一句话：首个基于执行的失败编码智能体轨迹审计，表明失败轨迹保留大量可恢复价值，为以轨迹为中心的自动智能体修复奠基。
自动程序修复（APR）表明有缺陷的程序往往可被修复而非重写。随着 LLM 编码智能体承担越来越多的软件工程任务，一个类似的问题浮现：失败的智能体执行能否被修复，而不是重新启动？本文给出据作者所知首个基于执行证据的失败编码智能体轨迹审计。初步结果显示，失败轨迹仍保留可观的可恢复价值：在 27 个任务中，轨迹中位完成 75% 的子目标；然而在 26 个被审计的智能体中，没有任何一个曾给出正确的修复方案。这些发现为以轨迹为中心的自动智能体修复（TC-AAR）奠定了经验基础，将 APR 从程序扩展到智能体的执行轨迹。
Automated Program Repair (APR) has shown that buggy programs can often be repaired rather than rewritten. As LLM coding agents increasingly perform software engineering tasks, an analogous question arises: can failed agent executions be repaired instead of restarted? We answer the prerequisite question with, to the best of our knowledge, the first execution-grounded audit of failed coding agent trajectories. Our preliminary results show that failed trajectories retain substantial recoverable value, completing a median of 75% of task subgoals (n = 27), while none of the audited agents ever states the correct fix (0/26). These findings establish the empirical foundation for Trajectory-Centric Automated Agent Repair (TC-AAR), extending APR from programs to agent executions.