BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks

2026-04-27

AI Summary

Current complex LLM agent benchmarks are often prone to misjudgment due to specification errors, implicit assumptions, or rigid evaluation scripts, mistakenly attributing benchmark flaws to agent failures. This work proposes the first automated auditing framework leveraging state-of-the-art large language models to cross-validate task-oriented, execution-driven benchmark components through structured LLM protocols, augmented by agent solutions and execution traces for diagnostic support. The approach establishes a novel AI-assisted paradigm for benchmark validation, overcoming the limitations of traditional manual review. Applied to ScienceAgentBench, it identified 12 author-confirmed issues—including critical errors—and reproduced 83.3% of expert-discovered problems on the BIXBench Verified-50 subset, with an auditing cost of under $15 for 50 bioinformatics tasks.

Abstract

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 first automated auditing framework for task-oriented, execution-based agent benchmarks. BenchGuard cross-verifies all benchmark artifacts via structured LLM protocols, optionally incorporating agent solutions or execution traces as additional diagnostic evidence. Deployed on two prominent scientific benchmarks, BenchGuard identified 12 author-confirmed issues in ScienceAgentBench - including fatal errors rendering tasks unsolvable - and exactly matched 83.3% of expert-identified issues on the BIXBench Verified-50 subset, catching defects that prior human review missed entirely. A full audit of 50 complex bioinformatics tasks costs under USD 15, making automated benchmark auditing a practical and valuable complement to human review. These findings point toward AI-assisted benchmark development, where frontier models serve not only as subjects of evaluation but as active participants in validating the evaluation infrastructure itself.

Problem

• benchmark auditing
• LLM agent evaluation
• evaluation infrastructure
• benchmark flaws
• automated validation

Innovation

• automated benchmark auditing
• LLM-based evaluation
• agent benchmark validation
• execution-based benchmarks
• AI-assisted benchmark development