Important dates
(All deadlines are at 23:59, Anywhere on Earth time zone):
- Submission Open: June 2025.
- Abstract Paper Submission Deadline: 1 September 2025.
- Full Paper Submission Deadline: 8 September 2025.
- Notification of Acceptance: 6 October 2025.
- Late Breaking Poster Submission Deadline: 13 October 2025.
- Camera Ready Submission Deadline: 27 October 2025
- Conference: 19-21 November 2025
Submission information
Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning. Additionally, high-quality research results already published in international AI/ML conference proceedings or journals are also welcome for presentation at the conference, and will be published as extended abstracts. Four types of submissions are invited:
- Type A: Regular papers Papers presenting original work that advances Artificial Intelligence and Machine Learning. Position and review papers are also welcome. These contributions should address a well- developed body of research, an important new area, or a promising new topic, and provide a big picture view. Type A papers can be long (up to 15 pages, excluding references and appendices) or short (at most 10 pages, excluding references and appendices). Contributions will be reviewed on the basis of their overall quality and relevance.
- Type B: Encore abstracts Abstracts of work published (or accepted) in an international conference or journal relating to AI/ML and closely related fields. These should have been accepted on or after 1st September 2025. Authors are invited to submit the authors’ version of their officially published paper together with an abstract of at most 2 pages (excluding references). Authors are encouraged to include further results obtained after the publication in their abstract and presentation. Submissions will be judged based on their relevance to the conference. Authors may submit at most one type B paper of which they are the corresponding author.
- Type C: Demonstration abstracts Proposals for demos should be submitted as a 2-page (excluding references) abstract. Demonstrations should also submit a short video illustrating the working of the system (not exceeding 15 minutes). Any system requirements should be mentioned in the submission. Demonstrations will be evaluated based on their originality and innovative character, the technology deployed, the purpose of the systems in interaction with users and/or other systems, and their economic and/or societal potential.
- Type D: Thesis abstracts Bachelor and Master students are invited to submit a 2-page abstract (excluding references) of their completed AI/ML-related thesis. Supervisors should be listed. The thesis should have been accepted on or after 1st September 2025. Submissions will be judged based on their originality and relevance to the conference.
- Type E: Late breaking abstracts. Original and ongoing AI/ML-related work can be submitted as a Late Breaking abstract of 2-pages (excluding references). These late-breaking abstracts will not be selected for oral presentations.
Reviews will be single-blind. All submissions should include author names and their affiliations.
Presentation and prizes
Type A, B and D papers can be accepted for either oral or poster presentation. There will be prizes for the best paper (type A), best demonstration (type C), and best bachelor's or master's thesis (type D).
Pre- & Post-proceedings
Accepted contributions in all four categories will be included in the (non-archival) conference pre- proceedings, published open access in the form of CEUR workshop proceedings. All contributions should be written in English, using the Springer CCIS/LNCS format and submitted electronically through OpenReview. Submission implies willingness of at least one author to register for BNAIC/BeNeLearn 2025 and present in person at the conference. For each paper, at least one author must register for the conference by the early registration deadline. Similar to previous years, we plan to organize post-proceedings in the Springer CCIS series. A selection of type A papers will be invited to submit to the post-proceedings.
Topics of interest
We invite contributions on any topic in the broad area of Artificial Intelligence and Machine Learning. In addition to fundamental work we encourage cross-domain and interdisciplinary work, as well as application of AI or ML-based techniques. A non-exhaustive list of topics includes:
AI methodology keywords
- Bayesian Learning
- Case-based Learning
- Causal Learning
- Clustering
- Computational Creativity
- Computational Learning Theory
- Computational Models of Human Learning
- Data Mining & Knowledge Discovery
- Data Visualisation
- Deep Learning
- Dimensionality Reduction
- Ensemble Methods
- Evaluation Frameworks
- Evolutionary Computation
- Graph Mining & Social Network Analysis
- Inductive Logic Programming
- Interactive AI / Human-in-the-loop Methods and Systems
- Kernel Methods
- Knowledge Representation and Reasoning
- Learning and Ubiquitous Computing
- Learning in Multi-Agent Systems
- Learning from Big Data
- Learning from User Interactions
- Logics and normative systems
- Media Mining and Text Analytics
- Natural Language Processing / Natural Language Understanding
- Online Learning
- Pattern Mining
- Ranking / Preference Learning / Information Retrieval
- Reinforcement Learning
- Representation Learning
- Robot Learning
- Social Networks
- Speech Recognition
- Structured Output Learning
- Time series modeling & prediction
- Transfer and Adversarial Learning
The impact area of research
- AI and Law
- AI and Ethics
- Bioinformatics, genomics and biomedical
- AI and Economics (game theory)
- AI and Educational science
- Fundamental research in AI
- Human-centered AI
- Medical imaging
- AI and Neuroscience
- AI and Physics (complex systems)
- Scientific Machine Learning
- AI Applications in Industry
- Ai for Scientific Discovery
- AI and Social sciences
- Robotics
- Ai and Gaming
- AI and Entertaining
- AI and Agriculture
- AI and Finance
- AI and Transport
- AI and Automotive
- AI and Social Media
- Data Security
- Healthcare
- E-Commerce
- AI and Art
- AI and Astronomy