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Grounded Evaluation and Repair for NL-to-PDDL Problem Generation
Large Language Models (LLMs) have shown promise for translating Natural Language (NL) planning descriptions into PDDL problem instances. However, standard evaluation criteria such as syntactic validity or planner success can substantially overestimate faithfulness to the described task: a generated problem may be parseable and solvable while misrepresenting the intended initial state, goal, object structure, or optimization target. This paper studies an end-to-end NL-to-PDDL pipeline that combines LLM generation, checks in terms of PDDL parsing, planning and validation, a domain-conformance ch
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T08:53:05.000Z
First collected: 2026-09-20T19:32:24.350Z. This is not the publication date.