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README.md

@@ -1,6 +1,6 @@
 # COGNITIVE-CORE Framework
 
-> Standard universel pour les architectures cognitives d'Ame Web Studio
+> 🧠 Standard universel pour les architectures cognitives d'Ame Web Studio
 
 ## 🏗️ Structure
 
@@ -9,151 +9,228 @@ cognitive-core/
 ├── __init__.py              # Exports du package
 ├── cognitive_base.py        # Classes de base (Config, Modules, PreTrainedModel)
 ├── cognitive_checkpoint.py  # Chargement/sauvegarde avec remappage auto
+├── cognitive_modules.py     # 🆕 TOUS les modules cognitifs réutilisables
+├── cognitive_training.py    # Utilitaires d'entraînement
 ├── cognitive_utils.py       # Utilitaires (device, mémoire, tokens)
 └── README.md               # Cette documentation
 ```
 
+
 ## 🚀 Installation
 
-```python
-# Ajouter à votre modèle
-import sys
-sys.path.append("/path/to/standardisation")
+### Option 1: Via Pip (Recommandé)
 
-from cognitive_core import (
-    CognitiveConfig,
-    CognitivePreTrainedModel,
-    setup_environment,
-    get_device
-)
-```
+```bash
+# Installation standard
+pip install cognitive-core
 
-## 📖 Guide d'Utilisation
+# Avec support Vision
+pip install "cognitive-core[vision]"
 
-### 1. Créer une Configuration
+# Avec support Audio
+pip install "cognitive-core[audio]"
 
-```python
-from cognitive_core import CognitiveConfig
+# Avec support Entraînement (WandB/Tensorboard)
+pip install "cognitive-core[training]"
 
-class MyModelConfig(CognitiveConfig):
-    model_type = "my_cognitive_model"
-    
-    def __init__(
-        self,
-        vocab_size: int = 50000,
-        # ... vos paramètres
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.vocab_size = vocab_size
+# Installation Complète
+pip install "cognitive-core[all]"
 ```
 
-### 2. Créer un Modèle Cognitif
+### Option 2: Via Git (Dernière version)
 
-```python
-from cognitive_core import CognitivePreTrainedModel, CognitiveModule
-import torch.nn as nn
+```bash
+pip install git+https://github.com/Volgat/nexus-standardisation.git
+```
 
-class MyMemoryModule(CognitiveModule):
-    def __init__(self, config):
-        super().__init__(config)
-        self.memory = nn.Parameter(torch.randn(1000, config.d_model))
-    
-    def forward(self, x, **kwargs):
-        # Votre logique
-        return {"output": x, "memory_used": True}
-    
-    def reset_state(self):
-        pass
+### Option 3: Via HuggingFace
 
-class MyModel(CognitivePreTrainedModel):
-    config_class = MyModelConfig
-    
-    def __init__(self, config):
-        super().__init__(config)
-        self.embeddings = nn.Embedding(config.vocab_size, config.d_model)
-        self.memory = MyMemoryModule(config)
-        self.lm_head = nn.Linear(config.d_model, config.vocab_size)
-        self.post_init()
-    
-    def forward(self, input_ids, **kwargs):
-        x = self.embeddings(input_ids)
-        mem_out = self.memory(x)
-        logits = self.lm_head(mem_out["output"])
-        return logits
+```bash
+pip install git+https://huggingface.co/amewebstudio/cognitive-core
 ```
 
-### 3. Chargement Automatique
+### Option 4: Mode Développement (Local)
+
+```bash
+git clone https://github.com/Volgat/nexus-standardisation.git
+cd nexus-standardisation
+pip install -e .
+```
 
-Le framework gère automatiquement:
-- ✅ Remappage des clés (avec/sans préfixe `model.`)
-- ✅ Validation du checkpoint
-- ✅ Compatibilité HuggingFace
+Si vous n'utilisez pas pip, vous pouvez simplement ajouter le chemin :
 
 ```python
-from cognitive_core import load_cognitive_checkpoint
+import sys
+sys.path.append("/path/to/standardisation")
 
-# Charger un checkpoint personnalisé
-info = load_cognitive_checkpoint(model, "path/to/checkpoint.pt", verbose=True)
-print(f"Clés chargées: {info['validation']['matched_keys']}")
+from cognitive_core import *
 ```
 
-### 4. Configuration Environnement (Kaggle/Colab)
+## 📦 Modules Disponibles
 
-```python
-from cognitive_core import setup_environment, get_device, get_hf_token
+### Normalisation
+| Module | Description |
+|--------|-------------|
+| `RMSNorm` | Root Mean Square Normalization (plus efficace que LayerNorm) |
 
-# Configure cache HuggingFace dans répertoire accessible
-cache_dir = setup_environment()
+### Encodage Positionnel
+| Module | Description |
+|--------|-------------|
+| `RotaryEmbedding` | RoPE avec scaling pour contextes longs |
+| `SinusoidalPositionalEncoding` | Encodage sinusoïdal classique |
 
-# Détection automatique GPU/CPU
-device = get_device()
+### Attention
+| Module | Description |
+|--------|-------------|
+| `GroupedQueryAttention` | GQA avec RoPE et KV-Cache |
+| `CrossAttention` | Attention croisée pour fusion multimodale |
 
-# Récupérer token HuggingFace
-token = get_hf_token()
-```
+### Réseaux Feed-Forward
+| Module | Description |
+|--------|-------------|
+| `SwiGLU` | Activation SwiGLU (meilleure que GELU) |
+| `MLP` | MLP standard avec GELU |
+
+### Mixture of Experts
+| Module | Description |
+|--------|-------------|
+| `Expert` | Expert unique avec SwiGLU |
+| `SparseMoE` | MoE sparse avec routing Top-K |
+
+### Systèmes de Mémoire
+| Module | Description |
+|--------|-------------|
+| `ContrastiveLPOL` | Mémoire LPOL avec 9 domaines de connaissances |
+| `MultiScaleMemory` | Mémoire court/long terme avec consolidation |
+| `EpisodicMemory` | Mémoire épisodique pour expériences |
+
+### World Model
+| Module | Description |
+|--------|-------------|
+| `WorldBuffer` | Buffer de monde unique avec prédiction |
+| `MultiWorldBuffer` | Buffers multi-domaines (physical, social, abstract, temporal) |
+
+### État Interne
+| Module | Description |
+|--------|-------------|
+| `NonVerbalTension` | Tracker de tension basé sur erreur de prédiction |
+| `InternalState` | État cognitif interne complet |
 
-## 🔧 Modules Disponibles
+### Rêve & Identité
+| Module | Description |
+|--------|-------------|
+| `DreamPhase` | Phase de rêve pour consolidation mémoire |
