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• The model processes current RGB observation, top-down explored map, and object-level episodic memory serialized into tokens.technical 80%
• The proposed memory-augmented vision-language agent improves standard captioning scores by up to +11.86% over baseline models.result 90%
• The model improves caption self-similarity by +7.39% over baseline models.result 90%
• The model uses a unified autoregressive framework that simultaneously handles data association, object captioning, and exploration policy.method 85%
• The model maintains persistent object identity and semantic consistency across extended sequences using object-level episodic memory serialized into tokens.technical 85%
• The model was trained in a self-supervised manner using a dataset collected in photorealistic 3D environments with a disagreement-based policy.method 80%
• The model enables scalable performance through a compact scene representation.technical 75%
• The model's reliance on specific datasets for training may limit transfer to different hardware platforms or operating environments.limitation 70%
• The proposed memory-augmented vision-language agent improves standard captioning scores by up to +11.86% over baseline models.result 90%
• The model improves caption self-similarity by +7.39% over baseline models.result 90%
• The model uses a unified autoregressive framework that simultaneously handles data association, object captioning, and exploration policy.method 85%
• The model maintains persistent object identity and semantic consistency across extended sequences using object-level episodic memory serialized into tokens.technical 80%

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