AgentMath MATH+ EF-LiOpt-3 ongoing

Agentic AI in Mathematics

This project aims to develop agentic AI systems for mathematical research: autonomous discovery of patterns and conjectures, design and execution of computational experiments, and integration with formal verification tools.

IOL Project Members

Sebastian PokuttaTU/PR/Dominic Simon
Sebastian Pokutta Principal Investigator
Max Zimmer
Max Zimmer Project Member
Nico Pelleriti
Nico Pelleriti Project Member

Funding

This project is being funded by the Berlin Mathematics Research Center MATH+ (project ID EF-LiOpt-3), itself funded by the German Research Foundation (DFG) under Germany's Excellence Strategy (EXC-2046/1, project ID 390685689) from September 2025 to August 2028.

Project Description

Recent AI breakthroughs—discovering knot invariant relationships, generating Hirsch conjecture counterexamples, finding new plane colorings—demonstrate the AI4Math paradigm’s potential.

This project pursues three directions: (1) multi-agent systems for pattern recognition and conjecture generation in discrete mathematics, (2) frameworks that design computational experiments with adaptive parameter tuning, and (3) integration with LEAN for automated proof formalization. A human-in-the-loop approach combines AI’s systematic exploration with mathematical intuition.

Talks and posters

Conference and workshop talks

Jul 2026
The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning by Nico Pelleriti
AI4Research Workshop @ ICML 2026, Seoul

Poster presentations

Jul 2026
The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning by Nico Pelleriti
AI4Research Workshop @ ICML 2026, Seoul

Publications and preprints

Preprints

  1. Pokutta, S. (2025). Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms. [arXiv]
    [BibTeX]
    @misc{2025_Pokutta_DcoptimizationFrankwolfe_2507-17545,
      archiveprefix = {arXiv},
      eprint = {2507.17545},
      arxiv = {arXiv:2507.17545},
      primaryclass = {math.OC},
      author = {Pokutta, Sebastian},
      title = {Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms},
      date = {2025-07-23}
    }
  2. Kaibel, V., and Pokutta, S. (2026). A Counterexample to Ziegler’s Cross-Polytope Conjecture for Simplicial 0/1-Polytopes. [arXiv]
    [BibTeX]
    @misc{2026_KaibelPokutta_Counterexampleziegler_2606-31640,
      archiveprefix = {arXiv},
      eprint = {2606.31640},
      arxiv = {arXiv:2606.31640},
      primaryclass = {math.CO},
      author = {Kaibel, Volker and Pokutta, Sebastian},
      title = {A Counterexample to Ziegler's Cross-Polytope Conjecture for Simplicial 0/1-Polytopes},
      date = {2026-06-30}
    }
  3. Pokutta, S. (2026). Symmetric Extension Complexity of the Spanning Tree Polytope. [arXiv]
    [BibTeX]
    @misc{2026_Pokutta_Symmetricextensioncomplexity_2606-17017,
      archiveprefix = {arXiv},
      eprint = {2606.17017},
      arxiv = {arXiv:2606.17017},
      primaryclass = {math.CO},
      author = {Pokutta, Sebastian},
      title = {Symmetric Extension Complexity of the Spanning Tree Polytope},
      date = {2026-06-15}
    }
  4. Xu, L., Zhou, Y., and Pokutta, S. (2026). Agentic MIP Research: Accelerated Constraint Handler Generation. [arXiv]
    [BibTeX]
    @misc{2026_XuZhouPokutta_Agenticmip_2605-09186,
      archiveprefix = {arXiv},
      eprint = {2605.09186},
      arxiv = {arXiv:2605.09186},
      primaryclass = {cs.AI},
      author = {Xu, Liding and Zhou, Yugeng and Pokutta, Sebastian},
      title = {Agentic MIP Research: Accelerated Constraint Handler Generation},
      date = {2026-05-09}
    }
  5. Zimmer, M., Pelleriti, N., Roux, C., and Pokutta, S. (2026). The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning. [arXiv]
    [BibTeX]
    @misc{2026_ZimmerPelleritiRouxPokutta_Airesearchframework_2603-15914,
      archiveprefix = {arXiv},
      eprint = {2603.15914},
      arxiv = {arXiv:2603.15914},
      primaryclass = {cs.LG},
      author = {Zimmer, Max and Pelleriti, Nico and Roux, Christophe and Pokutta, Sebastian},
      title = {The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning},
      date = {2026-03-16}
    }

Conference proceedings

  1. Pelleriti, N., Nelaturu, S. H., Zhou, Z., Li, Z., Zimmer, M., Han, B., and Pokutta, S. (2026). What Do Evolutionary Coding Agents Evolve? Proceedings of the NeurIPS 2026 Workshop on Self-Evolving Diversity-Driven Search for Robust AI Systems. [arXiv]
    [BibTeX]
    @inproceedings{2026_PelleritiEtAl_Evotrace_2605-20086,
      year = {2026},
      booktitle = {Proceedings of the NeurIPS 2026 Workshop on Self-Evolving Diversity-Driven Search for Robust AI Systems},
      archiveprefix = {arXiv},
      eprint = {2605.20086},
      arxiv = {arXiv:2605.20086},
      primaryclass = {cs.NE},
      author = {Pelleriti, Nico and Nelaturu, Sree Harsha and Zhou, Zhanke and Li, Zongze and Zimmer, Max and Han, Bo and Pokutta, Sebastian},
      title = {What Do Evolutionary Coding Agents Evolve?}
    }
  2. Pokutta, S. (2026). Frank-Wolfe Beyond 1/t Convergence. Proceedings of the Conference on Neural Information Processing Systems. [arXiv]
    [BibTeX]
    @inproceedings{2026_Pokutta_Localdualsharpness_2604-28006,
      year = {2026},
      booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
      archiveprefix = {arXiv},
      eprint = {2604.28006},
      arxiv = {arXiv:2604.28006},
      primaryclass = {math.OC},
      author = {Pokutta, Sebastian},
      title = {Frank-Wolfe Beyond 1/t Convergence}
    }