arXiv Notify me• Mar 16, 2026paper

Attention Residuals

Yang Zhilin(guest)

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Executive Summary

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Limitations of Standard Residual Connections Dynamic Layer Aggregation via Attention

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Concerns Raised

The memory and communication overhead of the full Attention Residuals approach can be prohibitive for large-scale training.
Increased computational complexity compared to simple additive residual connections, even with the Block AttnRes optimization.

Opportunities Identified

A new architectural primitive that can serve as a drop-in replacement for standard residual connections to improve future LLMs.
Enhanced training stability and performance for extremely deep neural networks, potentially enabling more powerful models.
Improved information flow across layers could lead to better long-context understanding and reasoning capabilities.

Key Themes

Research Findings11

Attention Residuals (AttnRes) replace fixed accumulation in LLMs with a softmax attention mechanism over preceding layer outputs, allowing for selective aggregation with learned, input-dependent weights.

Scaling law experiments confirm that the performance improvement from Attention Residuals (AttnRes) is consistent across different model sizes.

In the Kimi Linear architecture, Attention Residuals (AttnRes) mitigate Pre-Layer Normalization (PreNorm) dilution, leading to more uniform output magnitudes and gradient distribution across model depth.

The Kimi Linear architecture with Attention Residuals (AttnRes) improves downstream performance across all evaluated tasks.

The uniform aggregation in standard residual connections causes uncontrolled hidden-state growth with model depth, progressively diluting each layer's contribution.

Block AttnRes is a variant of Attention Residuals that partitions layers into blocks and attends over block-level representations to reduce memory and communication overhead in large-scale model training.

Block AttnRes reduces the memory footprint of full AttnRes while preserving most of its performance gains.

Combined with cache-based pipeline communication and a two-phase computation strategy, Block AttnRes is a practical drop-in replacement for standard residual connections with minimal overhead.

Ablation studies validated the benefit of the content-dependent, depth-wise selection mechanism within Attention Residuals (AttnRes).

Attention Residuals (AttnRes) were integrated into the Kimi Linear architecture, a model with 48 billion total parameters and 3 billion activated parameters.

The Kimi Linear architecture with Attention Residuals (AttnRes) was pre-trained on 1.4 trillion tokens.

Topics

Large Language Models (LLMs)Transformer Architecture Residual Connections Pre-Layer Normalization (PreNorm)Attention Mechanisms Attention Residuals (AttnRes)Block AttnRes Model Scaling Scaling Laws Deep Learning Neural Network Architecture Training Stability Gradient Flow Kimi Linear Model Optimization

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

Attention Residuals

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

Limitations of Standard Residual Connections

Dynamic Layer Aggregation via Attention

Pragmatic Engineering for Large-Scale Implementation

Empirical Validation and Scaling Laws

Research Findings11

Topics