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@INPROCEEDINGS{Mayet:637851,
author = {Mayet, Frank and Hellert, Thorsten and Sulc, Antonin and
Tennant, Christopher},
title = {{T}oward {P}article {A}ccelerator {M}achine {S}tate
{E}mbeddings as a {M}odality for {L}arge {L}anguage
{M}odels},
reportid = {PUBDB-2025-03902},
pages = {965-970},
year = {2025},
abstract = {Understanding and diagnosing the state of a particle
accelerator requires navigating high-dimensional control
system data, often involving hundreds of interdependent
parameters. We propose a novel multimodal embedding
framework that jointly learns representations of machine
states from both numerical control system readouts and
natural language descriptions. This enables the translation
of complex machine conditions into human-readable summaries
while maintaining fidelity to the underlying physical
system. The obtained embeddings are subsequently adapted to
an open-weights large language model via cross-attention
conditioning. We demonstrate a first implementation trained
on European XFEL machine state data. This work covers the
embedding model architecture, training methodology, and
presents initial examples demonstrating the model's
capabilities in action. Due to the general concept of
machine state, the model can be easily adapted to other
facilities and control system environments.},
month = {Sep},
date = {2025-09-20},
organization = {20th International Conference on
Accelerator and Large Experimental
Physics Control Systems, ICALEPCS,
Chicago (USA), 20 Sep 2025 - 26 Sep
2025},
cin = {MXL},
cid = {I:(DE-H253)MXL-20160301},
pnm = {6G13 - Accelerator of European XFEL (POF4-6G13) / 621 -
Accelerator Research and Development (POF4-621)},
pid = {G:(DE-HGF)POF4-6G13 / G:(DE-HGF)POF4-621},
experiment = {EXP:(DE-H253)XFEL(machine)-20150101},
typ = {PUB:(DE-HGF)8},
doi = {10.18429/JACoW-ICALEPCS2025-WEPD082},
url = {https://bib-pubdb1.desy.de/record/637851},
}