arXiv AI By Hongmin Li, Wanli Zhao

Where Scientific Search Agents Fail: Decision-Checkpoint Auditing of Exposure and Inspection Attempts

Read the original on arXiv AI →

The paper introduces decision checkpoints that log observations and tool actions during inference, enabling the assignment of outcome categories to scientific-search agent responses. Using these checkpoints on 540 AutoResearchBench questions, the authors compare keyword search and read-first strategies, finding that keyword search yields 24.6% accuracy while raw search achieves 17.8%. The checkpoint protocol reveals detailed differences in target exposure, inspection attempts, and evidence-search calls that aggregate accuracy alone obscures.

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