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arXiv 2602.00352cs.CL
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DETOUR:双智能体搜索与推理的交互基准

DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning

Patronus AI;DAP Lab(DAP实验室);Columbia University(哥伦比亚大学)

Li Siyan, Darshan Deshpande, Anand Kannappan, Rebecca Qian

AI总结:

DETOUR基准通过双智能体机制评估双智能体搜索与推理能力,揭示现有模型在模糊不明确场景下的不足。

摘要:

在对话中回忆信息时,人们往往在多次轮次后才能回忆起相关信息。然而,现有评估智能体在此类舌尖上的搜索过程能力的基准仅限于单轮次设置。为了更真实地模拟舌尖上的搜索,我们引入了基于双智能体的评估通过模糊不明确检索(DETOUR),该基准包含1,011个提示。基准设计涉及一个主智能体,即被评估的对象,其任务是通过查询一个在所有评估中保持一致的内存智能体来识别回忆的实体。我们的结果表明,当前最先进的模型在该基准上仍面临挑战,仅在所有模态(文本、图像、音频和视频)上实现了36%的准确率,突显了在模糊不明确场景中提升能力的重要性。

英文摘要:

When recalling information in conversation, people often arrive at the recollection after multiple turns. However, existing benchmarks for evaluating agent capabilities in such tip-of-the-tongue search processes are restricted to single-turn settings. To more realistically simulate tip-of-the-tongue search, we introduce Dual-agent based Evaluation Through Obscure Under-specified Retrieval (DETOUR), a dual-agent evaluation benchmark containing 1,011 prompts. The benchmark design involves a Primary Agent, which is the subject of evaluation, tasked with identifying the recollected entity through querying a Memory Agent that is held consistent across evaluations. Our results indicate that current state-of-the-art models still struggle with our benchmark, only achieving 36% accuracy when evaluated on all modalities (text, image, audio, and video), highlighting the importance of enhancing capabilities in underspecified scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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