Devs Say Pass/Fail Benchmark Scores Are Now Nearly Impossible to Interpret
geoffwolfe · x · 2026-09-12
AI practitioners are debating the reliability of model evals.
Developer trq212 argued that interpreting evals by pass/fail scores alone is "basically impossible" these days — many benchmark failures he sees stem from overly strict hidden tests, where the model's answer is sometimes more sensible than the expected eval result.
Satish Vutukuru of ReasonCoreAI responded that the eval debate will persist indefinitely: "there are no perfect evals — only great, good, good enough, or poor evals," and said his team is building evals that are fair to models and accurately reflect their capabilities.
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