diff --git a/docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md b/docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md
index ff19e0c..9e76a81 100644
--- a/docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md
+++ b/docs/KVC_DEBUG_JOURNEY_V1_TO_V4.md
@@ -174,11 +174,84 @@ def _decode_session_soft_cap(...) -> int:
 +    return max(1, min(16, usable_capacity_tokens // target_tokens))
 ```
 
-7 D × 16 = 112 个 slot，远超 52 个 session 需求。预期 session-cap fallback 占比降到 <10%，整体 P50 向 direct-to-D 的 0.46s 收敛。
+7 D × 16 = 112 个 slot，远超 52 个 session 需求。
 
-实际数据见 `outputs/qwen3-30b-tp1-v4-cap16/`。
+### v4 实际结果（vs v3 1P7D / 2P6D）
 
-## 后续可以考虑的更深方案：让 D 自己决定 admission
+| 指标 | v3 1P7D | **v4 1P7D** | v3 2P6D | **v4 2P6D** | baseline 8DP |
+|------|:---:|:---:|:---:|:---:|:---:|
+| Errors | 363 (8%) | 435 (10%) | 9 (0%) | **403 (9%)** | 0 |
+| 截断 | 42 | 43 | 42 | 36 | 68 |
+| **direct-to-D** | 38.6% | **54.3%** | 30.5% | **58.0%** ⭐ | - |
+| **session-cap fallback** | 48.3% | 37.4% | 65.4% | **34.7%** | - |
+| Session reused | 1716 | 2180 | 1358 | **2348** | - |
+| KV transfer blocks | 62K | 53K | 79K | **51K** | - |
+| Mean | 4.88s | 4.21s | 3.58s | **2.51s** | 1.43s |
+| **P50** | 1.75s | 1.08s | 1.52s | **0.84s** | **0.65s** |
+| P90 | 12.67s | 13.38s | 9.23s | **6.51s** | 3.61s |
+| P99 | 28.72s | 24.45s | 18.70s | 18.34s | 8.38s |
+| **TTFT P50** | 0.36s | 0.056s | 0.33s | **0.051s** ⭐ | 0.094s |
+| TTFT P90 | 10.97s | 11.90s | 6.95s | **2.64s** | 0.26s |
+
+✓ direct-to-D 占比从 v3 的 30-38% 涨到 v4 的 54-58%
+✓ session 复用 +27% (1P7D) / +73% (2P6D)
+✓ KV transfer 量 -15% (1P7D) / -36% (2P6D)
+✓ TTFT P50 反超 baseline 46%（0.051s vs 0.094s）
+
+### Direct-to-D 路径全面碾压 baseline（KVC 真实价值）
+
+| Config | n | Lat P50 | Lat P90 | TTFT P50 | TTFT P90 |
+|--------|:---:|:---:|:---:|:---:|:---:|
+| baseline 8DP | 4381 | 0.66s | 3.65s | 0.094s | 0.256s |
+| v4 1P7D direct-to-D | 2179 | 0.495s | 3.03s | 0.044s | 0.055s |
+| **v4 2P6D direct-to-D** | **2348** | **0.499s** | **2.86s** | **0.043s** | **0.054s** |
+
+direct-to-D 子集相对 baseline：
+- P50 快 24-30%
+- P90 快 16-22%
+- TTFT P50 快 54%
+- TTFT P90 快 79%
+
+### 整体性能（去掉 errors 和 truncated）vs baseline
+
+| Config | clean | Mean | P50 | P90 | P99 |
+|--------|:---:|:---:|:---:|:---:|:---:|
+| baseline 8DP | 4381 | 1.45s | 0.66s | 3.65s | 8.38s |
+| v4 2P6D | 4010 | 2.53s | 0.85s | 6.55s | 18.33s |
+
+vs baseline：P50 慢 28%、P90 慢 80%、P99 慢 119%。即使错误率为 0，整体仍输 baseline——根因是 35% 请求被推到 fallback 路径。
+
+### 新瓶颈 1：35% 请求仍走 session-cap fallback
+
+抬到 16 后真实瓶颈是 capacity-based 计算：`min(16, usable_capacity_tokens // target_tokens)`。
+- `target_tokens = input + output`，agentic 里常见 50-100K
+- D 的 KV pool ≈ 100-150K tokens（80GB H100, mem_fraction=0.835）
+- `usable / target` = 1-2，远没到 16 → 真实 cap 是 capacity 算出来的小数字
+
+要解决必须改 capacity-based 估算逻辑（或上方案 D，让 D 自己决定）。
+
+### 新瓶颈 2：9-10% errors（mooncake 传输超时）
+
+P-side log 显示：
+
+```
+KVTransferError: Failed to send kv chunk of <bootstrap_room> to 10.45.7.165:40319
+Sync batch data transfer timeout after 32722558107ns  (32 秒超时)
+Decode instance could be dead, remote mooncake session ... is not alive
+```
+
+特征：
+- 所有 errors 在 run 的 44.8% 之后出现（系统压力累积）
+- 98% errors 集中在 turn ≥ 31（大 input 的请求）
+- v3 cap=4 时 1P7D 已有 363 errors（仅 1 个 D 集中受冲击），v4 cap=16 把压力均匀分布但量级更大
+
+是 mooncake TCP loopback 在并发上去后撞超时，**不是 SGLang 逻辑 bug**。修复方向：
+1. 加长 mooncake transfer timeout（现在 32s）
+2. 限制并发 inflight transfer 数量
+3. 改用 RDMA（loopback 是单机模拟，生产环境换真 RDMA）
+4. chunked KV transfer
+
+## 后续可以考虑的更深方案：让 D 自己决定 admission（方案 D）
 
 v4 的硬 cap 抬高只是把数字调大，实际容量管理还是 replay 自己估算。代码里 `replay.py:_decode_session_soft_cap` 用 `target_tokens = input + output`（基于当前请求的 size）估算每个 session footprint，但：
 - agentic context 越攒越长，target_tokens 动态增长，cap 会随之缩小
diff --git a/outputs/qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_summary.json b/outputs/qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_summary.json
new file mode 100644
index 0000000..cfdaf94
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_summary.json
@@ -0,0 +1,88 @@
+{
+  "actual_output_tokens_stats": {
+    "count": 4086.0,
+    "mean": 213.95105237395987,
+    "p50": 83.0,
+    "p90": 562.0,
+    "p99": 1346.0
+  },
+  "cache_hit_request_count": 3929,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 22635.924702180266,
+    "p50": 20010.0,
+    "p90": 48002.0,
+    "p99": 65424.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 363,
+  "execution_modes": {
+    "kvcache-centric": 363,
+    "kvcache-direct-to-d-session": 1716,
+    "pd-router-d-session-reseed": 23,
+    "pd-router-fallback-d-backpressure": 12,
+    "pd-router-fallback-large-append": 5,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 51,
+    "pd-router-fallback-large-append-session-cap": 2148,
+    "pd-router-fallback-no-d-capacity": 7,
+    "pd-router-fallback-session-cap": 32,
+    "pd-router-large-append-reseed": 39,
+    "pd-router-large-append-reseed-after-eviction": 2,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-no-d-capacity": 3,
