CAS: Conformalized Agentic Search via Adaptive Retrieval and Policy Weighting
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TL;DR - CAS applies conformal prediction to agentic search, adapting retrieval depth and weighting reinforcement-learning trajectories by answer confidence. It aims to improve QA reasoning reliability while reducing unnecessary tool calls.
- Adaptive Prediction Sets dynamically truncate retrieved documents instead of relying on a fixed Top-K cutoff.
- Adaptive Conformal Inference estimates answer confidence with controllable coverage during training.
- CAS penalizes low-confidence trajectories within the Group Relative Policy Optimization objective.
- Experiments on single-hop and multi-hop QA report higher reasoning accuracy and substantially fewer redundant tool invocations.
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CAS: Conformalized Agentic Search via Adaptive Retrieval and Policy Weighting
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TL;DR - CAS applies conformal prediction to agentic search, adapting retrieval depth and weighting reinforcement-learning trajectories by answer confidence. It aims to improve QA reasoning reliability while reducing unnecessary tool calls.
- Adaptive Prediction Sets dynamically truncate retrieved documents instead of relying on a fixed Top-K cutoff.
- Adaptive Conformal Inference estimates answer confidence with controllable coverage during training.
- CAS penalizes low-confidence trajectories within the Group Relative Policy Optimization objective.
- Experiments on single-hop and multi-hop QA report higher reasoning accuracy and substantially fewer redundant tool invocations.