GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory
- URL: http://arxiv.org/abs/2511.12027v1
- Date: Sat, 15 Nov 2025 04:29:00 GMT
- Title: GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory
- Authors: Jeong Hun Yeo, Sangyun Chung, Sungjune Park, Dae Hoe Kim, Jinyoung Moon, Yong Man Ro,
- Abstract summary: We introduce GCAgent, a novel Global-Context-Aware Agent framework that achieves comprehensive long-video understanding.<n>Our core innovation is the Schematic and Narrative Episodic Memory, which structurally models events and their causal and temporal relations into a concise, organized context.<n>Experiments confirm that GCAgent significantly enhances long-video understanding, achieving up to 23.5% accuracy improvement on the Video-MME Long split over a strong MLLM baseline.
- Score: 59.869552603264076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Long-video understanding remains a significant challenge for Multimodal Large Language Models (MLLMs) due to inherent token limitations and the complexity of capturing long-term temporal dependencies. Existing methods often fail to capture the global context and complex event relationships necessary for deep video reasoning. To address this, we introduce GCAgent, a novel Global-Context-Aware Agent framework that achieves comprehensive long-video understanding. Our core innovation is the Schematic and Narrative Episodic Memory. This memory structurally models events and their causal and temporal relations into a concise, organized context, fundamentally resolving the long-term dependency problem. Operating in a multi-stage Perception-Action-Reflection cycle, our GCAgent utilizes a Memory Manager to retrieve relevant episodic context for robust, context-aware inference. Extensive experiments confirm that GCAgent significantly enhances long-video understanding, achieving up to 23.5\% accuracy improvement on the Video-MME Long split over a strong MLLM baseline. Furthermore, our framework establishes state-of-the-art performance among comparable 7B-scale MLLMs, achieving 73.4\% accuracy on the Long split and the highest overall average (71.9\%) on the Video-MME benchmark, validating our agent-based reasoning paradigm and structured memory for cognitively-inspired long-video understanding.
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