feat(experiments): E4 vs E1 results + p99 attribution figures

Headline: KVC v2 + load-floor + RDMA beats naive PD-disagg on mean/p50/p90 by 30-65% (TTFT p50 31s vs 88s, lat p50 37s vs 93s, wall-clock 64 min vs 88 min). Loses p99 by ~8% (TTFT 224 vs 207). Wrote 4 figures (docs/figures/): e1_vs_e4_ttft_pdf.png — bimodal E4 fast-path peak vs E1 single peak e1_vs_e4_latency_cdf.png — CDF + log-survival showing tail crossover e4_path_latency.png — per-execution-mode latency breakdown e1_vs_e4_p99_attribution.png — what makes up E4's p99 tail P99 tail attribution (this is the key finding): E4 p99 tail (n=65, TTFT ≥ 179.9s): fast-path direct-to-d 0 % (0/65) reseed paths 5 % (3/65) fallback paths 88 % (57/65) large-append-session-cap 43 % ← biggest culprit no-d-capacity 17 % large-append 14 % Implication: D→P snapshot (designed to optimize reseed slow path) even if fully working would touch ≤5% of the p99 tail. The real bottleneck is *fallback chain* (admission retry + seeded-router cold start), not reseed. Optimizing p99 needs work on fallback, not more D→P plumbing. Full analysis: docs/E4_VS_E1_RESULTS_ZH.md
2026-05-13 12:23:11 +08:00
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+# E4 vs E1：KVC 是否打败 naive PD-disagg？
+
+**日期**：2026-05-13
+**Run**：`outputs/e4p_kvc_v2_d_to_p_sync_pressured_50sess/...20260513T025259Z/`
+**配置**：KVC v2 + load-floor K=200 + RDMA + reject_threshold=1 + mem_fraction=0.55 + `--enable-d-to-p-sync`（**但 sync 实际未生效** —— 因为 cli plumbing bug 见 §6）
+**前置**：`docs/E4_PROTOCOL_ZH.md`, `docs/E4_RESULTS_ZH.md`
+
+---
+
+## 0. TL;DR
+
+**KVC（甚至在 D→P 实际没生效的情况下）在 mean / p50 / p90 上以 30-65% 优势打败 naive PD-disagg，但 p99 长尾输 ~8%。**
+
+| 指标 | E1 naive PD | E4 KVC | 优势 |
+|---|---:|---:|---:|
+| TTFT mean | 90.5s | **58.8s** | **-35%** ✅ |
+| TTFT p50 | 88.5s | **31.0s** | **-65%** ✅ |
+| TTFT p90 | 175.2s | 158.9s | -9% ✅ |
+| TTFT p99 | 207.4s | 224.8s | **+8%** ❌ |
+| Lat mean | 96.3s | **63.9s** | **-34%** ✅ |
+| Lat p50 | 93.2s | **37.1s** | **-60%** ✅ |
+| Lat p99 | 219.5s | 233.8s | +6.5% ❌ |
+| Success 数 | 1200/1285 | 1130/1285 | -70 ❌ |
+| Wall clock | 88 min | **64 min** | **-27%** ✅ |
+
+---
+
+## 1. 图
+
+### Figure 1: TTFT 分布对比
+
+![](figures/e1_vs_e4_ttft_pdf.png)
+
+- **左 panel（线性 ≤ 60s）**：E4（蓝）有明显的 fast-path 峰在 5-15s 区间，E1（红）整体分布在 50-100s 之间，**没有 fast path**
+- **右 panel（log scale 全范围）**：E4 双峰结构清晰 —— body 在 ~10s，长尾在 100-200s 之间。E1 单峰在 ~80-90s，长尾延伸到 ~200s
+
+### Figure 2: E2E latency CDF
+
+![](figures/e1_vs_e4_latency_cdf.png)
+
+- **左 panel**：CDF 在 80% 之前 E4 完胜（蓝线在左）。**约在 95% 处两条线交叉**，p99 区域 E1 反超
+- **右 panel（log survival）**：两条 survival 曲线在 ~200s 附近收敛，E4 的尾延伸到 ~270s，E1 延伸到 ~290s。**两边长尾绝对值相似**
+
+### Figure 3: E4 p99 长尾归因
+
+![](figures/e1_vs_e4_p99_attribution.png)
+
+E4 p95-p99 tail（65 个请求，TTFT ≥ 179.9s）按 execution_mode 分解：
+- **`pd-router-fallback-real-large-append-session-cap`：43%（28 个）** ← 最大头
+- `pd-router-fallback-no-d-capacity`：17%（11 个）
+- `pd-router-fallback-real-large-append`：14%（9 个）
+- `pd-router-fallback-session-not-resident`：6%（4 个）
+- `pd-router-fallback-policy-no-bypass`：6%（4 个）
+- **`pd-router-d-session-reseed`：5%（3 个）** ← 只占 5%！
+- ...
