MMCTAgent enables multimodal reasoning over large video collections  

Modern multimodal AI models can recognize objects, describe scenes, and answer questions about images and short video clips, but they struggle with long-form and large-scale visual data, where real-world reasoning requires moving beyond object recognition and short-clip analysis.

Real-world reasoning increasingly involves analyzing long-form video content, where context spans minutes or hours, far beyond the context limits of most models. It also entails querying across massive multimodal libraries of videos, images, and transcripts, where finding and integrating relevant evidence requires more than retrieval, it requires strategic reasoning. Existing models typically perform single-pass inference, producing one-shot answers. This limits their ability to handle tasks that require temporal reasoning, cross-modal grounding, and iterative refinement.

MMCTAgent

To meet these challenges, we developed the Multi-modal Critical Thinking Agent, or MMCTAgent, for…