arXiv (All)
open access
The Lattice of Transition Laws
Diffusion and autoregression (AR) have long been seen as different categories of generative models, with diffusion specialising in continuous fields and AR specialising in discrete tokens. Recent work seeks to combine the advantages of the two models, and each hybrid fixes its decoding schedule by d
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#798735
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#797321
- Why diffusion models do not memorize: the role of implicit dynamical regularization in training#1120376
- A PDE Perspective on Generative Diffusion Models#1247309
- Particle GFlowNets: Rethinking Generative Marginalization Models#999062
Record · ID 1248143
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive