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This paper proposes a reinforcement learning framework for evidence-seeking diagnostic reasoning using LLMs. The RL-trained 7B model outperforms larger models in multilingual clinical consultation tasks, showing that specialized RL can distill high-level clinical reasoning.
ClinSeekAgent is an automated agentic framework that enables large language models to actively acquire and synthesize multimodal clinical evidence from raw data sources, improving decision-making accuracy in both text-only and multimodal tasks. It introduces the ClinSeek-Bench benchmark and a distilled model ClinSeek-35B-A3B that achieves strong performance on agentic clinical reasoning.