+| `SelfTrace` | Tracking d'identité à travers le temps |
 
+### Neurogenèse
 | Module | Description |
 |--------|-------------|
-| `CognitiveConfig` | Configuration de base héritant de PretrainedConfig |
-| `CognitiveModule` | Interface abstraite pour modules cognitifs |
-| `MemoryModule` | Interface pour modules de mémoire (store/retrieve) |
-| `TemporalModule` | Interface pour modules temporels (predict) |
-| `WorldModelModule` | Interface pour modèles du monde (update/imagine) |
-| `CognitivePreTrainedModel` | Modèle HuggingFace avec remappage auto |
+| `NeurogenesisLayer` | Couche avec naissance/mort dynamique de neurones |
 
-## 🎯 Cas d'Usage
+### EARCP
+| Module | Description |
+|--------|-------------|
+| `EARCPModule` | Ensemble Auto-Regulated Coherence Protocol |
+
+### VAE (Vision/World Model)
+| Module | Description |
+|--------|-------------|
+| `VAEEncoder` | Encodeur VAE convolutionnel |
+| `VAEDecoder` | Décodeur VAE convolutionnel |
+
+### Espace Latent Universel
+| Module | Description |
+|--------|-------------|
+| `UniversalLatentSpace` | ULS pour alignement cross-modal (text, vision, audio) |
+
+## 🎯 Exemples d'Utilisation
+
+### Modèle de Langage Cognitif
 
-### Vision Cognitive
 ```python
-class CognitiveViTConfig(CognitiveConfig):
-    model_type = "cognitive_vit"
-    # ... config vision
+from cognitive_core import (
+    CognitiveConfig, CognitivePreTrainedModel,
+    GroupedQueryAttention, SparseMoE, ContrastiveLPOL,
+    MultiScaleMemory, RMSNorm
+)
+
+class MyLLMConfig(CognitiveConfig):
+    model_type = "cognitive_llm"
+    vocab_size = 50000
+
+class MyCognitiveLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attn = GroupedQueryAttention(config.d_model, config.n_heads)
+        self.moe = SparseMoE(config.d_model, config.d_ff, num_experts=8)
+        self.norm1 = RMSNorm(config.d_model)
+        self.norm2 = RMSNorm(config.d_model)
+    
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))[0]
+        moe_out, aux = self.moe(self.norm2(x))
+        return x + moe_out, aux
 ```
 
-### World Model
+### World Model Cognitif
+
 ```python
-class CognitiveWorldConfig(CognitiveConfig):
+from cognitive_core import (
+    CognitiveConfig, VAEEncoder, VAEDecoder,
+    MultiWorldBuffer, EpisodicMemory, NeurogenesisLayer
+)
+
+class WorldModelConfig(CognitiveConfig):
     model_type = "cognitive_world"
-    # ... config world model
+    world_state_dim = 256
+
+class CognitiveWorldModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.encoder = VAEEncoder(in_channels=3, latent_dim=256)
+        self.decoder = VAEDecoder(latent_dim=256, out_channels=3)
+        self.world = MultiWorldBuffer(config.d_model, config)
+        self.memory = EpisodicMemory(config.d_model, config)
+        self.neurogenesis = NeurogenesisLayer(256, 64, config)
 ```
 
-### Multimodal
+### Vision-Language Multimodal
+
 ```python
-class CognitiveMultimodalConfig(CognitiveConfig):
+from cognitive_core import (
+    CognitiveConfig, UniversalLatentSpace, CrossAttention,
+    ContrastiveLPOL, DreamPhase, SelfTrace
+)
+
+class MultimodalConfig(CognitiveConfig):
     model_type = "cognitive_multimodal"
-    vision_enabled = True
-    audio_enabled = True
+
+class CognitiveMultimodal(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.uls = UniversalLatentSpace(config.d_model, config)
+        self.cross_attn = CrossAttention(config.d_model)
+        self.memory = ContrastiveLPOL(config.d_model, config)
+        self.dream = DreamPhase(config.d_model, config)
+        self.self_trace = SelfTrace(config.d_model, config)
+    
+    def forward(self, text_features, vision_features):
+        # Fusion dans l'espace latent universel
+        uls_out = self.uls({"text": text_features, "vision": vision_features})
+        # Attention croisée
+        fused = self.cross_attn(text_features, vision_features)
+        # Mémoire
+        mem_out = self.memory(fused)
+        return mem_out["output"]
 ```
 
 ## 📊 Garanties du Standard
 
-- ✅ **Intégrité des poids** - Aucun poids réinitialisé silencieusement
-- ✅ **Compatibilité HuggingFace** - AutoModel fonctionne nativement
+- ✅ **Agnostique** - Fonctionne pour LLM, Vision, Audio, World Model, Multimodal
+- ✅ **Composable** - Tous les modules sont indépendants et combinables
+- ✅ **HuggingFace-Compatible** - Hérite de PreTrainedModel
+- ✅ **Remappage Auto** - Gère les différences de format de checkpoint
 - ✅ **Portabilité** - Kaggle, Colab, Local sans modification
-- ✅ **Extensibilité** - Ajouter vos modules facilement
 
 ## 📄 Licence
 

cognitive_modules.py

@@ -0,0 +1,1206 @@
+"""
+COGNITIVE-CORE: Reusable Cognitive Modules
+===========================================
+
+Complete library of cognitive modules that can be composed to build
+any cognitive model: vision, language, world model, multimodal, etc.
+
+All modules are agnostic and can be configured for different use cases.
+
+Copyright © 2026 Mike Amega (Logo) - Ame Web Studio
+License: Proprietary - All Rights Reserved
+"""
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Dict, List, Optional, Any, Tuple
+from collections import deque
+from abc import ABC, abstractmethod
+
+from .cognitive_base import CognitiveConfig, CognitiveModule
+
+
+# ==============================================================================
+# SECTION 1: NORMALIZATION LAYERS
+# ==============================================================================
+
+
+class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization - More efficient than LayerNorm."""
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        rms = torch.sqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps)
+        return x / rms * self.weight
+
+
+# ==============================================================================
+# SECTION 2: POSITIONAL ENCODINGS
+# ==============================================================================
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding (RoPE) with scaling support."""
