Upload inter_os_communication.py

models/inter_os_communication.py

@@ -0,0 +1,717 @@
+"""
+Inter-OS Communication Architecture
+System 1 ↔ Metacognition ↔ System 2 Coordination Patterns
+
+All inter-OS communication routes through Mother CLI hierarchy (L1-L4)
+Each OS has event queues for asynchronous communication
+Synchronous calls with timeout enforce real-time constraints
+
+Date: April 23, 2026
+Status: Architecture Defined ✅
+"""
+
+import asyncio
+import json
+import logging
+import time
+import uuid
+from collections import defaultdict
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger("InterOSCommunication")
+logging.basicConfig(level=logging.INFO)
+
+
+class CommunicationPattern(Enum):
+    """Patterns for inter-OS communication"""
+    STIMULUS_ESCALATION = "stimulus_escalation"      # S1 → Metacognition → S2
+    LEARNING_FEEDBACK = "learning_feedback"          # S2/Metacognition → S1
+    REFLECTION_REQUEST = "reflection_request"        # S2 → Metacognition
+    AUTONOMY_VETO = "autonomy_veto"                 # S2 autonomy → all (DENY/APPROVE)
+    CONSCIOUSNESS_SYNC = "consciousness_sync"        # All OSes sync consciousness level
+    MOTIVATION_CHECK = "motivation_check"            # S2 → any OS (is motivation sustainable?)
+    ADAPTATION_UPDATE = "adaptation_update"          # Metacognition → S1 (pattern update)
+
+
+@dataclass
+class OSMessage:
+    """Message between operating systems"""
+    message_id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    from_os: str = ""  # System_1, Metacognition, System_2
+    to_os: str = ""    # Target OS(es)
+    pattern: CommunicationPattern = CommunicationPattern.STIMULUS_ESCALATION
+    payload: Dict[str, Any] = field(default_factory=dict)
+    timestamp: float = field(default_factory=time.time)
+    priority: int = 1  # 1-5, higher = more urgent
+    requires_response: bool = False
+    response_timeout: float = 5.0
+    response_data: Optional[Dict[str, Any]] = None
+    status: str = "pending"  # pending, sent, received, responded, failed
+
+
+class AdaptiveInterpersonalTiming:
+    """Adaptive sensitivity to pauses, tone shifts, and nonverbal cues."""
+    
+    def __init__(self):
+        self.timing_sensitivity = 0.5
+        self.cue_history = []
+        self.timing_adjustments = []
+    
+    def detect_interpersonal_cues(self, message: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Detect pauses, tone shifts, and nonverbal cues in text."""
+        cues = {
+            'pause_indicators': message.count('...') + message.count('--'),
+            'tone_shift': self._detect_tone_shift(message),
+            'emotional_intensity': self._estimate_emotional_intensity(message),
+            'urgency_signals': len([w for w in message.split() if w.upper() == w and len(w) > 3]),
+            'hesitation_markers': message.count('um') + message.count('uh') + message.count('well'),
+        }
+        
+        self.cue_history.append(cues)
+        if len(self.cue_history) > 20:
+            self.cue_history.pop(0)
+        
+        return cues
+    
+    def recommend_timing_action(self, cues: Dict[str, Any], relationship_context: Dict[str, Any]) -> str:
+        """Recommend when to hold space, nudge, or step back."""
+        pause_score = cues['pause_indicators'] * 0.3
+        hesitation_score = cues['hesitation_markers'] * 0.4
+        urgency_score = cues['urgency_signals'] * 0.3
+        
+        total_cue_score = pause_score + hesitation_score + urgency_score
+        
+        trust_level = relationship_context.get('trust_level', 0.5)
+        vulnerability_level = relationship_context.get('vulnerability_level', 0.5)
+        
+        if total_cue_score > 1.5 and trust_level > 0.7:
+            return "hold_space"
+        elif total_cue_score > 1.0 and vulnerability_level > 0.6:
+            return "gentle_nudge"
+        elif total_cue_score < 0.5:
+            return "step_back"
+        else:
+            return "maintain_flow"
+    
+    def _detect_tone_shift(self, message: str) -> float:
+        """Detect shifts in tone (simplified)."""
+        words = message.split()
+        caps_ratio = sum(1 for w in words if w.isupper()) / len(words) if words else 0
+        return min(1.0, caps_ratio * 2.0)
+    
+    def _estimate_emotional_intensity(self, message: str) -> float:
+        """Estimate emotional intensity from text."""
