Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially modified, while subtle yet consequential edits outside the mask are treated as natural. We reformulate VLM image tampering from coarse region labels to a pixel-grounded, meaning and language-aware task. First, we introduce a taxonomy spanning edit primitives (replace/remove/splice/inpaint/attribute/colorization, etc.) and their semantic class of tampered object, linking low-level changes to high-level understanding. Second, we release a new benchmark with per-pixel tamper maps and paired category supervision to evaluate detection and classification within a unified protocol. Third, we propose a training framework and evaluation metrics that quantify pixel-level correctness with localization to assess confidence or prediction on true edit intensity, and further measure tamper meaning understanding via semantics-aware classification and natural language descriptions for the predicted regions. We also re-evaluate the existing strong segmentation/localization baselines on recent strong tamper detectors and reveal substantial over- and under-scoring using mask-only metrics, and expose failure modes on micro-edits and off-mask changes. Our framework advances the field from masks to pixels, meanings and language descriptions, establishing a rigorous standard for tamper localization, semantic classification and description. Code and benchmark data are available at https://github.com/VILA-Lab/PIXAR.
VisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision
University of Information Technology, VNU-HCM（越南国立胡志明市大学信息技术大学）；University College Cork（科克大学）；Hanoi University of Science and Technology（河内科技大学）；Trinity College Dublin（都柏林圣三一大学）
VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（香港科技大学新兴跨学科领域分部）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
School of Electrical Engineering, Korea University（韩国大学电气工程学院）；Department of Hospital Pathology, The Catholic University of Korea College of Medicine（韩国天主教大学医学院医院病理学系）
MVTamperBench: Evaluating Robustness of Vision-Language Models
Liverpool John Moores University（利物浦约翰·穆里斯大学）；Birla Institute of Technology（比拉理工学院）；Christ University（基督大学）；Columbia University（哥伦比亚大学）；New York University（纽约大学）；University of Washington（华盛顿大学）；Hanyang University（翰阳大学）
BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment
Department of Computer Science, Drexel University（德克萨斯大学计算机科学系）；Department of Neurological Surgery, Washington University（华盛顿大学神经外科系）；University of California, Berkeley（加州大学伯克利分校）
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
ETH Zürich（苏黎世联邦理工学院）；University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；EPFL（苏黎世联邦理工学院）
Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
Department of Computing, University of Turku（图尔库大学计算机系）；Institute of Computer Science, Zurich University of Applied Sciences（应用科学大学计算机科学研究所）；Centre for Artificial Ingelligence, Zurich University of Applied Sciences（应用科学大学人工智能中心）；Agentic Systems Lab, Department of Management, Technology and Economics, ETH Zürich（苏黎世联邦理工学院管理、科技与经济系代理系统实验室）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙技术大学数学与信息科学学院）
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models
University of British Columbia（不列颠哥伦比亚大学）；Zuse School（Zuse学校）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
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）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Oxford（牛津大学）；YouTu Lab, Tencent（腾讯优图实验室）
Qatar Computing Research Institute（卡塔尔计算研究所）；Sofia University "St. Kliment Ohridski"（索菲亚大学"圣克莱孟·奥赫里迪斯"）；University of Padova（帕多瓦大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
DuoTeach: Dual Role Self-Teaching for Coarse-to-Fine Decision Coordination in Vision--Language Models
Department of Computer, North China Electric Power University, Baoding, China（华北电力大学计算机学院）；AIMS Lab, HKUST (GZ), Guangzhou, China（香港科技大学（广州）人工智能实验室）；HKUST, Hong Kong SAR, China（香港科技大学）；School of Information and Intelligent Science, Donghua University, Shanghai, China（东华大学信息与智能科学学院）
Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies
1 School of Computation, Information；Technology, Technical University of Munich, Germany 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany 3 LTCI, Télécom Paris, Institut Polytechnique de Paris, France 4 Munich Center for Machine Learning (MCML) 5 School of Biomedical Engineering；Imaging Sciences, King's College London, UK 6 Department of Artificial Intelligence in Biomedical Imaging, FAU Erlangen-Nuremberg, Germany 7 Department of Computing, Imperial College London, UK
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Civil, Construction and Environmental Engineering, Iowa State University（土木、建设与环境工程系，爱荷华州立大学）
Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiology
Centre for Responsible Autonomous Systems in Healthcare (CRASH) Lab, Koita Centre for Digital Health（负责任的自主医疗系统中心（CRASH）实验室、Koita数字健康中心）；Ashoka University（阿什oka大学）；Independent Researcher（独立研究者）；Koita Centre for Digital Health（Koita数字健康中心）
Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies
Department of Computer Science and Engineering, Birbhum Institute of Engineering and Technology（计算机科学与工程系，比罗尔理工学院）；College of Computer Science and Artificial Intelligence, Fudan University（计算机科学与人工智能学院，复旦大学）；Faculty of Computer Science and Information Technology, Universiti Malaya（计算机科学与信息技术学院，马来亚大学）
