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
Results show that self-evolving agents should place experience inside the control flow, rather than only store it as text, and show how learned conventions must reach the right execution step on long tool-use environments where learned conventions must reach the right execution step.
Yun-He Su, Zi-Yi Dong, Tong Yu et al.· 0 citations
EvoSCM is introduced, which equips scientific agents with explicit structural causal models that evolve as new experimental evidence is collected, yielding more accurate explanations and predictions while making more effective use of experimental interactions.
Qing Zhao, Hao-Wei Li, Wei-Jian Deng et al.· 2 citations
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