arXiv Machine Learning By Richard Zhe Wang

Abstention and Noise Filtering: Two Missing Primitives of Softmax Attention

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The paper investigates how gating the value pathway in attention mechanisms provides two missing capabilities of softmax attention: abstention and noise filtering. Experiments on models ranging from 10M to 350M parameters show that abstention benefits smaller models while noise filtering becomes more advantageous as models scale, and that combining both primitives yields the best performance across all sizes. The authors also demonstrate that the gates effectively suppress interference and that each gate type has a distinct blind spot, all while adding negligible parameters and preserving compatibility with key‑value caching.

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