An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. Reusing this experience for learning requires identifying a decision to revise and testing a concrete alternative. We introduce the Agent Err...
Kun-Lun Zhu, Xu-Yan Ye, Yi-Bo Li et al.· 0 citations
Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where existing tests become stale. A conversation can resolve the episode, but when the participants change, what makes the lesson continue to go...
Hong-Yi Du, Tian-Yi Zhang, Wei-Jia Zhang et al.· 0 citations
Results show that CUA root-cause diagnosis can provide actionable repair signals rather than merely post-hoc explanations and show that CUA root-cause diagnosis can provide actionable repair signals.
Wei-Jia Zhang, Kunlun Zhu, Ze-Yi Liu et al.· 1 citation
DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline.
Kunlun Zhu, Xuyan Ye, Zhi-Guang Han et al.· arXiv.org· 3 citations
This work presents a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing.
Tao Feng, Fangxu Yu, Haozhen Zhang et al.· 1 citation
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