Property-guided LLM program synthesis with counterexample feedback creates direct heuristics for PDDL planning domains that require far fewer generations and less evaluation cost than score-based baselines.
Morgan Kaufmann
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HCL-GP learns parameterized policies and reuses extracted components to achieve 98% accuracy on AppWorld benchmark tasks for LLM agents, outperforming static synthesis by 15.8 points on challenges.
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Property-Guided LLM Program Synthesis for Planning
Property-guided LLM program synthesis with counterexample feedback creates direct heuristics for PDDL planning domains that require far fewer generations and less evaluation cost than score-based baselines.
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Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents
HCL-GP learns parameterized policies and reuses extracted components to achieve 98% accuracy on AppWorld benchmark tasks for LLM agents, outperforming static synthesis by 15.8 points on challenges.