cd /news/machine-learning/improving-matrix-multiplication-expo… · home topics machine-learning article
[ARTICLE · art-101529] src=arxiv.org ↗ pub= topic=machine-learning verified=true sentiment=↑ positive

Improving matrix multiplication exponent with optimization and AlphaEvolve

Researchers using a machine-learning-guided optimization algorithm called AlphaEvolve have improved the upper bound on the matrix multiplication exponent to ω < 2.371177, surpassing the previous best of 2.371339. The team, including authors from the paper submitted to arXiv on 17 Aug 2026, reformulated the core optimization problem and combined modern optimization techniques with AlphaEvolve to achieve this result.

read2 min views1 publishedAug 18, 2026
Improving matrix multiplication exponent with optimization and AlphaEvolve
Image: source
[Submitted on 17 Aug 2026]


[View PDF](/pdf/2608.16884)

[HTML (experimental)](https://arxiv.org/html/2608.16884v1)

Abstract:The current best bounds on the matrix multiplication exponent $\omega$ 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 $\omega$ < 2.371177, improving the previous best bound of 2.371339.

Current browse context:

cs.DS

References & Citations

...

Bibliographic Explorer

(What is the Explorer?) Connected Papers

(What is Connected Papers?) Litmaps

(What is Litmaps?) scite Smart Citations

(What are Smart Citations?)# Code, Data and Media Associated with this Article alphaXiv

(What is alphaXiv?) CatalyzeX Code Finder for Papers

(What is CatalyzeX?) DagsHub

(What is DagsHub?) Gotit.pub

(What is GotitPub?) Hugging Face

(What is Huggingface?) ScienceCast

(What is ScienceCast?)# Demos Influence Flower

(What are Influence Flowers?) CORE Recommender

(What is CORE?)# arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

── more in #machine-learning 4 stories · sorted by recency
── more on @alphaevolve 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
Live at https://your-agent.zahid.host
Get free account → Pricing
from €0/mo · no card required
LIVE [news/improving-matrix-mul…] indexed:0 read:2min 2026-08-18 ·