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

arXiv: https://arxiv.org/abs/2607.26464 · HTML: https://mlsanity.com/paper/2607.26464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories](https://mlsanity.com/paper/2405.13930/) (2024)
- [NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science](https://mlsanity.com/paper/2304.13927/) (2023)
- [MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation](https://mlsanity.com/paper/2601.13232/) (2026)
- [ARES OS 2.0: An Orchestration Software Suite for Autonomous Experimentation Systems and Self-Driving Labs](https://mlsanity.com/paper/2604.03440/) (2026)

## 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.