""" Self-Layer (S''') Implementation The Self-Layer optimizes the self-improvement process itself: 1. Monitors convergence of S'' 2. Prevents divergence via complexity penalties 3. Aligns optimization with goals 4. Triggers structural modifications S'''[S'', S'] is the outermost recursive loop. """ import numpy as np from typing import Optional, Tuple, List, Dict, Any, Callable from dataclasses import dataclass from .salience import SelfSalienceFunctional, SalienceWeights from .meta_layer import MetaLayer @dataclass class SelfConfig: """Configuration for the Self-Layer.""" stability_window: int = 10 divergence_threshold: float = 10.0 complexity_budget: float = 1000.0 alignment_weight: float = 0.5 adaptation_rate: float = 0.05 class SelfLayer: """ Self-Layer (S''') - Optimizes the self-improvement process. Monitors: - Meta-optimization convergence - System complexity (Kolmogorov-like proxy) - Goal alignment Actions: - Adjust meta-learning rate - Trigger architecture changes - Reset unstable components """ def __init__(self, meta_layer: MetaLayer, config: Optional[SelfConfig] = None): self.meta_layer = meta_layer self.config = config or SelfConfig() # Self-salience tracking self.self_salience = SelfSalienceFunctional() # Monitoring history self.s_pp_history: List[float] = [] # S'' values self.complexity_history: List[float] = [] self.stability_history: List[float] = [] self.alignment_history: List[float] = [] # Control signals self.meta_lr_multiplier: float = 1.0 self.architecture_growth_enabled: bool = True self.emergency_reset_triggered: bool = False # Goal tracking self.goal_function: Optional[Callable] = None self.goal_progress: List[float] = [] def set_goal_function(self, goal_fn: Callable[[Any], float]): """Set the goal function for alignment tracking.""" self.goal_function = goal_fn def step(self, t: int) -> Dict[str, Any]: """ Execute one step of self-optimization. 1. Observe meta-layer state 2. Compute self-salience 3. Apply control actions """ # Get current meta-state s_pp = self.meta_layer.get_meta_salience() meta_diagnostics = self.meta_layer.get_diagnostics() meta_params = self.meta_layer.current_weights.to_array() # Compute self-salience self_s, components = self.self_salience.compute_self_salience( t=t, current_s_pp=s_pp, meta_params=meta_params ) self.s_pp_history.append(s_pp) # Compute system complexity complexity = self._compute_complexity(meta_params) self.complexity_history.append(complexity) # Compute stability stability = self._compute_stability() self.stability_history.append(stability) # Compute alignment (if goal function set) alignment = self._compute_alignment() self.alignment_history.append(alignment) # Apply control actions actions_taken = self._apply_control(stability, complexity, alignment) return { 'self_salience': self_s, 'components': components.to_dict(), 'complexity': complexity, 'stability': stability, 'alignment': alignment, 'actions': actions_taken, 'meta_lr_multiplier': self.meta_lr_multiplier } def _compute_complexity(self, params: np.ndarray) -> float: """ Approximate system complexity. Uses parameter entropy and effective dimensionality. """ # Normalize parameters params_abs = np.abs(params) + 1e-10 params_norm = params_abs / np.sum(params_abs) # Entropy-based complexity entropy = -np.sum(params_norm * np.log(params_norm)) # Effective dimensionality (number of significant parameters) threshold = 0.01 * np.max(params_abs) effective_dim = np.sum(params_abs > threshold) complexity = entropy * np.log(effective_dim + 1) return float(complexity) def _compute_stability(self) -> float: """ Compute optimization stability. Low variance in S'' -> high stability. """ if len(self.s_pp_history) < self.config.stability_window: return 1.0 recent = self.s_pp_history[-self.config.stability_window:] variance = np.var(recent) mean_abs = np.abs(np.mean(recent)) + 1e-8 cv = np.sqrt(variance) / mean_abs # Coefficient of variation stability = np.exp(-cv) return float(stability) def _compute_alignment(self) -> float: """Compute alignment with goal function.""" if self.goal_function is None: return 0.5 try: alignment = self.goal_function(self.meta_layer) alignment = np.clip(alignment, 0, 1) self.goal_progress.append(alignment) return float(alignment) except: return 0.5 def _apply_control(self, stability: float, complexity: float, alignment: float) -> List[str]: """ Apply control actions based on system state. """ actions = [] # Stability control if stability < 0.3: self.meta_lr_multiplier *= 0.8 actions.append('decreased_meta_lr') if stability < 0.1: self.emergency_reset_triggered = True actions.append('emergency_reset_triggered') elif