The Orthogonalized Read Is a Removable Training Scaffold for Recurrent Memory
Orthogonalizing the mLSTM memory matrix at read time with five differentiable Newton-Schulz iterations improves noisy associative recall. We replicate this effect and investigate its mechanism. Training on MAD noisy recall exhibits a long chance-level plateau followed by a sharp increase in accuracy. The orthogonalized read improves conditioning during this plateau and can be removed after escape. Ablations support three findings. First, the benefit requires a self-consistent read and gradient: an exact recursive least-squares read (the Mesa layer) yields a similar benefit, while straight-through variants, delta-rule writes, frozen random keys, and Frobenius normalization show no improvem...
| Authors | Keston Aquino-Michaels |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 14 September 2026 |
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