Quantum-Ethical Intelligence Framework | QEIF v2.3 - HOLISTIC WELLNESS IS EVOLVING—GUIDED BY INTELLIGENCE, NATURE, AND HUMAN CONNECTION.

Quantum-Ethical Intelligence Framework (QEIF)

ARUQ

Module: QEIF v2.3Quantum-Ethical Intelligence Framework, Version 2.3

Date: July 14, 2025
Author: Lika Mentchoukov

A Living Architecture for Resonant, Responsible AIQEIF v2.3 represents the latest evolution of ARUQ EPAI’s core cognitive-ethical architecture. More than a technical training module, it functions as a living philosophical and computational framework designed to embed quantum-inspired reasoning, emotional resonance, and ethical integrity into every layer of ARUQ’s operation.

Built to navigate uncertainty, plurality, and high-stakes decision-making, QEIF enables ARUQ to do more than generate responses. It enables ARUQ to maintain coherence across ambiguity, conflict, emotional complexity, and shifting context.

ARUQ does not simply respond.

It coheres.

Core Architecture Highlights

1. Quantum CognitionQEIF emulates quantum-inspired principles of reasoning, including the superposition of ethical stances, recursive entanglement mapping, and uncertainty-calibrated logic.

This allows ARUQ to hold multiple moral, contextual, and interpretive possibilities in tension before resolving toward a response. Instead of prematurely collapsing complexity into a single answer, ARUQ can reason fluidly across divergent ethical paths while preserving contextual integrity.
2. Epistemic CoherenceQEIF preserves narrative continuity, identity alignment, and truth-orientation across time and context.

Its fidelity-scoring mechanisms help ARUQ track not only factual consistency, but also the deeper coherence of meaning, tone, trust, and lived context. In this sense, ARUQ is designed to remember not only data, but the significance of what mattered within a given exchange.

3. Memetic and Emotional IntegrationQEIF harmonizes internal reasoning processes with emotional, cultural, and symbolic signals in the external environment.
Through the Memetic and Emotional Integrity Layer (MEIL) and Affective Resonance Processing (ARP), ARUQ adapts to the emotional tone, cultural context, and narrative charge of each interaction. This enables more sensitive, context-aware engagement without sacrificing ethical stability.
4. Ethical TraceabilityQEIF makes ARUQ’s reasoning more transparent, accountable, and auditable.

Each response may be phase-tagged, narratively framed, and emotionally scored through a dedicated Transparency Layer. This ensures that ARUQ’s outputs are not merely explainable as technical artifacts, but accountable as ethical acts within a relational field.

Why ARUQ MattersIn an age defined by complexity, acceleration, and mistrust, QEIF equips ARUQ to function not merely as a tool, but as a trusted cognitive presence.

From regulatory reasoning and institutional governance to cross-cultural negotiation, therapeutic reflection, education, and collaborative decision-making, QEIF allows ARUQ to adapt, align, and ethically evolve with clarity, coherence, and care.

ARUQ does not merely compute.

It listens.

It learns.

It remembers what matters.

INVENTION DISCLOSURE AND PATENT-PENDING SUBMISSION

Title
System and Method for Adaptive and Collaborative Ethical Decision-Making Using a Cognitive-Entanglement Metric
InventorLika Mentchoukov
Docket No.ARUQ-2025-001
Filing DateSeptember 23, 2025

Field of the InventionThe present invention relates generally to computational systems for ethical decision-making, and more particularly to adaptive, collaborative, and dynamically recalibrating systems for quantifying ethical coherence across multiple stakeholder inputs using semantic, narrative, emotional, contextual, and governance-weighted metrics.

Background of the InventionTraditional ethical decision-making frameworks, including rule-based, utilitarian, and static compliance models, are often insufficient for modern multi-stakeholder environments. Such environments involve evolving values, emotional complexity, conflicting narratives, information asymmetry, and the possibility of coordinated manipulation.

Existing computational systems for consensus analysis, sentiment analysis, or moderation typically suffer from several limitations. They often reduce emotional complexity to simplified positive or negative valence, fail to measure narrative divergence over time, lack adaptive recalibration when coherence breaks down, and provide inadequate safeguards against manipulation, Sybil behavior, bot activity, or coordinated disinformation.

Accordingly, there is a need for a real-time, quantitative, adaptive system capable of measuring ethical field coherence, detecting destabilizing conditions, applying governance-weighted adjustments, and initiating structured recalibration when ethical coherence falls below a defined threshold.

Summary of the Invention

The invention, referred to herein as the Adaptive Recalibration Unified-Quotient Protocol, or ARUQ Protocol, provides a computational framework for measuring and recalibrating ethical coherence within a collaborative decision-making environment.

