Introducing ๐จ๐๐๐๐๐๐๐๐ ๐น๐๐๐๐ ๐๐๐๐: Rethinking depth-wise aggregation. Residual 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. ๐น Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. ๐น Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. ๐น Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. ๐น Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. ๐Full report: github.com/MoonshotAI/Attโฆ
