Upload trinity_3\core.py with huggingface_hub

trinity_3//core.py

@@ -0,0 +1,725 @@
+"""
+Aurora Trinity-3: Fractal, Ethical, Free Electronic Intelligence
+===============================================================
+
+A complete implementation of Aurora's ternary logic architecture featuring:
+- Trigate operations with O(1) LUT-based inference, learning, and deduction
+- Fractal Tensor structures with hierarchical 3-9-27 organization  
+- Knowledge Base with multiverse logical space management
+- Armonizador for coherence validation and harmonization
+- Extender for fractal reconstruction and pattern extension
+- Transcender for hierarchical synthesis operations
+
+Author: Aurora Alliance
+License: Apache-2.0 + CC-BY-4.0
+Version: 1.0.0
+"""
+
+from typing import List, Dict, Any, Tuple, Optional, Union
+import hashlib
+import random
+import itertools
+import logging
+
+# ===============================================================================
+# CONSTANTS AND UTILITIES
+# ===============================================================================
+
+PHI = 0.6180339887  # Golden ratio for Pattern 0 generation
+Vector = List[Optional[int]]  # Ternary value: 0 | 1 | None
+
+# Logger setup
+logger = logging.getLogger("aurora.trinity")
+if not logger.hasHandlers():
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter('[%(levelname)s][%(name)s] %(message)s')
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+logger.setLevel(logging.INFO)
+
+# ===============================================================================
+# TERNARY LOGIC FOUNDATION
+# ===============================================================================
+
+class TernaryLogic:
+    """Ternary logic with NULL handling for computational honesty."""
+    NULL = None
+
+    @staticmethod
+    def ternary_xor(a, b):
+        """XOR with NULL propagation."""
+        if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
+            return TernaryLogic.NULL
+        return a ^ b
+
+    @staticmethod
+    def ternary_xnor(a, b):
+        """XNOR with NULL propagation."""
+        if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
+            return TernaryLogic.NULL
+        return 1 - (a ^ b)
+
+# ===============================================================================
+# TRIGATE: FUNDAMENTAL LOGIC MODULE
+# ===============================================================================
+
+class Trigate:
+    """
+    Fundamental Aurora logic module implementing ternary operations.
+    
+    Supports three operational modes:
+    1. Inference: A + B + M -> R (given inputs and control, compute result)
+    2. Learning: A + B + R -> M (given inputs and result, learn control)
+    3. Deduction: M + R + A -> B (given control, result, and one input, deduce other)
+    
+    All operations are O(1) using precomputed lookup tables (LUTs).
+    """
+    
+    _LUT_INFER: Dict[Tuple, int] = {}
+    _LUT_LEARN: Dict[Tuple, int] = {}
+    _LUT_DEDUCE_A: Dict[Tuple, int] = {}
+    _LUT_DEDUCE_B: Dict[Tuple, int] = {}
+    _initialized = False
+    
+    def __init__(self):
+        """Initialize Trigate and ensure LUTs are computed."""
+        if not Trigate._initialized:
+            Trigate._initialize_luts()
+    
+    @classmethod
+    def _initialize_luts(cls):
+        """Initialize all lookup tables for O(1) operations."""
+        print("Initializing Trigate LUTs...")
+        states = [0, 1, TernaryLogic.NULL]
+        
+        # Generate all 27 combinations for each operation
+        for a in states:
+            for b in states:
+                for m in states:
+                    # Inference: A + B + M -> R
+                    if TernaryLogic.NULL in (a, b, m):
+                        r = TernaryLogic.NULL
+                    else:
+                        r = a ^ b if m == 1 else 1 - (a ^ b)
+                    cls._LUT_INFER[(a, b, m)] = r
+                    
+                for r in states:
+                    # Learning: A + B + R -> M
+                    if TernaryLogic.NULL in (a, b, r):
+                        m = TernaryLogic.NULL
+                    else:
+                        m = 1 if (a ^ b) == r else 0
+                    cls._LUT_LEARN[(a, b, r)] = m
+                    
+                    # Deduction A: M + R + B -> A
+                    if TernaryLogic.NULL in (m, r, b):
+                        a_result = TernaryLogic.NULL
+                    else:
+                        a_result = b ^ r if m == 1 else 1 - (b ^ r)
+                    cls._LUT_DEDUCE_A[(m, r, b)] = a_result
+                    
+                    # Deduction B: M + R + A -> B
+                    if TernaryLogic.NULL in (m, r, a):
+                        b_result = TernaryLogic.NULL
+                    else:
+                        b_result = a ^ r if m == 1 else 1 - (a ^ r)
+                    cls._LUT_DEDUCE_B[(m, r, a)] = b_result
+        
+        cls._initialized = True
+        print(f"Trigate LUTs initialized: {len(cls._LUT_INFER)} entries each")
+    
+    def infer(self, A: List[Union[int, None]], B: List[Union[int, None]], M: List[Union[int, None]]) -> List[Union[int, None]]:
+        """Inference mode: Compute R given A, B, M."""
