We present eMEM (Embodied Memory), a hybrid graph-based memory system for embodied agents operating in physical environments. Current agent memory architectures, such as Generative Agents, MemGPT, and A-MEM, treat memory as text streams or knowledge graphs, but embodied agents require memory that is simultaneously searchable by meaning, space, and time. eMEM fills this gap with a multi-index architecture (SQLITE for structured storage, hnswlib for approximate nearest neighbour semantic search, and an R-tree for spatial queries) unified behind a single graph model. A tiered consolidation pipeline transforms raw perceptual observations into compressed summaries, mirroring hippocampal-neocortical consolidation in biological systems. Ten agent-facing recall tools expose memory retrieval primitives, including concept-to-location resolution and cross layer recall, as first-class operations for LLM tool calling. The system is fully embedded and runs in-process alongside the agent. In addition we introduce eMEM-Bench v1, a benchmark we construct over ProcTHOR-10K scenes for embodied memory evaluation. The benchmark is organised explicitly around eight cognitive-psychology paradigms (DRM lures, pattern separation, pattern completion, source monitoring, context-dependent retrieval, long-horizon interference, serial position, and a foil augmented retention curve), each chosen so that the result is interpretable against the broader memory-systems literature in humans and prior agent-memory systems; a level of diagnostic that surface-task benchmarks like LoCoMo or OpenEQA cannot provide. eMEM scores 80.8 weighted mean over 988 probes, with a flat retention curve at ceiling from 1 h to 1 yr of simulated delay on room-unique items. We show that a pure RAG baseline (the flat_rag ablation) loses 30 pt on context dependent retrieval and 29 pt on DRM lure rejection, isolating the contribution of multi-layer storage and consolidation respectively. We release both the system and the benchmark code.
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
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）
SNOW: Spatio-Temporal Scene Understanding with World Knowledge for Open-World Embodied Reasoning
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃斯林根应用科学大学）；Dr. Ing. h.c. F. Porsche AG（德意志联邦汽车工业联合会）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
History-Conditioned Spatio-Temporal Visual Token Pruning for Efficient Vision-Language Navigation
Department of Computer and Information Sciences at the University of Delaware（德克萨斯大学达勒姆分校计算机与信息科学系）；University of Maryland’s Department of Computer Science（马里兰大学计算机科学系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement
GigaAI Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所GigaAI研究院）；Beijing Institute of Technology（北京理工大学）；GigaAI（极佳科技）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃斯林根应用科学大学）；Dr. Ing. h.c. F. Porsche AG（德意志联邦汽车工业协会）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics
Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；YXGN Robotics（YXGN机器人）；China University of Mining and Technology（中国矿业大学）；Shenzhen Technology University（深圳技术大学）；Huazhong University of Science and Technology（华中科技大学）；Xiamen University（厦门大学）
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Beijing Institute of Technology（北京理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
AirScape: An Aerial Generative World Model with Motion Controllability
Shenzhen International Graduate School, Tsinghua University Pengcheng Laboratory Shenzhen China；School of Computer Science \& Technology, Beijing Institute of Technology Beijing China；Department of Automation, Tsinghua University Beijing China；Communication Engineering College, Northeastern University Qinhuangdao China；Shenzhen International Graduate School, Tsinghua University Shenzhen China；Department of Electronic Engineering, Tsinghua University Beijing China；BNRist, Tsinghua University Beijing China；Department of Electronic Engineering, Tsinghua University BNRist, Tsinghua University Beijing China；Shenzhen International Graduate School, Tsinghua University Pengcheng Laboratory；School of Computer Science \& Technology, Beijing Institute of Technology；Department of Automation, Tsinghua University；Communication Engineering College, Northeastern University；Shenzhen International Graduate School, Tsinghua University；Department of Electronic Engineering, Tsinghua University；BNRist, Tsinghua University；Department of Electronic Engineering, Tsinghua University BNRist, Tsinghua University
