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.

Video-ToC: Video Tree-of-Cue Reasoning
Apr 22, 2026Qizhong Tan, Zhuotao Tian, Guangming Lu +2Video Reasoning VLM Reasoning
Harbin Institute of Technology, Shenzhen
HyLaR: Hybrid Latent Reasoning with Decoupled Policy Optimization
Apr 22, 2026Tao Cheng, Shi-Zhe Chen, Hao Zhang +3Multimodal Large Language Models Multimodal Reasoning
Tencent PCG, China · Tencent CSIG, China
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Apr 22, 2026Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5VLM Evaluation Autonomous Driving Benchmarks
Professorship of Autonomous Vehicle Systems, Technical University of Munich, Munich, Germany · Bayerische Motoren Werke AG, Munich, Germany · Data Analytics and Machine Learning Group, Technical University of Munich, Munich, Germany
Visual Reasoning through Tool-supervised Reinforcement Learning
Apr 21, 2026Qihua Dong, Gozde Sahin, Pei Wang +4Tool Use in VLMs VLM Reasoning
1Northeastern University · 2Amazon AGI
Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning
Apr 21, 2026Palawat Busaranuvong, Reza Saadati Fard, Emmanuel Agu +4Healthcare Medical Diagnosis
Worcester Polytechnic Institute, USA
Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
Apr 21, 2026Chuou Xu, Liya Ji, Qifeng ChenLarge Vision-Language Models VLM Reasoning
The Hong Kong University of Science and Technology
CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
Apr 21, 2026Shengli Zhou, Xiangchen Wang, Guanhua Chen +13D Spatial Reasoning VLM Reasoning
1Southern University of Science and Technology · 2SpatialTemporal AI
Disparities In Negation Understanding Across Languages In Vision-Language Models
Apr 21, 2026Charikleia Moraitaki, Sarah Pan, Skyler Pulling +3VLM Evaluation Vision-Language Models
Massachusetts Institute of Technology
S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models
Apr 20, 2026Nitish Shukla, Surgan Jandial, Arun RossDirect Preference Optimization Visual Reasoning
Michigan State University · Carnegie Mellon University
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
Apr 20, 2026Kangan Qian, ChuChu Xie, Yang Zhong +133D Spatial Reasoning Embodied QA
Tsinghua University · Automotive and Robotics, Xiaomi Corporation · National University of Singapore +2
Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models
Apr 20, 2026Haiweng Xu, Sipeng Zheng, Hao Luo +3VLM Evaluation VLM Reasoning
1Peking University · 3BeingBeyond · 2Tsinghua University
ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
Apr 20, 2026Tuan Van Vo, Tan Q. Nguyen, Khang Nguyen +5Robotic Manipulation VLM Reasoning
VinRobotics, Hanoi. Vietnam · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE. · University of Texas at Arlington, Texas, USA +4
RS-HyRe-R1: A Hybrid Reward Mechanism to Overcome Perceptual Inertia for Remote Sensing Images Understanding
Apr 19, 2026Gaozhi Zhou, Hu He, Peng Shen +8Vision-Language Models Visual Question Answering
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China · School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
Apr 19, 2026Yueyang Ding, HaoPeng Zhang, Rui Dai +4Vision-Language Models Time Series Reasoning
Amap, Alibaba Group
Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
Apr 17, 2026Yige Xu, Yongjie Wang, Zizhuo Wu +3Vision-Language Models VLM Adaptation
College of Computing and Data Science, Nanyang Technological University, Singapore · Alibaba-NTU Global e-Sustainability CorpLab (ANGEL) · Tongyi Lab, Alibaba Group, China
Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization
Apr 17, 2026Siddhant Bharadwaj, Ashish Vashist, Fahimul Aleem +1VLM Evaluation Large Vision-Language Models
University of Central Florida
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
Apr 17, 2026Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian +1Multimodal Model Evaluation Visual Spatial Reasoning
IIT, Hyderabad · Microsoft Research India
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
Apr 17, 2026Yaohui Han, Tianshuo Wang, Zixi Zhao +6Vision-Language Models VLM Reasoning
The Chinese University of Hong Kong · Central South University · Huawei Technologies Co., Ltd +1
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
Apr 17, 2026Shuyan Ke, Yifan Mei, Changli Wu +4Image Segmentation Remote Sensing Image Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China · Shanghai Innovation Institute
HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
Apr 17, 2026Yanbin Wei, Chun Kang, Siwei Li +11VLM Evaluation Large Vision-Language Models
1Southern University of Science and Technology · 2Hong Kong University of Science and Technology · 4Beihang University +1
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study
Apr 16, 2026Zhen Yang, Ping Jian, Zhongbin Guo +5Visual Spatial Reasoning Large Vision-Language Models
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding
Apr 16, 2026Hatice Merve Vural, Doga Kukul, Ege Erdem Ozlu +4VLM Reasoning Multimodal CoT Reasoning
Koç University, Istanbul, Turkey · KUIS AI Center, Istanbul, Turkey · Air Mail and Cartoon Collections +1
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards
Apr 16, 2026Jun Wang, Shuo Tan, Zelong Sun +5Document Understanding Multimodal RAG
Glint Lab
Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
Apr 16, 2026Danae Sánchez Villegas, Samuel Lewis-Lim, Nikolaos Aletras +1CoT Faithfulness Vision-Language Models
University of Copenhagen, Department of Computer Science · University of Sheffield, School of Computer Science
Learning Adaptive Reasoning Paths for Efficient Visual Reasoning
Apr 16, 2026Yixu Huang, Tinghui Zhu, Muhao ChenEfficient VLM Inference Visual Reasoning
♠Fudan University · ♣University of California, Davis
H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection
Apr 16, 2026Jianghong Huang, Luping Ji, Weiwei Duan +1Few-Shot Anomaly Detection Medical Image Anomaly Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Reward Design for Physical Reasoning in Vision-Language Models
Apr 15, 2026Derek Lilienthal, Manisha Mukherjee, Sameera HorawalavithanaVLM Reasoning Physical Reasoning
Pacific Northwest National Laboratory, Richland, WA 99352, USA
SmoGVLM: A Small, Graph-enhanced Vision-Language Model
Apr 15, 2026Debjyoti Mondal, Rituraj Singh, Subhadarshi PandaVLM Reasoning Vision-Language Grounding
Samsung R&D Institute India-Bangalore
Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA
Apr 15, 2026Yuanlei Zheng, Pei Fu, Hang Li +9Document QA Long-Document VQA
School of Software Engineering, Huazhong University of Science and Technology · MiLM Plus, Xiaomi Inc. · School of Computer Science and Technology, Huazhong University of Science and Technology +1
Why MLLMs Struggle to Determine Object Orientations
Apr 14, 2026Anju Gopinath, Nikhil Krishnaswamy, Bruce DraperRepresentation Geometry in VLMs Vision-Language Models
Department of Computer Science, Colorado State University, Fort Collins, CO, USA

