CRISP: Critical Step Perception for Training Efficient Deep Search Agents
Large language models (LLMs) are increasingly extended into deep search agents that solve complex questions through multi-step interaction with external search and browsing tools. However, existing agents often incur substantial computational and interaction costs, generating lengthy trajectories that contain redundant queries, inefficient exploration, and irrelevant observations. Existing efficiency-oriented methods usually encourage agents to use tools less frequently, but treating all tool interactions uniformly may also suppress steps that gather necessary evidence. In this paper, we propose CRISP, a framework for training efficient deep search agents through critical step perception....
| Authors | Haosi Mo, Zihao Yan, Ruiqing Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 4 August 2026 |
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