CAS: Conformalized Agentic Search via Adaptive Retrieval and Policy Weighting
Search Agents face a severe reliability crisis during reinforcement learning (RL) fine-tuning. Heuristic Top-K retrieval often causes critical evidence loss or noise inclusion, while over-confidence induced by progressive RL leads to hallucinated answers and redundant searches. To build highly reliable agents, we introduce Conformal Prediction (CP) and propose Conformalized Agentic Search (CAS). This framework establishes reliability guarantees on both the retrieval and training sides: on the retrieval side, an Adaptive Prediction Set (APS), a specific CP realization, translates statistical coverage into dynamic document truncation to construct prediction sets that are adaptive in size; o...
| Authors | Zixi Zhu, Jiayuan Su, Jian Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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