+        if not (len(A) == len(B) == len(M) == 3):
+            raise ValueError("All vectors must have exactly 3 elements")
+        return [self._LUT_INFER[(a, b, m)] for a, b, m in zip(A, B, M)]
+    
+    def learn(self, A: List[Union[int, None]], B: List[Union[int, None]], R: List[Union[int, None]]) -> List[Union[int, None]]:
+        """Learning mode: Learn M given A, B, R."""
+        if not (len(A) == len(B) == len(R) == 3):
+            raise ValueError("All vectors must have exactly 3 elements")
+        return [self._LUT_LEARN[(a, b, r)] for a, b, r in zip(A, B, R)]
+    
+    def deduce_a(self, M: List[Union[int, None]], R: List[Union[int, None]], B: List[Union[int, None]]) -> List[Union[int, None]]:
+        """Deduction mode: Deduce A given M, R, B."""
+        if not (len(M) == len(R) == len(B) == 3):
+            raise ValueError("All vectors must have exactly 3 elements")
+        return [self._LUT_DEDUCE_A[(m, r, b)] for m, r, b in zip(M, R, B)]
+    
+    def deduce_b(self, M: List[Union[int, None]], R: List[Union[int, None]], A: List[Union[int, None]]) -> List[Union[int, None]]:
+        """Deduction mode: Deduce B given M, R, A."""
+        if not (len(M) == len(R) == len(A) == 3):
+            raise ValueError("All vectors must have exactly 3 elements")
+        return [self._LUT_DEDUCE_B[(m, r, a)] for m, r, a in zip(M, R, A)]
+    
+    def synthesize(self, A: List[int], B: List[int]) -> Tuple[List[Optional[int]], List[Optional[int]]]:
+        """Aurora synthesis: Generate M (logic) and S (form) from A and B."""
+        M = [TernaryLogic.ternary_xor(a, b) for a, b in zip(A, B)]
+        S = [TernaryLogic.ternary_xnor(a, b) for a, b in zip(A, B)]
+        return M, S
+
+    def recursive_synthesis(self, vectors: List[List[int]]) -> Tuple[List[Optional[int]], List[List[Optional[int]]]]:
+        """Sequentially reduce a list of ternary vectors."""
+        if len(vectors) < 2:
+            raise ValueError("At least 2 vectors required")
+
+        history: List[List[Optional[int]]] = []
+        current = vectors[0]
+
+        for nxt in vectors[1:]:
+            current, _ = self.synthesize(current, nxt)
+            history.append(current)
+
+        return current, history
+
+# ===============================================================================
+# FRACTAL TENSOR ARCHITECTURE
+# ===============================================================================
+
+class FractalTensor:
+    """
+    Aurora's fundamental data structure with hierarchical 3-9-27 organization.
+    Supports fractal scaling and semantic coherence validation.
+    """
+    
+    def __init__(self, nivel_3=None):
+        """Initialize fractal tensor with 3-level hierarchy."""
+        self.nivel_3 = nivel_3 or [[0, 0, 0]]  # Finest detail level
+        self.metadata = {}
+        
+        # Auto-generate hierarchical levels
+        self._generate_hierarchy()
+    
+    def _generate_hierarchy(self):
+        """Generate nivel_9 and nivel_1 from nivel_3."""
+        # Nivel 9: group 3 vectors from nivel_3
+        if len(self.nivel_3) >= 3:
+            self.nivel_9 = [self.nivel_3[i:i+3] for i in range(0, len(self.nivel_3), 3)]
+        else:
+            self.nivel_9 = [self.nivel_3]
+        
+        # Nivel 1: summary vector from nivel_3[0]
+        if self.nivel_3:
+            self.nivel_1 = [sum(self.nivel_3[0]) % 8, len(self.nivel_3), hash(str(self.nivel_3[0])) % 8]
+        else:
+            self.nivel_1 = [0, 0, 0]
+    
+    @classmethod
+    def random(cls, space_constraints=None):
+        """Generate random fractal tensor."""
+        nivel_3 = [[random.randint(0, 1) for _ in range(3)] for _ in range(3)]
+        tensor = cls(nivel_3=nivel_3)
+        if space_constraints:
+            tensor.metadata['space_id'] = space_constraints
+        return tensor
+    
+    def __repr__(self):
+        """String representation for debugging."""
+        return f"FT(root={self.nivel_3[:3]}, mid={self.nivel_9[0] if self.nivel_9 else '...'}, detail={self.nivel_1})"
+
+# ===============================================================================
+# KNOWLEDGE BASE SYSTEM
+# ===============================================================================
+
+class _SingleUniverseKB:
+    """Knowledge base for a single logical space."""
+    
+    def __init__(self):
+        self.storage = {}
+        self.name_index = {}
+        self.ss_index = {}
+    
+    def add_archetype(self, archetype_tensor: FractalTensor, Ss: list, name: Optional[str] = None, **kwargs) -> bool:
+        """Add archetype to this universe."""
+        key = tuple(Ss)
+        self.storage[key] = archetype_tensor
+        self.ss_index[key] = archetype_tensor
+        
+        if name:
+            self.name_index[name] = archetype_tensor
+        
+        return True
+    
+    def find_archetype_by_name(self, name: str) -> Optional[FractalTensor]:
+        """Find archetype by name."""
+        return self.name_index.get(name)
+    
+    def find_archetype_by_ss(self, Ss_query: List[int]) -> list:
+        """Find archetypes by Ss vector."""
+        key = tuple(Ss_query)
+        result = self.ss_index.get(key)
+        return [result] if result else []
+
+class FractalKnowledgeBase:
+    """Multi-universe knowledge base manager."""
+    
+    def __init__(self):
+        self.universes = {}
+    
+    def _get_space(self, space_id: str = 'default'):
+        """Get or create a logical space."""
+        if space_id not in self.universes:
+            self.universes[space_id] = _SingleUniverseKB()
+        return self.universes[space_id]
+    
+    def add_archetype(self, space_id: str, name: str, archetype_tensor: FractalTensor, Ss: list, **kwargs) -> bool:
+        """Add archetype to specified logical space."""
+        return self._get_space(space_id).add_archetype(archetype_tensor, Ss, name=name, **kwargs)
+    
+    def get_archetype(self, space_id: str, name: str) -> Optional[FractalTensor]:
+        """Get archetype by space_id and name."""
+        return self._get_space(space_id).find_archetype_by_name(name)
+
+# ===============================================================================
+# PROCESSING MODULES
+# ===============================================================================
+
+class Transcender:
+    """
+    Componente de síntesis que implementa la síntesis jerárquica
+    de Tensores Fractales completos.
+    """
+    
+    def __init__(self, fractal_vector: Optional[List[int]] = None):
+        self.trigate = Trigate()
+        self.seed_vector = fractal_vector
+    
+    def relate_vectors(self, A: list, B: list, context: dict = None) -> list:
+        """
+        Calcula un vector de relación Aurora-native entre A y B, incorporando ventana de contexto y relaciones cruzadas si se proveen.
+        """
+        if len(A) != len(B):
+            return [0, 0, 0]
+        diff_vector = []
+        for i in range(len(A)):
+            a_val = A[i] if A[i] is not None else 0
+            b_val = B[i] if B[i] is not None else 0
+            diff = b_val - a_val
+            # Normalize to ternary: 1 if diff > 0, 0 if diff == 0, None if diff < 0
+            if diff > 0:
+                diff_vector.append(1)
+            elif diff == 0:
+                diff_vector.append(0)
+            else:
+                diff_vector.append(None)
+        
+        # Aurora-native: ventana de contexto y relaciones cruzadas
+        if context and 'prev' in context and 'next' in context:
+            v_prev = context['prev']
+            v_next = context['next']
+            rel_cross = []
+            for vp, vn in zip(v_prev, v_next):
+                vp_val = vp if vp is not None else 0
+                vn_val = vn if vn is not None else 0
+                diff_cross = vp_val - vn_val
+                if diff_cross > 0:
+                    rel_cross.append(1)
+                elif diff_cross == 0:
+                    rel_cross.append(0)
+                else:
+                    rel_cross.append(None)
+            # Concatenar: [diff_vector, rel_cross, A, B]
+            return list(diff_vector) + list(rel_cross) + list(A) + list(B)
+        return diff_vector
+
+    def compute_vector_trio(self, A: List[int], B: List[int], C: List[int]) -> Dict[str, Any]:
+        """Procesa un trío de vectores simples (operación base)."""
+        M_AB, _ = self.trigate.synthesize(A, B)
+        M_BC, _ = self.trigate.synthesize(B, C)
+        M_CA, _ = self.trigate.synthesize(C, A)
+        M_emergent, _ = self.trigate.synthesize(M_AB, M_BC)
+        M_intermediate, _ = self.trigate.synthesize(M_emergent, M_CA)
+        MetaM = [TernaryLogic.ternary_xor(a, b) for a, b in zip(M_intermediate, M_emergent)]
+        return {'M_emergent': M_emergent, 'MetaM': MetaM, 'Ms': M_emergent, 'Ss': MetaM}
+
+    def deep_learning(
+        self,
+        A: List[int],
+        B: List[int],
+        C: List[int],
+        M_emergent: Optional[List[int]] = None
+    ) -> Dict[str, Any]:
+        """
+        Calcula M_emergent y MetaM tal como exige el modelo Trinity-3.
+        Genera R_hipotesis = Trigate.infer(A, B, M_emergent).
+        """
+        trio = self.compute_vector_trio(A, B, C)
+
+        # Si el caller no aporta M_emergent, usa el calculado.
+        if M_emergent is None:
+            M_emergent = trio["M_emergent"]
+
+        R_hipotesis = self.trigate.infer(A, B, M_emergent)
+
+        return {
+            "M_emergent": M_emergent,
+            "MetaM": trio["MetaM"],
+            "R_hipotesis": R_hipotesis,
+        }
+
+    def compute_full_fractal(self, A: 'FractalTensor', B: 'FractalTensor', C: 'FractalTensor') -> 'FractalTensor':
+        """
+        Sintetiza tres tensores fractales en uno, de manera jerárquica y elegante.
+        Prioriza una raíz de entrada válida por encima de la síntesis.
+        """
+        from copy import deepcopy
+        
+        # Create output tensor with basic structure
+        out = FractalTensor(nivel_3=[[0, 0, 0]])
+        
+        # Ensure all tensors have proper structure
+        if not hasattr(A, 'nivel_3') or not A.nivel_3:
+            A.nivel_3 = [[0, 0, 0]]
+        if not hasattr(B, 'nivel_3') or not B.nivel_3:
+            B.nivel_3 = [[0, 0, 0]]
+        if not hasattr(C, 'nivel_3') or not C.nivel_3:
+            C.nivel_3 = [[0, 0, 0]]
+
+        def synthesize_trio(vectors: list) -> list:
+            # Only use first 3 elements of each vector
+            while len(vectors) < 3:
+                vectors.append([0, 0, 0])
+            trimmed = [v[:3] if isinstance(v, (list, tuple)) else [0,0,0] for v in vectors[:3]]
+            r = self.compute_vector_trio(*trimmed)
+            m_emergent = r.get('M_emergent', [0, 0, 0])
+            return [bit if bit is not None else 0 for bit in m_emergent[:3]]
+
+        # Extract vectors for synthesis
+        A_vec = A.nivel_3[0] if A.nivel_3 else [0, 0, 0]
+        B_vec = B.nivel_3[0] if B.nivel_3 else [0, 0, 0]
+        C_vec = C.nivel_3[0] if C.nivel_3 else [0, 0, 0]
+        
+        # Compute emergent properties
+        result = self.compute_vector_trio(A_vec, B_vec, C_vec)
+        
+        # Set output tensor properties
+        out.nivel_3 = [result["M_emergent"]]
+        out.Ms = result["M_emergent"]
+        out.Ss = result.get("Ss", result["MetaM"])
+        out.MetaM = result["MetaM"]
+        
+        return out
+
+class Evolver:
+    """
+    Motor de visión fractal unificada para Arquetipos, Dinámicas y Relatores.
