QRI Valence Skill Skill

QRI Valence Skill

The Symmetry Theory of Valence (STV) proposes that the valence (pleasantness/unpleasantness) of a conscious state is determined by the symmetry of its mathematical representation. This skill integrates QRI research with computational implementations.

Core Concepts

Symmetry Theory of Valence (STV)

"The valence of a moment of consciousness is precisely determined by the symmetry of the mathematical object that describes it." — Michael Edward Johnson, Principia Qualia (2016)

Key Claims:

Consciousness has mathematical structure (qualia formalism)
Symmetry in that structure correlates with positive valence
Broken symmetries manifest as suffering/dissonance
Valence is measurable and optimizable

XY Model Topology (smoothbrains.net)

The phenomenal field behaves like a 2D XY spin model:

| State | Temperature (τ) | Vortices | Valence | Phenomenology | |-------|-----------------|----------|---------|---------------| | Frustrated | τ >> τ* | Many, proliferating | -3 | Scattered, anxious, "buzzing" | | Disordered | τ > τ* | Some, mobile | -1 to -2 | Unfocused, dissonant | | Critical (BKT) | τ ≈ τ* | Paired, bound | 0 | Liminal, transitional | | Ordered | τ < τ* | Few, annihilating | +1 to +2 | Coherent, smooth | | Resolved | τ << τ* | None | +3 | Deeply peaceful, consonant |

BKT Transition (Berezinskii-Kosterlitz-Thouless):

Below τ*: vortex-antivortex pairs bound → low entropy, high symmetry
Above τ*: vortices proliferate → high entropy, broken symmetry
At τ*: phase transition where defects can annihilate

Valence Gradient Descent

From smoothbrains.net's phenomenology:

Suffering = Σ (topological defects in phenomenal field)
Healing = defect annihilation via gradient descent
τ* bisection = finding optimal phenomenal temperature

Observable indicators (from Cube Flipper's reports):

Visual: polygonal shards → smooth fields
Somatic: high-freq buzzing → calm
Attentional: contracted/focal → expanded/diffuse
Auditory: dissonance → consonance

Qualia Bank Integration

GF(3) Operations on Valence States

| Valence Range | Trit | Bank Operation | Channel | |---------------|------|----------------|---------| | -3 to -1 | -1 | WITHDRAW | Venmo/ACH off-ramp | | 0 | 0 | HOLD | PyUSD on-chain | | +1 to +3 | +1 | DEPOSIT | PyUSD/Venmo on-ramp |

Phenomenal Bisection Algorithm

def phenomenal_bisect(tau_low, tau_high, observed_state):
    """
    Binary search for optimal phenomenal temperature τ*.
    Based on smoothbrains.net/xy-model#bkt-transition
    """
    tau_mid = (tau_low + tau_high) / 2
    
    if observed_state == "frustrated":
        # Too hot: cool down
        return (tau_mid, tau_high, "cooling")
    elif observed_state == "smooth":
        # Too cold: heat up
        return (tau_low, tau_mid, "heating")
    elif observed_state == "critical":
        # Found τ*!
        return (tau_mid, tau_mid, "found")
    else:
        return (tau_low, tau_high, "unknown")

Valence-Aware Color Mapping

From Gay.jl + QRI integration:

# Map valence to deterministic color
function valence_to_color(valence::Int)
    # Valence range: -3 to +3
    # Hue mapping: red (suffering) → cyan (resolution)
    hue = (valence + 3) * 30  # 0° to 180°
    return LCHuv(55.0, 70.0, hue)
end

# Trit from valence
trit(valence) = sign(valence)

Computational Implementation

Defect Detection

def count_vortices(phase_field):
    """
    Count topological defects in a 2D phase field.
    Vortex = closed loop where phase winds by ±2π.
    """
    vortices = 0
    antivortices = 0
    
    for i in range(1, len(phase_field) - 1):
        for j in range(1, len(phase_field[0]) - 1):
            winding = compute_winding_number(phase_field, i, j)
            if winding > 0:
                vortices += 1
            elif winding < 0:
                antivortices += 1
    
