The Transformer Revolution, Part 1: Dynamic Processing through Output-Weight Interconnections
We reinterpret Transformer inference by developing a functionally equivalent mechanical-structural description of its functional architecture. Parameterized transformations of token representations, or transforming concepts, are identified with simple neural networks organized through output-input and output-weight interconnections. This redescription makes explicit an organizational feature that is not equally salient in the standard matrix description: during inference, the outputs of some networks determine the weights, and hence the transformations, of others. These output-weight interconnections generate prompt-dependent dynamic transformations and give rise to Sequence-level Interac...
| Authors | Marco Giunti, Fabrizia Giulia Garavaglia |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 20 September 2026 |
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