arXiv:2512.05774v2 Announce Type: replace-cross Abstract: Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrelevant content. While agentic pipelines improve video reasoning capabilities, prevailing frameworks rely on a query-agnostic captioner to perceive video information, which wastes computation on irrelevant content and blurs fine-grained temporal and spatial information. Motivated by active perception theory, we argue that LVU agents should actively decide what, w
The proliferation of long-form video content and the increasing sophistication of AI models necessitate more efficient and intelligent approaches to video understanding.
This research introduces agentic, active perception to video understanding, moving beyond passive processing to enable more accurate, efficient, and context-aware analysis of complex visual data.
Traditional query-agnostic video processing will be superseded by more intelligent, iterative systems that actively seek out relevant information, significantly improving the efficacy of long video analysis.
- · AI agents developers
- · Video analytics companies
- · Security and surveillance sectors
- · Content moderation platforms
- · Inefficient video processing models
- · Companies reliant on brute-force video captioning
- · Legacy video analysis software
More sophisticated and nuanced understanding of long video content becomes widely accessible.
This improved understanding fuels the development of advanced autonomous agents capable of complex decision-making based on visual input.
The enhanced ability to process and interpret visual data could lead to breakthroughs in areas requiring real-time, context-aware visual reasoning, from robotics to automated scientific discovery.
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Read at arXiv cs.CL