Scale ablation early-stop caps to the compressed window (scale=0.2)

At replay_time_scale=0.2 the 600s arrival window compresses to 120s, so
the inherited 900s wall-clock elapsed cap let overloaded TP1 probes burn
~15min each (the tractability hazard the brief flagged). Scale the caps
proportionately to the time axis: early_stop_max_elapsed_s 900->180,
early_stop_max_lag_s 120->30. Feasible probes (~120s arrival + drain)
finish well inside 180s; overloaded probes die in ~3min. Both configs
still differ only in use_harness + study_id. Adds the ablation doc
skeleton and a read-only trajectory-extraction helper.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/harness-ablation/harness-vs-naive-20260616.md

@@ -0,0 +1,62 @@
+# Harness vs naive agentic tuner — controlled ablation on dense Qwen3.5-27B — 2026-06-16
+
+Branch `main`. Quantifies the value of the paper's **harness** (domain-knowledge
+knob-family guidance) by running the agentic tuning loop twice on the *same*
+workload, identical in every respect except `llm.use_harness`:
+
+- **Harness ON** (`dash0_qwen27b_ablation_harness_on.json`, study
+  `dash0-qwen27b-ablation-harness-on`): the prompt carries the `Harnesses:`
+  section (ranked bottleneck hypotheses + per-knob-family use-when / procedure /
+  guards, with an `active_now` flag), the loop can emit a deterministic
+  harness-guided first probe, and a **Stop-B validator** gates the LLM's
+  `should_stop` (an unauthorized stop is vetoed).
+- **Naive OFF** (`dash0_qwen27b_ablation_naive_off.json`, study
+  `dash0-qwen27b-ablation-naive-off`): `use_harness=false`. No harness prompt
+  section, no deterministic guided/stop proposals, and the LLM's own `should_stop`
+  is honored without a validator veto. The prompt still tells the LLM that
+  TP/DP/EP are tunable and gives the full study/SLO/trial-history context — so the
+  difference is purely the harness guidance, this is the paper's "naive agentic
+  tuner."
+
+The two config files differ in **exactly two keys** (`llm.use_harness` and
+`study_id`); verified by diff.
+
+## Substrate (why these knobs, and the comparability caveat)
+
+This ablation measures the **tuning process** (proposal path + convergence), not
+absolute peak-rate, so a faster replay substrate is used to keep it tractable
+(at `replay_time_scale=1.0` a single TP4 trial took ~3 h — see
+`stop-b-e2e-27b-20260616.md`).
+
+| knob | value | rationale |
+| --- | --- | --- |
+| `trace.replay_time_scale` | **0.2** | arrival times are multiplied by 0.2, i.e. the same request set arrives in 1/5 the wall-clock → ~5× higher effective offered load. `arrival_s = timestamp * time_scale` (`trace.py:223`). Mild arrival-time compression: the lever the brief prescribes (compress time, do **not** just cut the elapsed cap). |
+| `search.high` | 0.25 | upper bound of the sampling_u binary search |
+| `search.max_probes` | 5 | probe budget per trial |
+| `--max-trials` | 8 | iteration budget |
+| Stop-A | **enabled** (unchanged) | converged-prefix replay truncation stays on for both runs |
+| SLO | length-aware TTFT (4s + L_in/8k) + TPOT ≤ 50 ms | unchanged from base |
+| GPUs | `CUDA_VISIBLE_DEVICES=2,3,4,5,6,7` | GPUs 0/1 avoided |
+
+**Comparability caveat.** Because arrival times are compressed 5×, the absolute
+`request_rate_per_gpu` values are **not** comparable to the scale=1.0 ground-truth
+climb (TP1 0.123 → TP2 0.29 → TP4 1.00). The ablation reads the **trajectory
+shape** (which knob family each iteration tries, whether the incumbent climbs
+monotonically, where each run stops) and the **relative** per-GPU ordering across
+topologies — not the absolute numbers.
+
+## Run 1 — Harness ON
+
+<!-- TRAJECTORY_ON -->
+
+## Run 2 — Naive OFF
+
+<!-- TRAJECTORY_OFF -->
+
+## The five comparison metrics
+
+<!-- METRICS -->
+
+## Analysis & caveats
+
+<!-- ANALYSIS -->