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Linguistic Trajectory Encoding for Efficient Long-Horizon Spatial Memory in Embodied Agents

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

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), keep it only as raw coordinates (geometric SLAM), or organise it around immediate task context (agent working memories). None of them gives the agent a per-object timeline whose state transitions are themselves queryable in language. Our key contribution is \textbf{Linguistic Trajectory Encoding} (LTE)

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