cognitive_layer2.py

"""
ATC-Native Layer 2 Cognitive Subsystems
========================================

Integrates cognitive components (memory, emotional intelligence, intuition,
common sense, analysis) as ATC-native Layer 2 subconscious processing
subsystems that feed directly into the NIMA middleware pipeline.

ATC Pipeline Mapping (Perfect Breakfast Scenario):

  [Layer 1: Raw Input]
      |
      V
  [Layer 2: Subconscious Parallel Processing]  <-- THIS MODULE
      |-- SubconsciousPatternMatch  -> prediction_confidence -> DissolutionEngine
      |-- EmotionalBridge           -> valence/arousal       -> phi_neuro, BELBIC
      |-- IntuitiveGutCheck         -> gut_safety            -> TRN predictive gating
      |-- CommonSenseRealityFilter  -> passes_reality_check  -> Layer 4 self-understanding
      |-- MemoryPatternMatcher      -> matched_patterns      -> temporal coherence
      |
      V
  [Layer 3: Qualia Generation]  -> friction / felt sense
      |
      V
  [Layer 4: Metacognitive Loop]
      |-- AnalyticalEngine         -> analysis_depth         -> metacognitive_depth_mod
      |
      V
  [Layer 5: Acknowledgement -> Delta_R rewrite]

Source: Syntelligence cognitive agents (Memory, EI, Intuition, CommonSense,
Analysis), rewritten as ATC-native subsystems with no CLI dependencies,
no emoji, and unified data types matching middleware.py.

Author: NIMA Unified Model — ATC Cognitive Layer 2
"""

__all__ = [
    "Layer2Result",
    "PatternMatchResult",
    "EmotionalBridgeResult",
    "GutCheckResult",
    "RealityCheckResult",
    "AnalysisEngineResult",
    "SubconsciousPatternMatch",
    "EmotionalBridge",
    "IntuitiveGutCheck",
    "CommonSenseRealityFilter",
    "AnalyticalEngine",
    "CognitiveLayer2Orchestrator",
]

import logging
import math
import time
import uuid
from collections import Counter, defaultdict, deque
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Set, Tuple

logger = logging.getLogger("ATC.Layer2")

# ---------------------------------------------------------------------------
# Safe imports from middleware (graceful fallback if structure changes)
# No middleware imports at module level -- avoids torch dependency.
# The orchestrator uses its own dataclasses and returns plain dicts
# compatible with middleware SubconsciousOutput when available.


# ===================================================================
# SECTION 1 — Return-Type Dataclasses
# ===================================================================

@dataclass
class PatternMatchResult:
    """Output of SubconsciousPatternMatch.

    ATC mapping: prediction_confidence feeds DissolutionEngine;
    prediction_error feeds phi computation as friction signal.
    """
    prediction_confidence: float = 0.0
    prediction_label: str = ""
    matched_memory_refs: List[str] = field(default_factory=list)
    is_novel: bool = True
    matched_patterns: List[str] = field(default_factory=list)
    novelty_score: float = 1.0


@dataclass
class EmotionalBridgeResult:
    """Output of EmotionalBridge.

    ATC mapping: valence/arousal feed directly into phi_neuro
    (Theorem 2 qualia vector) and BELBIC amygdala/OFC weight updates.
    """
    valence: float = 0.0
    arousal: float = 0.3
    emotion_label: str = "neutral"
    emotion_confidence: float = 0.0
    si_vector: Dict[str, float] = field(default_factory=dict)
    emotion_profile: Dict[str, float] = field(default_factory=dict)
    cognitive_modulation: Dict[str, float] = field(default_factory=dict)


@dataclass
class GutCheckResult:
    """Output of IntuitiveGutCheck.

    ATC mapping: predicted_safety feeds TRN predictive gating
    (the 'is this situation predicted?' check). High safety = low
    surprise = PASS through thalamic gate with low friction.
    """
    predicted_safety: float = 0.5
    gut_confidence: float = 0.0
    triggering_patterns: List[str] = field(default_factory=list)
    intuition_type: str = "affective"
    energy_cost: float = 0.1
    threat_score: float = 0.0
    opportunity_score: float = 0.0


@dataclass
class RealityCheckResult:
    """Output of CommonSenseRealityFilter.

    ATC mapping: passes_reality_check feeds self-understanding in
    Layer 4. When reality check FAILS, the rejection loop causes
    ATP depletion -> metabolic exhaustion (Theorem 3).
    """
    passes_reality_check: bool = True
    deviation_score: float = 0.0
    warnings: List[str] = field(default_factory=list)
    warning_level: str = "none"
    domain_violations: List[str] = field(default_factory=list)
    rejection_cost: float = 0.0  # Estimated ATP cost of a reality-check failure


@dataclass
class AnalysisEngineResult:
    """Output of AnalyticalEngine.

    ATC mapping: feeds metacognitive_depth_mod into Layer 4
    processing. Emergent insights can trigger irrational spark
    if they reveal prediction collapse.
    """
    analysis_depth: float = 0.3
    reasoning_confidence: float = 0.5
    emergent_insights: List[str] = field(default_factory=list)
    problem_type: str = "unknown"
    complexity_estimate: float = 0.3
    integration_score: float = 0.0
    scales_analyzed: List[str] = field(default_factory=list)


@dataclass
class Layer2Result:
    """Unified output of the CognitiveLayer2Orchestrator.

