ACM AI Summit – Author Track (Inaugural Edition)

The inaugural ACM AI Summit invites submissions to its Author Track, showcasing cutting-edge advances and forward-looking ideas in artificial intelligence. This track brings together researchers and practitioners from academia and industry to highlight recent breakthroughs, emerging methodologies, and visionary perspectives that will shape the future of AI.

Submission Categories

We solicit two types of contributions:

1. Breakthrough “impact” Highlights

Papers in this category should present recent, significant advances in artificial intelligence, including novel methods, systems, or applications. Submissions should clearly articulate the core innovation, its technical contributions, empirical or theoretical impact, and broader implications for the AI community.

2. Visionary Papers

This category invites forward-looking perspectives that identify emerging challenges, opportunities, and future research directions in AI. Visionary papers should provide insightful analysis, propose new paradigms or frameworks, and aim to inspire transformative research agendas.

Submission Guidelines

• Each author may submit up to 1 paper in the Breakthrough Highlights category and 1 paper in the Visionary category.
• All submissions must be 4 pages in length (excluding references) and follow the ACM format.
• Submissions will be evaluated based on originality, significance, clarity, and potential impact.
• Accepted papers will be presented as posters at the summit and published in the ACM AI Summit proceedings following the event.
• There is no article processing charge (APC) associated with publication.

Timeline

• Submission Deadline: June 30th 2026(tentative)
• Notification: July 30th 2026 (tentative)
• Camera-ready Deadline: TBD

Submission Site

https://easychair.org/conferences/?conf=acmaisummit20260

Topics of Interest

We welcome contributions across all areas of artificial intelligence, including but not limited to:

• Machine learning (supervised, unsupervised, reinforcement learning)
• Deep learningLarge language models and foundation models
• Multimodal mining and learning
• Natural language processing
• Computer vision
• Speech and audio processing
• Robotics and embodied AI
• Planning and reasoning
• Knowledge representation, discovery and reasoning
• Graph mining, learning and network analysis
• Causal inference and decision intelligence
• Trustworthy and responsible AI (fairness, robustness, interpretability, privacy, security and safety)
• Human-AI interaction and human-in-the-loop systems
• AI systems and infrastructure (efficient training, scaling, hardware–software co-design)
• Edge and distributed AI
• Federated learningContinual and lifelong learning
• Generative AI and creative AI
• Program synthesis and code generation
• Neurosymbolic AI and automated reasoning
• AI for science (biology, chemistry, materials, physics, climate)
• AI for health and medicineAI for engineering and design
• AI for social good and public policy
• Emerging AI systems (physical AI, digital twins, autonomous scientific discovery)
• Quantum machine learning
• Quantum computing for AI
• Quantum AI systems and algorithms
• Physics-informed machine learning and scientific modeling
• Physical AI and embodied physical intelligence systems