[R] Will NeurIPS 2025 proceedings ever get published?
Camera-ready papers submitted in October still unpublished, raising questions about the flagship AI conference's timeline.
The Neural Information Processing Systems (NeurIPS) conference, one of the most prestigious venues in artificial intelligence and machine learning, is facing an unexpected publication delay for its 2025 proceedings. Researchers who submitted their camera-ready papers in October, following the standard peer-review and revision cycle, have been waiting for official publication on the conference's papers.nips.cc website. This delay is unusual for NeurIPS, which typically maintains a predictable schedule, and has led to growing concern and speculation within the academic community about potential administrative or technical hurdles causing the holdup.
The delay has significant practical implications for the AI research ecosystem. Many early-career researchers and PhD students depend on timely publication to bolster their academic records for job applications and tenure considerations. Furthermore, the official proceedings serve as the canonical reference for citations, and delays can slow the dissemination of new techniques in fields like large language models (LLMs), computer vision, and reinforcement learning. While the exact cause remains unconfirmed, the situation highlights the critical infrastructure role major conferences play and the community's reliance on their operational consistency. Researchers are awaiting an official communication from the NeurIPS organizing committee regarding the revised publication timeline.
- Camera-ready papers for NeurIPS 2025 were submitted by authors in October 2024, per the standard deadline.
- The official proceedings website (papers.nips.cc) has not yet published the accepted papers, causing an atypical delay.
- The holdup impacts researchers needing official citations for academic progress, job markets, and establishing priority for new AI work.
Why It Matters
Delays in premier conference proceedings disrupt academic careers, slow research dissemination, and impact the entire AI innovation pipeline.