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Current memory systems, despite their complexity, are surprisingly worse than naive RAG when applied to continuous lifelogging scenarios, revealing a critical need for better context preservation.
By iteratively reasoning over video snippets with a Chain-of-Thought, $\text{R}^2$VLM achieves state-of-the-art long-horizon task progress estimation without needing to process entire videos at once.
