The International Conference on Complex Systems and Intelligent Computing (ICCSIC 2026) aims to bring together leading researchers, practitioners, and industry experts to explore cutting-edge developments in intelligent systems and their applications to complex, real-world problems. As artificial intelligence evolves beyond task-specific solutions, the first edition of ICCSIC focuses on scalable, trustworthy, and sustainable approaches to AI across diverse domains.

ICCSIC provides a platform for interdisciplinary exchange on emerging paradigms such as Large Language Models (LLMs), graph-based reasoning, multimodal intelligence, and neuromorphic computing, while also addressing critical dimensions like security, explainability, robustness, and green AI. Special emphasis is placed on systems-level challenges involving heterogeneous data, dynamic environments, and resource-constrained settings.

This inaugural edition invites contributions that advance theory, algorithms, and real-world applications in areas including, but not limited to, foundational AI models, adaptive learning architectures, adversarial robustness, and intelligent decision-making in complex ecosystems.


Conference Tracks

Track 1: Advanced Architectures and Foundation Models

This track highlights recent advances in the design and training of foundational and specialized neural architectures that power next-generation AI systems. It covers scalable, efficient, and biologically inspired models driving breakthroughs across modalities and tasks.

Track 2: Multimodal and Cross-modal Intelligence

This track explores the integration of multiple data modalities (such as text, images, audio, and sensor data) to build intelligent systems that can understand and generate rich, coherent representations across different data sources. The track highlights advancements in learning joint representations, reasoning, and generation across diverse modalities.

Track 3: AI Security, Privacy, and Adversarial Defenses

This track explores the critical challenges of securing AI systems, safeguarding user privacy, and defending against adversarial attacks. Topics include methods for making AI more resilient to malicious manipulation and ensuring that sensitive data used by AI systems remains protected.

Track 4: Efficient, Green, and Decentralized AI Systems

This track explores innovations aimed at optimizing the efficiency, scalability, and environmental sustainability of intelligent systems. It highlights cutting-edge techniques in compressing models, accelerating inference, and deploying AI across distributed and resource-constrained environments.

Track 5: Explainable AI, Causal Inference, and Model Transparency

This track addresses the growing demand for AI systems that are interpretable, transparent, and aligned with human understanding, particularly in high-stakes and regulated domains. It emphasizes the emerging convergence of explainability, causal reasoning, and responsible AI, pushing beyond post-hoc interpretation toward models that are inherently understandable and trustworthy by design.

Track 6: Applications of Intelligent Systems in Complex Environments

This track focuses on the application of advanced intelligent systems across various domains, particularly those involving complex, dynamic, and interconnected environments. Topics include AI's role in tackling global challenges, optimizing systems, and enhancing decision-making in complex settings.