Optimizing Information Retrieval via Reinforcement Learning

The core challenge in biomedical fact-checking is the high cost of error and the complexity of verifying claims against vast, technical datasets. This research proposes an agentic framework that moves beyond standard RAG (Retrieval-Augmented Generation) by integrating reinforcement learning (RL) to guide the agent's search behavior. Instead of relying on static retrieval, the agent learns an optimal policy for navigating biomedical literature, allowing it to dynamically decide when to perform additional searches, which queries to prioritize, and how to synthesize conflicting evidence.

Improving Fact-Checking Reliability

By utilizing RL to fine-tune the agent's decision-making process, the system achieves higher precision in identifying verifiable evidence. The framework focuses on:

• Strategic Query Formulation: The agent learns to decompose complex biomedical claims into sub-queries that are more likely to surface relevant, high-authority medical literature.
• Evidence Synthesis: The system is trained to weigh sources based on their reliability and relevance to the specific claim, reducing the likelihood of hallucination or misinterpretation of technical data.
• Iterative Refinement: The RL loop allows the agent to refine its search path based on the quality of retrieved documents, effectively 'learning' the optimal path to verify specific types of medical assertions.

This approach demonstrates that agentic workflows, when augmented with reinforcement learning, significantly outperform traditional retrieval methods in high-stakes domains where accuracy is paramount.