Summary
The Sessa paper introduces "Selective State Space Attention" as an alternative to Transformers, addressing the issue where self-attention dilutes the influence of individual (especially older) tokens as context length grows. This new approach utilizes structured state-space models to process sequences recurrently, aiming to overcome the scaling limitations and improve long-range dependency handling inherent in traditional self-attention mechanisms.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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