+
+    def __init__(
+        self, dim: int, max_seq_len: int = 4096, base: int = 10000, scaling: float = 1.0
+    ):
+        super().__init__()
+        self.dim = dim
+        self.scaling = scaling
+
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(max_seq_len).float() / scaling
+        freqs = torch.einsum("i,j->ij", t, inv_freq)
+        emb = torch.cat([freqs, freqs], dim=-1)
+        self.register_buffer("cos_cache", emb.cos()[None, None, :, :])
+        self.register_buffer("sin_cache", emb.sin()[None, None, :, :])
+
+    def forward(
+        self, q: torch.Tensor, k: torch.Tensor, seq_len: int, offset: int = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        cos = self.cos_cache[:, :, offset : offset + seq_len, :].to(q.dtype)
+        sin = self.sin_cache[:, :, offset : offset + seq_len, :].to(q.dtype)
+        q_rot = (q * cos) + (self._rotate_half(q) * sin)
+        k_rot = (k * cos) + (self._rotate_half(k) * sin)
+        return q_rot, k_rot
+
+    def _rotate_half(self, x: torch.Tensor) -> torch.Tensor:
+        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+        return torch.cat([-x2, x1], dim=-1)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    """Classical sinusoidal positional encoding."""
+
+    def __init__(self, d_model: int, max_seq_len: int = 4096, dropout: float = 0.1):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout)
+
+        pe = torch.zeros(max_seq_len, d_model)
+        position = torch.arange(0, max_seq_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
+        )
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+
+        self.register_buffer("pe", pe)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+# ==============================================================================
+# SECTION 3: ATTENTION MECHANISMS
+# ==============================================================================
+
+
+class GroupedQueryAttention(nn.Module):
+    """Grouped Query Attention (GQA) with RoPE and KV-Cache support."""
+
+    def __init__(
+        self,
+        d_model: int,
+        n_heads: int = 8,
+        n_kv_heads: int = 4,
+        max_seq_len: int = 4096,
+        dropout: float = 0.1,
+        use_rope: bool = True,
+    ):
+        super().__init__()
+        self.n_heads = n_heads
+        self.n_kv_heads = n_kv_heads
+        self.head_dim = d_model // n_heads
+        self.n_rep = n_heads // n_kv_heads
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(d_model, n_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(d_model, n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(d_model, n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(n_heads * self.head_dim, d_model, bias=False)
+
+        self.dropout = nn.Dropout(dropout)
+        self.rope = RotaryEmbedding(self.head_dim, max_seq_len) if use_rope else None
+
+    def _repeat_kv(self, x: torch.Tensor) -> torch.Tensor:
+        if self.n_rep == 1:
+            return x
+        B, n_kv, T, D = x.shape
+        return (
+            x[:, :, None, :, :]
+            .expand(B, n_kv, self.n_rep, T, D)
+            .reshape(B, self.n_heads, T, D)
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple]]:
+        B, T, C = x.shape
+
+        q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
+
+        offset = 0
+        if kv_cache is not None:
+            k_cache, v_cache = kv_cache
+            offset = k_cache.size(2)
+            k = torch.cat([k_cache, k], dim=2)
+            v = torch.cat([v_cache, v], dim=2)
+
+        if self.rope is not None:
+            q, _ = self.rope(q, q, T, offset)
+            _, k = self.rope(k, k, k.size(2), 0)
+
+        k = self._repeat_kv(k)
+        v = self._repeat_kv(v)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        if mask is not None:
+            attn = attn.masked_fill(mask == 0, float("-inf"))
+
+        attn = F.softmax(attn, dim=-1)
+        attn = self.dropout(attn)
+
+        out = (attn @ v).transpose(1, 2).reshape(B, T, -1)
+        out = self.o_proj(out)
+
+        new_cache = None
+        if use_cache:
+            k_to_cache = (
+                self.k_proj(x)
+                .view(B, T, self.n_kv_heads, self.head_dim)
+                .transpose(1, 2)
+            )
+            v_to_cache = (
+                self.v_proj(x)
+                .view(B, T, self.n_kv_heads, self.head_dim)
+                .transpose(1, 2)
+            )
+            if kv_cache is not None:
+                k_to_cache = torch.cat([kv_cache[0], k_to_cache], dim=2)
+                v_to_cache = torch.cat([kv_cache[1], v_to_cache], dim=2)
+            new_cache = (k_to_cache, v_to_cache)
+
+        return out, new_cache
+
+
+class CrossAttention(nn.Module):
+    """Cross-attention for multimodal fusion."""
+
+    def __init__(self, d_model: int, n_heads: int = 8, dropout: float = 0.1):
+        super().__init__()
+        self.n_heads = n_heads
+        self.head_dim = d_model // n_heads
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(d_model, d_model, bias=False)
+        self.k_proj = nn.Linear(d_model, d_model, bias=False)
+        self.v_proj = nn.Linear(d_model, d_model, bias=False)
+        self.o_proj = nn.Linear(d_model, d_model, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key_value: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        B, T, C = query.shape
+        _, S, _ = key_value.shape
+
+        q = self.q_proj(query).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
+        k = (
+            self.k_proj(key_value)
+            .view(B, S, self.n_heads, self.head_dim)
+            .transpose(1, 2)
+        )
+        v = (
+            self.v_proj(key_value)
+            .view(B, S, self.n_heads, self.head_dim)
+            .transpose(1, 2)
+        )
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        if mask is not None:
+            attn = attn.masked_fill(mask == 0, float("-inf"))
+
+        attn = F.softmax(attn, dim=-1)
+        attn = self.dropout(attn)
+
+        out = (attn @ v).transpose(1, 2).reshape(B, T, -1)
+        return self.o_proj(out)
+
+
+# ==============================================================================
+# SECTION 4: FEEDFORWARD NETWORKS
+# ==============================================================================
+
+
+class SwiGLU(nn.Module):
+    """SwiGLU activation - better than GELU for transformers."""
+
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
+        super().__init__()
+        hidden = int(d_ff * 2 / 3)
+        hidden = ((hidden + 63) // 64) * 64  # Align to 64
+
+        self.w1 = nn.Linear(d_model, hidden, bias=False)
+        self.w2 = nn.Linear(hidden, d_model, bias=False)
+        self.w3 = nn.Linear(d_model, hidden, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MLP(nn.Module):
+    """Standard MLP with GELU activation."""
+
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(d_model, d_ff),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(d_ff, d_model),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)
+
+
+# ==============================================================================
+# SECTION 5: SPARSE MIXTURE OF EXPERTS
+# ==============================================================================
+
+
+class Expert(nn.Module):
+    """Single expert module."""
