AIHOT 于 2026-08-17 收录了“AlphaEvolve 与机器学习优化将矩阵乘法指数上界改进至 ω < 2.371177”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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[2608.16884] Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
Computer Science > Data Structures and Algorithms
arXiv:2608.16884 (cs)
-
[Submitted on 17 Aug 2026]
Title:Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
Authors:Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii, Abbas Mehrabian, Francisco J. R. Ruiz, Abigail See, Renfei Zhou, Josh Alman, Virginia Vassilevska Williams, Matej Balog
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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.
Subjects:
Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Machine Learning (cs.LG)
Cite as:
arXiv:2608.16884 [cs.DS]
(or
arXiv:2608.16884v1 [cs.DS] for this version)
https://doi.org/10.48550/arXiv.2608.16884
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Emilien Dupont [view email]
[v1]
Mon, 17 Aug 2026 17:59:05 UTC (223 KB)
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arXiv:2608.16884 (cs)
-
[Submitted on 17 Aug 2026]
Title:Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
Authors:Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii, Abbas Mehrabian, Francisco J. R. Ruiz, Abigail See, Renfei Zhou, Josh Alman, Virginia Vassilevska Williams, Matej Balog
View a PDF of the paper titled Improving the matrix multiplication exponent with modern optimization and AlphaEvolve, by Emilien Dupont and 9 other authors
View PDF
HTML (experimental)
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.
Subjects:
Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Machine Learning (cs.LG)
Cite as:
arXiv:2608.16884 [cs.DS]
(or
arXiv:2608.16884v1 [cs.DS] for this version)
https://doi.org/10.48550/arXiv.2608.16884
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Emilien Dupont [view email]
[v1]
Mon, 17 Aug 2026 17:59:05 UTC (223 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Improving the matrix multiplication exponent with modern optimization and AlphaEvolve, by Emilien Dupont and 9 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.DS
< prev
|
next >
new
|
recent
| 2026-08
Change to browse by:
cs
cs.AI
cs.CC
cs.LG
References & Citations
- NASA ADS
- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
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Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
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Huggingface Toggle
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Spaces Toggle
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About arXivLabs
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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.
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AIHOT 摘要
一项新研究通过重构组合损失分析中的优化问题，并借助机器学习设计新算法、再用 AlphaEvolve 精炼，将矩阵乘法指数 ω 的上界从 2.371339 改进至 2.371177。该成果刷新了当前最佳界限，延续了激光法结合组合损失分析的路线。
为什么值得关注
矩阵乘法指数上界传统靠手工构造优化问题，本文改写成更通用形式并用 AlphaEvolve 搜索，思路可借用于同类极值优化研究。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：矩阵乘法指数上界传统靠手工构造优化问题，本文改写成更通用形式并用 AlphaEvolve 搜索，思路可借用于同类极值优化研究。
- AIHOT 评分：53；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。