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AEL: Agent Evolving Learning for Open-Ended Environments · Stentor Studio

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AEL: Agent Evolving Learning for Open-Ended Environments

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abstract

LLM agents increasingly operate in open-ended environments spanning hundreds of sequential episodes, yet they remain largely stateless: each task is solved from scratch without converting past experience into better future behavior. The central obstacle is not what to remember but how to use what has been remembered, including which retrieval policy to apply, how to interpret prior outcomes, and when the current…

Authors: Wujiang Xu, Jiaojiao Han, Minghao Guo, Kai Mei, Xi Zhu, Han Zhang, Dimitris N. Metaxas
Published: April 29, 2026
Duration: 2:23
arXiv: 2604.21725

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