@ -459,29 +401,6 @@ async def _run_request(
             requested_output_tokens=execution.requested_output_tokens,
             finish_reason=execution.finish_reason,
         )
-        if progress is not None:
-            await progress.record_completed(row)
-        return row
-
-
-async def _progress_heartbeat(
-    progress: ReplayProgress,
-    stop_event: asyncio.Event,
-) -> None:
-    while not stop_event.is_set():
-        try:
-            await asyncio.wait_for(stop_event.wait(), timeout=progress.interval_s)
-        except asyncio.TimeoutError:
-            await progress.emit("heartbeat")
-
-
-def _is_truncated(row: RequestMetrics) -> bool:
-    return (
-        row.actual_output_tokens is not None
-        and row.requested_output_tokens is not None
-        and row.requested_output_tokens > 1
-        and row.actual_output_tokens < row.requested_output_tokens * 0.5
-    )
 
 
 async def _invoke_router(
@@ -754,41 +673,16 @@ async def _open_streaming_session(
         request.input_length * 16,
         (request.input_length + request.output_length) * 16,
     )
-    payload = {
-        "capacity_of_str_len": capacity,
-        "session_id": session_id,
-        "streaming": True,
-    }
-    url = f"{server_url.rstrip('/')}/open_session"
-    response = await client.post(url, json=payload, timeout=_ADMISSION_PROBE_TIMEOUT_S)
-    try:
-        response.raise_for_status()
-    except httpx.HTTPStatusError:
-        if response.status_code != 400:
-            raise
-        await _structural_emit(
-            "session-lifecycle.jsonl",
-            {
-                "event": "open-session-400-retry",
-                "server_url": server_url,
-                "session_id": session_id,
-                "request_id": request.request_id,
-                "turn_id": request.turn_id,
-                "response_text": response.text[:512],
-            },
-        )
-        await _close_streaming_session(
-            client=client,
-            server_url=server_url,
-            session_id=session_id,
-            allow_missing=True,
-        )
-        response = await client.post(
-            url,
-            json=payload,
-            timeout=_ADMISSION_PROBE_TIMEOUT_S,
-        )
-        response.raise_for_status()
+    response = await client.post(
+        f"{server_url.rstrip('/')}/open_session",
+        json={
+            "capacity_of_str_len": capacity,
+            "session_id": session_id,
+            "streaming": True,
+        },
+        timeout=_ADMISSION_PROBE_TIMEOUT_S,
+    )
+    response.raise_for_status()
     opened_session_id = response.json()
     if opened_session_id != session_id:
         raise ValueError(
@@ -1485,6 +1379,49 @@ def _is_stale_decode_session_error(exc: Exception) -> bool:
     )
 
 
+# execution_mode substrings that signal D-side admission rejected this request.
+# Used by _run_request to update state.session_d_rejects so KvAwarePolicy can
+# migrate persistently-starved sessions to a different D next turn.
+_ADMISSION_REJECTION_SUBSTRINGS = (
+    "session-cap",
+    "no-d-capacity",
+    "d-backpressure",
+)
+
+
+def _is_admission_rejection_mode(execution_mode: str) -> bool:
+    return any(token in execution_mode for token in _ADMISSION_REJECTION_SUBSTRINGS)
+
+
+def _fallthrough_reason(
+    *,
+    request: TraceRequest,
+    config: ReplayConfig,
+    decision,
+    direct_append_length: int | None,
+    direct_session_reused: bool,
+    direct_session_reset: bool,
+) -> str:
+    """Classify why a turn-2+ KVC request fell through to the seed/large-append branch.
+
+    Returns a short label suffix used in execution_mode strings to replace the
+    misleading 'large-append' label (TEAM_REPORT §2.7). In particular,
+    'session-not-resident' is the §1 starvation signature — direct_session_reused
+    is False because the session was never opened on the policy-chosen D.
+    """
+    if not direct_session_reused:
+        return "session-not-resident"
+    if direct_session_reset:
+        return "session-was-evicted"
+    if direct_append_length is None:
+        return "no-direct-info"
+    if direct_append_length > config.kvcache_direct_max_uncached_tokens:
+        return "real-large-append"
+    if not _should_bypass_prefill(request=request, config=config, decision=decision):
+        return "policy-no-bypass"
+    return "other-large-append"
+
+
 def _dynamic_decode_headroom_tokens(
     *,
     residency: DecodeResidencyState,
@@ -2646,6 +2583,17 @@ async def _execute_request(
                 decode_residency=decode_residency,
             )
 
