Paper Submission
Submit your original research to NGEN-AI 2025, the International Conference on Next Generation AI Systems, focusing on federated learning, large language models, generative AI, and agentic AI.
Submission Guidelines
Submitted papers (.pdf) must be prepared in English using the Springer Lecture Notes in Computer Science (LNCS) format.
Authors must use the latest LNCS LaTeX or Word templates provided by Springer and follow the conference proceedings guidelines for LNCS.
- Full research papers: max. 16 pages in LNCS style.
- Short / demo papers: max. 8 pages in LNCS style.
- Poster or early-idea papers: recommended up to 6 pages in LNCS style.
Submissions that do not follow the LNCS formatting and length requirements may be rejected without review.
Submissions to NGEN-AI 2025 must present original, unpublished work and must not be under review or accepted for publication elsewhere. Prior peer-reviewed publications cannot be resubmitted.
Springer enforces strict ethical and plagiarism policies. All prior work must be properly cited, and submissions may be subject to similarity checks.
Please submit with the full and final list of authors in the correct order. The author list is expected to remain unchanged after the submission deadline.
Carefully proofread your submission. Language should be clear and correct so it is easily understandable. Either US English or UK English spelling conventions are acceptable, but spelling and style should be consistent throughout the paper.
All papers that are accepted, registered, and presented will be published in a Springer proceedings series (LNCS/LNAI or related), subject to Springer’s publication policy and production requirements.
- ✔ Paper uses the Springer LNCS template; PDF generated.
- ✔ Keywords added and topics aligned with NGEN-AI tracks.
- ✔ Length matches the selected category (Full / Short / Poster).
- ✔ Original work; not under review or accepted elsewhere.
- ✔ Final author list (names & order) confirmed.
- ✔ Proofread (US/UK English); references complete.
- • Systems, architectures, and deployments are welcome.
- • Strong emphasis on trustworthy and responsible AI.
- • Applied case studies and industrial experiences encouraged.