The paper introduces EDV, an Execute‑Distill‑Verify framework that constructs offline experience for large language model agents without updating model parameters. In EDV, multiple heterogeneous agents execute tasks, a distiller extracts candidate lessons from their trajectories and feedback, and executors review these lessons, keeping only those unanimously approved. Experiments on τ²‑bench, Mind2Web, and MMTB demonstrate improved performance on revisited tasks and within‑domain transfer, while a Retail subset evaluation shows EDV cuts the Self‑Confirmation Trap Rate from 11.0% to 5.0%.
By Shiding Zhu, Yudi Qi, Yajie Wang, Jiaze Li, Chao Song, Yaorui Shi, Yibo Miao, Hanqi Gao, Kai Zhang
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion.
arXiv:2609.36608v1 Announce Type: new
Abstract: On-policy distillation (OPD) trains multi-turn language agents with dense teacher supervision on student-generated responses. However, standard think-t...
By Zubin Zheng, Jiahao Wu, Shaofeng Zhang, Zhirui Zhang, Yew-Soon Ong, Shengcai Liu
arXiv:2609.21423v1 Announce Type: new
Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to...
By 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:2609.39957v1 Announce Type: cross
Abstract: Coding agents solve repository-level tasks through sequences of actions, where a single erroneous action can misdirect subsequent decisions and incre...
By Jiangrui Zhao, Chenglong Li, Meng Zhang, Xiaoting Du
TraceSpec is a framework that links trajectory evaluation with adaptive draft training to improve speculative decoding in large language model agents. It introduces a dynamic execution strategy combined with fixed‑prefix replay and provides a new corpus (TraceSpec‑Traj) of 10,000 training and 2,500 test states covering browsing and software engineering tasks. Experiments show that TraceSpec’s state‑conditioned draft allocation boosts replay throughput by 12.5%–22.6% and increases accepted length by up to 10.0% compared to fixed‑turn supervision.
By Jihao Zhao, Shuaishuai Zu, Pengfei Li, Biao Qin, Guanghuan Fang, Ben Feng, Baochuan Yang