+        # TEAM_REPORT §2.7: 'large-append' is misleading — most fallthroughs are
+        # actually 'session-not-resident-on-pinned-D' (§1 starvation). Classify
+        # the real reason and embed it in the execution_mode label.
+        fallthrough = _fallthrough_reason(
+            request=request,
+            config=config,
+            decision=decision,
+            direct_append_length=direct_append_length,
+            direct_session_reused=direct_session_reused,
+            direct_session_reset=direct_session_reset,
+        )
         seed_filter_reason = _seed_filter_reason(
             request=request,
             config=config,
@@ -2657,7 +2605,7 @@ async def _execute_request(
                 client=client,
                 config=config,
                 decision=decision,
-                execution_mode=f"pd-router-fallback-large-append-{seed_filter_reason}",
+                execution_mode=f"pd-router-fallback-{fallthrough}-{seed_filter_reason}",
                 decode_residency=decode_residency,
             )
         async with direct_session_lock:
@@ -2702,7 +2650,7 @@ async def _execute_request(
                 client=client,
                 config=config,
                 decision=decision,
-                execution_mode="pd-router-fallback-large-append-session-cap",
+                execution_mode=f"pd-router-fallback-{fallthrough}-session-cap",
                 decode_residency=decode_residency,
             )
         if can_seed:
@@ -2718,23 +2666,27 @@ async def _execute_request(
                 decode_residency=decode_residency,
                 reserved_tokens=reserved_tokens,
                 execution_mode=(
-                    "pd-router-large-append-reseed"
+                    f"pd-router-{fallthrough}-reseed"
                     + _eviction_suffix(
                         evicted_sessions,
                         prefill_backed_evictions,
                     )
                 ),
             )
+        # Preserve seed_reason in the label so migration feedback fires for
+        # 'd-no-space' / 'd-*-backpressure' (matched via _is_admission_rejection_mode).
+        if _is_decode_backpressure_reason(seed_reason):
+            mode_label = f"pd-router-fallback-{fallthrough}-d-backpressure"
+        elif seed_reason == "d-no-space":
+            mode_label = f"pd-router-fallback-{fallthrough}-no-d-capacity"
+        else:
+            mode_label = f"pd-router-fallback-{fallthrough}"
         return await _invoke_plain_router(
             request=request,
             client=client,
             config=config,
             decision=decision,
-            execution_mode=(
-                "pd-router-fallback-d-backpressure"
-                if _is_decode_backpressure_reason(seed_reason)
-                else "pd-router-fallback-large-append"
-            ),
+            execution_mode=mode_label,
             decode_residency=decode_residency,
         )
 

src/agentic_pd_hybrid/stack.py

@@ -201,6 +201,14 @@ def _build_process_env(topology: SingleNodeTopology) -> dict[str, str]:
         # Default to TCP when RDMA is not forced (e.g. loopback on same node)
         env.setdefault("MOONCAKE_PROTOCOL", "tcp")
 
+    # Mooncake C++ batch_transfer_sync default timeout is 30 s, which can
+    # fire as a false positive when a saturated D scheduler thread is busy
+    # with LRU eviction (see docs/E1_E2_RESULTS_ZH.md §5c). Default to 1800 s
+    # so the hair-trigger blacklist in conn.py:1270 doesn't latch on
+    # transient stalls. Caller can override via shell env (setup_env.sh).
+    if topology.transfer_backend == "mooncake":
+        env.setdefault("MC_TRANSFER_TIMEOUT", "1800")
+
     repo_root = Path(__file__).resolve().parents[2]
     python_paths = [
         str(repo_root / "src"),

tests/README.md

@@ -0,0 +1,39 @@
+# Tests
+
+Pure-Python unit + property tests for the algorithm layer. These tests do
+**not** import SGLang and do **not** need a GPU — they validate the routing
+algorithm (Algorithm 1/2/3 in `docs/KVC_ROUTER_ALGORITHM.md`) and its
+theorems against the pure functions extracted from `policies.py`.
+
+## Run
+
+```bash
+uv sync --group test
+uv run pytest
+```
+
+Or, without uv:
+
+```bash
+pip install pytest
+PYTHONPATH=src pytest tests
+```
+
+## Scope
+
+- `test_policy_scoring.py` — Algorithm 1 lex-score properties (overlap
+  dominates sticky, load-floor gating, tie-breakers).
+- `test_no_starvation.py` — Theorem 1: bounded retries before some D either
+  accepts or the least-rejected D is forced through the degenerate path.
+
+Future:
+- block-level eviction `MockRadixCache` tests (see
+  `docs/BLOCK_LEVEL_EVICTION_DESIGN_ZH.md` §5).
+- D→P sync `staleness_budget` property tests (see
+  `docs/D_TO_P_SYNC_CONTRACT_ZH.md` §1).
+
+## Why no integration tests here
+
+Anything that needs SGLang, mooncake, or a real model is an integration
+test and must run on hardware. Those tests live as `scripts/sweep_*.sh`
+under the evaluation protocol in `docs/EVALUATION_PROTOCOL_ZH.md`.