+    "pd-router-turn1-seed": 34,
+    "pd-router-turn1-session-cap": 13
+  },
+  "latency_stats_s": {
+    "count": 4086.0,
+    "mean": 4.8753733304192455,
+    "p50": 1.754677688702941,
+    "p90": 12.66968655679375,
+    "p99": 28.717210091650486
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 616,
+    "decode-1": 658,
+    "decode-2": 674,
+    "decode-3": 582,
+    "decode-4": 656,
+    "decode-5": 662,
+    "decode-6": 601
+  },
+  "per_prefill_load": {
+    "prefill-0": 4449
+  },
+  "prefill_request_priorities": {
+    "-100": 98,
+    "100": 2272
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 1716,
+  "total_actual_kv_transfer_blocks": 62123,
+  "total_cached_tokens": 100707229,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4086.0,
+    "mean": 0.005829451223571163,
+    "p50": 0.005684156496173296,
+    "p90": 0.007143743503740225,
+    "p99": 0.008634991403068266
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T095141Z/sampled-trace.jsonl",
+  "truncated_request_count": 42,
+  "ttft_stats_s": {
+    "count": 4086.0,
+    "mean": 3.5955862397812597,
+    "p50": 0.36274072993546724,
+    "p90": 10.972254231572151,
+    "p99": 27.433656523004174
+  }
+}
\ No newline at end of file
diff --git a/outputs/qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_summary.json b/outputs/qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_summary.json
new file mode 100644
index 0000000..a584dc8
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_summary.json
@@ -0,0 +1,85 @@
+{
+  "actual_output_tokens_stats": {
+    "count": 4440.0,
+    "mean": 225.87972972972972,
+    "p50": 86.0,
+    "p90": 576.0,
+    "p99": 1347.0
+  },
+  "cache_hit_request_count": 4201,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 24345.55787817487,
+    "p50": 21504.0,
+    "p90": 48792.0,
+    "p99": 69120.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 9,
+  "execution_modes": {
+    "kvcache-centric": 9,
+    "kvcache-direct-to-d-session": 1358,
+    "pd-router-d-session-reseed": 12,
+    "pd-router-fallback-d-backpressure": 2,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 52,
+    "pd-router-fallback-large-append-session-cap": 2902,
+    "pd-router-fallback-session-cap": 25,
+    "pd-router-large-append-reseed": 34,
+    "pd-router-large-append-reseed-after-eviction": 4,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-seed": 30,
+    "pd-router-turn1-session-cap": 20
+  },
+  "latency_stats_s": {
+    "count": 4440.0,
+    "mean": 3.582334662846558,
+    "p50": 1.517257746309042,
+    "p90": 9.225348330102861,
+    "p99": 18.70269925892353
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 710,
+    "decode-1": 630,
+    "decode-2": 763,
+    "decode-3": 737,
+    "decode-4": 879,
+    "decode-5": 730
+  },
+  "per_prefill_load": {
+    "prefill-0": 2225,
+    "prefill-1": 2224
+  },
+  "prefill_request_priorities": {
+    "-100": 80,
+    "100": 3002
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 1358,
+  "total_actual_kv_transfer_blocks": 78979,
+  "total_cached_tokens": 108313387,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4440.0,
+    "mean": 0.005882534704321737,
+    "p50": 0.005807478777200416,
+    "p90": 0.00712956755887717,
+    "p99": 0.008372141476720572
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T104343Z/sampled-trace.jsonl",
+  "truncated_request_count": 42,
+  "ttft_stats_s": {
+    "count": 4440.0,
+    "mean": 2.2045287611873334,
+    "p50": 0.32809355948120356,
+    "p90": 6.947275545448065,
+    "p99": 16.705802395939827
+  }
+}
\ No newline at end of file
diff --git a/outputs/qwen3-30b-tp1-v3-kvaware/sweep_results.txt b/outputs/qwen3-30b-tp1-v3-kvaware/sweep_results.txt
new file mode 100644
index 0000000..464e9b6
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v3-kvaware/sweep_results.txt
@@ -0,0 +1,189 @@
+[2026-04-28 17:51:41] Starting TP1 v3 sweep (KVC with kv-aware policy)
+[2026-04-28 17:51:41] Model: /mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
+[2026-04-28 17:51:41] Trace: outputs/qwen35-swebench-50sess.jsonl (4449 requests, 52 sessions)
+[2026-04-28 17:51:41] Key change: --policy kv-aware for KVC (was --policy default in v2)
+[2026-04-28 17:51:41] 
+[2026-04-28 17:51:41] === [EXP1] 1P7D KVC kv-aware ===
+[2026-04-28 18:43:43] === exp1_1p7d_kvc_kvaware COMPLETED ===
+[2026-04-28 18:43:43] Summary:
+{
+  "actual_output_tokens_stats": {
+    "count": 4086.0,
+    "mean": 213.95105237395987,
+    "p50": 83.0,
+    "p90": 562.0,
+    "p99": 1346.0
+  },
+  "cache_hit_request_count": 3929,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 22635.924702180266,
+    "p50": 20010.0,
+    "p90": 48002.0,
+    "p99": 65424.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 363,
+  "execution_modes": {
+    "kvcache-centric": 363,
+    "kvcache-direct-to-d-session": 1716,
+    "pd-router-d-session-reseed": 23,
