MMAL is dedicated to providing a global stage for the dissemination of advancements in multimodal artificial intelligence, machine learning, and their interdisciplinary applications. It offers an excellent opportunity for researchers and industry professionals to exchange innovative ideas, share cutting-edge findings, and discuss emerging trends in the field.

We invite all enthusiasts and experts to join us, contributing to the global dialogue that will shape the future of these crucial domains. Your participation will not only expand your knowledge but also help drive collective advancements toward a sustainable and technologically empowered future.

Potential topics include, but are not limited to:
Foundations and Theories of Multimodal AI
Multimodal representation learning and cross-modal alignment
Unified multimodal modeling theories and architectures
Pretraining and post-training methods for multimodal large models
World models and physical law learning
Self-supervised, semi-supervised, and few-shot multimodal learning
Explainable and trustworthy multimodal learning
Robust learning and uncertainty modeling
Cross-modal generation and reasoning
Autonomous Agents, Multi-Agent Collaboration, and Human-AI Hybrid Decision-Making
Autonomous agent theory and self-evolution mechanisms
Multi-agent collaboration and swarm intelligence
Autonomous planning and scheduling in complex dynamic environments
Reinforcement learning and autonomous decision-making
Multimodal intention understanding and human-robot interaction
Human-AI hybrid augmented intelligence and human-in-the-loop mechanisms
Embodied AI and physical interaction
Safety, ethics, and alignment in human-AI collaboration
Multimodal AI System and Implementation Technologies
Architectural design and optimization of multimodal intelligent systems
Edge-cloud collaboration and engineering deployment
Model lightweighting, compression, and embedded optimization
Multimodal data governance, evaluation, and synthetic data
Digital twin and simulation verification technologies
Large-scale data processing and distributed learning
System reliability, robustness, and fault diagnosis
Privacy preservation and security protection technologies
Cutting-Edge Applications and Interdisciplinary Intersections of Multimodal AI
Multimodal AI for scientific discovery (AI4Science)
Multimodal learning in healthcare and bioinformatics
Cross-disciplinary multimodal data analysis
Industrial manufacturing and digital twin applications
Multimodal AI in low-resource and real-world scenarios
Cross-scenario transfer learning and domain adaptation
Societal impacts and responsible AI in multimodal systems
Intelligent decision support and industry applications

Important Dates
Submission Deadline: October 15, 2026
Acceptance Deadline: November 15, 2026
Registration Deadline: December 15, 2026