From classroom to coding: SA teen builds AI diffusion model while achieving academic excellence

Despite personal hardships and pandemic disruptions, Trinityhouse student Tristan Jay Neale achieved six distinctions while ...
Tech Xplore

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 ...
Tech Xplore

One image is all robots need to find their way

While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and ...
IEEE

Motion Planning Diffusion: Learning and Adapting Robot Motion Planning With Diffusion Models

Abstract: The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain a ...
Microsoft

Machine Intelligence

Diffusion Language Models (DLMs) explores the potential of diffusion-based models to exceed the generative quality of traditional autoregressive language models, while overcoming their current ...
GitHub

Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving

Generative diffusion models for end-to-end autonomous driving often suffer from mode collapse, tending to generate conservative and homogeneous behaviors. While DiffusionDrive employs predefined ...
Risk.net

Volume 29, Number 3 (December 2025)

Peter was editor of The Journal of Computational Finance from 2008 to 2013. We have selected the papers in these two issues, contributed by his friends and colleagues worldwide, to reflect both the ...
GitHub

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...
IEEE

Aligning Text-to-Image Diffusion Models With Constrained Reinforcement Learning

Abstract: Reward finetuning has emerged as a powerful technique for aligning diffusion models with specific downstream objectives or user preferences. However, current approaches suffer from a ...