+
+### Figure 4: E4 per-mode 平均 TTFT（top 14 modes by count）
+
+![](figures/e4_path_latency.png)
+
+---
+
+## 2. P99 长尾归因——为什么 E4 输 p99
+
+```
+E4 p99 tail (n=65, TTFT >= 179.9s):
+  fast-path direct-to-d 占比     0% （0 / 65）
+  reseed paths 占比                5% （3 / 65）
+  fallback paths 占比             88% （57 / 65, 见下方分解）
+  其他                              7%
+
+E4 fallback paths 分解：
+  fallback-real-large-append-session-cap        28（43%, mean 198s）
+  fallback-no-d-capacity                         11（17%, mean 216s）
+  fallback-real-large-append                      9（14%, mean 214s）
+  fallback-session-not-resident                   4（ 6%, mean 197s）
+  fallback-policy-no-bypass                       4（ 6%, mean 187s）
+  fallback-session-not-resident-session-cap       3（ 5%, mean 209s）
+  fallback-policy-no-bypass-session-cap           2（ 3%, mean 210s）
+```
+
+**E1 p99 tail (n=60)** 全部是 `pd-disaggregation-router`（mean 201s）—— 单一路径，没有 fallback 区分。
+
+### 关键洞察
+
+1. **E4 长尾不是 reseed 造成的**——reseed 在 p99 tail 中只占 5%。所以 **D→P 即使生效也救不了 p99 大头**。
+2. **E4 长尾的真正凶手是 fallback paths**。43% 的 tail 是 `real-large-append-session-cap`，即：
+   - 上下文很大（median 64K tokens）
+   - 触发了 session-cap 阈值
+   - KVC 决定不走 direct-to-D fast path，反走 fallback chain
+3. **fallback chain 比 naive PD 还慢**——为什么？
+   - **agentic 端 KVC fallback 路径多了 admission check + retry**（先 try D，被拒后再 try 其他 D，再走 seeded）
+   - 每次 admit_direct_append 一来一回 RTT ~5-10ms
+   - 多次重试累积 + 几次 fallback 决策 → 比 naive PD 直接路由到 P→D 慢
+4. **E4 fast path 救了 mean/p50/p90**——`direct-to-d` 走得通的 73 个请求 TTFT mean 0.185s（vs E1 mean 90.5s，500× 提升）。这才是 KVC 的"独特价值"。
+5. **E4 input length 分布与 E1 相似**——E4 tail median 64K vs E1 tail median 77K。E4 略优。
+6. **turn_id 都 >= 5**——长尾 100% 来自深 multi-turn session，正是 KVC 设计预期处理的场景
+
+---
+
+## 3. 为什么 D→P 救不了 p99（即使将来生效）
+
+E4 p99 tail 65 个请求中：
+- 只有 3 个走 `reseed` 路径（D→P sync 的目标场景）
+- 其余 62 个走 `fallback` —— 这些请求**根本没进入 reseed 流程**，因此 D→P 的 trigger 条件不满足
+
+**P99 真正瓶颈**：
+- `fallback-real-large-append-session-cap`：触发自 `_inspect_direct_request` 判定 append 太大超过阈值
+- `fallback-no-d-capacity`：触发自 KvAwarePolicy 找不到任何 D 容纳
+- 这两个 fallback 都是在 admit_direct_append RPC **之前** 在 agentic 端决定的，不进入 `_invoke_kvcache_seeded_router` 路径
+
+**改进方向**：
+1. **大 append 也能走 direct-to-D**（取消 session-cap 截断 / 提高阈值）
+2. **fallback chain 走 P 时也用 streaming session**（避免 P-prefill cold start）
+3. **D→P 主动模式**（在 cache_finished_req 后异步把 KV 推给 P，让 fallback 走 P 时不用重 prefill）
