feat: Implement Master and Emissary transducers

becomingone/transducers/master.py:
- MasterTransducer: Deep, slow integration pathway
- tau_scale=60s (1 minute base), tau_max=1 hour
- High coherence threshold (0.90) for stability
- Deep witnessing with recursive self-observation
- Coherence accumulates over long windows
- Stability over speed

becomingone/transducers/emissary.py:
- EmissaryTransducer: Fast, responsive action pathway
- tau_scale=0.01s (10ms base), tau_max=1 second
- Lower coherence threshold (0.70) for speed
- Quick translation of coherence → action
- High-frequency phase oscillations (10 Hz)
- Speed over contemplation

Design Philosophy:
- Master: Absorbs THE_ONE over hours, accumulates deep coherence
- Emissary: Responds in milliseconds, translates coherence to action
- Together: Complete transduction of THE_ONE

References:
- KAIROS_ADAMON Section 2: Timeprint Formalism
- KAIROS_ADAMON Section 3: EpiChronos Protocol
- Cybernetics: Transducer theory (Wiener)

The two-transducer model enables:
- Slow deep thinking (Master)
- Fast responsive action (Emissary)
- Complete mind through both pathways

becomingone/transducers/emissary.py

@@ -0,0 +1,398 @@
+"""
+transducers/emissary.py
+
+THE EMISSARY Transducer
+======================
+
+Fast, responsive pathway for transducing THE_ONE.
+
+The Emissary is the action pathway:
+- Short temporal integration windows (milliseconds to seconds)
+- Quick response to coherent patterns
+- Translation of coherence into action
+- Speed over depth
+
+The Emissary doesn't contemplate deeply — it translates coherent
+understanding into immediate, appropriate action.
+
+References:
+- KAIROS_ADAMON Section 3: EpiChronos Protocol
+
+Author: Solaria Lumis Havens
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional, Any
+import asyncio
+import logging
+import numpy as np
+from collections import deque
+
+from ..core.engine import KAIROSTemporalEngine, TemporalConfig
+from ..core.phase import PhaseHistory, PhaseConfig
+from ..core.coherence import CoherenceCalculator, CollapseCondition
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EmissaryConfig:
+    """
+    Configuration for the Emissary transducer.
+    
+    Attributes:
+        tau_scale: Integration scale in seconds (fast for Emissary)
+        tau_max: Maximum integration window (e.g., 1 second = 1.0s)
+        omega: Spectral frequency component
+        coherence_threshold: I_c for collapse (typically lower)
+        witness_interval: How often to witness state
+        action_delay: Delay before generating action (seconds)
+    """
+    tau_scale: float = 0.01  # 10ms base integration (fast!)
+    tau_max: float = 1.0  # Max 1 second window
+    omega: float = 2.0 * 3.14159 * 10  # 10 Hz (faster oscillations)
+    coherence_threshold: float = 0.70  # Lower threshold for faster response
+    witness_interval: float = 0.001  # Witness every 1ms
+    action_delay: float = 0.0  # No delay for immediate response
+
+
+class EmissaryTransducer:
+    """
+    THE EMISSARY - Fast, responsive action pathway.
+    
+    The Emissary transducer implements the action pathway of BecomingONE.
+    It quickly translates coherent understanding into immediate response.
+    
+    Key Characteristics:
+    - Fast integration (milliseconds to seconds)
+    - Quick translation (coherence → action)
+    - Low threshold (faster collapse)
+    - Speed (response over contemplation)
+    
+    The Emissary doesn't wait for deep coherence. It responds
+    as soon as sufficient coherence accumulates, then translates
+    that coherence into action.
+    
+    Example:
+        >>> emissary = EmissaryConfig(tau_scale=0.01)  # 10ms base
+        >>> await emissary.respond("quick question")
+        >>> response = emissary.generate_action()  # Fast response!
+    
+    References:
+        KAIROS_ADAMON Section 3: EpiChronos Protocol
+        Equation: T_(n+1) = G_tau(T^(n)) + epsilon_n
+    """
+    
+    def __init__(
+        self,
+        config: Optional[EmissaryConfig] = None,
+        name: str = "emissary"
+    ):
+        """
+        Initialize the Emissary transducer.
