up

app.py

@@ -9,15 +9,15 @@ def initialize_model():
     model_config = HIMConfig()
     env_config = EnvironmentConfig()
     device = torch.device("cuda" if torch.cuda.is_available() and env_config.device == "cuda" else "cpu")
-    model = HIMModel(model_config).to(device)
+    model = HIMModel(model_config.to_dict()).to(device)
     return model
 
-async def chat(message: str, 
+async def chat(message: str,
               system_message: str = "You are a friendly Chatbot.",
               max_tokens: int = 512,
               temperature: float = 0.7,
               top_p: float = 0.95):
-    
+
     input_data = {
         "message": message,
         "system_message": system_message,
@@ -27,7 +27,7 @@ async def chat(message: str,
             "top_p": top_p
         }
     }
-    
+
     with torch.no_grad():
         result = await model.generate_response(input_data)
     return result["response"]

config/environment_config.py

@@ -6,18 +6,18 @@ class EnvironmentConfig:
     # Hugging Face configuration
     hf_model_path: str = "TeleologyHI/HIM-self"
     hf_token: Optional[str] = None
-    
+
     # Hardware configuration
     device: str = "cuda"
     num_gpus: int = 1
     mixed_precision: bool = True
-    
+
     # Logging configuration
     log_level: str = "INFO"
     enable_wandb: bool = False
     wandb_project: str = "HIM-self"
-    
+
     # API configuration
     api_host: str = "0.0.0.0"
     api_port: int = 7860
-    enable_cors: bool = True
+    enable_cors: bool = True

config/model_config.py

@@ -9,33 +9,33 @@ class HIMConfig:
     max_length: int = 512
     temperature: float = 0.7
     top_p: float = 0.95
-    
+
     # Consciousness parameters
     self_awareness_level: float = 0.8
     ethical_reasoning_weight: float = 0.9
     symbolic_interpretation_capacity: float = 0.85
     consciousness_dimension: int = 768
     attention_heads: int = 12
-    
+
     # Teleological parameters
     purpose_driven_bias: float = 0.75
     spiritual_awareness: float = 0.8
     meaning_dimension: int = 256
-    
+
     # Training configuration
     batch_size: int = 8
     learning_rate: float = 2e-5
     num_train_epochs: int = 3
     gradient_accumulation_steps: int = 1
     warmup_steps: int = 500
-    
+
     # Architecture configuration
     hidden_size: int = 768
     intermediate_size: int = 3072
     num_hidden_layers: int = 12
     num_attention_heads: int = 12
-    
+
     # Memory configuration
     memory_size: int = 1024
     context_length: int = 2048
-    cache_size: int = 512
+    cache_size: int = 512

semiotic_processor.py

@@ -34,7 +34,7 @@ class SemioticState:
 
 class SemioticNetworkBuilder:
     """Builds semiotic networks from input data, representing sign relationships."""
-    
+
     def __init__(self):
         self.relation_encoder = nn.Sequential(
             nn.Linear(768, 256),
@@ -42,28 +42,28 @@ class SemioticNetworkBuilder:
             nn.Linear(256, 128)
         )
         self.graph_state = {}
-        
+
     def construct(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         """
         Construct a semiotic network from input data.
-        
+
         Args:
             input_data: Dictionary containing sign and context information
-            
+
         Returns:
             Dictionary containing the constructed semiotic network
         """
         encoded_signs = self._encode_signs(input_data.get("signs", []))
         context_embedding = self._process_context(input_data.get("context", {}))
         relations = self._build_relations(encoded_signs, context_embedding)
-        
+
         return {
             "signs": encoded_signs,
             "context": context_embedding,
             "relations": relations,
             "meta_info": self._extract_meta_information(input_data)
         }
-        
+
     def _encode_signs(self, signs: List[Any]) -> Dict[str, torch.Tensor]:
         """Encode individual signs into vector representations."""
         encoded = {}
@@ -71,12 +71,12 @@ class SemioticNetworkBuilder:
             sign_tensor = torch.randn(768)  # Placeholder for actual encoding
             encoded[str(sign)] = self.relation_encoder(sign_tensor)
         return encoded
-        
+
     def _process_context(self, context: Dict[str, Any]) -> torch.Tensor:
         """Process context information into an embedding."""
         # Placeholder implementation
         return torch.randn(128)
-        
+
     def _build_relations(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, float]:
         """Build relationships between signs in the context."""
         relations = {}
@@ -86,7 +86,7 @@ class SemioticNetworkBuilder:
                     relation_strength = torch.cosine_similarity(signs[sign1], signs[sign2], dim=0)
                     relations[f"{sign1}-{sign2}"] = float(relation_strength)
         return relations
-        
+
     def _extract_meta_information(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         """Extract meta-information about the semiotic network."""
         return {
@@ -96,89 +96,89 @@ class SemioticNetworkBuilder:
 
