LLM-Only PDDL Domain Repair with Open-Weight Models
Quick summary
arXiv:2608.17341v1 Announce Type: new Abstract: AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represented in the Planning Domain Definition Language (PDDL). An active line of research investigates how errors in such models can be detected and repaired. For example, users may provide positive test plans that are solutions, and negative test plans that fail during execution. Automated repair methods then modify the PDDL model to satisfy these constraints. In this paper, we evaluate the ability of re
Key takeaways
- arXiv:2608.17341v1 Announce Type: new Abstract: AI planning is concerned with finding a sequence of actions that achieves a specified goal.
- It relies on explicit models of the world, commonly represented in the Planning Domain Definition Language (PDDL).
- An active line of research investigates how errors in such models can be detected and repaired.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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