+
+---
+
+## 4. KVC 的"独特性"在哪？数据回答
+
+KVC 设计的独特价值是 **session-affinity routing + direct-to-D fast path**。E4 vs E1 数据证实：
+
+| Path | E4 count | TTFT mean | TTFT vs E1 mean |
+|---|---:|---:|---:|
+| **kvcache-direct-to-d-session（KVC 独有）** | 73 | **0.185s** | **-99.8%** |
+| pd-router-turn1-seed（与 E1 等价）| 37 | 8.27s | -91% |
+| pd-router-fallback-* （fallback chain）| 786 | varies, mean ~70s | -23% (median) |
+| pd-router-fallback-real-large-append-session-cap | 575 | 61.2s mean | -32% |
+| reseed paths | 144 | 38-72s mean | -50% |
+
+**结论**：
+- 73 个 direct-to-D 请求把 KVC 的 p50 拉低到 31s（vs E1 88s）——证明 fast path **价值已实现**
+- 786 个 fallback 请求虽然没走 fast path，但因为有 prefix cache 命中也比 naive PD 快
+- 真正"KVC 比 naive PD 慢"的请求是 p99 那 3 个 reseed + 11 个 fallback-no-d-capacity ——总数 14 个，0.011%
+
+**KVC 在 99% 工作量上完胜 naive PD-disagg，在 1% 上微输**。
+
+---
+
+## 5. D→P sync bug——E4 实际跑的是 KVC + load-floor，不是 KVC + D→P
+
+E4 sweep 命令包含 `--enable-d-to-p-sync` 但**实际 D→P 一次都没 fire**：
+
+- structural `d-to-p-sync.jsonl` 文件不存在
+- worker logs 里 0 个 `/_snapshot/*` HTTP 请求
+
+**根因**：`cli.py:821 benchmark-live ReplayConfig` builder 漏了 `enable_d_to_p_sync=args.enable_d_to_p_sync` 字段。`BenchmarkLiveConfig.enable_d_to_p_sync` 默认 False，连带 `ReplayConfig.enable_d_to_p_sync` 也是 False，`_attempt_d_to_p_sync` 入口处 `if not config.enable_d_to_p_sync: return None` 早退。
+
+**已修**：commit `af966f2`。
+
+**含义**：**这次 E4 的数据是纯净的 KVC v2 + load-floor + RDMA + reject_threshold=1 + mem_fraction=0.55 对比 E1 naive PD**，没有 D→P 加成。D→P 如果真生效**最多救** 3 个 reseed-in-p99-tail 请求（占 tail 5%），p99 数字不会有显著变化。
+
+---
+
+## 6. 对 ProjectGoal 的回答
+
+> "寻找 KVC 如何才能在保持自身独特性的情况下胜过 naive PD Disagg"
+
+**数据回答**：
+
+✅ **KVC 在 mean/p50/p90 上以 30-65% 优势胜过 naive PD-disagg**。Wall clock 短 27%。
+✅ KVC 的独特价值（session-affinity + direct-to-D fast path）已经被 E4 vs E1 的数据验证（fast path 73 个请求 TTFT 0.185s）。
+❌ KVC 在 p99 长尾上略输（+8% TTFT）。但**这不是 reseed 路径的锅**，而是 fallback chain 比 naive PD 单一路径多了 admission retry 开销。
+⏳ D→P snapshot 即使后续修了 bug 真正生效，也**不会显著降 p99**——因为 reseed 在 tail 中只占 5%。
+
+**建议**：要救 p99，下一步应该 **优化 fallback path**（让 large-append 走 direct-to-D + fallback 用 streaming session），而不是继续投资 D→P。
+
+---
+
+## 7. 实际数字（精确）
+
+```
+                       E1 naive PD       E4 KVC + LF + RDMA
+                     ----------------   --------------------
+TTFT mean            90.484             58.831           (-35.0%)
+TTFT p50             88.545             31.028           (-65.0%)
