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Exploring and evaluating a novel, data-dependent query formulation for Attention Residuals to potentially enhance its representational power and dynamic routing capabilities, moving beyond static query vectors.
This issue proposes an alternative, data-dependent query formulation for Attention Residuals, moving beyond the current static query vector. The proposed method involves calculating unnormalized routing scalars for future layers via an affine projection of $v_i$ at each layer, followed by softmax normalization and a sum reduction. This demonstrates active engagement with the core architectural design. For B2B SaaS developing foundational AI models, such theoretical explorations are critical for pushing performance boundaries. Investigating dynamic, data-dependent routing mechanisms could unlock significant improvements in model efficiency, capacity, or generalization, offering a competitive edge in the rapidly evolving AI landscape.
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MoonshotAI/Attention-Residuals is analyzed by our AI as: Exploring and evaluating a novel, data-dependent query formulation for Attention Residuals to potentially enhance its representational power and dynamic routing capabilities, moving beyond static query vectors.. It focuses on This issue proposes an alternative, data-dependent query formulation for Attention Residuals, moving beyond the current static query vector. The pr...
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Data for MoonshotAI/Attention-Residuals was aggregated directly from the GitHub Open Source community ecosystem, representing raw developer and early-adopter sentiment.
When was MoonshotAI/Attention-Residuals publicly launched?
The initial public indexing or launch date for MoonshotAI/Attention-Residuals within our tracked developer communities was recorded on March 15, 2026.
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MoonshotAI/Attention-Residuals has achieved measurable traction, logging over 2,587 traction score and facilitating 116 recorded discussions or engagements.
Are there active development issues for MoonshotAI/Attention-Residuals?
Yes, we are currently tracking open architectural debates and bug reports for this project on GitHub. There are currently 5 active high-priority issues logged recently.
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Yes. It has been covered by media outlets like Github.com. This indicates the concept has reached a level of mainstream or scientific viability beyond just developer forums.
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Active Developer Issues (GitHub)
Logged: Mar 28, 2026
Logged: Mar 25, 2026
Logged: Mar 19, 2026
Logged: Mar 19, 2026
Logged: Mar 18, 2026
Community Voice & Feedback
俺只是随手发了一个，大体就是，对残差链接做注意力也有文章，而且效果普遍都不是很好，却只字不提，之前的线性层那篇文章，也是和 RWKV 如出一辙，而且两篇一起看的话，作者明显是读过类似文章的
I’m a bit confused by the flow of this thread.
The OP originally linked to the "DeepCrossAttention paper" (published Feb 10, 2025). Since that paper's concepts seem very closely related to this repository's "Attention Residuals" (published Mar 16, 2026), the OP's concern about a missing citation feels completely valid. (similar concern on X: https://x.com/behrouz_ali/status/2033581834953453853 )
The response however seems to focus on comparing Attention Residuals to Hyper Connection, bypassing DeepCrossAttention. I checked the edit history, and it doesn't look like the OP ever changed their URL...
Was there a mix up when reading the initial issue?
The OP originally linked to the "DeepCrossAttention paper" (published Feb 10, 2025). Since that paper's concepts seem very closely related to this repository's "Attention Residuals" (published Mar 16, 2026), the OP's concern about a missing citation feels completely valid. (similar concern on X: https://x.com/behrouz_ali/status/2033581834953453853 )
The response however seems to focus on comparing Attention Residuals to Hyper Connection, bypassing DeepCrossAttention. I checked the edit history, and it doesn't look like the OP ever changed their URL...
Was there a mix up when reading the initial issue?
啊？咱们下载的不是同一篇技术报告？
是的，我就是这个意思：用AttnRes替换mHC里的residual部分，即在每个stream内部做跨层attention，而不是在两者之上再叠一层。
关于这点你们有一些实验数据吗？
关于这点你们有一些实验数据吗？
可能不会带来显著的叠加收益
1.两者都在解决"信息在深度方向的传递和选择"问题，只是角度不同，功能上有相当程度的重叠
2.multihead AttnRes退化的实验结果是一个反向信号——增加深度聚合的表达能力并不总是有帮助
4.组合会带来双倍的复杂度和额外的超参（block数、stream数如何联合调优）
更可能有价值的方向反而是用AttnRes替换mHC里的residual部分，即在每个stream内部做跨层attention，而不是在两者之上再叠一层。
1.两者都在解决"信息在深度方向的传递和选择"问题，只是角度不同，功能上有相当程度的重叠
2.multihead AttnRes退化的实验结果是一个反向信号——增加深度聚合的表达能力并不总是有帮助
4.组合会带来双倍的复杂度和额外的超参（block数、stream数如何联合调优）
更可能有价值的方向反而是用AttnRes替换mHC里的residual部分，即在每个stream内部做跨层attention，而不是在两者之上再叠一层。
> https://arxiv.org/abs/2502.06785 和这篇几乎一样，但是文章中一点也不提及 之前也是这样 [MoonshotAI/Kimi-Linear#4](https://github.com/MoonshotAI/Kimi-Linear/issues/4)
Attention Residual是Layer Dimension的Quadratic Attention, Hyper Connection是Layer Dimension的Linear Attention. 这种关于layer维度的，之前有过一些 相关 工作，比如 [Learning Deep Transformer Models for Machine Translation ](https://arxiv.org/abs/1906.01787) , [Depth-Wise Attention (DWAtt)](https://arxiv.org/abs/2209.15168). 这种Attention Residual的工作相比于线性，可能在层数比较多(e.g. more than 1000)时候有一些明显效果提升相比于线性注意力(e.g. Hyper Connection)
Attention在层间流动的信息本质是在做gradient descent，Pre-Norm 和 Post-Norm是在流形上的优化，x是当前点，ffn(x)或者att(x)是对应的梯度，所以可以做很多有意思的东西。从这个角度理解 Attention Residual在做类似SGD+momentum。同时，我们也可以将信息流动建模成流形上的梯度下降 [GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization](https://arxiv.org/abs/2601.22095)
我对架构方向做的比较多，有问题可以多交流
Attention Residual是Layer Dimension的Quadratic Attention, Hyper Connection是Layer Dimension的Linear Attention. 这种关于layer维度的，之前有过一些 相关 工作，比如 [Learning Deep Transformer Models for Machine Translation ](https://arxiv.org/abs/1906.01787) , [Depth-Wise Attention (DWAtt)](https://arxiv.org/abs/2209.15168). 这种Attention Residual的工作相比于线性，可能在层数比较多(e.g. more than 1000)时候有一些明显效果提升相比于线性注意力(e.g. Hyper Connection)
Attention在层间流动的信息本质是在做gradient descent，Pre-Norm 和 Post-Norm是在流形上的优化，x是当前点，ffn(x)或者att(x)是对应的梯度，所以可以做很多有意思的东西。从这个角度理解 Attention Residual在做类似SGD+momentum。同时，我们也可以将信息流动建模成流形上的梯度下降 [GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization](https://arxiv.org/abs/2601.22095)
我对架构方向做的比较多，有问题可以多交流
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