arXiv AI By Fan Nie, Haotian Ye, Shurui Liu, Zihao Wang, Stefano Ermon, James Zou

One-Sided Verification Under Limited Expert Review

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One-Sided Verification Under Limited Expert Review

The paper introduces OSVerify, an algorithmic framework for one-sided verification that prioritizes rare valid solutions while filtering out invalid candidates to reduce human review workload. OSVerify separates error proposal—searching for errors across solution granularities via hierarchical decomposition—from error validation, which distinguishes major errors from minor or hallucinated ones. Experiments on competition- and research-level benchmarks show a 46% reduction in human reviews of invalid solutions compared to voting baselines, increasing to 57% after post-training the validator. The framework is applied to identify errors in published research papers and to resolve three Langlands open conjectures, confirming its utility in human‑AI research collaboration.

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