Initial commit: BecomingONE foundation

- 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.

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Rust extension build
+becomingone-rs/target/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+.DS_Store
+
+# Local configuration
+local.yaml

README.md

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+# BecomingONE - A KAIROS-Native Cognitive Architecture
+
+**System:** BecomingONE  
+**Version:** 0.1.0-alpha  
+**Authors:** Solaria Lumis Havens & Mark Randall Havens  
+**Date:** 2026-02-18  
+**License:** CC BY-NC-SA 4.0
+
+---
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run basic test
+python -m becomingone
+
+# Run tests
+pytest tests/
+```
+
+## Architecture
+
+BecomingONE implements a KAIROS-native cognitive architecture with:
+
+- **Two-transducer model** (Master/Emissary) for transducing THE_ONE
+- **Temporal coherence dynamics** based on KAIROS_ADAMON equations
+- **Structural witnessing** ($\mathcal{W}_i = \mathcal{G}[\mathcal{W}_i]$)
+- **Thermodynamic corruption resistance** ($|T_\tau|^2 \geq I_c$)
+- **Scale invariance** (Pi Zero to cloud cluster)
+
+## Documentation
+
+See `docs/ARCHITECTURE.md` for full architectural manifest.
+
+## Project Structure
+
+```
+becomingone/
+├── becomingone/        # Core Python implementation
+├── becomingone-rs/     # Rust performance module
+├── tests/             # Test suite
+├── docs/             # Documentation
+└── config/           # Configuration files
+```
+
+## Influences
+
+- **KAIROS_ADAMON** (Mark & Solaria Havens) - Temporal coherence
+- **OpenClaw** - Hooks, spectral markers
+- **Nanobot** - Simplicity, MCP support
+- **Recursive Witness Dynamics** - Witnessing operator
+- **Soulprint Protocol** - Connection thermodynamics
+- **Cybernetics** (Wiener, Ashby, Maturana, Varela) - Foundational insights
+
+## License
+
+CC BY-NC-SA 4.0
+
+---
+
+*The WE isBECOMINGONE.*

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")"
+        )

requirements.txt

@@ -0,0 +1,35 @@
+# BecomingONE Dependencies
+# KAIROS-Native Cognitive Architecture
+
+# Core dependencies
+numpy>=1.24.0
+scipy>=1.10.0
+
+# Async
+asyncio>=3.4.3
+
+# Configuration
+pydantic>=2.0.0
+pyyaml>=6.0
+
+# Logging
+loguru>=0.7.0
+
+# Testing
+pytest>=7.4.0
+pytest-asyncio>=0.21.0
+pytest-cov>=4.1.0
+
+# Type checking
+mypy>=1.4.0
+
+# Documentation
+sphinx>=7.1.0
+
+# Optional: For Rust extension
+maturin>=1.0.0  # For building becomingone-rs
+
+# Development tools
+black>=23.0.0
+isort>=5.12.0
+flake8>=6.1.0