mlsanityData to 7 Oct 2026

Diffusion Models for Cayley Graphs

arXiv 2503.05558 ↗First posted 7 Mar 2025cs.LG math.CO math.GRcited by 1

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

We review the problem of finding paths in Cayley graphs of groups and group actions, using the Rubik's cube as an example, and we list several more examples of significant mathematical interest. We then show how to formulate these problems in the framework of diffusion models. The exploration of the graph is carried out by the forward process, while finding the target nodes is done by the inverse backward process. This systematizes the discussion and suggests many generalizations. To improve exploration, we propose a ``reversed score'' ansatz which substantially improves over previous comparable algorithms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 4 of its 21 references that are in the map. First posted on arXiv 7 Mar 2025.

Builds on

4 of 21 references.

References · 4

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2024An analytic theory of creativity in convolutional diffusion modelscited by 103
  3. 2018Solving the Rubik's Cube Without Human Knowledgecited by 15
  4. 2024What makes math problems hard for reinforcement learning: a case studycited by 9

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Citing papers per year: 2026: 1.

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  1. 2024SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups
  2. 2025HOG-Diff: Higher-Order Guided Diffusion for Graph Generation
  3. 2024CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models
  4. 2024Towards understanding Diffusion Models (on Graphs)
  5. 2021ℓ2-norm Flow Diffusion in Near-Linear Time
  6. 2026Graph Representation via Elements of Discrete Morse and Cobordism Theories
  7. 2025DDPS: Discrete Diffusion Posterior Sampling for Paths in Layered Graphs
  8. 2026Learning Discrete Diffusion of Graphs via Free-Energy Gradient Flows