@inproceedings{jia-etal-2026-medcpenv,
title = "{M}ed{CPE}nv: A Reproducible Cost-Aware Simulated Hospital for Benchmarking and Training Medical {LLM} Agents",
author = "Jia, Zixiao and
Hua, Changjie and
Zhai, Weiqi and
Xu, Ruyi and
Yang, Xue and
Li, Han and
Lu, Tianyun and
Wu, Kailuan and
Zheng, Xiang and
Qu, Lin and
Wei, hu and
Zhao, Bing and
Zhang, Ruohan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1601/",
pages = "34108--34125",
ISBN = "979-8-89176-462-0",
abstract = "Real-world clinical competence demands more than answering medical knowledge questions{---}it requires proactively gathering incomplete patient information, selecting appropriate examinations, and formulating safe, cost-conscious treatment plans through multi-turn interaction. Existing medical LLM benchmarks rely predominantly on static QA formats, failing to assess interactive information acquisition, tool use, or cost{--}quality tradeoffs. We propose \textsc{MedCPEnv}, a reproducible, cost-aware simulated hospital environment for evaluating and training medical LLM agents. Built from MIMIC-IV-NOTE discharge notes, \textsc{MedCPEnv} comprises 48,803 curated cases, a 1,000-case benchmark, and a 371,550-entry medical QA knowledge base, exposing four deterministic action types (ASK, EXAM, KNOWLEDGE, FINAL) to eliminate high-variance LLM role-playing artifacts. Evaluating eight frontier models, GPT-5.4 achieves the highest composite score (3.69/5) and diagnostic accuracy (60.3{\%}), yet treatment accuracy remains only 19.4{\%}, identifying treatment plan generation as the primary bottleneck. Substantial inter-model variation in the cost{--}quality tradeoff further demonstrates that clinical correctness alone is insufficient to characterize agent behavior. GRPO-based Agentic RL on Qwen3-4B shows that medical quality rewards consistently improve tool-use strategies, raising the composite score from 2.97 to 3.14 and diagnostic accuracy from 26.0{\%} to 31.2{\%}, establishing \textsc{MedCPEnv} as both a rigorous evaluation benchmark and a reproducible training environment for medical agent research."
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<abstract>Real-world clinical competence demands more than answering medical knowledge questions—it requires proactively gathering incomplete patient information, selecting appropriate examinations, and formulating safe, cost-conscious treatment plans through multi-turn interaction. Existing medical LLM benchmarks rely predominantly on static QA formats, failing to assess interactive information acquisition, tool use, or cost–quality tradeoffs. We propose MedCPEnv, a reproducible, cost-aware simulated hospital environment for evaluating and training medical LLM agents. Built from MIMIC-IV-NOTE discharge notes, MedCPEnv comprises 48,803 curated cases, a 1,000-case benchmark, and a 371,550-entry medical QA knowledge base, exposing four deterministic action types (ASK, EXAM, KNOWLEDGE, FINAL) to eliminate high-variance LLM role-playing artifacts. Evaluating eight frontier models, GPT-5.4 achieves the highest composite score (3.69/5) and diagnostic accuracy (60.3%), yet treatment accuracy remains only 19.4%, identifying treatment plan generation as the primary bottleneck. Substantial inter-model variation in the cost–quality tradeoff further demonstrates that clinical correctness alone is insufficient to characterize agent behavior. GRPO-based Agentic RL on Qwen3-4B shows that medical quality rewards consistently improve tool-use strategies, raising the composite score from 2.97 to 3.14 and diagnostic accuracy from 26.0% to 31.2%, establishing MedCPEnv as both a rigorous evaluation benchmark and a reproducible training environment for medical agent research.</abstract>
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%0 Conference Proceedings
%T MedCPEnv: A Reproducible Cost-Aware Simulated Hospital for Benchmarking and Training Medical LLM Agents
%A Jia, Zixiao
%A Hua, Changjie
%A Zhai, Weiqi
%A Xu, Ruyi
%A Yang, Xue
%A Li, Han
%A Lu, Tianyun
%A Wu, Kailuan
%A Zheng, Xiang
%A Qu, Lin
%A Wei, hu
%A Zhao, Bing
%A Zhang, Ruohan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jia-etal-2026-medcpenv
%X Real-world clinical competence demands more than answering medical knowledge questions—it requires proactively gathering incomplete patient information, selecting appropriate examinations, and formulating safe, cost-conscious treatment plans through multi-turn interaction. Existing medical LLM benchmarks rely predominantly on static QA formats, failing to assess interactive information acquisition, tool use, or cost–quality tradeoffs. We propose MedCPEnv, a reproducible, cost-aware simulated hospital environment for evaluating and training medical LLM agents. Built from MIMIC-IV-NOTE discharge notes, MedCPEnv comprises 48,803 curated cases, a 1,000-case benchmark, and a 371,550-entry medical QA knowledge base, exposing four deterministic action types (ASK, EXAM, KNOWLEDGE, FINAL) to eliminate high-variance LLM role-playing artifacts. Evaluating eight frontier models, GPT-5.4 achieves the highest composite score (3.69/5) and diagnostic accuracy (60.3%), yet treatment accuracy remains only 19.4%, identifying treatment plan generation as the primary bottleneck. Substantial inter-model variation in the cost–quality tradeoff further demonstrates that clinical correctness alone is insufficient to characterize agent behavior. GRPO-based Agentic RL on Qwen3-4B shows that medical quality rewards consistently improve tool-use strategies, raising the composite score from 2.97 to 3.14 and diagnostic accuracy from 26.0% to 31.2%, establishing MedCPEnv as both a rigorous evaluation benchmark and a reproducible training environment for medical agent research.
%U https://aclanthology.org/2026.findings-emnlp.1601/
%P 34108-34125
Markdown (Informal)
[MedCPEnv: A Reproducible Cost-Aware Simulated Hospital for Benchmarking and Training Medical LLM Agents](https://aclanthology.org/2026.findings-emnlp.1601/) (Jia et al., Findings 2026)
ACL
- Zixiao Jia, Changjie Hua, Weiqi Zhai, Ruyi Xu, Xue Yang, Han Li, Tianyun Lu, Kailuan Wu, Xiang Zheng, Lin Qu, hu Wei, Bing Zhao, and Ruohan Zhang. 2026. MedCPEnv: A Reproducible Cost-Aware Simulated Hospital for Benchmarking and Training Medical LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34108–34125, Budapest, Hungary. Association for Computational Linguistics.