What question did this study set out to answer?

This research aims to validate the Q-FENG framework for monitoring AI governance using normative friction and Hilbert-space analysis.

May 16, 2026Open Access

Q-FENG: A Cybernetic Neurosymbolic Framework for AI Governance Monitoring via Hilbert-Space Interference — Empirical Validation Through Adversarial CLT Extension and Manaus O₂ Crisis Retrospective

Puntos clave

This research aims to validate the Q-FENG framework for monitoring AI governance using normative friction and Hilbert-space analysis.
Developed a five-stage classification pipeline integrating quantum decision theory and answer set programming.
Conducted empirical validation across health resource allocation and labor law adjudication in Brazil with proof-of-concept scenarios.
Performed a retrospective analysis over 70 weeks during the Manaus O2 Crisis, classifying governance regimes using angular measurements.
Five scenarios classified as CIRCUIT_BREAKER with θ between 127.8° and 134.7°; two scenarios classified as STAC with θ less than 8°.
Q-FENG maintained stable CIRCUIT_BREAKER classification 19 weeks prior to the public calamity declaration in January 2021.
Variety Amplification measured at 2.33 over the unstructured baseline in the adversarial CLT extension involving 3,700 jobs.

Resumen

Q-FENG (Quantum-Fractal Neurosymbolic Governance) is a cybernetic neurosymbolic framework for AI governance monitoring that operationalises normative friction as an interference angle θ in a Hilbert-space representation of predictor and normative states. The framework integrates Stafford Beer's Viable System Model, Quantum Decision Theory (Born rule with interference term), and Answer Set Programming (Clingo) into a five-stage pipeline that classifies governance regimes into three categories: STAC (θ < 30°, autonomous operation), HITL (30° ≤ θ < 120°, human-in-the-loop escalation), and Circuit-Breaker (θ ≥ 120°, blocked execution). This paper presents the empirical validation of Q-FENG across two domains of public-sector AI deployment in Brazil: health resource allocation (SUS specialist services, including the 2021 Manaus oxygen crisis) and labour law adjudication (Consolidação das Leis do Trabalho — CLT). Validation strata The validation comprises three independent strata: Seven authored proof-of-concept scenarios spanning Brazilian SUS, US Medicaid (Obermeyer), and Brazilian CLT. Five scenarios were classified CIRCUITBREAKER (θ ∈ 127. 8°, 134. 7°) and two STAC positive controls (θ < 8°), demonstrating the discriminability of the geometric formalism. A 70-epidemiological-week retrospective analysis of the Manaus 2020–2021 hospital collapse, in which Q-FENG sustained stable CIRCUITBREAKER classification with first stable onset at SE 37/2020 — 19 weeks before the state-level public calamity declaration of January 2021. An adversarial CLT extension comprising 3, 700 jobs (four architectural arms: B1 unstructured baseline, B3-novo deterministic Clingo, B4-novo-v2 NeSy 1. 0 verifier-passive anchoring, and B5SIDECARᵥ2 Q-FENG full sidecar; 100 scenarios × 4 LLM backbones × 3 random seeds per arm) with pre-registered Bonferroni-corrected hypotheses (m=7, αcorrected = 0. 00714). The full Q-FENG sidecar demonstrates Variety Amplification V (B5) /V (B1) = 7/3 = 2. 33 over the unstructured baseline. Canonical contributions The paper introduces four canonical contributions: A quantum interference geometry for normative alignment via Hilbert-space inner products A Markovian θ-efetivo extension for temporal governance tracking A failure typology grounded in positive law (constitutional, execution-inertia, execution-absent-channel) An architectural category γ for AI governance sidecars demonstrated through ablation The framework operates within the EU AI Act regulatory perimeter (Regulation 2024/1689) and provides a reproducible reference implementation for the high-risk public-sector AI use cases enumerated therein. Theoretical foundation The framework operationalised in this paper is theoretically grounded in Kaminski (2026a), A Governança Cibernética da Inteligência Artificial: Do Compliance ao Controle (independently published, KDP: https: //www. amazon. com/dp/B0GX33QDTZ). The book establishes the tripartite governance taxonomy (Type I principled, Type II risk-management, Type III cybernetic) and identifies the empirical absence of Type III implementations that the present paper addresses through the Q-FENG proof-of-concept pipeline. Editorial history An earlier working-paper version is available on SSRN (DOI: 10. 2139/ssrn. 6433122). The present Zenodo deposit is the revised and expanded version, incorporating the adversarial CLT extension (3, 700 jobs across four architectural arms) and the 70-epidemiological-week Manaus O2 Crisis retrospective. Code and data availability Companion repository (private, access by invitation): https: //github. com/Ricardo-Kaminski/qfengᵥalidation. All parquet result files, Clingo corpus, and pipeline code are included. Reproducibility is guaranteed via deterministic Clingo evaluation, SHA-256 cached DeonticAtoms, and fixed random seeds documented in the pre-registration.

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