arXiv:2512.11415v3 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of latent-state cycles. We introduce a model in which visible and hidden variables interact through two independently parametrized transition matrices, defining a Markov chain whose steady state is intrinsically out of equilibrium. Likelihood maximization drives this system toward nonequilibrium steady states with finite entropy production, reduced self-transition probabilities, and persistent probabil
The continuous drive for more efficient and robust unsupervised learning methods in AI research leads to exploring novel dynamics like nonequilibrium systems.
This research suggests a new paradigm for generative AI, potentially leading to more stable and powerful models with emergent properties not previously observed.
The understanding of how latent cycles can emerge constructively in generative models, opening new avenues for designing and training AI systems.
- · AI researchers
- · Generative AI developers
- · Machine learning startups
Improved performance and stability in certain types of generative AI models.
Development of new AI architectures inspired by nonequilibrium dynamics.
Enhanced AI capabilities contributing to advances in fields like materials science or drug discovery through generative design.
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