# Stop-A validation (Phase 3) — 2026-06-15

Branch `feat/two-stop`. Stop-A = truncate each probe's replay once the offered
L-C-A of the replayed prefix converges to the full set (pure L-C-A criterion +
C-gate). This note records the CPU calibration and the GPU fidelity check.

## 1. Calibration (CPU, no serving)

`scripts/stop_a_calibration.py` on the dash0 0321 10:00–10:10 windows:

| dim | chat (19239 req, hit≈7%) | coder (2451 req, structured reuse) |
| --- | --- | --- |
| A | ≥0.95 by frac 0.10 | fast |
| L | ≥0.96 from frac 0.05 | 0.05=0.75 (heavy tail) → ≥0.94 by 0.20 |
| **C (slowest)** | noisy, dips (0.50→0.885, 0.55→0.835), stable ≥0.92 only ~0.85 | smooth, stable ≥0.92 by ~0.70 |

Stop fraction (τ_L=τ_A=0.90, W=3):

| τ_c | chat | coder |
| --- | --- | --- |
| 0.85 | 0.45 (273s) | 0.45 (255s) |
| 0.90 | 0.70 (423s) | 0.55 (318s) |
| 0.92 | 0.85 (513s) | 0.70 (411s) |

Findings:
- **C is the slowest dimension in both workloads** — reproduces paper §5.2 / Fig 9.
- **What makes C hard to call converged is signal *noise*, not reuse magnitude.**
  Low-reuse chat has a sparse/spiky ideal-hit-length series, so its C similarity
  oscillates and is *harder* to stabilize than the structured, higher-reuse coder.
  Consequence: a strict τ_c (0.92) gives chat only ~15% saving. A more robust C
  feature for the low-reuse regime is future work.

## 2. GPU fidelity check (Qwen3-30B-A3B, vLLM 0.11.1, H20)

One full-window run (`adaptive_stop` disabled, `replay_time_scale=1.0`, window
`chat_w20260311_1000`, 0–8k, out=128), then `scripts/stop_a_validate.py`
recomputes each probe's convergence prefix and compares the truncated verdict to
the full verdict — so a single GPU run validates truncation fidelity (no second run).

Trial result: best feasible `sampling_u=0.078125`, request_rate **2.30 req/s**,
pass_rate 0.973.

Per-probe verdict (τ=0.9):

| τ_c | verdict matches | mean replay saved |
| --- | --- | --- |
| 0.85 | 3/4 | 54% |
| 0.90 | 3/4 | 52% |
| 0.92 | 3/4 | 38% |

The mismatch is the same probe at every τ_c — the feasibility knee `0.08594`:

```
thresh    full_pass  prefix_pass  full_feas  prefix_feas
0.08594   0.946      0.956–0.961  False      True   <- mismatch
0.07812   0.973      0.987–0.990  True       True
0.06250   0.986      1.000        True       True
0.09375   0.268      0.49–0.54    False      False
```

## 3. Interpretation

- **Stop-A works and saves ~50% of replay** (vs the full 600 s window) while
  preserving 3/4 probe verdicts. (The paper's ~70% is vs a 30-min fixed baseline;
  our baseline is the 600 s window, so the percentages are not directly comparable.)
- **The one failure is a boundary false-positive at the feasibility knee.** At
  `0.08594` the full window is 0.946 (just below the 0.95 SLO) but the prefix is
  0.956–0.961 (just above): the *second half* of the window degraded — engine-state
  drift (KV fill / fragmentation / later-arriving harder requests) that the
  *offered* L-C-A cannot see. The C-gate did not help because offered-C had
  converged; the divergence is in the measured pass-rate, not in C.
- If Stop-A were enabled, the binary search would accept `0.08594`, overestimating
  the peak sustainable rate by one binary step (~10%).

**This is the boundary jitter we accepted when choosing the pure-L-C-A criterion.**
The data now argues for revisiting the previously-declined **SLO-boundary guard**:
keep replaying while the measured pass-rate is within ±δ of the target, even after
L-C-A converges. It targets exactly this knee case at low extra cost (it only
extends replay on probes sitting on the feasibility boundary). Recommend adding it
as a small Stop-A enhancement before enabling Stop-A in production studies.

## 4. SLO-boundary guard (implemented + validated)

Added `trace.adaptive_stop.boundary_delta` (default 0.02): when a truncated probe's
measured pass-rate lands within ±δ of the SLO target, re-measure on the full window
and use that verdict. Re-ran the same config with `adaptive_stop` enabled
(τ=0.9, τ_c=0.90, δ=0.02):

| threshold | feasible | pass | selected | replayed | boundary_extended |
| --- | --- | --- | --- | --- | --- |
| 0.06250 | True | 1.000 | 1086 | 487 (45%) | — |
| 0.09375 | False | 0.444 | 1656 | 822 (50%) | — |
| 0.07812 | True | 0.994 | 1378 | 682 (49%) | — |
| 0.08594 | **False** | 0.947 | 1523 | **1523 (100%)** | **True** |

Result: best feasible `sampling_u=0.078125` (rate 2.30 req/s) — **identical to the
full-replay baseline**. The guard fired on exactly the one knee probe and
re-measured it to the correct infeasible verdict; the other three probes truncated
to ~45–50%. Net replayed 3514/5643 requests ≈ **38% replay saved on this trial
while recovering the correct peak rate** (no one-step overestimate).

**Conclusion: Stop-A with the boundary guard is correct (verdict matches full
replay) and still saves replay time. Safe to enable.** Configs:
`dash0_qwen30b_a3b_stopA_fulldata.json` (OFF baseline) and
`dash0_qwen30b_a3b_stopA_on.json` (ON).

## Repro

```
# calibration
PYTHONPATH=src python3 scripts/stop_a_calibration.py \
  --jsonl <DIR>/qwen_chat_blksz_64_032109-032111.jsonl --block-size 64 \
  --window-start 3600 --window-end 4200 --gpu-count 8 --label chat
# GPU run + fidelity
PYTHONPATH=src python3 -m aituner.cli study tune \
  --spec configs/examples/dash0_qwen30b_a3b_stopA_fulldata.json \
  --store-root .aituner/stopA-fulldata --max-trials 1
PYTHONPATH=src python3 scripts/stop_a_validate.py \
  --spec configs/examples/dash0_qwen30b_a3b_stopA_fulldata.json \
  --store-root .aituner/stopA-fulldata --tau 0.9 --tau-c 0.90
```