tests/_fixtures.py

@@ -0,0 +1,66 @@
+"""Lightweight fixtures for algorithm-layer tests.
+
+Builds minimal TraceRequest / SingleNodeTopology / RoutingState instances
+without invoking build_single_node_topology() (which validates GPU budgets
+we don't care about in unit tests).
+"""
+
+from __future__ import annotations
+
+from agentic_pd_hybrid.topology import SingleNodeTopology, WorkerSpec
+from agentic_pd_hybrid.trace import TraceRequest
+
+
+def make_topology(decode_count: int = 3, prefill_count: int = 1) -> SingleNodeTopology:
+    prefill_workers = tuple(
+        WorkerSpec(
+            role="prefill",
+            ordinal=i,
+            gpu_ids=(i,),
+            host="127.0.0.1",
+            port=30000 + i,
+        )
+        for i in range(prefill_count)
+    )
+    decode_workers = tuple(
+        WorkerSpec(
+            role="decode",
+            ordinal=i,
+            gpu_ids=(prefill_count + i,),
+            host="127.0.0.1",
+            port=31000 + i,
+        )
+        for i in range(decode_count)
+    )
+    return SingleNodeTopology(
+        model_path="/dev/null/test-model",
+        prefill_workers=prefill_workers,
+        decode_workers=decode_workers,
+        direct_workers=(),
+        router_host="127.0.0.1",
+        router_port=8000,
+        transfer_backend="mooncake",
+        trust_remote_code=True,
+    )
+
+
+def make_request(
+    *,
+    session_id: str = "sess-1",
+    turn_id: int = 0,
+    hash_ids: tuple[int, ...] = (),
+    input_length: int = 1024,
+    output_length: int = 64,
+) -> TraceRequest:
+    return TraceRequest(
+        request_id=f"{session_id}-t{turn_id}",
+        session_id=session_id,
+        chat_id=int(turn_id),
+        parent_chat_id=-1 if turn_id == 0 else int(turn_id - 1),
+        timestamp_s=float(turn_id),
+        input_length=input_length,
+        output_length=output_length,
+        request_type="user",
+        turn_id=turn_id,
+        hash_ids=hash_ids,
+    )

