---
title: "The Dark Side of the Moon proposes the Attention Residuals architecture to optimize the Transformer model"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/279393793.md"
description: "Moonshot AI recently launched a new architecture called Attention Residuals (AttnRes), aimed at optimizing information processing in Transformer-based large language models. This architecture employs a deep attention mechanism that allows network layers to dynamically select and weight combinations of information from previous layers, addressing the information blurring issues caused by traditional residual connections. AttnRes significantly enhances the model's stability and efficiency in long-context reasoning, marking an evolution of residual components towards a more scalable and adaptive direction, laying the foundation for the next generation of high-performance AI systems"
datetime: "2026-03-17T08:30:35.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/279393793.md)
  - [en](https://longbridge.com/en/news/279393793.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/279393793.md)
---

# The Dark Side of the Moon proposes the Attention Residuals architecture to optimize the Transformer model

PingWest reported on March 17 that Moonshot AI recently released a new architectural concept called Attention Residuals (AttnRes), aimed at revolutionizing the information processing mechanism of Transformer-based large language models. To address the limitations of traditional residual connections, where outputs from various layers are equally combined, leading to information blurring, AttnRes introduces a deep attention mechanism that allows network layers to dynamically select and weight combinations of information from previous layers.

This method treats model depth as a sequence dimension, enabling layers to actively retrieve historical features rather than passively receiving mixed signals. This effectively resolves issues of hidden state redundancy and lack of selective access in deep networks, significantly enhancing the model's stability and efficiency in long-context reasoning. As a technological breakthrough behind the Kimi series models, AttnRes reflects the trend of extending attention mechanisms to network hierarchical structures.

Moonshot AI continues to drive the development of large models through architectural innovation, with its trillion-parameter mixture of experts system already applied to complex reasoning tasks. The introduction of AttnRes signifies that even the most fundamental residual components are evolving towards more scalable and adaptive directions, laying a theoretical foundation for building the next generation of high-performance AI systems.

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