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English(EN) Same Patient, Different Order: Action-Level Reliability of Clinical LLM Agents Under Repeated Runs

临床LLM代理在重复运行时显示出动作级差异

一项发表在arXiv上的新研究介绍了一种名为“相同输入重跑”的方法,用于评估临床大型语言模型(LLM)代理的动作级可靠性。该方法多次重放相同的输入,以检查代理是否始终如一地产生相同的动作,例如开具检查单或处方药物。研究发现,即使基准测试报告了相同的成功判决,动作也存在显著差异,这凸显了当前临床LLM代理评估实践中的不足。 AI

影响 强调了临床LLM代理潜在的不可靠性,促使制定新的评估标准以实现更安全的部署。

排序理由 研究论文,详细介绍了一种LLM代理的新评估方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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临床LLM代理在重复运行时显示出动作级差异

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研究论文,详细介绍了一种LLM代理的新评估方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rohith Reddy Bellibatlu, Manpreet Singh, Zhoutian Han, Wenbin Zhang ·

    同一患者,不同顺序:临床LLM代理在重复运行下的动作级可靠性

    arXiv:2609.13582v1 Announce Type: cross Abstract: A clinical agent benchmark can report the same verdict on identical inputs while the agent files a materially different order on each run. Such agents order tests, request medications and place referrals, yet benchmarks typically …