arXiv AI July 20, 2026 By Zeyu Yun, Alexander Belsten, Dasheng Bi, Zahra Kadkhodaie, Yubei Chen, Bruno A. Olshausen

Toward a mechanistic understanding of inference in visual cortex and diffusion models

arXiv:2607. 15693v1 Announce Type: cross Abstract: We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters.

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diffusion

arXiv AI

Sep 2

V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

V-Co investigates visual co-denoising for pixel-space diffusion models, using a unified JiT-based framework to isolate key design choices. The study identifies two essential components: a dual-stream architecture with flexible cross-stream interaction and a perceptual-drifting hybrid loss combined with RMS-based feature rescaling for stronger semantic supervision. Experiments on ImageNet-256 demonstrate that V-Co surpasses baseline pixel-space diffusion and strong prior pixel-diffusion methods at comparable model sizes while requiring fewer training epochs.

By Han Lin, Xichen Pan, Zun Wang, Yue Zhang, Chu Wang, Jaemin Cho, Mohit Bansal

diffusion safety

Hugging Face Trending Papers

Jun 3

Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction

Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.

computer-vision benchmarks

arXiv Machine Learning

Sep 22

Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

arXiv:2411.05005v2 Announce Type: replace-cross Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, m...

By Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao, Martial Hebert, Yu-Xiong Wang

diffusion multimodal

arXiv AI

Jun 16

DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing

arXiv:2606. 15796v1 Announce Type: cross Abstract: Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits.

By Artyom Mazur, Nina Konovalova, Aibek Alanov

llms diffusion efficiency multimodal benchmarks safety

arXiv AI

Jul 7

Steering Optimisation Trajectories in Diffusion Representation Learning

arXiv:2607. 05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures.

By Rajat Rasal, Avinash Kori, Tian Xia, Ben Glocker

diffusion computer-vision benchmarks

arXiv AI

Jul 21

Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm

arXiv:2607. 16295v1 Announce Type: cross Abstract: Mechanistic interpretability has made significant strides in understanding neural network representations, with sparse dictionary learning (SDL) methods, most prominently sparse autoencoders, as a central paradigm.

By Yiming Tang, Qinglin Qi, Zhaoqian Yao, Harshvardhan Saini, Dianbo Liu

computer-vision efficiency benchmarks safety

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V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction

Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

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Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm