newsNature Machine IntelligenceTrust 88 · LabPublished 3d agoLive · yesterday
A unifying framework from neural superposition to sparse interpretable codes
Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.
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paperSteering Neural Network Training through Interpretable Constraints Based on Partial DependencepaperWhen Structured Sparse Autoencoders Learn Consistent Concepts Across ModalitiespaperCross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse AutoencoderspaperFourier Preconditioning for Neural Feature Learningrepotimjm25/QuantumAgent
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paperSteering Neural Network Training through Interpretable Constraints Based on Partial DependencepaperWhen Structured Sparse Autoencoders Learn Consistent Concepts Across ModalitiespaperCross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencodersrepotimjm25/QuantumAgentpaperFourier Preconditioning for Neural Feature Learning