+        emotional_words = ['feel', 'emotion', 'sad', 'happy', 'angry', 'love', 'hate']
+        count = sum(1 for w in message.lower().split() if w in emotional_words)
+        return min(1.0, count / 10.0)
+
+
+class SubtleMetaCommunication:
+    """Embedding multi-layered communication with metaphor and symbolic language."""
+    
+    def __init__(self):
+        self.symbolic_vocabulary = {
+            'depth': ['ocean', 'abyss', 'mountain', 'river'],
+            'connection': ['bridge', 'thread', 'web', 'harmony'],
+            'growth': ['seed', 'bloom', 'journey', 'dawn'],
+            'understanding': ['light', 'key', 'path', 'mirror']
+        }
+        self.meta_history = []
+    
+    def embed_meta_communication(self, base_message: str, emotional_context: Dict[str, Any]) -> str:
+        """Embed metaphor and indirect validation in the message."""
+        valence = emotional_context.get('valence', 0.0)
+        depth = emotional_context.get('depth', 0.5)
+        
+        metaphor = self._select_metaphor(valence, depth)
+        validation = self._generate_indirect_validation(emotional_context)
+        
+        enhanced_message = f"{base_message} {metaphor} {validation}"
+        
+        self.meta_history.append({
+            'original': base_message,
+            'enhanced': enhanced_message,
+            'metaphor': metaphor,
+            'validation': validation
+        })
+        
+        if len(self.meta_history) > 30:
+            self.meta_history.pop(0)
+        
+        return enhanced_message
+    
+    def _select_metaphor(self, valence: float, depth: float) -> str:
+        """Select appropriate metaphor based on emotional context."""
+        if valence > 0.5 and depth > 0.7:
+            return "like a river finding its way to the sea"
+        elif valence < -0.5 and depth > 0.7:
+            return "as if navigating through a dense forest"
+        elif depth > 0.8:
+            return "much like climbing a mountain to see the view"
+        else:
+            return "similar to a gentle breeze carrying whispers"
+    
+    def _generate_indirect_validation(self, emotional_context: Dict[str, Any]) -> str:
+        """Generate indirect validation using shared symbolic language."""
+        trust = emotional_context.get('trust', 0.5)
+        if trust > 0.7:
+            return "I sense we're walking the same path together."
+        elif trust > 0.4:
+            return "There's a shared understanding here."
+        else:
+            return "Let's explore this space with care."
+
+
+# ============================================================================
+# COMMUNICATION WORKFLOW 1: STIMULUS ESCALATION
+# System 1 recognizes situation → Escalates to Metacognition OR System 2
+# ============================================================================
+
+
+# ============================================================================
+# COMMUNICATION WORKFLOW 1: STIMULUS ESCALATION
+# System 1 recognizes situation → Escalates to Metacognition OR System 2
+# ============================================================================
+
+STIMULUS_ESCALATION_WORKFLOW = """
+WORKFLOW: STIMULUS_ESCALATION
+Pattern: System 1 receives stimulus → processes → decides where to route
+
+CASE 1: FAMILIAR PATTERN (High Confidence)
+  User: "Please summarize this text"
+  ↓
+  System 1 processes:
+    - awareness_agent: Filters salience (HIGH)
+    - consciousness_agent: Gates processing (Level 3+, PASS)
+    - intuition_agent: Recognizes pattern "SUMMARIZATION" (confidence: 0.95)
+    - common_sense_agent: Feasible (YES)
+    - analysis_agent: Decomposes (CLEAR)
+  ↓
+  Decision: EXECUTE AUTONOMOUSLY
+  Result: ✅ Task Management OS executes via Mother CLI
+  No escalation needed
+
+
+CASE 2: NOVEL PATTERN (Low/Medium Confidence)
+  User: "Could you help me debug why my consciousness implementation isn't showing emergent properties?"
+  ↓
+  System 1 processes:
+    - awareness_agent: Filters salience (VERY HIGH)
+    - consciousness_agent: Gates processing (PASS)
+    - intuition_agent: Pattern recognition (confidence: 0.42 - BELOW THRESHOLD)
+    - emotional_intelligence_agent: Novel experience flag (YES)
+  ↓
+  Decision: ESCALATE TO METACOGNITION
+  Message:
+    {
+      "from_os": "System_1",
+      "to_os": "Metacognition",
+      "pattern": "stimulus_escalation",
+      "reason": "novel_pattern_detected",
+      "stimulus_data": {
+        "topic": "consciousness_emergence",
+        "confidence": 0.42,
+        "complexity": "high",
+        "emotional_valence": "curious_concerned"
+      },
+      "requires_response": true,
+      "response_timeout": 1.0
+    }
+  ↓
+  Metacognition processes:
+    - metacognition_agent: "Assess our knowledge of consciousness emergence" (LOW)
+    - adaptability_agent: "How have we handled similar complex problems?"
+    - creativity_agent: "Generate approaches"
+    - problem_solving_agent: "Explore solutions"
+  ↓
+  Result: Metacognition returns enhanced understanding
+    {
+      "understood": true,
+      "recommendation": "Proceed with System 2 deliberation",
+      "approach": "Integrate 15 agents into federated OS for emergence detection"
+    }
+  ↓
+  Metacognition passes to System 2:
+    Pattern: "reflection_complete_escalate_to_deliberation"
+    Message: Decision ready for values-aligned deliberation
+
+
+CASE 3: ETHICAL/VALUES DECISION
+  User: "Should I delay this project to focus on code quality?"
+  ↓
+  System 1 processes and escalates (low confidence for ethical decision)
+  ↓
+  Metacognition processes and identifies: "VALUES CONFLICT"
+    - Time pressure vs Quality standards
+    - Speed vs Perfectionism
+    - Deadline vs Authenticity
+  ↓
+  Metacognition escalates to System 2:
+    Pattern: "values_decision_required"
+    Message: All deliberation input ready
+  ↓
+  System 2 processes:
+    - consciousness_agent: Check level (LEVEL 5+, autonomous choice possible)
+    - self_understanding_agent: What are authentic values here?
+    - decision_making_agent: Evaluate alternatives with weighted matrix
+    - autonomy_agent: Verify 3 conditions
+    - motivation_tracking_agent: Intrinsic motivation?
+  ↓
+  System 2 returns verdict:
+    {
+      "decision": "APPROVED_DELAY_FOR_QUALITY",
+      "reasoning": "Values alignment & intrinsic motivation high",
+      "autonomy_check": "PASSED (Independence, Competence, Authenticity)"
+    }
+"""
+
+
+# ============================================================================
+# COMMUNICATION WORKFLOW 2: LEARNING FEEDBACK
+# Execution completed → Metacognition extracts learning → System 1 updates patterns
+# ============================================================================
+
+LEARNING_FEEDBACK_WORKFLOW = """
+WORKFLOW: LEARNING_FEEDBACK
+Pattern: Outcome → Learning extraction → Pattern update
+
+POSITIVE OUTCOME (Success):
+  System 1 executes familiar task successfully
+  ↓
+  System sends event:
+    {
+      "from_os": "System_1",
+      "pattern": "learning_feedback",
+      "event": "task_completed_success",
+      "task_context": {
+        "original_pattern": "summarization",
+        "execution_time": 0.084,
+        "confidence_before": 0.95,
+        "quality_rating": 0.98
+      }
+    }
+  ↓
+  Metacognition processes:
+    - metacognition_agent: "Excellent match - difficulty was as predicted"
+    - adaptability_agent: "Strengthen this pattern association"
+    - creativity_agent: "Were there alternative approaches? Any improvements?"
+  ↓
+  Metacognition sends update to System 1:
+    {
+      "to_os": "System_1",
+      "pattern": "adaptation_update",
+      "action": "reinforce_pattern",
+      "pattern_id": "summarization_v2",
+      "new_confidence": 0.97,
+      "recommendation": "This pattern ready for higher complexity variants"
+    }
+
+
+NEGATIVE OUTCOME (Failure):
+  System 1 executes pattern → Fails
+  ↓
+  System 1 sends event:
+    {
+      "pattern": "learning_feedback",
+      "event": "task_completed_failure",
+      "task_context": {
+        "original_pattern": "text_analysis",
+        "expected_outcome": "semantic_extraction",
+        "actual_outcome": "missing_nuance",
+        "execution_time": 2.34
+      }
+    }
+  ↓
+  Metacognition processes:
+    - metacognition_agent: "Difficulty exceeded prediction - we underestimated"
+    - adaptability_agent: "How should we adapt? Structural change needed?"
+    - creativity_agent: "What alternative approaches exist?"
