Why some AI neurons hide many ideas — a simple model explains it
Deep nets sometimes cram lots of different ideas into a single unit, a weird thing called polysemanticity.
It means one neuron can light up for apples, faces, or a color, all at once, making the model hard to read.
A tiny, clear model shows this happens because the system stores extra, rare bits of info in superposition, like hiding extra messages in the same place.
As the model grows these hidden bits can suddenly shift behavior, like a quick flip — think of it as a phase change in how the network uses its parts.
The work also finds a surprising tie to the shape of things in many dimensions, simple geometry ideas explain why this packing happens.
This also helps explain why small, strange tweaks can trick networks — the same tricks that make adversarial examples.
Knowing this gives a new path to make models easier to understand and safer.
The idea is simple, but it might change how we read the inner life of AI, and how we fix it when it breaks.
Read article comprehensive review in Paperium.net:
Toy Models of Superposition
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