Towards Real-World Multimodal Intelligence: From Understanding to Reasoning and Action
Year: 2026
Type: other
Abstract: Recent advances in multimodal foundation models have significantly improved the ability of AI systems to perceive and interpret visual information, yet real-world intelligence requires more than passive understanding. It demands the ability to reason over complex multimodal inputs, actively acquire missing information, and take actions toward open-ended goals. This project, Towards Real-World Multimodal Intelligence: From Understanding to Reasoning and Action, aims to develop multimodal intelligence along this progression. We first study multimodal understanding and reasoning, with works such as Video-R1 and OneThinker, which enhance models’ capabilities to reason over rich visual and video inputs and solve complex multimodal problems. Building on these foundations, we further move from passive perception and reasoning toward multimodal agents that can interact with external environments and tools. In Gen-Searcher, we explore an agentic framework in which a multimodal model can actively search for relevant visual knowledge, reason over retrieved evidence, and use it to improve downstream image generation. Together, these efforts establish a unified research direction from understanding, to reasoning, and ultimately to action, with the broader goal of enabling multimodal AI systems that can perceive, think, acquire knowledge, and act effectively in complex real-world environments.
Source: California Digital Library
DOI: 10.48321/d114af59a3
Author: Kaituo Feng
Institution: Chinese University of Hong Kong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204439742