EP238: SepsisAgent outperforms clinicians using clinical world models

Title: Agentifying Patient Dynamics within LLMs through Interacting with Clinical World ModelSource: http://arxiv.org/abs/2605.14723v1Summary:This work presents a novel world-model-augmented agentic reasoning loop that utilizes a 'propose-simulate-refine' workflow to ground LLM decisions in action-conditioned dynamics. It demonstrates how integrating world models with agentic reinforcement learning can significantly improve decision-making safety and efficacy in complex environments.