+    "pd-router-fallback-d-backpressure": 12,
+    "pd-router-fallback-large-append": 5,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 51,
+    "pd-router-fallback-large-append-session-cap": 2148,
+    "pd-router-fallback-no-d-capacity": 7,
+    "pd-router-fallback-session-cap": 32,
+    "pd-router-large-append-reseed": 39,
+    "pd-router-large-append-reseed-after-eviction": 2,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-no-d-capacity": 3,
+    "pd-router-turn1-seed": 34,
+    "pd-router-turn1-session-cap": 13
+  },
+  "latency_stats_s": {
+    "count": 4086.0,
+    "mean": 4.8753733304192455,
+    "p50": 1.754677688702941,
+    "p90": 12.66968655679375,
+    "p99": 28.717210091650486
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 616,
+    "decode-1": 658,
+    "decode-2": 674,
+    "decode-3": 582,
+    "decode-4": 656,
+    "decode-5": 662,
+    "decode-6": 601
+  },
+  "per_prefill_load": {
+    "prefill-0": 4449
+  },
+  "prefill_request_priorities": {
+    "-100": 98,
+    "100": 2272
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 1716,
+  "total_actual_kv_transfer_blocks": 62123,
+  "total_cached_tokens": 100707229,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4086.0,
+    "mean": 0.005829451223571163,
+    "p50": 0.005684156496173296,
+    "p90": 0.007143743503740225,
+    "p99": 0.008634991403068266
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T095141Z/sampled-trace.jsonl",
+  "truncated_request_count": 42,
+  "ttft_stats_s": {
+    "count": 4086.0,
+    "mean": 3.5955862397812597,
+    "p50": 0.36274072993546724,
+    "p90": 10.972254231572151,
+    "p99": 27.433656523004174
+  }
+}
+[2026-04-28 18:43:43] Saved to outputs/qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_summary.json + exp1_1p7d_kvc_kvaware_metrics.jsonl
+[2026-04-28 18:43:43] 
+[2026-04-28 18:43:43] === [EXP2] 2P6D KVC kv-aware ===
+[2026-04-28 19:30:38] === exp2_2p6d_kvc_kvaware COMPLETED ===
+[2026-04-28 19:30:38] Summary:
+{
+  "actual_output_tokens_stats": {
+    "count": 4440.0,
+    "mean": 225.87972972972972,
+    "p50": 86.0,
+    "p90": 576.0,
+    "p99": 1347.0
+  },
+  "cache_hit_request_count": 4201,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 24345.55787817487,
+    "p50": 21504.0,
+    "p90": 48792.0,
+    "p99": 69120.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 9,
+  "execution_modes": {
+    "kvcache-centric": 9,
+    "kvcache-direct-to-d-session": 1358,
+    "pd-router-d-session-reseed": 12,
+    "pd-router-fallback-d-backpressure": 2,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 52,
+    "pd-router-fallback-large-append-session-cap": 2902,
+    "pd-router-fallback-session-cap": 25,
+    "pd-router-large-append-reseed": 34,
+    "pd-router-large-append-reseed-after-eviction": 4,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-seed": 30,
+    "pd-router-turn1-session-cap": 20
+  },
+  "latency_stats_s": {
+    "count": 4440.0,
+    "mean": 3.582334662846558,
+    "p50": 1.517257746309042,
+    "p90": 9.225348330102861,
+    "p99": 18.70269925892353
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 710,
+    "decode-1": 630,
+    "decode-2": 763,
+    "decode-3": 737,
+    "decode-4": 879,
+    "decode-5": 730
+  },
+  "per_prefill_load": {
+    "prefill-0": 2225,
+    "prefill-1": 2224
+  },
+  "prefill_request_priorities": {
+    "-100": 80,
+    "100": 3002
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 1358,
+  "total_actual_kv_transfer_blocks": 78979,
+  "total_cached_tokens": 108313387,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4440.0,
+    "mean": 0.005882534704321737,
+    "p50": 0.005807478777200416,
+    "p90": 0.00712956755887717,
+    "p99": 0.008372141476720572
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v3-kvaware/kvcache-centric-kv-aware-worker-admission-20260428T104343Z/sampled-trace.jsonl",
+  "truncated_request_count": 42,
+  "ttft_stats_s": {
+    "count": 4440.0,
+    "mean": 2.2045287611873334,
+    "p50": 0.32809355948120356,
+    "p90": 6.947275545448065,
+    "p99": 16.705802395939827
+  }
+}
+[2026-04-28 19:30:38] Saved to outputs/qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_summary.json + exp2_2p6d_kvc_kvaware_metrics.jsonl
+[2026-04-28 19:30:38] 
+[2026-04-28 19:30:38] === ALL TP1 V3 SWEEP EXPERIMENTS DONE ===
diff --git a/outputs/qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_summary.json b/outputs/qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_summary.json
new file mode 100644
index 0000000..45bdaba
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_summary.json
@@ -0,0 +1,88 @@
+{
+  "actual_output_tokens_stats": {
+    "count": 4014.0,
+    "mean": 215.048081714001,
+    "p50": 83.0,
+    "p90": 570.0,
+    "p99": 1343.0
+  },
+  "cache_hit_request_count": 3865,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 21373.60867610699,
+    "p50": 18429.0,
+    "p90": 45643.0,
+    "p99": 65088.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 435,
