CaltechMathAI

We are a team of Math & AI researchers at Caltech, focused on developing AI systems that can tackle hard research-level math problems. Solving challenging mathematical tasks — such as proving or disproving long-standing conjectures, or establishing difficult theorems — often requires discovering intricate, multi-step solutions. Our mission is to use these hard mathematical problems as environments to design new AI algorithms and architectures that can identify rare solutions carrying disproportionately high rewards. In other words, we aspire to be one of the best AI research labs focused on sparse-reward, long-horizon tasks.

A sparse infinite wave graph with isolated orange peaks rising above hills and valleys
ICML2026

Algebraic
Hirsch.

The agent builds a linearly presented square-free monomial ideal one generator at a time, searching for a generator graph whose diameter exceeds the ideal’s degree. Valid counterexamples are exceptionally rare, so reward arrives only at the end of a successful construction.

Mathematical resultRare non-Hirsch ideals constructed across a wide range of degrees.
Machine-learning resultFirst successful use of hierarchical RL in commutative algebra.
ICMLAccepted · 2026
ALGEBRAIC_HIRSCH.ENV
Environment Demo
GENERATOR GRAPH / GI READY
Interactive Algebraic Hirsch generator graphNodes are joined by level-one edges when their labels intersect in d minus one symbols. Orange marks a diameter path; curved off-red edges are irreducible level-two edges.
INITIAL GENERATOR024578Presampled trajectory ready

HOW TO READ IT Nodes connect when |Si ∩ Sj| = d − 1. Orange traces a diameter path; curved off-red arcs mark irreducible level-two edges.

The hierarchical run is presampled: πS builds the line, then πL completes linearity. Uniform search samples distinct generators randomly.

ICML2026NeurIPS2025

Andrews–
Curtis.

Starting from a balanced presentation, the agent applies Andrews–Curtis moves: relator inversions, multiplications, and conjugations. Each move preserves the underlying group while reshaping the presentation, so progress can require a long sequence of locally valid but strategically meaningful moves.

Benchmark progress153 more presentations solved than the prior RL baseline.
Open mathematical cases550 unsolved examples reduced to 261 equivalence classes.
Released datasetsAC-19: 125K · AC-1M: 1.1M presentations.
ICMLNeurIPS
Accepted · ICML 2026 / NeurIPS 2025
ANDREWS_CURTIS.ENV
INTERACTIVE SANDBOX
BALANCED PRESENTATION / ⟨x,y | r₁,r₂⟩ READY
r₁5 letters
r₂4 letters

TRY IT Apply legal transformations manually, or run the stored agent trajectory back toward ⟨x,y | x,y⟩.

Free reductions happen automatically after every move. This is a small word sandbox, not a general conjecture solver.

The Caltech
Math-AI team.

Sergei Gukov
Principal Investigator

Sergei
Gukov

CALTECH
Giorgi Butbaia
Postdoctoral Scholar

Giorgi
Butbaia

CALTECH
Davide Passaro
Postdoctoral Scholar

Davide
Passaro

CALTECH
Michele Tarquini
Graduate Student

Michele
Tarquini

CALTECH
Lucas Fagan
Research Scientist

Lucas
Fagan

CALTECH
Paul Orland
External Collaborator

Paul
Orland

CALTECH
Portrait of Angus Gruen
External Collaborator

Angus
Gruen

Portrait of Elli Heyes
External Collaborator

Elli
Heyes

IMPERIAL COLLEGE LONDON
Coco Xiaoyu Huang
External Collaborator

Coco Xiaoyu
Huang

TEMPLE UNIVERSITY
Maksymilian Manko
External Collaborator

Maksymilian
Manko

UNIVERSITÄT ZÜRICH
Justin Tan
External collaborator

Justin
Tan

CAMBRIDGE

The Lab is grateful to the institutions and partners whose support makes this work possible.