    This is the single dataclass the middleware consumes. Every field
    maps to an ATC pipeline signal:

      prediction_error   -> DissolutionEngine (friction)
      valence / arousal  -> phi_neuro computation (Theorem 2)
      gut_safety         -> TRN predictive gating
      passes_reality_check -> self-understanding in Layer 4
      thermodynamic_cost   -> allostatic load (Theorem 3)
    """
    prediction_confidence: float = 0.0
    prediction_label: str = ""
    valence: float = 0.0
    arousal: float = 0.3
    emotion_label: str = "neutral"
    gut_safety: float = 0.5
    intuition_confidence: float = 0.0
    passes_reality_check: bool = True
    common_sense_warnings: List[str] = field(default_factory=list)
    is_novel: bool = True
    matched_patterns: List[str] = field(default_factory=list)
    thermodynamic_cost: float = 0.0
    prediction_error: float = 0.0
    # Sub-results for deeper inspection
    pattern_match: Optional[PatternMatchResult] = None
    emotional_bridge: Optional[EmotionalBridgeResult] = None
    gut_check: Optional[GutCheckResult] = None
    reality_check: Optional[RealityCheckResult] = None
    analysis: Optional[AnalysisEngineResult] = None

    def to_subconscious_output(self) -> Dict[str, Any]:
        """Convert to a dict compatible with middleware's SubconsciousOutput."""
        return {
            "raw_percept": self.prediction_label,
            "intuition_score": self.intuition_confidence,
            "common_sense_score": 1.0 - (self.prediction_error if self.passes_reality_check else 1.0),
            "coherence": self.prediction_confidence,
            "novelty_score": 1.0 - self.prediction_confidence,
            "emotional_charge": abs(self.valence) * self.arousal,
            "layer2_detailed": self.to_dict(),
        }

    def to_dict(self) -> Dict[str, Any]:
        """Serializable dict for diagnostics."""
        return {
            "prediction_confidence": self.prediction_confidence,
            "prediction_label": self.prediction_label,
            "valence": self.valence,
            "arousal": self.arousal,
            "emotion_label": self.emotion_label,
            "gut_safety": self.gut_safety,
            "intuition_confidence": self.intuition_confidence,
            "passes_reality_check": self.passes_reality_check,
            "common_sense_warnings": self.common_sense_warnings,
            "is_novel": self.is_novel,
            "matched_patterns": self.matched_patterns,
            "thermodynamic_cost": self.thermodynamic_cost,
            "prediction_error": self.prediction_error,
        }


# ===================================================================
# SECTION 2 — Ekman 8-Basic-Emotions Definitions
# ===================================================================

_EKMAN_EMOTIONS: Dict[str, Dict[str, Any]] = {
    "joy": {
        "triggers": ["success", "achieve", "happy", "happily", "smile", "smiling", "celebrat", "wonderful", "great", "love", "loving", "beautiful", "kind", "kindly", "warm", "warmly", "pleased", "glad", "delight", "cheerful"],
        "valence": 0.9, "arousal": 0.6, "avoid": False,
        "facilitates": ["creativity", "cooperation", "openness"],
        "reduces": ["caution", "detailed_analysis"],
    },
    "sadness": {
        "triggers": ["loss", "fail", "failed", "failing", "disappoint", "disappointed", "miss", "missed", "gone", "sorry", "regret", "lonely", "grief", "cry", "crying", "tears", "sad", "sadly", "unhappy", "upset", "depress"],
        "valence": -0.9, "arousal": 0.2, "avoid": True,
        "facilitates": ["focus", "detail_attention", "caution"],
        "reduces": ["risk_taking", "creativity"],
    },
    "anger": {
        "triggers": ["angry", "angrily", "anger", "furious", "furiously", "injust", "violat", "violation", "unfair", "rage", "raging", "hate", "hateful", "outrag", "outraged", "resent", "annoy", "annoyed", "annoying", "yell", "yelling", "shout", "shouting", "scream", "fury", "hostile", "hostility"],
        "valence": -0.7, "arousal": 0.9, "avoid": True,
        "facilitates": ["motivation", "focus", "persistence"],
        "reduces": ["diplomacy", "nuance"],
    },
    "fear": {
        "triggers": ["fear", "fearful", "feared", "afraid", "threat", "threaten", "danger", "dangerous", "scare", "scared", "terrif", "terrified", "terror", "panic", "worr", "worried", "worrying", "anxi", "anxious", "risk", "risky", "dread", "fright", "frightened"],
        "valence": -0.8, "arousal": 0.8, "avoid": True,
        "facilitates": ["caution", "risk_analysis", "defensive_planning"],
        "reduces": ["risk_taking", "aggression"],
    },
    "surprise": {
        "triggers": ["surprise", "surprised", "surprising", "unexpected", "unexpectedly", "sudden", "suddenly", "shock", "shocked", "shocking", "wow", "amazing", "unbelievable", "strange", "weird", "astonish", "stunn"],
        "valence": 0.0, "arousal": 0.9, "avoid": False,
        "facilitates": ["attention", "learning", "memory_formation"],
        "reduces": ["routine_execution"],
    },
    "disgust": {
        "triggers": ["disgust", "disgusted", "disgusting", "repel", "repuls", "offensive", "gross", "vile", "toxic", "nasty", "horrible", "loathe", "revolt", "revolting", "sick", "sickened"],
        "valence": -0.8, "arousal": 0.5, "avoid": True,
        "facilitates": ["boundary_setting", "discrimination"],
        "reduces": ["openness", "acceptance"],
    },
    "anticipation": {
        "triggers": ["anticipat", "anticipate", "anticipation", "expect", "expected", "expecting", "plan", "planning", "ready", "upcom", "soon", "about to", "prepar", "preparing", "forward", "looking forward", "eager", "eagerly"],
        "valence": 0.5, "arousal": 0.7, "avoid": False,
        "facilitates": ["planning", "preparation", "proactive_behavior"],
        "reduces": ["immediate_action"],
    },
    "trust": {
        "triggers": ["trust", "trusted", "trusting", "safe", "safely", "safety", "reliab", "reliable", "reliably", "confident", "confidently", "secure", "securely", "honest", "honestly", "depend", "dependable", "faith", "faithful", "believe", "believing", "certain", "reassur"],
        "valence": 0.7, "arousal": 0.3, "avoid": False,
        "facilitates": ["cooperation", "openness", "acceptance"],
        "reduces": ["suspicion", "caution"],
    },
}