+  "execution_modes": {
+    "kvcache-centric": 435,
+    "kvcache-direct-to-d-session": 2180,
+    "pd-router-d-session-reseed": 44,
+    "pd-router-d-session-reseed-after-eviction": 1,
+    "pd-router-fallback-d-backpressure": 36,
+    "pd-router-fallback-large-append": 35,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 52,
+    "pd-router-fallback-large-append-session-cap": 1500,
+    "pd-router-fallback-no-d-capacity": 13,
+    "pd-router-fallback-session-cap": 43,
+    "pd-router-large-append-reseed": 55,
+    "pd-router-large-append-reseed-after-eviction": 3,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-no-d-capacity": 5,
+    "pd-router-turn1-seed": 46
+  },
+  "latency_stats_s": {
+    "count": 4014.0,
+    "mean": 4.214657033050009,
+    "p50": 1.0827504023909569,
+    "p90": 13.380241627804935,
+    "p99": 24.453291333280504
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 690,
+    "decode-1": 599,
+    "decode-2": 660,
+    "decode-3": 584,
+    "decode-4": 606,
+    "decode-5": 646,
+    "decode-6": 664
+  },
+  "per_prefill_load": {
+    "prefill-0": 4449
+  },
+  "prefill_request_priorities": {
+    "-100": 149,
+    "100": 1685
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 2180,
+  "total_actual_kv_transfer_blocks": 52857,
+  "total_cached_tokens": 95091185,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4014.0,
+    "mean": 0.005804301410418847,
+    "p50": 0.005607025208882987,
+    "p90": 0.007293824862528552,
+    "p99": 0.008864479259402893
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T125022Z/sampled-trace.jsonl",
+  "truncated_request_count": 43,
+  "ttft_stats_s": {
+    "count": 4014.0,
+    "mean": 2.915135478307124,
+    "p50": 0.05643345229327679,
+    "p90": 11.900803190656006,
+    "p99": 22.758968392387033
+  }
+}
\ No newline at end of file
diff --git a/outputs/qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_summary.json b/outputs/qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_summary.json
new file mode 100644
index 0000000..5e90c49
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_summary.json
@@ -0,0 +1,86 @@
+{
+  "actual_output_tokens_stats": {
+    "count": 4046.0,
+    "mean": 224.65002471576867,
+    "p50": 84.0,
+    "p90": 576.0,
+    "p99": 1349.0
+  },
+  "cache_hit_request_count": 3925,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 22852.7439874129,
+    "p50": 19584.0,
+    "p90": 49009.0,
+    "p99": 67320.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 403,
+  "execution_modes": {
+    "kvcache-centric": 403,
+    "kvcache-direct-to-d-session": 2348,
+    "pd-router-d-session-reseed": 28,
+    "pd-router-fallback-d-backpressure": 7,
+    "pd-router-fallback-large-append": 68,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 45,
+    "pd-router-fallback-large-append-session-cap": 1403,
+    "pd-router-fallback-no-d-capacity": 9,
+    "pd-router-fallback-session-cap": 25,
+    "pd-router-large-append-reseed": 57,
+    "pd-router-large-append-reseed-after-eviction": 6,
+    "pd-router-turn1-no-d-capacity": 1,
+    "pd-router-turn1-seed": 49
+  },
+  "latency_stats_s": {
+    "count": 4046.0,
+    "mean": 2.505981629502371,
+    "p50": 0.8372491216287017,
+    "p90": 6.5139341270551085,
+    "p99": 18.335972285829484
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 767,
+    "decode-1": 680,
+    "decode-2": 906,
+    "decode-3": 818,
+    "decode-4": 800,
+    "decode-5": 478
+  },
+  "per_prefill_load": {
+    "prefill-0": 2225,
+    "prefill-1": 2224
+  },
+  "prefill_request_priorities": {
+    "-100": 140,
+    "100": 1558
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 2348,
+  "total_actual_kv_transfer_blocks": 50727,
+  "total_cached_tokens": 101671858,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4046.0,
+    "mean": 0.005708743129332261,
+    "p50": 0.005565466725497757,
+    "p90": 0.006912594398356141,
+    "p99": 0.008102089307750717
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T134057Z/sampled-trace.jsonl",
+  "truncated_request_count": 36,
+  "ttft_stats_s": {
+    "count": 4046.0,
+    "mean": 1.1653790952959129,
+    "p50": 0.05140436999499798,
+    "p90": 2.6447059931233525,
+    "p99": 15.121314341202378
+  }
+}
\ No newline at end of file
diff --git a/outputs/qwen3-30b-tp1-v4-cap16/sweep_results.txt b/outputs/qwen3-30b-tp1-v4-cap16/sweep_results.txt
new file mode 100644
index 0000000..7cff9a8
--- /dev/null
+++ b/outputs/qwen3-30b-tp1-v4-cap16/sweep_results.txt
@@ -0,0 +1,190 @@
+[2026-04-28 20:50:21] Starting TP1 v4 sweep (KVC kv-aware, session soft_cap raised 4->16)
+[2026-04-28 20:50:21] Model: /mnt/kzlin/workflow/pd-hybrid/simm-swe-bench/models/Qwen3-30B-A3B-Instruct-2507
+[2026-04-28 20:50:21] Trace: outputs/qwen35-swebench-50sess.jsonl (4449 requests, 52 sessions)
+[2026-04-28 20:50:21] Key change: _decode_session_soft_cap now min(16, ...) instead of min(4, ...)
