Rohan Paul@rohanpaul_ai · XAI 摘要
Rohan Paul引用新论文指出，尽管LLM智能体有时能通过交互发现隐藏结构，但其推断世界模型的能力存在根本局限：随着隐藏世界复杂度增加，AI智能体的表现迅速落后，难以将积累的反馈转化为稳定的内部模型，尤其在提问规划、记忆利用和反馈整合方面表现薄弱。结论是，在复杂环境中，LLM智能体建立可靠心智模型的速度跟不上难度增长。
Can LLM agents actually discover hidden rules by interacting？
The answer is uncomfortable. The more complicated the hidden world gets， the faster AI agents fall behind.
LLMs often cannot turn growing evidence into a stable internal model.
Current LLM agents can sometimes discover hidden structure through interaction， but they are still weak at planning questions， using memory， and turning feedback into a reliable world model.
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Link - arxiv. org/abs/2606.16576
Title： "Can LLM Agents Infer World Models？ Evidence from Agentic Automata Learning"