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This work argues that streaming video memory should explicitly encode structured and semantically meaningful representations, particularly at the entity level, and proposes MEMO, a novel framework that models streaming video through multi-level, entity-aware structured memory.
This work proposes GroundingVLN, which uses visual grounding as a shared interface between reasoning and action, and introduces Grounded and Execution-Aware Reinforcement Learning (GEAR), which aligns grounded reasoning and spatial decisions with downstream execution.
This work proposes Change and Invariance Motion Editing (CIME), a unified framework that comprehensively decouples change and invariance into spatial pose and temporal rhythm dimensions and introduces the Riemannian Non-uniform Integral Manifold Mapping module.
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