InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
Researchers have introduced InternVideo3, a new framework designed to improve long-horizon video understanding and agentic capabilities. The system utilizes Multimodal Contextual Reasoning (MCR) to process video content as an evolving context, enabling evidence accumulation and verification over extended periods. To maintain efficiency, InternVideo3 incorporates Multimodal Multi-head Latent Attention (M^2LA), which compresses key-value cache states without losing token information. The model has demonstrated strong performance on various video understanding benchmarks and has been adapted into a video agent capable of evidence-grounded retrieval tasks. AI
IMPACT Introduces novel methods for long-horizon video understanding and agentic behavior, potentially advancing multimodal AI capabilities.