Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Scientific experimentation and manufacturing rely on prolonged protocol development and complex, multi-step implementation, which require continuous human expertise for precise execution and decision-making, limiting interpretability and scalability. Here, we introduce human-artificial intelligence (AI) co-embodied intelligence, a new form of physical AI that unites human researchers, agentic AI, and wearable hardware. In this paradigm, humans provide precise execution, while agentic AI contributes contextual reasoning, adaptive planning, and analysis. The wearable interface continuously captures experimentation and manufacturing, facilitating seamless communication between humans and AI. We instantiate this paradigm in a microfabrication cleanroom, leading to the agentic-physical experimentation (APEX) system which understands fabrication procedure with accuracy 51% higher than state-of-the-art multimodal large language models/vision language models (LLMs/VLMs), detects and corrects fabrication errors in real-time, and transfers procedural expertise to novice users. Critically, APEX system enables the co-development of fabrication protocols in cleanrooms, overcoming the incompatibility of elastomeric materials in standard microfabrication processes and enabling previously unattainable fabrication outcomes, as demonstrated by the wafer-scale realization of brain-level soft neural probe capable of single-unit-resolution neural recording. These results establish the human-AI co-embodied intelligence that extends agentic reasoning beyond computation into the physical domain, transforming scientific experimentation and manufacturing into autonomous, traceable, interpretable and scalable processes.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 48 references that are in the map. First posted on arXiv 3 Nov 2025.
Builds on
13 of 48 references.
References · 13
- 2023GPT-4 Technical Reportcited by 18,132
- 2020Shortcut Learning in Deep Neural Networkscited by 1,928
- 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,842
- 2022Competition-Level Code Generation with AlphaCodecited by 1,427
- 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 732
- 2024A Survey on Large Language Models for Code Generationcited by 646
- 2021CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Taskscited by 564
- 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 405
- 2023Self-collaboration Code Generation via ChatGPTcited by 255
- 2024Empowering Biomedical Discovery with AI Agentscited by 117
- 2024ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterizationcited by 60
- 2024CRISPR-GPT for Agentic Automation of Gene-editing Experimentscited by 56
1 more
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI
- 2024FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration
- 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI
- 2025Virtuous Machines: Towards Artificial General Science
- 2024MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
- 2025Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
- 2026Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
- 2025RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration