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RT @Kimi_Moonshot: Introducing π¨ππππππππ πΉππππ ππππ: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniβ¦
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RT @Kimi_Moonshot: Introducing π¨ππππππππ πΉππππ ππππ: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniβ¦
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ππππ: Rethinking depth-wise aggregation.\n\nResidual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers.\n\nπΉ Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth.\nπΉ Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale.\nπΉ Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead.\nπΉ Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains.\n\nπFull report:\nhttps://t.co/u3EHICG05h",
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