arXiv (All)
open access
Discrete Diffusion Models are Minimax Optimal Under Sparsity
Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than those of their continuous counterparts. For continuous diffusion models, a prominent line of theory su
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
- Jumping up and down: Denoiser diffusion models for discrete ordinal data#1187068
- DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech#1342548
- Subliminal Clocks: Latent Time Modelling in Diffusion Language Models#1198963
- Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement#1117414
- HaloBlock: Faster and Better Decoding for Diffusion Large Language Models#1343147
Record · ID 1344843
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