Goodfire AI Says Neural Networks Think in Shapes, Launches Geometry Research Series
A new interpretability paradigm frames neural network activations as geometric objects — curves and manifolds — rather than discrete features. Goodfire AI's research series could reshape how we understand model internals.
Goodfire AI launched a research series on what it calls "neural geometry" — the study of how neural network activations form structured geometric shapes in high-dimensional space. As @GoodfireAI explained, "Neural networks think in shapes," proposing that the meaningful unit of analysis isn't individual features or neurons but the manifolds and curves that activations trace through representation space.
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