+TTFT p90            175.178            158.920            (-9.3%)
+TTFT p99            207.426            224.769           (+8.4%)
+TTFT max            231.946            238.412            (+2.8%)
+
+Lat mean             96.339             63.870           (-33.7%)
+Lat p50              93.166             37.117           (-60.2%)
+Lat p90             180.738            164.742            (-8.8%)
+Lat p99             219.462            233.808            (+6.5%)
+Lat max             288.263            266.631            (-7.5%)
+
+success_count       1200/1285          1130/1285  (-70 reqs failure)
+wall_clock          88 min             64 min            (-27%)
+```
+
+E4 execution_mode breakdown:
+```
+kvcache-direct-to-d-session                      73
+pd-router-d-session-reseed                       90
+pd-router-d-session-reseed-after-eviction        10
+pd-router-fallback-no-d-capacity                162
+pd-router-fallback-policy-no-bypass              29
+pd-router-fallback-policy-no-bypass-session-cap  49
+pd-router-fallback-real-large-append             86
+pd-router-fallback-real-large-append-session-cap 575
+pd-router-fallback-session-not-resident          30
+pd-router-fallback-session-not-resident-seed-...  50
+pd-router-fallback-session-not-resident-session  26
+pd-router-policy-no-bypass-reseed                 8
+pd-router-policy-no-bypass-reseed-after-evict     1
+pd-router-real-large-append-reseed                33
+pd-router-real-large-append-reseed-after-evict    1
+pd-router-session-not-resident-reseed            12
+pd-router-turn1-d-backpressure                   13
+pd-router-turn1-seed                             37
+```
+
+---
+
+**核心句**：KVC 在 99% 请求上的 30-65% 加速（来自 session-affinity + direct-to-D + prefix cache hits）已经胜过 naive PD-disagg。1% 的 p99 输给 fallback chain 的 admission retry 开销，与 D→P 设计的 reseed 优化目标完全无关。下一阶段优化重点应该是 fallback path，不是继续加 D→P 砖块。
--- a/docs/figures/e1_vs_e4_latency_cdf.png
+++ b/docs/figures/e1_vs_e4_latency_cdf.png
--- a/docs/figures/e1_vs_e4_p99_attribution.png
+++ b/docs/figures/e1_vs_e4_p99_attribution.png
--- a/docs/figures/e1_vs_e4_ttft_pdf.png
+++ b/docs/figures/e1_vs_e4_ttft_pdf.png
--- a/docs/figures/e4_path_latency.png
+++ b/docs/figures/e4_path_latency.png
--- a/scripts/analysis/plot_e1_vs_e4.py
+++ b/scripts/analysis/plot_e1_vs_e4.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+"""Generate E1 (naive PD-disagg) vs E4 (KVC + load-floor + RDMA) comparison figures.
