Latent Process Generator Matching
The paper titled 'Latent Process Generator Matching' introduces a new framework for generative models in machine learning. This framework allows for the treatment of observed generative states as deterministic images of tractable Markov processes. It extends existing generator matching theory to include time-dependent latent conditional processes.
- ▪The framework treats observed generative states as deterministic images of tractable Markov processes.
- ▪It generalizes existing results from discrete latent processes to a broader family of time-dependent processes.
- ▪The paper aims to improve the training of flow-matching and diffusion-style generative models.
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Computer Science > Machine Learning arXiv:2605.20547 (cs) [Submitted on 19 May 2026] Title:Latent Process Generator Matching Authors:Lukas Billera, Hedwig Nora Nordlinder, Ben Murrell View a PDF of the paper titled Latent Process Generator Matching, by Lukas Billera and 2 other authors View PDF HTML (experimental) Abstract:Many recent flow-matching and diffusion-style generative models rely on auxiliary stochastic dynamics during training: a richer process is simulated to define conditional targets, but the auxiliary state is either intractable to sample at generation time or simply not part of the desired output.
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