% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Mayet:637851,
      author       = {Mayet, Frank and Tennant, Chris and Sulc, Antonin and
                      Tennant, C.},
      title        = {{T}oward particle accelerator machine state embeddings as a
                      modality for large language models},
      journal      = {2226-0358},
      address      = {Geneva},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2025-03902},
      isbn         = {978-3-95450-255-4},
      pages        = {1233 - 1238},
      year         = {2025},
      note         = {Literaturangaben;},
      comment      = {International Conference on Accelerator and Large
                      Experimental Physics Control Systems : Proceedings, 20th
                      conference, ICALEPCS, Chicago, USA, 20.-26.09.2025},
      booktitle     = {International Conference on
                       Accelerator and Large Experimental
                       Physics Control Systems : Proceedings,
                       20th conference, ICALEPCS, Chicago,
                       USA, 20.-26.09.2025},
      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  = {International Conference on
                       Accelerator and Large Experimental
                       Physics Control Systems, Chicago (USA),
                       20 Sep 2025 - 26 Sep 2025},
      keywords     = {Accelerator Physics (Other) / MC13 - MC13: Artificial
                      Intelligence $\&$ Machine Learning (Other)},
      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)16 / PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACoW-ICALEPCS2025-WEPD082},
      url          = {https://bib-pubdb1.desy.de/record/637851},
}