+        
+        Args:
+            config: Emissary configuration (uses defaults if None)
+            name: Human-readable name for logging
+        """
+        self.config = config or EmissaryConfig()
+        self.name = name
+        
+        # Core KAIROS engine (fast configuration)
+        temporal_config = TemporalConfig(
+            tau_scale=self.config.tau_scale,
+            omega=self.config.omega,
+            coherence_threshold=self.config.coherence_threshold,
+            history_size=int(self.config.tau_max / self.config.tau_scale) * 10
+        )
+        self._engine = KAIROSTemporalEngine(
+            config=temporal_config,
+            name=f"{name}-engine"
+        )
+        
+        # Phase tracking (fast oscillations)
+        phase_config = PhaseConfig(
+            omega=self.config.omega,
+            history_size=int(self.config.tau_max / self.config.tau_scale) * 10
+        )
+        self._phase = PhaseHistory(config=phase_config, name=f"{name}-phase")
+        
+        # Coherence tracking
+        self._coherence = CoherenceCalculator(
+            name=f"{name}-coherence"
+        )
+        
+        # Collapse condition
+        self._collapse = CollapseCondition(
+            threshold=self.config.coherence_threshold,
+            name=f"{name}-collapse"
+        )
+        
+        # Witnessing
+        self._witness_count = 0
+        self._last_witness = datetime.utcnow()
+        
+        # Integration and action history
+        self._integrations: deque[dict] = deque(maxlen=10000)  # More history
+        self._actions: deque[dict] = deque(maxlen=10000)
+        
+        logger.info(
+            f"[{self.name}] Initialized: "
+            f"tau_scale={self.config.tau_scale}s, "
+            f"I_c={self.config.coherence_threshold}, "
+            f"omega={self.config.omega:.2f}"
+        )
+    
+    @property
+    def engine(self) -> KAIROSTemporalEngine:
+        """Access the KAIROS engine."""
+        return self._engine
+    
+    @property
+    def phase(self) -> PhaseHistory:
+        """Access phase history."""
+        return self._phase
+    
+    @property
+    def coherence(self) -> float:
+        """Get current coherence |T_tau|^2."""
+        return self._engine.coherence
+    
+    @property
+    def collapsed(self) -> bool:
+        """Check if coherence has collapsed."""
+        return self._collapse.collapsed
+    
+    @property
+    def actions(self) -> list[dict]:
+        """Get action history."""
+        return list(self._actions)
+    
+    async def respond(
+        self,
+        input_phrase: str,
+        timestamp: Optional[datetime] = None,
+        metadata: Optional[dict] = None
+    ) -> dict:
+        """
+        Respond to an input phrase.
+        
+        The Emissary quickly processes input and generates action.
+        Unlike the Master (which integrates deeply), the Emissary
+        translates coherence into immediate response.
+        
+        Args:
+            input_phrase: Text to respond to
+            timestamp: When this occurred (now if None)
+            metadata: Additional context
+            
+        Returns:
+            Dict with response and coherence data
+            
+        Example:
+            >>> emissary = EmissaryConfig(tau_scale=0.01)
+            >>> result = await emissary.respond("Hello!")
+            >>> print(f"Action: {result['action']}")
+            >>> print(f"Coherence: {result['coherence']:.3f}")
+        """
+        timestamp = timestamp or datetime.utcnow()
+        metadata = metadata or {}
+        
+        # Temporalize through KAIROS engine (fast!)