tests/test_no_starvation.py

@@ -0,0 +1,150 @@
+"""Theorem 1 — no permanent starvation under bounded retries.
+
+Reference: docs/KVC_ROUTER_ALGORITHM.md §4.1.
+
+    For any session s with τ_reject ≥ 1, after at most |D| · τ_reject
+    consecutive admission rejects on s, the routing policy MUST still
+    return a valid decision (via the degenerate "least-rejected D"
+    fallback). The session cannot be permanently starved at the policy
+    layer.
+
+We can't exercise the full Dispatch loop here (it lives in replay.py and
+needs HTTP, mooncake, etc.). What we CAN test is the policy-layer
+guarantee: after K = |D| · τ_reject reject bumps, select() never raises
+and never returns a worker that's both blacklisted *and* has positive
+overlap (the degenerate path chooses by least-rejected).
+
+This is the property-layer companion to test_policy_scoring.py's
+quantitative checks.
+"""
+
+from __future__ import annotations
+
+from agentic_pd_hybrid.policies import KvAwarePolicy, RoutingState
+
+from ._fixtures import make_request, make_topology
+
+
+def test_select_returns_valid_decision_under_full_blacklist():
+    """Bump all (s, d) reject counters past τ_reject. select() must still
+    pick a worker (degenerate fallback, no exception, no None)."""
+    topology = make_topology(decode_count=3)
+    state = RoutingState.create(topology)
+    request = make_request(session_id="s-stuck", turn_id=0)
+    policy = KvAwarePolicy(migration_reject_threshold=3)
+
+    # Pre-fill the blacklist for every D.
+    for worker in topology.route_workers:
+        for _ in range(3):
+            state.record_admission_reject(request.session_id, worker.worker_id)
+
+    decision = policy.select(request=request, topology=topology, state=state)
+    assert decision.decode_worker_id is not None
+    assert decision.decode_worker_id in {w.worker_id for w in topology.route_workers}
+
+
+def test_bounded_retries_to_force_degenerate_path():
+    """Theorem 1: at most |D| · τ_reject rejects suffice to either exhaust
+    every D or to force the degenerate fallback. Simulate the worst case
+    where each retry picks a fresh D and is immediately rejected."""
+    topology = make_topology(decode_count=4)
+    state = RoutingState.create(topology)
+    request = make_request(session_id="s-worst", turn_id=0)
+    threshold = 3
+    policy = KvAwarePolicy(migration_reject_threshold=threshold)
+
+    seen_decoders: set[str] = set()
+    max_retries = len(topology.route_workers) * threshold
+
+    for retry in range(max_retries):
+        decision = policy.select(request=request, topology=topology, state=state)
+        seen_decoders.add(decision.decode_worker_id)
+        # Adversary: this D rejects this session.
+        state.record_admission_reject(request.session_id, decision.decode_worker_id)
+
+    # After |D|·τ_reject rejects every D must be blacklisted, so the next
+    # select() takes the degenerate "least-rejected" branch and STILL
+    # returns a valid worker.
+    final = policy.select(request=request, topology=topology, state=state)
+    assert final.decode_worker_id in {w.worker_id for w in topology.route_workers}
+    # And we should have explored every D over the bounded retries — the
+    # algorithm cannot trap a session on a single D when all are rejecting.
+    assert seen_decoders == {w.worker_id for w in topology.route_workers}
+
+
+def test_least_rejected_d_chosen_when_all_blacklisted():
+    """When every D is past threshold, the degenerate fallback chooses the
+    one with the *fewest* rejects (Algorithm 1, line 4)."""
+    topology = make_topology(decode_count=3)
+    state = RoutingState.create(topology)
+    request = make_request(session_id="s-lr", turn_id=0)
+    policy = KvAwarePolicy(migration_reject_threshold=3)
+
+    # Skew rejections: decode-0 has 5, decode-1 has 10, decode-2 has 3.
+    # All are >= threshold=3, so the filter wipes out every candidate.
+    # The fallback should pick decode-2 (smallest rejection count).
+    workers = list(topology.route_workers)
+    bumps = {workers[0].worker_id: 5, workers[1].worker_id: 10, workers[2].worker_id: 3}
+    for wid, n in bumps.items():
+        for _ in range(n):
+            state.record_admission_reject(request.session_id, wid)
+
+    decision = policy.select(request=request, topology=topology, state=state)
+    assert decision.decode_worker_id == workers[2].worker_id
+
+
+def test_other_session_unaffected_by_blacklist():
+    """Algorithm 1's filter is per-(session, D), not per-D. Session A's
+    rejects must not influence session B's routing."""
+    topology = make_topology(decode_count=2)
+    state = RoutingState.create(topology)
+    policy = KvAwarePolicy(migration_reject_threshold=3)
+
+    # Blacklist decode-0 for session A.
+    workers = list(topology.route_workers)
+    for _ in range(3):
+        state.record_admission_reject("session-A", workers[0].worker_id)
+
+    # Session B sees a clean slate — should be able to pick decode-0
+    # (which is the iteration-order winner under empty state).
+    decision_b = policy.select(
+        request=make_request(session_id="session-B"),
+        topology=topology,
+        state=state,
+    )
+    # decode-0 wins iteration-order tiebreak when all scores are (0,0,0,0).
+    assert decision_b.decode_worker_id == workers[0].worker_id
+
+
+def test_threshold_zero_disables_blacklist():
+    """migration_reject_threshold=0 means the migration mechanism is off:
+    every D stays a candidate regardless of its reject count."""
+    topology = make_topology(decode_count=2)
+    state = RoutingState.create(topology)
+    request = make_request(session_id="s-no-mig")
+    policy = KvAwarePolicy(migration_reject_threshold=0)
+
+    workers = list(topology.route_workers)
+    # Pile a huge number of rejects on decode-0.
+    for _ in range(100):
+        state.record_admission_reject(request.session_id, workers[0].worker_id)
+
+    decision = policy.select(request=request, topology=topology, state=state)
+    # decode-0 should still be eligible; with empty overlap/sticky/inflight,
+    # iteration order picks decode-0 first.
+    assert decision.decode_worker_id == workers[0].worker_id
+
+
+def test_reject_counter_only_grows_on_record():
+    """RoutingState.record_admission_reject is the ONLY mutator for the
+    counter. select() must not silently bump it."""
+    topology = make_topology(decode_count=2)
+    state = RoutingState.create(topology)
+    request = make_request(session_id="s-clean")
+    policy = KvAwarePolicy()
+
+    for _ in range(5):
+        policy.select(request=request, topology=topology, state=state)
+
+    # No explicit record_admission_reject -> all counters stay zero.
+    assert sum(state.session_d_rejects.values()) == 0

tests/test_policy_scoring.py

@@ -0,0 +1,189 @@
+"""Unit tests for Algorithm 1 (KvAwarePolicy score_candidate).
+
+Reference: docs/KVC_ROUTER_ALGORITHM.md §3.1. The lex-score is
+
+    (overlap + sticky_bonus*sticky + floor_bonus,
+     sticky,
+     -inflight,
+     -assigned)
+
+These tests pin down the qualitative properties that the algorithm's
+correctness arguments rely on. They run without SGLang/GPU.
+"""
+
+from __future__ import annotations
+
+from agentic_pd_hybrid.policies import score_candidate
+
+
+def _score(**overrides):
+    """Helper: build a score with all defaults and per-test overrides."""
+    args = dict(
+        overlap=0,
+        sticky=False,
+        inflight=0,
+        assigned=0,
+        mean_assigned=0.0,
+        sticky_bonus=1,
+        load_floor_bonus=0,
+    )
+    args.update(overrides)
+    return score_candidate(**args)
+
+
+# -- Determinism ----------------------------------------------------------------
+
+
+def test_score_is_pure():
+    """Same kwargs must produce the same tuple (no hidden state)."""
+    a = _score(overlap=3, sticky=True, inflight=1, assigned=7)
+    b = _score(overlap=3, sticky=True, inflight=1, assigned=7)
+    assert a == b
+
+
+def test_score_returns_4_tuple():
+    s = _score()
+    assert isinstance(s, tuple)
+    assert len(s) == 4
+    assert all(isinstance(x, int) for x in s)
+
+
+# -- Primary term: overlap dominates sticky --------------------------------------
+
+
+def test_overlap_strictly_dominates_pure_sticky():
+    """Theorem-2 building block: any positive overlap on a non-sticky D wins
+    against a sticky-only D with zero overlap (sticky_bonus=1)."""
+    overlap = _score(overlap=2, sticky=False)
+    sticky_only = _score(overlap=0, sticky=True)
+    assert overlap > sticky_only
+
+
+def test_overlap_plus_sticky_beats_overlap_alone():
+    """Two D's with equal overlap: sticky one wins (sticky_bonus contributes
+    to primary AND wins tie-1)."""
+    sticky_d = _score(overlap=5, sticky=True)
+    fresh_d = _score(overlap=5, sticky=False)
+    assert sticky_d > fresh_d
+
+
+# -- Tie breakers ----------------------------------------------------------------
+
+
+def test_tiebreaker_inflight_lower_wins():
+    """Equal primary & sticky: prefer the D with fewer in-flight requests."""
+    low = _score(overlap=3, sticky=False, inflight=0, assigned=10)
+    high = _score(overlap=3, sticky=False, inflight=5, assigned=10)
+    assert low > high
+
+
+def test_tiebreaker_assigned_lower_wins():
+    """Equal primary & sticky & inflight: prefer rarely-picked D."""
+    rare = _score(overlap=3, sticky=False, inflight=2, assigned=1)
+    frequent = _score(overlap=3, sticky=False, inflight=2, assigned=99)
+    assert rare > frequent
+
+
+def test_tiebreaker_strict_lex_order():
+    """Sticky always beats non-sticky on tie-1 even if non-sticky has lower
+    inflight (the lex order is strict, position 1 outranks positions 2/3)."""
+    sticky_busy = _score(overlap=4, sticky=True, inflight=10, assigned=10)
+    fresh_idle = _score(overlap=4, sticky=False, inflight=0, assigned=0)
+    # Note: with sticky_bonus=1 added to position 0, sticky_busy actually wins
+    # on position 0 first (5 > 4). Force equal primary by lowering sticky's
+    # overlap.
+    sticky_busy_eq_primary = _score(overlap=3, sticky=True, inflight=10, assigned=10)
+    fresh_idle_eq_primary = _score(overlap=4, sticky=False, inflight=0, assigned=0)
+    # Now equal primary (3+1=4 vs 4). Sticky wins position 1.
+    assert sticky_busy_eq_primary > fresh_idle_eq_primary
+
+
+# -- Load-floor bonus ------------------------------------------------------------
+
+
+def test_load_floor_disabled_by_default():
+    """load_floor_bonus=0 → no contribution to primary."""
+    s = _score(overlap=0, sticky=False, mean_assigned=10, assigned=0)
+    assert s[0] == 0
+
+
+def test_load_floor_gated_off_when_sticky():
+    """Even with load_floor_bonus>0, sticky D does NOT receive the boost.
+    Otherwise a session would migrate away from its warm D under load."""
+    sticky_under_loaded = _score(
+        overlap=0, sticky=True, mean_assigned=10, assigned=0, load_floor_bonus=200
+    )
+    # primary = overlap(0) + sticky_bonus(1) + floor(0) = 1
+    assert sticky_under_loaded[0] == 1
+
+
+def test_load_floor_zero_when_mean_zero():
+    """Warmup case: mean_assigned=0 -> no D gets boost -> degenerate to lex
+    tiebreak by iteration order."""
+    s = _score(
+        overlap=0, sticky=False, mean_assigned=0, assigned=0, load_floor_bonus=200
+    )
+    assert s[0] == 0
+
+
+def test_load_floor_proportional_to_deficit():
+    """floor_bonus = K * deficit / mean. assigned=0, mean=10, K=200 -> 200."""
+    s_zero = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=0, load_floor_bonus=200
+    )
+    s_half = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=5, load_floor_bonus=200
+    )
+    s_full = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=10, load_floor_bonus=200
+    )
+    # deficit = max(0, 10-0)=10 -> bonus = int(200*10/10) = 200
+    # deficit = max(0, 10-5)=5  -> bonus = int(200*5/10)  = 100
+    # deficit = max(0, 10-10)=0 -> bonus = 0
+    assert s_zero[0] == 200
+    assert s_half[0] == 100
+    assert s_full[0] == 0
+
+
+def test_load_floor_does_not_underflow_when_overloaded():
+    """assigned > mean -> deficit clamped to 0, no negative bonus."""
+    s = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=50, load_floor_bonus=200
+    )
+    assert s[0] == 0
+
+
+# -- Routing intent: real overlap beats load-floor bonus -------------------------
+
+
+def test_real_prefix_overlap_beats_load_floor_on_warm_d():
+    """E1_E2_FIX_DESIGN_ZH §Q2: load_floor should be set such that
+    real per-session prefix overlap outweighs the cold-D bonus.
+    With overlap=800 (a per-session prefix) and load_floor_bonus=200,
+    a warm D (high overlap, possibly high load) should still win against
+    a cold D with floor bonus."""
+    warm = _score(
+        overlap=800, sticky=True, mean_assigned=10, assigned=10, load_floor_bonus=200
+    )
+    cold = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=0, load_floor_bonus=200
+    )
+    # warm primary = 800 + 1 + 0 = 801. cold primary = 0 + 0 + 200 = 200.
+    assert warm[0] == 801
+    assert cold[0] == 200
+    assert warm > cold
+
+
+def test_boilerplate_overlap_loses_to_load_floor_for_cold_d():
+    """Same §Q2: load_floor should beat cross-session boilerplate overlap.
+    If load_floor_bonus=200 and the worst-case boilerplate overlap is ~50,
+    a fresh cold D should still win against a slightly-warm-from-boilerplate D."""
+    warm_boilerplate = _score(
+        overlap=50, sticky=False, mean_assigned=10, assigned=10, load_floor_bonus=200
+    )
+    cold_under_loaded = _score(
+        overlap=0, sticky=False, mean_assigned=10, assigned=0, load_floor_bonus=200
+    )
+    # warm_boilerplate primary = 50 + 0 + 0 = 50 (assigned=mean, no deficit).
+    # cold_under_loaded primary = 0 + 0 + 200 = 200.
+    assert cold_under_loaded > warm_boilerplate