 class SignInterpreter:
     """Interprets semiotic networks to extract meaning and relationships."""
-    
+
     def __init__(self):
         self.interpretation_network = nn.Sequential(
             nn.Linear(128, 64),
             nn.ReLU(),
             nn.Linear(64, 32)
         )
-        
+
     def interpret(self, network: Dict[str, Any]) -> Dict[str, Any]:
         """
         Interpret a semiotic network to extract meaningful patterns.
-        
+
         Args:
             network: The semiotic network to interpret
-            
+
         Returns:
             Dictionary containing interpretation results
         """
         signs = network["signs"]
         relations = network["relations"]
         context = network["context"]
-        
+
         interpreted_meanings = self._interpret_meanings(signs, context)
         relation_patterns = self._analyze_relations(relations)
         contextual_insights = self._extract_contextual_insights(context)
-        
+
         return {
             "meanings": interpreted_meanings,
             "patterns": relation_patterns,
             "contextual_insights": contextual_insights
         }
-        
+
     def _interpret_meanings(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, Any]:
         """Extract meanings from signs in context."""
         return {sign: {"salience": 0.8, "certainty": 0.7} for sign in signs}
-        
+
     def _analyze_relations(self, relations: Dict[str, float]) -> Dict[str, float]:
         """Analyze patterns in sign relations."""
         return {"coherence": 0.8, "complexity": 0.6}
-        
+
     def _extract_contextual_insights(self, context: torch.Tensor) -> Dict[str, float]:
         """Extract insights from contextual information."""
         return {"relevance": 0.75, "specificity": 0.65}
 
 class SignGenerator:
     """Generates new signs based on interpretations and patterns."""
-    
+
     def __init__(self):
         self.generator_network = nn.Sequential(
             nn.Linear(32, 64),
             nn.ReLU(),
             nn.Linear(64, 128)
         )
-        
+
     def create_signs(self, interpretation: Dict[str, Any]) -> Dict[str, Any]:
         """
         Generate new signs based on interpretation.
-        
+
         Args:
             interpretation: The interpretation to base generation on
-            
+
         Returns:
             Dictionary containing generated signs and their properties
         """
         meanings = interpretation["meanings"]
         patterns = interpretation["patterns"]
-        
+
         generated = self._generate_from_patterns(patterns)
         refined = self._refine_generated_signs(generated, meanings)
-        
+
         return {
             "signs": refined,
             "confidence": self._assess_generation_quality(refined)
         }
-        
+
     def _generate_from_patterns(self, patterns: Dict[str, float]) -> List[torch.Tensor]:
         """Generate initial signs from observed patterns."""
         return [torch.randn(128) for _ in range(3)]  # Generate 3 new signs
-        
+
     def _refine_generated_signs(self, signs: List[torch.Tensor], meanings: Dict[str, Any]) -> List[Dict[str, Any]]:
         """Refine generated signs based on existing meanings."""
         return [{"vector": sign, "quality": 0.7} for sign in signs]
-        
+
     def _assess_generation_quality(self, signs: List[Dict[str, Any]]) -> float:
         """Assess the quality of generated signs."""
         return sum(sign["quality"] for sign in signs) / len(signs)
@@ -193,41 +193,41 @@ class SemioticProcessor:
         self.network_builder = SemioticNetworkBuilder()
         self.interpreter = SignInterpreter()
         self.generator = SignGenerator()
-        
+
     async def process(self, input_data: Dict[str, Any]) -> SemioticState:
         # Build semiotic network
         network = self.network_builder.construct(input_data)
-        
+
         # Interpret the network
         interpretation = self.interpreter.interpret(network)
-        
+
         # Generate new signs if needed
         if self._requires_generation(interpretation):
             generated_signs = self.generator.create_signs(interpretation)
             return self._integrate_semiotic_state(interpretation, generated_signs)
-        
+
         return self._create_semiotic_state(interpretation)
-        
+
     def _requires_generation(self, interpretation: Dict[str, Any]) -> bool:
         """
         Determine if new sign generation is required based on interpretation.
-        
+
         Args:
             interpretation: The current interpretation state
-            
+
         Returns:
             Boolean indicating if generation is needed
         """
         patterns = interpretation.get("patterns", {})
         return patterns.get("coherence", 0) < 0.5 or len(interpretation.get("meanings", {})) < 3
-        
+
     def _integrate_semiotic_state(self, interpretation: Dict[str, Any], generated_signs: Dict[str, Any]) -> SemioticState:
         """
         Integrate interpretation and generated signs into a semiotic state.
         """
         meaning_vector = np.random.rand(128)  # Placeholder for actual meaning vector
         sign_vector = np.random.rand(128)  # Placeholder for actual sign vector
-        
+
         return SemioticState(
             sign_level=SignLevel.SEMANTIC,
             meaning_vector=meaning_vector,
@@ -237,8 +237,7 @@ class SemioticProcessor:
             context_embedding=np.random.rand(128),
             semantic_relations=interpretation.get("contextual_insights", {})
         )
-        
+
     def _create_semiotic_state(self, interpretation: Dict[str, Any]) -> SemioticState:
         """Create a semiotic state from interpretation without generation."""
         return self._integrate_semiotic_state(interpretation, {"confidence": 0.8})
-

