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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.14723 |
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| _version_ | 1866909043409289216 |
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| author | Wu, Minghao Yan, Yuting Cai, Zhenyang Ji, Ke Fang, Chuangsen Sheng, Ziying Wang, Xidong Wang, Rongsheng Zhang, Hejia Li, Shuang Wang, Benyou Zha, Hongyuan |
| author_facet | Wu, Minghao Yan, Yuting Cai, Zhenyang Ji, Ke Fang, Chuangsen Sheng, Ziying Wang, Xidong Wang, Rongsheng Zhang, Hejia Li, Shuang Wang, Benyou Zha, Hongyuan |
| contents | Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) encode broad clinical knowledge and can reason over guidelines, they are not inherently grounded in action-conditioned patient dynamics. We introduce SepsisAgent, a world model-augmented LLM agent for sepsis treatment recommendation. SepsisAgent uses a learned Clinical World Model to simulate patient responses under candidate fluid--vasopressor interventions, and follows a propose--simulate--refine workflow before committing to a prescription. We first show that world-model access alone yields inconsistent LLM decision performance, motivating agent-specific training. We then train SepsisAgent through a three-stage curriculum: patient-dynamics supervised fine-tuning, propose--simulate--refine behavior cloning, and world-model-based agentic reinforcement learning. On MIMIC-IV sepsis trajectories, SepsisAgent outperforms all traditional RL and LLM-based baselines in off-policy value while achieving the best safety profile under guideline adherence and unsafe-action metrics. Further analysis shows that repeated interaction with the Clinical World Model enables the agent to learn regularities in patient evolution, which remain useful even when simulator access is removed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14723 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model Wu, Minghao Yan, Yuting Cai, Zhenyang Ji, Ke Fang, Chuangsen Sheng, Ziying Wang, Xidong Wang, Rongsheng Zhang, Hejia Li, Shuang Wang, Benyou Zha, Hongyuan Artificial Intelligence Computation and Language Machine Learning Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) encode broad clinical knowledge and can reason over guidelines, they are not inherently grounded in action-conditioned patient dynamics. We introduce SepsisAgent, a world model-augmented LLM agent for sepsis treatment recommendation. SepsisAgent uses a learned Clinical World Model to simulate patient responses under candidate fluid--vasopressor interventions, and follows a propose--simulate--refine workflow before committing to a prescription. We first show that world-model access alone yields inconsistent LLM decision performance, motivating agent-specific training. We then train SepsisAgent through a three-stage curriculum: patient-dynamics supervised fine-tuning, propose--simulate--refine behavior cloning, and world-model-based agentic reinforcement learning. On MIMIC-IV sepsis trajectories, SepsisAgent outperforms all traditional RL and LLM-based baselines in off-policy value while achieving the best safety profile under guideline adherence and unsafe-action metrics. Further analysis shows that repeated interaction with the Clinical World Model enables the agent to learn regularities in patient evolution, which remain useful even when simulator access is removed. |
| title | Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.14723 |