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#feature-representation News & Analysis

2 articles tagged with #feature-representation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles
AINeutralarXiv – CS AI · Jun 86/10
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A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders

Researchers propose a mathematical framework for understanding how sparse autoencoders learn and represent concepts, formalizing concept learning as a set-alignment problem and establishing geometric conditions for neuron-level concept representation. The work connects concept learning to formal concept analysis, revealing that neuron interpretation involves complex many-to-many mappings rather than simple one-to-one relationships.

AINeutralarXiv – CS AI · May 286/10
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Singular Vectors of Attention Heads Align with Features

Researchers demonstrate that singular vectors of attention matrices in language models reliably align with learned feature representations, providing theoretical justification for using this mathematical approach to identify interpretable features. The work bridges mechanistic interpretability research by validating why this alignment occurs and proposing testable predictions for detecting it in real models.