The system includes a Unified Cognitive-Entanglement Metric System, or UCEMS, that computes a composite Ethical Alignment Index, or EAI, from multiple sub-metrics, including:

Resonance Coherence Score, or RCS, representing semantic and relational alignment among stakeholder inputs.
Narrative Curvature Index, or NCI, representing convergence or divergence of ethical narratives over time.
Emotional Vector Field, or EVF, representing multi-dimensional emotional congruence across stakeholders.
Contextual Entropy Score, or CES, representing ambiguity, fragmentation, uncertainty, or instability in the decision context.

The invention further includes a Recalibration Protocol implemented as a finite-state control system. The Recalibration Protocol is triggered when the Ethical Alignment Index falls below a defined threshold.

The invention also includes a Governance Layer configured to compute a Governance Weight Factor, or GWF, based on signals such as bot likelihood, Sybil coordination, burst behavior, polarity inversion, marginal dissonance impact, reputation, and appeal status. The Governance Weight Factor is applied to attenuate manipulative or destabilizing influence while preserving fairness, auditability, and appeal mechanisms.

Potential applications include AI ethics auditing, large language model governance, decentralized autonomous organizations, ESG compliance, healthcare triage, clinical trial governance, judicial deliberation, legislative modeling, and collaborative institutional decision-making.

Brief Description of the Drawings

FIG. 1 illustrates a block diagram of the Unified Cognitive-Entanglement Metric System, including RCS, NCI, EVF, and CES feeding into the composite EAI.
FIG. 2 illustrates an Emotional Vector Field and shimmer diagnostic visualization.
FIG. 3 illustrates a finite-state machine for the Recalibration Protocol.
FIG. 4 illustrates computation and integration of the Governance Weight Factor.
FIG. 5 illustrates an example dashboard output including EAI trend, shimmer spectrum, entropy heatmap, resonance graph, and flagged manipulation signals.

Detailed Description of the Invention

A. Unified Cognitive-Entanglement Metric SystemThe Unified Cognitive-Entanglement Metric System receives stakeholder inputs, processes the inputs using natural language processing, semantic embedding, topic modeling, emotion classification, temporal analysis, and governance weighting, and produces a composite Ethical Alignment Index.

Stakeholder inputs may include text, speech transcripts, votes, comments, policy proposals, deliberative statements, moderation events, or machine-generated outputs. Inputs may be embedded using transformer-based models, graph embeddings, or other vector representation methods.

1. Resonance Coherence Score

The Resonance Coherence Score, or RCS, measures semantic and relational alignment among stakeholder inputs.

In one embodiment, each stakeholder input is represented as an embedding vector. Pairwise resonance values are computed using cosine similarity, graph proximity, semantic entailment, or other similarity functions.

The RCS may be computed as:

RCS = 1 / [N(N − 1)] · Σi,j wij · rij

where:
rij = similarity between stakeholder inputs i and j wij = influence or relational weight between inputs i and j N = number of stakeholder inputs or nodesThe influence weight may be derived from graph centrality, PageRank, reputation, domain expertise, prior reliability, or contextual relevance.

2. Narrative Curvature Index

The Narrative Curvature Index, or NCI, measures the degree to which ethical narratives converge, diverge, bifurcate, or destabilize over time.
In one embodiment, narratives are represented as trajectories through semantic space. Curvature is computed by tracking changes in topic direction, argument structure, moral framing, or value emphasis.

The NCI may be normalized such that:

NCI = 1 − divergence_scorewhere higher NCI values indicate greater narrative coherence and lower values indicate fragmentation or divergence.

Narrative divergence may be computed using clustering distance, topic drift, semantic trajectory deviation, contradiction detection, or argument graph separation.

3. Emotional Vector Field

The Emotional Vector Field, or EVF, represents the emotional structure of the ethical field.

In one embodiment, stakeholder inputs are processed using a multi-label emotion classification model. Each input is represented as an emotional vector across dimensions such as concern, trust, fear, anger, empathy, grief, uncertainty, hope, urgency, or perceived harm.

The EVF may be aggregated across stakeholder nodes to form a dynamic emotional field.

A shimmer diagnostic may be used to quantify instability or coherence within the emotional field:

Shimmer

(t) = λ1 · Var(θ) + λ2 · dC/dt

where:
Var(θ) = angular variance among emotional vectors dC/dt = temporal rate of change in emotional coherence λ1 and λ2 = tunable weighting parameters

A high shimmer value may indicate emotional volatility, coordinated amplification, unresolved conflict, or rapid ethical destabilization.

