Foundation models have emerged as a transformative technology in AI, revolutionizing tasks across vision, language, and multimodal domains. These models, trained on massive datasets and capable of self-supervised learning, serve as a backbone for a wide array of applications, from content generation to complex decision-making systems. Despite remarkable practical results, many fundamental theoretical questions remain unanswered. There is an ongoing debate among researchers regarding the true potential and limitations of these models, with some claiming they approach general artificial intelligence (AGI) capabilities, while others remain skeptical due to the lack of robust theoretical understanding.
This workshop seeks to bring together leading experts from academia and industry to foster a deeper understanding of the theoretical foundations of foundation models while simultaneously addressing practical advances and real-world deployments. A particular emphasis will be also placed on understanding and improving the security and robustness of these models. Topics such as adversarial vulnerabilities, watermarking for copyright protection, and robustness to distributional shifts will be core aspects of the discussion. Given the increasing reliance on foundation models in critical applications, this workshop will also explore methods to secure these models against malicious attacks.
This workshop will feature interactive sessions with invited talks by world-class experts in theory, practice, and security of foundation models. It will provide a unique platform for researchers and practitioners to engage in discussions that shape the next generation of robust, secure, and theoretically sound foundation models.
