Fast-Weight Time Machine tests temporary variable binding with an explicit, sequence-local weight matrix. A controller receives key/value writes, produces a write strength, and accumulates outer products in a fast memory. A later query key reads the memory in one matrix-vector operation. The learned parameters stay fixed between examples; only the fast matrix changes inside each sequence.

The historical anchor is Schmidhuber's 1992 Learning to Control Fast-Weight Memories, which described feedforward controllers producing context-dependent weight changes and included adaptive temporary-variable binding. This project is a modern outer-product interpretation tested against a similarly sized GRU. It does not claim to reproduce the original implementation or experiments exactly.

The models trained on 16,000 sequences containing four writes and 12 distractors. Each held-out condition contained 4,000 new sequences.

This task is intentionally aligned with the fast-weight architecture: an explicit outer-product matrix can bind a key and value, while the GRU must compress all bindings into one recurrent vector. The comparison demonstrates the inductive bias; it is not a general claim that fast weights outperform GRUs on arbitrary sequences.