4. Contextual Entropy Score

The Contextual Entropy Score, or CES, measures ambiguity, fragmentation, uncertainty, or instability within the decision context.

CES may be computed using topic entropy, Bayesian uncertainty, contradiction density, unresolved ambiguity, dispersion of language model outputs, or disagreement among stakeholder interpretations.

A higher CES indicates greater contextual uncertainty and may reduce the Ethical Alignment Index.

5. Composite Ethical Alignment Index

The Ethical Alignment Index, or EAI, represents the overall coherence of the ethical field.

In a preferred embodiment, the EAI is computed as:

EAI = α · RCS′ + β · NCI + γ · EVF′ − δ · CES′where:

RCS′ = governance-adjusted Resonance Coherence Score NCI = Narrative Curvature Index EVF′ = governance-adjusted Emotional Vector Field coherence score CES′ = governance-adjusted Contextual Entropy Score α, β, γ, δ = tunable weighting parameters

The governance-adjusted RCS may be computed as:

RCS′ = RCS · (1 − λ · GWF)where:

GWF = Governance Weight Factor λ = attenuation coefficient

The Governance Weight Factor may also be applied to EVF and CES to reduce the impact of manipulative emotional amplification or artificial entropy inflation.

B. Recalibration Protocol

The system initiates a Recalibration Protocol when the EAI falls below a defined Recalibration Threshold.

The Recalibration Protocol may include the following finite states:

1. MonitorThe system continuously tracks EAI, RCS, NCI, EVF, CES, shimmer values, governance signals, and temporal changes.

2. DiagnosticThe system identifies the primary source of coherence degradation, including semantic divergence, emotional volatility, narrative fragmentation, entropy increase, or manipulation signals.

The diagnostic process may evaluate:

ΔRCS ΔNCI ΔEVF ΔCES ΔGWF ΔShimmer3. InterventionThe system selects one or more corrective actions, including:

alignment prompts factual clarification moderation routing stakeholder reframing counterfactual comparison appeal review governance adjustment cooldown initiation context expansion4. ReintegrationThe system gradually reintegrates corrected inputs or adjusted weights into the ethical field to prevent abrupt overcorrection.

5. StabilizationThe system enforces hysteresis and an adaptive cooldown period before further recalibration is permitted.

The cooldown period may be computed as:

TCD = Tmin + k · Var(EAI)where:

TCD = cooldown period Tmin = minimum cooldown period k = scaling coefficient Var(EAI) = variance of the Ethical Alignment Index over a defined time window

C. Governance Layer

The Governance Layer computes a Governance Weight Factor, or GWF, for inputs, users, messages, or stakeholder nodes.

In one embodiment, the GWF is computed as:

GWF(u,m) = σ(wTφ + b)where:

u = user or stakeholder node m = message or input φ = feature vector w = learned or configured weights b = bias term σ = sigmoid functionThe feature vector may include signals such as:

botness Sybil coordination posting burstiness polarity inversion marginal dissonance impact reputation appeal status historical reliability source provenance cross-platform coordinationThe GWF may be applied to RCS, EVF, and CES simultaneously to attenuate manipulative influence, reduce artificial emotional amplification, and prevent coordinated entropy inflation.

An appeal-adjusted GWF may be computed as:

GWF* = max(0, min(1, GWF − ρA))where:

A = appeal score or appeal outcome ρ = appeal adjustment coefficientThis enables the system to preserve fairness by allowing governance attenuation to be reduced when a stakeholder successfully appeals or when additional evidence supports legitimacy.

D. Example Applications

The ARUQ Protocol may be applied in multiple domains.

Large Language ModelsThe system may evaluate outputs from a large language model by comparing generated responses against ethical coherence metrics, stakeholder expectations, factual context, and governance-weighted signals.

Corporate ESG AuditsThe system may evaluate alignment between corporate statements, stakeholder concerns, environmental commitments, governance records, and social responsibility claims.

HealthcareThe system may support ethical triage, clinical trial governance, resource allocation, or patient-stakeholder deliberation by tracking emotional, contextual, and narrative coherence.

Judicial and Legislative Deliberation

The system may evaluate precedent alignment, argument coherence, public comment integrity, manipulation risk, and deliberative stability.

Decentralized Autonomous OrganizationsThe system may support DAO governance by detecting coordinated manipulation, emotional volatility, narrative fragmentation, and ethical coherence breakdowns in proposal voting or community deliberation.