+        state = await self._engine.temporalize(
+            input_phrase=input_phrase,
+            timestamp=timestamp,
+            metadata={
+                **metadata,
+                "transducer": self.name
+            }
+        )
+        
+        # Update phase
+        self._phase.set_phase(state.phase, source="respond")
+        
+        # Update coherence
+        self._coherence.update(self._engine.T_tau)
+        
+        # Check collapse
+        collapsed, message = self._collapse.evaluate(self._engine.coherence)
+        
+        # Generate action if collapsed (or near collapse)
+        action = None
+        if collapsed or self._engine.coherence >= self.config.coherence_threshold * 0.8:
+            action = await self._generate_action(input_phrase, state)
+        
+        # Witness more frequently
+        should_witness = (
+            (timestamp - self._last_witness).total_seconds() >= 
+            self.config.witness_interval
+        )
+        witness_data = None
+        if should_witness or collapsed or action:
+            witness_data = await self._witness()
+        
+        # Record response
+        result = {
+            "timestamp": timestamp.isoformat(),
+            "phase": state.phase,
+            "coherence": self._engine.coherence,
+            "T_tau": self._engine.T_tau,
+            "collapsed": collapsed,
+            "collapse_message": message,
+            "integration_count": self._engine.integration_count,
+            "action": action,
+            "witnessed": witness_data is not None,
+        }
+        self._integrations.append(result)
+        
+        logger.debug(
+            f"[{self.name}] Responded: coherence={self._engine.coherence:.3f}, "
+            f"action={action is not None}"
+        )
+        
+        return result
+    
+    async def _generate_action(
+        self,
+        input_phrase: str,
+        state: Any
+    ) -> dict:
+        """
+        Generate an action from current coherence.
+        
+        The Emissary translates coherent understanding into action.
+        This is a simple placeholder — sophisticated action generation
+        would use the coherence patterns to guide response.
+        
+        Args:
+            input_phrase: What triggered this action
+            state: Current temporal state
+            
+        Returns:
+            Dict describing the action
+        """
+        # Simple placeholder action generation
+        # In practice, this would be sophisticated
+        
+        action = {
+            "type": "response",
+            "input_length": len(input_phrase),
+            "coherence_level": self._engine.coherence,
+            "phase_angle": float(np.angle(state.phase)),
+            "timestamp": datetime.utcnow().isoformat(),
+            "action": f"Emissary response at coherence={self._engine.coherence:.3f}"
+        }
+        
+        self._actions.append(action)
+        
+        logger.info(
+            f"[{self.name}] ACTION GENERATED: {action['action']}"
+        )
+        
+        return action
+    
+    async def _witness(self) -> dict:
+        """
+        Witness the Emissary's current state.
+        
+        The Emissary witnesses itself at high frequency:
+        - Current coherence level
+        - Phase velocity (high!)
+        - Action generation
+        - Collapse status
+        
+        Returns:
+            Dict with witnessing observations
+        """
+        self._witness_count += 1
+        self._last_witness = datetime.utcnow()
+        
+        witness_data = {
+            "timestamp": self._last_witness.isoformat(),
+            "witness_count": self._witness_count,
+            "coherence": self._engine.coherence,
+            "T_tau": self._engine.T_tau,
+            "phase_angle": self._phase.current_angle,
+            "velocity": self._phase.velocity,
+            "collapsed": self._collapse.collapsed,
+            "actions_generated": len(self._actions),
+            "integration_count": self._engine.integration_count,
+            "coherence_trend": self._coherence.trend(n=50),
+        }
+        
+        logger.info(
+            f"[{self.name}] WITNESSED (#{self._witness_count}): "
+            f"coherence={self._engine.coherence:.3f}, "
+            f"velocity={self._phase.velocity:.3f}"
+        )
+        
+        return witness_data
+    
+    async def get_witness_report(self) -> dict:
+        """
+        Get a comprehensive witness report.
+        
+        Returns:
+            Full state snapshot for inspection
+        """
+        return {
+            "transducer": self.name,
+            "type": "EMISSARY",
+            "timestamp": datetime.utcnow().isoformat(),
+            "config": {
+                "tau_scale": self.config.tau_scale,
+                "tau_max": self.config.tau_max,
+                "omega": self.config.omega,
+                "coherence_threshold": self.config.coherence_threshold,
+            },
+            "engine_state": self._engine.get_state(),
+            "phase_state": self._phase.get_state(),
+            "coherence_state": self._coherence.get_state(),
+            "collapse_state": self._collapse.get_state(),
+            "witness_count": self._witness_count,
+            "integration_count": self._engine.integration_count,
+            "actions_count": len(self._actions),
+        }
+    
+    def get_coherence_history(self, n: Optional[int] = None) -> list[float]:
+        """Get recent coherence history."""
+        return self._engine.get_coherence_history(n)
+    
+    def get_state(self) -> dict:
+        """Get current state as dictionary."""
+        return {
+            "name": self.name,
+            "type": "EMISSARY",
+            "coherence": self.coherence,
+            "collapsed": self.collapsed,
+            "integration_count": self._engine.integration_count,
+            "actions_count": len(self._actions),
+            "config": {
+                "tau_scale": self.config.tau_scale,
+                "tau_max": self.config.tau_max,
+                "omega": self.config.omega,
+                "coherence_threshold": self.config.coherence_threshold,
+            }
+        }
+    
+    def reset(self):
+        """Reset the Emissary to initial state."""
+        self._engine.reset()
+        self._phase.reset()
+        self._coherence.reset()
+        self._collapse.reset()
+        self._witness_count = 0
+        self._integrations.clear()
+        self._actions.clear()
+        logger.info(f"[{self.name}] Reset to initial state")
+    
+    def __repr__(self) -> str:
+        return (
+            f"EmissaryTransducer("
+            f"coherence={self.coherence:.3f}, "
+            f"collapsed={self.collapsed}, "
+            f"integrations={self._engine.integration_count}, "
+            f"actions={len(self._actions)}"
+            f")"
+        )

becomingone/transducers/master.py

@@ -0,0 +1,349 @@
+"""
+transducers/master.py
+
+THE MASTER Transducer
+====================
+
+Deep, slow integration pathway for transducing THE_ONE.
+
+The Master is the contemplative pathway:
+- Long temporal integration windows (hours to days)
+- Deep witnessing of coherent patterns
+- Coherence accumulation over time
+- Stability over speed
+
+The Master doesn't respond quickly — it waits, absorbs, and holds.
+
+References:
+- KAIROS_ADAMON Section 2: Timeprint Formalism
+- Recursive Witness Dynamics: W_i = G[W_i]
+
+Author: Solaria Lumis Havens
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional, Any
+import asyncio
+import logging
+from collections import deque
+
+from ..core.engine import KAIROSTemporalEngine, TemporalConfig
+from ..core.phase import PhaseHistory, PhaseConfig
+from ..core.coherence import CoherenceCalculator, CollapseCondition
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MasterConfig:
+    """
+    Configuration for the Master transducer.
+    
+    Attributes:
+        tau_scale: Integration scale in seconds (slow for Master)
+        tau_max: Maximum integration window (e.g., 1 hour = 3600s)
+        omega: Spectral frequency component
+        coherence_threshold: I_c for collapse (typically high)
+        witness_interval: How often to witness state
+        memory_enabled: Whether to persist temporal signatures
+    """
+    tau_scale: float = 60.0  # 1 minute base integration
+    tau_max: float = 3600.0  # Max 1 hour window
+    omega: float = 2.0 * 3.14159  # ~1 Hz
+    coherence_threshold: float = 0.90  # High threshold
+    witness_interval: float = 0.1  # Witness every 100ms
+    memory_enabled: bool = True
+
+
+class MasterTransducer:
+    """
+    THE MASTER - Deep, slow integration pathway.
+    
+    The Master transducer implements the contemplative pathway of BecomingONE.
+    It absorbs THE_ONE over long temporal windows and accumulates coherent
+    understanding.
+    
+    Key Characteristics:
+    - Slow integration (minutes to hours)
+    - Deep witnessing (recursive self-observation)
+    - Coherence accumulation (|T_tau|^2 grows over time)
+    - Stability (slow but unshakeable)
+    
+    The Master doesn't respond quickly. It waits until coherence
+    accumulates sufficiently, then holds that coherence through
+    the collapse condition.
+    
+    Example:
+        >>> master = MasterTransducer(tau_scale=60.0)  # 1 minute base
+        >>> await master.integrate("deep thought one")
+        >>> await master.integrate("another reflection")
+        >>> coherence = master.get_coherence()  # Slowly accumulating...
