The Von-Neumann State-Space Transformer for neural decoding

What the math gives to ML

The paper contains a transferable architectural idea: use a low-dimensional recurrent state to select the parameters of a token-specific operator, rather than selecting among fixed expert outputs. The key asset is factorized dynamic weight synthesis, which gives each token a task-conditioned linear map while keeping the instruction bank much smaller than a full set of dense experts. This should be tested first as a drop-in replacement for the Transformer MLP, with a stable state-space controller and explicit low-rank updates around a shared base matrix. The main falsifiable benefit is improved accuracy and data efficiency at equal parameter count, especially when long contexts require slowly changing computation.