Voltic Separates Volatility from Noise in Memory

The Voltic architecture outperforms gated baselines in average performance across eight reasoning tasks and long-context retrieval accuracy in 45M-parameter language models.

Traditional recurrent memories like Gated Delta-Rule treat uncertainty as isotropic, failing to distinguish between 'volatility' (how fast associations change) and 'stochasticity' (observation noise). This rigidity limits adaptation to dynamic environments.

Voltic maintains anisotropic covariance and makes noise variances input-dependent, allowing write operations to carry accumulated uncertainty. It uses diagonal and quasi-diagonal approximations to preserve parallel training efficiency by reusing chunked kernels.

These results are based on author-run controlled recall tasks and small-scale experiments, with no independent reproduction or large-scale deployment verification yet.