BWhat changed
Computed attention through a learned difference of two softmax maps, seeking to suppress irrelevant context while improving retrieval and language-model behavior in reported experiments.
Scientific scope
Computed attention through a learned difference of two softmax maps, seeking to suppress irrelevant context while improving retrieval and language-model behavior in reported experiments. Noise cancellation is an architectural intuition and empirical finding, not universal elimination of hallucinations or attention error. Subtractive attention changes the architecture and is not exact dense-attention acceleration.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Noise cancellation is an architectural intuition and empirical finding, not universal elimination of hallucinations or attention error. Subtractive attention changes the architecture and is not exact dense-attention acceleration.
Contributors
- Source-listed authors and technical contributors · SCIENCE: 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.
- Differential Transformer
- Ye et al.: Differential attention for context-noise suppression — official project source
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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