arXiv Machine Learning September 11, 2026 By Antoine Saillenfest

MUtE: A Dual Framework for Concept Erasure and Counterfactual Interventions

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The paper introduces MUtE, a dual framework that simultaneously erases concept-specific information from representations and generates counterfactual mappings. By deriving new erasure functions based on optimal bounds, MUtE imposes a translational bias on counterfactual trajectories, aligning with geometric properties of concepts in language models. The authors demonstrate that this approach improves downstream algorithmic fairness and enables the generation of counterfactual texts.

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