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EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can fail when their beliefs about environment dynamics are incomplete, incorrect,…

arXiv — cs.AIBy: Janet Wang, Jiang Guo, Yi Xiang, Jiaqing Yuan, Mingwen Dong, Hideo Kobayashi, Shuaichen Chang, Chandan K. Reddy, Jiarong JiangOctober 12, 2026 (Oct 12)
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  • arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills
  • Yet even procedurally capable agents can fail when their beliefs about environment dynamics are incomplete, incorrect,…
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EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

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