arXiv:2605.24752v1 Announce Type: new Abstract: We study \emph{learning-to-sample} -- a basic algorithmic task underlying generative modeling -- for Ising models, a standard testbed for algorithmic ideas in both theoretical computer science and machine learning. Given i.i.d. samples of an unknown target distribution, the goal of learning-to-sample is to learn a computationally efficient generation procedure that produces new samples following approximately the same distribution. We construct a family of Ising models of constantly bounded-width which lie just beyond the spectral threshold $\lam
This research is published as AI moves toward more sophisticated generative modeling and efficient sampling techniques become crucial for advancements.
This paper identifies a computational phase transition in learning-to-sample for Ising models, indicating potential fundamental limits or new approaches for generative AI.
Understanding these computational limits can guide the development of more efficient and theoretically sound generative AI models, particularly for complex distributions.
- · AI researchers
- · Generative AI developers
- · Machine learning theoreticians
- · Inefficient generative modeling approaches
Improved theoretical understanding of the computational complexity of generative modeling.
Development of more robust and scalable generative AI algorithms by circumventing identified computational bottlenecks.
Enhanced capabilities for AI agents to learn and generate complex data distributions, accelerating progress in various AI applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG