13 papers
Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models
Shijun Shi, Zhe Liu, Zhi Hou +5
World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level instructions may leave the next…
LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making
Jingyuan Li, Yujie Liu, Yijie Dai +18
Laboratory experiments involve more than following predefined protocols; they require adaptive decision-making as the experimental state changes. Existing scientific benchmarks rar…
LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols
Jingyuan Li, Dongzhan Zhou, Minting Pan +10
Closing the loop of AI-driven science requires reliably translating natural-language protocols into executable laboratory workflows despite protocol defects and heterogeneous hardw…
SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control
Ruihua Han, Rui Gao, Zhe Liu +6
Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneous robots, sparse workspaces, s…
ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions
Zhe Liu, Jiaming Gu, Zhaohui Du +7
Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual parameters implicit, making them dif…
BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
Zhe Liu, Quan Lu, Zhaohui Du +7
Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments…
Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Zhe Liu, Huanbo Jin, Zhaohui Du +10
Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproduci…
ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
Brain Team, Ziyang Gong, Haoming Gu +28
Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve f…
IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking
Ruihua Han, Shuai Wang, Chengyang Li +8
Developing navigation policies requires simulation scenarios that support repeatable training and evaluation. Despite the availability of numerous simulators, constructing diverse…
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
From Passive Consumption to Active Interaction: Exploring Interactive LLM Scaffolding to Support Learning Engagement
Zixin Chen, Haotian Li, Zhe Liu +2
Large Language Models (LLMs) are increasingly used as learning companions, providing scaffolded explanations, hints, or step-by-step guidance. However, in current LLM-based learnin…
ACE-Brain-0: Spatial Intelligence as a Shared Scaffold for Universal Embodiments
Ziyang Gong, Zehang Luo, Anke Tang +21
Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and unmanned aerial vehicles (UAVs). However,…
Dr. Zero: Self-Evolving Search Agents without Training Data
Zhenrui Yue, Kartikeya Upasani, Xianjun Yang +5
As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm. This approach allows large language…