[CI] Add opt-in statistically-calibrated lm-eval accuracy gate (Wilson lower bound)

[yongzhe2160cs]

Purpose

The lm-eval accuracy gate in .buildkite/lm-eval-harness/test_lm_eval_correctness.py decides pass/fail with a fixed relative tolerance:

rtol = eval_config.get("rtol", DEFAULT_RTOL)  # DEFAULT_RTOL = 0.08
min_acceptable = ground_truth * (1 - rtol)
success = success and measured_value >= min_acceptable

That threshold ignores the eval sample count (limit, often 1000). A fixed epsilon is not noise-aware: at gsm8k baseline ≈0.755 with n=1000, the binomial standard error is ≈1.4pp, so the 8% relative band (≈6pp) sits ~4 SE below baseline — loose enough to let a real ~3–5pp regression pass as long as the point estimate happens to clear the bar, while at small limit the same fixed band can flag pure sampling noise. Today this is handled manually (e.g. #43570 stabilised a flaky gsm8k gate by switching to greedy decode, bumping N to 200, and hand-tuning rtol/expected values).

This is a concrete first increment toward #34333 ("Automated baselining and degradation detection"), which calls for statistically principled accuracy gating. It does not close that RFC — it lands the minimal, dependency-free building block.

Change (opt-in, backward compatible): a new pure-stdlib accuracy_gate.py adds a gate: "ci" mode. A proportion metric must clear the same baseline threshold via a one-sided Wilson score lower confidence bound at limit samples and confidence (default 0.95), instead of trusting the point estimate:

z      = NormalDist().inv_cdf(confidence)
center = (p + z^2/(2n)) / (1 + z^2/n)
half   = (z / (1 + z^2/n)) * sqrt( p(1-p)/n + z^2/(4 n^2) )
lower  = center - half          # pass iff lower >= ground_truth * (1 - rtol)

Wilson is used over the Wald approximation because it stays in [0,1] and behaves at the tails (e.g. p=1.0 gives a sensible bound, not zero width). The CI gate is strictly at least as strict as the rtol gate (the lower bound never exceeds the point estimate), so it only adds protection against borderline/under-sampled passes; set rtol: 0 for a pure "confidently ≥ baseline" gate. It applies only to proportion metrics (accuracies in [0,1]) and raises a clear error for non-proportion metrics (e.g. perplexity) or a missing/invalid limit.

Backward compatibility: gate defaults to "rtol"; that path reproduces the existing comparison exactly. No existing config sets gate, so current behaviour is unchanged.

Test Plan

uv venv --python 3.12 .venv && uv pip install --python .venv pytest ruff
# unit tests for the new pure gating logic (CPU, no lm_eval / no GPU):
.venv/bin/python -m pytest .buildkite/lm-eval-harness/test_accuracy_gate.py -q
# lint/format (repo ruff config):
ruff check  .buildkite/lm-eval-harness/{accuracy_gate,test_accuracy_gate,test_lm_eval_correctness}.py
ruff format --check .buildkite/lm-eval-harness/{accuracy_gate,test_accuracy_gate,test_lm_eval_correctness}.py

Test Result

• Unit tests: 26 passed in 0.01s. Coverage includes: Wilson lower bound cross-checked against the textbook value (50/100 → 0.4038), tail/boundary behaviour (p=1.0→0.9973, p=0.0→0.0), monotonicity in n, the rtol path matching the legacy comparison exactly, the headline case (measured 0.72 vs baseline 0.755, rtol 0.08: passes rtol but the CI gate FAILS at n=100 and PASSES at n=1000), and input guards (non-proportion metric, invalid n, unknown gate).
• Lint: ruff check → All checks passed; ruff format --check → 3 files already formatted.
• Not run locally: the full lm-eval accuracy harness (test_lm_eval_correctness.py) launches a model and requires a GPU, so it was not executed here — only the new gating logic's CPU unit tests were. The integration wiring is a minimal, type-checked refactor of the existing loop; CI/maintainers with GPU should exercise it.

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Not a duplicate: searched open/closed issues & PRs — no open PR touches test_lm_eval_correctness.py / accuracy gating / Wilson. The only related item is RFC #34333 (the parent proposal); this PR is a concrete partial step toward it, posted as a comment there for coordination, not a competing RFC.

AI assistance disclosure (per AGENTS.md): this change was implemented with AI assistance (Claude); the commit carries a Co-authored-by: Claude trailer. It is submitted by a human author who is reviewing the change and will run/defend the GPU harness path.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

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