Clinical trial failure remains a central bottleneck in drug development, where minor protocol design flaws can irreversibly compromise outcomes despite promising therapeutics. Although cutting-edge AI methods achieve strong performance in predicting trial success, they are inherently reactive for merely diagnosing risk without offering actionable remedies once failure is anticipated. To fill this gap, this paper proposes ClinicalReTrial, a self-evolving AI agent framework that addresses this gap by casting clinical trial reasoning as an iterative protocol redesign problem. Our method integrates failure diagnosis, safety-aware modification, and candidate evaluation in a closed-loop, reward-driven optimization framework. Serving the outcome prediction model as a simulation environment, ClinicalReTrial enables low-cost evaluation of protocol modifications and provides dense reward signals for continuous self-improvement. To support efficient exploration, the framework maintains hierarchical memory that captures iteration-level feedback within trials and distills transferable redesign patterns across trials. Empirically, ClinicalReTrial improves 83.3% of trial protocols with a mean success probability gain of 5.7%, and retrospective case studies demonstrate strong alignment between the discovered redesign strategies and real-world clinical trial modifications.

翻译：临床试验失败仍然是药物研发的核心瓶颈，其中微小的方案设计缺陷可能不可逆转地损害试验结果，即使治疗手段本身前景良好。尽管前沿AI方法在预测试验成功率方面表现出色，但其本质上是反应式的，仅能诊断风险而无法在预期失败时提供可行的补救措施。为填补这一空白，本文提出ClinicalReTrial——一种自进化AI智能体框架，该框架通过将临床试验推理构建为迭代式方案重设计问题来解决此缺陷。我们的方法将失败诊断、安全感知修改与候选方案评估整合于一个闭环的、奖励驱动的优化框架中。通过将结果预测模型作为仿真环境，ClinicalReTrial能够对方案修改进行低成本评估，并为持续自我改进提供密集的奖励信号。为支持高效探索，该框架维护分层记忆系统，既能捕获试验内的迭代级反馈，又能提炼跨试验的可迁移重设计模式。实证结果表明，ClinicalReTrial改进了83.3%的试验方案，平均成功概率提升达5.7%；回顾性案例研究显示，所发现的重设计策略与实际临床试验修改方案高度吻合。