Attention sinks and rescaling in transformers
Published on October 5, 2026, the paper examines attention sinks and residual sinks in large language models and proposes how these outliers, softmax attention, and RMSNorm rescale other components.
Source: Como attention sinks e residual sinks reescalam componentes de transformers (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on October 5, 2026, the paper examines attention sinks and residual sinks in large language models. It proposes that these outliers, along with softmax attention and RMSNorm, rescale other components, offering a functional explanation for outliers that emerge during training and their role in transformers.
For a company training or operating models, a practical response is to instrument evaluations that track activations and other internal signals across controlled models and versions, comparing behavior on relevant tasks. The consultancy can help define these measurements and build a dashboard to support training, evaluation, or deployment decisions, without assuming that outliers are inherently a problem.
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Wendelmaques can diagnose the evaluation opportunity for your models, define metrics and an implementation scope for collection, analysis, and monitoring, and support operation of the solution.
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