arXiv:2606.19341v1 Announce Type: cross Abstract: Passive models for long video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to grow with video duration. Although interactive frameworks have emerged, they often rely on global pre-scanning, and their context cost still scales with video length. We propose OmniAgent, the first native omni-modal agent that formulates video understanding as a POMDP-based iterative Observation-Thought-Action cycle. OmniAgent executes on-demand actions to selectively
The proliferation of complex, long-form video data and the computational constraints of current passive AI models are driving the need for more efficient and intelligent video understanding approaches.
This development indicates a significant step towards more resource-efficient and human-like AI perception, crucial for scaling AI applications to real-world, dynamic environments.
AI models will move from 'watch-it-all' passive processing to active, selective, and iterative perception for complex data, enabling more sophisticated and less computationally intensive analysis.
- · AI agents developers
- · Robotics
- · Surveillance systems
- · Autonomous vehicles
- · High-compute cloud providers (paradoxically, as efficiency reduces demand)
- · Passive video processing model developers
- · Inefficient AI systems
AI systems will become significantly more efficient at processing large, complex datasets like long videos.
This efficiency will accelerate the deployment of autonomous AI agents in real-world environments requiring continuous omni-modal understanding.
More capable and resource-efficient AI agents could lead to new forms of automation and interaction, reshaping industries reliant on visual and contextual data processing.
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Read at arXiv cs.CL