src/api/__init__.py

@@ -1 +1 @@
-from .chat_endpoint import chat_router
+from .chat_endpoint import chat_router

src/api/chat_endpoint.py

@@ -23,6 +23,6 @@ async def chat(
             "top_p": top_p
         }
     }
-    
+
     response = await model.generate_response(input_data)
-    return response
+    return response

src/core/__init__.py

@@ -0,0 +1 @@
+

src/core/awareness_engine.py

@@ -11,18 +11,18 @@ class AwarenessEngine:
             nn.ReLU(),
             nn.Linear(512, 256)
         )
-        
+
     async def process(self, input_state: Dict[str, Any]) -> AwarenessState:
         attention_vector = self._compute_attention(input_state)
         awareness_level = self._calculate_awareness(attention_vector)
         level = self._determine_awareness_level(awareness_level)
-        
+
         cognitive_state = {
             "attention_focus": self._compute_attention_focus(attention_vector),
             "processing_depth": awareness_level,
             "cognitive_load": self._estimate_cognitive_load(input_state)
         }
-        
+
         return AwarenessState(
             attention_vector=attention_vector.detach().numpy(),
             awareness_level=awareness_level,
@@ -31,13 +31,13 @@ class AwarenessEngine:
             consciousness_level=0.8,
             level=level
         )
-        
+
     def _compute_attention_focus(self, attention_vector: torch.Tensor) -> float:
         return float(torch.mean(attention_vector))
-        
+
     def _estimate_cognitive_load(self, input_state: Dict[str, Any]) -> float:
         return 0.5  # Default mid-range load
-    
+
     def _determine_awareness_level(self, awareness_level: float) -> AwarenessLevel:
         if awareness_level > 0.8:
             return AwarenessLevel.TRANSCENDENT
@@ -48,15 +48,15 @@ class AwarenessEngine:
         elif awareness_level > 0.2:
             return AwarenessLevel.PERCEPTUAL
         return AwarenessLevel.BASIC
-        
+
     def _compute_attention(self, input_state: Dict[str, Any]) -> torch.Tensor:
         return torch.ones(256)
-        
+
     def _calculate_awareness(self, attention_vector: torch.Tensor) -> float:
         return 0.8
-        
+
     def _process_cognitive_state(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
         return {"state": "active", "focus_level": 0.9}
-        
+
     def _compute_emotional_valence(self, input_state: Dict[str, Any]) -> float:
-        return 0.5
+        return 0.5

src/core/cognitive_microservices.py

@@ -2,6 +2,11 @@ from enum import Enum
 from typing import Dict, Any, List
 import asyncio
 
