InternVideo3 enhances long-video understanding with multimodal reasoning

Researchers have introduced InternVideo3, a framework designed to improve multimodal contextual reasoning for long-horizon video understanding. The system employs a closed-loop process over an evolving context that includes observations, instructions, reasoning, and tool actions. To enhance efficiency, it utilizes Multimodal Multi-head Latent Attention (M^2LA) for compressing key-value cache states while preserving the full token stream. Experiments demonstrate strong performance on benchmarks like Video-MME and MLVU, and the model has been instantiated as a video agent capable of robust, evidence-grounded behavior. AI

IMPACT Enhances agentic capabilities for long-horizon visual tasks, potentially improving applications requiring sustained video analysis and interaction.

RANK_REASON The cluster contains a research paper detailing a new framework and model for multimodal reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• EgoSchema
• InternVideo3
• MLVU
• Multimodal Contextual Reasoning (MCR)
• Multimodal Multi-head Latent Attention (M^2LA)
• Video-MME

• paper
• model release

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