Researchers have proposed Conformalized Agentic Search (CAS), a framework for building reliable search agents. CAS addresses the reliability crisis in reinforcement learning fine-tuning by incorporating Conformal Prediction and Adaptive Conformal Inference. These methods provide reliability guarantees on both retrieval and training sides, ensuring accurate and efficient search results. The framework has been tested on single-hop and multi-hop QA datasets, demonstrating improved reasoning accuracy and reduced redundant tool invocations. --- Why it matters: This matters to AI researchers because it tackles a significant issue in reinforcement learning fine-tuning: the reliability crisis. CAS provides a solution by establishing reliable guarantees for both retrieval and training, which is crucial for developing accurate and efficient search agents. Source: https://arxiv.org/abs/2608.20771