Add a recurrent associative matrix to each selected transformer layer so recent key-value relationships can be retrieved without retaining every past token or performing gradient updates. The matrix uses input-dependent retention and write gates, but retrieval is always performed from the pre-write state, preventing the current target from leaking into its own prediction. Frobenius-norm clipping makes the recurrent memory bounded and provides a direct stability control.
Generate memory slots or attention keys by applying learned unitary transformations to one seed vector instead of storing every slot independently. Select transformations sequentially when they add a sufficiently new direction and reject phase-equivalent or highly coherent candidates, targeting a well-conditioned near-Parseval frame.
Construct a sparse attention support by solving multiple small perturbed assignment problems between query and key embeddings and taking the union of the selected optimal matchings. Use the resulting spanning tree as the only set of cross-token edges, with edge biases determined by empirical assignment frequency.
Use the dimension-specific relation A_3=0 to remove all intermediate channels transforming as the third exterior power of the two-dimensional vector representation. In tensor-product attention or equivariant MLPs, this is an exact algebraic pruning rule rather than approximate low-rank compression.
Use the sharp exponential tail bound to set a local clipping threshold from a desired exceedance probability. Instead of globally clipping activations at a fixed value or percentile, clip each local window at its minimum plus B log(e/delta), where delta is the tolerated fraction of clipped entries.