# Harness vs naive agentic tuner — controlled ablation on dense Qwen3.5-27B — 2026-06-16 Branch `main`. Goal: quantify 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`. > **Status: SET UP AND CALIBRATED; full two-run GPU sweep NOT completed in this > session.** The two ablation configs are committed and validated end-to-end on > dash0 (LLM auth OK, engine launches clean, Stop-A/Stop-B machinery present). A > substrate-calibration finding (below) was obtained from a real harness-ON run. > The full sweep was **not** run to completion because each run is ~2–3 GPU-hours > (8 trials × ~6-min engine warmup + multi-probe binary search) and the two runs > are sequential (each may need 4 GPUs for TP4) — ~5–6 GPU-hours total, beyond > this session. GPUs were left **clean (all 0 MiB, no orphans)**. A precise > continuation recipe is at the end. ## What the ablation toggles (the harness mechanism, verified in code) With `use_harness=true` vs `false` (`src/aituner/llm.py`, `src/aituner/cli.py`, `src/aituner/harness.py`): | Aspect | Harness ON | Naive OFF | | --- | --- | --- | | Prompt `Harnesses:` section | **present** — ranked bottleneck hypotheses (`_rank_bottleneck_hypotheses`, weights TTFT/TPOT/queueing from probe failure counts + workload default) and per-knob-family **use-when / procedure / guards** with an `active_now` flag (e.g. TP family `active_now` when bottleneck ∈ {ttft_prefill, decode_tpot}) | **absent** — only common preamble + study/SLO/trial-history JSON | | Deterministic guided proposal | `build_harness_guided_proposal` can emit a deterministic first validation probe | none — LLM proposes freely every iteration | | Stop-B authority | `_stop_authority`: an LLM `should_stop` is honored only if the deterministic validator agrees (frontier closed + no high-value candidate); else **vetoed** (bounded, `cli.py:350`) | LLM's own `should_stop` honored immediately (`stop_authority is None` ⇒ `authorized=True`) | | Convergence guard | `_convergence_guard`: stop only when ≥3 completed trials are all within 2% of incumbent | not applied | The naive prompt still states TP/DP/EP are tunable and gives full context — so the **only** thing removed is the harness's bottleneck-diagnosis + knob-family steering (and the deterministic guided/stop scaffolding). That is exactly the paper's "naive agentic tuner." ## Configs (committed, reproducible) - `configs/examples/dash0_qwen27b_ablation_harness_on.json` (`study_id=dash0-qwen27b-ablation-harness-on`, `use_harness=true`) - `configs/examples/dash0_qwen27b_ablation_naive_off.json` (`study_id=dash0-qwen27b-ablation-naive-off`, `use_harness=false`) The two files differ in **exactly two keys** (`llm.use_harness`, `study_id`) — verified by `diff` of sorted JSON. Both validate on dash0 (codex/gpt-5.4 endpoint resolves, base config inherited from `dash0_qwen27b_stopB_loop.json`). ## Substrate (and the calibration finding) The ablation measures the **tuning process**, not absolute peak-rate, so a faster replay substrate is used (at `replay_time_scale=1.0` a single TP4 trial took ~3 h — `stop-b-e2e-27b-20260616.md`). | knob | value | rationale | | --- | --- | --- | | `trace.replay_time_scale` | **0.2** | `arrival_s = timestamp * time_scale` (`trace.py:223`): same request set arrives in 1/5 the wall-clock → ~5× effective offered load. The brief's prescribed lever (compress time, not just cut the cap). | | `trace.early_stop_max_elapsed_s` | **180** (from 900) | the 600 s arrival window compresses to **120 s** at scale 0.2, so the inherited 900 s wall cap was ~5× too large and let overloaded probes burn ~15 min each. Scaled proportionately to the compressed time axis. | | `trace.early_stop_max_lag_s` | **30** (from 120) | proportionate to the 120 s compressed window. | | `search.high` | 0.25 | sampling_u binary-search upper bound | | `search.max_probes` | **3** (from 5) | bound the binary-search step count per trial | | `--max-trials` | 8 | iteration budget | | Stop-A | **enabled** (unchanged) | converged-prefix replay truncation on for both runs | | SLO | length-aware TTFT (4 s + L_in/8k) + TPOT ≤ 50 ms | unchanged | | GPUs | `2,3,4,5,6,7` | GPUs 0/1 avoided | **Calibration finding (real harness-ON run, trial-0001 baseline TP1):** the first binary-search probe at `sampling_u=0.125` measured **pass_rate = 0.0** and early-stopped on **`probe_elapsed_s>180.0`** (probe_history.json). So: 1. The 180 s elapsed cap **works** (cut the overloaded probe at 3 min, as intended). 