MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism
arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into an agentic exploration process. As a plug-and-play framework, it incrementally streams videos to construct a Hierarchical Graph Memory, a top-down three-tier architecture for semantic abstraction, anchored by a foundational graph capturing spatiotemporal and c
The proliferation of long-form video content and the increasing sophistication of multimodal AI necessitate improved methods for efficient video understanding without overwhelming computational resources.
This development is crucial for advancing AI's ability to process and reason over extended temporal data, unlocking new applications in video analytics, perception, and agentic systems.
A framework for decoupling perception and reasoning in long video understanding is now available, potentially enabling more scalable and efficient agentic exploration of video data.
- · AI agents
- · Video analytics platforms
- · Surveillance and security industries
- · Autonomous systems developers
- · Traditional sequential video processing methods
- · Cloud providers without optimized long video processing solutions
AI agents can now more effectively process and understand hours-long video content.
This could lead to a significant acceleration in the development and deployment of agentic systems capable of continuous observation and complex environmental interpretation.
The enhanced ability of AI to 'perceive' and 'reason' over extended real-world events could fundamentally alter human-AI interaction paradigms and decision-making processes in complex environments.
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Read at arXiv cs.AI