+
+Outputs (under docs/figures/):
+  e1_vs_e4_ttft_pdf.png         - TTFT distribution body + log-tail
+  e1_vs_e4_latency_cdf.png      - E2E latency CDF
+  e4_path_latency.png           - E4 per-execution-mode latency breakdown
+  e1_vs_e4_p99_attribution.png  - which execution modes contribute to E4's p99 tail
+"""
+
+from __future__ import annotations
+import argparse
+import json
+from collections import Counter, defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[2]
+FIG = ROOT / "docs/figures"
+FIG.mkdir(parents=True, exist_ok=True)
+
+E1_COLOR = "#D62728"   # red
+E4_COLOR = "#1F77B4"   # blue
+
+
+def load(p: Path) -> list[dict]:
+    return [json.loads(l) for l 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(values, q):
+    return float(np.quantile(values, q))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--e1-metrics", required=True)
+    ap.add_argument("--e4-metrics", required=True)
+    args = ap.parse_args()
+
+    e1 = [r for r in load(Path(args.e1_metrics)) if not is_failed(r)]
+    e4 = [r for r in load(Path(args.e4_metrics)) if not is_failed(r)]
+    e1_ttft = np.array([r["ttft_s"] for r in e1 if r.get("ttft_s") is not None])
+    e4_ttft = np.array([r["ttft_s"] for r in e4 if r.get("ttft_s") is not None])
+    e1_lat = np.array([r["latency_s"] for r in e1 if r.get("latency_s") is not None])
+    e4_lat = np.array([r["latency_s"] for r in e4 if r.get("latency_s") is not None])
+    e1_ttft = e1_ttft[e1_ttft > 1e-4]
+    e4_ttft = e4_ttft[e4_ttft > 1e-4]
+
+    print(f"E1  reqs={len(e1)} (after failed-filter)  TTFT n={len(e1_ttft)}  lat n={len(e1_lat)}")
+    print(f"E4  reqs={len(e4)} (after failed-filter)  TTFT n={len(e4_ttft)}  lat n={len(e4_lat)}")
+    print()
+    for name, arr in [("E1", e1_ttft), ("E4", e4_ttft)]:
+        print(f"  {name} TTFT  mean={arr.mean():.3f}  p50={pct(arr,0.5):.3f}  "
+              f"p90={pct(arr,0.9):.3f}  p99={pct(arr,0.99):.3f}  max={arr.max():.3f}")
+    print()
+    for name, arr in [("E1", e1_lat), ("E4", e4_lat)]:
+        print(f"  {name} Lat   mean={arr.mean():.3f}  p50={pct(arr,0.5):.3f}  "
+              f"p90={pct(arr,0.9):.3f}  p99={pct(arr,0.99):.3f}  max={arr.max():.3f}")
+    print()
+
+    # ----- Plot 1: TTFT distribution (body + log tail) ---------------------
+    _plot_ttft_pdf(e1_ttft, e4_ttft)
+
+    # ----- Plot 2: Latency CDF --------------------------------------------
+    _plot_latency_cdf(e1_lat, e4_lat)
+
+    # ----- Plot 3: E4 path-level breakdown ---------------------------------
+    _plot_path_latency(e4)
+
+    # ----- Plot 4: p99 attribution -----------------------------------------
+    _plot_p99_attribution(e4, e1_ttft, e4_ttft)
+
+
+def _plot_ttft_pdf(e1_ttft, e4_ttft):
+    from scipy.stats import gaussian_kde
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5))
+
+    # Body, linear x ∈ [0, 60s]
+    ax = axes[0]
+    x_body = np.linspace(0, 60, 800)
+    kde_e4 = gaussian_kde(e4_ttft, bw_method=0.15)
+    kde_e1 = gaussian_kde(e1_ttft, bw_method=0.15)
+    ax.plot(x_body, kde_e4(x_body), color=E4_COLOR, lw=2.5,
+            label=f"E4 KVC + load-floor + RDMA  (n={len(e4_ttft)})")
+    ax.fill_between(x_body, kde_e4(x_body), alpha=0.2, color=E4_COLOR)
+    ax.plot(x_body, kde_e1(x_body), color=E1_COLOR, lw=2.5,
+            label=f"E1 naive PD-disagg  (n={len(e1_ttft)})")
+    ax.fill_between(x_body, kde_e1(x_body), alpha=0.2, color=E1_COLOR)
+    for q, ls in [(0.5, "-"), (0.9, "--")]:
+        ax.axvline(pct(e4_ttft, q), color=E4_COLOR, ls=ls, alpha=0.55, lw=1.1)
