Vision-language models excel at 2D visual understanding but remain limited in 3D spatial reasoning. Existing approaches either depend on explicit 3D modalities, which limits scalability, or inject partial, view-conditioned geometric priors and leave the language model to recover global scene structure from sparse cues. We introduce Spa3R, a self-supervised framework that learns a unified, view-invariant spatial representation from unposed multi-view RGB images. Its Predictive Spatial Field Modeling objective compresses context views into a compact latent representation and predicts aligned geometric and semantic feature fields at novel viewpoints, thereby encouraging coherent encoding of scene geometry and layout. We integrate the pre-trained Spa3R Encoder into a vision-language model through a lightweight residual cross-attention adapter, yielding Spa3-VLM and grounding language reasoning in global spatial context. Spa3-VLM achieves an average score of 58.6% on VSI-Bench and delivers leading or competitive performance across three additional spatial reasoning benchmarks. These results demonstrate that predictive spatial representation learning provides an effective visual foundation for 3D reasoning.
University of Nottingham Ningbo China（宁波诺丁汉大学）；Nanyang Technological University（南洋理工大学）
Raven's Progressive Matrices (RPMs) are frequently used in evaluating human's visual reasoning ability. Researchers have made considerable efforts in developing systems to automatically solve the RPM problem, often through a black-box end-to-end convolutional neural network for both visual recognition and logical reasoning tasks. Based on the two intrinsic natures of RPM problem, visual recognition and logical reasoning, we propose a Two-stage Rule-Induction Visual Reasoner (TRIVR), which consists of a perception module and a reasoning module, to tackle the challenges of real-world visual reco…
Position: The Systemic Lack of Agency in Visual Reasoning
National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University（武汉大学计算机学院人工智能研究所多媒体软件工程研究中心）；Hubei Key Laboratory of Multimedia（湖北省多媒体与网络通信工程重点实验室）；Zhongguancun Academy, Beijing, China. 100094（中关村学院，北京，中国）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；Shanda AI Research Tokyo（上海大势人工智能研究东京）
Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions. It is crucial for machines to have this capacity to reason using these two modalities to invent new robot-human collaborative systems. Recent advances in deep learning have built separate sophisticated representations of both visual scenes and languages. However, understanding the associations between the two modalities in a shared context for multimodal reasoning remains a challenge. Focusing on language and vision modali…
Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning
Zhejiang University（浙江大学）；Hunan University（湖南大学）；Tianjin University（天津大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Jilin University（吉林大学）
VisualQuest: A Benchmark for Abstract Visual Reasoning in MLLMs
School of Computer Science and Technology, Dalian University of Technology, Dalian, China（大连理工大学计算机科学与技术学院）；School of Foreign Languages, Dalian University of Technology, Dalian, China（大连理工大学外语学院）；School of Computer Science and Technology, Xinjiang Normal University, Urumqi, China（新疆师范大学计算机科学与技术学院）
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；University of Central Florida（中佛罗里达大学）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM). Our model considers explicit spatial semantics of visual elements within each image in the puzzle, encoded as spatio-visual tokens, and learns the intra-image as well as the inter-image token dependencies, highly relevant for the visual reasoning task. Token-wise relationship, modeled through a transformer-based SAViR-T architecture, extract group (row or column) driven representations by leveraging the group-rule coherence and use this as the induc…
UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；Zhongguancun Academy（中关村学院）
Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning
Fudan University（复旦大学）；Alibaba Group Holding Limited（阿里巴巴集团控股有限公司）；Future Living Lab of Alibaba（阿里巴巴未来生活实验室）；University of Southern California（南加州大学）；Shanghai Innovation Institute（上海创新研究院）
Artificial and Natural Intelligence Toulouse Institute（图卢兹人工智能与自然智能研究所）；Université de Toulouse（图卢兹大学）；Centre de Recherche Cerveau & Cognition (CerCo), CNRS（法国国家科学研究中心大脑与认知研究中心）
Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the "Synthetic Visual Reasoning Test" (SVRT) challenge, a collection of twenty-three visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and…
MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering
Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine（埃默里大学医学院放射肿瘤学系与温希普癌症研究所）；Department of Biostatistics and Bioinformatics, Emory University（埃默里大学生物统计学与生物信息学系）
University of California, Los Angeles（加州大学洛杉矶分校）；Air Force Research Laboratory（空军研究实验室）
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here …
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
Warsaw University of Technology（华沙工业大学）；LIGM, Ecole des Ponts, Univ Gustave Eiffel（古斯塔夫·埃菲尔大学桥路学院LIGM实验室）；Tooploox（Tooploox公司）；IDEAS NCBR（IDEAS国家研究与发展中心）；Jagiellonian University of Cracow（克拉科夫雅盖隆大学）
Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks. This work aims to assess how well these features preserve information about the objects, such as their spatial location, their visual properties and their relative relationships. We propose to do so by evaluating them in the context of visual reasoning, where multiple objects with complex relationships and different attributes are at play. More specifically, we introduce a protocol to evaluate visual representations for the task…
