Teaching an LLM agent to fit XRR curves with X-Ray Calc 3
Abstract
The structure of a periodic multilayer X-ray mirror is obtained by fitting its X-ray reflectivity (XRR) curve, and the result depends on how the operator normalizes and trims the curve, frees parameters, and accepts a fit. The manual of the fitting program and the papers describing its engine leave these decisions to the operator, whose practice is tacit, so the fitting stays with the expert. To solve this problem, we proposed to develop a skill for a large language model (LLM) agent via elicitation: the expert's decisions were recorded during fitting and written as thirteen steps and a 22-item report template. The agent runs X-Ray Calc 3 through a Model Context Protocol (MCP) tool server. Fresh sessions, each given the skill, one curve, and a nominal design, were scored against fits the expert had withheld, under six tolerances fixed beforehand. The skill was developed on XRR curves of Co/C mirrors and of Ru/C mirrors from a public data deposit. The final version of the skill was tested on W/B4C multilayers. It was demonstrated that the skill recovered the mean period within 0.3 Å of the expert's fits and the period drift through the stack on both W/B4C specimens, and the W and B4C thicknesses within 1 Å on one of them.
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BibTeX
@misc{penkov2026teaching,
title = {{Teaching an LLM agent to fit XRR curves with X-Ray Calc 3}},
author = {Oleksiy V. Penkov and Jingjing Peng and Haoyu Fu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.28926},
eprint = {2609.28926},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28926},
}
RIS
TY - UNPB
TI - Teaching an LLM agent to fit XRR curves with X-Ray Calc 3
AU - Oleksiy V. Penkov
AU - Jingjing Peng
AU - Haoyu Fu
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.28926
UR - https://arxiv.org/abs/2609.28926
AB - The structure of a periodic multilayer X-ray mirror is obtained by fitting its X-ray reflectivity (XRR) curve, and the result depends on how the operator normalizes and trims the curve, frees parameters, and accepts a fit. The manual of the fitting program and the papers describing its engine leave these decisions to the operator, whose practice is tacit, so the fitting stays with the expert. To solve this problem, we proposed to develop a skill for a large language model (LLM) agent via elicitation: the expert's decisions were recorded during fitting and written as thirteen steps and a 22-item report template. The agent runs X-Ray Calc 3 through a Model Context Protocol (MCP) tool server. Fresh sessions, each given the skill, one curve, and a nominal design, were scored against fits the expert had withheld, under six tolerances fixed beforehand. The skill was developed on XRR curves of Co/C mirrors and of Ru/C mirrors from a public data deposit. The final version of the skill was tested on W/B4C multilayers. It was demonstrated that the skill recovered the mean period within 0.3 Å of the expert's fits and the period drift through the stack on both W/B4C specimens, and the W and B4C thicknesses within 1 Å on one of them.
ER -