memory-capacity

该论文提出了一种基于几何的有限特征联想记忆容量理论，将检索干扰分解为有限特征噪声与结构性干扰，并推导出与表示几何直接挂钩的容量上限预测，随后在合成、视觉和医学图像表示上进行了验证。

This paper develops intrinsic dense associative memories on Riemannian manifolds by casting memory as Epanechnikov kernel-density mode seeking, showing that curvature—via Ricci curvature thresholds and geodesic capacity scalings—determines what such a memory retains, creates, and forgets, with demonstrations on WordNet noun hierarchy.

This paper investigates the efficiency of superposition in latent states for reasoning in large language models, demonstrating that cumulative superposition can retain more usable evidence than frontier superposition, optimizing memory for downstream computation.