Modern forest monitoring workflows increasingly benefit from the growing availability of high-resolution satellite imagery and advances in deep learning. Two persistent challenges in this context are accurate pixel-level change detection and meaningful semantic change captioning for complex forest dynamics. While large language models (LLMs) are being adapted for interactive data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored. To address this gap, we introduce an LLM-driven agent for integrated forest change analysis that supports natural language querying across multiple RSICI tasks. The proposed system builds upon a multi-level change interpretation (MCI) vision-language backbone with LLM-based orchestration. To facilitate adaptation and evaluation in forest environments, we further introduce the Forest-Change dataset, which comprises bi-temporal satellite imagery, pixel-level change masks, and multi-granularity semantic change captions generated using a combination of human annotation and rule-based methods. Experimental results show that the proposed system achieves mIoU and BLEU-4 scores of 67.10% and 40.17% on the Forest-Change dataset, and 88.13% and 34.41% on LEVIR-MCI-Trees, a tree-focused subset of LEVIR-MCI benchmark for joint change detection and captioning. These results highlight the potential of interactive, LLM-driven RSICI systems to improve accessibility, interpretability, and efficiency of forest change analysis. All data and code are publicly available at https://github.com/JamesBrockUoB/ForestChat.
From Perception to Action: An Interactive Benchmark for Vision Reasoning
Singapore University of Technology（新加坡科技设计大学）；Singapore Management University (SMU), Singapore（新加坡管理学院）；Nanyang Technological University (NTU), Singapore（南洋理工大学）；University of Science（科学大学）
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Hunan University（湖南大学）
M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息学院）；College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Engineering, Westlake University（西湖大学工程学院）；College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology（成都理工大学计算机与网络安全学院（软件学院））；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南大学云南省软件工程重点实验室）
Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions
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）；Wuhan AI Research（武汉人工智能研究院）
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences (UCAS)（中国科学院大学先进交叉学科学院）；School of Electronic, Electrical and Communication Engineering, UCAS（中国科学院大学电子、电气与通信工程学院）；School of Computer Science and Technology, UCAS（中国科学院大学计算机科学与技术学院）
From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Xiamen University（厦门大学）；The Hong Kong University of Science and Technology（香港理工大学）；Nanyang Technological University（南洋理工大学）
EAA: Automating materials characterization with vision language model agents
Argonne National Laboratory（阿贡国家实验室）；Advanced Photon Source（先进光子源）；Data Science and Learning Division（数据科学与学习 division）；Department of Radiation Oncology（放射肿瘤学系）；Northwestern University（西北大学）
MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion
organization= College of Computer Science；Technology, Jilin University , city= Changchun , country= China；organization= Georgia Institute of Technology , city= Atlanta , country= USA；organization= Qingdao Institute of Software, College of Computer Science；Technology, China University of Petroleum (East China) , city= Qingdao , country= China；organization= Institute for Math \& AI, Wuhan University , city= Wuhan , country= China；organization= University of Michigan, Ann Arbor , country= USA；organization= Thrust of Artificial Intelligence, Hong Kong University of Science；organization= School of Artificial Intelligence；Computer Science, Nantong University , city= Nantong , country= China
Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis
Shenzhen University（深圳大学）；Research Institute for Smart Cities（智慧城市研究院）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑与城市科学国家重点实验室）；Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities（粤港澳智慧城市联合实验室）
3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy
Two-Stream Interactive Joint Learning of Scene Parsing and Geometric Vision Tasks
College of Electronic and Information Engineering, Tongji University（同济大学电子信息学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统研究所）；College of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Department of Vehicle Control System and Software Development, NIO（蔚来汽车车辆控制系统与软件开发部门）；Department of Automotive Engineering, Jilin University（吉林大学汽车工程学院）；Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University（同济大学嵌入式系统与服务计算重点实验室）；College of Electronic and Information Engineering, Shanghai Institute of Intelligent Science and Technology（上海智能科学与技术研究院电子信息学院）；Shanghai Key Laboratory of Intelligent Autonomous Systems（上海智能自主系统重点实验室）
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery
Bonn-Rhein-Sieg University of Applied Sciences（博恩-莱茵-锡格应用科学大学）；Department of Computer Science（计算机科学系）；Institute for Artificial Intelligence and Autonomous Systems (A2S)（人工智能与自主系统研究所）；Institute of Technology, Resource and Energy-efficient Engineering (TREE)（技术与资源节能工程研究所）
本文针对无人机林地地表分割难题，采用 PEFT 微调 SAM 并改造其掩码解码器实现无提示自动分割，证明适配器方法 mIoU 最高，LoRA 更轻量。
SpinalSAM-R1: A Vision-Language Multimodal Interactive System for Spine CT Segmentation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；Department of Orthopedics, Qilu Hospital, Shandong University（骨科部，齐鲁医院，山东大学）；Key Laboratory of Qingdao in Medicine and Engineering（医学与工程青岛重点实验室）
