Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling

Generative diffusion models have achieved remarkable success in producing high-quality images. However, because these models typically operate in continuous intensity spaces - diffusing independently per pixel and color channel - they are fundamentally ill-suited for applications where quantities such as particle counts or material units are inherently discrete and governed by strict conservation laws such as mass preservation, limiting their applicability in scientific workflows. To address this limitation, we propose Discrete Spatial Diffusion (DSD), a framework based on a continuous-time, discrete-state jump stochastic process that operates directly in discrete spatial domains while strictly preserving mass in both forward and reverse diffusion processes. By using spatial diffusion to achieve mass preservation, we introduce stochasticity naturally through a discrete formulation. We demonstrate the expressive flexibility of DSD by performing image synthesis, class conditioning, and image inpainting across widely-used image benchmarks, with the ability to condition on image intensity. Additionally, we highlight its applicability to domain-specific scientific data for materials microstructure, bridging the gap between diffusion models and mass-conditioned scientific applications.

Patchfinder: Leveraging Visual Language Models for Accurate Information Retrieval using Model Uncertainty

For decades, corporations and governments have relied on scanned documents to record vast amounts of information. However, extracting this information is a slow and tedious process due to the overwhelming amount of documents. The rise of vision language models presents a way to efficiently and accurately extract the information out of these documents. The current automated workflow often requires a two-step approach involving the extraction of information using optical character recognition software, and subsequent usage of large language models for processing this information. Unfortunately, these methods encounter significant challenges when dealing with noisy scanned documents. The high information density of such documents often necessitates using computationally expensive language models to effectively reduce noise. In this study, we propose PatchFinder, an algorithm that builds upon Vision Language Models (VLMs) to address the information extraction task. First, we devise a confidence-based score, called Patch Confidence, based on the Maximum Softmax Probability of the VLMs' output to measure the model's confidence in its predictions. Then, PatchFinder utilizes that score to determine a suitable patch size, partition the input document into overlapping patches of that size, and generate confidence-based predictions for the target information. Our experimental results show that PatchFinder can leverage Phi-3v, a 4.2 billion parameter vision language model, to achieve an accuracy of 94% on our dataset of 190 noisy scanned documents, surpassing the performance of ChatGPT-4o by 18.5 percentage points.