Qualia Research Institute's Symmetry Theory of Valence (STV) for consciousness research. Maps phenomenal states to bankable assets via XY model topology, BKT transitions, and defect annihilation. Source: smoothbrains.net + QRI wiki. Use for qualia computing, valence gradient optimization, and consciousness-aware system design.
/plugin marketplace add plurigrid/asi/plugin install asi-skills@asi-skillsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
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.
"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:
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):
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):
| 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 |
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")
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)
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
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
Principia Qualia (2016) - Michael Edward Johnson
QRI Wiki - Symmetry Theory of Valence
smoothbrains.net - Cube Flipper
LessWrong Primer on STV
| 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 |
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")
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}"
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
This skill is ERGODIC (0) - it coordinates between:
Conservation: suffering_detected + healing_applied + coordination = 0
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
general: 734 citations in bib.duckdbThis 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
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.