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
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"""
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")"
)
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"""
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")"
)