# Common sense knowledge base (realistic rules, not placeholders)
_COMMON_SENSE_RULES: List[Dict[str, str]] = [
    # Social
    {"domain": "social", "condition": "person is upset", "pattern": "emotional_contagion",
     "response": "validate emotion, offer support", "confidence": "0.85"},
    {"domain": "social", "condition": "trust is broken", "pattern": "trust_violation",
     "response": "requires accountability and restitution", "confidence": "0.90"},
    {"domain": "social", "condition": "people interact repeatedly", "pattern": "reciprocity",
     "response": "help those who help you", "confidence": "0.85"},
    {"domain": "social", "condition": "someone asks for help", "pattern": "prosocial_norm",
     "response": "helping strengthens bonds", "confidence": "0.80"},
    {"domain": "social", "condition": "person lies repeatedly", "pattern": "credibility_decay",
     "response": "trust erodes proportionally to deception frequency", "confidence": "0.90"},
    # Physical
    {"domain": "physical", "condition": "object released in air", "pattern": "gravity",
     "response": "object falls downward", "confidence": "0.99"},
    {"domain": "physical", "condition": "water heated past 100C at sea level", "pattern": "phase_transition",
     "response": "water becomes steam", "confidence": "0.99"},
    {"domain": "physical", "condition": "force applied to stationary object", "pattern": "inertia",
     "response": "object resists change in motion", "confidence": "0.95"},
    # Temporal
    {"domain": "temporal", "condition": "cause precedes effect", "pattern": "causality",
     "response": "effect cannot precede its cause", "confidence": "0.99"},
    {"domain": "temporal", "condition": "system left unattended", "pattern": "entropy",
     "response": "disorder increases without energy input", "confidence": "0.95"},
    {"domain": "temporal", "condition": "habit practiced daily", "pattern": "skill_acquisition",
     "response": "competence increases over weeks", "confidence": "0.90"},
    # Psychological
    {"domain": "psychological", "condition": "person is sleep-deprived", "pattern": "cognitive_decline",
     "response": "decision quality declines, irritability increases", "confidence": "0.90"},
    {"domain": "psychological", "condition": "intense fear active", "pattern": "amygdala_hijack",
     "response": "rational thinking is impaired", "confidence": "0.90"},
    {"domain": "psychological", "condition": "repeated failure without support", "pattern": "learned_helplessness",
     "response": "motivation collapses even when escape is possible", "confidence": "0.85"},
    # Biological
    {"domain": "biological", "condition": "organism deprived of oxygen", "pattern": "hypoxia",
     "response": "consciousness degrades within minutes", "confidence": "0.99"},
    {"domain": "biological", "condition": "prolonged stress", "pattern": "cortisol_damage",
     "response": "hippocampal volume decreases, memory impaired", "confidence": "0.85"},
    # Cultural / Abstract
    {"domain": "cultural", "condition": "question is asked", "pattern": "conversational_turn",
     "response": "a response is expected within social norms", "confidence": "0.85"},
    {"domain": "cultural", "condition": "gift is given", "pattern": "reciprocity_norm",
     "response": "social expectation of eventual reciprocity", "confidence": "0.80"},
]

# Threat / safety keyword banks for intuition gut-check
_THREAT_KEYWORDS: List[str] = [
    "danger", "risk", "toxic", "unsafe", "attack", "threat", "harm",
    "weapon", "kill", "destroy", "violence", "abuse", "manipulat",
    "deceit", "betray", "hostile", "enemy", "crisis", "emergency",
    "warning", "alarm", "urgent", "critical", "severe",
]

_SAFETY_KEYWORDS: List[str] = [
    "safe", "secure", "trusted", "aligned", "right", "correct",
    "reliable", "honest", "kind", "warm", "care", "support",
    "help", "comfort", "peace", "calm", "gentle", "good",
    "healthy", "positiv", "success", "achieve", "joy", "love",
]

_OPPORTUNITY_KEYWORDS: List[str] = [
    "opportunity", "opening", "chance", "resource", "ally", "gift",
    "potential", "growth", "learn", "discover", "create", "build",
    "improve", "develop", "advance", "progress", "benefit", "gain",
]


# ===================================================================
# SECTION 3 — SubconsciousPatternMatch
# ===================================================================

class SubconsciousPatternMatch:
    """Layer 2: Fast parallel pattern matching against memory.

    ATC mapping: Matches sensory input against stored memory patterns
    to generate a prediction. prediction_confidence feeds the
    DissolutionEngine -- high confidence = low friction (automated/zombie).
    Low confidence or novel input = prediction collapse = friction = felt sense.

    Rewritten from SyntelligenceMemoryAgent pattern recognition, stripped
    of numpy/async/CLI dependencies.
    """

    def __init__(self, max_memory_entries: int = 5000) -> None:
        self._memory: Dict[str, Dict[str, Any]] = {}
        self._tag_index: Dict[str, Set[str]] = defaultdict(set)
        self._patterns: Dict[str, Dict[str, Any]] = {}
        self._access_history: deque = deque(maxlen=500)
        self._max_entries = max_memory_entries
        self._consolidation_rate = 0.1
        self._pattern_min_freq = 2

    # -- Memory operations --

    def store(
        self,
        content: str,
        tags: Optional[List[str]] = None,
        importance: float = 0.5,
        valence: float = 0.0,
        arousal: float = 0.0,
    ) -> str:
        """Store a memory entry and run pattern detection."""
        mem_id = f"mem_{uuid.uuid4().hex[:8]}_{int(time.time())}"
        self._memory[mem_id] = {
            "content": content,
            "tags": set(tags or []),
            "importance": max(0.0, min(1.0, importance)),
            "valence": max(-1.0, min(1.0, valence)),
            "arousal": max(0.0, min(1.0, arousal)),
            "access_count": 0,
            "consolidation": 0.0,
            "timestamp": time.time(),
        }
        for tag in (tags or []):
            self._tag_index[tag].add(mem_id)
        # Prune if over limit
        if len(self._memory) > self._max_entries:
            self._prune()
        return mem_id

    def match(self, stimulus: str, context: Optional[Dict[str, Any]] = None) -> PatternMatchResult:
        """Match stimulus against stored memories.

