提出多模型规划框架SCOPE，提升临床试验表格的隐式推理准确率
SCOPE:Planning for Hybrid Querying over Clinical Trial Data
- 通过显式规划分解任务：行选择、结构化规划、执行三阶段
- 在1500个混合推理问题上，准确率优于零样本和链式思维等方法
- 适合需要高精度医疗数据推理的研究者与临床决策支持系统
我们研究临床试验表格推理，答案不直接存在于可见单元格中，需通过语义理解进行归一化、分类、提取或轻量级领域推理。针对现有大模型在隐式规划假设下常出现“错误推理”的现象，本文聚焦于从部分观测的临床试验表格中恢复隐式属性（如疗法类型、添加药物、终点角色、随访状态）。提出SCOPE（面向临床试验证据检索的结构化混合规划框架），采用多大模型规划器将任务分解为行选择、结构化规划与执行三个阶段。规划器在生成答案前显式明确源字段、推理规则和输出约束，相比直接提示显著降低歧义。在1500个肿瘤学临床试验表格上的混合推理问题上评估，相较于零样本、少样本、链式思维、TableGPT2、Blend-SQL和EHRAgent，SCOPE在推理类问题上提升了准确率，并在准确率-效率权衡上优于更重的代理基线。研究结果表明，临床试验推理是独特的表格理解问题，而混合规划分解是一种有效解决方案。
原文摘要 · Abstract (English)
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM approaches often suffer from "bad reasoning" under implicit planning assumptions, we focus on settings in which the model must recover implicit attributes such as therapy type, added agents, endpoint roles, or follow-up status from partially observed clinical-trial tables. We propose SCOPE (Structured Clinical hybrid Planning for Evidence retrieval in clinical trials), a multi-LLM planner-based framework that decomposes the task into row selection, structured planning, and execution. The planner makes the source field, reasoning rules, and output constraints explicit before answer generation, reducing ambiguity relative to direct prompting. We evaluate SCOPE on 1,500 hybrid reasoning questions over oncology clinical-trial tables against zero-shot, few-shot, chain-of-thought, TableGPT2, Blend-SQL, and EHRAgent. Results show that explicit multi-LLM planning improves accuracy for reasoning-based questions while offering a stronger accuracy-efficiency tradeoff than heavier agentic baselines. Our findings position clinical trial reasoning as a distinct table understanding problem and highlight hybrid planner-based decomposition as an effective solution
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