The Universal Observable Pipeline for Sectorized Observable Frameworks
Abstract
Part X of the RIME program. Papers VIII and IX define the static and dynamic SOF architecture. Paper X asks why different species can share the same observable architecture at all, and whether represented or finite observable systems pass through a common realization pipeline before their diagnostics are compared. We formulate the Universal Observable Pipeline: represented or finite species enter through sectorization, observable families, observable shadows, and observable diagnostics. This pipeline is the theorem-level object of Paper X. We then introduce the SOF Registry as evidence architecture for the pipeline. Each registered species must supply a SOF object, observable ladder, dynamics when present, diagnostics, and claim-status metadata. The main structural result is the Universal Observable Pipeline Principle: any admitted species equipped with a finite space, a sectorization, and an observable extraction rule induces an SOF and hence a canonical observable ladder. The current registry contains Rubik, Type III/IV mechanism instances, synthetic boundary controls, Xu-style ridge rate separation, finite spectral-triple, control, PDE, combinatorial, quantum gate, barrier-option stochastic-finance, Markov, graph, neural-network, and Yang-like filtration instances. Paper X shifts the meaning of universality from "all systems obey the same law" to "all admitted systems pass through the same observable pipeline." It does not claim a universal wall law or universal rate theorem. Its main causal claim is the Calibrated Mechanism-Separation Principle: when distinct observable channels are driven by distinct mechanisms and the response constants are ordered, observable proxy rates separate for structural reasons rather than by post-hoc fitting. Source repository: https://github.com/dooven-prime/rime-lite This work is part of the Representation-Induced Mechanics and Evolution (RIME) research program.
Fields of Study & Subjects
Game Theory and Applications
Computability
Logic
AI Algorithms
Bayesian Modeling and Causal Inference
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 10 July 2026
- Document type
- Preprint