DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement

Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to distill these traces into reusable feedback without post-hoc outcome labels, drawing on their evidence of local progress,…

Siyuan Liu (Fudan University, Meituan Longcat Team), Fan Yu (Fudan University, Meituan Longcat Team), Dongyu Ru (Meituan Longcat Team), Yizhu Liu (Meituan Longcat Team), Yifan Yang (Meituan Longcat Team), Xuezhi Cao (Meituan Longcat Team), Xunliang Cai (Meituan Longcat Team), Yixin Cao (Fudan University)·arXiv — cs.AI

Original Source

Key Takeaways

01.Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale
02.We study how to distill these traces into reusable feedback without post-hoc outcome labels, drawing on their evidence of local progress,…

This material is provided as an extended analytical summary.

Read full article at source

Aggregated within AI Qazaqstan national intelligence stream with author attribution.