搜索代理何时应该提问：面向澄清感知的深度搜索基准DiscoBench

摘要

基于大语言模型（LLMs）的搜索代理越来越多地被用于解决复杂的信息寻求任务，这类任务需要通过多步检索和推理来实现用户目标。然而，现有基准测试通常假设用户查询是完整且明确的，忽视了现实世界中搜索请求往往模糊不清、描述不充分甚至包含事实错误的情况。在深度搜索场景中，这种歧义性会沿着多步推理链传播，导致代理偏离正确的搜索轨迹。为填补这一空白，我们提出了DiscoBench——一个面向澄清感知深度搜索的基准测试，旨在评估搜索代理是否能主动识别歧义、提出有效的澄清问题，并通过用户交互恢复正确的推理路径。DiscoBench包含211个样本和463个歧义实例，覆盖11个现实领域和四种歧义类型。我们进一步设计了一个用于多轮交互的用户模拟器，并从任务效用、歧义检测、交互策略和成本效率四个角度评估模型性能。对代表性大语言模型的实验表明，歧义检测与有效澄清是两种不同的能力，而持续搜索而不主动澄清的效果往往比直接猜测更差，这凸显了当前搜索代理在检索能力与交互式问题解决之间的关键差距。

English

Search agents powered by large language models (LLMs) are increasingly used to solve complex information-seeking tasks, requiring multi-step retrieval and reasoning to fulfill user goals. However, existing benchmarks often assume that user queries are complete and explicit, overlooking the fact that real-world search requests are frequently vague, underspecified, or even factually incorrect. In deep search scenarios, such ambiguity can propagate along multi-step reasoning chains and lead agents toward incorrect search trajectories. To address this gap, we introduce DiscoBench, a benchmark for clarification-aware deep search, designed to evaluate whether search agents can proactively identify ambiguity, ask effective clarification questions, and recover correct reasoning paths through user interaction. DiscoBench contains 211 samples and 463 ambiguity instances across 11 real-world domains, covering four ambiguity types. We further design a user simulator for multi-turn interaction and evaluate model performance from four perspectives: task utility, ambiguity detection, interaction strategy, and cost efficiency. Experiments on representative LLMs show that ambiguity detection and effective clarification are distinct capabilities, and that repeatedly searching instead of asking for clarification often performs worse than direct guessing, highlighting a critical gap between retrieval ability and interactive problem-solving in current search agents.