Vision Language Models: Challenges of Real World Deployment

Vision language models (VLMs) have demonstrated remarkable capabilities in integrating visual perception with natural language understanding, powering applications such as multimodal assistants, robotics, autonomous systems, and accessibility tools. However, their real-world deployment faces significant challenges in efficiency, scalability, and reliability. This workshop will bring together researchers and practitioners from academia and industry to highlight cutting-edge research, systems-level optimizations, and evaluation methodologies that are often overlooked yet pivotal for robust real-world integration. Efficiency, robustness, and reliability will be emphasized as core design principles, essential to advancing VLMs from experimental systems to dependable deployed technologies. By convening researchers at the intersection of multimodal learning, efficient inference and training, robustness and uncertainty estimation, and large-scale systems design, the workshop aims to establish concrete pathways toward building VLMs that can operate reliably under practical constraints. We hope this workshop will serve as a venue for exchanging insights on model design, efficiency techniques, and robustness evaluation that bridge the gap between research and real-world systems.