MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

Hugging Face Daily Papers Papers

Summary

MemDreamer decouples perception and reasoning for long video understanding using hierarchical graph memory and agentic retrieval, achieving state-of-the-art performance with reduced computational overhead.

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 causal relations. During inference, the reasoning model employs agentic tool-augmented retrieval, navigating hierarchies, searching nodes, and traversing logical edges via an Observation-Reason-Action loop. Experiments show MemDreamer achieves SOTA results across four mainstream benchmarks, narrowing the gap with human experts to only 3.7 points. It constrains the reasoning context window to merely 2% of full-context ingestion while delivering a 12.5 point absolute accuracy gain. Furthermore, statistical analysis uncovers a strong positive linear correlation between an VLM's performance on logic reasoning and long-video understanding benchmarks, establishing agentic capability scaling as a new paradigm for multimodal comprehension.
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Paper page - MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

Source: https://huggingface.co/papers/2606.07512

Abstract

MemDreamer addresses long-video understanding challenges by decoupling perception and reasoning through hierarchical graph memory and agentic exploration, achieving state-of-the-art performance with reduced computational overhead.

CurrentVision-Language Modelsstruggle with hours-long videos because processing full-length visual sequences induces prohibitivetoken explosionandattention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into anagentic explorationprocess. As a plug-and-play framework, it incrementally streams videos to construct aHierarchical Graph Memory, a top-down three-tier architecture for semantic abstraction, anchored by a foundational graph capturing spatiotemporal andcausal relations. During inference, the reasoning model employs agentictool-augmented retrieval, navigating hierarchies, searching nodes, and traversing logical edges via anObservation-Reason-Action loop. Experiments show MemDreamer achieves SOTA results across four mainstream benchmarks, narrowing the gap with human experts to only 3.7 points. It constrains the reasoning context window to merely 2% of full-context ingestion while delivering a 12.5 point absolute accuracy gain. Furthermore, statistical analysis uncovers a strong positive linear correlation between an VLM’s performance on logic reasoning and long-video understanding benchmarks, establishing agentic capability scaling as a new paradigm formultimodal comprehension.

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