arXiv:2608. 16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans.
By Veit Laule, Jiangtao Shuai, Manfred Hauswirth, Sonja Schimmler
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng
arXiv:2609.06071v1 Announce Type: new
Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predi...
By Nicola J. M\"uller, Naya Rudolph, Katharina Stein, J\"org Hoffmann, Ayal Taitler, Timo P. Gros
arXiv:2608. 09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promising software that is correct by construction.
By Zenan Li, Ziran Yang, Peiyang Song, Zhaoyu Li, Kaiyu Yang
arXiv:2607. 21414v1 Announce Type: new Abstract: In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula.
By Connor Little, Christian Muise
The paper evaluates how well open-weight large language models can repair Planning Domain Definition Language (PDDL) models using only LLMs. Experiments show that while the best LLM achieves an F1 score of 0.87—an improvement of 0.38 over a symbolic baseline—it still fails to reliably satisfy test constraints, with a mean test pass rate of only 0.82 and as low as 0.06 on the Thoughtful domain. The study concludes that current open-weight models cannot guarantee the necessary test constraint satisfaction for dependable automated model repair.
By Nader Karimi Bavandpour, Pascal Bercher