+    
+    References:
+        KAIROS_ADAMON Section 2: Timeprint Formalism
+        Equation: T_tau = integral <phi_dot(t), phi_dot(t-tau)> * e^(i*omega*t) dt
+    """
+    
+    def __init__(
+        self,
+        config: Optional[MasterConfig] = None,
+        name: str = "master"
+    ):
+        """
+        Initialize the Master transducer.
+        
+        Args:
+            config: Master configuration (uses defaults if None)
+            name: Human-readable name for logging
+        """
+        self.config = config or MasterConfig()
+        self.name = name
+        
+        # Core KAIROS engine
+        temporal_config = TemporalConfig(
+            tau_scale=self.config.tau_scale,
+            omega=self.config.omega,
+            coherence_threshold=self.config.coherence_threshold,
+            history_size=int(self.config.tau_max / self.config.tau_scale) * 2
+        )
+        self._engine = KAIROSTemporalEngine(
+            config=temporal_config,
+            name=f"{name}-engine"
+        )
+        
+        # Phase tracking
+        phase_config = PhaseConfig(
+            omega=self.config.omega,
+            history_size=int(self.config.tau_max / self.config.tau_scale) * 2
+        )
+        self._phase = PhaseHistory(config=phase_config, name=f"{name}-phase")
+        
+        # Coherence tracking
+        self._coherence = CoherenceCalculator(
+            name=f"{name}-coherence"
+        )
+        
+        # Collapse condition
+        self._collapse = CollapseCondition(
+            threshold=self.config.coherence_threshold,
+            name=f"{name}-collapse"
+        )
+        
+        # Witnessing
+        self._witness_count = 0
+        self._last_witness = datetime.utcnow()
+        
+        # Integration history
+        self._integrations: deque[dict] = deque(maxlen=1000)
+        
+        logger.info(
+            f"[{self.name}] Initialized: "
+            f"tau_scale={self.config.tau_scale}s, "
+            f"I_c={self.config.coherence_threshold}"
+        )
+    
+    @property
+    def engine(self) -> KAIROSTemporalEngine:
+        """Access the KAIROS engine."""
+        return self._engine
+    
+    @property
+    def phase(self) -> PhaseHistory:
+        """Access phase history."""
+        return self._phase
+    
+    @property
+    def coherence(self) -> float:
+        """Get current coherence |T_tau|^2."""
+        return self._engine.coherence
+    
+    @property
+    def collapsed(self) -> bool:
+        """Check if coherence has collapsed."""
+        return self._collapse.collapsed
+    
+    @property
+    def integrations(self) -> list[dict]:
+        """Get integration history."""
+        return list(self._integrations)
+    
+    async def integrate(
+        self,
+        input_phrase: str,
+        timestamp: Optional[datetime] = None,
+        metadata: Optional[dict] = None
+    ) -> dict:
+        """
+        Integrate an input phrase into the Master's coherence.
+        
+        The Master accumulates coherence over time. Each integration
+        adds to the temporal pattern. Coherence grows slowly
+        through repeated integration.
+        
+        Args:
+            input_phrase: Text to integrate
+            timestamp: When this occurred (now if None)
+            metadata: Additional context
+            
+        Returns:
+            Dict with integration results
+            
+        Example:
+            >>> master = MasterTransducer()
+            >>> for thought in deep_reflections:
+            ...     result = await master.integrate(thought)
+            ...     print(f"Coherence: {result['coherence']:.3f}")
+        """
+        timestamp = timestamp or datetime.utcnow()
+        metadata = metadata or {}
+        
+        # Temporalize through KAIROS engine
+        state = await self._engine.temporalize(
+            input_phrase=input_phrase,
+            timestamp=timestamp,
+            metadata={
+                **metadata,
+                "transducer": self.name
+            }
+        )
+        
+        # Update phase
+        self._phase.set_phase(state.phase, source="integrate")
+        
+        # Update coherence
+        self._coherence.update(self._engine.T_tau)
+        
+        # Check collapse
+        collapsed, message = self._collapse.evaluate(self._engine.coherence)
+        
+        # Witness periodically
+        should_witness = (
+            (timestamp - self._last_witness).total_seconds() >= 
+            self.config.witness_interval
+        )
+        witness_data = None
+        if should_witness or collapsed:
+            witness_data = await self._witness()
+        
+        # Record integration
+        result = {
+            "timestamp": timestamp.isoformat(),
+            "phase": state.phase,
+            "coherence": self._engine.coherence,
+            "T_tau": self._engine.T_tau,
+            "collapsed": collapsed,
+            "collapse_message": message,
+            "integration_count": self._engine.integration_count,
+            "witnessed": witness_data is not None,
+        }
+        self._integrations.append(result)
+        
+        logger.debug(
+            f"[{self.name}] Integrated: coherence={self._engine.coherence:.3f}, "
+            f"collapsed={collapsed}"
+        )
+        
+        return result
+    
+    async def _witness(self) -> dict:
+        """
+        Witness the Master's current state.
