Author

Jin-Luan Yang

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#artificial intelligence Preprint Oct 2026

Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents

Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-improving agents. Current evaluations score the co-evolved policy on the final bank it trained with (the delivered bank). After deployment, however, skills may be rewritten...

Yu-Jie Feng, Xiao-Bin Wang, Jin-Luan Yang et al. · 0 citations

#machine learning Mar 2026

TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training

This work proposes TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology that explicitly captures how tool invocations and environmental responses drive the divergence between effective strategies and failure modes.

Jin-Luan Yang, Yuxin Liu, Zheng-Yu Chen et al. · arXiv.org · 3 citations · ⚡1