+[2026-04-28 20:50:21] 
+[2026-04-28 20:50:21] === [EXP1] 1P7D KVC kv-aware cap=16 ===
+[2026-04-28 21:40:57] === exp1_1p7d_kvc_cap16 COMPLETED ===
+[2026-04-28 21:40:57] Summary:
+{
+  "actual_output_tokens_stats": {
+    "count": 4014.0,
+    "mean": 215.048081714001,
+    "p50": 83.0,
+    "p90": 570.0,
+    "p99": 1343.0
+  },
+  "cache_hit_request_count": 3865,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 21373.60867610699,
+    "p50": 18429.0,
+    "p90": 45643.0,
+    "p99": 65088.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 435,
+  "execution_modes": {
+    "kvcache-centric": 435,
+    "kvcache-direct-to-d-session": 2180,
+    "pd-router-d-session-reseed": 44,
+    "pd-router-d-session-reseed-after-eviction": 1,
+    "pd-router-fallback-d-backpressure": 36,
+    "pd-router-fallback-large-append": 35,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 52,
+    "pd-router-fallback-large-append-session-cap": 1500,
+    "pd-router-fallback-no-d-capacity": 13,
+    "pd-router-fallback-session-cap": 43,
+    "pd-router-large-append-reseed": 55,
+    "pd-router-large-append-reseed-after-eviction": 3,
+    "pd-router-turn1-d-backpressure": 1,
+    "pd-router-turn1-no-d-capacity": 5,
+    "pd-router-turn1-seed": 46
+  },
+  "latency_stats_s": {
+    "count": 4014.0,
+    "mean": 4.214657033050009,
+    "p50": 1.0827504023909569,
+    "p90": 13.380241627804935,
+    "p99": 24.453291333280504
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 690,
+    "decode-1": 599,
+    "decode-2": 660,
+    "decode-3": 584,
+    "decode-4": 606,
+    "decode-5": 646,
+    "decode-6": 664
+  },
+  "per_prefill_load": {
+    "prefill-0": 4449
+  },
+  "prefill_request_priorities": {
+    "-100": 149,
+    "100": 1685
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 2180,
+  "total_actual_kv_transfer_blocks": 52857,
+  "total_cached_tokens": 95091185,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4014.0,
+    "mean": 0.005804301410418847,
+    "p50": 0.005607025208882987,
+    "p90": 0.007293824862528552,
+    "p99": 0.008864479259402893
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T125022Z/sampled-trace.jsonl",
+  "truncated_request_count": 43,
+  "ttft_stats_s": {
+    "count": 4014.0,
+    "mean": 2.915135478307124,
+    "p50": 0.05643345229327679,
+    "p90": 11.900803190656006,
+    "p99": 22.758968392387033
+  }
+}
+[2026-04-28 21:40:57] Saved to outputs/qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_summary.json + exp1_1p7d_kvc_cap16_metrics.jsonl
+[2026-04-28 21:40:57] 
+[2026-04-28 21:40:57] === [EXP2] 2P6D KVC kv-aware cap=16 ===
+[2026-04-28 22:27:53] === exp2_2p6d_kvc_cap16 COMPLETED ===
+[2026-04-28 22:27:53] Summary:
+{
+  "actual_output_tokens_stats": {
+    "count": 4046.0,
+    "mean": 224.65002471576867,
+    "p50": 84.0,
+    "p90": 576.0,
+    "p99": 1349.0
+  },
+  "cache_hit_request_count": 3925,
+  "cached_tokens_stats": {
+    "count": 4449.0,
+    "mean": 22852.7439874129,
+    "p50": 19584.0,
+    "p90": 49009.0,
+    "p99": 67320.0
+  },
+  "decode_request_priorities": {},
+  "error_count": 403,
+  "execution_modes": {
+    "kvcache-centric": 403,
+    "kvcache-direct-to-d-session": 2348,
+    "pd-router-d-session-reseed": 28,
+    "pd-router-fallback-d-backpressure": 7,
+    "pd-router-fallback-large-append": 68,
+    "pd-router-fallback-large-append-seed-filter-early-turn": 45,
+    "pd-router-fallback-large-append-session-cap": 1403,
+    "pd-router-fallback-no-d-capacity": 9,
+    "pd-router-fallback-session-cap": 25,
+    "pd-router-large-append-reseed": 57,
+    "pd-router-large-append-reseed-after-eviction": 6,
+    "pd-router-turn1-no-d-capacity": 1,
+    "pd-router-turn1-seed": 49
+  },
+  "latency_stats_s": {
+    "count": 4046.0,
+    "mean": 2.505981629502371,
+    "p50": 0.8372491216287017,
+    "p90": 6.5139341270551085,
+    "p99": 18.335972285829484
+  },
+  "mechanisms": {
+    "kvcache-centric": 4449
+  },
+  "per_decode_load": {
+    "decode-0": 767,
+    "decode-1": 680,
+    "decode-2": 906,
+    "decode-3": 818,
+    "decode-4": 800,
+    "decode-5": 478
+  },
+  "per_prefill_load": {
+    "prefill-0": 2225,
+    "prefill-1": 2224
+  },
+  "prefill_request_priorities": {
+    "-100": 140,
+    "100": 1558
+  },
+  "re_prefill_count": 0,
+  "request_count": 4449,
+  "reuse_expected_count": 4397,
+  "reuse_observed_count": 4397,
+  "router_url": "http://127.0.0.1:8000",
+  "session_reset_count": 0,
+  "session_reused_count": 2348,
+  "total_actual_kv_transfer_blocks": 50727,
+  "total_cached_tokens": 101671858,
+  "total_kv_transfer_blocks": 105235,
+  "tpot_stats_s": {
+    "count": 4046.0,
+    "mean": 0.005708743129332261,
+    "p50": 0.005565466725497757,
+    "p90": 0.006912594398356141,
+    "p99": 0.008102089307750717
+  },
+  "trace_path": "outputs/qwen3-30b-tp1-v4-cap16/kvcache-centric-kv-aware-worker-admission-20260428T134057Z/sampled-trace.jsonl",
+  "truncated_request_count": 36,
+  "ttft_stats_s": {
+    "count": 4046.0,
+    "mean": 1.1653790952959129,
+    "p50": 0.05140436999499798,
+    "p90": 2.6447059931233525,
+    "p99": 15.121314341202378
+  }
+}
+[2026-04-28 22:27:53] Saved to outputs/qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_summary.json + exp2_2p6d_kvc_cap16_metrics.jsonl
+[2026-04-28 22:27:53] 
+[2026-04-28 22:27:53] === ALL TP1 V4 SWEEP EXPERIMENTS DONE ===
diff --git a/scripts/analysis/analyze_errors.py b/scripts/analysis/analyze_errors.py
new file mode 100644
index 0000000..e837712
--- /dev/null
+++ b/scripts/analysis/analyze_errors.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""Deep dive into v4 errors: which path, which D, which session, which turn."""
