Embodied agents are expected to assist humans by actively exploring unknown environments and reasoning about spatial contexts. When deployed in real life, agents often face sequential tasks where each new task follows the completion of the previous one and may include infeasible objectives, such as searching for non-existent objects. However, most existing research focuses on isolated goals, overlooking the core challenge of sequential tasks: the ability to reuse spatial knowledge accumulated from previous explorations to guide subsequent reasoning and exploration. In this work, we investigate this underexplored yet practically significant embodied AI challenge. Specifically, we propose 3DSPMR, a 3D SPatial Memory Reasoning framework that utilizes Field-of-View (FoV) coverage as an explicit geometric prior. By integrating FoV-based constraints, 3DSPMR significantly enhances an agent's memory, reasoning, and exploration capabilities across sequential tasks. To facilitate research in this area, we further introduce SEER-Bench, a novel Sequential Embodied Exploration and Reasoning Benchmark that spans two foundational tasks: Embodied Question Answering (EQA) and Embodied Multi-modal Navigation (EMN). SEER-Bench uniquely incorporates both feasible and infeasible tasks to provide a rigorous and comprehensive evaluation of agent performance. Extensive experiments verify that 3DSPMR achieves substantial performance gains on both sequential EQA and EMN tasks.
ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning
School of Traffic and Transportation, Beijing Jiaotong University（交通与运输学院，北京交通大学）；Tencent Robotics X & Futian Laboratory（腾讯机器人X与福田实验室）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
NavBench: Probing Multimodal Large Language Models for Embodied Navigation
The University of Adelaide（阿德莱德大学）；The University of Queensland（昆士兰大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织数据61研究中心）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Tongji University（同济大学）
SELU: Self-Learning Embodied MLLMs in Unknown Environments
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）；School of Computer Science, Peking University（北京大学计算机学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration
Beijing University of Posts and Telecommunications（北京邮电大学）；QiYuanLab（启元实验室）；Tsinghua University（清华大学）；University of Electronic Science and Technology of China（电子科技大学）
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息多媒体国家重点实验室，计算机学院，北京大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Institute for Brain and Intelligence, Fudan University（脑与智能研究院，复旦大学）；University of Science and Technology Beijing（北京科技大学）；Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）
Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis
Northeastern University, Boston, MA, USA；The Chinese University of Hong Kong, Hong Kong, China；Peking University, Beijing, China；Westlake University, Hangzhou, China；Harvard University, Cambridge, MA, USA；Purdue University, West Lafayette, IN, USA；University of Oxford, Oxford, United Kingdom
ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；School of Materials Science and Engineering（材料科学与工程学院）；China Mobile Research Institute（中国移动研究院）；College of Computing and Data Science（计算与数据科学学院）
MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；School of Software, Beihang University（北京航空航天大学软件学院）；Tsinghua University（清华大学）；The University of Sydney（悉尼大学）
Think 360°: Evaluating the Width-centric Reasoning Capability of MLLMs Beyond Depth
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；NLPR&MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所NLPR&MAIS）；School of Advanced Interdisciplinary Science, University of Chinese Academy of Sciences（中国科学院大学交叉学科学院）；Zhongguancun Academy（中关村学院）
Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
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）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Wuhan AI Research（武汉人工智能研究院）；Institute of Microelectronics of the Chinese Academy of Sciences（中国科学院微电子研究所）
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
Beijing University of Posts and Telecommunications（北京邮电大学）；Tianjin University（天津大学）；South China Hospital, Medical School, Shenzhen University（深圳大学医学院南方医院）
DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs
Xiamen University（厦门大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；China University of Mining and Technology（中国矿业大学）；Shenzhen Technology University（深圳技术大学）
PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments
Beijing Jiaotong University（北京交通大学）；Tencent Robotics X & Futian Laboratory, Shenzhen（腾讯机器人X及福田实验室，深圳）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，中国科学院自动化研究所）；ObjectEye Inc（ObjectEye公司）
Grounding Everything in Tokens for Multimodal Large Language Models
MoE Key Lab of Artificial Intelligence（人工智能混合专家模型关键实验室）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）；Central Research Institute, Huawei（华为中央研究院）
ViC-Bench: Benchmarking Visual-Interleaved Chain-of-Thought Capability in MLLMs with Free-Style Intermediate State Representations
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；Meituan Inc.（美团公司）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；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）；Research Center for Space Computing System, Zhejiang Lab（浙江实验室空间计算系统研究中心）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Robotics and Automation, Nanjing University（南京大学机器人与自动化学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
ERMV: Editing 4D Robotic Multi-view images to enhance embodied agents
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China（自动化与智能感知学院，上海交通大学；系统控制与信息处理重点实验室，中华人民共和国教育部）
Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Tencent Youtu Lab（腾讯优图实验室）；Nanjing University（南京大学）
See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm
University College London（伦敦大学学院）；Imperial College London（伦敦帝国学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；ShanghaiTech University（上海科技大学）；Southern University of Science（南方科技大学）