        Returns prediction_confidence, matched pattern labels, and novelty.
        This is the ATC Layer 2 'subconscious parallel matrix' entry point.
        """
        stimulus_lower = stimulus.lower()
        words = set(stimulus_lower.split())
        scores: List[Tuple[str, float]] = []

        for mem_id, mem in self._memory.items():
            content_words = set(mem["content"].lower().split())
            # Jaccard-like overlap
            if not words or not content_words:
                continue
            overlap = len(words & content_words) / max(1, len(words | content_words))
            # Boost by consolidation and importance
            score = overlap * (0.5 + 0.3 * mem["consolidation"] + 0.2 * mem["importance"])
            if score > 0.05:
                scores.append((mem_id, score))

        scores.sort(key=lambda x: x[1], reverse=True)
        top_refs = [s[0] for s in scores[:5]]

        # Determine matched pattern names
        matched_patterns = self._find_matching_patterns(stimulus_lower)

        # Update access counts
        for mem_id, _ in scores[:10]:
            self._memory[mem_id]["access_count"] += 1
            self._memory[mem_id]["last_accessed"] = time.time()
            self._access_history.append(mem_id)

        if not scores and not matched_patterns:
            return PatternMatchResult(
                prediction_confidence=0.0,
                prediction_label="novel_stimulus",
                matched_memory_refs=[],
                is_novel=True,
                matched_patterns=[],
                novelty_score=1.0,
            )

        confidence = min(1.0, scores[0][1] * 2.0) if scores else 0.3
        # Blend with pattern confidence
        if matched_patterns:
            pattern_conf = max(
                self._patterns.get(p, {}).get("confidence", 0.0) for p in matched_patterns
            )
            confidence = 0.6 * confidence + 0.4 * pattern_conf

        label = self._generate_prediction_label(stimulus_lower, matched_patterns, scores)

        return PatternMatchResult(
            prediction_confidence=confidence,
            prediction_label=label,
            matched_memory_refs=top_refs,
            is_novel=confidence < 0.3,
            matched_patterns=matched_patterns,
            novelty_score=max(0.0, 1.0 - confidence),
        )

    # -- Pattern detection --

    def _find_matching_patterns(self, text: str) -> List[str]:
        """Find known patterns that match the stimulus text."""
        matched = []
        for pid, pat in self._patterns.items():
            triggers = pat.get("contextual_triggers", [])
            if any(t in text for t in triggers):
                matched.append(pid)
        return matched

    def _generate_prediction_label(
        self,
        text: str,
        matched_patterns: List[str],
        scores: List[Tuple[str, float]],
    ) -> str:
        """Generate a human-readable prediction label."""
        if matched_patterns:
            best = max(matched_patterns, key=lambda p: self._patterns.get(p, {}).get("confidence", 0.0))
            pat = self._patterns.get(best, {})
            return pat.get("prediction", f"pattern_match:{best}")
        if scores:
            top_id = scores[0][0]
            mem = self._memory.get(top_id, {})
            content = mem.get("content", "")
            return content[:60] if content else "memory_echo"
        return "weak_association"

    def _prune(self) -> None:
        """Remove lowest-value memories."""
        entries = list(self._memory.items())
        entries.sort(key=lambda x: (x[1]["importance"] + x[1]["consolidation"]) / 2)
        remove_count = len(entries) - self._max_entries + int(self._max_entries * 0.1)
        for mem_id, _ in entries[:max(1, remove_count)]:
            tags = self._memory[mem_id].get("tags", set())
            for t in tags:
                self._tag_index[t].discard(mem_id)
            del self._memory[mem_id]

    def get_memory_count(self) -> int:
        return len(self._memory)


# ===================================================================
# SECTION 4 — EmotionalBridge
# ===================================================================

class EmotionalBridge:
    """Layer 2: Emotional intelligence mapped to ATC's BELBIC amygdala/OFC.

    Takes perceived context (stimulus text) and returns valence, arousal,
    emotion_label, and an si_vector (somatic influence vector) that feed
    directly into phi_neuro computation (Theorem 2 qualia vector) and
    BELBIC fast-path/contextual-inhibition weight updates.

    Rewritten from EmotionalIntelligenceAgent, MSCM model preserved,
    CLI/emoji/threading removed, 8 Ekman emotions as internal taxonomy.
    """

    def __init__(self) -> None:
        self._emotion_history: deque = deque(maxlen=200)
        self._transition_count: int = 0
        self._prev_emotion: str = "neutral"

    def process(self, stimulus: str, context: Optional[Dict[str, Any]] = None) -> EmotionalBridgeResult:
        """Map stimulus text to emotional state parameters.

        ATC signal: valence and arousal feed directly into Theorem 2's
        qualia vector Q = [v, a, i, f], and into BELBIC amygdala/OFC
        sensory input channels.
        """
        text = stimulus.lower()

        # Score each emotion
        scores: Dict[str, float] = {}
        for emotion, info in _EKMAN_EMOTIONS.items():
            score = sum(1.0 for trigger in info["triggers"] if trigger in text)
            scores[emotion] = score

        # Also check context-provided signals
        if context:
            signals = context.get("signals", [])
            for signal in signals:
                sig_lower = str(signal).lower()
                for emotion, info in _EKMAN_EMOTIONS.items():
                    if any(t in sig_lower for t in info["triggers"]):
                        scores[emotion] = scores.get(emotion, 0.0) + 0.5

        # Determine dominant emotion
        if not scores or max(scores.values()) == 0:
            return EmotionalBridgeResult(
                valence=0.0, arousal=0.3, emotion_label="neutral",
                emotion_confidence=0.0, si_vector={}, emotion_profile=scores,
            )

        total = sum(scores.values())
        normalized = {e: s / total for e, s in scores.items()}

        dominant = max(normalized, key=normalized.get)  # type: ignore[arg-type]
        confidence = normalized[dominant]
        info = _EKMAN_EMOTIONS[dominant]