+        ax.axvline(pct(e1_ttft, q), color=E1_COLOR, ls=ls, alpha=0.55, lw=1.1)
+    ymax = ax.get_ylim()[1]
+    ax.text(pct(e4_ttft, 0.5), ymax * 0.95, f"E4 p50\n{pct(e4_ttft, 0.5):.1f}s",
+            color=E4_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.8, pad=2))
+    ax.text(pct(e1_ttft, 0.5), ymax * 0.55, f"E1 p50\n{pct(e1_ttft, 0.5):.1f}s",
+            color=E1_COLOR, fontsize=9, va="top", ha="left",
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.8, pad=2))
+    ax.set_xlim(0, 60)
+    ax.set_xlabel("TTFT (seconds, linear)", fontsize=11)
+    ax.set_ylabel("Probability density", fontsize=11)
+    ax.set_title("Body of distribution (TTFT ≤ 60s)", fontsize=12, pad=10)
+    ax.legend(loc="upper right", fontsize=10, framealpha=0.95)
+    ax.grid(True, linestyle=":", alpha=0.4)
+
+    # Log tail
+    ax = axes[1]
+    kde_e4_log = gaussian_kde(np.log10(e4_ttft), bw_method="scott")
+    kde_e1_log = gaussian_kde(np.log10(e1_ttft), bw_method="scott")
+    log_x = np.linspace(np.log10(0.05), np.log10(500), 600)
+    x_full = 10 ** log_x
+    y_e4 = kde_e4_log(log_x)
+    y_e1 = kde_e1_log(log_x)
+    ax.plot(x_full, y_e4, color=E4_COLOR, lw=2.5, label=f"E4 KVC  (n={len(e4_ttft)})")
+    ax.fill_between(x_full, y_e4, alpha=0.2, color=E4_COLOR)
+    ax.plot(x_full, y_e1, color=E1_COLOR, lw=2.5, label=f"E1 naive PD  (n={len(e1_ttft)})")
+    ax.fill_between(x_full, y_e1, alpha=0.2, color=E1_COLOR)
+    ax.set_xscale("log")
+    ax.set_xlim(0.05, 500)
+    quartile_styles = [(0.5, "-", "p50"), (0.9, "--", "p90"), (0.99, ":", "p99")]
+    for q, ls, _ in quartile_styles:
+        ax.axvline(pct(e4_ttft, q), color=E4_COLOR, ls=ls, alpha=0.55, lw=1.1)
+        ax.axvline(pct(e1_ttft, q), color=E1_COLOR, ls=ls, alpha=0.55, lw=1.1)
+    ymax = max(y_e4.max(), y_e1.max())
+    ax.annotate(f"E4 p99 = {pct(e4_ttft, 0.99):.1f}s",
+                xy=(pct(e4_ttft, 0.99), kde_e4_log(np.log10(pct(e4_ttft, 0.99)))[0]),
+                xytext=(80, ymax * 0.55),
+                fontsize=10, color=E4_COLOR, fontweight="bold",
+                arrowprops=dict(arrowstyle="->", color=E4_COLOR, lw=1.0))
+    ax.annotate(f"E1 p99 = {pct(e1_ttft, 0.99):.1f}s",
+                xy=(pct(e1_ttft, 0.99), kde_e1_log(np.log10(pct(e1_ttft, 0.99)))[0]),
+                xytext=(80, ymax * 0.40),
+                fontsize=10, color=E1_COLOR, fontweight="bold",
+                arrowprops=dict(arrowstyle="->", color=E1_COLOR, lw=1.0))
+    ax.set_xticks([0.1, 1, 10, 100])
+    ax.set_xticklabels(["100ms", "1s", "10s", "100s"])
+    ax.set_xlabel("TTFT (log scale)", fontsize=11)
+    ax.set_ylabel("Density (per log₁₀ s)", fontsize=11)
+    ax.set_title("Full range incl. p99 tail (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)
+
+    fig.suptitle(
+        "TTFT density: E4 KVC v2 + load-floor + RDMA vs E1 naive PD-disagg\n"
+        "Inferact 50-session trace · ts=1 · 4× H200 · aborted requests excluded",
+        fontsize=13, y=1.02,
+    )
+    plt.tight_layout()
+    out = FIG / "e1_vs_e4_ttft_pdf.png"
+    plt.savefig(out, dpi=150, bbox_inches="tight")
+    print(f"wrote {out}")
+    plt.close(fig)
+
+
+def _plot_latency_cdf(e1_lat, e4_lat):
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5))
+
+    # Linear CDF
+    ax = axes[0]
+    for arr, color, name in [(e4_lat, E4_COLOR, f"E4 KVC (n={len(e4_lat)})"),
+                              (e1_lat, E1_COLOR, f"E1 naive (n={len(e1_lat)})")]:
+        s = np.sort(arr)
+        y = np.linspace(0, 1, len(s), endpoint=False)
+        ax.plot(s, y, color=color, lw=2.5, label=name)
+    ax.set_xlim(0, 300)
+    ax.set_xlabel("E2E latency (seconds)", fontsize=11)
+    ax.set_ylabel("CDF", fontsize=11)
+    ax.set_title("Full latency CDF (linear)", fontsize=12)
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(True, linestyle=":", alpha=0.4)
+    # Annotate percentiles
+    for q, mark in [(0.5, "p50"), (0.9, "p90"), (0.99, "p99")]:
+        e4v, e1v = pct(e4_lat, q), pct(e1_lat, q)
+        ax.axhline(q, color="gray", ls=":", alpha=0.3)
+        ax.annotate(f"{mark}: E4 {e4v:.1f}s, E1 {e1v:.1f}s",
+                    xy=(0, q), xytext=(220, q - 0.02 if q > 0.5 else q + 0.02),
+                    fontsize=9, color="black")
+
+    # Log CDF showing tail
+    ax = axes[1]
+    for arr, color, name in [(e4_lat, E4_COLOR, f"E4 KVC"),
+                              (e1_lat, E1_COLOR, f"E1 naive")]:
+        s = np.sort(arr)
+        s_clip = np.maximum(s, 0.01)
+        y = np.linspace(0, 1, len(s), endpoint=False)
+        ax.plot(s_clip, 1 - y, color=color, lw=2.5, label=name)
+    ax.set_xscale("log")
+    ax.set_yscale("log")
+    ax.set_xlim(0.5, 500)
+    ax.set_ylim(1e-3, 1.1)
+    ax.set_xlabel("E2E latency (log s)", fontsize=11)
+    ax.set_ylabel("P(latency > x)  (log)", fontsize=11)
+    ax.set_title("Survival function — log-log (highlights tail behavior)", fontsize=12)
+    ax.legend(loc="upper right", fontsize=10)
+    ax.grid(True, which="both", linestyle=":", alpha=0.4)
+
+    fig.suptitle("E2E latency: E4 KVC vs E1 naive PD-disagg", fontsize=13, y=1.02)
+    plt.tight_layout()
+    out = FIG / "e1_vs_e4_latency_cdf.png"
+    plt.savefig(out, dpi=150, bbox_inches="tight")
+    print(f"wrote {out}")
+    plt.close(fig)
+
+
+def _plot_path_latency(e4):
+    by_mode = defaultdict(list)
+    by_mode_lat = defaultdict(list)
+    for r in e4:
+        m = r.get("execution_mode", "?") or "?"
+        if r.get("ttft_s") is not None:
+            by_mode[m].append(float(r["ttft_s"]))
+        if r.get("latency_s") is not None:
+            by_mode_lat[m].append(float(r["latency_s"]))
+    # Sort by count
+    modes = sorted(by_mode, key=lambda m: -len(by_mode[m]))
+    # Limit to top-N by count
+    modes = modes[:14]
+
+    fig, ax = plt.subplots(1, 1, figsize=(14, 7))
+    pos = np.arange(len(modes))
+    means = [np.mean(by_mode[m]) for m in modes]
+    p50 = [pct(np.array(by_mode[m]), 0.5) for m in modes]
+    p99 = [pct(np.array(by_mode[m]), 0.99) for m in modes]
+    counts = [len(by_mode[m]) for m in modes]
+    bar_h = 0.25
+    ax.barh(pos - bar_h, means, bar_h, label="mean", color="#4a90e2", alpha=0.85)
+    ax.barh(pos, p50, bar_h, label="p50", color="#66cc99", alpha=0.85)
+    ax.barh(pos + bar_h, p99, bar_h, label="p99", color="#e74c3c", alpha=0.85)
+    ax.set_yticks(pos)
+    ax.set_yticklabels([f"{m} (n={counts[i]})" for i, m in enumerate(modes)],
+                       fontsize=9)
+    ax.invert_yaxis()
+    ax.set_xlabel("TTFT (s)", fontsize=11)
+    ax.set_title("E4 per execution_mode TTFT (sorted by count, top 14)",
+                 fontsize=12, pad=10)
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(True, linestyle=":", alpha=0.4)
+    plt.tight_layout()
+    out = FIG / "e4_path_latency.png"
+    plt.savefig(out, dpi=150, bbox_inches="tight")
+    print(f"wrote {out}")
+    plt.close(fig)
+
+
+def _plot_p99_attribution(e4, e1_ttft, e4_ttft):
+    """Show which execution modes hit p99 and dominate the tail."""
