Researchers prove AI-generated plans solve all cases in 12 domains
A new method lets large language models generate generalized plans in Lean—a formal language—and prove they work for every possible case in a domain. The approach converts PDDL planning problems into Lean code, then uses an LLM to write both the plan and a proof of its correctness. Lean’s kernel verifies the proof, ensuring no edge case is missed.
Key points
- New method generates plans in Lean and proves they solve every case in a domain
- Uses GPT-5.6-Sol to create both plan and formal proof, verified by Lean’s kernel
- Works for 12 of 13 tested benchmark domains, marking progress in automated completeness proofs
The team tested it on 13 standard benchmark domains, achieving valid proofs for 12. The method uses GPT-5.6-Sol as the LLM. This advances automated planning by shifting from manual verification to automated proof generation, though the work remains theoretical for now.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- Study finds multimodal AI models shift answers based on evidence order · 1 src
- PLLM+ pipeline solves 1,500 Python dependency issues in benchmark · 1 src
- Researchers propose method to balance conflicting AI objectives without retraining models · 1 src
- Researchers release agimud for Multi-Agent simulations with human interaction · 1 src
- Microsoft Copilot AI predicts bitcoin could reach $180,000 by early 2027 · 2 src
Comments
via GitHub Discussions