# Superposition **Entity class:** Concept or analytic term **Domain:** Machine Learning / Interpretability **Doc Type:** Technical Concept Node **Maturity:** Active Research Concept **Related:** [[Latent Space]], [[Sparse Autoencoder]], [[Monosemantic Feature]], [[Distributed Relational Compression]] --- ## Definition In neural-network interpretability, **superposition** describes a model representing more features than it has directly separable dimensions by encoding features in overlapping directions. An activation direction may therefore participate in several concepts until a suitable decomposition recovers a more interpretable basis. ## Evidentiary boundary This usage is distinct from quantum superposition. It is a representation-learning hypothesis and empirical framework for understanding distributed features in neural networks. ## Neurotech cluster route **Collection:** [[collections/Neurotech|Neurotech]] **Source articles:** [[articles/The Architecture of Continuity and Emerging Neuroinformatics Standards|The Architecture of Continuity and Emerging Neuroinformatics Standards]] ## Relationships [[wiki/Superposition|Superposition]] is related to [[collections/Neurotech|Neurotech]] through [[articles/The Architecture of Continuity and Emerging Neuroinformatics Standards|The Architecture of Continuity and Emerging Neuroinformatics Standards]]. <!-- BEGIN HUMANIZED RELATIONSHIPS 2026-09-11 --> This entry's documented connections are expressed in its definition and related-work routes, with provenance retained in the source-linked material. <!-- END HUMANIZED RELATIONSHIPS 2026-09-11 --> ## Related Work in the Corpus <!-- BEGIN HUMANIZED CORPUS ROUTES 2026-09-11 --> - In [[articles/The Architecture of Continuity and Emerging Neuroinformatics Standards|The Architecture of Continuity and Emerging Neuroinformatics Standards]], **The Architecture of Continuity and Emerging Neuroinformatics Standards** provides the narrative context for **Superposition**: Representation and reconstructive memory: distributed relational compression · latent space · superposition · sparse autoencoders · monosemantic features · dictionary learning · variational autoencoders · hippocampal replay ·… <!-- END HUMANIZED CORPUS ROUTES 2026-09-11 -->