# Toward a Fully Autonomous, AI-Native Particle Accelerator

arXiv: https://arxiv.org/abs/2602.17536 · HTML: https://mlsanity.com/paper/2602.17536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory](https://mlsanity.com/paper/2006.06562/) (2020)
- [Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators](https://mlsanity.com/paper/2007.03555/) (2020)
- [Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.08141/) (2020)
- [Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations](https://mlsanity.com/paper/2401.05815/) (2024)
- [AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation](https://mlsanity.com/paper/2504.18328/) (2025)
- [Osprey: Production-Ready Agentic AI for Safety-Critical Control Systems](https://mlsanity.com/paper/2508.15066/) (2025)
- [Rethinking Certification for Higher Trust and Ethical Safeguarding of Autonomous Systems](https://mlsanity.com/paper/2303.09388/) (2023)
- [Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time](https://mlsanity.com/paper/2601.08910/) (2026)
- [Outlook Towards Deployable Continual Learning for Particle Accelerators](https://mlsanity.com/paper/2504.03793/) (2025)
- [Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution](https://mlsanity.com/paper/2511.17850/) (2025)
- [From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure](https://mlsanity.com/paper/2512.18779/) (2025)

## Abstract (arXiv)

This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.