KunLunBaize-VoT-R1 introduces a novel approach to video-language modeling that significantly enhances inference efficiency while maintaining high performance.

Key Innovations:

• Image Packing Technology reduces computational overhead for video processing
• Autonomy-of-Experts (AoE) Architecture optimizes multimodal data handling
• Video of Thought (VoT) integration leverages LLM capabilities for superior understanding
• Engineering Efficiency balances performance and resource utilization

Engineering Impact: This research addresses critical efficiency bottlenecks in video-language models, enabling more practical deployments for real-time video understanding applications in resource-constrained environments.

Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture