Topological Semantic Compression — Unified Framework

This record presents Topological Semantic Compression (TSC), together with an ML-native translation for alignment and interpretability research The framework observes that certain abstract relational structures—such as feedback loops, equilibrium dynamics, temporal asymmetries, and ethical constraints—can be compressed into minimal latent representations that preserve relational topology while discarding surface semantics. When decoded by different systems, these compressed kernels reconstruct diverse concrete instantiations that share the same underlying structural geometry. This is not Shannon-style lossless compression; instead, it is topology-preserving compression evaluated via acceptance-threshold metrics rather than optimization objectives. Empirical observations include extreme token reduction (up to ~197:1), cross-model structural convergence, and stable performance on semantic faithfulness, calibration, and safety metrics. The work is presented as exploratory and falsifiable, intended to make the original symbolic framework legible to machine learning researchers for independent validation and further study.