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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20547 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | hrgBYuRxuX3P |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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