arXiv:2602.13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA). Instead of uniformly sampling frames, \revise selects a small set of informative frames, maintains a summary-as-state across rounds, and stops early when confident. It supports proprietary vision-language models (VLMs) in a ``plug-and-play'' setting and enables reinforcement fine-tuning for open-source models. For fine-tuning, we introduce EAGER (Evidence-Adjusted Gain for Efficient Reasoning),
The proliferation of video data and the computational cost of processing it is driving innovation in efficient AI models, making sparse video understanding crucial for scalability.
This development represents a significant step towards more efficient and autonomous AI agents capable of understanding and reasoning about dynamic environments, reducing computational overhead and enabling new applications.
AI models can now process video data much more efficiently by focusing on informative frames, potentially accelerating the development and deployment of complex video-based AI systems without requiring uniform frame sampling.
- · AI Agent Developers
- · Cloud Computing Providers (due to optimized resource use)
- · Vision-Language Model Developers
- · Robotics and Autonomous Systems
- · Traditional Video Processing Architectures
- · AI Models reliant on brute-force, full-frame processing
More sophisticated and computationally cheaper video understanding capabilities become widely available for AI applications.
The reduced computational burden allows for more complex, real-time AI agents to operate in dynamic video-rich environments, including robotics and surveillance.
This efficiency could democratize advanced AI agent development by lowering compute barriers, leading to a broader range of intelligent systems in various sectors.
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Read at arXiv cs.LG