VLM Reasoning

VLM: Vision-Language Model

This field studies how models connect images or video with language to solve tasks involving spatial relations, time, causality, diagrams, and action. Common approaches include chain-of-thought prompting, visual tools and retrieval, structured representations such as scene graphs, and fine-tuning with process-level rewards. Benchmarks increasingly test whether answers and intermediate steps are grounded in visual evidence, including in multi-image, video, scientific, and embodied settings. A central tension is that longer reasoning can help on complex tasks but can also weaken visual grounding, amplify language-based shortcuts, or produce explanations that do not reflect how an answer was reached.

Latest papers

853 papers96 papers in the last four weeks, up 48% on the four weeks before. 0.6% of all new papers.

CRIT: Graph-Based Automatic Data Synthesis to Enhance Cross-Modal Multi-Hop Reasoning
Apr 2, 2026Junyoung Sung, Seungwoo Lyu, Minjun Kim +3Multimodal Reasoning VLM Reasoning
Dept. of CSE, Korea University · Google DeepMind
A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation
Apr 1, 2026Yabin Zhang, Chong Wang, Yunhe Gao +19Radiology VLMs VLM Reasoning
Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, CA, USA. · Department of Radiology, Stanford University, Stanford, CA, USA. · Department of Computer Science, Stanford University, Stanford, CA, USA. +4
All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Models
Apr 1, 2026Xinyu Tian, Shu Zou, Zhaoyuan Yang +3RL for VLMs VLM Reasoning
Australian National University · Shanghai AI Lab · GE Research
PRISM: A Multi-View Multi-Capability Retail Video Dataset for Embodied Vision-Language Models
Mar 31, 2026Amirreza Rouhi, Parikshit Sakurikar, Satya Sai Reddy +6VLMs for Robotics VLM Reasoning
DreamVu
Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding
Mar 31, 2026Jingqi XuVLM Adaptation VLM Reasoning
University of Southern California, Los Angeles, CA, USA
Internalized Reasoning for Long-Context Visual Document Understanding
Mar 31, 2026Austin VeselkaMultimodal Understanding Document Image Understanding
LightOn
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
Mar 30, 2026Yanjie Zhang, Yafei Li, Rui Sheng +5VLM Robustness Chart QA
HKUST, Hong Kong SAR, China · HKUST(GZ), Guangzhou, China
Learning Multi-View Spatial Reasoning from Cross-View Relations
Mar 30, 2026Suchae Jeong, Jaehwi Song, Haeone Lee +9Multimodal Spatial Reasoning Visual Spatial Reasoning
KAIST · Config · Hanyang University +2
Difference Feedback: Generating Multimodal Process-Level Supervision for VLM Reinforcement Learning
Mar 29, 2026Feiding, Yongkang Zhang, Yuhao Liao +10RL for VLMs Reasoning for VLMs
Shenzhen International Graduate School, Tsinghua University, China
Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models
Mar 28, 2026Yuhang Han, Yuyang Wu, Zhengbo Jiao +6VLM Reasoning RL for VLMs
EPIC Lab, SJTU · HKUST (GZ) · Huawei
Inference-Time Structural Reasoning for Compositional Vision-Language Understanding
Mar 28, 2026Amartya BhattacharyaCompositional Reasoning in VLMs VLM Reasoning
Biomedical Data Science, Geisel School of Medicine, Dartmouth College
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
Mar 28, 2026Yizhou Jin, Yuezhu Feng, Jinjin Zhang +3Anomaly Localization Multimodal Anomaly Detection
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China · Hangzhou Innovation Institute, Beihang University, Hangzhou, China
VIRST: Video-Instructed Reasoning Assistant for SpatioTemporal Segmentation
Mar 28, 2026Jihwan Hong, Jaeyoung DoVideo Object Segmentation Referring Video Object Segmentation
AIDAS Laboratory, 1IPAI, Seoul National University · AIDAS Laboratory, 2ECE, Seoul National University
HandVQA: Diagnosing and Improving Fine-Grained Spatial Reasoning about Hands in Vision-Language Models
Mar 27, 2026MD Khalequzzaman Chowdhury Sayem, Mubarrat Tajoar Chowdhury, Yihalem Yimolal Tiruneh +4VLM Reasoning Spatial Reasoning
UNIST · University of Aberdeen · University College London +1
Mitigating the Reasoning Tax in Vision-Language Fine-Tuning with Input-Adaptive Depth Aggregation
Mar 27, 2026Yiming Ren, Yujiu Yang, Junjie WangVLM Adaptation VLM Reasoning
Tsinghua University
From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning
Mar 27, 2026Alberto G. Rodriguez SalgadoVLM Reasoning Visual Spatial Reasoning
Independent Researcher
LanteRn: Latent Visual Structured Reasoning
Mar 26, 2026André G. Viveiros, Nuno Gonçalves, Matthias Lindemann +1Latent Visual Reasoning VLM Reasoning
Instituto de Telecomunicações · Instituto Superior Técnico, Universidade de Lisboa · Carnegie Mellon University
Bridging Perception and Reasoning: Token Reweighting for RLVR in Multimodal LLMs
Mar 26, 2026Jinda Lu, Junkang Wu, Jinghan Li +6RL for VLMs VLM Reasoning
University of Science and Technology of China · Peking University · Independent Researcher
Sparse Visual Thought Circuits in Vision-Language Models
Mar 26, 2026Yunpeng ZhouVLM Interpretability Sparse Autoencoders
University of Reading, Reading, UK
Video-Only ToM: Enhancing Theory of Mind in Multimodal Large Language Models
Mar 25, 2026Siqi Liu, Xinyang Li, Bochao Zou +3Theory of Mind Multimodal Large Language Models
University of Science and Technology Beijing
RVLM: Recursive Vision-Language Models with Adaptive Depth
Mar 25, 2026Nicanor Mayumu, Zeenath Khan, Melodena Stephens +2VLM Reasoning Medical VLMs
Department of Computer Science, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE · Department of Academic Affairs, Mohammed Bin Rashid School of Government, City Walk, Building B02, Dubai, UAE
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Mar 25, 2026Weixin Chen, Antonio Vergari, Han ZhaoVLM Robustness Visual Reasoning
University of Illinois Urbana-Champaign · University of Edinburgh
Limits of Spatial Imagery Reasoning in Frontier LLM Models
Mar 25, 2026Sergio Y. Hayashi, Nina S. T. HirataVisual Spatial Reasoning 3D Spatial Reasoning
Institute of Mathematics and Statistics – University of S˜ao Paulo, Brazil
Edge Reliability Gap in Vision-Language Models: Quantifying Failure Modes of Compressed VLMs Under Visual Corruption
Mar 24, 2026Mehmet Kaan ErolCompositional Reasoning in VLMs VLM Evaluation
Marmara University, Institute of Pure and Applied Sciences
Cog3DMap: Multi-View Vision-Language Reasoning with 3D Cognitive Maps
Mar 24, 2026Chanyoung Gwak, Yoonwoo Jeong, Byungwoo Jeon +3Multimodal Spatial Reasoning 3D Spatial Reasoning
POSTECH · KAIST · RLWRLD
Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought
Mar 24, 2026Yunheng Li, Hangyi Kuang, Hengrui Zhang +4VLM Reasoning Multimodal CoT Reasoning
VCIP, School of Computer Science, Nankai University · Kuaishou Technology
Q-Tacit: Image Quality Assessment via Latent Visual Reasoning
Mar 23, 2026Yuxuan Jiang, Yixuan Li, Hanwei Zhu +3Image Quality Assessment Latent Visual Reasoning
Visual Information Lab, University of Bristol · Nanyang Technological University
Let's Think with Images Efficiently! An Interleaved-Modal Chain-of-Thought Reasoning Framework with Dynamic and Precise Visual Thoughts
Mar 23, 2026Xu Liu, Yongheng Zhang, Qiguang Chen +3Multimodal CoT Reasoning VLM Reasoning
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen · School of Computer Science and Engineering, Central South University · Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center, Guizhou University +1
Getting to the Point: Pointing Improves LVLMs at Counting
Mar 23, 2026Simone Alghisi, Massimo Rizzoli, Seyed Mahed Mousavi +1VLM Reasoning Large Vision-Language Models
Signals and Interactive Systems Lab, University of Trento, Italy
Do Multilingual VLMs Reason Equally? A Cross-Lingual Visual Reasoning Audit for Indian Languages
Mar 23, 2026Swastik RMultilingual VLM Evaluation VLM Reasoning
Indian Institute of Information Technology, Raichur

