[2508.11524] Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models

Computer Science > Artificial Intelligence arXiv:2508.11524 (cs) [Submitted on 15 Aug 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models Authors:Wenkai Yu, Jianhang Tang, Yang Zhang, Yixiong Feng, Celimuge Wu, Kebing Jin, Hankz Hankui Zhuo View a PDF of the paper titled Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models, by Wenkai Yu and 5 other authors View PDF HTML (experimental) Abstract:Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to ensure valid plans. In this paper, we propose a novel LLM-assisted planner integrated with problem decomposition, which first decomposes large planning problems into multiple simpler sub-tasks with dependency construction and conflict detection. Then we explore two novel paradigms to utilize LLMs, i.e., LLM4Inspire and LLM4Predict, to assist problem decomposition, where LLM4Inspire provides heuristic guidance according to general knowledge and LLM4Predict employs domain-specific knowle...