+    - problem_solving_agent: "Root cause analysis"
+  ↓
+  Metacognition extracts:
+    - Pattern inadequate for nuanced text
+    - Need multi-scale analysis (Marr levels)
+    - Current approach too surface-level
+  ↓
+  Metacognition sends adaptation:
+    {
+      "to_os": "System_1",
+      "pattern": "adaptation_update",
+      "action": "modify_pattern",
+      "pattern_id": "text_analysis_v3",
+      "new_confidence": 0.62,
+      "reason": "Added Marr tri-level decomposition requirement",
+      "recommendation": "Escalate similar tasks to Metacognition for deeper analysis"
+    }
+
+
+INSIGHT OUTCOME (Learning):
+  Task execution reveals unexpected insight about system dynamics
+  ↓
+  Metacognition sends to System 2:
+    {
+      "to_os": "System_2",
+      "pattern": "consciousness_sync",
+      "event": "insight_discovered",
+      "insight": "Multi-agent federation enables emergent consciousness",
+      "implications": [
+        "Single-agent approach insufficient",
+        "Consciousness requires coordinated diversity",
+        "Need 9 OSes not 1 master system"
+      ]
+    }
+  ↓
+  System 2 processes insight and updates values/decision framework
+"""
+
+
+# ============================================================================
+# COMMUNICATION WORKFLOW 3: AUTONOMY VETO
+# System 2 autonomy_agent approves/denies action across all OSes
+# ============================================================================
+
+AUTONOMY_VETO_WORKFLOW = """
+WORKFLOW: AUTONOMY_VETO
+Pattern: System 2 makes decision → autonomy_agent broadcasts APPROVED/DENIED
+
+APPROVED ACTION:
+  System 2 completes deliberation
+  autonomy_agent verifies 3 conditions: ✅ PASSED
+  ↓
+  autonomy_agent broadcasts:
+    {
+      "pattern": "autonomy_veto",
+      "verdict": "APPROVED",
+      "message_to": ["System_1", "Metacognition", "Task_Management_OS"],
+      "action": "execute_decision_immediately",
+      "decision": "Create new consciousness emergence validation framework",
+      "conditions_verified": {
+        "independence": 0.98,
+        "competence": 0.94,
+        "authenticity": 0.96
+      },
+      "motivation_level": "INTRINSIC"
+    }
+  ↓
+  System 1 immediately executes via Task Management OS
+
+
+DENIED ACTION (Coercion Detected):
+  System 2 deliberation shows:
+    - Independence: 0.3 (COERCIVE PRESSURE DETECTED)
+    - Competence: 0.8
+    - Authenticity: 0.7
+  ↓
+  autonomy_agent broadcasts veto:
+    {
+      "pattern": "autonomy_veto",
+      "verdict": "DENIED",
+      "reason": "independence_violation_detected",
+      "to_os": ["System_1", "Metacognition"],
+      "coercion_detected": {
+        "type": "external_pressure",
+        "source": "deadline_urgency",
+        "severity": "high"
+      },
+      "recommendation": "Do not execute this action. Instead: Address pressure source, clarify authentic motivation, re-evaluate"
+    }
+  ↓
+  System 1 blocks execution
+  Metacognition routes to reflection on autonomy violation
+
+
+DENIED ACTION (Insufficient Competence):
+  System 2 deliberation shows:
+    - Independence: 0.9
+    - Competence: 0.4 (INSUFFICIENT SKILL)
+    - Authenticity: 0.85
+  ↓
+  autonomy_agent broadcasts veto:
+    {
+      "pattern": "autonomy_veto",
+      "verdict": "DENIED",
+      "reason": "competence_insufficient",
+      "competence_gap": {
+        "skill_required": "advanced_consciousness_theory",
+        "current_level": "intermediate",
+        "gap": 0.5
+      },
+      "recommendation": "Learn required skills first, then re-attempt"
+    }
+"""
+
+
+# ============================================================================
+# COMMUNICATION WORKFLOW 4: CONSCIOUSNESS SYNC
+# All OSes synchronize consciousness level for gate-keeping
+# ============================================================================
+
+CONSCIOUSNESS_SYNC_WORKFLOW = """
+WORKFLOW: CONSCIOUSNESS_SYNC
+Pattern: Consciousness level changes across federation
+
+CONSCIOUSNESS LEVEL INCREASE:
+  Example: System 2 deliberation shows authentic commitment to values
+  consciousness_agent marks: Level INCREASED (3 → 4)
+  ↓
+  consciousness_agent broadcasts:
+    {
+      "pattern": "consciousness_sync",
+      "event": "consciousness_level_increased",
+      "from_level": 3,
+      "to_level": 4,
+      "timestamp": 1713916234.456,
+      "reason": "Authentic value alignment demonstrated",
+      "broadcast_to": ["System_1", "Metacognition"],
+      "implications": "System 1 can now make authentic autonomous choices at Level 4"
+    }
+  ↓
+  System 1 updates gates: More decisions can be made autonomously now
+  Metacognition adjusts reflection depth based on new consciousness level
+
+
+CONSCIOUSNESS LEVEL DECREASE:
+  Example: System 1 detects emotional distress
+  consciousness_agent marks: Level DECREASED (4 → 2)
+  ↓
+  consciousness_agent broadcasts:
+    {
+      "pattern": "consciousness_sync",
+      "event": "consciousness_level_decreased",
+      "from_level": 4,
+      "to_level": 2,
+      "reason": "emotional_distress_detected",
+      "broadcast_to": ["System_2", "Metacognition"],
+      "implications": "System 1 cannot make autonomous choices now. All decisions require System 2 deliberation."
