arXiv (All)
open access
Feature Information Dynamics in Diffusion
Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail. Yet, this intuition is mostly empirical and qualitative. We introduce feature information dynamics, an information-theoretic framework for localizing when
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
- Feature Information Dynamics in Diffusion#1210298
- Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion#346491
- Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models#1343069
- Diffusion Language Models for Speech Recognition#14019
- DiffReID: Discriminative Diffusion Model for Object Re-Identification#1132095
Record · ID 1218418
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