SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?
For the AI research community, this benchmark reveals a critical bottleneck in using LLMs as autonomous research agents: they cannot reliably assess proposal viability before resource expenditure.
SoundnessBench tests whether LLMs can judge the methodological soundness of ML research proposals. Across 12 frontier LLMs, models show a pervasive optimism bias, frequently rating low-soundness proposals as sound, and are not yet reliable as standalone evaluators of scientific rigor.
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.