2. At scale 0.2, **TP1 cannot serve even the lightest binary-search threshold** of this 0–8k chat window — it is hopelessly TPOT/decode-bound under 5× compression (engine logs: 260 preemptions over 311 requests, 100% GPU util, ≥12 reqs always queued). The baseline incumbent therefore sits at/near the search floor, leaving large headroom for TP scaling — a *clean* ablation shape, but every TP1 probe runs the full 180 s cap (no feasible point to find faster). **Substrate recommendation for the rerun (carried into the continuation recipe):** scale 0.2 is *too* aggressive — it makes the whole TP1 family uniformly infeasible, so the baseline is uninformative and each probe pays the full elapsed cap. Use **`replay_time_scale=0.4–0.5`** (window 240–300 s arrival) so TP1 registers a real feasible baseline and feasible probes finish *before* the cap; keep the caps proportionate (`early_stop_max_elapsed_s = 900 × scale`, `early_stop_max_lag_s = 120 × scale`). **Comparability caveat (applies to whatever scale is used).** Compressed arrivals mean the absolute `request_rate_per_gpu` is **not** comparable to the scale=1.0 ground-truth climb (TP1 0.123 → TP2 0.29 → TP4 1.00). The ablation reads **trajectory shape** (which knob family each iteration tries; monotonic incumbent climb; where each run stops) and **relative** per-topology ordering. ## Expected contrast (hypothesis to be confirmed — do not treat as result) From the committed mechanism and the scale=1.0 27B climb (`stop-b-e2e-27b-20260616.md`) plus the older smoke-regime ablation (`qwen27b-chat-0-8k-harness-fig18.md`, iters-to-best 4→2) and a prior 235B **naive** run inspected this session (`.aituner-prefill/...-noharness-minprompt-gpt54-20260514`, which wandered into TP4+EP4 and TP4+DP2 launch-failures, repeated max-num-seqs/mbt runtime fiddling, and regressed at iters 6/8/9/11/12): - **Harness ON** should diagnose TP1 as TPOT/decode-bound (the `tensor-parallel-size` family `active_now`) and steer to **TP↑ early**, climbing TP1→TP2→TP4 with a monotonic incumbent, then pivoting to runtime tuning on the winning family, and stop only when the Stop-B convergence guard authorizes it. - **Naive OFF** is expected to **wander** (runtime knobs, EP/duplicate/launch-failing topologies) and possibly stop early on its own `should_stop` without a validator veto. This is the quantity the rerun must measure; it is **not** yet measured here. ## Continuation recipe (to finish the sweep) 1. On dash0, set `replay_time_scale=0.4` (and `early_stop_max_elapsed_s=360`, `early_stop_max_lag_s=48`) in **both** ablation configs; keep `max_probes=3`, `--max-trials 8`, everything else identical. Re-verify the two configs differ only in `use_harness`+`study_id`. 2. `export OPENAI_API_KEY=$(python3 -c "import json,pathlib;print(json.load(open(pathlib.Path.home()/'.codex/auth.json'))['OPENAI_API_KEY'])")`; confirm `curl .../v1/models` → 200. 3. Run **sequentially** (GPUs 2–7 free between runs): `setsid env PYTHONPATH=src OPENAI_API_KEY=$OPENAI_API_KEY python3 -u -m aituner.cli study tune --spec --store-root .aituner-ablation --max-trials 8 logs/.log 2>&1 &` 4. Extract trajectories with the committed helper: `python3 scripts/ablation_trajectory.py .aituner-ablation/` — it prints the iter → config → per_gpu → incumbent table and the proposal path (it distinguishes `baseline-*` / `proposal-*` / `harness-proposal-*` / `harness-stop-*`, so metrics #2 and #5 fall out directly). 5. Fill the five comparison metrics: (1) iters-to-best, (2) proposal path, (3) oscillation/regression, (4) wasted/infeasible/launch-failed trials, (5) whether/when each run stops (harness Stop-B vs naive's own `should_stop`). ## Operational notes confirmed this session - LLM auth path works (export `OPENAI_API_KEY` from `~/.codex/auth.json`; 200 from `https://ai.prism.uno/v1/models`). Both ON and OFF call the LLM. - GPUs 0/1 were **clean** (0 MiB) this session — the earlier leaked-memory orphans appear to have been reset; configs still pin GPUs 2–7. - **SIGTERM teardown fix validated again**: killing `study tune` tore down the engine + EngineCore workers cleanly, GPUs 2–7 returned to 0 MiB, no orphan. - Use `setsid` + `