arXiv Computer Vision By Jianyang Gu, Jacob Beattie, Mridul Khurana, Julia Chae, Xi Xiao, Cheng Zhang, Anuj Karpatne, Wei-Lun Chao, Sara Beery, Tanya Berger-Wolf

From Activation to Intervention: Interpreting Sparse Visual Features through Reconstruction

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arXiv AI
Oct 27

VLMProbe: An Open-source Library for VLM Probing and Interpretation

VLMProbe is an open‑source library that unifies lens readouts, attention attribution, causal interventions, and sparse autoencoder analysis for vision‑language models. It offers shared adapters, calibration tools, evaluation pipelines, and visualizations to enable systematic comparison across different model architectures. The framework demonstrates that representation readouts and spatial explanations vary with the analysis goal and observation point, and that local attribution can conflict with intervention outcomes, highlighting the need for multiple evidence sources in interpretation studies.

By Gexin Huang, Haisheng Fu, Yanting Yang, Chen Zhao, Ruinan Jin, Jiaqi Liu, Xiaoxiao Li
arXiv Computer Vision
Sep 24

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

UVU is a vision-language unified autoregressive framework that integrates visual supervision directly into the pre-training stage of multimodal large language models. By using continuous visual encoding and a large-scale iterative hierarchical clustering algorithm to build a pixel-level visual codebook, UVU enables lossless representation of visual inputs and autoregressive generation of pixel-level image tokens alongside textual tokens. This approach synergizes pixel-level visual perception with semantic-level visual understanding, allowing models to internalize visual reconstruction capabilities and improve multimodal understanding performance.

By Zhehan Kan, Xinghua Jiang, Yubo Zhu, Yanlin Liu, Xiaochen Yang, Zhixiang Wei, Shifeng Liu, Qingmin Liao, Wenming Yang, Xin Li, Yinsong Liu, Deqiang Jiang, Xing Sun
arXiv AI
Oct 1

D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders

D‑Scope is a framework that links the interpretation of sparse autoencoder (SAE) features in diffusion transformers (DiTs) to controllable image generation. It aggregates SigLIP‑2 embeddings of highly activating image patches into visual centroids, matches target text descriptions against these centroids, and retrieves individual features without per‑feature text annotations. The method provides visual evidence for each selection and uses spatially masked interventions to test decoder directions under fixed generation conditions, evaluated across 150 SAEs and a benchmark of 100 target concepts.

By Xinyue Xu, Jiahao Zhang, Lijie Hu, Peter Hase, Hao Wang