A Geometry-Based Capacity Theory for Finite-Feature Associative Memory
The theory develops a geometry-based capacity law for exact-key retrieval in compressed random-feature Hebbian memory. It shows that retrieval interference separates into two parts: finite-feature noise and structural interference caused by the kernel overlap among stored keys. This allows researchers to predict memory limits based on representation geometry, distinguishing when improving feature budget helps versus when changing the representation is necessary.