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Linguistic Trajectory Encoding for Efficient Long-Horizon Spatial Memory in Embodied Agents
Embodied agents performing long-horizon tasks require a memory representation in which the state transitions of dynamic objects remain queryable in natural language across hours-to-days observation horizons. Existing systems either drop fine-grained motion (clip-level video-language embeddings), kee
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