Morning Edition · Tuesday, July 28, 2026Published at 1:47 AM EDT · New York
CausalGate Prunes Transformer Modules by Causal Importance Rather Than Correlation Heuristics
The paper argues that dropping redundant modules based on hidden-state similarity or activation magnitude misjudges which parts of a model actually matter for a given input.
A paper introducing CausalGate challenges how adaptive-inference methods decide which transformer modules to skip. Existing approaches drop modules using observational heuristics such as hidden-state similarity or activation magnitude, but…
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