What is recorded here?
Replaced uniform residual accumulation with learned input-dependent attention over earlier layer outputs, with a blockwise variant and communication/computation strategy for scalable training.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
A depth-wise representation routing method, distinct from token attention and DeepNorm initialization. Empirical scaling improvements are not a general optimization/convergence guarantee; report-front collective controls attribution.
Read the original work
- Attention Residuals
- Kimi Team: attention-weighted residual aggregation across depth — official method source
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English catalogue narrative
Kimi Team: attention-weighted residual aggregation across depth
Replaced uniform residual accumulation with learned input-dependent attention over earlier layer outputs, with a blockwise variant and communication/computation strategy for scalable training.
Scientific scope
A depth-wise representation routing method, distinct from token attention and DeepNorm initialization. Empirical scaling improvements are not a general optimization/convergence guarantee; report-front collective controls attribution.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.