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This paper introduces Simplified Sparse Attention (SSA), a method that uses gist tokens during continued pretraining to enable efficient chunk selection at inference without architectural changes, achieving high compression ratios and outperforming baselines on long-context tasks like LongBench and retrieval-augmented generation.
This paper proposes a bridging action representation based on relative wrist translation in the head-camera frame to transfer human manipulation skills to bi-manual robots, using a vision-language-action model with interleaved action tokens and attention masking to handle embodiment differences.