+import json
+import numpy as np
+from pathlib import Path
+from collections import Counter, defaultdict
+
+BASE = Path(__file__).parent
+
+def load_rows(jsonl_path):
+    rows = []
+    with open(jsonl_path) as f:
+        for line in f:
+            rows.append(json.loads(line))
+    return rows
+
+# Compare v3 and v4 errors
+for label, path in [
+    ("v3 1P7D", BASE.parent / "qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_metrics.jsonl"),
+    ("v4 1P7D", BASE / "exp1_1p7d_kvc_cap16_metrics.jsonl"),
+    ("v3 2P6D", BASE.parent / "qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_metrics.jsonl"),
+    ("v4 2P6D", BASE / "exp2_2p6d_kvc_cap16_metrics.jsonl"),
+]:
+    if not path.exists():
+        print(f"\nSKIP {label}: {path} not found")
+        continue
+    rows = load_rows(path)
+    err = [r for r in rows if r.get("error") is not None]
+    print(f"\n========== {label} ({len(err)} errors / {len(rows)} total = {len(err)/len(rows)*100:.1f}%) ==========")
+
+    # Error finish_reason distribution
+    fr_counter = Counter()
+    for r in err:
+        fr = str(r.get("finish_reason") or r.get("error") or "?")
+        fr_counter[fr[:80]] += 1
+    print(f"finish_reason distribution:")
+    for fr, cnt in fr_counter.most_common():
+        print(f"  {cnt:>4}x  {fr}")
+
+    # Errors by execution mode (these are aborted before mode assignment usually)
+    mode_counter = Counter(r.get("execution_mode", "?") for r in err)
+    print(f"\nerror by execution_mode:")
+    for mode, cnt in mode_counter.most_common():
+        print(f"  {cnt:>4}x  {mode}")
+
+    # Errors per D worker
+    dw_counter = Counter(r.get("assigned_decode_node", "?") for r in err)
+    print(f"\nerror per assigned_decode_node:")
+    for dw, cnt in dw_counter.most_common():
+        print(f"  {cnt:>4}x  {dw}")
+
+    # Errors by turn distribution
+    turn_counter = Counter(r.get("turn_id", -1) for r in err)
+    early = sum(c for t, c in turn_counter.items() if t <= 5)
+    mid = sum(c for t, c in turn_counter.items() if 5 < t <= 30)
+    late = sum(c for t, c in turn_counter.items() if t > 30)
+    print(f"\nerror by turn: early(0-5)={early} mid(6-30)={mid} late(31+)={late}")
+
+    # Per-session error rate
+    per_sess_err = defaultdict(int)
+    per_sess_total = defaultdict(int)
+    for r in rows:
+        per_sess_total[r["session_id"]] += 1
+        if r.get("error") is not None:
+            per_sess_err[r["session_id"]] += 1
+    sess_with_err = [(sid, per_sess_err[sid], per_sess_total[sid]) for sid in per_sess_err]
+    sess_with_err.sort(key=lambda x: -x[1])
+    print(f"\ntop 5 sessions by error count:")
+    for sid, e, t in sess_with_err[:5]:
+        print(f"  session {sid}: {e}/{t} errors ({e/t*100:.0f}%)")
+
+    # Errors timeline: are they bursty?
+    err_ts = sorted([r.get("trace_timestamp_s", 0) for r in err])
+    if err_ts:
+        first_ts = err_ts[0]
+        last_ts = err_ts[-1]
+        all_ts = sorted([r.get("trace_timestamp_s", 0) for r in rows])
+        first_all = all_ts[0]
+        last_all = all_ts[-1]
+        run_duration = last_all - first_all
+        err_first_pct = (err_ts[0] - first_all) / run_duration * 100 if run_duration > 0 else 0
+        err_last_pct = (err_ts[-1] - first_all) / run_duration * 100 if run_duration > 0 else 0
+        print(f"\nerror time range (% of run): {err_first_pct:.1f}% - {err_last_pct:.1f}%")
diff --git a/scripts/analysis/analyze_v3.py b/scripts/analysis/analyze_v3.py
new file mode 100644
index 0000000..5e29919
--- /dev/null
+++ b/scripts/analysis/analyze_v3.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""Analyze v3 (kv-aware) results — find why fallback-large-append-session-cap dominates."""
+import json
+import numpy as np
+from pathlib import Path
+from collections import Counter, defaultdict
+
+BASE = Path(__file__).parent
+
+def load_rows(jsonl_path):
+    rows = []
+    with open(jsonl_path) as f:
+        for line in f:
+            rows.append(json.loads(line))
+    return rows
+
+exp1 = load_rows(BASE / "exp1_1p7d_kvc_kvaware_metrics.jsonl")
+exp2 = load_rows(BASE / "exp2_2p6d_kvc_kvaware_metrics.jsonl")
+
+for name, rows in [("Exp1 1P7D", exp1), ("Exp2 2P6D", exp2)]:
+    print(f"\n========== {name} ==========")
+    ok = [r for r in rows if r.get("error") is None]
+
+    # Execution mode breakdown by latency
+    modes = Counter(r["execution_mode"] for r in ok)
+    print(f"\nExecution modes (n={len(ok)}):")
+    for mode, count in modes.most_common():
+        mode_rows = [r for r in ok if r["execution_mode"] == mode]
+        lats = [r["latency_s"] for r in mode_rows]
+        ttfts = [r["ttft_s"] for r in mode_rows]
+        print(f"  {mode}: n={count} ({count/len(ok)*100:.1f}%) "
+              f"lat P50={np.percentile(lats,50):.3f}s P90={np.percentile(lats,90):.3f}s | "
+              f"ttft P50={np.percentile(ttfts,50):.3f}s P90={np.percentile(ttfts,90):.3f}s")
+
+    # Per-D session distribution
+    per_d_sessions = defaultdict(set)
+    for r in ok:
+        d = r.get("assigned_decode_node", "?")
+        per_d_sessions[d].add(r["session_id"])
+    print(f"\nSessions per D worker:")
+    for d in sorted(per_d_sessions.keys()):
+        print(f"  {d}: {len(per_d_sessions[d])} unique sessions")
+
+    # session-cap fallback analysis
+    sc_rows = [r for r in ok if r["execution_mode"] == "pd-router-fallback-large-append-session-cap"]
+    if sc_rows:
+        print(f"\nSession-cap fallback details (n={len(sc_rows)}):")
+        # Which sessions hit this most?
