发表机构
Korea University; Kangwon National University（高丽大学; 江原国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
STRIVE将临床推理分解为多智能体并引入两阶段验证，在Longitudinal-MIMIC数据集上实现纵向放射报告生成的最佳临床效能，纵向变化一致性较最强基准提升超一倍。
AI 中文摘要
纵向放射报告生成（LRRG）需要识别当前影像发现及其与先前研究的变化。现有方法在隐式表示中联合建模诊断、属性估计、时序对比与语言生成，易引发任务干扰、掩盖各决策的底层证据、限制错误可追溯性，且将进展状态建模为独立标签，忽略其有序结构，导致同等对待遗漏变化与方向反转。本文提出面向LRRG的STRIVE——多智能体结构化时序推理与集成验证框架，其将临床推理分解为专门的诊断智能体、属性智能体及时序变化智能体，生成显式中间证据。其中，时序变化智能体进一步采用进展感知GRPO进行后训练，该可验证的塑形奖励为方向一致的错误分配部分信用，同时将方向反转的评分设为最低。STRIVE在两个阶段执行验证：确定性一致性门在报告生成前协调智能体输出，验证智能体检查生成报告是否符合聚合临床证据。在Longitudinal-MIMIC数据集上，STRIVE在近期方法中取得最佳临床效能，且纵向变化一致性（LCC，即与参考报告时序一致性的度量）较最强基准提升超一倍。
英文摘要
Longitudinal radiology report generation (LRRG) requires identifying both current findings and their changes relative to a prior study. Existing methods jointly model diagnosis, attribute estimation, temporal comparison, and language generation within implicit representations, which can cause task interference, obscure the evidence underlying each decision, and limit error traceability. They also model progression states as independent labels, ignoring their ordered structure and thus treating missed changes and direction reversals equally. We present STRIVE, Multi-Agent Structured Temporal Reasoning with Integrated Verification for LRRG, which decomposes clinical reasoning into specialized Diagnosis, Attribute, and Temporal Change Agents that produce explicit intermediate evidence. In particular, the Temporal Change Agent is further post-trained using Progression-Aware GRPO, a verifiable, shaped reward that assigns partial credit to direction-preserving errors while scoring direction reversals lowest. STRIVE performs verification at two stages: a deterministic Consistency Gate reconciles the agent outputs before report generation, and a Validation Agent checks whether the generated report is supported by the aggregated clinical evidence. On Longitudinal-MIMIC, STRIVE attains the best clinical efficacy among recent methods and more than doubles Longitudinal Change Concordance (LCC), a measure of temporal agreement with the reference report, over the strongest baseline.
