Fractal Semantic Architecture: Scale-Parameterized Relational Training Across Semantic Granularities (v2.2)

A formal white paper proposing a complementary training paradigm for neural language models that adds multi-scale relational learning and version-differential training to existing generative architectures. FSA defines a parameterized family of relational classifiers operating on typed relationships between semantic units at variable granularity (sentence through version-sequence), with automated extraction pipelines, bidirectional cross-scale consistency constraints, and inference-time integration via skeleton planning, coherence gating, revision scoring, and reranking. Includes a falsifiable experimental design for testing the hypothesis that discrete relational structures improve collapse resistance under recursive synthetic retraining.