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DiscoBench: When Search Agents Should Ask for ClarificationDiscoBench：搜索Agent何时应请求澄清的基准测试

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Researchers introduce DiscoBench, a benchmark evaluating when LLM search agents should stop searching and ask users for clarification on vague or underspecified queries instead of continuing retrieval.
The benchmark tests 211 samples across 11 domains with 463 ambiguity instances, finding that agents requesting clarification (SearchThenAsk) achieve higher pass rates than those repeatedly searching (SearchHeavyGuess) or directly guessing (DirectGuess).
The study reveals agents often detect uncertainty in retrieval results but fail to convert that uncertainty into appropriate external actions, pointing to a broader evaluation gap: assessing whether agents choose the right next action when the current path becomes unreliable.

研究人员推出DiscoBench基准，用于评估基于大语言模型的搜索Agent在何时应停止搜索并向用户请求澄清，而非对模糊或不完整查询进行持续检索。
该基准测试了来自11个领域的211个样本和463个歧义实例，发现请求澄清的Agent（SearchThenAsk）的通过率高于反复搜索（SearchHeavyGuess）或直接猜测（DirectGuess）的Agent。
研究表明Agent虽能检测检索结果中的不确定性，但未能将其转化为适当的外部行为，指向更广泛的评估缺陷：评估当前路径变得不可靠时Agent是否选择了正确的下一步行动。

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