Memory Without Training: A Complementary Learning System on Frozen Multimodal Encoders

We report a complementary learning system that is never trained, and whose every behaviour is derived before it is measured. A fast store binds role–filler pairs by circular convolution and completes them through an attractor read; a slow store accumulates the same content in a Hebbian outer-product matrix under passive decay; replay of confidence-gated retrievals carries episodes from one to the other. Binding capacity, the forgetting horizon, the consolidation schedule, the onset of semanticisation and the magnitude of a priming effect are each predicted from a single interference calculation, with no fitted parameter. Content comes from frozen encoders: MiniLM and CLIP codes enter through a codec whose crosstalk we predict from the codes' power spectra alone, and an untrained random projection into a shared workspace binds language and vision slots into one episode, within 0.011 of the derived accuracy and with no new constant. Three findings stand out. Binding capacity belongs to the workspace dimension rather than to the encoder that produced the content, so projection alone buys 15.3 points of accuracy at depth. The spacing effect reverses direction with the clock it is measured from, and both directions follow from the same law. And the full circuit adds nothing over the storeless codec law, agreeing within 0.009 at every load, so end-to-end behaviour is the product of independently derived parts. We registered all 23 predictions before measuring them; four failed, and we report the failures, the diagnoses they carried, and three corrected laws re-validated on grids they had never seen.