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Video-ToC: Video Tree-of-Cue Reasoning

Qizhong Tan, Zhuotao Tian, Guangming Lu, Jun Yu+1

Harbin Institute of Technology, Shenzhen

Video Reasoning VLM Reasoning RL for VLMs

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Existing Video Large Language Models (Video LLMs) struggle with complex video understanding, exhibiting limited reasoning capabilities and potential hallucinations. In particular, these methods tend to perform reasoning solely relying on the pretrained inherent reasoning rationales whilst lacking perception-aware adaptation to the input video content. To address this, we propose Video-ToC, a novel video reasoning framework that enhances video understanding through tree-of-cue reasoning. Specifically, our approach introduces three key innovations: (1) A tree-guided visual cue localization mechanism, which endows the model with enhanced fine-grained perceptual capabilities through structured reasoning patterns; (2) A reasoning-demand reward mechanism, which dynamically adjusts the reward value for reinforcement learning (RL) based on the estimation of reasoning demands, enabling on-demand incentives for more effective reasoning strategies; and (3) An automated annotation pipeline that constructs the Video-ToC-SFT-1k and Video-ToC-RL-2k datasets for supervised fine-tuning (SFT) and RL training, respectively. Extensive evaluations on six video understanding benchmarks and a video hallucination benchmark demonstrate the superiority of Video-ToC over baselines and recent methods. Code is available at https://github.com/qizhongtan/Video-ToC.