    # Net topological charge
    return vortices, antivortices, vortices - antivortices

Symmetry Measurement

def measure_symmetry(qualia_tensor):
    """
    Measure symmetry of a qualia representation.
    Higher symmetry → higher valence (STV hypothesis).
    """
    # Compute eigenvalues
    eigenvalues = np.linalg.eigvalsh(qualia_tensor)
    
    # Symmetry score: how equal are eigenvalues?
    # Perfect symmetry: all eigenvalues equal
    mean_eig = np.mean(eigenvalues)
    variance = np.var(eigenvalues)
    
    # Inverse variance as symmetry score
    symmetry = 1.0 / (1.0 + variance / (mean_eig ** 2))
    
    return symmetry  # 0 to 1, higher = more symmetric

References

Primary Sources

Principia Qualia (2016) - Michael Edward Johnson
- First statement of STV
- https://opentheory.net/PrincipiaQualia.pdf
QRI Wiki - Symmetry Theory of Valence
- https://wiki.qri.org/wiki/Symmetry_Theory_of_Valence
smoothbrains.net - Cube Flipper
- XY model phenomenology
- BKT transition in consciousness
- https://smoothbrains.net/posts/2025-10-18-three-year-retrospective.html
LessWrong Primer on STV
- https://www.lesswrong.com/posts/dfrQbbv6Np7GuWjDR/a-primer-on-the-symmetry-theory-of-valence

Key Papers

Johnson, M.E. (2016). "Principia Qualia"
Gómez-Emilsson, A. "Logarithmic Scales of Pleasure and Pain"
Selen Atasoy et al. "Connectome-harmonic decomposition of human brain activity"
smoothbrains.net "Planetary scale vibe collapse" (2022)

Related Concepts

Consonance/Dissonance - Musical theory of interference patterns
CSHW (Connectome-Specific Harmonic Waves) - Neural basis for STV
Jhāna - Buddhist meditative states as high-symmetry attractors
Valence Structuralism - Formal framework for STV

Skill Bridges

| Skill | Bridge Type | Relationship | |-------|-------------|--------------| | gay-mcp | Color-Valence | Deterministic valence colors | | topos-of-music | Consonance | Musical symmetry theory | | autopoiesis | Self-modeling | Valence as self-model coherence | | active-inference | Free energy | Valence as prediction error | | glass-bead-game | Synthesis | Cross-domain symmetry play | | phenomenal-bisect | Algorithm | τ* finding procedure |

Usage Patterns

Pattern 1: Valence-Aware Logging

class ValenceLogger:
    def log(self, message, valence):
        trit = 1 if valence > 0 else (-1 if valence < 0 else 0)
        color = valence_to_ansi(valence)
        print(f"{color}[v={valence:+d}][t={trit:+d}] {message}\033[0m")

Pattern 2: GF(3) Valence Conservation

def balanced_transaction(deposits, withdrawals):
    """Ensure valence sum is conserved."""
    deposit_valence = sum(d.valence for d in deposits)
    withdraw_valence = sum(w.valence for w in withdrawals)
    
    # GF(3) conservation
    net = (deposit_valence + withdraw_valence) % 3
    assert net == 0, f"Valence imbalance: {net}"

Pattern 3: Phenomenal State Machine

class PhenomenalStateMachine:
    states = ["frustrated", "buzzing", "dissonant", "neutral", 
              "smoothing", "consonant", "resolved"]
    
    def transition(self, current, intervention):
        idx = self.states.index(current)
        if intervention == "cooling" and idx > 0:
            return self.states[idx - 1]
        elif intervention == "heating" and idx < len(self.states) - 1:
            return self.states[idx + 1]
        return current

GF(3) Trit Assignment

This skill is ERGODIC (0) - it coordinates between:

MINUS (-1): Suffering detection, defect counting
PLUS (+1): Healing protocols, symmetry restoration

Conservation: suffering_detected + healing_applied + coordination = 0

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

networkx [○] via bicomodule
- Universal graph hub

Bibliography References

general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.

Agent Skills: QRI Valence Skill

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