+
+    def __init__(self, d_model: int, d_ff: int, expert_type: str = "general"):
+        super().__init__()
+        self.expert_type = expert_type
+        self.ffn = SwiGLU(d_model, d_ff)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.ffn(x)
+
+
+class SparseMoE(nn.Module):
+    """Sparse Mixture of Experts with Top-K routing."""
+
+    def __init__(
+        self,
+        d_model: int,
+        d_ff: int,
+        num_experts: int = 8,
+        top_k: int = 2,
+        expert_types: Optional[List[str]] = None,
+        aux_loss_weight: float = 0.01,
+    ):
+        super().__init__()
+        self.num_experts = num_experts
+        self.top_k = top_k
+        self.aux_loss_weight = aux_loss_weight
+
+        if expert_types is None:
+            expert_types = ["general"]
+
+        self.router = nn.Linear(d_model, num_experts, bias=False)
+        self.experts = nn.ModuleList(
+            [
+                Expert(d_model, d_ff, expert_types[i % len(expert_types)])
+                for i in range(num_experts)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        B, T, C = x.shape
+        x_flat = x.view(-1, C)
+
+        router_logits = self.router(x_flat)
+        topk_weights, topk_indices = torch.topk(
+            F.softmax(router_logits, dim=-1), self.top_k, dim=-1
+        )
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+        output = torch.zeros_like(x_flat)
+
+        for i, expert in enumerate(self.experts):
+            mask = (topk_indices == i).any(dim=-1)
+            if not mask.any():
+                continue
+            expert_weight = torch.where(
+                topk_indices == i, topk_weights, torch.zeros_like(topk_weights)
+            ).sum(dim=-1)
+            expert_out = expert(x_flat[mask])
+            output[mask] += expert_out * expert_weight[mask].unsqueeze(-1)
+
+        # Auxiliary load balancing loss
+        router_probs = F.softmax(router_logits, dim=-1)
+        expert_usage = router_probs.mean(dim=0)
+        aux_loss = (
+            self.num_experts
+            * (expert_usage * expert_usage).sum()
+            * self.aux_loss_weight
+        )
+
+        return output.view(B, T, C), aux_loss
+
+
+# ==============================================================================
+# SECTION 6: MEMORY SYSTEMS
+# ==============================================================================
+
+
+class ContrastiveLPOL(CognitiveModule):
+    """
+    LPOL Memory System with configurable knowledge domains.
+    Uses contrastive learning for memory retrieval.
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        config: CognitiveConfig,
+        domains: Optional[List[str]] = None,
+        slots_per_domain: int = 512,
+        retrieval_k: int = 8,
+    ):
+        super().__init__(config)
+
+        if domains is None:
+            domains = [
+                "semantic",
+                "episodic",
+                "procedural",
+                "spatial",
+                "temporal",
+                "causal",
+                "social",
+                "emotional",
+                "conceptual",
+            ]
+
+        self.domains = domains
+        self.k = retrieval_k
+
+        self.memories = nn.ParameterDict(
+            {
+                domain: nn.Parameter(torch.randn(slots_per_domain, d_model) * 0.01)
+                for domain in domains
+            }
+        )
+
+        self.domain_clf = nn.Sequential(
+            nn.Linear(d_model, len(domains) * 2),
+            nn.GELU(),
+            nn.Linear(len(domains) * 2, len(domains)),
+        )
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.out_proj = nn.Linear(d_model * 2, d_model)
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        B, T, C = x.shape
+
+        domain_probs = F.softmax(self.domain_clf(x.mean(dim=1)), dim=-1)
+        all_mem = torch.cat([self.memories[d] for d in self.domains], dim=0)
+
+        q = self.q_proj(x)
+        k = self.k_proj(all_mem)
+        v = self.v_proj(all_mem)
+
+        sim = torch.matmul(q, k.T) / math.sqrt(C)
+        topk_sim, topk_idx = torch.topk(sim, min(self.k, all_mem.size(0)), dim=-1)
+        weights = F.softmax(topk_sim, dim=-1)
+        retrieved = (weights.unsqueeze(-1) * v[topk_idx]).sum(dim=2)
+        output = self.out_proj(torch.cat([x, retrieved], dim=-1))
+
+        return {
+            "output": output,
+            "domain_probs": domain_probs,
+            "retrieval_weights": weights,
+        }
+
+    def reset_state(self):
+        pass
+
+    def update_memory(self, x: torch.Tensor, domain: str, lr: float = 0.01):
+        """Online memory update."""
+        if domain in self.memories:
+            with torch.no_grad():
+                mem = self.memories[domain]
+                sim = F.cosine_similarity(
+                    x.mean(dim=1, keepdim=True), mem.unsqueeze(0), dim=-1
+                )
+                _, idx = sim.min(dim=-1)
+                mem[idx] = (1 - lr) * mem[idx] + lr * x.mean(dim=1)
+
+
+class MultiScaleMemory(CognitiveModule):
+    """Short-term and long-term memory with consolidation."""
+
+    def __init__(
+        self,
+        d_model: int,
+        config: CognitiveConfig,
+        short_term_dim: int = 512,
+        long_term_dim: int = 256,
+        st_decay: float = 0.95,
+        lt_decay: float = 0.99,
+        consolidation_threshold: float = 0.7,
+    ):
+        super().__init__(config)
+
+        self.st_decay = st_decay
+        self.lt_decay = lt_decay
+        self.consolidation_threshold = consolidation_threshold
+
+        # Short-term memory
+        self.st_compress = nn.Sequential(
+            nn.Linear(d_model, short_term_dim),
+            nn.GELU(),
+            nn.Linear(short_term_dim, short_term_dim),
+        )
+        self.st_gate = nn.GRUCell(short_term_dim, short_term_dim)
+
+        # Long-term memory
+        self.consolidation = nn.Sequential(
+            nn.Linear(short_term_dim + long_term_dim, 256),
+            nn.SiLU(),
+            nn.Linear(256, 1),
+            nn.Sigmoid(),
+        )
+        self.st_to_lt = nn.Linear(short_term_dim, long_term_dim)
+        self.lt_gate = nn.GRUCell(long_term_dim, long_term_dim)
+
+        # Fusion
+        self.fusion = nn.Sequential(
+            nn.Linear(short_term_dim + long_term_dim, d_model), nn.Tanh()
+        )
+
+        # State buffers
+        self.register_buffer("st_state", torch.zeros(1, short_term_dim))
+        self.register_buffer("lt_state", torch.zeros(1, long_term_dim))
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        B = x.size(0)
+        h_compressed = self.st_compress(x.mean(dim=1))
+
+        st_prev = self.st_state.expand(B, -1)
+        st_new = self.st_decay * st_prev + (1 - self.st_decay) * self.st_gate(
+            h_compressed, st_prev
+        )
+
+        lt_prev = self.lt_state.expand(B, -1)
+        consolidation_score = self.consolidation(torch.cat([st_new, lt_prev], dim=-1))
+
+        if (consolidation_score > self.consolidation_threshold).any():
+            lt_input = self.st_to_lt(st_new)
+            lt_new = self.lt_decay * lt_prev + (1 - self.lt_decay) * self.lt_gate(
+                lt_input, lt_prev
+            )
+        else:
+            lt_new = lt_prev
+
+        self.st_state = st_new[:1].detach()
+        self.lt_state = lt_new[:1].detach()
+
+        fused = self.fusion(torch.cat([st_new, lt_new], dim=-1))
+
+        return {
+            "st": st_new,
+            "lt": lt_new,
+            "fused": fused,
+            "consolidation_score": consolidation_score.mean().item(),
+        }
+
+    def reset_state(self):
+        self.st_state.zero_()
+        self.lt_state.zero_()
+
+
+class EpisodicMemory(CognitiveModule):
+    """Episodic memory for experience storage and retrieval."""
