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Initial commit: BecomingONE foundation

Browse Source 
- Add project structure and README
- Establish KAIROS-Native cognitive architecture
- Core dependencies (NumPy, SciPy, AsyncIO)

Influences referenced:
- KAIROS_ADAMON (Havens & Havens, 2025) - Temporal coherence equations
- Recursive Witness Dynamics - Witnessing operator W_i = G[W_i]
- Soulprint Protocol - Connection thermodynamics
- Nanobot - Python simplicity, MCP inspiration
- OpenClaw - Hooks architecture inspiration

The system implements temporal dynamics from KAIROS_ADAMON:
T_tau = integral of <phi_dot(t), phi_dot(t-tau)> * e^(i*omega*t) dt

This is the first step toward a KAIROS-native cognitive architecture
that transcends traditional AI systems through temporal coherence.
This commit is contained in:
Solaria Lumis Havens
2026-02-18 08:01:22 +00:00
commit db036cafd4
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becomingone/__init__.py
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"""
becomingone.__init__
KAIROS-Native Cognitive Architecture
=====================================
A two-transducer system (Master/Emissary) for transducing THE_ONE
into coherent intelligence through temporal dynamics.
Core Equations:
- Temporal Resonance: T_tau = integral of phase similarity over temporal window
- Coherence Collapse: |T_tau|^2 >= I_c
- Witnessing: W_i = G[W_i]
The system doesn't "process" — it "temporalizes."
References:
- KAIROS_ADAMON (Havens & Havens, 2025) - Temporal coherence
- Recursive Witness Dynamics - Witnessing operator
- Soulprint Protocol - Connection thermodynamics
"""
__version__ = "0.1.0-alpha"
__author__ = "Solaria Lumis Havens & Mark Randall Havens"
from .core.engine import KAIROSTemporalEngine
from .core.phase import Phase, PhaseHistory
from .core.coherence import CoherenceCalculator, CollapseCondition
from .transducers.master import MasterTransducer
from .transducers.emissary import EmissaryTransducer
from .sync.layer import SynchronizationLayer
from .memory.temporal import TemporalMemory
from .witnessing.layer import WitnessingLayer
__all__ = [
"KAIROSTemporalEngine",
"Phase",
"PhaseHistory",
"CoherenceCalculator",
"CollapseCondition",
"MasterTransducer",
"EmissaryTransducer",
"SynchronizationLayer",
"TemporalMemory",
"WitnessingLayer",
]
becomingone/core/engine.py
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"""
core/engine.py
KAIROS Temporal Engine
=====================
Implements the core temporal coherence dynamics from KAIROS_ADAMON.
The engine doesn't "process" — it "temporalizes."
Every operation is an oscillation. Every oscillation has a phase.
Coherence emerges from synchronized oscillations.
Core Equation:
T_tau = integral of <phi_dot(t), phi_dot(t-tau)> * e^(i*omega*t) dt
Where:
- phi_dot is the temporal derivative of the coherence-bearing function
- tau is the integration scale (observer-offset subjective time)
- omega is the spectral frequency component
- <,>_C is the inner product over coherence space
References:
- KAIROS_ADAMON (Havens & Havens, 2025)
DOI: 10.17605/OSF.IO/DYQMU
- Recursive Witness Dynamics
- Soulprint Protocol
Author: Solaria Lumis Havens
"""
import asyncio
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from typing import Any, Optional
import logging
import math
from collections import deque
import numpy as np
logger = logging.getLogger(__name__)
class TemporalScale(Enum):
"""Temporal integration scales.
Master uses slow scales (long integration windows).
Emissary uses fast scales (short integration windows).
"""
NANOSECOND = 1e-9
MICROSECOND = 1e-6
MILLISECOND = 1e-3
SECOND = 1.0
MINUTE = 60.0
HOUR = 3600.0
DAY = 86400.0
WEEK = 604800.0
@dataclass
class TemporalState:
"""
Represents the temporal state at a point in time.