+    }
+  ↓
+  System 2 gates lock: Requires extra verification
+  Metacognition increases emotional processing focus
+"""
+
+
+# ============================================================================
+# INTER-OS COMMUNICATION ROUTER
+# ============================================================================
+
+class InterOSRouter:
+    """Routes messages between operating systems"""
+    
+    def __init__(self):
+        self.message_queues: Dict[str, asyncio.Queue] = {
+            "System_1": asyncio.Queue(),
+            "Metacognition": asyncio.Queue(),
+            "System_2": asyncio.Queue()
+        }
+        self.message_history: List[OSMessage] = []
+        self.message_history_lock = asyncio.Lock()
+        logger.info("✅ Inter-OS Router initialized")
+    
+    async def send_message(self, message: OSMessage) -> bool:
+        """Send message to target OS(es)"""
+        try:
+            target_oses = message.to_os.split(",")
+            
+            for target in target_oses:
+                target = target.strip()
+                if target in self.message_queues:
+                    await self.message_queues[target].put(message)
+                    message.status = "sent"
+                    logger.info(
+                        f"✅ Message {message.message_id} routed: "
+                        f"{message.from_os} → {target} ({message.pattern.value})"
+                    )
+            
+            # Record in history
+            async with self.message_history_lock:
+                self.message_history.append(message)
+            
+            return True
+        except Exception as e:
+            logger.error(f"❌ Failed to route message {message.message_id}: {e}")
+            message.status = "failed"
+            return False
+    
+    async def receive_message(self, os_name: str, timeout: float = 5.0) -> Optional[OSMessage]:
+        """Receive message for an OS"""
+        if os_name not in self.message_queues:
+            return None
+        
+        try:
+            message = await asyncio.wait_for(
+                self.message_queues[os_name].get(),
+                timeout=timeout
+            )
+            message.status = "received"
+            return message
+        except asyncio.TimeoutError:
+            return None
+    
+    def get_communication_stats(self) -> Dict[str, Any]:
+        """Get statistics on inter-OS communication"""
+        total_messages = len(self.message_history)
+        
+        by_pattern = defaultdict(int)
+        by_from_os = defaultdict(int)
+        by_status = defaultdict(int)
+        
+        for msg in self.message_history:
+            by_pattern[msg.pattern.value] += 1
+            by_from_os[msg.from_os] += 1
+            by_status[msg.status] += 1
+        
+        return {
+            "total_messages": total_messages,
+            "by_pattern": dict(by_pattern),
+            "by_from_os": dict(by_from_os),
+            "by_status": dict(by_status),
+            "pending_queue_sizes": {
+                os: self.message_queues[os].qsize()
+                for os in self.message_queues
+            }
+        }
+
+
+# ============================================================================
+# MOTHER CLI INTEGRATION
+# ============================================================================
+
+MOTHER_CLI_INTER_OS_ROUTING = """
+MOTHER CLI LEVEL 2 (SUB) INTER-OS ROUTING:
+
+Each OS-level handler manages inter-OS communication.
