§2.3 reframe: dispatch coupling is regime-dependent, not binary chatbot/agentic

The previous §2.3 narrative said "chatbot has T_human ≈ 30 s think-time,
agentic has T_external ≈ 0, so agentic is always closed-loop and chatbot
never is". The new T_external measurements on the production chatbot
trace (qwen3-max, n=42 k inter-turn gaps from formatted parent_chat_id
sessions) show the binary framing is wrong:

  agentic   p50 1.6 s,  39% gaps < 1 s,  p99 738 s
  chatbot   p50 7.2 s,   4% gaps < 1 s,  p99  43 s

Both have nonzero T_external. The right distinction is the *shape*:
chatbot is unimodal around 5–15 s (human cadence); agentic is bimodal
with a sub-second tool-call mass (39 % vs chatbot's 4 %) plus a long-
pause tail (13 % > 30 s). The agentic sub-second mass is what activates
dispatch coupling — for any W_turn > 1 s scheduler those turns satisfy
W_turn ≫ T_external by construction.

The empirical regime split:
                 unified  TTFT p90 = 7.3 s   →  agentic 73% closed-loop, chatbot 32%
                 lmetric  TTFT p90 = 15.7s   →  agentic 80%,             chatbot 88%

lmetric is bad enough that it drags the chatbot regime into closed-loop
too. This is a direct empirical explanation for lmetric underperforming
on both workloads.

Updates:
- PAPER_OUTLINE.md §2.3: lead with the regime threshold W_turn ≷
  T_external, replace the "T_human dominates" Little's Law with the
  general form L = Λ · N · (W_turn(L) + T_external), embed f3a CDF,
  add the empirical regime table; correct the small-perturbation
  formula to include the +T_external dampening term.
- MEETING.md §1: same reframe, condensed (CDF figure, two-row regime
  table, one-line conclusion).

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

MEETING.md

@@ -6,18 +6,26 @@
 
 ## 1. 关键洞察：Dispatch Coupling
 
-Chatbot：turn 间有人类 think-time，系统快慢 ⊥ 下一 turn 到达率。
-Agentic：turn 间只有 tool-call 返回 (≈0)，**系统跑慢 → session 停留长 → 并发多 → KV pool 紧 → 更慢**。
+每个 turn 间有一段外部 gap `T_external`（chatbot 是人类读+想+打字；agentic 是 tool 执行）。**Little's Law `L = Λ · N · (W_turn + T_external)`** 在两种 workload 下都成立 —— 差异在于 `T_external` 的分布相对于 `W_turn` 的位置：
+- `T_external ≫ W_turn` → 开环 regime：scheduler 退一步不动 L
+- `T_external ≲ W_turn` → 闭环 regime：`W_turn(L)` 因 KV 竞争耦合到 L，反馈环把 scheduler 的 ε 退步放大几倍
 
-Little's Law 隐式方程：
+**Production trace 实测 `T_external` 分布**（next.start − prev.end，formatted session 链作 ground truth）：
 
-```
-L = Λ · N · W_turn(L)        # agentic, T_human≈0
-```
+![](figs/f3a_inter_turn_gap.png)
 
-小扰动分析：amplification = `1 / (1 − Λ·N·W'(L*))`，系统接近 KV 饱和时发散。
+| | Agentic | Chatbot |
+|---|---:|---:|
+| p50 | **1.6s** | **7.2s** |
+| gap < 1s | **39%** | 4% |
+| gap < 5s | 67% | 29% |
+| p99 | 738s | 43s |
 
-**实测**：lmetric 跑 600s trace 用 49 min wall-clock = **8x amplification**。同硬件 unified 比 lmetric session 清空速度 ~3x。**per-turn W 的小差异被放大成 wall-clock 数量级差距** —— 这意味着 locality 不是 nice-to-have，是 dominant lever。
+两个分布形状完全不同：chatbot unimodal 集中在 5–15s（人类节奏）；agentic bimodal —— **39% 的 gap 在 sub-second 里（autonomous tool-call mode）**，外加 13% > 30s 的长尾。**Agentic 的 sub-second mass 是 chatbot 没有的**，正是 dispatch coupling 激活的来源。
+
+**实测 regime**：在 unified（TTFT p90 = 7.3s）下，**73% 的 agentic turn 把系统推进闭环**（W_turn > T_external），chatbot 仅 32%。在 lmetric（15.7s）下 agentic 80%、**chatbot 也到 88%** —— lmetric 把 chatbot 自己也拖进闭环，这就是它在两种 workload 都 underperform 的根因。
+
+**结果**：lmetric 跑 600s trace 用 49 min wall-clock = **8x amplification**。**per-turn W 的小差异被放大成 wall-clock 数量级差距** —— locality 不是 nice-to-have，是 dominant lever。
 
 ---