In the rapidly evolving landscape of AI, the development of adaptive foundation models represents a ground-breaking shift towards AI systems that can continually learn, adapt, and evolve in response to new information, changing environments, and user preferences. This workshop aims to explore cutting-edge advancements in adaptive foundation models, focusing on methodologies that enable continual weight updates, memory-efficient fine-tuning, and personalized adaptation to diverse tasks and domains. We feature invited talks by experts in LLMs, diffusion models, multimodal learning, continual learning, and efficient ML to explore this interdisciplinary topic. We host workshop paper submissions and invite oral papers for contributed talks. In addition, there is a panel discussion with the invited speakers.