18 Sheaf-Based Distributed Learning

SID

Foundries maintain reusable local knowledge across contexts. SID instantiates them as sheaf-valued predictive models and makes the descent diagram itself the learning object. It separates local compatibility, global effectivity, tangent updates that preserve descent, and transverse repairs that return an obstructed family toward the descent locus.

¹1. SID abbreviates Sheaf Infinitesimal Descent. It replaces a representation-specific gluing penalty with a typed descent-and-admission interface. ↩