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Spatial intelligence can be solved 5.6x faster than autoregressive VLMs by treating candidate answers as classes and using native attention matching for direct scoring.
A unified proactive generation framework enables LLMs to form reusable factual memories from streaming video without compromising real-time perception.
TimeLens2 outperforms models with up to 397B parameters by effectively grounding temporal evidence in videos, redefining expectations for multimodal LLM capabilities.
Current MLLMs fail to provide adequate support for visually impaired individuals, particularly in anticipating navigation-critical events in real-time.
VideoChat3 achieves unprecedented generalization in video understanding while maintaining high efficiency, outperforming larger models with just 4 billion parameters.
Current video LLMs falter when faced with the demands of real-time interaction, a gap RIVER Bench directly addresses by providing a challenging new evaluation framework.