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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-langu
推荐理由「Linguistic Trajectory Encoding f…」偏Agent，对造物者有用的是方法能不能迁到自己的 Agent 或生成管线，不是把标题再念一遍。