[2507.16334] Higher Gauge Flow Models

Computer Science > Artificial Intelligence arXiv:2507.16334 (cs) [Submitted on 22 Jul 2025 (v1), last revised 3 Mar 2026 (this version, v3)] Title:Higher Gauge Flow Models Authors:Alexander Strunk, Roland Assam View a PDF of the paper titled Higher Gauge Flow Models, by Alexander Strunk and Roland Assam View PDF HTML (experimental) Abstract:This paper introduces Higher Gauge Flow Models, a novel class of Generative Flow Models. Building upon ordinary Gauge Flow Models (arXiv:2507.13414), these Higher Gauge Flow Models leverage an L$_{\infty}$-algebra, effectively extending the Lie Algebra. This expansion allows for the integration of the higher geometry and higher symmetries associated with higher groups into the framework of Generative Flow Models. Experimental evaluation on a Gaussian Mixture Model dataset revealed substantial performance improvements compared to traditional Flow Models. Comments: Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Differential Geometry (math.DG) Cite as: arXiv:2507.16334 [cs.AI]   (or arXiv:2507.16334v3 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2507.16334 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alexander Strunk [view email] [v1] Tue, 22 Jul 2025 08:16:06 UTC (126 KB) [v2] Wed, 6 Aug 2025 09:42:01 UTC (126 KB) [v3] Tue, 3 Mar 2026 11:16:43 UTC (120 KB) Full-text links: Access Paper: View a PDF of the paper titled Higher Gauge Flow Models, by Alexander Strunk and Roland ...