third_party/sglang/python/sglang/srt/managers/schedule_batch.py

@@ -1564,6 +1564,74 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             # For DLLM, we use a separate forward mode
             self.forward_mode = ForwardMode.DLLM_EXTEND
 
+        # Pre-filter pass: drop streaming-session reqs whose committed prefix
+        # already covers fill_ids. The streaming-session correction below would
+        # set extend_input_len = max(0, fill_len - prefix_len) = 0 for these
+        # reqs, but the downstream invariant at the per-req loop
+        # (`assert seq_len - pre_len == req.extend_input_len`) is computed from
+        # raw fill_ids/prefix_indices lengths and has no path to be satisfied
+        # when fill_len < prefix_len. Treat the condition as upstream state
+        # inconsistency, abort the affected reqs (so the client sees an error
+        # response instead of the worker crashing), and continue with the
+        # remaining batch. See docs/E3_FINDINGS_ZH.md for the failure mode
+        # this guards against.
+        if self.reqs:
+            kept_reqs = []
+            for req in self.reqs:
+                if (
+                    req.session is not None
+                    and req.session.streaming
+                    and len(req.fill_ids) < len(req.prefix_indices)
+                ):
+                    logger.error(
+                        "Dropping streaming-session req with fill_ids shorter than "
+                        "prefix_indices (rid=%s, session_id=%s, fill_len=%d, "
+                        "prefix_len=%d, kv_committed_len=%d). Upstream state "
+                        "inconsistency would crash prepare_for_extend's invariant; "
+                        "aborting this req. See docs/E3_FINDINGS_ZH.md.",
+                        req.rid,
+                        req.session.session_id,
+                        len(req.fill_ids),
+                        len(req.prefix_indices),
+                        req.kv_committed_len,
+                    )
+                    req.finished_reason = FINISH_ABORT(
+                        message=(
+                            "streaming-session inconsistency: fill_ids "
+                            f"({len(req.fill_ids)}) < prefix_indices "
+                            f"({len(req.prefix_indices)})"
+                        ),
+                    )
+                else:
+                    kept_reqs.append(req)
+            if len(kept_reqs) != len(self.reqs):
+                self.reqs = kept_reqs
+
+        if not self.reqs:
+            # Whole batch filtered. Set empty tensor / list state so
+            # downstream callers (model_runner.forward, batch_result handlers)
+            # see a valid no-op batch and skip the model pass cleanly.
+            _pin = is_pin_memory_available(self.device)
+            empty_long = torch.zeros(0, dtype=torch.int64, pin_memory=_pin).to(
+                self.device, non_blocking=True
+            )
+            empty_int = torch.zeros(0, dtype=torch.int32, pin_memory=_pin).to(
+                self.device, non_blocking=True
+            )
+            self.input_ids = empty_long
+            self.req_pool_indices = empty_int
+            self.seq_lens = empty_long
+            self.seq_lens_cpu = torch.zeros(0, dtype=torch.int64)
+            self.orig_seq_lens = empty_int
+            self.prefix_lens = []
+            self.extend_lens = []
+            self.extend_num_tokens = 0
+            self.out_cache_loc = empty_int
+            self.input_embeds = None
+            self.multimodal_inputs = []
+            self.token_type_ids = None
+            return
+
         # Init tensors
         reqs = self.reqs
         for req in reqs:

uv.lock

@@ -2,15 +2,33 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'win32'",
-    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
-    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [options]
@@ -30,7 +48,7 @@ dependencies = [
 requires-dist = [
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "mooncake-transfer-engine" },
-    { name = "sglang", specifier = "==0.5.10" },
+    { name = "sglang", editable = "third_party/sglang/python" },
 ]
 
 [[package]]
@@ -457,7 +475,8 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "loguru" },
     { name = "pydantic" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
     { name = "transformers" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/98/c0/8fb99aa86bc538d3a025749633d1d0105d849b35eb240ba7ba30e22de49b/compressed_tensors-0.15.1a20260409.tar.gz", hash = "sha256:a9a477691c2887bc8d2c46aef82aa60c85fe1f014cacb2218b423904aff04f4d", size = 238217, upload-time = "2026-04-09T21:21:52.922Z" }
@@ -565,8 +584,8 @@ name = "decord2"
 version = "3.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/51/c3/fbc81c2cc18b2b7ca8a3a26ca2e8dfa243a2c7f5c4431f4b3839a8f12f0a/decord2-3.3.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:3a67fb644041a031bc3f21b2e1adcf92b9742d980bd90f3bc45396c2a0ddcbfa", size = 25036754, upload-time = "2026-04-06T18:09:46.005Z" },
@@ -664,7 +683,8 @@ dependencies = [
     { name = "einops" },
     { name = "nvidia-cutlass-dsl" },
     { name = "quack-kernels" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
     { name = "torch-c-dlpack-ext" },
     { name = "typing-extensions" },
 ]
@@ -699,7 +719,8 @@ dependencies = [
     { name = "packaging" },
     { name = "requests" },
     { name = "tabulate" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/95/81eafb78574312db79ef7144a4e77f2fee015343f413ef3000f279c8a118/flashinfer_python-0.6.7.post2.tar.gz", hash = "sha256:924cb1788d0335225293eea384da40f40daa6b4e32b6a5ebc214ab679b4e2125", size = 6509418, upload-time = "2026-04-04T07:10:25.516Z" }
@@ -904,34 +925,34 @@ wheels = [
 
 [[package]]
 name = "hf-xet"
-version = "1.5.0.dev1"
+version = "1.5.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c9/b5/73db543ba19129c23b2ca52d837373eb4243f0332130093f31b3ecc6739f/hf_xet-1.5.0.dev1.tar.gz", hash = "sha256:a21c9c85869ee122747543dd93471826cc0e9b5f61b11411aabd4adf72e345b1", size = 823729, upload-time = "2026-04-17T08:22:19.349Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/74/d8/5c06fc76461418326a7decf8367480c35be11a41fd938633929c60a9ec6b/hf_xet-1.5.0.tar.gz", hash = "sha256:e0fb0a34d9f406eed88233e829a67ec016bec5af19e480eac65a233ea289a948", size = 837196, upload-time = "2026-05-06T06:18:15.583Z" }
 wheels = [
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+    { name = "sglang", extras = ["diffusion"], marker = "extra == 'all'" },
+    { name = "sglang", extras = ["test"], marker = "extra == 'dev'" },
+    { name = "sglang", extras = ["tracing"], marker = "extra == 'all'" },
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+    { name = "torch-memory-saver", specifier = "==0.0.9" },
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+    { name = "torchaudio", specifier = "==2.9.1" },
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+    { name = "watchfiles" },
+    { name = "xatlas", marker = "extra == 'diffusion'" },
+    { name = "xgrammar", specifier = "==0.1.32" },
 ]
+provides-extras = ["checkpoint-engine", "runai", "diffusion", "ray", "tracing", "test", "dev", "all"]
 
