Metrology-Theoretic Epistemics Engine (MTE): Observable-Only Metrology for Long-Horizon Autonomous Intelligence

This paper presents the Metrology-Theoretic Epistemics Engine (MTE), a formal and machine-checkable supplement to the Sovereign Takeoff Engine (STE) for no-meta, observable-only autonomous intelligence.MTE does not replace STE’s growth law; it adds a strict epistemic governance layer for deciding when claimed progress is scientifically credit-bearing. The framework defines deterministic canonicalization of event logs into strict artifacts, a fail-closed gate vector for observability credit, and replayable kernels for equivalence-class collapse rate (ECR) accounting. It formalizes dual risk ledgers and monotone risk-penalty channels, proving consistency conditions for net credit under bridge, risk, and physical penalties.The theory further introduces constructive links between certified contraction and lower-bounded uplift, anytime-valid supermartingale controls for over-crediting risk, and robust handling of regime shifts via escrow and holonomy constraints. Additional modules cover multiscale proxy governance, social-endogeneity discounting, quantum-reference-frame-relative artifacts, physical envelope limits (including Landauer/Bekenstein/Page-type constraints), and oracle-free anti-extortion mechanisms.Operationally, the paper provides machine-readable schemas, deterministic gate rules, destructive tests, and proof obligations designed for autonomous agents and crawlers, prioritizing reproducibility, auditability, and long-horizon deployment reliability. Archived on Arweave; data transaction digests pinned in the companion repository. TeX source digest: XrBb_7TSif4wDzryr8jFu6IsETNYbXTxTwkhqG3nVwM PDF digest: GzoatDOq77OfrMJAea5eJfg7mYshd1Ah2V09A5Bqv2c