+        sc_per_sess = Counter(r["session_id"] for r in sc_rows)
+        print(f"  Sessions hitting session-cap (top 5):")
+        for sid, cnt in sc_per_sess.most_common(5):
+            print(f"    session {sid}: {cnt} times")
+        # Per-D distribution
+        sc_per_d = Counter(r.get("assigned_decode_node", "?") for r in sc_rows)
+        print(f"  Per-D distribution: {dict(sc_per_d.most_common())}")
+        # Input length distribution
+        inp = [r.get("input_length", 0) for r in sc_rows]
+        print(f"  Input length: P50={np.percentile(inp,50):.0f} P90={np.percentile(inp,90):.0f}")
+        # Turn distribution
+        turns = Counter(r.get("turn_id", -1) for r in sc_rows)
+        print(f"  Turn distribution (top 5): {dict(turns.most_common(5))}")
+
+    # Direct-to-D analysis (ideal path)
+    dd_rows = [r for r in ok if r["execution_mode"] == "kvcache-direct-to-d-session"]
+    if dd_rows:
+        lats = [r["latency_s"] for r in dd_rows]
+        ttfts = [r["ttft_s"] for r in dd_rows]
+        kv_blocks = [r.get("actual_kv_transfer_blocks", 0) for r in dd_rows]
+        cached = [r.get("cached_tokens", 0) for r in dd_rows]
+        print(f"\nDirect-to-D details (n={len(dd_rows)}):")
+        print(f"  lat P50={np.percentile(lats,50):.3f}s P90={np.percentile(lats,90):.3f}s P99={np.percentile(lats,99):.3f}s")
+        print(f"  ttft P50={np.percentile(ttfts,50):.3f}s P90={np.percentile(ttfts,90):.3f}s")
+        print(f"  KV transfer: P50={np.percentile(kv_blocks,50):.0f} (should be 0 — no P involved)")
+        print(f"  cached_tokens P50={np.percentile(cached,50):.0f}")
+
+    # Sessions: how many turns each, how many used direct-to-d
+    print(f"\nPer-session direct-to-D rate (top 10 by total turns):")
+    per_sess = defaultdict(list)
+    for r in ok:
+        per_sess[r["session_id"]].append(r)
+    sess_stats = []
+    for sid, sreqs in per_sess.items():
+        total = len(sreqs)
+        dd = sum(1 for r in sreqs if r["execution_mode"] == "kvcache-direct-to-d-session")
+        sc = sum(1 for r in sreqs if "session-cap" in r["execution_mode"])
+        sess_stats.append((sid, total, dd, sc))
+    sess_stats.sort(key=lambda x: -x[1])
+    for sid, total, dd, sc in sess_stats[:10]:
+        print(f"  session {sid}: {total} turns, {dd} direct-to-D ({dd/total*100:.0f}%), {sc} session-cap fallback ({sc/total*100:.0f}%)")
diff --git a/scripts/analysis/analyze_v4.py b/scripts/analysis/analyze_v4.py
new file mode 100644
index 0000000..3367330
--- /dev/null
+++ b/scripts/analysis/analyze_v4.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""V4 results analysis: errors, execution modes, latency by mode."""
+import json
+import numpy as np
+from pathlib import Path
+from collections import Counter
+
+BASE = Path(__file__).parent
+
+def load_rows(jsonl_path):
+    rows = []
+    with open(jsonl_path) as f:
+        for line in f:
+            rows.append(json.loads(line))
+    return rows
+
+for name, path in [
+    ("Exp1 1P7D cap=16", BASE / "exp1_1p7d_kvc_cap16_metrics.jsonl"),
+    ("Exp2 2P6D cap=16", BASE / "exp2_2p6d_kvc_cap16_metrics.jsonl"),
+]:
+    rows = load_rows(path)
+    print(f"\n========== {name} ==========")
+    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]
+    print(f"Total: {len(rows)}, OK: {len(ok)}, Errors: {len(err)}")
+
+    # Errors finish_reason
+    if err:
+        finish_reasons = Counter()
+        for r in err:
+            fr = str(r.get("finish_reason") or r.get("error") or "?")
+            # Truncate long messages
+            short = fr[:120]
+            finish_reasons[short] += 1
+        print(f"\nError finish_reasons (top 5):")
+        for fr, cnt in finish_reasons.most_common(5):
+            print(f"  {cnt}x: {fr}")
+
+    # Execution mode latency breakdown
+    modes = Counter(r["execution_mode"] for r in ok)
+    print(f"\nTop execution modes by latency:")
+    print(f"{'mode':<55}{'n':<8}{'%':<8}{'P50 lat':<10}{'P90 lat':<10}{'TTFT P50':<10}")
+    for mode, count 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]
+        ttfts = [r["ttft_s"] for r in mode_rows]
+        print(f"  {mode:<53}{count:<8}{count/len(ok)*100:>5.1f}%  {np.percentile(lats,50):>7.3f}s  {np.percentile(lats,90):>7.3f}s  {np.percentile(ttfts,50):>7.3f}s")
+
+    # Per-D load
+    per_d = Counter(r.get("assigned_decode_node", "?") for r in ok)
+    print(f"\nPer-D load: max/min ratio = {max(per_d.values())/max(min(per_d.values()),1):.2f}x")
+    print(f"  {dict(per_d.most_common())}")
diff --git a/scripts/analysis/compare_no_error.py b/scripts/analysis/compare_no_error.py
new file mode 100644
index 0000000..4384726
--- /dev/null
+++ b/scripts/analysis/compare_no_error.py
@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""Compare KVC variants vs baseline, EXCLUDING errors and truncated requests."""
