Call For Papers 2026
The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026) is an interdisciplinary conference that brings together researchers in deep learning, generative AI, core machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:
- Computer vision
- Language and multimodal language models
- Robotics, embodied systems, and engineering
- AI/ML for physical sciences
- AI/ML for health and biotechnology
- AI/ML for sustainability
- AI/ML for social sciences
- AI/ML for creatives
- Neuroscience and cognitive science
- Socio-technical aspects of AI
- Human interaction in AI systems
- Decision-making, reinforcement learning, and control
- Generalization and multi-task learning
- Optimization
- Probabilistic methods
- AI and network science
- Data-centric aspects of AI
- SysML Infrastructure
- Theory
- Deep learning
- General machine learning: core contributions in supervised and unsupervised methods
Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories. We also encourage in-depth analysis of existing methods that provide new insights in terms of their limitations or behavior beyond the scope of the original work.
Key dates:
- Abstract submission deadline: May 4, 2026 (AOE). All authors must have an OpenReview profile when submitting
- Full paper submission deadline, including all supplementary materials: May 6, 2026 AOE
- Author notification: September 24, 2026 AoE
Key links:
- Paper template:
- Submission link:
- How to choose your track (main, evaluations and benchmarks, position papers): Decision Tree. Note that different tracks have different calls for papers, different submission portals, and possibly different deadlines.
- MainTrackHandbook, including our policies regarding reciprocal reviewing, dual submissions and LLMs: