The Agentic AI Kerr Lab: LLM Agents for Kerr Microscopy Experiments with Magnetic Skyrmions
Abstract
Magnetic skyrmions are topological spin textures that can be observed directly by magnetic microscopy and driven by fields and currents, which makes them a model system for fundamental physics and candidates for spintronic devices. Magneto-optical Kerr microscopy is a method to image them in real time; however, most experiments use an inefficient cycle of consecutive imaging, analysis and excitation in which every step requires the judgement of the experimentalist operating the setup. Here we present the Agentic AI Kerr Lab (AgentKerr), in which an agent based on a large language model operates this cycle automatically and efficiently on a Kerr microscope. Rather than relying on pattern detection by a language model, a convolutional neural network trained on Kerr images turns live images into skyrmion observables such as position, size and shape, on which the agent bases its decisions. The architecture lets agents operate the instruments safely, as every hardware action is checked deterministically against an action policy, independently of the agent that issues it. With no procedure given, the agent determines by itself how to create, tune and move skyrmions, and writes its own analyses. The approach can be transferred to other spin textures and points towards magnetic imaging experiments in which the researcher poses the question and the agent finds the way to realize the experiments that yield the answer.
Related
- Derivation of the Thiele equation for magnetic skyrmions using the Mori-Zwanzig formalism
- Uniqueness and radial symmetry of magnetic skyrmions
- Spin-Orbit-Torque-Driven Recurrent Motion of Synthetic Antiferromagnetic Skyrmions for Magnetic Clocking
- Observation of Magnetic-Anisotropy Crossover and High-Temperature Skyrmions in the Dirac Magnet Fe3Ge with a Distorted Kagome Lattice
- AI-Assisted Identification of Magnetic Orders and Skyrmions
BibTeX
@misc{leutner2026agentic,
title = {{The Agentic AI Kerr Lab: LLM Agents for Kerr Microscopy Experiments with Magnetic Skyrmions}},
author = {Kilian Leutner and Yuean Zhou and Aditya Kumar and Thomas B. Winkler and Robert Frömter and Hans Fangohr and Mathias Kläui},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00299},
eprint = {2611.00299},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00299},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Agentic AI Kerr Lab: LLM Agents for Kerr Microscopy Experiments with Magnetic Skyrmions
AU - Kilian Leutner
AU - Yuean Zhou
AU - Aditya Kumar
AU - Thomas B. Winkler
AU - Robert Frömter
AU - Hans Fangohr
AU - Mathias Kläui
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00299
UR - https://arxiv.org/abs/2611.00299
AB - Magnetic skyrmions are topological spin textures that can be observed directly by magnetic microscopy and driven by fields and currents, which makes them a model system for fundamental physics and candidates for spintronic devices. Magneto-optical Kerr microscopy is a method to image them in real time; however, most experiments use an inefficient cycle of consecutive imaging, analysis and excitation in which every step requires the judgement of the experimentalist operating the setup. Here we present the Agentic AI Kerr Lab (AgentKerr), in which an agent based on a large language model operates this cycle automatically and efficiently on a Kerr microscope. Rather than relying on pattern detection by a language model, a convolutional neural network trained on Kerr images turns live images into skyrmion observables such as position, size and shape, on which the agent bases its decisions. The architecture lets agents operate the instruments safely, as every hardware action is checked deterministically against an action policy, independently of the agent that issues it. With no procedure given, the agent determines by itself how to create, tune and move skyrmions, and writes its own analyses. The approach can be transferred to other spin textures and points towards magnetic imaging experiments in which the researcher poses the question and the agent finds the way to realize the experiments that yield the answer.
ER -