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What's the Plan? Evaluating and Developing Planning-Aware Techniques for Language Models
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Planning is a fundamental task in artificial intelligence that involves finding a sequence of actions that achieve a specified goal in a given environment. Large language models (LLMs) are increasingly used for applications that require planning capabilities, such as web or embodied agents. In line with recent studies, we demonstrate through experimentation that LLMs lack necessary skills required for planning. Based on these observations, we advocate for the potential of a hybrid approach that combines LLMs with classical planning methodology. Then, we introduce SimPlan, a novel hybrid-method, and evaluate its performance in a new challenging setup. Our extensive experiments across various planning domains demonstrate that SimPlan significantly outperforms existing LLM-based planners.
Forward citations
Cited by 4 Pith papers
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A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners
Supervised fine-tuning lets LLMs linearly encode action validity and state predicates, with broader state-space coverage during training improving world-model recovery.
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Verbalized Algorithms: Classical Algorithms are All You Need (Mostly)
Verbalized algorithms integrate LLMs as oracles for simple string operations within classical algorithms to improve accuracy-runtime tradeoffs on sorting, clustering, submodular maximization, and multi-hop QA.
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End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
FRAMe combines an LLM planner with RAG-based memory and a multi-modal coach agent to generate valid, preference-aligned eVTOL flight plans, achieving up to 93.8% validity across four LLMs.
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An LLM-Based Assistance System for Intuitive and Flexible Capability-Based Planning
Hybrid LLM-SMT assistance system for capability-based planning that supports natural-language interaction, result interpretation, and iterative knowledge-model adaptation under human approval.
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