arXiv:2605.26014v1 Announce Type: cross Abstract: Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often address this challenge by externalizing reasoning through textual chain-of-thought (CoT), keyframe selection, repeated frame reinsertion, or external tool use. While effective, such pipelines increase inference-time latency and engineering complexity, and they force temporal-visual evidence to be serialized into text or repeatedly re-encoded from frames. Inspired by th
The paper addresses the current limitations of large vision-language models (LVLMs) in complex video reasoning by proposing 'internalized modeling,' indicating a frontier in AI capabilities.
This research suggests a more efficient and integrated approach to spatial-temporal reasoning, potentially enabling AI to process and understand video data with greater sophistication and less computational overhead, which is crucial for autonomous systems and intelligent agents.
Current methods relying on externalized reasoning (like CoT or repeated re-encoding) will be challenged by new models that internalize temporal-visual evidence, leading to faster and more robust video understanding.
- · AI researchers and developers
- · Video-driven AI applications
- · Developers of embodied AI and robotics
- · AI models reliant on externalized reasoning pipelines
- · Compute-inefficient video processing methods
Improved efficiency and accuracy in AI video analysis tasks such as surveillance, autonomous navigation, and content generation.
Accelerated development of more capable AI agents and robotic systems that can interpret dynamic environments in real-time.
Enhanced AI understanding of human behavior and complex operational sequences, impacting fields from healthcare to logistics and defense.
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