Add full latency grid (mean/p50/p90/p99 × TTFT/TPOT/E2E) as f6 companion

The headline f6_e2e_latency_bars only shows p90, hiding three regimes:
  - mean: unified dominates (3.3s TTFT, 7.0s E2E vs sticky 5.6s / 12.1s)
  - p50: sticky and unified are tied on first-turn TTFT (0.5s each) —
    sticky's first turn of each session is free, after which queues
    accumulate. Unified beats sticky everywhere else.
  - p99: tail amplification reveals unified's biggest gap —
    TTFT 42.3s vs sticky 74.1s; E2E 68.8s vs sticky 139.7s.

The 12-panel figure is the honest full picture; the 3-panel headline
stays for slide-friendly summary.

- analysis/characterization/window_1_results/raw_stats/{policy}.json:
  cached ttft/tpot/e2e {mean,p50,p90,p99} pulled from dash0
  /home/admin/cpfs/wjh/agentic-kv/outputs/b3_sweep_20260525_095043/
  (b3_policy_comparison.json doesn't record mean, only percentiles).
- analysis/characterization/render_window1_figures.py:
  new fig_b3_latency_full_grid renders the 4×3 grid from the cache.
- figs/f6_e2e_latency_full_grid.png: 12-panel companion.
- PAPER_OUTLINE.md §5.2: both figures embedded; main table column
  renamed from "Hotspot idx" to "Worker p90 (median / max)" to match
  the new metric convention.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

PAPER_OUTLINE.md

@@ -241,13 +241,17 @@ KV transfer 发生在触发该 migration 的 request 的 critical path 上，但
 
 ### §5.2 End-to-end Performance
 
-**Figure 6: End-to-end performance** — ✅ (PARTIAL，缺 PD-disagg 列)
+**Figure 6 (headline, p90 only)** — ✅ (PARTIAL，缺 PD-disagg 列)
 
-![F6 E2E latency bars — 5 policies](figs/f6_e2e_latency_bars.png)
+![F6 E2E latency bars — 4 policies, p90 only](figs/f6_e2e_latency_bars.png)
 
-> **🚧 TBD (NEW DATA)**：上图缺 `static PD-disagg` 那一列；EAR 列也是 TBD（需 migration validation）。要再补一张同样格式但包含全 6 个 baseline 的图。
+**Figure 6 full (mean / p50 / p90 / p99 × TTFT / TPOT / E2E)** — ✅ 数据完备：
 
-| Scheduler | TTFT p50 | TTFT p90 | TPOT p90 | APC | Hotspot idx | Wall-clock factor |
+![F6 full latency grid — 4 percentiles × 3 metrics](figs/f6_e2e_latency_full_grid.png)
+
+> **🚧 TBD (NEW DATA)**：两张图都缺 `static PD-disagg` 那一列；EAR 列也是 TBD（需 migration validation）。要再补同样格式但包含全 6 个 baseline 的版本。Headline 图用 p90 一行进 main paper，完整 grid 可进附录或 supplementary。
+
+| Scheduler | TTFT p50 | TTFT p90 | TPOT p90 | APC | Worker p90 (median / max) | Wall-clock factor |
 |---|---|---|---|---|---|---|
 | load-balance | TBD | TBD | TBD | TBD | TBD | TBD |
 | LMetric | TBD | TBD | TBD | 56.9% | 6.53 | ~8x |

analysis/characterization/render_window1_figures.py

@@ -89,6 +89,48 @@ def fig_b3_latency_bars(comp: dict, out: Path) -> None:
     plt.close(fig)
 
 
+def fig_b3_latency_full_grid(results_dir: Path, out: Path) -> None:
+    """4 rows (mean / p50 / p90 / p99) × 3 cols (TTFT / TPOT / E2E) per policy.
+
+    Reads per-policy metrics.summary.json caches under raw_stats/, which
+    expose mean alongside the percentiles (b3_policy_comparison.json does
+    not record mean).
+    """
+    raw_dir = results_dir / "raw_stats"
+    pols = [p for p in POLICY_ORDER if (raw_dir / f"{p}.json").exists()]
+    if not pols:
+        return
+    stats = {p: json.loads((raw_dir / f"{p}.json").read_text()) for p in pols}
+
+    rows = [("mean", "mean"), ("p50", "p50"), ("p90", "p90"), ("p99", "p99")]
+    cols = [
+        ("TTFT (s)", "ttft", 1.0),
+        ("TPOT (ms)", "tpot", 1000.0),
+        ("E2E (s)", "e2e", 1.0),
+    ]
+    fig, axes = plt.subplots(len(rows), len(cols), figsize=(11, 11), sharex=True)
+    for i, (row_label, agg_key) in enumerate(rows):
+        for j, (col_label, metric_key, scale) in enumerate(cols):
+            ax = axes[i][j]
+            vals = [stats[p][metric_key][agg_key] * scale for p in pols]
+            ax.bar(pols, vals,
+                   color=[POLICY_COLOR.get(p, "gray") for p in pols],
+                   edgecolor="black", linewidth=0.5)
+            for k, v in enumerate(vals):
+                ax.text(k, v, f"{v:.1f}", ha="center", va="bottom", fontsize=8)
+            if j == 0:
+                ax.set_ylabel(row_label, fontsize=11)
+            if i == 0:
+                ax.set_title(col_label, fontsize=11)
+            ax.grid(alpha=0.3, axis="y")
+            ax.tick_params(axis="x", rotation=20, labelsize=9)
+            ax.margins(y=0.18)
+    fig.suptitle("B3 latencies per policy — mean / p50 / p90 / p99")
+    fig.tight_layout()
+    fig.savefig(out, dpi=120)
+    plt.close(fig)
+
+
 def fig_b3_apc_vs_upper(comp: dict, upper: dict, out: Path) -> None:
     by = {r["policy"]: r for r in comp["rows"]}
     pols = [p for p in POLICY_ORDER if p in by]
@@ -307,6 +349,9 @@ def main() -> None:
 
