Testing the Planning Claims of LLM-Based Agents against Formal Semantics
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·来源: ArXiv AI
arXiv:2610.22469v1 Announce Type: new Abstract: LLM-based agents are increasingly credited with the competences of practical reasoning: planning, monitoring, and correcting their own actions. Their success is attributed to the prompting strategies and agent architectures built around the model, such...
arXiv:2610.22469v1 Announce Type: new Abstract: LLM-based agents are increasingly credited with the competences of practical reasoning: planning, monitoring, and correcting their own actions. Their success is attributed to the prompting strategies and agent architectures built around the model, such...
arXiv:2610.22469v1 Announce Type: new Abstract: LLM-based agents are increasingly credited with the competences of practical reasoning: planning, monitoring, and correcting their own actions. Their success is attributed to the prompting strategies and agent architectures built around the model, such...