소개
The condensed-analytic-stacks skill provides a rigorous computational bridge between abstract condensed mathematics and practical sheaf neural network architectures. By implementing the Scholze-Clausen framework of condensed sets and liquid vector spaces alongside a 6-functor formalism, it enables the translation of high-level algebraic geometry into diffusion-based learning systems. This is particularly valuable for researchers and developers working at the intersection of geometric deep learning, categorical logic, and decentralized consensus systems where topological consistency and profinite approximation are critical.
