Call for Papers
Accepted papers will be presented in either a poster session or an oral session.
Important Dates
All dates have deadlines 11:59 pm AoE (Anywhere on Earth)
- Paper submission deadline:
May 27th, 2025May 29th, 2025 The deadline has been extended by two days. - Notification of acceptance:
June 16th, 2025June 11th, 2025 - Camera-ready deadline: June 30th, 2025
Submission Instructions
Authors are invited to submit a short abstract of new work or a re-focused write-up of recently published work. To submit your work, please visit the OpenReview.
- Please format your submission according to the ICML style.
- Submissions must be 8 pages maximum, not including references.
- Submissions may include unlimited appendices after the reference section. In addition, a zip file of up to 50MB can be submitted as supplemental material.
- Submissions are not anonymized and should include author names and affiliations. That is, please change \usepackage{icml2025} in the template to \usepackage[accepted]{icml2025}. This will display the author names.
Submissions will undergo a lightweight review process and will be judged on originality, relevance, interest, and clarity. This workshop aims to provide a forum for practitioners to discuss topics related to Vector DBs, and we also encourage the submission of technical articles (white papers) on Vector DB systems and ANN libraries.
The workshop will not have formal proceedings and is not intended to preclude later publication at another venue. We encourage in-person attendance at the workshop; however, we will consider appropriate measures for those who cannot attend due to visa issues or other circumstances.
Topics
We welcome submissions of papers on all aspects of vector databases, including but not limited to:
- Retrieval-augmented generation (RAG)
- Algorithms and data structures for approximate nearest neighbor search (ANN)
- Data management systems for handling vector data
- Query languages
- Embedding models
- Applications of vector databases
- Performance evaluation
- Cross-modal retrieval
- Recommendation
- Privacy, fairness, and security