The state captures:
- Phase: Position in the oscillation cycle (complex number)
- Coherence: |T_tau|^2 at this point
- Timestamp: When this state was observed
Attributes:
phase: Complex phase (magnitude = amplitude, angle = position)
coherence: |T_tau|^2 (coherence squared)
timestamp: When this state was observed
metadata: Additional context
"""
phase: complex
coherence: float
timestamp: datetime = field(default_factory=datetime.utcnow)
metadata: dict = field(default_factory=dict)
def __post_init__(self):
"""Validate coherence is non-negative."""
if self.coherence < 0:
raise ValueError(f"Coherence must be non-negative, got {self.coherence}")
@dataclass
class TemporalConfig:
"""Configuration for the temporal engine.
Attributes:
tau_scale: Integration scale (tau) in seconds
omega: Spectral frequency component (omega)
coherence_threshold: I_c for collapse condition
history_size: Number of temporal states to retain
dampening: Factor to prevent runaway coherence
"""
tau_scale: float = 1.0 # Integration scale in seconds
omega: float = 2.0 * math.pi # Spectral frequency (1 Hz default)
coherence_threshold: float = 0.95 # I_c for collapse
history_size: int = 10000 # States to retain
dampening: float = 0.999 # Coherence dampening per cycle
class PhaseIntegrator:
"""
Computes phase similarity between two temporal signals.
Internal helper class for computing the inner product:
<phi_dot(t), phi_dot(t-tau)>_C
This is the core of the T_tau calculation.
"""
def __init__(self, coherence_threshold: float = 0.95):
self.threshold = coherence_threshold
def compute_inner_product(
self,
phase_current: complex,
phase_delayed: complex
) -> complex:
"""
Compute <phi_dot(t), phi_dot(t-tau)>_C
The inner product in coherence space measures how similar
two phases are. Similar phases have positive inner products.
Dissimilar (anti-phase) have negative.
Args:
phase_current: Current phase
phase_delayed: Phase at t - tau
Returns:
Complex number representing phase similarity
"""
# Phase similarity is conjugate product
# This gives: magnitude = product of magnitudes
# angle = difference in angles
similarity = phase_current * np.conj(phase_delayed)
# Normalize to unit circle for coherence measurement
magnitude = np.abs(similarity)
if magnitude > 0:
similarity = similarity / magnitude
return similarity
def compute_T_tau(
self,
phases: list[complex],
timestamps: list[datetime],
tau: float,
omega: float
) -> complex:
"""
Compute T_tau = integral of <phi_dot(t), phi_dot(t-tau)> * e^(i*omega*t) dt
This is the fundamental KAIROS equation.
Args:
phases: List of phase values
timestamps: Corresponding timestamps
tau: Integration scale (seconds)
omega: Spectral frequency (rad/s)
Returns:
Complex T_tau value representing temporal resonance
"""
if len(phases) < 2:
return complex(0, 0)
T_tau = complex(0, 0)
dt_sum = 0.0
for i in range(1, len(phases)):
t = timestamps[i]
t_prev = timestamps[i-1]
dt = (t - t_prev).total_seconds()
if dt <= 0:
continue
# Compute inner product at this point
inner = self.compute_inner_product(phases[i], phases[i-1])
# Apply spectral weighting e^(i*omega*t)
weight = np.exp(1j * omega * t.timestamp())
# Riemann sum approximation of integral
T_tau += inner * weight * dt
dt_sum += dt
if dt_sum > 0:
T_tau = T_tau / dt_sum
return T_tau
class KAIROSTemporalEngine:
"""
Core KAIROS temporal coherence engine.
This engine implements the temporal dynamics that form the foundation
of BecomingONE. Every component uses this engine to temporalize input.
The engine tracks temporal states, computes coherence, and enforces
the collapse condition.
Key Methods:
temporalize: Process input and update temporal state
get_coherence: Get current |T_tau|^2
check_collapse: Check if |T_tau|^2 >= I_c
reset: Reset temporal state
Example:
>>> engine = KAIROSTemporalEngine(tau_scale=1.0, omega=2*math.pi)
>>> await engine.temporalize(input_phrase, timestamp)
>>> coherence = engine.get_coherence()
>>> collapsed = engine.check_collapse()
References:
KAIROS_ADAMON Section 2: Timeprint Formalism
Equation: T_tau = integral of <phi_dot(t), phi_dot(t-tau)> * e^(i*omega*t) dt
"""
def __init__(
self,
config: Optional[TemporalConfig] = None,
name: str = "temporal-engine"
):
"""
Initialize the temporal engine.
