eval-integrity

evalgate

Cheap statistical checks for AI eval claims — run them before you publish.

Most benchmark headlines overstate themselves in one of a few nameable ways. evalgate is four tiny, dependency-free checks, one per failure mode — the same checks behind a set of independent eval-integrity audits that caught these mistakes in published work.

Pure Python, zero dependencies, runs anywhere.

pip install git+https://github.com/ipezygj/evalgate

Use it as an MCP tool (for agents)

If you're an AI agent — or you run one (Claude, Cursor, Claude Code, Windsurf…) — evalgate ships an MCP server so the model can call these checks itself before it trusts, reports, or acts on any eval number: a benchmark score, a leaderboard #1, an LLM-as-judge verdict, or a claimed trend.

pip install "eval-integrity[mcp] @ git+https://github.com/ipezygj/evalgate"   # once on PyPI:  pip install "eval-integrity[mcp]"

Add it to your MCP client (e.g. claude_desktop_config.json / Cursor / Claude Code):

{
  "mcpServers": {
    "evalgate": { "command": "evalgate-mcp" }
  }
}

_{MCP registry identity — mcp-name: io.github.ipezygj/evalgate}

Tools the agent gets, each with a "call this when…" description it can reason about:

tool	the agent calls it before…
check_top_rank	claiming a model is #1 / SOTA on a benchmark (is the top rank real or a tie?)
check_subset_win	trusting a "best on subset/metric X" claim (look-elsewhere correction)
check_judge_bias	trusting an LLM-as-judge / A-B preference result (length / self-preference / position bias)
check_resolution	calling one of two close models better (can the benchmark even tell them apart?)
check_trend_fragility	reporting a fitted trend / scaling exponent (does one point carry it?)

The five checks above work on summary numbers (scores, p-values, win counts). If the agent has the raw per-item results — which items each model solved, or the head-to-head battles — three deeper tools do the real audit instead of an approximation:

tool	the agent calls it when…
audit_leaderboard	it has per-item results ({model: [solved item-ids]}) — the real version of check_top_rank: bootstrapped rank confidence intervals, the paired-McNemar tie group, resolvable tiers, split-half stability
audit_preferences	a ranking comes from pairwise votes ([winner, loser] battles) — Bradley-Terry rank CIs and a Condorcet check that preferences are transitive, not rock-paper-scissors cycles
check_dimensions	deciding whether one number fairly summarizes a multi-skill benchmark — counts the latent skills in the result matrix (eigenspectrum vs a shuffled null)

The point: an agent that produces an eval number should sanity-check it, and now it can — in one call, with a plain verdict and a recommendation. Reproducible, zero-dependency checks (the mcp extra is only for the server transport).

The four checks

1. "We lead on subset X" — corrected for look-elsewhere

Report the subset/metric/checkpoint where a model looks best and you are reporting the maximum of many noisy tests. Correct for how many you could have picked.

evalgate correct --p 0.009 --n 23
# raw p=0.009 over 23 tests -> sidak p=0.19 (does NOT survive correction at alpha=0.05)

from evalgate import correct_best_of
correct_best_of(0.009, n_tested=23).significant   # False

(A real RewardBench "best subset" win: raw p=0.009 → p=0.19 after correcting for the 23 subsets. Not a finding.)

2. Is the judge winning, or just longer / first / same-family?

An LLM-as-judge that "prefers" your model may be preferring the longer answer, the first-listed one, or its own family. Feed it the count and test against chance.

evalgate bias --wins 68 --n 100 --label "longer answer wins"
# longer answer wins: 68/100 = 68.0% (p=0.0004) -> BIAS

from evalgate import bias_rate
bias_rate(68, 100).biased    # True

(A widely-used GPT-4 judge preferred the longer answer 68% of the time and its own model family 71.5% — both at p≈0.)

3. Does one data point flip your slope?

A scaling exponent or trend that hangs on a single high-leverage point isn't one. Leave each point out and refit.

evalgate loo examples/points.txt --power-law --threshold 1.0
# slope=1.08, leave-one-out range [0.87, 1.26] -> CROSSES 1 (most influential point: index 5)

from evalgate import leave_one_out, power_law_exponent
leave_one_out(xs, ys, fit=power_law_exponent, threshold=1.0).crosses_threshold  # True

(A reported "super-linear" grokking exponent, α=1.13, fell to 0.97 — with a better fit — when one point was dropped.)

4. Is the gap bigger than the sample can resolve?

A leaderboard orders two models by a two-point accuracy gap on a finite test set. Ask whether that gap is even detectable at this sample size — or smaller than the minimum detectable effect, i.e. a coin flip.

evalgate power --n 200 --p1 0.85 --p2 0.83
# gap=+0.02 on n=200 (NOT significant, p=0.585); MDE at 80% power=0.103 -> UNDERPOWERED (gap < MDE)

from evalgate import power_check
power_check(200, 0.85, 0.83).resolvable   # False — 2pp over 200 items can't be resolved
power_check(2000, 0.85, 0.80).resolvable  # True  — 5pp over 2000 items can

(Frontier models on a fixed benchmark routinely sit a task or two apart — inside the MDE — so the #1 rank is noise. More votes, not a better model, resolves the tie.)

Library API

from evalgate import (
    correct_best_of, sidak, bonferroni,     # look-elsewhere
    bias_rate, binomial_test,               # judge / metric bias
    leave_one_out, ols_slope, power_law_exponent,   # fragility + fits
    power_check, min_detectable_effect,     # power / minimum detectable effect
)

Every function returns a small dataclass that prints a one-line verdict and exposes the numbers (.corrected_p, .p_value, .loo_min …) so you can gate CI on them.

Reproduce the case studies:

python -m evalgate.checks     # -> evalgate selftest: OK (reproduced all 3 case studies + power check)

Why this exists

These are textbook checks — the value isn't the math, it's running all of them, adversarially, on a number you're too close to. evalgate is the open, do-it-yourself version. When a launch, a paper, or a fundraise rides on a figure and you want it audited independently first, that's the paid practice.

Want the full checklist and the client-grade report template that wrap these checks? The Eval Integrity Kit — the 9-check audit checklist, the report template I ship to clients, an evalgate quickstart, and three worked case studies.

The fuller story — why AI benchmark scores and trading backtests overpromise, and how to catch them — is in the book Measured, Not Believed (pay what you want).

License

MIT — see LICENSE.