arXiv:2608. 16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans.
The paper presents a method for automatically generating generalized plans in Lean, along with formal proofs of their completeness for given domain constraints. It introduces a semantic‑preserving conversion from PDDL to Lean and uses an LLM to produce both the plan and its proof, whose correctness is verified by Lean’s kernel. Evaluated on 13 benchmark domains with GPT‑5.6‑Sol, the approach yields complete plans and valid proofs for 12 of them, marking a significant advance in automated generalized‑plan completeness.
arXiv:2606. 02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning.
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
arXiv:2501. 18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning.
arXiv:2602.00276v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning...
arXiv:2406. 03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios.
arXiv:2602. 22067v2 Announce Type: replace Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increases.
arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
arXiv:2601. 09097v3 Announce Type: replace Abstract: Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints.
arXiv:2606. 24965v1 Announce Type: cross Abstract: Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances that are harder than those seen in training.