2 papers
cs.AI2026
Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution
Fengrui Fan, Huahao Wang, Jingyi Fang +3
LLM agent frameworks heavily rely on external skill assets to standardize tool usage, encode operational workflows, and supply executable runtime resources. Despite their widesprea…
cs.AI2026
MemArbiter: Decision-Time Memory Arbitration for Long-Horizon LLM Agents
Jiajun Dong, Yutao Hu, Fengrui Fan +3
Large language model (LLM) agents must retain and use cross-step information to act coherently in long-horizon tasks. Existing methods improve memory accessibility, yet action-rele…