auto_philosophy_1

Snippet from Philosophy.md

# Illustrative pseudocode only -- NOT a literal extract from router.py.
# Real signal names differ: see utility_components (quality/latency/cost/distance/evidence/uncertainty),
# constraints.shadow_prices (not "slacks"), and drift.drift_ema (not "drift_signal").
import numpy as np

def instantaneous_update(theta: np.ndarray, signals: dict, eta: float = 0.01) -> np.ndarray:
    """
    Mechanistic rt: No judge required
    
    Args:
        theta: Current policy parameters
        signals: Dictionary containing drift_ema, constraints, utility_components (illustrative keys)
        eta: Learning rate (default 0.01)
    
    Returns:
        Updated theta with bounded step
    """
    # Decompose mechanistic signals
    delta_E = signals['drift_ema']
    shadow_prices = signals['constraints']['shadow_prices']
    coh = signals['utility_components']['evidence']
    
    # Intrinsic reward: No external judge
    r_t = -delta_E - np.sum(np.maximum(0, -np.array(list(shadow_prices.values())))) + 0.1 * coh  # beta_s=0.1
    
    # Guarded gradient: Trust radius bounds step
    grad_log_pi = np.random.randn(*theta.shape)  # Proxy policy grad
    step = eta * r_t * grad_log_pi
    
    # Apply trust region constraint
    theta_new = theta + np.clip(step, -signals['trust_radius'], signals['trust_radius'])
    
    return theta_new

# Example: Stable Descent in Action
theta = np.zeros(5)
signals = {
    'drift_ema': -0.02,
    'constraints': {'shadow_prices': {'lambda_0': 0.1, 'lambda_1': 0.0}},
    'utility_components': {'evidence': -0.5},
    'trust_radius': 0.5
}

theta_updated = instantaneous_update(theta, signals)
print(f"Theta shift: {theta_updated}")
print(f"Step magnitude: {np.linalg.norm(theta_updated - theta):.4f}")