The normalized inverse-scale direction of LayerNorm's affine parameters is an exact algebraic kernel of the post-final-norm centred activation covariance for any input distribution in LayerNorm transformers.
Attention Layers Add Into Low-Dimensional Residual Subspaces , url =
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Contribution Weights combine attention, value magnitude, and directional alignment to measure token influence more faithfully than attention alone, and show attention sinks actively suppress information via a convex sink-rate to output-norm relationship.
Linear probes recover day-of-year from LM activations for temporal reasoning but are orthogonal to the model's causal 4D subspace identified by DAS, with the angle matching the Haar-uniform random null, replicated across scales and families.
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.
citing papers explorer
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Algebraic Dead Directions in LayerNorm Transformers: A Forward-Pass-Only Diagnostic at LLM Scale
The normalized inverse-scale direction of LayerNorm's affine parameters is an exact algebraic kernel of the post-final-norm centred activation covariance for any input distribution in LayerNorm transformers.
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Contribution Weights: A Geometrical Analysis of Self-Attention Transformers
Contribution Weights combine attention, value magnitude, and directional alignment to measure token influence more faithfully than attention alone, and show attention sinks actively suppress information via a convex sink-rate to output-norm relationship.
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When and How Long? The Readout-Mediator Angle in Temporal Reasoning
Linear probes recover day-of-year from LM activations for temporal reasoning but are orthogonal to the model's causal 4D subspace identified by DAS, with the angle matching the Haar-uniform random null, replicated across scales and families.
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RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.