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CRIT: Graph-Based Automatic Data Synthesis to Enhance Cross-Modal Multi-Hop Reasoning

Junyoung Sung, Seungwoo Lyu, Minjun Kim, Sumin An+2

Dept. of CSE, Korea University · Google DeepMind

Multimodal Reasoning VLM Reasoning Multimodal Model Evaluation Synthetic Data Generation

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Real-world reasoning often requires combining information across modalities, connecting textual context with visual cues in a multi-hop process. Yet, most multimodal benchmarks fail to capture this ability: they typically rely on single images or set of images, where answers can be inferred from a single modality alone. This limitation is mirrored in the training data, where interleaved image-text content rarely enforces complementary, multi-hop reasoning. As a result, Vision-Language Models (VLMs) frequently hallucinate and produce reasoning traces poorly grounded in visual evidence. To address this gap, we introduce CRIT, a new dataset and benchmark built with a graph-based automatic pipeline for generating complex cross-modal reasoning tasks. CRIT consists of diverse domains ranging from natural images, videos, and text-rich sources, and includes a manually verified test set for reliable evaluation. Experiments on this benchmark reveal that even state-of-the-art models struggle on such reasoning tasks. Models trained on CRIT show significant gains in cross-modal multi-hop reasoning, including strong improvements on SPIQA and other standard multimodal benchmarks.