     fig_b3_apc_vs_hotspot(comp, upper, args.out_dir / "fig_b3_apc_vs_hotspot.png")
     fig_b3_latency_bars(comp, args.out_dir / "fig_b3_latency_bars.png")
+    fig_b3_latency_full_grid(
+        args.results_dir, args.out_dir / "fig_b3_latency_full_grid.png"
+    )
     fig_b3_apc_vs_upper(comp, upper, args.out_dir / "fig_b3_apc_vs_upper.png")
     fig_b3_failure_breakdown(comp, args.out_dir / "fig_b3_failure_breakdown.png")
     fig_b3_per_worker_ttft(args.results_dir, comp,

analysis/characterization/window_1_results/raw_stats/lmetric.json

@@ -0,0 +1,23 @@
+{
+  "ttft": {
+    "count": 1214.0,
+    "mean": 5.111546324698484,
+    "p50": 0.9387824369769078,
+    "p90": 15.671339168207492,
+    "p99": 53.56683189840049
+  },
+  "tpot": {
+    "count": 1214.0,
+    "mean": 0.01757124870168204,
+    "p50": 0.008854518407308914,
+    "p90": 0.02122720699121469,
+    "p99": 0.18280341184277568
+  },
+  "e2e": {
+    "count": 1214.0,
+    "mean": 9.518126648903337,
+    "p50": 2.754255389008904,
+    "p90": 24.8209177934099,
+    "p99": 80.59924928059091
+  }
+}

analysis/characterization/window_1_results/raw_stats/load_only.json

@@ -0,0 +1,23 @@
+{
+  "ttft": {
+    "count": 1214.0,
+    "mean": 6.268620166597892,
+    "p50": 1.2609447415161412,
+    "p90": 20.197147866390882,
+    "p99": 52.84285237012196
+  },
+  "tpot": {
+    "count": 1214.0,
+    "mean": 0.02406975794215626,
+    "p50": 0.009231464695980247,
+    "p90": 0.026851662550158716,
+    "p99": 0.3211630676943426
+  },
+  "e2e": {
+    "count": 1214.0,
+    "mean": 11.702793988628443,
+    "p50": 3.58568156149704,
+    "p90": 33.459180271782685,
+    "p99": 93.95083751494239
+  }
+}

analysis/characterization/window_1_results/raw_stats/sticky.json

@@ -0,0 +1,23 @@
+{
+  "ttft": {
+    "count": 1214.0,
+    "mean": 5.55315460854824,
+    "p50": 0.5415176274836995,
+    "p90": 18.021296651283045,
+    "p99": 74.09429564891524
+  },
+  "tpot": {
+    "count": 1214.0,
+    "mean": 0.027834537397398284,
+    "p50": 0.008952101894096181,
+    "p90": 0.03641285916619554,
+    "p99": 0.35152006935195085
+  },
+  "e2e": {
+    "count": 1214.0,
+    "mean": 12.109200157184377,
+    "p50": 2.081947358994512,
+    "p90": 34.62592205510591,
+    "p99": 139.68334607904353
+  }
+}

analysis/characterization/window_1_results/raw_stats/unified.json

@@ -0,0 +1,23 @@
+{
+  "ttft": {
+    "count": 1213.0,
+    "mean": 3.2790960856202394,
+    "p50": 0.4997710260213353,
+    "p90": 7.345769894809922,
+    "p99": 42.34170345296613
+  },
+  "tpot": {
+    "count": 1213.0,
+    "mean": 0.012493800538265787,
+    "p50": 0.008079791456705824,
+    "p90": 0.017110194704198407,
+    "p99": 0.12655874612209597
+  },
+  "e2e": {
+    "count": 1213.0,
+    "mean": 6.961301470549104,
+    "p50": 1.7495028690318577,
+    "p90": 18.033410895219994,
+    "p99": 68.80023987947489
+  }
+}