mlsanityData to 14 Oct 2026

Improving the matrix multiplication exponent with modern optimization and AlphaEvolve

arXiv 2608.16884 ↗First posted 17 Aug 2026cs.DS cs.AI cs.CCcited by 2

Not on the map. Why?

Abstract (arXiv)

The current best bounds on the matrix multiplication exponent ω are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and propose several improvements. First, we reformulate the optimization problem allowing us to solve it in a larger setting than was previously possible. Second, we leverage recent advances in machine learning to design a new optimization algorithm for this problem. Finally, we refine the resulting optimization algorithm with AlphaEvolve. Our combined approach yields an upper bound of ω < 2.371177, improving the previous best bound of 2.371339.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 17 Aug 2026.

Builds on

8 of 24 references.

References · 8

  1. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  2. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 867
  3. 2020A Refined Laser Method and Faster Matrix Multiplicationcited by 56
  4. 2023New Bounds for Matrix Multiplication: from Alpha to Omegacited by 34
  5. 2022A Unified Framework for Implicit Sinkhorn Differentiationcited by 28
  6. 2024More Asymmetry Yields Faster Matrix Multiplicationcited by 27
  7. 2022Faster Matrix Multiplication via Asymmetric Hashingcited by 19
  8. 2020Supervised Quantile Normalization for Low-rank Matrix Approximationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.