Large language model (LLM) agents now fix software, operate computers and serve customers, but their harnesses, the programs that run them, call a large LLM at every step, which makes long tasks slow and costly. Most of these calls end in a multiple-choice decision, such as which tool to call next. Decision models such...
Kun-Lun Zhu, Tao Feng, Xue-Ning Han et al.· 0 citations
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objectives, simulation, verification, and repeated correction. AI now faces a similar transition: frontier models can solve difficult tasks once...
Brian Wang, Bin Feng, Xiaoman Pan et al.· 1 citation
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.· 1 citation
Across Harness-Bench and NewtonBench, full-bank meta-skills improve macro-average performance by 8.95 percentage points over no-skill construction, and 12.02 points over direct delivery of the same bank to the Target.
Cheng Qian, Kun-Lun Zhu, Bei-Bin Li et al.· 0 citations
The introduction of Relic, which turns recurring collaboration failures into organization-owned, executable protocols, and how collaboration experience can become persistent organizational state that remains useful beyond the members who created it are shown.
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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