When Search Agents Should Ask: DiscoBench for Clarification-Aware Deep Search
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 ident...
| Authors | Yiling Tao, Shihan Deng, Meiling Tao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 June 2026 |
| Last updated at source | 1 July 2026 |
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