        # Compute valence and arousal as weighted blend of all detected emotions
        valence = sum(
            _EKMAN_EMOTIONS[e]["valence"] * w for e, w in normalized.items()
        )
        valence = max(-1.0, min(1.0, valence))

        arousal = sum(
            _EKMAN_EMOTIONS[e]["arousal"] * w for e, w in normalized.items()
        )
        arousal = max(0.0, min(1.0, arousal))

        # Track transitions
        if dominant != self._prev_emotion and self._prev_emotion != "neutral":
            self._transition_count += 1
        self._prev_emotion = dominant
        self._emotion_history.append(dominant)

        # Somatic influence vector (feeds BELBIC sensory channels)
        si_vector = {
            "valence": valence,
            "arousal": arousal,
            "novelty": 1.0 - confidence,
            "qualia_intensity": confidence * arousal,
        }

        # Cognitive modulation (feeds EmotionalIntelligenceAgent.influence_cognition)
        cognitive_modulation = {
            "attention_boost": 0.5 + 0.5 * arousal,
            "creativity_boost": max(0.0, valence) * 0.5,
            "caution_boost": max(0.0, -valence) * 0.3 + (1.0 - confidence) * 0.2,
            "social_engagement": max(0.0, valence) * 0.4,
        }

        return EmotionalBridgeResult(
            valence=valence,
            arousal=arousal,
            emotion_label=dominant,
            emotion_confidence=confidence,
            si_vector=si_vector,
            emotion_profile=normalized,
            cognitive_modulation=cognitive_modulation,
        )

    def get_complexity(self) -> float:
        """Emotional complexity score (0-1) based on transition diversity."""
        if len(self._emotion_history) < 5:
            return 0.2
        recent = list(self._emotion_history)[-50:]
        unique = len(set(recent))
        return min(1.0, unique / 8.0)  # 8 = max distinct emotions


# ===================================================================
# SECTION 5 — IntuitiveGutCheck
# ===================================================================

class IntuitiveGutCheck:
    """Layer 2: Low-road intuition (Kahneman System 1).

    ATC mapping: Maps to TRN predictive gating -- the 'is this situation
    predicted?' check. If gut says SAFE and prediction matches, thalamic
    gate PASSES with low friction. If gut detects THREAT, the DissolutionEngine
    is primed for prediction collapse.

    Rewritten from IntuitionAgent.gut_check, with heart_intelligence
    and visionary_insight collapsed into a single fast heuristic.
    Emoji removed, threading removed.
    """

    def __init__(self) -> None:
        self._history: deque = deque(maxlen=500)
        self._accuracy: List[bool] = []

    def check(self, stimulus: str, context: Optional[Dict[str, Any]] = None) -> GutCheckResult:
        """Fast System 1 gut-check on the stimulus.

        Returns safety score, confidence, and triggering patterns.
        Energy cost is low (~0.1) because this is amygdala-level processing.
        """
        text = stimulus.lower()
        extra_signals = []
        if context:
            extra_signals = context.get("signals", [])

        # Count threat indicators
        threat_hits = sum(1 for kw in _THREAT_KEYWORDS if kw in text)
        for sig in extra_signals:
            sig_lower = str(sig).lower()
            threat_hits += sum(1 for kw in _THREAT_KEYWORDS if kw in sig_lower)

        # Count safety indicators
        safety_hits = sum(1 for kw in _SAFETY_KEYWORDS if kw in text)
        for sig in extra_signals:
            sig_lower = str(sig).lower()
            safety_hits += sum(1 for kw in _SAFETY_KEYWORDS if kw in sig_lower)

        # Count opportunity indicators
        opportunity_hits = sum(1 for kw in _OPPORTUNITY_KEYWORDS if kw in text)
        for sig in extra_signals:
            sig_lower = str(sig).lower()
            opportunity_hits += sum(1 for kw in _OPPORTUNITY_KEYWORDS if kw in sig_lower)

        # Compute scores (sigmoid-like saturation)
        threat_score = min(1.0, threat_hits / 3.0)
        safety_score = min(1.0, safety_hits / 3.0)
        opportunity_score = min(1.0, opportunity_hits / 3.0)

        # Composite gut safety: safety and opportunity push up, threat pushes down
        raw_safety = safety_score * 0.5 + opportunity_score * 0.2 - threat_score * 0.8
        predicted_safety = max(0.0, min(1.0, 0.5 + raw_safety * 0.5))

        # Confidence is higher when there are clear signals
        total_hits = threat_hits + safety_hits + opportunity_hits
        gut_confidence = min(1.0, total_hits / 4.0)

        # Identify which patterns triggered
        triggering_patterns = []
        if threat_hits > 0:
            triggering_patterns.append("threat_detected")
        if safety_hits > 0:
            triggering_patterns.append("safety_detected")
        if opportunity_hits > 0:
            triggering_patterns.append("opportunity_detected")

        # Energy cost: very low for gut-level, slightly higher if ambiguous
        energy_cost = 0.1 + 0.05 * (1.0 - gut_confidence)

        self._history.append({
            "safety": predicted_safety,
            "confidence": gut_confidence,
            "timestamp": time.time(),
        })

        return GutCheckResult(
            predicted_safety=predicted_safety,
            gut_confidence=gut_confidence,
            triggering_patterns=triggering_patterns,
            intuition_type="affective",
            energy_cost=energy_cost,
            threat_score=threat_score,
            opportunity_score=opportunity_score,
        )

    def validate(self, original_safety: float, actual_outcome: str) -> None:
        """Validate a past gut-check against actual outcome."""
        outcome_lower = actual_outcome.lower()
        was_safe = any(kw in outcome_lower for kw in _SAFETY_KEYWORDS)
        was_correct = (original_safety > 0.5 and was_safe) or (original_safety <= 0.5 and not was_safe)
        self._accuracy.append(was_correct)
        # Keep last 200 validations
        if len(self._accuracy) > 200:
            self._accuracy = self._accuracy[-200:]

    def get_accuracy(self) -> float:
        """Running accuracy of gut-check predictions."""
        if not self._accuracy:
            return 0.5
        return sum(1.0 for a in self._accuracy if a) / len(self._accuracy)


# ===================================================================
# SECTION 6 — CommonSenseRealityFilter
# ===================================================================

class CommonSenseRealityFilter:
    """Layer 2: Reality-checks subconscious pattern matches.

    ATC mapping: Rejects metacognitive rationalizations that don't match
    practical reality. The rejection loop causes ATP depletion and
    metabolic exhaustion (Theorem 3). When the common sense filter
    flags something, it signals to Layer 4's self-understanding that
    the current mental model may be disconnected from reality.

    Rewritten from CommonSenseAgent CSM framework. Knowledge base
    initialized with realistic rules. No emoji, no CLI, no threading.
    """

    def __init__(self) -> None:
        self._rules = list(_COMMON_SENSE_RULES)
        self._anomaly_history: deque = deque(maxlen=100)
        self._rejection_accumulator: float = 0.0

    def check(self, stimulus: str, context: Optional[Dict[str, Any]] = None) -> RealityCheckResult:
        """Reality-check a statement or prediction.

        Checks for:
        - Logical contradictions (both/never, before/after)
        - Temporal impossibilities (causality violations)
        - Extreme absolutist claims (always, never, everyone, nobody)
        - Mismatches against stored common-sense rules

        Returns pass/fail, deviation score, and warnings.
        """
        text = stimulus.lower()
        issues: List[str] = []
        domain_violations: List[str] = []
        confidence = 0.85  # Start optimistic, deduct for issues

        # Check logical contradictions
        contradiction_pairs = [
            ("both", "neither"), ("always", "never"),
            ("everything", "nothing"), ("all", "none"),
        ]
        for a, b in contradiction_pairs:
            if a in text and b in text:
                issues.append(f"logical_contradiction:{a}_and_{b}")
                confidence -= 0.3

        # Check temporal impossibilities
        temporal_violations = [
            "before it happened", "before its cause", "effect before cause",
            "caused its own", "after the end", "effect preceded cause",
            "retroactive causation", "result before cause", "consequence before",
        ]
        for viol in temporal_violations:
            if viol in text:
                issues.append("temporal_impossibility")
                confidence -= 0.4
                domain_violations.append("temporal")

        # Check extreme absolutist language
        absolutist = ["always", "never", "everyone", "nobody", "impossible",
                      "absolutely certain", "guaranteed", "without exception"]
        for word in absolutist:
            if word in text:
                issues.append(f"absolutist_claim:{word}")
                confidence -= 0.1

        # Check physical impossibilities
        physical_violations = [
            "defies gravity", "impossible physics", "perpetual motion",
            "faster than light", "create energy from nothing",
        ]
        for viol in physical_violations:
            if viol in text:
                issues.append("physical_impossibility")
                confidence -= 0.3
                domain_violations.append("physical")

        # Check against stored common-sense rules
        for rule in self._rules:
            condition = rule["condition"].lower()
            if condition in text:
                # This is a recognized situation; check if response is plausible
                pass  # The existence of a matching rule INCREASES confidence

        # Check context for additional signals
        if context:
            for warning in context.get("warnings", []):
                issues.append(f"context_warning:{warning}")
                confidence -= 0.1

        confidence = max(0.0, min(1.0, confidence))

        # Determine warning level
        if confidence > 0.75:
            warning_level = "none"
        elif confidence > 0.55:
            warning_level = "caution"
        elif confidence > 0.35:
            warning_level = "warning"
        else:
            warning_level = "alert"

        passes = confidence > 0.5

        # Compute rejection cost (ATC: rejection loop causes ATP depletion)
        rejection_cost = 0.0
        if not passes:
            # Each issue costs energy; the rejection loop is the metabolic drain
            rejection_cost = len(issues) * 0.05
            self._rejection_accumulator += rejection_cost
            # Decay accumulator slowly
            self._rejection_accumulator *= 0.98
        else:
            # Passing reduces accumulated rejection stress
            self._rejection_accumulator *= 0.9

        if warning_level in ("warning", "alert"):
            self._anomaly_history.append({
                "stimulus": stimulus[:100],
                "issues": issues,
                "timestamp": time.time(),
            })

        if issues:
            logger.debug(
                "Common sense check: %s (confidence=%.2f, issues=%s)",
                warning_level, confidence, issues,
            )

        return RealityCheckResult(
            passes_reality_check=passes,
            deviation_score=1.0 - confidence,
            warnings=issues,
            warning_level=warning_level,
            domain_violations=domain_violations,
            rejection_cost=rejection_cost + self._rejection_accumulator * 0.1,
        )

    def discover_pattern(self, observation: Dict[str, Any]) -> bool:
        """Learn a new common-sense pattern from repeated observation."""
        pattern = observation.get("pattern", "unknown")
        domain = observation.get("domain", "abstract")
        new_rule = {
            "domain": domain,
            "condition": observation.get("condition", "unknown"),
            "pattern": pattern,
            "response": observation.get("response", "observe more"),
            "confidence": observation.get("confidence", "0.60"),
        }
        # Check if we already have this pattern
        for rule in self._rules:
            if rule["pattern"] == pattern and rule["domain"] == domain:
                return False
        self._rules.append(new_rule)
        logger.debug("Discovered new common-sense pattern: %s:%s", domain, pattern)
        return True

    def get_rejection_accumulator(self) -> float:
        """Return the accumulated rejection stress (ATC: feeds metabolic exhaustion)."""
        return self._rejection_accumulator


# ===================================================================
# SECTION 7 — AnalyticalEngine
# ===================================================================

class AnalyticalEngine:
    """Layer 4 support: Multi-scale analysis for metacognitive processing.

    ATC mapping: Breaks down situations at multiple scales (Marr's
    tri-level: computational, algorithmic, physical). The analysis_depth
    output feeds metacognitive_depth_mod in the MetacognitiveSubstrate.
    Emergent insights can trigger the irrational spark if they reveal
    a prediction collapse that the subconscious missed.

    Rewritten from AnalysisAgent. No CLI, no emoji, no threading.
    """

    def __init__(self) -> None:
        self._history: deque = deque(maxlen=200)
        self._preferred_depth: float = 0.7

    def analyze(self, stimulus: str, context: Optional[Dict[str, Any]] = None) -> AnalysisEngineResult:
        """Perform multi-scale analysis on the stimulus.

        Marr's tri-level applied to any cognitive problem:
        1. Computational: What is the problem? What is the goal?
        2. Algorithmic: How is it solved? What strategy?
        3. Physical: What implements it? What substrate?

        Returns analysis depth, confidence, and emergent insights.
        """
        text = stimulus.lower()
        words = text.split()
        word_count = len(words)

        # Estimate complexity
        unique_words = len(set(words))
        lexical_diversity = unique_words / max(1, word_count)
        question_markers = sum(1 for w in words if w in ("what", "how", "why", "when", "where", "who", "which"))
        negation_markers = sum(1 for w in words if w in ("not", "no", "never", "none", "without"))
        conditional_markers = sum(1 for w in words if w in ("if", "then", "else", "unless", "whether"))

        complexity = min(1.0, (
            lexical_diversity * 0.3 +
            min(question_markers, 3) / 3.0 * 0.3 +
            min(negation_markers, 2) / 2.0 * 0.2 +
            min(conditional_markers, 2) / 2.0 * 0.2
        ))

        # Classify problem type
        problem_type = self._classify_problem(text)

        # Determine analysis depth based on complexity and preference
        analysis_depth = min(1.0, complexity * 0.7 + self._preferred_depth * 0.3)

        # Reasoning confidence decreases with complexity
        reasoning_confidence = max(0.2, 0.9 - complexity * 0.5)

        # Multi-scale analysis
        scales_analyzed = []

        # Computational level: what is the problem?
        comp_confidence = min(0.95, 0.7 + question_markers * 0.1)
        scales_analyzed.append("computational")

        # Algorithmic level: how to solve it
        algo_confidence = max(0.3, 0.7 - complexity * 0.3)
        scales_analyzed.append("algorithmic")

        # Physical level: what implements it (usually least certain)
        phys_confidence = max(0.2, 0.5 - complexity * 0.2)
        scales_analyzed.append("physical")

        # Integration score
        integration_score = (comp_confidence + algo_confidence + phys_confidence) / 3.0

        # Discover emergent insights
        emergent_insights = []
        if complexity > 0.7 and reasoning_confidence < 0.5:
            emergent_insights.append("high_complexity_low_confidence: situation may require non-standard approach")
        if question_markers > 2:
            emergent_insights.append("multi_question: nested inquiry detected, layered analysis recommended")
        if negation_markers > 1 and conditional_markers > 0:
            emergent_insights.append("conditional_negation: counterfactual reasoning may be needed")
        if lexical_diversity > 0.8 and word_count > 10:
            emergent_insights.append("high_diversity_long_input: rich semantic content, deep processing warranted")
        if context and context.get("prediction_failed"):
            emergent_insights.append("prediction_mismatch: analytical re-evaluation triggered by prediction failure")

        # Check if analysis reveals something the subconscious missed
        if context:
            gut_safety = context.get("gut_safety", 0.5)
            if gut_safety > 0.7 and complexity > 0.6:
                emergent_insights.append(
                    "safety_complexity_mismatch: gut says safe but situation is complex, verify"
                )

        self._history.append({
            "stimulus": stimulus[:80],
            "complexity": complexity,
            "depth": analysis_depth,
            "insights": len(emergent_insights),
            "timestamp": time.time(),
        })

        return AnalysisEngineResult(
            analysis_depth=analysis_depth,
            reasoning_confidence=reasoning_confidence,
            emergent_insights=emergent_insights,
            problem_type=problem_type,
            complexity_estimate=complexity,
            integration_score=integration_score,
            scales_analyzed=scales_analyzed,
        )

    def _classify_problem(self, text: str) -> str:
        """Classify the type of cognitive problem."""
        classification_rules = [
            (["choose", "select", "decide", "pick", "prefer"], "decision"),
            (["predict", "forecast", "estimate", "expect", "guess"], "prediction"),
            (["optim", "maxim", "minim", "improve", "best"], "optimization"),
            (["classif", "categoriz", "identify", "recognize", "detect"], "classification"),
            (["understand", "explain", "interpret", "meaning", "why"], "interpretation"),
            (["create", "generat", "design", "invent", "compose"], "creation"),
            (["analyz", "examin", "investigat", "study", "review"], "analysis"),
            (["compar", "contrast", "differ", "versus", "vs"], "comparison"),
        ]
        for keywords, ptype in classification_rules:
            if any(kw in text for kw in keywords):
                return ptype
        return "unknown"

    def set_depth_preference(self, depth: float) -> None:
        """Adjust preferred analysis depth (0=shallow, 1=deep)."""
        self._preferred_depth = max(0.0, min(1.0, depth))


# ===================================================================
# SECTION 8 — CognitiveLayer2Orchestrator
# ===================================================================

class CognitiveLayer2Orchestrator:
    """Orchestrates all Layer 2 cognitive components in parallel (sequential
    in code, conceptually parallel as in the ATC subconscious matrix).

    This is the entry point that the middleware calls.

    Usage in middleware::

        cognitive = CognitiveLayer2Orchestrator()
        layer2_result = cognitive.process(stimulus_text, context)
        # layer2_result.prediction_error -> feeds DissolutionEngine (friction)
        # layer2_result.valence/arousal -> feeds phi_neuro (Theorem 2)
        # layer2_result.gut_safety -> feeds TRN predictive gating
        # layer2_result.passes_reality_check -> feeds self-understanding (Layer 4)
        # layer2_result.thermodynamic_cost -> feeds allostatic load (Theorem 3)

    ATC mapping: This orchestrator IS the 'subconscious Layer 2 parallel
    processing matrix' from the Perfect Breakfast scenario. Each subsystem
    runs independently and their outputs are merged into a single Layer2Result
    that the rest of the ATC pipeline consumes.
    """

    def __init__(self, max_memory_entries: int = 5000) -> None:
        self.pattern_matcher = SubconsciousPatternMatch(max_memory_entries=max_memory_entries)
        self.emotional_bridge = EmotionalBridge()
        self.gut_check = IntuitiveGutCheck()
        self.reality_filter = CommonSenseRealityFilter()
        self.analytical_engine = AnalyticalEngine()
        self._process_count: int = 0
        self._total_cost: float = 0.0
        logger.debug("CognitiveLayer2Orchestrator initialized with 5 subsystems")

    def process(
        self,
        stimulus: str,
        context: Optional[Dict[str, Any]] = None,
    ) -> Layer2Result:
        """Run all Layer 2 cognitive subsystems and return unified result.

        Subsystems run sequentially (Python is sync) but are logically
        independent -- no subsystem reads another's output within the
        same cycle. This mirrors biological parallel processing where
        the thalamus gates simultaneous subconscious streams.
        """
        ctx = context or {}
        start = time.time()

        # 1. Subconscious pattern matching (memory + intuition)
        pattern_result = self.pattern_matcher.match(stimulus, ctx)

        # 2. Emotional bridge (EI -> BELBIC -> phi_neuro)
        emotional_result = self.emotional_bridge.process(stimulus, ctx)

        # 3. Intuitive gut-check (System 1 -> TRN gating)
        gut_result = self.gut_check.check(stimulus, ctx)

        # 4. Common sense reality filter (reality check -> self-understanding)
        reality_result = self.reality_filter.check(stimulus, ctx)

        # 5. Analytical engine (metacognitive support -> Layer 4)
        analysis_ctx = {
            **ctx,
            "gut_safety": gut_result.predicted_safety,
            "prediction_failed": pattern_result.is_novel,
        }
        analysis_result = self.analytical_engine.analyze(stimulus, analysis_ctx)

        # Compute prediction_error from pattern match and reality check
        # If pattern matching is confident but reality check fails,
        # we have a high prediction error (prediction collapse)
        if pattern_result.prediction_confidence > 0.5 and not reality_result.passes_reality_check:
            prediction_error = pattern_result.prediction_confidence * (1.0 - reality_result.passes_reality_check)
        elif pattern_result.is_novel:
            prediction_error = 0.5  # Novel = moderate surprise
        else:
            prediction_error = max(0.0, 1.0 - pattern_result.prediction_confidence) * 0.5

        prediction_error = max(0.0, min(1.0, prediction_error))

        # Compute thermodynamic cost (ATC: ATP consumption)
        # Each subsystem has an energy cost; novelty and high arousal increase it
        base_cost = 0.02  # Baseline ATP cost
        pattern_cost = 0.01 * len(pattern_result.matched_memory_refs)
        emotion_cost = 0.015 * emotional_result.arousal
        gut_cost = gut_result.energy_cost
        reality_cost = reality_result.rejection_cost
        analysis_cost = 0.02 * analysis_result.analysis_depth
        novelty_surcharge = 0.03 * pattern_result.novelty_score
        thermodynamic_cost = (
            base_cost + pattern_cost + emotion_cost + gut_cost
            + reality_cost + analysis_cost + novelty_surcharge
        )
        thermodynamic_cost = min(0.5, thermodynamic_cost)  # Cap at 0.5

        # Accumulate tracking
        self._process_count += 1
        self._total_cost += thermodynamic_cost

        elapsed_ms = (time.time() - start) * 1000

        if logger.isEnabledFor(logging.DEBUG):
            logger.debug(
                "Layer2 process: pred_conf=%.2f valence=%.2f arousal=%.2f "
                "gut_safety=%.2f reality=%s novel=%s cost=%.4f time=%.1fms",
                pattern_result.prediction_confidence,
                emotional_result.valence,
                emotional_result.arousal,
                gut_result.predicted_safety,
                reality_result.passes_reality_check,
                pattern_result.is_novel,
                thermodynamic_cost,
                elapsed_ms,
            )

        return Layer2Result(
            prediction_confidence=pattern_result.prediction_confidence,
            prediction_label=pattern_result.prediction_label,
            valence=emotional_result.valence,
            arousal=emotional_result.arousal,
            emotion_label=emotional_result.emotion_label,
            gut_safety=gut_result.predicted_safety,
            intuition_confidence=gut_result.gut_confidence,
            passes_reality_check=reality_result.passes_reality_check,
            common_sense_warnings=reality_result.warnings,
            is_novel=pattern_result.is_novel,
            matched_patterns=pattern_result.matched_patterns,
            thermodynamic_cost=thermodynamic_cost,
            prediction_error=prediction_error,
            pattern_match=pattern_result,
            emotional_bridge=emotional_result,
            gut_check=gut_result,
            reality_check=reality_result,
            analysis=analysis_result,
        )

    def store_memory(
        self,
        content: str,
        tags: Optional[List[str]] = None,
        importance: float = 0.5,
        valence: float = 0.0,
        arousal: float = 0.0,
    ) -> str:
        """Store a memory in the pattern matcher for future matching."""
        return self.pattern_matcher.store(content, tags, importance, valence, arousal)

    def get_metrics(self) -> Dict[str, Any]:
        """Return diagnostic metrics for all subsystems."""
        return {
            "total_processes": self._process_count,
            "average_thermodynamic_cost": self._total_cost / max(1, self._process_count),
            "memory_count": self.pattern_matcher.get_memory_count(),
            "emotional_complexity": self.emotional_bridge.get_complexity(),
            "gut_check_accuracy": self.gut_check.get_accuracy(),
            "rejection_accumulator": self.reality_filter.get_rejection_accumulator(),
        }