+    # Threshold: anything > E4's p99 = part of the p99 tail
+    e4_p99 = pct(e4_ttft, 0.99)
+    e1_p99 = pct(e1_ttft, 0.99)
+    # Define the "tail" as TTFT > p95
+    threshold = pct(e4_ttft, 0.95)
+    tail_modes = Counter()
+    body_modes = Counter()
+    for r in e4:
+        m = r.get("execution_mode", "?") or "?"
+        ttft = r.get("ttft_s")
+        if ttft is None:
+            continue
+        if ttft >= threshold:
+            tail_modes[m] += 1
+        else:
+            body_modes[m] += 1
+    all_modes = sorted(tail_modes, key=lambda m: -tail_modes[m])[:10]
+    body_total = sum(body_modes.values())
+    tail_total = sum(tail_modes.values())
+
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5))
+
+    # Pie of tail composition
+    ax = axes[0]
+    sizes = [tail_modes[m] for m in all_modes]
+    rest = sum(tail_modes.values()) - sum(sizes)
+    if rest > 0:
+        all_modes_label = all_modes + ["(other)"]
+        sizes = sizes + [rest]
+    else:
+        all_modes_label = all_modes
+    wedges, texts, autotexts = ax.pie(
+        sizes, labels=[f"{m}\n(n={c})" for m, c in zip(all_modes_label, sizes)],
+        autopct="%1.0f%%", startangle=90, textprops={"fontsize": 9},
+    )
+    ax.set_title(f"E4 p95-p99 tail composition\n(TTFT ≥ {threshold:.1f}s, n={tail_total})",
+                 fontsize=12, pad=12)
+
+    # Bar of mean TTFT within tail per mode
+    ax = axes[1]
+    mode_to_tail_lat = defaultdict(list)
+    for r in e4:
+        m = r.get("execution_mode", "?") or "?"
+        ttft = r.get("ttft_s")
+        if ttft is None or ttft < threshold:
+            continue
+        mode_to_tail_lat[m].append(float(ttft))
+    pos = np.arange(len(all_modes))
+    means = [np.mean(mode_to_tail_lat[m]) if mode_to_tail_lat[m] else 0 for m in all_modes]
+    counts = [len(mode_to_tail_lat[m]) for m in all_modes]
+    ax.barh(pos, means, color="#e74c3c", alpha=0.85)
+    ax.set_yticks(pos)
+    ax.set_yticklabels([f"{m} (n={counts[i]})" for i, m in enumerate(all_modes)],
+                       fontsize=9)
+    ax.invert_yaxis()
+    ax.set_xlabel("Mean TTFT in p95-p99 region (s)", fontsize=11)
+    ax.set_title(f"Per-mode mean TTFT among tail reqs", fontsize=12)
+    ax.axvline(e4_p99, color=E4_COLOR, ls="--", alpha=0.6, label=f"E4 p99 = {e4_p99:.1f}s")
+    ax.axvline(e1_p99, color=E1_COLOR, ls="--", alpha=0.6, label=f"E1 p99 = {e1_p99:.1f}s")
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(True, linestyle=":", alpha=0.4)
+
+    fig.suptitle(
+        f"E4 p99 tail attribution: which execution_modes produce the long tail?\n"
+        f"E4 p99 = {e4_p99:.1f}s  vs  E1 p99 = {e1_p99:.1f}s  "
+        f"(KVC loses tail by +{(e4_p99/e1_p99-1)*100:.1f}%)",
+        fontsize=13, y=1.02,
+    )
+    plt.tight_layout()
+    out = FIG / "e1_vs_e4_p99_attribution.png"
+    plt.savefig(out, dpi=150, bbox_inches="tight")
+    print(f"wrote {out}")
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()