+
+    def __init__(self, d_model: int, config: CognitiveConfig, max_episodes: int = 1000):
+        super().__init__(config)
+
+        self.encoder = nn.Sequential(
+            nn.Linear(d_model, d_model // 2),
+            nn.GELU(),
+            nn.Linear(d_model // 2, d_model),
+        )
+
+        self.register_buffer("episodes", torch.zeros(max_episodes, d_model))
+        self.register_buffer("count", torch.tensor(0))
+        self.max = max_episodes
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        encoded = self.encoder(x)
+        return {"encoded": encoded}
+
+    def store(self, x: torch.Tensor):
+        """Store an experience."""
+        with torch.no_grad():
+            idx = self.count.item() % self.max
+            self.episodes[idx] = x.mean(dim=(0, 1)) if x.dim() == 3 else x.mean(dim=0)
+            self.count += 1
+
+    def retrieve(self, query: torch.Tensor, k: int = 5) -> torch.Tensor:
+        """Retrieve k most similar episodes."""
+        n = min(self.count.item(), self.max)
+        if n == 0:
+            return torch.zeros_like(query)
+
+        episodes = self.episodes[:n]
+        sim = F.cosine_similarity(query.unsqueeze(1), episodes.unsqueeze(0), dim=-1)
+        _, indices = sim.topk(min(k, n), dim=-1)
+        return episodes[indices].mean(dim=1)
+
+    def reset_state(self):
+        self.count.zero_()
+
+
+# ==============================================================================
+# SECTION 7: WORLD MODEL COMPONENTS
+# ==============================================================================
+
+
+class WorldBuffer(CognitiveModule):
+    """Single domain world buffer with state prediction."""
+
+    def __init__(self, d_model: int, config: CognitiveConfig, domain: str = "physical"):
+        super().__init__(config)
+        self.domain = domain
+
+        state_dim = getattr(config, "world_state_dim", 256)
+
+        self.encoder = nn.Sequential(
+            nn.Linear(d_model, state_dim), nn.GELU(), nn.Linear(state_dim, state_dim)
+        )
+
+        self.dynamics = nn.GRUCell(state_dim, state_dim)
+
+        self.predictor = nn.Sequential(
+            nn.Linear(state_dim, state_dim), nn.Tanh(), nn.Linear(state_dim, state_dim)
+        )
+
+        self.register_buffer("state", torch.zeros(1, state_dim))
+        self.register_buffer("prediction", torch.zeros(1, state_dim))
+        self.register_buffer("surprise", torch.tensor(0.0))
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        if x.dim() == 3:
+            x = x.mean(dim=1)
+
+        encoded = self.encoder(x)
+
+        # Compute surprise
+        if self.prediction.norm() > 0:
+            surprise = F.mse_loss(
+                encoded, self.prediction.expand(encoded.size(0), -1)
+            ).item()
+        else:
+            surprise = 0.0
+
+        self.surprise = torch.tensor(surprise)
+
+        # Update state
+        new_state = self.dynamics(encoded, self.state.expand(encoded.size(0), -1))
+        update_rate = getattr(self.config, "world_update_rate", 0.1)
+        self.state = (
+            update_rate * new_state[:1] + (1 - update_rate) * self.state
+        ).detach()
+        self.prediction = self.predictor(self.state).detach()
+
+        return {"surprise": surprise, "state": new_state}
+
+    def reset_state(self):
+        self.state.zero_()
+        self.prediction.zero_()
+        self.surprise.zero_()
+
+
+class MultiWorldBuffer(CognitiveModule):
+    """Multi-domain world model buffers."""
+
+    def __init__(
+        self, d_model: int, config: CognitiveConfig, domains: Optional[List[str]] = None
+    ):
+        super().__init__(config)
+
+        if domains is None:
+            domains = ["physical", "social", "abstract", "temporal"]
+
+        self.world_buffers = nn.ModuleDict(
+            {d: WorldBuffer(d_model, config, d) for d in domains}
+        )
+        self.register_buffer("aggregate_surprise", torch.tensor(0.0))
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        results = {}
+        total_surprise = 0.0
+
+        for domain, buffer in self.world_buffers.items():
+            result = buffer(x)
+            results[domain] = result
+            total_surprise += result["surprise"]
+
+        self.aggregate_surprise = torch.tensor(total_surprise / len(self.world_buffers))
+
+        return {
+            "domain_results": results,
+            "aggregate_surprise": self.aggregate_surprise.item(),
+        }
+
+    def reset_state(self):
+        for buffer in self.world_buffers.values():
+            buffer.reset_state()
+
+
+# ==============================================================================
+# SECTION 8: INTERNAL STATE SYSTEMS
+# ==============================================================================
+
+
+class NonVerbalTension(nn.Module):
+    """Tracks prediction error as internal tension signal."""
