Axiom-Aware FunSearch for Non-Constructive Mathematics
Max Esposito, Besart Shyti
NeurIPS 2025
Over the past sixty years, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve previously unaddressed planning problems. This was done through established practices of rigorous design and evaluation of planning systems. The experience and expertise of the planning community are not just important from a historical perspective; the lessons learned could play a crucial role in accelerating the development of LLM-based planners. The purpose of this tutorial is to share the knowledge with the wider AI community, with the aim of incorporating the insights, tools, and data from the automated planning community into the design and evaluation of LLM-based planners. We believe that exposing the NeurIPS community to the theory and practices from the planning community will contribute greatly to the progress in building LLM-based planners and to planning in general.
Website: https://planning-llm-era.github.io
Max Esposito, Besart Shyti
NeurIPS 2025
Jung koo Kang
NeurIPS 2025
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Isha Puri, Shivchander Sudalairaj, et al.
NeurIPS 2025