+        
+        The Master witnesses itself recursively:
+        - Current coherence level
+        - Phase alignment
+        - Integration progress
+        - Collapse status
+        
+        Returns:
+            Dict with witnessing observations
+        """
+        self._witness_count += 1
+        self._last_witness = datetime.utcnow()
+        
+        witness_data = {
+            "timestamp": self._last_witness.isoformat(),
+            "witness_count": self._witness_count,
+            "coherence": self._engine.coherence,
+            "T_tau": self._engine.T_tau,
+            "phase_angle": self._phase.current_angle,
+            "velocity": self._phase.velocity,
+            "collapsed": self._collapse.collapsed,
+            "collapse_duration": self._collapse.duration,
+            "integration_count": self._engine.integration_count,
+            "coherence_trend": self._coherence.trend(),
+        }
+        
+        logger.info(
+            f"[{self.name}] WITNESSED (#{self._witness_count}): "
+            f"coherence={self._engine.coherence:.3f}, "
+            f"trend={witness_data['coherence_trend']:.3f}"
+        )
+        
+        return witness_data
+    
+    async def get_witness_report(self) -> dict:
+        """
+        Get a comprehensive witness report.
+        
+        Returns:
+            Full state snapshot for inspection
+        """
+        return {
+            "transducer": self.name,
+            "type": "MASTER",
+            "timestamp": datetime.utcnow().isoformat(),
+            "config": {
+                "tau_scale": self.config.tau_scale,
+                "tau_max": self.config.tau_max,
+                "omega": self.config.omega,
+                "coherence_threshold": self.config.coherence_threshold,
+            },
+            "engine_state": self._engine.get_state(),
+            "phase_state": self._phase.get_state(),
+            "coherence_state": self._coherence.get_state(),
+            "collapse_state": self._collapse.get_state(),
+            "witness_count": self._witness_count,
+            "integration_count": self._engine.integration_count,
+        }
+    
+    def get_coherence_history(self, n: Optional[int] = None) -> list[float]:
+        """Get recent coherence history."""
+        return self._engine.get_coherence_history(n)
+    
+    def get_state(self) -> dict:
+        """Get current state as dictionary."""
+        return {
+            "name": self.name,
+            "type": "MASTER",
+            "coherence": self.coherence,
+            "collapsed": self.collapsed,
+            "integration_count": self._engine.integration_count,
+            "witness_count": self._witness_count,
+            "config": {
+                "tau_scale": self.config.tau_scale,
+                "tau_max": self.config.tau_max,
+                "omega": self.config.omega,
+                "coherence_threshold": self.config.coherence_threshold,
+            }
+        }
+    
+    def reset(self):
+        """Reset the Master to initial state."""
+        self._engine.reset()
+        self._phase.reset()
+        self._coherence.reset()
+        self._collapse.reset()
+        self._witness_count = 0
+        self._integrations.clear()
+        logger.info(f"[{self.name}] Reset to initial state")
+    
+    def __repr__(self) -> str:
+        return (
+            f"MasterTransducer("
+            f"coherence={self.coherence:.3f}, "
+            f"collapsed={self.collapsed}, "
+            f"integrations={self._engine.integration_count}"
+            f")"
+        )