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This work introduces LatentStream, a progressive latent working memory framework that shifts streaming memory from store-and-retrieve to retrieve-and-internalize, and achieves new state-of-the-art results on existing online and offline video benchmarks.
A novel training-free Condensed Dynamic Adapter C ON DA is proposed, which leverages vision-text alignment to guide vision-vision alignment and is compatible with arbitrary VLM and generalizes well across complex scenarios, such as long-term and egocentric scenarios.
LaMem-VLA is introduced, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning, and enables memory to directly participate in VLA reasoning and guide action generation under a bounded context.
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