+
+    def __init__(self, integration_rate: float = 0.1, buffer_size: int = 100):
+        super().__init__()
+        self.integration_rate = integration_rate
+        self.register_buffer("prediction_errors", torch.zeros(buffer_size))
+        self.register_buffer("error_idx", torch.tensor(0))
+        self.register_buffer("integrated_tension", torch.tensor(0.0))
+
+    def update(self, pred: torch.Tensor, actual: torch.Tensor):
+        with torch.no_grad():
+            error = F.mse_loss(pred.float(), actual.float()).item()
+            idx = self.error_idx.item() % len(self.prediction_errors)
+            self.prediction_errors[idx] = error
+            self.error_idx += 1
+
+    def integrate(self) -> float:
+        n = min(self.error_idx.item(), len(self.prediction_errors))
+        if n > 0:
+            raw = self.prediction_errors[:n].mean().item()
+            self.integrated_tension = (
+                1 - self.integration_rate
+            ) * self.integrated_tension + self.integration_rate * raw
+        return self.integrated_tension.item()
+
+
+class InternalState(CognitiveModule):
+    """Complete internal cognitive state tracker."""
+
+    def __init__(self, d_model: int, config: CognitiveConfig):
+        super().__init__(config)
+
+        internal_dim = getattr(config, "internal_state_dim", 128)
+        latent_dim = getattr(config, "latent_state_dim", 768)
+
+        self.tension = NonVerbalTension()
+
+        self.encoder = nn.Sequential(nn.Linear(latent_dim, internal_dim), nn.Tanh())
+
+        self.register_buffer("discomfort", torch.zeros(1, internal_dim))
+
+    def forward(
+        self,
+        fused: torch.Tensor,
+        pred: Optional[torch.Tensor] = None,
+        actual: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        if pred is not None and actual is not None:
+            self.tension.update(pred, actual)
+
+        tension = self.tension.integrate()
+
+        encoded = self.encoder(fused)
+        if encoded.dim() == 3:
+            encoded = encoded.mean(dim=1)
+
+        self.discomfort = 0.9 * self.discomfort + 0.1 * encoded[:1].detach()
+
+        return {
+            "tension": tension,
+            "discomfort": self.discomfort,
+            "encoded_state": encoded,
+        }
+
+    def reset_state(self):
+        self.discomfort.zero_()
+
+
+# ==============================================================================
+# SECTION 9: DREAM & SELF-TRACE
+# ==============================================================================
+
+
+class DreamPhase(CognitiveModule):
+    """Dream phase for memory consolidation."""
+
+    def __init__(
+        self,
+        d_model: int,
+        config: CognitiveConfig,
+        buffer_size: int = 256,
+        dream_threshold: float = 0.7,
+    ):
+        super().__init__(config)
+
+        internal_dim = getattr(config, "internal_state_dim", 128)
+
+        self.buffer = deque(maxlen=buffer_size)
+        self.is_dreaming = False
+        self.dream_steps = 0
+        self.dream_threshold = dream_threshold
+        self.total_dreams = 0
+
+        self.consolidator = nn.Sequential(
+            nn.Linear(internal_dim, internal_dim),
+            nn.GELU(),
+            nn.Linear(internal_dim, internal_dim),
+            nn.Tanh(),
+        )
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        return {"is_dreaming": self.is_dreaming, "dream_steps": self.dream_steps}
+
+    def record(self, state: torch.Tensor, tension: float):
+        """Record state for potential dream consolidation."""
+        self.buffer.append((state.detach().cpu(), tension))
+
+    def should_dream(self) -> bool:
+        if len(self.buffer) < 10:
+            return False
+        recent = [t for _, t in list(self.buffer)[-10:]]
+        return sum(recent) / len(recent) > self.dream_threshold
+
+    def enter_dream(self):
+        self.is_dreaming = True
+        self.dream_steps = 0
+        self.total_dreams += 1
+
+    def dream_step(self, identity: torch.Tensor) -> Optional[torch.Tensor]:
+        """Execute one dream consolidation step."""
+        if not self.is_dreaming or len(self.buffer) == 0:
+            return None
+
+        self.dream_steps += 1
+
+        # Sample from buffer
+        idx = torch.randint(0, len(self.buffer), (1,)).item()
+        state, _ = self.buffer[idx]
+        state = state.to(identity.device)
+
+        # Consolidate
+        consolidated = self.consolidator(state)
+
+        # Exit dream after some steps
+        if self.dream_steps > 50:
+            self.is_dreaming = False
+
+        return consolidated
+
+    def reset_state(self):
+        self.buffer.clear()
+        self.is_dreaming = False
+        self.dream_steps = 0
+
+
+class SelfTrace(CognitiveModule):
+    """Identity tracking across time."""
+
+    def __init__(self, d_model: int, config: CognitiveConfig):
+        super().__init__(config)
+
+        internal_dim = getattr(config, "internal_state_dim", 128)
+
+        self.register_buffer("identity", torch.zeros(1, internal_dim))
+        self.register_buffer("n_traces", torch.tensor(0))
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        return {"identity": self.identity, "n_traces": self.n_traces.item()}
+
+    def record(self, state: torch.Tensor, tension: float):
+        """Update identity based on state and tension."""
+        with torch.no_grad():
+            if state.dim() > 2:
+                state = state.mean(dim=1)
+
+            # Weight by tension (high tension = more salient)
+            weight = min(0.1, 0.01 * max(1.0, tension))
+            self.identity = (1 - weight) * self.identity + weight * state[:1]
+            self.n_traces += 1
+
+    def get_identity(self) -> torch.Tensor:
+        return self.identity
+
+    def reset_state(self):
+        self.identity.zero_()
+        self.n_traces.zero_()
+
+
+# ==============================================================================
+# SECTION 10: NEUROGENESIS
+# ==============================================================================
+
+
+class NeurogenesisLayer(CognitiveModule):
+    """Layer with dynamic neuron birth/death based on usage."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        n_neurons: int,
+        config: CognitiveConfig,
+        max_neurons: int = 256,
+        usage_decay: float = 0.99,
+        birth_threshold: float = 0.8,
+        death_threshold: float = 0.01,
+    ):
+        super().__init__(config)
+
+        self.input_dim = input_dim
+        self.max_neurons = max_neurons
+        self.usage_decay = usage_decay
+        self.birth_threshold = birth_threshold
+        self.death_threshold = death_threshold
+
+        self.weights = nn.Parameter(torch.randn(max_neurons, input_dim) * 0.02)
+        self.bias = nn.Parameter(torch.zeros(max_neurons))
+
+        self.register_buffer("n_neurons", torch.tensor(n_neurons))
+        self.register_buffer("usage", torch.ones(max_neurons))
+        self.register_buffer("lifetime", torch.zeros(max_neurons))
+        self.register_buffer("births", torch.tensor(0))
+        self.register_buffer("deaths", torch.tensor(0))
+
+    def forward(self, x: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        n = self.n_neurons.item()
+        out = torch.tanh(F.linear(x, self.weights[:n], self.bias[:n]))
+
+        with torch.no_grad():
+            activation = out.abs().mean(dim=0) if out.dim() > 1 else out.abs()
+            if activation.size(-1) >= n:
+                self.usage[:n] = (
+                    self.usage_decay * self.usage[:n]
+                    + (1 - self.usage_decay) * activation[..., :n].mean(dim=0)
+                    if activation.dim() > 1
+                    else activation[:n]
+                )
+            self.lifetime[:n] += 1
+
+        return {
+            "output": out,
+            "n_neurons": n,
+            "avg_usage": self.usage[:n].mean().item(),
+        }
+
+    def maybe_birth(self, coherence: float) -> bool:
+        """Try to add a neuron if coherence is high."""
+        n = self.n_neurons.item()
+        if coherence > self.birth_threshold and n < self.max_neurons:
+            with torch.no_grad():
+                nn.init.normal_(self.weights[n], std=0.02)
+                self.bias[n] = 0
+                self.usage[n] = 1.0
+                self.lifetime[n] = 0
+                self.n_neurons += 1
+                self.births += 1
+            return True
+        return False
+
+    def maybe_death(self) -> int:
+        """Remove underused neurons."""
+        n = self.n_neurons.item()
+        if n <= 8:
+            return 0
+
+        dead = 0
+        with torch.no_grad():
+            for i in range(n - 1, 7, -1):
+                if self.usage[i] < self.death_threshold and self.lifetime[i] > 100:
+                    # Swap with last active
+                    last = self.n_neurons.item() - 1
+                    if i < last:
+                        self.weights.data[i] = self.weights.data[last]
+                        self.bias.data[i] = self.bias.data[last]
+                        self.usage[i] = self.usage[last]
+                        self.lifetime[i] = self.lifetime[last]
+                    self.n_neurons -= 1
+                    self.deaths += 1
+                    dead += 1
+        return dead
+
+    def get_stats(self) -> Dict[str, Any]:
+        n = self.n_neurons.item()
+        return {
+            "total_neurons": n,
+            "births": self.births.item(),
+            "deaths": self.deaths.item(),
+            "avg_usage": self.usage[:n].mean().item() if n > 0 else 0,
+        }
+
+    def reset_state(self):
+        pass
+
+
+# ==============================================================================
+# SECTION 11: EARCP MODULE
+# ==============================================================================
+
+
+class EARCPModule(CognitiveModule):
+    """
+    Ensemble Auto-Regulated Coherence Protocol.
+    Compresses hidden states and regulates information flow.
+    """
+
+    def __init__(self, d_model: int, config: CognitiveConfig):
+        super().__init__(config)
+
+        latent_dim = getattr(config, "latent_state_dim", 768)
+        d_ff = getattr(config, "d_ff", 2048)
+
+        self.compress = nn.Sequential(
+            nn.Linear(d_model, (d_model + latent_dim) // 2),
+            nn.SiLU(),
+            nn.Linear((d_model + latent_dim) // 2, latent_dim),
+        )
+
+        self.state_gate = nn.Linear(latent_dim * 2, latent_dim)
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(latent_dim, d_model)
+        self.v_proj = nn.Linear(latent_dim, d_model)
+        self.out_proj = nn.Linear(d_model, d_model)
+
+        self.coherence_proc = nn.Sequential(
+            nn.Linear(d_model, d_ff), nn.SiLU(), nn.Linear(d_ff, d_model)
+        )
+
+        # Initialize small for residual
+        nn.init.zeros_(self.out_proj.weight)
+        nn.init.zeros_(self.coherence_proc[-1].weight)
+
+    def forward(self, h: torch.Tensor, fused: torch.Tensor, **kwargs) -> Dict[str, Any]:
+        h_compressed = self.compress(h.mean(dim=1))
+
+        gate = torch.sigmoid(self.state_gate(torch.cat([h_compressed, fused], dim=-1)))
+        state = (1 - gate) * fused + gate * h_compressed
+
+        q = self.q_proj(h)
+        k = self.k_proj(state).unsqueeze(1)
+        v = self.v_proj(state).unsqueeze(1)
+
+        attn = F.softmax(q @ k.transpose(-2, -1) / math.sqrt(h.size(-1)), dim=-1)
+        h = h + 0.02 * self.out_proj(attn @ v)
+        h = h + 0.1 * self.coherence_proc(h)
+
+        coherence = torch.sigmoid(h.mean()).item()
+
+        return {"hidden": h, "state": state, "coherence": coherence}
+
+    def reset_state(self):
+        pass
+
+
+# ==============================================================================
+# SECTION 12: VAE COMPONENTS (for World Models / Vision)
+# ==============================================================================
+
+
+class VAEEncoder(nn.Module):
+    """Convolutional VAE Encoder for visual inputs."""
+
+    def __init__(
+        self, in_channels: int = 3, latent_dim: int = 256, channels: List[int] = None
+    ):
+        super().__init__()
+
+        if channels is None:
+            channels = [32, 64, 128, 256]
+
+        layers = []
+        prev_c = in_channels
+
+        for c in channels:
+            layers.extend(
+                [
+                    nn.Conv2d(prev_c, c, 4, 2, 1),
+                    nn.BatchNorm2d(c),
+                    nn.LeakyReLU(0.2, inplace=True),
+                ]
+            )
+            prev_c = c
+
+        self.encoder = nn.Sequential(*layers)
+
+        # Calculate flattened size (assumes 64x64 input)
+        self.flat_size = channels[-1] * 4 * 4
+
+        self.fc_mu = nn.Linear(self.flat_size, latent_dim)
+        self.fc_logvar = nn.Linear(self.flat_size, latent_dim)
+
+    def forward(
+        self, x: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        h = self.encoder(x)
+        h = h.view(h.size(0), -1)
+
+        mu = self.fc_mu(h)
+        logvar = self.fc_logvar(h)
+
+        # Reparameterization
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        z = mu + eps * std
+
+        return z, mu, logvar
+
+
+class VAEDecoder(nn.Module):
+    """Convolutional VAE Decoder for visual outputs."""
+
+    def __init__(
+        self, latent_dim: int = 256, out_channels: int = 3, channels: List[int] = None
+    ):
+        super().__init__()
+
+        if channels is None:
+            channels = [256, 128, 64, 32]
+
+        self.fc = nn.Linear(latent_dim, channels[0] * 4 * 4)
+        self.init_channels = channels[0]
+
+        layers = []
+        for i in range(len(channels) - 1):
+            layers.extend(
+                [
+                    nn.ConvTranspose2d(channels[i], channels[i + 1], 4, 2, 1),
+                    nn.BatchNorm2d(channels[i + 1]),
+                    nn.ReLU(inplace=True),
+                ]
+            )
+
+        # Final layer
+        layers.extend(
+            [nn.ConvTranspose2d(channels[-1], out_channels, 4, 2, 1), nn.Sigmoid()]
+        )
+
+        self.decoder = nn.Sequential(*layers)
+
+    def forward(self, z: torch.Tensor) -> torch.Tensor:
+        h = self.fc(z)
+        h = h.view(h.size(0), self.init_channels, 4, 4)
+        return self.decoder(h)
+
+
+# ==============================================================================
+# SECTION 13: UNIVERSAL LATENT SPACE
+# ==============================================================================
+
+
+class UniversalLatentSpace(CognitiveModule):
+    """Universal Latent Space for cross-modal alignment."""
+
+    def __init__(
+        self,
+        d_model: int,
+        config: CognitiveConfig,
+        uls_dim: int = 1024,
+        n_anchors: int = 64,
+    ):
+        super().__init__(config)
+
+        self.uls_dim = uls_dim
+
+        self.anchors = nn.Parameter(torch.randn(n_anchors, uls_dim) * 0.02)
+
+        # Modality projections
+        self.text_to_uls = nn.Sequential(
+            nn.Linear(d_model, d_model),
+            nn.GELU(),
+            nn.Linear(d_model, uls_dim),
+            RMSNorm(uls_dim),
+        )
+
+        self.vision_to_uls = nn.Sequential(
+            nn.Linear(d_model, d_model),
+            nn.GELU(),
+            nn.Linear(d_model, uls_dim),
+            RMSNorm(uls_dim),
+        )
+
+        self.audio_to_uls = nn.Sequential(
+            nn.Linear(d_model, d_model),
+            nn.GELU(),
+            nn.Linear(d_model, uls_dim),
+            RMSNorm(uls_dim),
+        )
+
+        self.uls_to_model = nn.Sequential(
+            nn.Linear(uls_dim, d_model),
+            nn.GELU(),
+            nn.Linear(d_model, d_model),
+            RMSNorm(d_model),
+        )
+
+        self.anchor_attn = nn.MultiheadAttention(uls_dim, num_heads=4, batch_first=True)
+
+    def forward(self, features: Dict[str, torch.Tensor], **kwargs) -> Dict[str, Any]:
+        unified_features = []
+
+        if "text" in features and features["text"] is not None:
+            unified_features.append(self.text_to_uls(features["text"]))
+
+        if "vision" in features and features["vision"] is not None:
+            unified_features.append(self.vision_to_uls(features["vision"]))
+
+        if "audio" in features and features["audio"] is not None:
+            unified_features.append(self.audio_to_uls(features["audio"]))
+
+        if not unified_features:
+            B = 1
+            device = self.anchors.device
+            unified = torch.zeros(B, 1, self.uls_dim, device=device)
+        else:
+            # Average all modalities
+            unified = torch.stack(unified_features, dim=0).mean(dim=0)
+
+        # Anchor attention
+        anchors_expanded = self.anchors.unsqueeze(0).expand(unified.size(0), -1, -1)
+        enhanced, _ = self.anchor_attn(unified, anchors_expanded, anchors_expanded)
+        enhanced = unified + 0.1 * enhanced
+
+        output = self.uls_to_model(enhanced)
+
+        return {"unified": unified, "enhanced": enhanced, "output": output}
+
+    def reset_state(self):
+        pass

setup.py

@@ -0,0 +1,105 @@
+"""
+COGNITIVE-CORE: Universal Cognitive Architecture Framework
+===========================================================
+
+A robust, agnostic framework for building cognitive AI models.
+Supports vision, language, world model, audio, and multimodal architectures.
+
+Installation:
+    pip install cognitive-core
+
+Or from HuggingFace:
+    pip install git+https://huggingface.co/amewebstudio/cognitive-core
+
+Copyright © 2026 Mike Amega (Logo) - Ame Web Studio
+License: Proprietary - All Rights Reserved
+"""
+
+from setuptools import setup, find_packages
+
+with open("cognitive-core/README.md", "r", encoding="utf-8") as f:
+    long_description = f.read()
+
+setup(
+    name="cognitive-core",
+    version="1.0.0",
+    author="Mike Amega",
+    author_email="contact@amewebstudio.com",
+    description="Universal Cognitive Architecture Framework for AI Models",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/Volgat/nexus-standardisation",
+    project_urls={
+        "HuggingFace": "https://huggingface.co/amewebstudio/cognitive-core",
+        "Documentation": "https://github.com/Volgat/nexus-standardisation#readme",
+        "Bug Tracker": "https://github.com/Volgat/nexus-standardisation/issues",
+    },
+    packages=find_packages(),
+    package_dir={"cognitive_core": "cognitive-core"},
+    py_modules=["cognitive_core"],
+    classifiers=[
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Science/Research",
+        "License :: Other/Proprietary License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+    python_requires=">=3.8",
+    install_requires=[
+        "torch>=2.0.0",
+        "transformers>=4.35.0",
+        "datasets>=2.14.0",
+        "huggingface_hub>=0.19.0",
+        "accelerate>=0.24.0",
+    ],
+    extras_require={
+        "dev": [
+            "pytest>=7.0.0",
+            "black>=23.0.0",
+            "ruff>=0.1.0",
+        ],
+        "training": [
+            "wandb>=0.15.0",
+            "tensorboard>=2.14.0",
+        ],
+        "vision": [
+            "torchvision>=0.15.0",
+            "pillow>=9.0.0",
+        ],
+        "audio": [
+            "torchaudio>=2.0.0",
+            "librosa>=0.10.0",
+        ],
+        "all": [
+            "wandb>=0.15.0",
+            "tensorboard>=2.14.0",
+            "torchvision>=0.15.0",
+            "pillow>=9.0.0",
+            "torchaudio>=2.0.0",
+            "librosa>=0.10.0",
+        ],
+    },
+    keywords=[
+        "cognitive-ai",
+        "neural-network",
+        "transformer",
+        "llm",
+        "world-model",
+        "multimodal",
+        "huggingface",
+        "pytorch",
+        "deep-learning",
+        "neurogenesis",
+        "memory-system",
+    ],
+    include_package_data=True,
+    zip_safe=False,
+)