微软新论文提出OAT，用成功轨迹就能定位Agent失败步骤，不用费钱费力的错误标注，思路很巧妙。
微软等机构在论文中提出OAT（One-class Attribution for Trajectories），一种轻量级属性器。它仅需成功轨迹训练，通过神经控制微分方程建模成功轨迹的动力学。然后识别失败轨迹中偏离成功流的步骤，实现故障归因。不需要失败数据或错误标签，避免了昂贵的提示管道或后训练。论文编号2607.12747。
NEW paper from Microsoft and colleagues. Debugging agent trajectories at scale is challenging. Thi...
NEW paper from Microsoft and colleagues. Debugging agent trajectories at scale is challenging. This is a clever approach to monitor and improve agents in production. The problem: Finding which step in a failed agent run caused the failure usually means one of two costly options. Run an expensive prompting pipeline over the whole trajectory, or post-train on failure data with step-level error labels that are hard to collect and difficult to scale. The solution: They propose OAT, a lightweight attributor that needs neither. It trains only on successful trajectories, models their dynamics with neural controlled differential equations, then flags the steps where a failure trajectory departs from that learned flow of success. Failure attribution becomes one-class learning over what success looks like, so you never need labeled error steps or failure data at all. Paper: arxiv.org/abs/2607.12747 Learn to build effective AI agents in our academy: academy.dair.ai 💬 6 🔄 2 ❤️ 30 👀 2840 📊 13 ⚡