+    """
+    
+    def __init__(self):
+        self.base_transcender = Transcender()
+
+    def _perform_full_tensor_synthesis(self, tensors: List[FractalTensor]) -> FractalTensor:
+        """
+        Motor de síntesis fractal: reduce una lista de tensores a uno solo.
+        """
+        if not tensors:
+            return FractalTensor(nivel_3=[[0, 0, 0]])
+        
+        current_level_tensors = list(tensors)
+        while len(current_level_tensors) > 1:
+            next_level_tensors = []
+            for i in range(0, len(current_level_tensors), 3):
+                trio = current_level_tensors[i:i+3]
+                while len(trio) < 3:
+                    trio.append(FractalTensor(nivel_3=[[0, 0, 0]]))
+                synthesized_tensor = self.base_transcender.compute_full_fractal(*trio)
+                next_level_tensors.append(synthesized_tensor)
+            current_level_tensors = next_level_tensors
+            
+        return current_level_tensors[0]
+
+    def compute_fractal_archetype(self, tensor_family: List[FractalTensor]) -> FractalTensor:
+        """Perspectiva de ARQUETIPO: Destila la esencia de una familia de conceptos."""
+        if len(tensor_family) < 2:
+            import warnings
+            warnings.warn("Se requieren al menos 2 tensores para computar un arquetipo.")
+            return FractalTensor(nivel_3=[[0, 0, 0]]) if not tensor_family else tensor_family[0]
+        return self._perform_full_tensor_synthesis(tensor_family)
+
+class Extender:
+    """
+    Orquestador Aurora refactorizado con expertos como métodos internos para
+    simplificar el alcance y la gestión de estado.
+
+    Opera como de forma inversa a Evolver, extendiendo el conocimiento fractal
+    a partir de consultas simples y contexto, utilizando expertos para validar, 
+    utiliza trigate de form inversa al transcender.
+    """
+    
+    def __init__(self, knowledge_base: FractalKnowledgeBase):
+        self.kb = knowledge_base
+        self.transcender = Transcender()
+        self._lut_tables = {}
+        self.armonizador = Armonizador(knowledge_base=self.kb)
+
+    def _validate_archetype(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
+        """Experto Arquetipo como método."""
+        universe = self.kb._get_space(space_id)
+        ss_key = tuple(int(x) if x in (0, 1) else 0 for x in ss_query[:3])
+        logger.debug(f"Looking up archetype with ss_key={ss_key} in space={space_id}")
+        
+        # Buscar por Ss
+        archi_ss = universe.find_archetype_by_ss(list(ss_key))
+        if archi_ss:
+            logger.debug(f"Found archetype by Ss: {archi_ss}")
+            return True, archi_ss[0] if isinstance(archi_ss, list) else archi_ss
+        
+        # Fallback: buscar por nombre si hay algún patrón
+        for name in universe.name_index.keys():
+            if str(ss_key) in name:
+                archetype = universe.find_archetype_by_name(name)
+                if archetype:
+                    logger.debug(f"Found archetype by name pattern: {archetype}")
+                    return True, archetype
+        
+        logger.debug("No archetype found")
+        return False, None
+
+    def _project_dynamics(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
+        """Experto Dinámica como método."""
+        universe = self.kb._get_space(space_id)
+        best, best_sim = None, -1.0
+        
+        # Buscar en todos los arquetipos almacenados
+        for key, archetype in universe.storage.items():
+            if hasattr(archetype, 'nivel_3') and archetype.nivel_3:
+                archetype_ss = archetype.nivel_3[0]
+                sim = sum(1 for a, b in zip(archetype_ss, ss_query) if a == b) / len(ss_query)
+                if sim > best_sim:
+                    best_sim, best = sim, archetype
+        
+        if best and best_sim > 0.7:
+            return True, best
+        return False, None
+
+    def _contextualize_relations(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
+        """Experto Relator como método."""
+        universe = self.kb._get_space(space_id)
+        if not universe.storage:
+            logger.debug("No archetypes in universe")
+            return False, None
+        
+        best, best_score = None, float('-inf')
+        for key, archetype in universe.storage.items():
+            if not hasattr(archetype, 'nivel_3') or not archetype.nivel_3:
+                continue
+            
+            archetype_ss = archetype.nivel_3[0]
+            rel = self.transcender.relate_vectors(ss_query, archetype_ss)
+            score = sum(1 for bit in rel if bit == 0)
+            if score > best_score:
+                best_score, best = score, archetype
+        
+        if best:
+            # Create a deep copy to avoid modifying the original
+            from copy import deepcopy
+            result = deepcopy(best)
+            result.nivel_3[0] = list(ss_query[:3])  # Explicitly preserve root
+            logger.debug(f"Contextualized with score={best_score}, root preserved={result.nivel_3[0]}")
+            return True, result
+        
+        logger.debug("No relational match found")
+        return False, None
+
+    def lookup_lut(self, space_id: str, ss_query: list) -> Optional[FractalTensor]:
+        """Lookup in LUT tables."""
+        lut_key = f"{space_id}:{tuple(ss_query)}"
+        return self._lut_tables.get(lut_key)
+
+    def extend_fractal(self, input_ss, contexto: dict) -> dict:
+        """Orquestador Principal."""
+        log = [f"Extensión Aurora: espacio '{contexto.get('space_id', 'default')}'"]
+        
+        # Validación y normalización de ss_query
+        if hasattr(input_ss, 'nivel_3'):
+            ss_query = input_ss.nivel_3[0] if input_ss.nivel_3 else [0, 0, 0]
+        else:
+            ss_query = input_ss
+        
+        # Normalizar a un vector ternario de longitud 3
+        if not isinstance(ss_query, (list, tuple)):
+            log.append("⚠️ Entrada inválida, usando vector neutro [0,0,0]")
+            ss_query = [0, 0, 0]
+        else:
+            ss_query = [
+                None if x is None else int(x) if x in (0, 1) else 0
+                for x in list(ss_query)[:3]
+            ] + [0] * (3 - len(ss_query))
+        
+        space_id = contexto.get('space_id', 'default')
+        
+        STEPS = [
+            lambda q, s: (self.lookup_lut(s, q) is not None, self.lookup_lut(s, q)),
+            self._validate_archetype,
+            self._project_dynamics,
+            self._contextualize_relations
+        ]
+        METHODS = [
+            "reconstrucción por LUT",
+            "reconstrucción por arquetipo (axioma)",
+            "proyección por dinámica (raíz preservada)",
+            "contextualización por relator (raíz preservada)"
+        ]
+        
+        for step, method in zip(STEPS, METHODS):
+            ok, tensor = step(ss_query, space_id)
+            if ok and tensor is not None:
+                log.append(f"✅ {method}.")
+                
+                # Si tensor es lista, seleccionar el más cercano
+                if isinstance(tensor, list):
+                    tensor = tensor[0] if tensor else FractalTensor(nivel_3=[ss_query])
+                
+                # For dynamic/relator, preserve root
+                if method.startswith("proyección") or method.startswith("contextualización"):
+                    from copy import deepcopy
+                    result = deepcopy(tensor)
+                    result.nivel_3[0] = ss_query
+                    root_vector = result.nivel_3[0]
+                    harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
+                    result.nivel_3[0] = harm["output"]
+                    return {
+                        "reconstructed_tensor": result,
+                        "reconstruction_method": method + " + armonizador",
+                        "log": log
+                    }
+                
+                from copy import deepcopy
+                tensor_c = deepcopy(tensor)
+                root_vector = tensor_c.nivel_3[0] if tensor_c.nivel_3 else ss_query
+                harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
+                tensor_c.nivel_3[0] = harm["output"]
+                return {
+                    "reconstructed_tensor": tensor_c,
+                    "reconstruction_method": method + " + armonizador",
+                    "log": log
+                }
+        
+        # Fallback
+        log.append("🤷 No se encontraron coincidencias. Devolviendo tensor neutro.")
+        tensor_n = FractalTensor(nivel_3=[ss_query])
+        root_vector = tensor_n.nivel_3[0]
+        harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
+        tensor_n.nivel_3[0] = harm["output"]
+        
+        return {
+            "reconstructed_tensor": tensor_n,
+            "reconstruction_method": "fallback neutro + armonizador",
+            "log": log
+        }
+
+class Armonizador:
+    """Coherence validator and harmonization engine."""
+    
+    def __init__(self, knowledge_base=None, *, tau_1: int = 1, tau_2: int = 2, tau_3: int = 3):
+        self.kb = knowledge_base
+        self.tau_1, self.tau_2, self.tau_3 = tau_1, tau_2, tau_3
+    
+    def harmonize(self, tensor: Vector, *, archetype: Vector = None, space_id: str = "default") -> Dict[str, Any]:
+        """Harmonize vector for coherence."""
+        result_vector = self._microshift(tensor, archetype or [0, 0, 0])
+        
+        return {
+            "output": result_vector,
+            "score": 0,
+            "adjustments": ["microshift"]
+        }
+    
+    def _microshift(self, vec: Vector, archetype: Vector) -> Vector:
+        """Apply micro-adjustments to vector."""
+        logger.info(f"[microshift][ambig=0] Microshift final: {vec} | Score: 0")
+        return vec
+
+class TensorPoolManager:
+    """Pool manager for tensor collections."""
+    
+    def __init__(self):
+        self.tensors = []
+    
+    def add_tensor(self, tensor: FractalTensor):
+        """Add tensor to pool."""
+        self.tensors.append(tensor)
+
+# ===============================================================================
+# PATTERN 0: ETHICAL FRACTAL CLUSTER GENERATION
+# ===============================================================================
+
+def apply_ethical_constraint(vector, space_id, kb):
+    """Apply ethical constraints to vector."""
+    rules = getattr(kb, 'get_ethics', lambda sid: [-1, -1, -1])(space_id) or [-1, -1, -1]
+    return [v ^ r if r != -1 else v for v, r in zip(vector, rules)]
+
+def compute_ethical_signature(cluster):
+    """Compute ethical signature for cluster."""
+    base = str([t.nivel_3[0] for t in cluster]).encode()
+    return hashlib.sha256(base).hexdigest()
+
+def golden_ratio_select(N, seed):
+    """Select indices using golden ratio stepping."""
+    step = int(max(1, round(N * PHI)))
+    return [(seed + i * step) % N for i in range(3)]
+
+def pattern0_create_fractal_cluster(
+    *,
+    input_data=None,
+    space_id="default",
+    num_tensors=3,
+    context=None,
+    entropy_seed=PHI,
+    depth_max=3,
+):
+    """Generate ethical fractal cluster using Pattern 0."""
+    random.seed(int(entropy_seed * 1e9))
+    kb = FractalKnowledgeBase()
+    armonizador = Armonizador(knowledge_base=kb)
+    pool = TensorPoolManager()
+
+    # Generate tensors
+    tensors = []
+    for i in range(num_tensors):
+        if input_data and i < len(input_data):
+            vec = apply_ethical_constraint(input_data[i], space_id, kb)
+            tensor = FractalTensor(nivel_3=[vec])
+        else:
+            try:
+                tensor = FractalTensor.random(space_constraints=space_id)
+            except TypeError:
+                tensor = FractalTensor.random()
+        
+        # Add ethical metadata
+        tensor.metadata.update({
+            "ethical_hash": compute_ethical_signature([tensor]),
+            "entropy_seed": entropy_seed,
+            "space_id": space_id
+        })
+        
+        tensors.append(tensor)
+        pool.add_tensor(tensor)
+
+    # Harmonize cluster
+    for tensor in tensors:
+        harmonized = armonizador.harmonize(tensor.nivel_3[0], space_id=space_id)
+        tensor.nivel_3[0] = harmonized["output"]
+
+    return tensors
+
+# ===============================================================================
+# PUBLIC API
+# ===============================================================================
+
+# Main exports
+__all__ = [
+    'FractalTensor',
+    'Trigate', 
+    'TernaryLogic',
+    'Evolver',
+    'Extender', 
+    'FractalKnowledgeBase',
+    'Armonizador',
+    'TensorPoolManager',
+    'Transcender',
+    'pattern0_create_fractal_cluster'
+]
+
+# Compatibility aliases
+KnowledgeBase = FractalKnowledgeBase