Attention Residuals — Interactive Architecture Demo

An interactive single-page explainer of the Kimi team paper Attention Residuals: animated walkthroughs of depth dilution, Full AttnRes, and Block AttnRes two-phase compute, with KaTeX formulas. This case was generated by Kimi K3.

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Start with this prompt

An animated explainer of depth dilution, Full AttnRes, and Block AttnRes two-phase compute with KaTeX formulas and speed control.

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Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Make a Chinese edition

Same animations and formulas, rendered as a natural Chinese explainer.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Switch to linear attention

Keep the three-act animated form but explain Mamba and linear attention instead.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Add quiz checkpoints

Add short quizzes at the end of each section that jump back to the matching animation when answered wrong.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Start with this prompt

An animated explainer of depth dilution, Full AttnRes, and Block AttnRes two-phase compute with KaTeX formulas and speed control.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Make a Chinese edition

Same animations and formulas, rendered as a natural Chinese explainer.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Switch to linear attention

Keep the three-act animated form but explain Mamba and linear attention instead.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.

Add quiz checkpoints

Add short quizzes at the end of each section that jump back to the matching animation when answered wrong.

Try Deep Research

Build an English single-file interactive explainer for the Kimi team paper Attention Residuals. For readers with a deep-learning background: first animate the depth-dilution problem of PreNorm residual accumulation (hidden-state magnitude grows with depth and early layers get diluted), then explain Full AttnRes, where a learnable pseudo-query attends over all previous layer outputs with softmax weights, and finally land on Block AttnRes with its two-phase computation (plain residuals inside a block, attention across blocks), closing with a comparison of memory and communication costs. Render formulas with KaTeX (fall back to raw LaTeX text if the CDN fails), section navigation, adjustable animation speed, clean academic layout, single self-contained HTML file that works offline.