Categories: AI/ML News

Discrete spatial diffusion models data while obeying scientific principles

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI models. Unlike generative diffusion models, the team’s Discrete Spatial Diffusion approach honors scientific and physics principles. The team validated their model on two challenging scientific applications—subsurface rock microstructures and lithium-ion battery electrodes—with promising results.

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling

Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an ‘information void’ where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented Discrete Diffusion (CADD), a framework that augments the discrete…

December 11, 2025

In "FAANG"

GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

May 26, 2023

In "AI/ML Research"

FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models

Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency for long sequences. Diffusion Language Models (DLMs) parallelize across positions and thus appear promising for language generation, yet standard discrete diffusion typically needs hundreds to thousands…

October 14, 2025

In "FAANG"

AI Generated Robotic Content