# Autonomous discovery of accelerator commissioning algorithms

arXiv: https://arxiv.org/abs/2608.07138 · HTML: https://mlsanity.com/paper/2608.07138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [GAIA: A General AI Assistant for Intelligent Accelerator Operations](https://mlsanity.com/paper/2405.01359/) (2024)
- [Osprey: Production-Ready Agentic AI for Safety-Critical Control Systems](https://mlsanity.com/paper/2508.15066/) (2025)

## Abstract (arXiv)

Simulated commissioning has become essential for de-risking modern light-source design and commissioning, but the procedures being simulated are still designed entirely by human experts. Their labor-intensive redevelopment after lattice changes makes such studies hard to repeat and limits their use during early design iteration. This Letter demonstrates a closed research loop in which a language-model agent writes commissioning code, tests it in simulation, and improves the algorithm from the results. Applied to RF beam capture in the ALS-U accumulator-ring model, the loop substantially improves a working expert procedure and can construct a working one from a minimal starting point, with more capable models succeeding from less initial code. Extending the same framework to multiple objectives produces 16 non-dominated algorithms spanning physically distinct trade-offs between rapid beam capture and correction of seeded machine errors. This reframes commissioning studies from evaluating human-designed procedures toward a mode in which agents participate directly in discovering accelerator algorithms.