Reading guide: the three ideas behind MSAR, from residuals to depth attention
LChoshen · x · 2026-11-08
Liad Bercovitch, a member of the #ActiveAIReading group, published a guide to the background MSAR assumes, walking through three earlier ideas and the problems they leave open:
- Residual connections: every layer adds into a single running sum — simple and stable, but early-layer contributions get diluted with depth and can't be recovered
- Hyper-Connections: replace the single residual stream with several parallel streams mixed by learned matrices; DeepSeek's mHC variant constrains the mixing for training stability, but the mixing is frozen after training
- Attention Residuals: single-stream softmax attention along the depth axis, enabling content-dependent retrieval across layers — which leads directly to MSAR
Reading tip from the guide: the paper calls a convolution "depth-wise," but it runs over tokens, not layers — attention along depth, convolution along the sequence.
Related event: MSAR Multi-Stream Attention Residuals Speed Up Compute by 1.58x(2 posts)→
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