Submission details
Reviews will be single-blind. All submissions should include the authors' names and their
affiliations. All contributions should be written in English, using the Springer CCIS/LNCS
format and submitted electronically through OpenReviews. Submission implies the willingness of at least one author to register for BNAIC/BeNeLearn 2026 and present in person at the conference.
Please submit a single self-contained PDF file. For full and short submissions, please include links to any code/dataset you use. If you need to submit additional files or additional information that might be
important for reviewers to make a decision, you should prepare a repository (e.g., Github, Gitlab) with these files and specify in "Additional files" the link to the repository.
Please include any relevant information needed to use them.
Authors of selected FULL papers from the AI4DT workshop will be invited to submit extended versions to Digital Twins and Applications (IET/Wiley), subject to the journal's standard peer review process.
We will accept the following submission types:
Full Submissions (max. 12 pages, not considering refs):
- Original full papers: This must be unpublished work
- Transfers from related conferences: Papers submitted to other venues that
have not been accepted. Please attach the PDF of your previous submission, the reviews you
received, and a list of changes made to the current submission
Research Highlights (1-2 pages, not considering refs):
- This format is intended for PhD students, early-career postdoctoral researchers,
and industry practitioners to present work that may not yet qualify for a full paper category but
that represents an innovative idea or ongoing work.
Authors are encouraged to review the Preprint Policy of the Digital Twins and Applications Journal prior to submission.
We also encourage authors to make presentation handouts available to the community, either via their personal webpages or through the workshop webpage, subject to prior agreement with the organizers.
Generative AI Usage Disclosure
Authors. Authors must disclose any use of generative AI tools in the preparation of
their submission, including but not limited to generating, drafting, editing, or refining text,
figures, code, or results. Disclosure should specify which tools were used and for what purpose.
Authors remain fully responsible for the entire content of their submission, including any portion
produced with the assistance of such tools, and for ensuring its accuracy, originality, and
compliance with the conference's ethical and plagiarism standards.
Reviewers. The use of generative AI tools to generate or review submissions is not encouraged.
Reviewers who choose to use such tools do so at their own responsibility and must ensure that any
generated output is accurate and consistent with the content of the reviewed paper. Reviewers must not
upload, paste, or otherwise submit any part of a manuscript or its confidential content to external AI
tools, as this may compromise the confidentiality of the peer-review process.