stability > 0.9 and self.meta_layer.stagnation_counter > 5: self.meta_lr_multiplier *= 1.2 actions.append('increased_meta_lr') # Complexity control if complexity > self.config.complexity_budget: self.architecture_growth_enabled = False actions.append('disabled_growth') else: self.architecture_growth_enabled = True # Alignment control if alignment < 0.3: # Increase meaning weight to prioritize goal alignment self.meta_layer.current_weights.w_M *= 1.1 actions.append('boosted_meaning_weight') # Apply meta-LR multiplier self.meta_layer.meta_lr *= self.meta_lr_multiplier self.meta_layer.meta_lr = np.clip(self.meta_layer.meta_lr, 0.001, 1.0) self.meta_lr_multiplier = 1.0 # Reset multiplier return actions def should_grow_architecture(self) -> bool: """Check if architecture growth is allowed and needed.""" if not self.architecture_growth_enabled: return False return self.meta_layer.check_architecture_trigger() def emergency_reset(self): """ Emergency reset when system is diverging. Resets meta-layer to known good state. """ if self.emergency_reset_triggered: # Reset to best known weights if self.meta_layer.best_weights is not None: self.meta_layer.current_weights = self.meta_layer.best_weights # Reset meta learning rate self.meta_layer.meta_lr = self.meta_layer.config.meta_learning_rate # Clear stagnation self.meta_layer.stagnation_counter = 0 self.emergency_reset_triggered = False return True return False def get_self_salience(self) -> float: """Get cumulative self-salience S'''.""" return self.self_salience.cumulative_salience def get_diagnostics(self) -> Dict[str, Any]: """Get diagnostic information.""" return { 'self_salience': self.get_self_salience(), 'complexity': self.complexity_history[-1] if self.complexity_history else 0, 'stability': self.stability_history[-1] if self.stability_history else 1, 'alignment': self.alignment_history[-1] if self.alignment_history else 0.5, 'growth_enabled': self.architecture_growth_enabled, 'emergency_reset': self.emergency_reset_triggered } class RecursiveAGICore: """ Complete three-layer recursive AGI system. Integrates S', S'', S''' into a unified optimization loop. AGI = argmax_{π, θ, L} [ S'[ω] + S''[π, θ, L] + S'''[S'', S'] ] """ def __init__(self, trajectory_config: Optional[Any] = None, meta_config: Optional[Any] = None, self_config: Optional[SelfConfig] = None): from .trajectory_layer import TrajectoryLayer, TrajectoryConfig from .meta_layer import MetaConfig # Build layers self.trajectory_layer = TrajectoryLayer(trajectory_config or TrajectoryConfig()) self.meta_layer = MetaLayer(self.trajectory_layer, meta_config or MetaConfig()) self.self_layer = SelfLayer(self.meta_layer, self_config or SelfConfig()) # Unified tracking self.total_steps: int = 0 self.s_prime_history: List[float] = [] self.s_double_prime_history: List[float] = [] self.s_triple_prime_history: List[float] = [] self.agi_score_history: List[float] = [] def step(self, task) -> Dict[str, Any]: """Execute one full AGI step.""" # Trajectory layer step obs = task.get_observation() action, traj_info = self.trajectory_layer.step(obs, goal_state=task.get_goal()) obs_next, reward, done = task.step(action) s_prime = traj_info['salience'] self.s_prime_history.append(s_prime) # Meta layer step (periodic) s_double_prime = self.meta_layer.get_meta_salience() self.s_double_prime_history.append(s_double_prime) # Self layer step (periodic) self_info = self.self_layer.step(self.total_steps) s_triple_prime = self_info['self_salience'] self.s_triple_prime_history.append(s_triple_prime) # Compute AGI score agi_score = s_prime + s_double_prime + s_triple_prime self.agi_score_history.append(agi_score) # Check for architecture growth if self.self_layer.should_grow_architecture(): self._trigger_growth() # Check for emergency reset self.self_layer.emergency_reset() self.total_steps += 1 return { 's_prime': s_prime, 's_double_prime': s_double_prime, 's_triple_prime': s_triple_prime, 'agi_score': agi_score, 'action': action, 'done': done, 'self_diagnostics': self_info } def _trigger_growth(self): """Trigger architecture growth event.""" # This would trigger neural architecture growth # Implementation depends on the network type used pass def evolve(self, task, generations: int = 10) -> float: """Run evolutionary optimization.""" self.meta_layer.evolve_weights(task, generations) return self.meta_layer.best_fitness def get_total_salience(self) -> Tuple[float, float, float]: """Get total salience from all layers.""" return ( self.trajectory_layer.get_cumulative_salience(), self.meta_layer.get_meta_salience(), self.self_layer.get_self_salience() )