SPIN Unprocessed October 23, 2026 ai_technology research
MemPlan: Memory-Conditioned PDDL Planning for Partially Observable Text Environments
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arXiv:2610.30379v1 Announce Type: new Abstract: LLM agents are flexible in language interaction, but using them as long-horizon planners is costly and prone to hallucinated or invalid actions. This paper targets a different role for LLMs: constrained bridges between language and symbolic planning. We propose MemPlan, a memory-conditioned PDDL planning framework for partially observable interactive text environments. MemPlan keeps the PDDL domain fixed and reconstructs the PDDL problem at each st
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