 [[package]]
 name = "sglang-kernel"
@@ -3574,7 +3937,8 @@ dependencies = [
     { name = "huggingface-hub" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
     { name = "torchvision" },
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@@ -3612,30 +3976,50 @@ wheels = [
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 version = "2.9.1"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
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+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'",
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+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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+    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'emscripten'",
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+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'",
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+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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+]
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-    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "filelock", marker = "platform_machine != 'aarch64'" },
+    { name = "fsspec", marker = "platform_machine != 'aarch64'" },
+    { name = "jinja2", marker = "platform_machine != 'aarch64'" },
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-    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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+    { name = "nvidia-cusolver-cu12", version = "11.7.3.90", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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-    { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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-    { name = "setuptools" },
-    { name = "sympy" },
+    { name = "nvidia-nvtx-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "setuptools", marker = "platform_machine != 'aarch64'" },
+    { name = "sympy", marker = "platform_machine != 'aarch64'" },
     { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "platform_machine != 'aarch64'" },
 ]
 wheels = [
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@@ -3660,12 +4044,61 @@ wheels = [
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 ]
 
+[[package]]
+name = "torch"
+version = "2.9.1+cu129"
+source = { registry = "https://download.pytorch.org/whl/cu129" }
+resolution-markers = [
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+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+dependencies = [
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+    { name = "fsspec", marker = "platform_machine == 'aarch64'" },
+    { name = "jinja2", marker = "platform_machine == 'aarch64'" },
+    { name = "networkx", marker = "platform_machine == 'aarch64'" },
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+    { name = "nvidia-cuda-cupti-cu12", version = "12.9.79", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
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+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
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+    { name = "nvidia-cusolver-cu12", version = "11.7.5.82", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
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+    { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
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+    { name = "nvidia-nvshmem-cu12", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "nvidia-nvtx-cu12", version = "12.9.79", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "setuptools", marker = "platform_machine == 'aarch64'" },
+    { name = "sympy", marker = "platform_machine == 'aarch64'" },
+    { name = "triton", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "typing-extensions", marker = "platform_machine == 'aarch64'" },
+]
+wheels = [
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+    { url = "https://download-r2.pytorch.org/whl/cu129/torch-2.9.1%2Bcu129-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:ab44cf28e6ca2df679f0845fb4b950c81834431218840ca01c0a1583892a0986", upload-time = "2026-01-26T17:26:26Z" },
+    { url = "https://download-r2.pytorch.org/whl/cu129/torch-2.9.1%2Bcu129-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:794482180a4f2d92a960f470fcd47e066dbe2eeb27816880e618d3ce031805f7", upload-time = "2026-01-26T17:26:04Z" },
+    { url = "https://download-r2.pytorch.org/whl/cu129/torch-2.9.1%2Bcu129-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:4559e1254e2c8e1a337758626d1cf33ca5a5ded3509fa012070334bf886b686b", upload-time = "2026-01-26T17:25:38Z" },
+    { url = "https://download-r2.pytorch.org/whl/cu129/torch-2.9.1%2Bcu129-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:cbe8955514ace826d3638a5d5dc1faa2f9dda1de4de74941d2e86b1a0859477c", upload-time = "2026-01-26T17:25:36Z" },
+]
+
 [[package]]
 name = "torch-c-dlpack-ext"
 version = "0.1.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/37/de/921b6491efce5c389a5ef9bbed3d2d6660005840dae488124173180859ab/torch_c_dlpack_ext-0.1.5.tar.gz", hash = "sha256:d06f0357d575d22a168cc77acb9020fc4bae30968ceb6718a055dcbe92bacabe", size = 12913, upload-time = "2026-01-12T11:25:08.484Z" }
 wheels = [
@@ -3706,7 +4139,8 @@ name = "torchaudio"
 version = "2.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
 ]
 wheels = [
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@@ -3755,7 +4189,8 @@ dependencies = [
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     { name = "pillow" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
 ]
 wheels = [
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@@ -3827,10 +4262,15 @@ name = "triton"
 version = "3.5.1"
 source = { registry = "https://pypi.org/simple" }
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@@ -4029,7 +4469,8 @@ dependencies = [
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     { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
     { name = "pydantic" },
-    { name = "torch" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64'" },
+    { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'aarch64'" },
     { name = "transformers" },
     { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "typing-extensions" },