This workshop aims to advance the frontier of multimodal AI systems that can effectively reason across specialized domains requiring extensive domain knowledge. Recent advancements in multimodal AI—combining information from text, images, audio, and structured data—have unlocked impressive capabilities in general-purpose reasoning. However, significant challenges persist when these systems encounter scenarios demanding deep domain expertise in fields such as medicine, engineering, and scientific research. Such contexts require expert-level perception and reasoning grounded in extensive subject knowledge, highlighting the need for specialized strategies to handle domain-specific complexity. Through invited talks, panel discussions, and interactive poster sessions, researchers and practitioners from diverse backgrounds will share the latest developments, ongoing hurdles, and promising future directions for knowledge-intensive multimodal reasoning. The workshop aims to foster collaboration and stimulate innovation towards the development of next-generation multimodal AI systems capable of reliable, transparent, and contextually grounded reasoning in specialized, high-stakes environments.