mlsanityData to 7 Oct 2026

PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories

arXiv 2607.26464 ↗First posted 29 Jul 2026cond-mat.mtrl-sci cs.AIcited by 0

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Abstract (arXiv)

Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (GUI) orchestration layer, PUDA creates a command-line runtime environment that lets agents observe, orient, decide, and act over experiments while hardware execution remains deterministic, atomic, and auditable. Headless by design, devices appear through discoverable command-line interfaces, JSON protocols are routed through a distributed messaging system, and command responses, data products, and reports are preserved as structured records. PUDA organizes protocols, runs, samples, measurements, and command logs into an AI-native data structure linked by run identifiers and timestamps, preserving provenance from submitted protocol through hardware response to resulting data products. PUDA separates scientific orchestration from physical operation and data telemetry: agents choose experiments, while PUDA executes validated commands and captures provenance-linked state, responses, and data. The contribution is not another optimizer, orchestrator, or recipe language. It is a practical execution and data environment for agentic SDLs; the broader physical AI implication is that PUDA provides an AI-native hardware harness for AI systems to interact with physical tools.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 32 references that are in the map. First posted on arXiv 29 Jul 2026.

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6 of 32 references.

References · 6

  1. 2025Accelerating scientific discovery with Co-Scientistcited by 353
  2. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  3. 2024AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratoriescited by 10
  4. 2023NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials sciencecited by 3
  5. 2026MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automationcited by 3
  6. 2026ARES OS 2.0: An Orchestration Software Suite for Autonomous Experimentation Systems and Self-Driving Labscited by 1

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  1. 2026Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories
  2. 2026NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
  3. 2026Agentic AI for Self-Driving Laboratories in Soft Matter: Taxonomy, Benchmarks,and Open Challenges
  4. 2025The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories
  5. 2026Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts
  6. 2026Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer
  7. 2026LAP: An Agent-to-Instrument Protocol for Autonomous Science
  8. 2026Beyond Executable Models: The Pufibara Agent Harness and the Modelica Agent Workflow Benchmark for Physical System Modeling