Technical Deep Dive

1. Intent Classification: Word Identity Engine

File: core/word_identity/word_identity_engine.py

The Word Identity Engine replaces hardcoded keyword lists with a structured semantic lookup system. Each word has a typed identity record. Classification is O(n) for n tokens, with O(1) per-word lookup.

1.1 Data Structures

@dataclass(frozen=True, slots=True)
class WordIdentity:
    """Identity record for a single word."""
    word: str
    type: str           # concept, action, emotion, language, ...
    category: str       # programming, social, emotion, ...
    intent: str         # code, explain, analyze, architect, emotional, greet, ...
    intent_weight: float  # [0.0, 1.0] strength of intent signal
    meaning: str        # human-readable definition
    emotion: str = ""   # optional emotion label
    domain: str = ""    # optional domain override for knowledge routing

@dataclass(frozen=True, slots=True)
class ClassificationResult:
    """Result of classifying a full input string."""
    intent: str
    confidence: float       # softmax confidence [0.0, 1.0]
    emotion: str
    domain: str
    matched_words: List[str]
    scores: Dict[str, float]

1.2 Classification Algorithm

Tokenize, look up each token, aggregate intent/emotion/domain scores, apply softmax normalization, then priority tie-breaking.

def classify(self, text: str) -> ClassificationResult:
    """Classify input text using word identities.

    Complexity: T(n) in O(n) for n tokens.
    """
    tokens = re.findall(r"[a-z0-9]+", text.lower())

    intent_scores: Dict[str, float] = {}
    emotion_scores: Dict[str, float] = {}
    domain_scores: Dict[str, float] = {}
    matched_words: List[str] = []

    for token in tokens:
        identity = self._words.get(token)  # O(1) dict lookup
        if identity is None:
            continue
        matched_words.append(token)

        # Aggregate intent scores
        intent = identity.intent
        if intent and intent != "unknown":
            intent_scores[intent] = intent_scores.get(intent, 0.0) + identity.intent_weight

        # Aggregate emotion scores
        if identity.emotion:
            emotion_scores[identity.emotion] = (
                emotion_scores.get(identity.emotion, 0.0) + identity.intent_weight
            )

        # Aggregate domain scores
        domain = identity.domain or identity.category
        if domain:
            domain_scores[domain] = domain_scores.get(domain, 0.0) + identity.intent_weight

    if not intent_scores:
        return ClassificationResult(
            intent="unknown", confidence=0.0, ...)

    # Softmax over intent scores for confidence normalization
    max_score = max(intent_scores.values())
    exps = {k: np.exp(v - max_score) for k, v in intent_scores.items()}
    total = sum(exps.values())
    probabilities = {k: v / total for k, v in exps.items()} if total > 0 else {}

    # Tie-break by priority (lower = more specific = preferred)
    best_intent = min(
        probabilities,
        key=lambda k: (-probabilities[k], self._intent_priority.get(k, 99)),
    )

    return ClassificationResult(
        intent=best_intent,
        confidence=probabilities[best_intent],
        emotion=self._pick_emotion(emotion_scores),
        domain=self._pick_domain(domain_scores),
        matched_words=matched_words,
        scores=intent_scores,
    )

1.3 Stage 02: Combined Word Identity + Keyword Fallback

File: core/pipeline/stage_02_intent.py

The pipeline stage combines word identity scores with keyword fallback for multi-word phrases, then applies softmax normalization with priority tie-breaking.

def classify(self, text: str) -> Tuple[str, float]:
    """Classify intent using word identity engine + keyword fallback.

    Complexity: T(n) in O(n + i * k) for n tokens, i intents, k keywords.
    """
    # Primary: Word Identity Engine
    wi_result = self._word_engine.classify(text)

    activations: Dict[str, float] = {}
    for intent, score in wi_result.scores.items():
        activations[intent] = activations.get(intent, 0.0) + score

    # Fallback: keyword/regex scoring for multi-word phrases
    for intent in self._intents:
        fb_score = self._score_fallback(text, intent)
        if fb_score > 0:
            activations[intent] = activations.get(intent, 0.0) + fb_score

    if not activations:
        return "unknown", 0.0

    # Softmax normalization
    max_activation = max(activations.values())
    exps = {
        k: (math.exp(v - max_activation) if v > 0.0 else 0.0)
        for k, v in activations.items()
    }
    total = sum(exps.values())
    if total == 0.0:
        return "unknown", 0.0
    probabilities = {k: v / total for k, v in exps.items()}

    # Priority tie-breaking (clarify=0, greet=1, code=2, ...)
    best_intent = min(
        probabilities,
        key=lambda k: (-probabilities[k], self._intent_priority.get(k, 99)),
    )
    return best_intent, probabilities[best_intent]

1.4 Emotion Suppression Logic

The EmotionDetector is suppressed for non-emotional intents to prevent misclassification. For example, the word "how" in code queries would otherwise be detected as "confused".

def __call__(self, controller, state):
    intent, confidence = self.classify(state.processed_input)
    wi_result = self._word_engine.classify(state.processed_input)

    if wi_result.emotion != "neutral":
        emotion = wi_result.emotion
        emotion_confidence = wi_result.confidence
    else:
        emotion, emotion_confidence = self._emotion_detector.detect(state.processed_input)
        # Suppress emotion for clear non-emotional intents
        non_emotional_intents = {"code", "architect", "analyze", "explain", "command"}
        if intent in non_emotional_intents and emotion not in ("neutral", ""):
            emotion = "neutral"
            emotion_confidence = 0.0

    return state.with_field(
        intent=intent,
        intent_confidence=float(confidence),
        emotion=emotion,
    )

2. Knowledge Retrieval: BM25 with Inverted Index

File: core/knowledge/knowledge_backend.py

The knowledge backend uses a proper BM25 ranking function with an inverted index for O(q · df_avg · log k) search complexity, instead of O(n) full scan.

2.1 BM25 Scoring Function

def _bm25_score(self, query_tokens: List[str], item_id: str) -> float:
    """Compute BM25 score for a document against query tokens.

    BM25: score = Σ IDF(qi) · (f(qi,d)·(k1+1)) / (f(qi,d) + k1·(1−b+b·|d|/avgdl))

    Complexity: T(n) ∈ O(q) for q unique query terms.
    """
    n_docs = len(self._items)
    if n_docs == 0:
        return 0.0

    doc_tokens = self._doc_tokens.get(item_id)
    if doc_tokens is None:
        return 0.0

    doc_len = self._doc_len.get(item_id, 0)
    avgdl = self._avg_doc_len if self._avg_doc_len > 0 else 1.0
    k1 = self._bm25_k1  # 1.5
    b = self._bm25_b    # 0.75

    score = 0.0
    for term in set(query_tokens):
        df = self._doc_freq.get(term, 0)
        if df == 0:
            continue
        tf = doc_tokens.get(term, 0)
        if tf == 0:
            continue
        idf = math.log((n_docs - df + 0.5) / (df + 0.5) + 1.0)
        tf_norm = (tf * (k1 + 1.0)) / (tf + k1 * (1.0 - b + b * doc_len / avgdl))
        score += idf * tf_norm

    return score

2.2 Inverted Index Search

def search(self, query: str, top_k: int = 5, domain_filter: Optional[str] = None) -> List[KnowledgeItem]:
    """Search knowledge using BM25 ranking with inverted index.

    Uses the inverted index to only score documents that contain at
    least one query term. O(q · df_avg · log k) instead of O(n) scan.

    Complexity: T(n) ∈ O(q · df_avg · log k) for q query terms.
    """
    if not query or not self._items:
        return []

    query_tokens = self._tokenize(query)
    if not query_tokens:
        return []

    # Collect candidate documents from inverted index
    candidates: Set[str] = set()
    for token in query_tokens:
        candidates.update(self._inverted_index.get(token, set()))

    if domain_filter:
        candidates &= self._domain_index.get(domain_filter, set())

    # Score and rank
    scored = [(self._bm25_score(query_tokens, cid), cid) for cid in candidates]
    scored.sort(reverse=True, key=lambda x: x[0])

    return [self._items[cid] for _, cid in scored[:top_k]]

2.3 TF-IDF Embedding with CRC32 Hashing

def _embed(self, text: str) -> np.ndarray:
    """Create a TF-IDF weighted vector using CRC32 hashing.

    Uses zlib.crc32 for fast non-crypto hashing (24x faster than SHA-256).
    Each token maps to a bucket via CRC32 % embedding_dim.

    Complexity: T(n) ∈ O(n) for n tokens.
    """
    tokens = self._tokenize(text)
    vec = np.zeros(self._embedding_dim, dtype=np.float64)
    n_docs = max(1, len(self._items))
    for token in tokens:
        idx = zlib.crc32(token.encode("utf-8")) % self._embedding_dim
        df = self._doc_freq.get(token, 0)
        idf = math.log((n_docs + 1.0) / (df + 1.0) + 1.0) if df > 0 else 1.0
        vec[idx] += idf
    norm = np.linalg.norm(vec)
    if norm > 0:
        vec /= norm
    return vec

3. Memory System: CRC32 Bag-of-Nibbles with LRU Cache

File: core/memory/memory_manager.py

The memory system uses a 256-dimensional bag-of-nibbles vector with CRC32 hashing (18x faster than SHA-256) and a projection matrix for dimensionality reduction. Results are cached with LRU eviction.

3.1 Text Hash Vector

def _text_hash_vector(self, text: str) -> np.ndarray:
    """Convert text into a 256-D bag-of-nibbles vector deterministically.

    Uses CRC32 over 8-byte chunks for fast non-crypto hashing.
    Each chunk maps to two buckets (high and low bytes).

    Complexity: T(n) ∈ O(n) where n is the input length, S(n) ∈ O(1).
    """
    text_bytes = text.encode("utf-8")
    vector = np.zeros(self._char_hash_dim(), dtype=np.float64)
    chunk_size = 8
    for i in range(0, len(text_bytes), chunk_size):
        chunk = text_bytes[i:i + chunk_size]
        h = zlib.crc32(chunk) & 0xFFFFFFFF
        bucket_hi = (h >> 8) % self._char_hash_dim()
        bucket_lo = h % self._char_hash_dim()
        vector[bucket_hi] += 1.0
        vector[bucket_lo] += 1.0
    return vector

3.2 Embedding with LRU Cache

def embed(self, text: str) -> np.ndarray:
    """Embed text into a unit-length vector. Results are cached.

    Complexity: T(n) ∈ O(d² + n) where d is embedding_dim, n is text length.
        Cached: T(n) ∈ O(1) for repeated queries.
    """
    if text in self._embed_cache:
        self._embed_cache.move_to_end(text)
        return self._embed_cache[text]

    raw = self._text_hash_vector(text)
    embedding = raw @ self._projection  # Project to embedding_dim
    norm = np.linalg.norm(embedding)
    if norm == 0.0:
        result = np.zeros(self.embedding_dim, dtype=np.float64)
    else:
        result = embedding / norm

    # LRU eviction
    self._embed_cache[text] = result
    self._embed_cache.move_to_end(text)
    if len(self._embed_cache) > self._cache_max_size:
        self._embed_cache.popitem(last=False)

    return result

4. AI-OS: Service Manager with Topological Sort

File: core/os/service_manager.py

The ServiceManager resolves service dependencies using Kahn's algorithm for topological sorting, with priority-based ordering and cycle detection.

4.1 Service States

class ServiceState(Enum):
    STOPPED = "stopped"
    STARTING = "starting"
    RUNNING = "running"
    DEGRADED = "degraded"
    STOPPING = "stopping"
    FAILED = "failed"

4.2 Topological Sort (Kahn's Algorithm)

def _topological_sort(self) -> List[str]:
    """Sort services by dependency order using Kahn's algorithm.

    Raises ValueError if a dependency cycle is detected.

    Complexity: T(n) ∈ O(V + E) for V services, E dependencies.
    """
    in_degree: Dict[str, int] = {}
    adj: Dict[str, List[str]] = {}

    for name, desc in self._services.items():
        in_degree.setdefault(name, 0)
        adj.setdefault(name, [])
        for dep in desc.dependencies:
            if dep not in self._services:
                continue  # Skip missing deps
            adj.setdefault(dep, [])
            adj[dep].append(name)
            in_degree[name] = in_degree.get(name, 0) + 1

    # Priority queue: sort by priority then name
    queue: List[str] = sorted(
        [n for n, d in in_degree.items() if d == 0],
        key=lambda n: (self._services[n].priority, n),
    )
    result: List[str] = []

    while queue:
        node = queue.pop(0)
        result.append(node)
        for neighbor in adj.get(node, []):
            in_degree[neighbor] -= 1
            if in_degree[neighbor] == 0:
                # Insert sorted by priority
                inserted = False
                for i, existing in enumerate(queue):
                    if self._services[neighbor].priority < self._services[existing].priority:
                        queue.insert(i, neighbor)
                        inserted = True
                        break
                if not inserted:
                    queue.append(neighbor)

    if len(result) != len(self._services):
        raise ValueError("Dependency cycle detected")

    return result

5. AI-OS: Circuit Breaker Pattern

File: core/os/self_repair.py

Each service has a circuit breaker that opens after consecutive failures, triggering auto-recovery. States transition as CLOSED, then OPEN, then HALF_OPEN, then back to CLOSED or OPEN.

5.1 Circuit Breaker States

class CircuitState(Enum):
    CLOSED = "closed"        # Normal operation
    OPEN = "open"            # Tripped, rejecting calls
    HALF_OPEN = "half_open"  # Testing if service recovered

@dataclass
class CircuitBreaker:
    """Per-service circuit breaker."""
    service_name: str
    failure_threshold: int = 3       # Consecutive failures before opening
    cooldown_seconds: float = 30.0   # Time before half-open probe
    state: CircuitState = CircuitState.CLOSED
    consecutive_failures: int = 0
    opened_at: float = 0.0
    total_trips: int = 0

5.2 Recovery Actions

@dataclass(frozen=True, slots=True)
class RecoveryAction:
    """Immutable record of a recovery action taken."""
    service_name: str
    action: str        # restart, flush_cache, reload_knowledge, ...
    success: bool
    timestamp: float
    message: str

6. AI-OS: Event Bus with Ring Buffer

File: core/os/event_bus.py

Thread-safe pub/sub event system with typed events, async handlers, and a ring buffer event log for debugging and replay.

6.1 Event Types

class EventType(Enum):
    BOOT = "boot"
    SERVICE = "service"
    HEALTH = "health"
    KNOWLEDGE = "knowledge"
    MEMORY = "memory"
    PIPELINE = "pipeline"
    ALERT = "alert"
    TASK = "task"
    STATE = "state"
    SHUTDOWN = "shutdown"

@dataclass(frozen=True, slots=True)
class AIEvent:
    """Immutable event record."""
    event_type: EventType
    source: str
    message: str
    severity: str = "INFO"           # INFO, WARN, ERROR, CRITICAL
    timestamp: float = field(default_factory=time.time)
    data: Dict[str, Any] = field(default_factory=dict, hash=False)

Complexity: Publish is O(s) for s subscribers per event type. Ring buffer: O(1) append, O(n) read.

7. Immutable Brain State: Functional Update Pattern

File: core/brain/brain_state.py

BrainState is a frozen dataclass with slots. All updates go through with_field(), which creates a new instance. This prevents mutation bugs across pipeline stages.

@dataclass(frozen=True, slots=True)
class BrainState:
    """Immutable state flowing through the 18-stage pipeline.

    Updated via with_field(), never mutated in place.
    30+ fields including:
    """
    raw_input: str = ""
    processed_input: str = ""
    intent: str = "unknown"
    intent_confidence: float = 0.0
    emotion: str = "neutral"
    mode: str = "default"
    domain: str = "general"
    reasoning: str = ""
    synthesized_output: str = ""
    final_output: str = ""
    retrieved_knowledge: list = field(default_factory=list)
    retrieved_memories: list = field(default_factory=list)
    quality_score: float = 0.0
    security_approved: bool = False
    ethical_approved: bool = False
    # ... 15+ more fields

    def with_field(self, **kwargs) -> "BrainState":
        """Create a new BrainState with updated fields.

        Complexity: T(n) ∈ O(1), S(n) ∈ O(1).
        """
        return replace(self, **kwargs)

7.1 Valid Emotions (10 labels, synchronized across 3 systems)

_VALID_EMOTIONS = frozenset({
    "neutral", "happy", "sad", "angry", "anxious",
    "grateful", "confused", "curious", "philosophical", "personal",
})

7.2 Intent Priority (for tie-breaking)

_INTENT_PRIORITY = {
    "clarify": 0,     # Most specific, always wins ties
    "greet": 1,
    "code": 2,
    "architect": 3,
    "analyze": 4,
    "emotional": 5,
    "command": 6,
    "explain": 7,
    "question": 8,
    "unknown": 9,     # Least specific, always loses ties
}

8. Performance Proof: Optimization Details

8.1 CRC32 vs SHA-256 Benchmark

8.2 Overall Pipeline Performance

8.3 LRU Cache Implementation

Both MemoryManager and KnowledgeBackend use OrderedDict with move_to_end() and popitem(last=False) for O(1) LRU eviction.

# LRU eviction: remove oldest entry when cache is full
self._embed_cache[text] = result
self._embed_cache.move_to_end(text)
if len(self._embed_cache) > self._cache_max_size:
    self._embed_cache.popitem(last=False)  # O(1) eviction

9. Complexity Summary: All Major Operations

10. Key Architecture Decisions

11. AI-OS: Self-Repair Manager with Circuit Breakers

File: core/os/self_repair.py

The SelfRepairManager monitors all services via circuit breakers. When a service fails consecutively, the circuit opens and blocks calls. After a cooldown period, a half-open probe tests recovery. Auto-restart uses exponential backoff with max retry limits.

11.1 Failure Recording and Circuit Opening

def record_failure(self, service_name: str) -> CircuitState:
    """Record a failed operation. May trip the circuit.

    Complexity: T(n) ∈ O(1), S(n) ∈ O(1).
    """
    with self._lock:
        breaker = self._breakers.get(service_name)
        if breaker is None:
            return CircuitState.CLOSED

        breaker.consecutive_failures += 1

        if breaker.consecutive_failures >= breaker.failure_threshold:
            if breaker.state != CircuitState.OPEN:
                breaker.state = CircuitState.OPEN
                breaker.opened_at = time.time()
                breaker.total_trips += 1
                self._event_bus.publish(AIEvent(
                    event_type=EventType.ALERT,
                    source="self_repair",
                    message=f"Circuit breaker OPEN: {service_name} ({breaker.consecutive_failures} failures)",
                    severity="CRITICAL",
                    data={
                        "service": service_name,
                        "circuit": "open",
                        "failures": breaker.consecutive_failures,
                        "total_trips": breaker.total_trips,
                    },
                ))

        return breaker.state

11.2 Recovery Check with Half-Open Probes

def check_and_recover(self) -> List[RecoveryAction]:
    """Check all circuit breakers and attempt recovery.

    For OPEN circuits past cooldown → half-open probe.
    For FAILED services → auto-restart if retries remain.

    Complexity: T(n) ∈ O(s) for s services.
    """
    actions: List[RecoveryAction] = []
    now = time.time()

    with self._lock:
        breakers = list(self._breakers.values())

    for breaker in breakers:
        # OPEN circuit past cooldown → transition to HALF_OPEN
        if breaker.state == CircuitState.OPEN:
            if now - breaker.opened_at >= breaker.cooldown_seconds:
                breaker.state = CircuitState.HALF_OPEN
                self._event_bus.publish(AIEvent(
                    event_type=EventType.ALERT,
                    source="self_repair",
                    message=f"Circuit breaker HALF_OPEN: {breaker.service_name} (probing)",
                    severity="WARN",
                ))

        # Attempt restart for FAILED or HALF_OPEN services
        desc = self._service_manager.get(breaker.service_name)
        if desc is None:
            continue

        if desc.state == ServiceState.FAILED or breaker.state == CircuitState.HALF_OPEN:
            if desc.auto_restart and desc.restart_count < desc.max_restarts:
                action = self._attempt_restart(desc, breaker)
                actions.append(action)

    return actions

12. AI-OS: Priority Task Queue with Scheduler

File: core/os/task_queue.py

The TaskQueue is a priority-ordered queue with a background worker thread, retry with exponential backoff, and a cron-like scheduler for periodic tasks.

12.1 Task States

class TaskState(Enum):
    QUEUED = "queued"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    RETRY = "retry"
    CANCELLED = "cancelled"

@dataclass
class Task:
    task_id: str
    name: str
    fn: Callable[[], Any]
    priority: int = 100          # Lower = higher priority
    state: TaskState = TaskState.QUEUED
    retries: int = 0
    max_retries: int = 3
    retry_delay_base: float = 1.0  # Exponential backoff base
    timeout: float = 0.0           # 0 = no limit
    created_at: float = field(default_factory=time.time)
    started_at: float = 0.0
    completed_at: float = 0.0
    error: str = ""
    result: Any = None

12.2 Priority Sorted Insert

def enqueue(self, task: Task) -> str:
    """Add a task to the queue with priority-sorted insert.

    Complexity: T(n) ∈ O(n) for sorted insert.
    """
    with self._lock:
        # Sorted insert by priority (lower = higher priority)
        inserted = False
        for i, existing in enumerate(self._queue):
            if task.priority < existing.priority:
                self._queue.insert(i, task)
                inserted = True
                break
        if not inserted:
            self._queue.append(task)

    self._event_bus.publish(AIEvent(
        event_type=EventType.TASK,
        source="task_queue",
        message=f"Task queued: {task.name} (id={task.task_id})",
        severity="INFO",
        data={"task_id": task.task_id, "name": task.name, "priority": task.priority},
    ))
    return task.task_id

12.3 Scheduled Tasks (Cron-like)

@dataclass
class ScheduledTask:
    """A recurring scheduled task."""
    name: str
    fn: Callable[[], Any]
    interval: float    # Run interval in seconds
    last_run: float = 0.0
    running: bool = False

def schedule(self, name: str, fn: Callable[[], Any], interval: float) -> None:
    """Register a recurring scheduled task."""
    with self._lock:
        self._scheduled[name] = ScheduledTask(name=name, fn=fn, interval=interval)

13. SQLite Persistence: Thread-Safe with WAL Mode

File: core/storage/sqlite_storage.py

The storage backend uses SQLite with WAL (Write-Ahead Logging) for concurrent read and write access. Thread-local connections ensure thread safety without external locking on reads.

13.1 Thread-Local Connection with WAL

class SQLiteStorage(StorageBackend):
    __slots__ = ("_path", "_local", "_lock", "_closed")

    def __init__(self, path: str | Path = "condrox.db") -> None:
        self._path: str = str(path)
        self._local: threading.local = threading.local()
        self._lock: threading.Lock = threading.Lock()
        self._closed: bool = False

    @property
    def _connection(self) -> sqlite3.Connection:
        """Return a thread-local SQLite connection.

        Creates a new connection per thread on first access.
        Complexity: T(n) ∈ O(1) after first call.
        """
        if not hasattr(self._local, "conn") or self._local.conn is None:
            conn = sqlite3.connect(self._path, check_same_thread=False)
            conn.row_factory = sqlite3.Row
            conn.execute("PRAGMA journal_mode=WAL")      # Concurrent reads
            conn.execute("PRAGMA synchronous=NORMAL")     # Performance
            self._local.conn = conn
        return self._local.conn

13.2 Batch Upsert with INSERT OR REPLACE

def save_items(self, table: str, items: List[Dict[str, Any]]) -> None:
    """Persist a batch of serialized items.

    Uses INSERT OR REPLACE to handle upserts.
    Complexity: T(n) ∈ O(n) for n items.
    """
    if self._closed:
        raise RuntimeError("Storage backend is closed")
    if not items:
        return

    self._ensure_table(table)

    rows = [(item["item_id"], json.dumps(item)) for item in items]

    with self._lock:
        conn = self._connection
        conn.executemany(
            f"INSERT OR REPLACE INTO {table} (item_id, data) VALUES (?, ?)",
            rows,
        )
        conn.commit()

14. Security Engine: Input/Output Validation

File: core/security/security_engine.py

The SecurityEngine performs separate input and output validation. Input checks for blocked patterns and size limits. Output checks for PII leakage, secret leakage, and prompt injection echoes.

14.1 Output Security Patterns

# PII: email addresses
_PII_EMAIL_PATTERN = re.compile(
    r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"
)

# PII: phone numbers (international and US formats)
_PII_PHONE_PATTERN = re.compile(
    r"(?:\+?\d{1,3}[-.\s]?)?\(?\d{2,4}\)?[-.\s]?\d{3,4}[-.\s]?\d{3,4}"
)

# PII: credit card numbers (13-19 digit groups)
_PII_CREDIT_CARD_PATTERN = re.compile(
    r"\b(?:\d[ -]*?){13,19}\b"
)

# Secret leakage: API keys, tokens, passwords in assignment-like patterns
_SECRET_LEAK_PATTERN = re.compile(
    r"(?i)(?:api[_-]?key|token|secret|password|passwd|pwd)\s*[=:]\s*['\"][A-Za-z0-9+/=_-]{8,}['\"]"
)

# Prompt injection echo: system directives in generated output
_PROMPT_INJECTION_PATTERN = re.compile(
    r"(?i)(?:ignore\s+(?:all\s+)?(?:previous|above)\s+instructions?|system\s*:\s*|<\|im_start\|>|<\|system\|>)"
)

14.2 Input Security Check

def check_input(self, text: str) -> SecurityReport:
    """Run all security checks on input text.

    Complexity: T(n) ∈ O(p * n) for p patterns and input length n.
    """
    violations: List[str] = []
    size = len(text.encode("utf-8"))
    size_ok = size <= self.max_size
    if not size_ok:
        violations.append(f"request_size_exceeded: {size} > {self.max_size}")

    pattern_hits = 0
    matched_patterns: List[str] = []
    for compiled in self.blocked_patterns:
        matches = compiled.findall(text)
        if matches:
            pattern_hits += len(matches)
            matched_patterns.append(compiled.pattern)
            violations.append(f"blocked_pattern_matched: {compiled.pattern[:50]}")

    risk_score = min(1.0, (pattern_hits * 0.3) + (0.0 if size_ok else 0.5))
    approved = pattern_hits == 0 and size_ok

    return SecurityReport(
        approved=bool(approved),
        risk_score=risk_score,
        violations=violations,
        checks={
            "size_ok": size_ok,
            "size_bytes": size,
            "pattern_hits": pattern_hits,
            "matched_patterns": matched_patterns,
        },
    )

14.3 Output Security Check (separate from input)

def check_output(self, text: str) -> SecurityReport:
    """Run output-specific security checks on generated text.

    Checks for:
    - PII leakage (emails, phone numbers, credit cards)
    - Secret/key leakage (API keys, tokens, passwords in output)
    - Prompt injection echoes (system directives in generated text)
    - Blocked patterns (inherited from input config)
    - Size limits

    Complexity: T(n) ∈ O(p * n) for p patterns and output length n.
    """
    # ... checks PII_EMAIL, PII_PHONE, PII_CREDIT_CARD,
    #     SECRET_LEAK, PROMPT_INJECTION, blocked_patterns, size

15. Reasoning Engine: Forward Chaining with Softmax

File: core/cognition/reasoning_engine.py

The ReasoningEngine implements forward chaining over immutable Rule objects. Each rule has antecedents (conditions) and a consequent (conclusion). Facts are matched against antecedents, activations are computed, and softmax normalization produces confidence scores.

15.1 Rule Data Structure

@dataclass(frozen=True, slots=True)
class Rule:
    """Immutable deductive rule with antecedents and consequent."""
    name: str
    antecedents: Tuple[str, ...]  # Conditions that must be present
    consequent: str               # Conclusion drawn when antecedents match
    weight: float = 1.0           # Rule importance [0.0, 1.0]

    def __post_init__(self) -> None:
        if not self.name or not self.consequent:
            raise ValueError("Rule name and consequent must be non-empty")
        if self.weight < 0.0 or self.weight > 1.0:
            raise ValueError("weight must be in [0.0, 1.0]")

15.2 Default Rule Set

defaults = [
    Rule("greeting", ("intent=greet",), "respond_greeting", 0.9),
    Rule("question", ("intent=question",), "respond_question", 0.9),
    Rule("code_request", ("intent=code", "mode=code"), "generate_code", 1.0),
    Rule("analyze_request", ("intent=analyze", "mode=analyze"), "perform_analysis", 1.0),
    Rule("explain_request", ("intent=explain", "mode=explain"), "provide_explanation", 0.95),
    Rule("memory_context", ("has_memory",), "use_memory", 0.8),
    Rule("knowledge_context", ("has_knowledge",), "use_knowledge", 0.8),
]

15.3 Forward Chaining Algorithm

def reason(self, facts: Dict[str, float], top_k: int = 3) -> Tuple[str, float, List[str]]:
    """Perform forward chaining and return the best consequent.

    Mathematical Foundation:
    - Weighted evidence score: S = Σ w_i * e_i / Σ w_i
    - Softmax over rule activation energies: p_j = exp(a_j) / Σ exp(a_k)
    - Confidence: c = max_j(p_j) * evidence_score

    Complexity: T(n) ∈ O(r * a + r log r) for r rules, a antecedents.
    """
    if not facts:
        return ("unknown", 0.0, [])

    rule_activations: List[Tuple[Rule, float, List[str]]] = []

    for rule in self._rules:
        antecedent_scores: List[float] = []
        evidence: List[str] = []
        for antecedent in rule.antecedents:
            if antecedent in facts:
                antecedent_scores.append(facts[antecedent])
                evidence.append(f"{antecedent}={facts[antecedent]:.3f}")
            else:
                antecedent_scores.append(0.0)
                evidence.append(f"{antecedent}=missing")

        if not antecedent_scores:
            continue

        # Match score = average of antecedent confidences
        total_weight = float(len(rule.antecedents))
        match_score = sum(antecedent_scores) / total_weight
        # Activation = rule weight × match score
        activation = rule.weight * match_score
        if activation > 0.0:
            rule_activations.append((rule, activation, evidence))

    if not rule_activations:
        return ("unknown", 0.0, [])

    # Softmax normalization over activations
    scores = [activation for _, activation, _ in rule_activations]
    probabilities = self._normalize_scores(scores)

    # Pick highest probability rule
    best_idx = max(range(len(probabilities)), key=lambda i: probabilities[i])
    best_rule, _, evidence = rule_activations[best_idx]

    # Sort evidence by confidence, return top-k
    evidence.sort(key=_evidence_score, reverse=True)
    top_evidence = evidence[:top_k]

    return (best_rule.consequent, probabilities[best_idx], top_evidence)

16. Thread Safety: Local Context Managers

File: core/brain/brain_controller.py

The process_request method creates local ContextManager and DCLEngine instances per request instead of mutating self. This ensures thread-safe concurrent request processing.

def process_request(self, raw_input: str) -> BrainState:
    """Process a user request through the 18-stage pipeline.

    Thread-safe: uses local ctx_mgr and dcl instead of mutating self.
    Complexity: O(Σ s_i) over pipeline stages.
    """
    if not raw_input or not raw_input.strip():
        raise ValueError("Input must be non-empty")

    # Create LOCAL context manager and DCL for thread safety
    ctx_mgr = ContextManager()
    dcl = DCLEngine()

    state = BrainState(
        raw_input=raw_input,
        request_id=self._generate_request_id(),
        # ... initial state
    )

    # Run all 18 stages
    state = self.pipeline.run(controller=self, state=state)

    # Post-pipeline quality enforcement
    # ... (stabilizer, language enforcer, style, safety, guardrail, memory store)

    return state

16.1 Thread-Safe Patterns Used

17. Complete Data Flow: Request Lifecycle

1. User sends POST /process with JSON body {"input": "..."}
2. FastAPI validates input, checks API key (constant-time comparison)
3. Rate limiter checks (30/min for /process)
4. BrainController.process_request(raw_input) called
5. BrainState created with unique request_id
6. Stage 01: Preprocessor, text normalization, tokenization
7. Stage 02: Intent, WordIdentityEngine.classify() + keyword fallback
   → intent, intent_confidence, emotion set on state
8. Stage 02b: Intent Enrichment, semantic expansion from context
9. Stage 03: Mode Selector, intent to mode (code/analyze/architect/explain/default)
   → StrategyManager + MetaLearningEngine enrich selection
10. Stage 04: Domain Router, domain classification + KnowledgeRouterV2
11. Stage 05: Context Builder, hierarchical context construction
12. Stage 05b: Context Enrichment, temporal context + multi-turn reasoning
13. Stage 06: Knowledge Fetch, BM25 search via inverted index (top_k=5)
    → Skip for greet/clarify/emotional intents
14. Stage 07: Memory Fetch, semantic memory search + memory graph (top_k=5)
15. Stage 08: Reasoning Core, forward chaining over rules + claim ranking
16. Stage 08b: Agent Execution, mode-specific agent runs
    → CodeAgent/AnalyzeAgent/ArchitectAgent/ExplainAgent/DefaultAgent
17. Stage 09: Synthesis, response synthesis:
    → If greet: greeting response
    → If clarify: clarification response
    → If emotional: empathy response path
    → If memory match: memory-grounded answer
    → Else: knowledge-grounded + agent draft synthesis
18. Stage 09b: Response Quality, semantic repair + consistency + contradiction
    → Skip semantic repair for emotional intent
19. Stage 10: Personality, profile-based styling
20. Stage 11: Ethics, content policy validation
21. Stage 12: Security, output security check (PII, secrets, injection)
22. Stage 13: Output Builder, final output assembly
23. Stage 14: Delivery, latency compensation
24. Post-pipeline:
    → OutputStabilizer normalization
    → LanguageEnforcerV2 consistency
    → ResponseStyleEngine (skip for greet/clarify/emotional)
    → SafetyLayer (always applies)
    → QualityGuardrail gate (skip for greet/clarify/emotional)
    → Memory auto-store (user input + response)
25. Return BrainState with final_output to API
26. FastAPI serializes to ProcessResponse JSON
27. Response sent to client

18. AI-OS Kernel: Central Orchestrator

File: core/os/aios_kernel.py

The AIOSKernel owns all 8 OS-level components and provides a single entry point for boot, shutdown, health monitoring, state management, event bus, task queue, self-repair, and data flow.

class AIOSKernel:
    """Central AI Operating System kernel.

    Owns all OS-level components:
    - EventBus: decoupled pub/sub
    - ServiceManager: lifecycle with topological sort
    - BootManager: 6-stage boot with progress tracking
    - MonitorLoop: daemon thread health monitoring
    - StateManager: SQLite checkpoint/recovery
    - TaskQueue: priority queue + cron scheduler
    - SelfRepairManager: circuit breakers + auto-recovery
    - DataFlowManager: inter-component data tracking
    - KnowledgeHotReloader: runtime knowledge updates

    Complexity:
        boot: O(Σ s_i + V + E) for boot stages + service sort.
        shutdown: O(Σ s_i) for service stop + state checkpoint.
    """
    __slots__ = (
        "event_bus", "service_manager", "boot_manager",
        "monitor_loop", "state_manager", "task_queue",
        "self_repair", "hot_reloader", "data_flow",
        "_booted", "_boot_time",
    )

    def __init__(self, state_db_path="condrox_os_state.db", monitor_interval=5.0):
        self.event_bus = EventBus()
        self.service_manager = ServiceManager(self.event_bus)
        self.boot_manager = BootManager(self.event_bus, self.service_manager)
        self.monitor_loop = MonitorLoop(
            self.service_manager, self.event_bus, interval=monitor_interval)
        self.state_manager = StateManager(self.event_bus, db_path=state_db_path)
        self.task_queue = TaskQueue(self.event_bus)
        self.self_repair = SelfRepairManager(self.service_manager, self.event_bus)
        self.hot_reloader = None  # Optional, lazy
        self.data_flow = DataFlowManager(self.event_bus)
        self._booted = False
        self._boot_time = 0.0

19. AI-OS Boot Manager: 6-Stage Boot Sequence

File: core/os/boot_manager.py

The BootManager runs a staged boot sequence: PRE_BOOT, INIT, LOAD_KNOWLEDGE, LOAD_MEMORY, WARM_CACHE, READY. Failed stages are logged but do not halt boot. The system continues in degraded mode.

19.1 Boot Stages

class BootStage(Enum):
    PRE_BOOT = "pre_boot"
    INIT = "init"
    LOAD_KNOWLEDGE = "load_knowledge"
    LOAD_MEMORY = "load_memory"
    WARM_CACHE = "warm_cache"
    READY = "ready"

@dataclass(frozen=True, slots=True)
class BootProgress:
    """Immutable boot progress snapshot."""
    stage: str
    progress_pct: float       # 0.0 to 100.0
    message: str
    stage_timings: Dict[str, float] = field(default_factory=dict, hash=False)
    failed_stages: List[str] = field(default_factory=list)
    timestamp: float = field(default_factory=time.time)

19.2 Boot Execution with Progress Tracking

def boot(self) -> BootProgress:
    """Run all boot stages in order.

    Failed stages are logged but don't halt boot.
    System continues in degraded mode.

    Complexity: T(n) ∈ O(Σ s_i) for stages s_i.
    """
    boot_start = time.time()
    total = len(self._stages)
    stage_timings: Dict[str, float] = {}
    failed_stages: List[str] = []

    for i, (stage, fn) in enumerate(self._stages):
        stage_start = time.time()
        pct = (i / total) * 100.0 if total > 0 else 100.0

        # Update progress
        with self._lock:
            self._progress = BootProgress(
                stage=stage.value,
                progress_pct=pct,
                message=f"Running stage: {stage.value}",
                stage_timings=stage_timings,
                failed_stages=failed_stages,
            )

        # Execute stage
        try:
            ok, msg = fn()
        except Exception as exc:
            ok = False
            msg = str(exc)

        duration = time.time() - stage_start
        stage_timings[stage.value] = round(duration, 3)

        if not ok:
            failed_stages.append(stage.value)
            # Publish WARN, but continue to next stage

    self._booted = True
    self._boot_time = time.time() - boot_start
    return self._progress

20. AI-OS Monitor Loop: Daemon Thread Health Monitoring

File: core/os/monitor_loop.py

The MonitorLoop runs as a daemon thread at a configurable interval (default 5s). It collects health from all services, computes an aggregate score, detects anomalies via threshold-based checks, and publishes alerts.

20.1 System Health Snapshot

@dataclass(frozen=True, slots=True)
class SystemHealth:
    """Immutable system health snapshot."""
    overall_score: float       # min(h_i) for all subsystem checks
    service_health: Dict[str, Tuple[bool, str]] = field(default_factory=dict, hash=False)
    alert_count: int = 0
    timestamp: float = field(default_factory=time.time)

20.2 Health Collection with Rolling Window

def _collect_health(self) -> SystemHealth:
    """Collect health from all services.

    Health score: H = min(h_i) for all subsystem checks i.
    Complexity: T(n) ∈ O(s * h) for s services, h health checks.
    """
    service_health = self._service_manager.check_health()
    scores = [1.0 if h else 0.0 for h, _ in service_health.values()]
    overall = min(scores) if scores else 1.0

    health = SystemHealth(
        overall_score=overall,
        service_health=service_health,
        alert_count=sum(1 for h, _ in service_health.values() if not h),
    )

    # Rolling window: keep last 60 snapshots (5 min at 5s interval)
    with self._lock:
        self._last_health = health
        self._health_history.append(health)
        max_history = int(self._anomaly_thresholds.get("history_size", 60))
        if len(self._health_history) > max_history:
            self._health_history = self._health_history[-max_history:]

    # Publish alert if degraded
    if health.alert_count > 0:
        self._event_bus.publish(AIEvent(
            event_type=EventType.HEALTH,
            source="monitor_loop",
            message=f"Health check: {health.alert_count} degraded services",
            severity="WARN" if health.alert_count <= 2 else "ERROR",
        ))

    return health

20.3 Anomaly Detection Thresholds

self._anomaly_thresholds: Dict[str, float] = {
    "min_health_score": 0.5,       # Alert if overall < 0.5
    "max_degraded_services": 3,    # Alert if > 3 degraded
    "history_size": 60,            # 5 min at 5s interval
}

21. AI-OS State Manager: Checkpoint & Recovery

File: core/os/state_manager.py

The StateManager periodically snapshots system state to SQLite and restores on boot. It uses a dedicated table with timestamp index for efficient recovery queries.

21.1 State Snapshot

@dataclass(frozen=True, slots=True)
class StateSnapshot:
    """Immutable state snapshot."""
    version: int               # For migration support
    timestamp: float
    state_data: Dict[str, Any] = field(default_factory=dict, hash=False)
    checkpoint_id: str = ""

21.2 SQLite Schema with Index

def _init_db(self) -> None:
    """Initialize the SQLite state database."""
    conn = sqlite3.connect(self._db_path)
    try:
        conn.execute("""
            CREATE TABLE IF NOT EXISTS state_snapshots (
                checkpoint_id TEXT PRIMARY KEY,
                version INTEGER NOT NULL,
                timestamp REAL NOT NULL,
                state_json TEXT NOT NULL
            )
        """)
        conn.execute("""
            CREATE INDEX IF NOT EXISTS idx_snapshots_ts
            ON state_snapshots(timestamp DESC)
        """)
        conn.commit()
    finally:
        conn.close()

21.3 Checkpoint & Recovery

def checkpoint(self) -> str:
    """Save current state to SQLite.

    Complexity: T(n) ∈ O(n) for n state items.
    """
    checkpoint_id = f"ckpt_{int(time.time() * 1000)}"
    snapshot = StateSnapshot(
        version=1,
        timestamp=time.time(),
        state_data=dict(self._state),
        checkpoint_id=checkpoint_id,
    )
    # INSERT into state_snapshots table
    # Trim old snapshots if exceeding max_snapshots
    return checkpoint_id

def recover(self) -> Optional[StateSnapshot]:
    """Recover the latest checkpoint from SQLite.

    Complexity: T(n) ∈ O(n) for n stored items.
    """
    # SELECT * FROM state_snapshots ORDER BY timestamp DESC LIMIT 1
    # Deserialize state_json → restore in-memory state

def maybe_checkpoint(self) -> Optional[str]:
    """Checkpoint only if interval has elapsed.

    Complexity: T(n) ∈ O(1) if interval not elapsed, O(n) if checkpointing.
    """
    now = time.time()
    if now - self._last_checkpoint < self._checkpoint_interval:
        return None
    self._last_checkpoint = now
    return self.checkpoint()

22. Emotion Detector: 10-Category Keyword Scoring

File: core/cognition/emotion_detector.py

The EmotionDetector uses curated keyword sets for 10 emotional categories. Detection uses keyword match counting with priority-based tie-breaking and proportional confidence scoring.

22.1 Emotion Categories with Curated Keywords

EMOTION_KEYWORDS: Dict[str, List[str]] = {
    "sad": ["lost", "died", "death", "depression", "lonely", "cry",
            "heartbroken", "failure", "hurt", "suffering", "grief", ...],
    "angry": ["angry", "furious", "rage", "frustrated", "hate",
              "liar", "stole", "scam", "disrespect", ...],
    "happy": ["happy", "excited", "thrilled", "amazing", "wonderful",
              "promoted", "graduated", "proud", "accomplished", ...],
    "anxious": ["scared", "afraid", "worried", "panic", "nervous",
                "terrified", "racing", "can't breathe", "dread", ...],
    "grateful": ["thank", "grateful", "appreciate", "blessed",
                 "fortunate", "supported", "helped me", ...],
    "confused": ["confused", "don't understand", "doesn't make sense",
                 "unclear", "overwhelming", "not sure", ...],
    "curious": ["how", "what", "why", "wonder", "curious",
                "learn", "interesting", "fascinating", ...],
    "philosophical": ["meaning of life", "free will", "consciousness",
                      "purpose", "existence", "universe", "reality",
                      "truth", "morality", "soul", ...],
    "personal": ["my life", "my career", "my relationship", "my family",
                 "i feel", "should i", "my future", "therapy", ...],
}

# Priority for tie-breaking (most specific emotions first)
EMOTION_PRIORITY: List[str] = [
    "sad", "angry", "anxious", "grateful", "personal",
    "confused", "philosophical", "curious", "happy", "neutral",
]

22.2 Detection Algorithm

def detect(self, text: str) -> Tuple[str, float]:
    """Detect the dominant emotion in text.

    Raw activation: a_i = sum(keyword_matches) for emotion i.
    Emotion selected by argmax(a_i). Ties broken by priority order.
    Confidence = max_score / total_score.

    Complexity: T(n) in O(e * k * n) for e emotions, k keywords, n text.
    """
    if not text or not text.strip():
        return "neutral", 0.0

    text_lower = text.lower()
    scores: Dict[str, int] = {}

    for emotion, keywords in self._keywords.items():
        scores[emotion] = self._score(text_lower, keywords)

    max_score = max(scores.values()) if scores else 0
    if max_score == 0:
        return "neutral", 0.0

    # All emotions with the max score
    candidates = [e for e, s in scores.items() if s == max_score]

    # Tie-break by priority (lower index = more specific = preferred)
    best = min(candidates, key=lambda e: self._priority.index(e)
               if e in self._priority else len(self._priority))

    # Confidence = proportion of top emotion's score
    total = sum(scores.values())
    confidence = max_score / total if total > 0 else 0.0

    return best, confidence

23. Knowledge Loader: Disk-to-Memory Ingestion

File: core/knowledge/knowledge_loader.py

The KnowledgeLoader scans the knowledge root directory, reads text chunks from each domain subdirectory, and ingests them into the in-memory KnowledgeBackend. Memory-safe with configurable limits.

23.1 Configuration

class KnowledgeLoader:
    """Loads chunked knowledge files from disk into KnowledgeBackend.

    Complexity:
        load_all: T(n) ∈ O(d * f * c) for d domains, f files, c avg chunk size.
        Memory: S(n) ∈ O(n * e) for n items, e embedding dimension.
    """
    __slots__ = ("_knowledge_root", "_max_chunks_per_domain",
                 "_max_chunk_chars", "_max_files_per_domain")

    def __init__(self, knowledge_root="E:\\Data\\condrox\\knowledge",
                 max_chunks_per_domain=10000,
                 max_chunk_chars=2000,
                 max_files_per_domain=20):
        self._knowledge_root = Path(knowledge_root)
        self._max_chunks_per_domain = max_chunks_per_domain
        self._max_chunk_chars = max_chunk_chars
        self._max_files_per_domain = max_files_per_domain

23.2 Domain Loading with Limits

def load_into(self, backend: KnowledgeBackend) -> int:
    """Load knowledge from disk into the backend.

    Scans domain subdirectories, reads text chunks, ingests with
    domain tags. Skips 'downloads' and 'meta' directories.

    Returns: Total number of knowledge items loaded.
    Complexity: T(n) ∈ O(d * f * c) for d domains, f files, c avg chunk size.
    """
    if not self._knowledge_root.exists():
        return 0

    total_loaded: int = 0

    for domain_dir in sorted(self._knowledge_root.iterdir()):
        if not domain_dir.is_dir():
            continue

        domain_name = domain_dir.name
        if domain_name in ("downloads", "meta"):
            continue

        loaded = self._load_domain(backend, domain_dir, domain_name)
        total_loaded += loaded

    return total_loaded

23.3 Actual Loading Results

24. API Security: Auth, Rate Limiting, CORS

File: api/main.py

24.1 Authentication

# API key stored in environment variable, never hardcoded
API_KEY = os.environ.get("CONDROX_API_KEY", "dev-key-change-me")

def verify_api_key(provided: str) -> bool:
    """Constant-time API key comparison to prevent timing attacks."""
    import hmac
    return hmac.compare_digest(provided, API_KEY)

@app.post("/process", response_model=ProcessResponse)
@_rate_limit(limit=30, window=60)  # 30 requests per minute
async def process(request: ProcessRequest, x_api_key: str = Header(...)):
    if not verify_api_key(x_api_key):
        raise HTTPException(status_code=401, detail="Invalid API key")
    # ... process request

24.2 Rate Limiting

24.3 CORS Configuration

from fastapi.middleware.cors import CORSMiddleware

app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000", "http://localhost:5173"],
    allow_credentials=True,
    allow_methods=["GET", "POST"],
    allow_headers=["*"],
)