Draft

Holy: Kimi did an amazing work because it changes one of the most basic parts of how deep AI models pass information from layer to layer. Instead of blindly mixing in everything from earlier layers equally, the model can now choose which past information is actually useful for each token and task. That helps deep models keep important signals from getting washed out, making training more stable and efficient. The big deal is that Kimi shows this idea works at scale too: better results, about 25% more compute efficiency, and almost no extra inference slowdown.