反事实评估揭示临床LLM和智能体的隐藏能力画像
Counterfactual Evaluation Reveals Hidden Capability Profiles in Clinical LLMs and Agents
AI总结：
提出因果敏感性评分（CSS），通过沿五个临床维度变异肿瘤病例来评估模型是否按预期方向更新推荐，发现与覆盖度指标排名相反，并揭示所有前沿模型在手术状态干预上的安全盲点。
摘要：
两个临床AI系统在基于覆盖度的评分标准上得分几乎相同，但当患者输入变化时行为却截然不同：一个更新其推荐以匹配新的临床信号，而另一个无论输入如何都产生相同输出。我们引入因果敏感性评分（CSS），这是一个预注册的干预性指标，沿五个临床有意义的维度——生物标志物翻转、先前治疗失败、生物标志物移除、手术状态变化和分期扰动——变异肿瘤肿瘤委员会病例，并使用{0, 0.5, 1.0}量表对每个模型是否在预注册的正确方向上更新其推荐进行评分。与基于覆盖度的加权召回指标共识匹配评分（CMS）相比，来自三个实验室的六个前沿模型在224个病例的单次推理中评估，排名几乎完全相反：所有六个模型排名发生变化，CMS最差的模型成为CSS最好的模型，而一个中上CMS模型在CSS上排名最后。我们进一步揭示了一个普遍的安全盲点：每个前沿模型在手术状态干预上失败（D家族最多17.2%的CSS），这是CMS未暴露的发现。该指标也适用于使用工具的智能体：在ReAct风格的实验中，工具使用改善了六个模型中五个的CSS（+2.5到+20.3个百分点），然而CSS最低的模型检索相同的图表部分但仍未能更新其推荐——揭示了仅在反事实评估下可见的结构性响应缺陷。跨评判者复制和三位评估者的医学专业验证确认了总体发现。像CSS这样的干预性预注册指标补充了临床AI智能体的基于覆盖度的评估：它们捕捉了覆盖度指标遗漏的响应性，并为未来的智能体强化学习系统提供了候选的密集奖励信号。
英文摘要：
Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one updates its recommendations to match the new clinical signal, while the other produces the same output regardless. We introduce the Causal Sensitivity Score (CSS), a pre-registered interventional metric that mutates oncology tumor-board cases along five clinically meaningful dimensions - biomarker flips, prior-treatment failures, biomarker removals, surgery-status changes, and stage perturbations - and scores whether each model updates its recommendations in the pre-registered correct direction using a {0, 0.5, 1.0} scale. Benchmarked against the Consensus Match Score (CMS), a coverage-based weighted recall metric, six frontier models from three labs evaluated in single-shot inference across 224 cases rank in nearly opposite orders: all six models change rank, the CMS-worst model becomes CSS-best, and one upper-mid CMS model ranks last on CSS. We further surface a universal safety blind spot: every frontier model fails on surgery-status interventions (at most 17.2% CSS on Family D), a finding CMS does not expose. The metric also transfers to tool-using agents: in a ReAct-style experiment, tool use improves CSS for five of six models (+2.5 to +20.3 percentage points), yet the lowest-CSS model retrieves the same chart sections and still fails to update its recommendations - revealing a structural responsiveness deficit visible only under counterfactual evaluation. Cross-judge replication and three-rater medical-professional validation confirm the aggregate findings. Interventional pre-registered metrics like CSS complement coverage-based evaluation for clinical AI agents: they capture responsiveness that coverage metrics miss and offer a candidate dense reward signal for future agentic RL systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
