AXIOM 2026

Topics of Interest

We welcome submissions addressing theoretical, algorithmic, and systems aspects of efficient deep learning, including but not limited to:

• Predictive Theory for Efficient Learning: Scaling laws for efficient models, compute-optimal training and inference, predicting capability under resource constraints, optimization and learning dynamics, generalization under limited compute or data, phase transitions in efficient learning;
• Sparsity, Compression, and Model Structure: Foundations of pruning and quantization, sparse and modular neural networks, lottery tickets and subnetworks, adaptive computation, neural architecture design, representation learning for efficiency;
• Efficient Foundation Models: Efficient LLMs and multimodal models, efficient reasoning and adaptive inference, mixture-of-experts and modular architectures, test-time adaptation, memory-efficient training and inference, distillation and compression;
• Foundations and Future Directions: Theoretical limits of efficient AI, new efficiency metrics and benchmarks, predictive models of training dynamics, interpretability of efficient models, mathematical foundations of efficient deep learning, emerging theoretical paradigms for efficient AI.

Interdisciplinary work connecting theory, algorithms, systems, and hardware is particularly encouraged.

Paper Submission

We invite submissions presenting original research, preliminary results, novel ideas, and emerging research directions that advance the theoretical and practical foundations of efficient deep learning. We particularly encourage work that connects deep learning theory with efficient machine learning, including studies that improve our understanding of resource-efficient learning, reveal new theoretical principles, or bridge the gap between mathematical foundations and practical AI systems.

We invite short paper submissions (4 pages, excluding references) which follow the NeurIPS workshop formatting guidelines. This workshop is non-archival. Outstanding submissions will be invited for a 15-minute oral presentation. All accepted papers will be presented during the poster session and published on the workshop website. A Best Paper Award will be presented during the workshop.

Grand Challenges Track

Beyond research papers, AXIOM introduces a Grand Challenges track designed to identify the most important open questions for the future of efficient AI. Instead of reporting completed research, Grand Challenges submissions should articulate important unanswered questions, theoretical gaps, surprising empirical observations, or future research opportunities. We particularly encourage contributions on:

• Open theoretical questions motivated by efficient AI;
• Efficiency methods lacking theoretical explanations;
• Contradictions between theory and practice;
• Missing benchmarks, evaluation methodologies, or efficiency metrics.

Accepted submissions will be published on the workshop website and synthesized into a community report that will serve as the starting point for an interactive discussion session during the workshop. The insights from this discussion will contribute to a community position paper outlining a research agenda for predictive and efficient AI.

Grand Challenges submissions should consist of a one-page abstract plus references, with no appendix. They will undergo a light review and accepted submissions will be published on the workshop webpage.

Important Dates

Paper submission

August 29, 2026 (11:59 PM UTC-0)

Author notification

September 29, 2026 (AoE)

Camera-ready deadline

TBA

Submission Details

Submission site: OpenReview.

Submission format instructions:

• Papers: 4 pages excluding references and appendix, double-blind;
• Grand Challenges: 1 page excluding references, no appendix, double-blind.

Please use the NeurIPS 2026 LaTeX template. All papers will undergo peer review by the Program Committee.

Following the Guidance for NeurIPS Workshop Proposals 2026, workshop submissions must not duplicate work previously published at machine learning or related conferences. Work presented at the main NeurIPS conference must not also appear in the workshop, including as part of an invited talk. All authors, reviewers, presenters, and participants are expected to follow the NeurIPS Code of Conduct.

Submit to OpenReview