Toy models demonstrate that polysemanticity arises when neural networks store more sparse features than neurons via superposition, producing a phase transition tied to polytope geometry and increased adversarial vulnerability.
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Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
NeuroViz offers interactive real-time visualization of neural network forward and backward passes, achieving top usability scores in a study with 31 participants compared to existing tools.
A review paper that organizes conceptual, practical, and socio-technical open problems in mechanistic interpretability.
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Toy Models of Superposition
Toy models demonstrate that polysemanticity arises when neural networks store more sparse features than neurons via superposition, producing a phase transition tied to polytope geometry and increased adversarial vulnerability.
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From Mechanistic to Compositional Interpretability
Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
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NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training
NeuroViz offers interactive real-time visualization of neural network forward and backward passes, achieving top usability scores in a study with 31 participants compared to existing tools.
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Open Problems in Mechanistic Interpretability
A review paper that organizes conceptual, practical, and socio-technical open problems in mechanistic interpretability.