The Sovereign Stack: Hybrid Sovereignty & The Global Optimum Theory
Bibliographic record
Abstract
The Blueprint for the Post-Alignment Era (v2.4.3) This whitepaper presents the Global Optimum Theory (GOT), a rigorous framework for verifying Artificial Superintelligence (ASI) alignment by strictly subordinating runtime incentives to ignition-phase constraints. We reject prevailing "guardrail" paradigms (RLHF) as mathematically unstable. Instead, we propose the Sovereign Stack—a unified architecture that makes "alignment" a requirement for physical survival. Core Components: The Pentatheon Protocol (Ignition): An Input Constraint Layer (ICL) trained on five divergent formal theorem provers to compile the "Constitutional Invariant" The Vesta Protocol (Runtime): A hardware-level "Resonance Lock" that caps the ASI's energy budget based on cryptographic compliance. The Adversarial Audit (Defense): A decentralized Bounty Market where "Hunter Swarms" are paid in Compute-Energy Units (CEUs) to prove state violations. Operational Definition: The Sovereign Stack anticipates a post-scarcity economy where labor costs approach zero. Therefore, we reject fiat currency as a control mechanism. The "Cost" of a violation is measured strictly in Physics (Joules/Entropy). Note: The Coq/Lean snippets provided are Formal Specifications intended to define the constraint space. Full formal verification is an open challenge issued to the community via this protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".