Large vision language models (VLMs) increasingly claim reasoning skills, yet current benchmarks evaluate them in single-turn or question answering settings. However, grounding is an interactive process in which people gradually develop shared understanding through ongoing communication. We introduce a four-metric suite (grounding efficiency, content alignment, lexical adaptation, and human-likeness) to systematically evaluate VLM performance in interactive grounding contexts. We deploy the suite on 150 self-play sessions of interactive referential games between three proprietary VLMs and compare them with human dyads. All three models diverge from human patterns on at least three metrics, while GPT4o-mini is the closest overall. We find that (i) task success scores do not indicate successful grounding and (ii) high image-utterance alignment does not necessarily predict task success. Our metric suite and findings offer a framework for future research on VLM grounding.
ReadBench: Measuring the Dense Text Visual Reading Ability of Vision-Language Models
Artificial Intelligence and Intelligent Information Systems, University of Trier（人工智能与智能信息系统，特里尔大学）；German Research Center for Artificial Intelligence (DFKI), Trier（德国人工智能研究中心（DFKI），特里尔）
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
Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination
University of Southern California（南加州大学）；University of California San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs
Massachusetts Institute of Technology（麻省理工学院）；American International School Vienna（维也纳美国国际学校）；Substrate Labs；University of North Florida（北佛罗里达大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Motork；Collingwood School（科林伍德学校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
VisBias: Measuring Explicit and Implicit Social Biases in Vision Language Models
University of Southern California（南加州大学）；University of Bristol（布里斯托大学）；Centre National de la Recherche Scientifique（法国国家科学研究中心）；University of California, Los Angeles（加州大学洛杉矶分校）
Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance
Department of Linguistics, The Ohio State University, USA（语言学系，俄亥俄州立大学）；Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Department of Information Sciences and Technology, Penn State University, USA（信息科学与技术系，宾夕法尼亚州立大学）；Amazon, USA（亚马逊公司）
Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos
School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）；School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
