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Outlier dimensions, not global coefficients, are the key to accurate Transformer attention linearization, enabling a 9% MSE reduction and improved downstream performance.
A new topological framework reveals structural discrepancies in neural representations that traditional methods miss, enhancing both diagnosis and benchmarking.