Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything

The Agent-Omni framework presents a new method for multimodal reasoning by coordinating existing foundation models to enhance large language models' capabilities. This system integrates multiple modalities, including text, images, audio, and video, aiming to improve the models' reasoning and understanding abilities. By leveraging coordination among specialized models, Agent-Omni seeks to address the challenges of processing diverse data types simultaneously. The framework's goal is to enable more effective comprehension across various forms of input, potentially advancing the state of multimodal AI. Recent connected coverage on arXiv highlights the framework's innovative approach and its potential impact on understanding complex multimodal information. Overall, Agent-Omni represents a significant step toward more versatile and capable AI systems that can interpret and reason about diverse data sources in real time.