Invisible XML (ixml) is a language and process for identifying structure in documents. Invisible markup allows users to exploit the implicit structures in documents without the need for explicit markup. Since the release of the ixml standard in 2022, we have seen a steady increase in the use of ixml, as well as many interesting conference presentations about the language. The community group therefore feels the time is right for an event exclusively dedicated to invisible markup.
The Symposium will be held via Zoom on two afternoons (Western European time): Thursday/Friday 26/27 February 2026. The sessions will be four hours each day.
Attendance will be free.
Slides will be published on the ixml website after the Symposium, along with videos of the presentations if these have been made available.
Timeline
- Call for presentations open through 15 December 2025.
- Program announcement, and registration open, early January.
- Symposium 26/27 February.
Code of conduct
Call for presentations
We invite proposals from new users and experts alike. Share your experiences, techniques, ideas, implementations, use cases, grammars, and anything else ixml-related.
Presentations will be by Zoom. Timeslots are flexible, from 10 to 30 minutes in length, so each presenter can judge how long their talk requires.
Proposals should include a title, requested timeslot duration, and summary (100–200 words).
There is no requirement for a written paper. Presenters will be asked to submit a copy of their slides after the event. With presenter permission, talks will be recorded and the videos published.
You can submit your proposals to submit@invisiblexml.org, on or before 15 December, 2025.
Accepted presenters can contact the committee if they would like to discuss or ask for advice about any aspect of their presentation. Please contact the committee before 19 February to ensure that there is time for discussion before the symposium.
Presentations may not be written in whole or part by a large language model (LLM); authors who make use of an LLM or other generative AI tool must sign a statement that the tool has been trained on data acquired legally and with the informed consent of its original creators; please contact submit@invisiblexml.org if you have any queries about this policy.