ACMCV 2025

Vision-Language Models (VLMs) have demonstrated remarkable progress in natural image understanding and creative generation, yet their performance often falls short on enterprise-critical tasks such as document analysis, chart reasoning, workflow automation, and user interface navigation. In this talk, will be presented recent advances in adapting multimodal foundation models to enterprise applications, with a focus on text-rich visual understanding, document intelligence, and visual content–to–code generation. Also, will be introduced datasets and benchmarks such as BigDocs, BigCharts, StarFlow, and StarVector, designed to push VLMs toward real-world enterprise use cases. It will also be discussed AlignVLM, a robust architecture that bridges visual and textual representations to achieve competitive results on challenging document benchmarks. Finally, it will be highlighted how these models enable the next generation of AI agents—systems capable of reasoning, planning, and acting—by grounding natural language instructions in complex graphical user interfaces. Together, these directions illustrate a path toward enterprise-ready multimodal AI that is accurate, reliable, and adaptable.