Variational Linear Attention: Stable Associative Memory for Long-Context Transformers

Linear attention reduces the quadratic cost of softmax attention to O(T), but its memory state grows as O(T) in Frobenius norm, causing progressive interference between stored associations. We introduce Variational Linear Attention (VLA), which reframes the memory update as an online regularised least-squares problem with an adaptive penalty matrix maintained via the Sherman-Morrison rank-1 formula. We prove that normalising the write direction to unit length gives the recurrence Jacobian spectral norm exactly 1 for all sequence lengths and head dimensions (Proposition 2), and that the state norm is self-limiting under bounded inputs (Proposition 1). Empirically, VLA reduces |St|F by 109× relative to standard linear attention at T=1,000, achieves near-perfect exact-match accuracy on multi-query associative recall within the effective per-head memory regime (npairs < dh), maintaining substantially higher retrieval performance than DeltaNet and standard linear attention under increasing memory load, and maintains 62% accuracy at the per-head capacity boundary. A Triton-fused kernel achieves 14× speedup over sequential Python and O(T) scaling, crossing below softmax attention latency at approximately 43,000 tokens.