Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We present a privacy-preserving pipeline that analyzes classroom videos to extract insights about student attention, without storing any identifiable footage. Our system runs on a single GPU, using OpenPose for skeletal extraction and Gaze-LLE for visual attention estimation. Original video frames are deleted immediately after pose extraction, thus only geometric coordinates (stored as JSON) are retained, ensuring compliance with FERPA. The extracted pose and gaze data is processed by QwQ-32B-Reasoning, which performs zero-shot analysis of student behavior across lecture segments. Instructors access results through a web dashboard featuring attention heatmaps and behavioral summaries. Our preliminary findings suggest that LLMs may show promise for multimodal behavior understanding, although they still struggle with spatial reasoning about classroom layouts. We discuss these limitations and outline directions for improving LLM spatial comprehension in educational analytics contexts.
Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos
Department of Computer Science, Drexel University（德雷塞尔大学计算机科学系）；Department of Teaching and Learning, Washington State University（华盛顿州立大学教学与学习系）；College of Chemistry, Beijing Normal University（北京师范大学化学学院）；Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校计算机科学系）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）
Classroom Final Exam: An Instructor-Tested Reasoning Benchmark
Northwestern University（西北大学）；UC Santa Cruz（加州大学圣克鲁兹分校）；Duke University（杜克大学）；University of Birmingham（伯明翰大学）；University of Rochester（罗切斯特大学）；Analogy AI, Inc.（Analogy AI 公司）
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；ByteDance（字节跳动）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究所）；Xi’an Jiaotong University（西安交通大学）；Renmin University of China（中国人民大学）
Perception-R1: Advancing Multimodal Reasoning Capabilities of MLLMs via Visual Perception Reward
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；iFLYTEK AI Research（iFLYTEK人工智能研究院）；Hong Kong University of Science and Technology（香港科学与技术大学）
Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
The Hong Kong Polytechnic University（香港理工大学）；Singapore Management University（新加坡国立大学）；Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning
Zhejiang University（浙江大学）；Fudan University（复旦大学）；Soochow University（苏州大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models
The Hong Kong Polytechnic University（香港理工大学）；Zhejiang University（浙江大学）；University of Electronic Science and Technology of China（电子科技大学）；Reallm Labs（Reallm 实验室）；Amazon（亚马逊）；The Hong Kong University of Science and Technology（香港理工大学）
Faithful-First Reasoning, Planning, and Acting for Multimodal LLMs
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Australian National University（澳大利亚国立大学）；Fudan University（复旦大学）；University of Sydney（悉尼大学）
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院眼科学系）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
Geoint-R1: Formalizing Multimodal Geometric Reasoning with Dynamic Auxiliary Constructions
Shenyang institute of computing technology, Chinese academy of sciences（沈阳计算技术研究所，中国科学院）；Shanghai AI Laboratory（上海人工智能实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations
Seoul National University（首尔国立大学）；Human-Computer Interaction Institute, Carnegie Mellon University（人机交互研究所，卡内基梅隆大学）；Language Technologies Institute, Carnegie Mellon University（语言技术研究所，卡内基梅隆大学）
Learning to Reason About Implicit Multimodal Harm: From Diagnosis to Generalization
Max Planck Institute for Informatics（马克斯·普朗克院信息研究所）；Saarland Informatics Campus（萨尔兰州信息校园）；Saarland University（萨尔兰州大学）；The University of Edinburgh（爱丁堡大学）；Samsung AI Center, Cambridge（三星AI中心，剑桥）