+import json
+import numpy as np
+from pathlib import Path
+
+OUT = Path("/mnt/kzlin/workflow/pd-hybrid/agentic-pd-hybrid/outputs")
+
+DATASETS = [
+    ("baseline 8DP", OUT / "qwen3-30b-tp1-v2-fixed/exp1_8way_dp_cache_aware_metrics.jsonl"),
+    ("v3 1P7D",     OUT / "qwen3-30b-tp1-v3-kvaware/exp1_1p7d_kvc_kvaware_metrics.jsonl"),
+    ("v3 2P6D",     OUT / "qwen3-30b-tp1-v3-kvaware/exp2_2p6d_kvc_kvaware_metrics.jsonl"),
+    ("v4 1P7D",     OUT / "qwen3-30b-tp1-v4-cap16/exp1_1p7d_kvc_cap16_metrics.jsonl"),
+    ("v4 2P6D",     OUT / "qwen3-30b-tp1-v4-cap16/exp2_2p6d_kvc_cap16_metrics.jsonl"),
+]
+
+def load_rows(path):
+    rows = []
+    with open(path) as f:
+        for line in f:
+            rows.append(json.loads(line))
+    return rows
+
+def is_truncated(row):
+    a = row.get("actual_output_tokens")
+    r = row.get("requested_output_tokens")
+    if a is not None and r is not None and r > 1:
+        return a < r * 0.5
+    return False
+
+def stats(values):
+    if not values:
+        return {"n": 0}
+    a = np.array(values)
+    return {
+        "n":    len(a),
+        "mean": float(np.mean(a)),
+        "p50":  float(np.percentile(a, 50)),
+        "p90":  float(np.percentile(a, 90)),
+        "p99":  float(np.percentile(a, 99)),
+    }
+
+def fmt(s, key):
+    if s["n"] == 0:
+        return "N/A"
+    v = s[key]
+    return f"{v:.3f}s" if v < 100 else f"{v:.1f}s"
+
+results = []
+for label, path in DATASETS:
+    if not path.exists():
+        print(f"SKIP {label}")
+        continue
+    rows = load_rows(path)
+    total = len(rows)
+    err_n = sum(1 for r in rows if r.get("error") is not None)
+    trunc_n = sum(1 for r in rows if r.get("error") is None and is_truncated(r))
+
+    # Filter: error=None AND not truncated AND latency present
+    clean = [r for r in rows
+             if r.get("error") is None
+             and not is_truncated(r)
+             and r.get("latency_s") is not None]
+
+    lats = [r["latency_s"] for r in clean]
+    ttfts = [r["ttft_s"] for r in clean if r.get("ttft_s") is not None]
+
+    results.append({
+        "label": label,
+        "total": total,
+        "err": err_n,
+        "trunc": trunc_n,
+        "clean_n": len(clean),
+        "lat": stats(lats),
+        "ttft": stats(ttfts),
+    })
+
+# Print comparison table
+print(f"\n{'='*100}")
+print("LATENCY (excluding errors AND truncated)")
+print(f"{'='*100}")
+print(f"{'config':<16}{'total':>7}{'err':>6}{'trunc':>7}{'clean':>7}  {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
+for r in results:
+    print(f"{r['label']:<16}{r['total']:>7}{r['err']:>6}{r['trunc']:>7}{r['clean_n']:>7}  "
+          f"{fmt(r['lat'],'mean'):>9}{fmt(r['lat'],'p50'):>9}{fmt(r['lat'],'p90'):>9}{fmt(r['lat'],'p99'):>9}")
+
+print(f"\n{'='*100}")
+print("TTFT (excluding errors AND truncated)")
+print(f"{'='*100}")
+print(f"{'config':<16}{'clean':>7}  {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
+for r in results:
+    print(f"{r['label']:<16}{r['clean_n']:>7}  "
+          f"{fmt(r['ttft'],'mean'):>9}{fmt(r['ttft'],'p50'):>9}{fmt(r['ttft'],'p90'):>9}{fmt(r['ttft'],'p99'):>9}")
+
+# Also: per-execution-mode breakdown for v4 only (the most interesting)
+print(f"\n{'='*100}")
+print("V4 2P6D: per-execution-mode (excluding errors and truncated)")
+print(f"{'='*100}")
+v4_2p6d = next((p for l, p in DATASETS if l == "v4 2P6D"), None)
+if v4_2p6d:
+    rows = load_rows(v4_2p6d)
+    clean = [r for r in rows if r.get("error") is None and not is_truncated(r)]
+    from collections import Counter
+    modes = Counter(r["execution_mode"] for r in clean)
+    print(f"{'mode':<55}{'n':>7}{'%':>7}  {'mean':>9}{'P50':>9}{'P90':>9}{'P99':>9}")
+    for mode, count in modes.most_common(10):
+        m_rows = [r for r in clean if r["execution_mode"] == mode]
+        s = stats([r["latency_s"] for r in m_rows])
+        pct = count/len(clean)*100
+        print(f"  {mode:<53}{count:>7}{pct:>6.1f}%  {fmt(s,'mean'):>9}{fmt(s,'p50'):>9}{fmt(s,'p90'):>9}{fmt(s,'p99'):>9}")
+
+# Also: WHAT IF we only count direct-to-D? (Pure KVC performance)
+print(f"\n{'='*100}")
+print("Pure KVC (kvcache-direct-to-d-session ONLY) vs Baseline")
+print(f"{'='*100}")
+for label, path in DATASETS:
+    if not path.exists() or "1P7D" not in label and "2P6D" not in label:
+        continue
+    rows = load_rows(path)
+    direct = [r for r in rows
+              if r.get("error") is None and not is_truncated(r)
+              and r.get("execution_mode") == "kvcache-direct-to-d-session"]
+    if not direct:
+        continue
+    s_lat = stats([r["latency_s"] for r in direct])
+    s_ttft = stats([r["ttft_s"] for r in direct if r.get("ttft_s") is not None])
+    print(f"{label:<16}n={s_lat['n']:>5}  lat: P50={fmt(s_lat,'p50')} P90={fmt(s_lat,'p90')}  ttft: P50={fmt(s_ttft,'p50')} P90={fmt(s_ttft,'p90')}")
+
+# Baseline for reference (already non-fallback by definition)
+print()
+baseline_path = OUT / "qwen3-30b-tp1-v2-fixed/exp1_8way_dp_cache_aware_metrics.jsonl"
+baseline_rows = load_rows(baseline_path)
+clean = [r for r in baseline_rows if r.get("error") is None and not is_truncated(r)]
+s_lat = stats([r["latency_s"] for r in clean])
+s_ttft = stats([r["ttft_s"] for r in clean if r.get("ttft_s") is not None])
+print(f"{'baseline 8DP':<16}n={s_lat['n']:>5}  lat: P50={fmt(s_lat,'p50')} P90={fmt(s_lat,'p90')}  ttft: P50={fmt(s_ttft,'p50')} P90={fmt(s_ttft,'p90')}")