newsNature Machine IntelligenceTrust 88 · LabPublished 1mo agoLive · 1mo ago
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.
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 50%Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence →
- PossiblePossibly related (embedding) · 49%When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities →
- PossiblePossibly related (embedding) · 48%Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders →
- PossiblePossibly related (embedding) · 46%Fourier Preconditioning for Neural Feature Learning →
- PossiblePossibly related (embedding) · 45%timjm25/QuantumAgent →
- PossiblePossibly related (embedding) · 47%Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations →
- PossiblePossibly related (embedding) · 52%SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events →
- PossiblePossibly related (embedding) · 49%Sparse Competition during Training For the Emergence of Specialized Modules →
Covers
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
Covers (incoming)
paperCatching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature PermutationspaperSAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth EventspaperSparse Competition during Training For the Emergence of Specialized ModulespaperFinding and using interpretable latents in a neutrino foundation model with sparse autoencoderspaperConstrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints
Related across the graph
paperSparse Competition during Training For the Emergence of Specialized ModulespaperSteering Neural Network Training through Interpretable Constraints Based on Partial DependencepaperWhen Structured Sparse Autoencoders Learn Consistent Concepts Across ModalitiespaperConstrained Hebbian Learning Supports Efficient Representational Allocation under Structural ConstraintspaperCross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse AutoencoderspaperCatching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature PermutationspaperFinding and using interpretable latents in a neutrino foundation model with sparse autoencodersrepotimjm25/QuantumAgentpaperFourier Preconditioning for Neural Feature LearningpaperSAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events