f2b: replace top-1/5/10% bars with full CDF; align all docs to replay-trace numbers

The previous f2b_session_skew.png was a 3-bar chart (top 1/5/10%) computed
from the production trace summary (which is not present locally, only its
precomputed JSON). The new figure is a continuous CDF of cumulative
input-token mass vs session rank percentile, generated directly from the
replay trace traces/w600_r0.0015_st30.jsonl so any percentile is readable.

Headline numbers update accordingly:
  replay trace (n=274 sessions): top 1% = 24.3%, top 5% = 61.9%, top 10% = 75.8%
  production trace (n=1.3M):     top 1% = 46.5%, top 5% = 66.5%, top 10% = 74.6%

Both show extreme skew well above the y=x uniform reference; the replay
trace is less extreme at top-1% because n=274 makes that bucket only
~3 sessions. We standardize §2/§3 narrative on the replay-trace numbers
so motivation matches §5 evaluation; production numbers kept as a side
note for context.

- scripts/plot_session_skew_cdf.py: reproducible figure generator
- MEETING.md / PAPER_OUTLINE.md: update narrative + caption

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

MEETING.md

@@ -26,7 +26,7 @@ L = Λ · N · W_turn(L)        # agentic, T_human≈0
 | | 数据 | 图 |
 |---|---|---|
 | KV reuse 几乎只在 session 内 | intra 93.2% / cross 5.7% / shared 1.1% | ![](figs/f2a_reuse_topology.png) |
-| Session 极度偏斜 | top 1% = 46.5% input mass | ![](figs/f2b_session_skew.png) |
+| Session 极度偏斜 | replay 上 top 1% / 5% / 10% = 24% / 62% / 76% input mass（production 全 trace 更陡，top 1% = 46.5%） | ![](figs/f2b_session_skew.png) |
 | 单请求 KV footprint 已经很大 | p99 = 11.8 GiB ≈ H20 12% | ![](figs/f2c_kv_footprint_cdf.png) |
 
 理论 APC 上界 = intra-session 79.6% / any-session 80.3%，差 <1pp。**任何不 affinity 的调度都丢绝大部分 reuse。**
@@ -58,7 +58,7 @@ agentic 平均请求 33.6k token 需 3.3GB KV；4P+4D / 6P+2D 在 agentic regime
 | sticky | **20.3s** | 55.4s | **34.6s** |
 | unified | **10.3s** | 37.7s | **18.0s** |
 
-机制：top 1% 的 session 占 46.5% input 量、且 hot session 数量多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。
+机制：top 5% 的 session 占 ~62% input 量、且 hot session 数量远多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。
 
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