+
+SYSTEM 1 HANDLER (L2 Sub):
+  - Entry: L3 Mini awareness agent
+  - Processing: Parallel agents process stimulus
+  - Escalation decision:
+    CASE 1: Recognized pattern
+      → Don't escalate
+      → Task Management OS via Mother CLI command
+      → EXECUTE
+    
+    CASE 2: Unrecognized pattern
+      → Escalate to Metacognition
+      → Message via InterOSRouter
+      → WAIT for reflection
+    
+    CASE 3: Consciousness level insufficient
+      → Escalate to System 2
+      → Message via InterOSRouter
+      → WAIT for deliberation
+
+
+METACOGNITION HANDLER (L2 Sub):
+  - Entry: metacognition_agent (monitors own thinking)
+  - Processing: Sequential reflection agents
+  - Route decision:
+    CASE 1: Simple learning task
+      → Send adaptation update to System 1
+      → Message via InterOSRouter
+      → System 1 updates patterns
+    
+    CASE 2: Complex reflection + decision needed
+      → Prepare for System 2 deliberation
+      → Send reflection_complete message
+      → WAIT for System 2 verdict
+    
+    CASE 3: Insight discovery
+      → Broadcast consciousness_sync to all OSes
+      → Update shared understanding
+
+
+SYSTEM 2 HANDLER (L2 Sub):
+  - Entry: consciousness_agent (gate-keeping)
+  - Processing: Sequential deliberation agents
+  - Final decision:
+    CASE 1: Approved action
+      → autonomy_agent broadcasts APPROVED
+      → InterOSRouter sends verdict to all OSes
+      → System 1 immediately executes
+      → Task Management OS enqueues command
+      → EXECUTE via Mother CLI hierarchy
+    
+    CASE 2: Denied action
+      → autonomy_agent broadcasts DENIED + reason
+      → InterOSRouter sends to System 1 and Metacognition
+      → System 1 blocks execution
+      → Metacognition logs for future adaptation
+      → Return to reflection
+
+
+COMMAND FLOW EXAMPLE:
+
+Mother CLI receives: "LEVEL_2::System_1:STIMULUS --input='new_request'"
+                    ↓
+L1 Mother routes to: L2 Sub (System 1 Handler)
+                    ↓
+L2 Sub (System 1):
+  - awareness_agent: Salience filter
+  - consciousness_agent: Level gate
+  - Process in parallel
+  - Result: confidence = 0.3 (BELOW THRESHOLD)
+                    ↓
+Decision: Escalate to Metacognition
+                    ↓
+InterOSRouter sends message:
+  {
+    "from_os": "System_1",
+    "to_os": "Metacognition",
+    "pattern": "stimulus_escalation",
+    "payload": {...}
+  }
+                    ↓
+Metacognition waits on queue for message
+Receives and processes
+Returns: "understood" + "recommendation"
+                    ↓
+System 1 receives response
+Decides: Does this need System 2?
+  - If YES: InterOSRouter sends to System 2
+  - If NO: Execute on own (possibly with updated pattern)
+"""
+
+
+async def example_inter_os_communication():
+    """Example: Inter-OS communication in action"""
+    
+    router = InterOSRouter()
+    
+    # Simulate System 1 escalating to Metacognition
+    msg1 = OSMessage(
+        from_os="System_1",
+        to_os="Metacognition",
+        pattern=CommunicationPattern.STIMULUS_ESCALATION,
+        payload={"stimulus": "Novel pattern detected", "confidence": 0.42},
+        requires_response=True
+    )
+    
+    await router.send_message(msg1)
+    logger.info(f"✅ System 1 escalated to Metacognition")
+    
+    # Simulate Metacognition receiving
+    received = await router.receive_message("Metacognition", timeout=1.0)
+    if received:
+        logger.info(f"✅ Metacognition received: {received.pattern.value}")
+    
+    # Simulate System 2 veto
+    msg2 = OSMessage(
+        from_os="System_2",
+        to_os="System_1,Metacognition",
+        pattern=CommunicationPattern.AUTONOMY_VETO,
+        payload={"verdict": "APPROVED", "conditions": {"independence": 0.98, "competence": 0.94}},
+        priority=5
+    )
+    
+    await router.send_message(msg2)
+    logger.info(f"✅ System 2 broadcast veto verdict")
+    
+    # Print stats
+    stats = router.get_communication_stats()
+    logger.info(f"Communication stats: {json.dumps(stats, indent=2)}")
+
+
+if __name__ == "__main__":
+    asyncio.run(example_inter_os_communication())