针对临床文本推理中模型易出错的问题，提出分模块智能体框架提升准确率。
Compartmentalised Agentic Reasoning for Clinical NLI
- 将推理任务按类型分组，匹配专用解法器并验证结果
- 在200个实例上平均准确率从23%提升至57%，提升34个百分点
- 适合需要高可靠性和可审计性的医疗AI场景
大语言模型在临床自然语言推断任务中虽能生成流畅判断，但在需要正确推理模式而非表面匹配时常失败。本文提出CARENLI——一种分隔式智能体框架，将每个前提-陈述对路由至对应的推理类别，并使用具有显式验证和针对性优化的专用求解器。在包含200个实例、涵盖因果归因、组合基底、认知验证和风险状态抽象四类推理的扩展CTNLI基准上评估，相较直接提示，四种主流基础模型的平均准确率从约23%提升至约57%，增益约34个百分点，尤其在结构复杂的推理类型上收益最大。结果表明，分隔化与验证机制是实现更可靠、可审计临床推理的有效路径。
原文摘要 · Abstract (English)
Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rather than surface matching. We introduce CARENLI, a compartmentalised agentic framework that routes each premise-statement pair to a reasoning family and then applies a specialised solver with explicit verification and targeted refinement. We evaluate on an expanded CTNLI benchmark of 200 instances spanning four reasoning families: Causal Attribution, Compositional Grounding, Epistemic Verification, and Risk State Abstraction. Across four contemporary backbone models, CARENLI improves mean accuracy from about 23% with direct prompting to about 57%, a gain of roughly 34 points, with the largest benefits on structurally demanding reasoning types. These results support compartmentalisation plus verification as a practical route to more reliable and auditable clinical inference.
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