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SoundnessBench, a new benchmark constructed from ICLR submissions, evaluates LLMs' ability to assess the methodological soundness of research proposals based on reviewer soundness scores. Experiments across 12 LLMs reveal a systematic optimism bias, where models tend to overestimate the soundness of weak proposals. Controls for data contamination and surface features suggest this bias is intrinsic, indicating LLMs are not yet reliable for standalone evaluation of scientific rigor.
LLMs exhibit a pervasive optimism bias when evaluating research proposals, frequently rating methodologically unsound ideas as promising, suggesting they're not ready to replace human reviewers.
Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. However, existing benchmarks rarely test a fundamental bottleneck: whether Large Language Models can judge the methodological viability of a research idea before expending time and computational resources. We introduce SoundnessBench, a curated benchmark of 1,099 machine-learning research proposals reconstructed from ICLR submissions, labeled with reviewer soundness sub-scores, and audited against source papers. SoundnessBench should be interpreted as a benchmark for recoverable proposal-stage soundness rather than exact prediction of full-paper review outcomes. Across 12 frontier LLMs, we find a pervasive optimism bias: under standard prompting, models frequently rate low-soundness proposals as sound, while aggressive prompting largely shifts errors from false positives to false negatives. Additional controls for public-corpus contamination, paper-identifying phrases, surface features, and human audit quality suggest that this behavior is not explained by a single confounder. Our results indicate that current LLMs are not yet reliable as standalone first-gate evaluators for scientific rigor.
