Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by wh...
Dongfang Li, Zhuo-En Chen, Bo-Nian Jia et al.· 0 citations
CyberArena is a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat, and introduces a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities.
Bangya Liu, Jianhong Tu, Yu-Jing Pan et al.· 0 citations
Large language model agents increasingly undertake long-horizon tasks in complex environments characterized by fragmented evidence, partially completed states, and dependencies across resources and artifacts, and Interlock, a framework for task synthesis that represents each task as a typed completion graph is proposed...
Lin-Ji Hao, Yan-Ji He, Zi-Han Wang et al.· 0 citations
Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group of sibling rollouts, useful action comparisons depend on their context, evidence for both alternatives, and how that evidence enters learning. We propose Matched-Context G...
Chen-Xu Zhang, Yao-Rui Shi, Wen-Long Zhang et al.· 0 citations
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