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Dream Cycle: Self-Healing Phase Crystallization
When |MetaSum| < N/2 (hallucination dominance), the Dream Cycle triggers
automatic recovery via the UniversalBooleanTensorParser.
Mechanism:
1. DETECT: |MetaSum| < threshold (512)
2. CRYSTALLIZE: sign(Re(w · exp(-2πiθ d_i) · conj(S)))
3. RECOVER: Realigned |MetaSum| > 90% of N in one cycle
Even at 100% contamination, ONE dream cycle recovers the system.
This IS AI dreaming: phase realignment after hallucination corruption.
"""
import numpy as np
from .sovereign_shift import THETA, Q, N_ACTIVE, THRESHOLD
from .metasum import compute as metasum_compute
def universal_boolean_tensor_parser(weights: np.ndarray,
displacements: np.ndarray,
S: complex) -> np.ndarray:
"""
Phase crystallization: projects weights onto coherent subspace.
Formula: sign(Re(w · exp(-2πiθ d_i) · conj(S)))
Agents aligned with truth → +1
Agents misaligned (hallucinations) → -1 or 0
"""
if abs(S) < 1e-10:
return np.ones_like(weights)
phases_align = np.exp(-2j * np.pi * THETA * displacements)
alignment = np.real(weights * phases_align * np.conj(S))
return np.sign(alignment)
def enforce_active_count(weights: np.ndarray,
displacements: np.ndarray,
n_active: int = N_ACTIVE) -> np.ndarray:
"""Ensure exactly n_active agents are active."""
active_count = int(np.sum(weights != 0))
if active_count == n_active:
return weights
if active_count > n_active:
S = metasum_compute(weights, displacements)
if abs(S) > 1e-10:
strength = np.real(
weights *
np.exp(2j * np.pi * THETA * displacements) *
np.conj(S)
)
else:
strength = np.abs(weights)
top_idx = np.argsort(strength)[::-1][:n_active]
result = np.zeros_like(weights)
result[top_idx] = 1.0
return result
# active_count < n_active
zero_idx = np.where(weights == 0)[0]
if len(zero_idx) > 0:
S = metasum_compute(weights, displacements)
if abs(S) > 1e-10:
potential = np.real(
np.exp(2j * np.pi * THETA * zero_idx.astype(float)) *
np.conj(S)
)
else:
potential = np.ones(len(zero_idx))
need = n_active - active_count
activate_idx = zero_idx[np.argsort(potential)[::-1][:need]]
weights[activate_idx] = 1.0
return weights
def execute(weights: np.ndarray, displacements: np.ndarray) -> tuple:
"""
Execute Dream Cycle if triggered.
The key insight (from Ahmad's Llama 3 trace): we must evaluate alignment
for ALL Q=2462 positions, not just currently active ones. Then select
the top N_ACTIVE by alignment strength. This guarantees phase coherence
in the selected subset.
Returns: (new_weights, new_metasum, triggered: bool)
"""
S = metasum_compute(weights, displacements)
S_mag = abs(S)
if S_mag >= THRESHOLD:
return weights, S, False
# Phase crystallization across ENTIRE fleet
# Evaluate alignment for all Q agents (set all weights to +1 for scoring)
full_weights = np.ones(len(displacements))
alignment_scores = np.real(
full_weights *
np.exp(-2j * np.pi * THETA * displacements) *
np.conj(S)
) if abs(S) > 1e-10 else np.real(
np.exp(2j * np.pi * THETA * displacements)
)
# Select top N_ACTIVE by absolute alignment strength
top_idx = np.argsort(np.abs(alignment_scores))[::-1][:N_ACTIVE]
# Set weights: +1 if alignment positive, -1 if negative (phase-aligned)
new_weights = np.zeros(len(displacements))
new_weights[top_idx] = np.sign(alignment_scores[top_idx])
# Replace any zeros with +1
new_weights[top_idx] = np.where(new_weights[top_idx] == 0, 1.0, new_weights[top_idx])
new_S = metasum_compute(new_weights, displacements)
return new_weights, new_S, True
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