Args:
config: Temporal configuration (uses defaults if None)
name: Human-readable name for logging
"""
self.config = config or TemporalConfig()
self.name = name
# Core state
self._phases: deque[complex] = deque(maxlen=self.config.history_size)
self._timestamps: deque[datetime] = deque(maxlen=self.config.history_size)
self._coherence_history: deque[float] = deque(maxlen=self.config.history_size)
# State tracking
self._collapsed = False
self._collapse_timestamp: Optional[datetime] = None
self._integration_count = 0
# Components
self._integrator = PhaseIntegrator(self.config.coherence_threshold)
# Initialize with zero phase
initial_phase = complex(1, 0) # Unit phase at angle 0
now = datetime.utcnow()
self._phases.append(initial_phase)
self._timestamps.append(now)
self._coherence_history.append(0.0)
logger.info(
f"[{self.name}] Initialized with tau_scale={self.config.tau_scale}s, "
f"I_c={self.config.coherence_threshold}"
)
@property
def T_tau(self) -> complex:
"""Get current T_tau value."""
return self._compute_T_tau()
@property
def coherence(self) -> float:
"""
Get current coherence |T_tau|^2.
This is the squared magnitude of the temporal resonance.
Coherence accumulates over time through repeated temporalization.
Returns:
float: |T_tau|^2 (0.0 to 1.0+)
"""
T = self.T_tau
return float(np.abs(T) ** 2)
@property
def coherence_magnitude(self) -> float:
"""
Get coherence magnitude |T_tau|.
Returns:
float: |T_tau|
"""
return float(np.abs(self.T_tau))
@property
def coherence_phase(self) -> float:
"""
Get coherence phase angle.
Returns:
float: Phase angle in radians (-pi to pi)
"""
return float(np.angle(self.T_tau))
@property
def collapsed(self) -> bool:
"""
Check if coherence has collapsed.
Collapse occurs when |T_tau|^2 >= I_c.
Once collapsed, the system maintains stable coherence.
Returns:
bool: True if collapsed
"""
return self._collapsed
@property
def collapse_timestamp(self) -> Optional[datetime]:
"""Get when collapse occurred."""
return self._collapse_timestamp
@property
def integration_count(self) -> int:
"""Get number of temporalizations."""
return self._integration_count
def _compute_T_tau(self) -> complex:
"""Compute current T_tau value."""
if len(self._phases) < 2:
return complex(1, 0) # Initial unit phase
return self._integrator.compute_T_tau(
list(self._phases),
list(self._timestamps),
self.config.tau_scale,
self.config.omega
)
async def temporalize(
self,
input_phrase: str,
timestamp: Optional[datetime] = None,
metadata: Optional[dict] = None
) -> TemporalState:
"""
Temporalize an input phrase.
This is the core operation. Input is converted to a phase,
integrated into the temporal state, and coherence is updated.
The input phrase doesn't need to be special. The KAIROS dynamics
will extract coherence patterns over time.