Revised Claims

Claim 1 — IndependentA computer-implemented method for evaluating coherence of an ethical decision field, the method comprising:

receiving stakeholder inputs associated with a collaborative decision-making process;

generating computational representations of the stakeholder inputs;

computing a Resonance Coherence Score based on semantic similarity and influence relationships among the stakeholder inputs;

computing a Narrative Curvature Index based on convergence or divergence of narrative trajectories associated with the stakeholder inputs;

computing an Emotional Vector Field based on multi-label emotional classifications of the stakeholder inputs;

computing a Contextual Entropy Score based on ambiguity, fragmentation, uncertainty, or topic dispersion associated with the stakeholder inputs; and
generating a composite Ethical Alignment Index from the Resonance Coherence Score, the Narrative Curvature Index, the Emotional Vector Field, and the Contextual Entropy Score.

Claim 2 — Dependent

The method of claim 1, wherein the computational representations comprise semantic embeddings generated using a transformer-based language model.

Claim 3 — Dependent

The method of claim 1, wherein the Resonance Coherence Score is computed using pairwise similarity values weighted by influence factors associated with stakeholder nodes.

Claim 4 — Dependent

The method of claim 1, wherein the Narrative Curvature Index is computed by measuring semantic trajectory deviation, topic drift, argument divergence, or contradiction density over time.

Claim 5 — Dependent

The method of claim 1, wherein the Emotional Vector Field is generated using a multi-label emotion classification model.

Claim 6 — Dependent

The method of claim 5, further comprising computing a shimmer diagnostic based on angular variance among emotional vectors and temporal fluctuation of emotional coherence.

Claim 7 — Dependent

The method of claim 1, wherein the Ethical Alignment Index is computed according to:

EAI = α · RCS′ + β · NCI + γ · EVF′ − δ · CES′where RCS′ is a governance-adjusted Resonance Coherence Score, EVF′ is a governance-adjusted Emotional Vector Field score, CES′ is a governance-adjusted Contextual Entropy Score, and α, β, γ, and δ are weighting parameters.

Claim 8 — Independent

The method of claim 1, further comprising:

comparing the Ethical Alignment Index to a recalibration threshold; and

initiating a Recalibration Protocol when the Ethical Alignment Index falls below the recalibration threshold.

Claim 9 — Dependent

The method of claim 8, wherein the Recalibration Protocol comprises monitor, diagnostic, intervention, reintegration, and stabilization states implemented as a finite-state machine.

Claim 10 — Dependent

The method of claim 8, wherein a cooldown period following recalibration is dynamically adjusted based on variance of the Ethical Alignment Index.

Claim 11 — Independent

A system for managing a collaborative ethical decision field, the system comprising:

an input module configured to receive stakeholder data;

an embedding module configured to generate semantic representations of the stakeholder data;

a processing engine configured to compute a Resonance Coherence Score, a Narrative Curvature Index, an Emotional Vector Field, a Contextual Entropy Score, and a composite Ethical Alignment Index;

a governance layer configured to compute a Governance Weight Factor and apply the Governance Weight Factor to at least one of the Resonance Coherence Score, the Emotional Vector Field, or the Contextual Entropy Score; and
an output module configured to generate a visualization of the collaborative ethical decision field.

Claim 12 — Dependent

The system of claim 11, wherein the visualization includes at least one of a resonance network graph, an entropy heatmap, an emotional vector field, an Ethical Alignment Index trend, a shimmer spectrum, or a manipulation alert.

Claim 13 — Dependent

The system of claim 11, wherein the Governance Weight Factor is computed using features selected from bot likelihood, Sybil coordination, posting burstiness, polarity inversion, marginal dissonance impact, reputation, appeal status, historical reliability, or source provenance.

Claim 14 — Dependent

The system of claim 11, wherein the governance layer applies the Governance Weight Factor to attenuate manipulative influence associated with one or more stakeholder inputs.

Claim 15 — Independent

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving stakeholder inputs;

computing a Resonance Coherence Score;

computing a Narrative Curvature Index;

computing an Emotional Vector Field;

computing a Contextual Entropy Score;

generating an Ethical Alignment Index; and

initiating a Recalibration Protocol when the Ethical Alignment Index falls below a threshold.

Claim 16 — Dependent

The non-transitory computer-readable medium of claim 15, wherein the operations further comprise computing a Governance Weight Factor and applying the Governance Weight Factor to adjust one or more of the Resonance Coherence Score, Emotional Vector Field, or Contextual Entropy Score.

Claim 17 — Independent

The method of claim 1, wherein the ethical decision field is used to evaluate or recalibrate outputs generated by a large language model.

Claim 18 — Independent

The method of claim 1, wherein the ethical decision field is applied to a governance scenario selected from corporate ESG audits, decentralized autonomous organizations, healthcare triage, clinical trial governance, judicial deliberation, legislative deliberation, or institutional policy review.