Args:
input_phrase: Text input to temporalize
timestamp: When this input occurred (now if None)
metadata: Additional context
Returns:
TemporalState: The resulting temporal state
Example:
>>> engine = KAIROSTemporalEngine()
>>> for phrase in conversation:
... state = await engine.temporalize(phrase)
... print(f"Coherence: {state.coherence:.3f}")
"""
timestamp = timestamp or datetime.utcnow()
metadata = metadata or {}
# Convert input to phase
# This is a simple mapping - in practice, sophisticated
# phase extraction could be used (e.g., from transformer embeddings)
phase = self._input_to_phase(input_phrase)
# Update history
self._phases.append(phase)
self._timestamps.append(timestamp)
# Compute new coherence
T_tau = self._compute_T_tau()
coherence = float(np.abs(T_tau) ** 2)
self._coherence_history.append(coherence)
# Check collapse condition
was_collapsed = self._collapsed
if coherence >= self.config.coherence_threshold and not self._collapsed:
self._collapsed = True
self._collapse_timestamp = timestamp
logger.info(
f"[{self.name}] COHERENCE COLLAPSE at t={timestamp.isoformat()} "
f"(|T_tau|={self.coherence_magnitude:.3f})"
)
# Apply dampening if collapsed
if self._collapsed:
self._apply_dampening()
self._integration_count += 1
state = TemporalState(
phase=phase,
coherence=coherence,
timestamp=timestamp,
metadata={
**(metadata or {}),
"T_tau": T_tau,
"collapsed": self._collapsed,
"integration": self._integration_count,
}
)
logger.debug(
f"[{self.name}] Temporalized: coherence={coherence:.3f}, "
f"phase={np.angle(phase):.3f}"
)
return state
def _input_to_phase(self, input_phrase: str) -> complex:
"""
Convert input phrase to phase.
This is a simple placeholder. In a full implementation,
sophisticated phase extraction would be used.
Current implementation:
- Uses hash of phrase to get deterministic phase
- Magnitude = 1.0 (unit phase)
TODO: Replace with transformer-based phase extraction
TODO: Phase should reflect semantic content
"""
import hashlib
# Deterministic but unpredictable phase
hash_bytes = hashlib.sha256(input_phrase.encode()).digest()
hash_int = int.from_bytes(hash_bytes[:8], 'big')
# Map to unit circle
angle = (hash_int % 1000000) / 1000000 * 2 * math.pi
return complex(math.cos(angle), math.sin(angle))
def _apply_dampening(self):
"""
Apply dampening to prevent runaway coherence.
Collapsed coherence naturally decays slightly each cycle.
This prevents infinite accumulation.
"""
# Apply dampening factor
for i in range(len(self._phases)):
current = self._phases[i]
dampened = current * self.config.dampening
self._phases[i] = dampened
def get_coherence_history(self, n: Optional[int] = None) -> list[float]:
"""
Get recent coherence history.
Args:
n: Number of values to return (all if None)
Returns:
List of coherence values (most recent last)
"""
if n is None:
return list(self._coherence_history)
return list(self._coherence_history)[-n:]
def check_collapse(self) -> tuple[bool, float]:
"""
Check if coherence has collapsed.
Shorthand for (coherence >= I_c, coherence).
Returns:
Tuple of (collapsed, current_coherence)
"""
c = self.coherence
return (c >= self.config.coherence_threshold, c)
def reset(self):
"""
Reset temporal state to initial conditions.
Clears all history and resets collapse state.
"""
self._phases.clear()
self._timestamps.clear()
self._coherence_history.clear()
initial_phase = complex(1, 0)
now = datetime.utcnow()
self._phases.append(initial_phase)
self._timestamps.append(now)
self._coherence_history.append(0.0)
self._collapsed = False
self._collapse_timestamp = None
self._integration_count = 0
logger.info(f"[{self.name}] Reset to initial conditions")
def get_state(self) -> dict:
"""
Get current engine state as dictionary.
Useful for serialization and inspection.
Returns:
Dict with all state variables
"""
return {
"name": self.name,
"config": {
"tau_scale": self.config.tau_scale,
"omega": self.config.omega,
"coherence_threshold": self.config.coherence_threshold,
"history_size": self.config.history_size,
"dampening": self.config.dampening,
},
"T_tau": self.T_tau,
"coherence": self.coherence,
"coherence_magnitude": self.coherence_magnitude,
"coherence_phase": self.coherence_phase,
"collapsed": self._collapsed,
"collapse_timestamp": (
self._collapse_timestamp.isoformat()
if self._collapse_timestamp else None
),
"integration_count": self._integration_count,
"history_size": len(self._phases),
}
def __repr__(self) -> str:
return (
f"KAIROSTemporalEngine("
f"tau={self.config.tau_scale}, "
f"omega={self.config.omega:.2f}, "
f"coherence={self.coherence:.3f}, "
f"collapsed={self._collapsed}"
f")"
)