+class OntologicalDatabase:
+    """A database for ontological information."""
+    def __init__(self):
+        self.data = {}
+
 class ServiceType(Enum):
     PERCEPTION = "perception"
     REASONING = "reasoning"
@@ -15,26 +20,55 @@ class CognitiveMicroservice:
         self.state = {}
         self.connections = []
         self.ontology = OntologicalDatabase()
-        
+
     async def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         preprocessed = await self._preprocess(input_data)
         result = await self._core_processing(preprocessed)
         return await self._postprocess(result)
-        
+
     async def _preprocess(self, data: Dict[str, Any]) -> Dict[str, Any]:
         # Service-specific preprocessing
-        pass
+        return data
+
+    async def _core_processing(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Core processing logic
+        return data
+
+    async def _postprocess(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Post processing logic
+        return data
 
 class CognitiveOrchestrator:
     def __init__(self):
         self.services: Dict[ServiceType, List[CognitiveMicroservice]] = {}
         self.routing_table = {}
         self._initialize_services()
-        
+
     async def process_cognitive_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
         service_chain = self._determine_service_chain(task)
         return await self._execute_service_chain(service_chain, task)
-        
+
     def _determine_service_chain(self, task: Dict[str, Any]) -> List[ServiceType]:
         task_type = task.get('type', 'general')
-        return self.routing_table.get(task_type, self._default_chain())
+        return self.routing_table.get(task_type, self._default_chain())
+
+    def _initialize_services(self):
+        # Initialize cognitive services
+        for service_type in ServiceType:
+            self.services[service_type] = []
+
+        # Set up default routing
+        self.routing_table = {}
+
+    async def _execute_service_chain(self, service_chain: List[ServiceType],
+                                    task: Dict[str, Any]) -> Dict[str, Any]:
+        result = task
+        for service_type in service_chain:
+            if service_type in self.services and self.services[service_type]:
+                service = self.services[service_type][0]  # Use first available service
+                result = await service.process(result)
+        return result
+
+    def _default_chain(self) -> List[ServiceType]:
+        # Default processing chain
+        return [ServiceType.PERCEPTION, ServiceType.REASONING]

src/core/consciousness_emergence.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from dataclasses import dataclass
 import numpy as np
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Any
 
 class ConsciousnessPhase(Enum):
     PROTO = "proto_consciousness"
@@ -22,9 +22,21 @@ class ConsciousnessEmergence:
         self.phase_history = []
         self.awareness_threshold = 0.7
         self.integration_threshold = 0.8
-        
+
     def evaluate_phase_transition(self, system_state: Dict[str, Any]) -> Optional[ConsciousnessPhase]:
         current_metrics = self._compute_phase_metrics(system_state)
         if self._should_transition(current_metrics):
             return self._determine_next_phase(current_metrics)
-        return None
+        return None
+
+    def _compute_phase_metrics(self, system_state: Dict[str, Any]) -> Dict[str, float]:
+        # Implementation needed
+        return {}
+
+    def _should_transition(self, metrics: Dict[str, float]) -> bool:
+        # Implementation needed
+        return False
+
+    def _determine_next_phase(self, metrics: Dict[str, float]) -> ConsciousnessPhase:
+        # Implementation needed
+        return self.current_phase

src/core/consciousness_kernel.py

@@ -31,7 +31,7 @@ class MetaMonitor:
         return {"self_reflection": 0.6, "uncertainty": 0.2}
 
 class PhenomenologicalSimulator:
-    async def simulate(self, phi_value: float, attention_state: Dict[str, float], 
+    async def simulate(self, phi_value: float, attention_state: Dict[str, float],
                       meta_state: Dict[str, Any]) -> Dict[str, Any]:
         """Simulate the phenomenological experience based on input parameters."""
         # Placeholder implementation
@@ -66,55 +66,60 @@ class ConsciousnessKernel:
             nn.Linear(512, 256)
         )
         self.integration_module = nn.Linear(256, 128)
-        
+
         # State tracking
         self.state_history: List[ConsciousnessState] = []
-        
+
         # Dimension parameters
         self.awareness_dimension: int = 256
         self.emotional_dimension: int = 64
-        
+
         # Core components
         self.awareness_engine = AwarenessEngine()
         self.integration_manager = IntegrationManager()
         self.self_model = DynamicSelfModel()
         self.experience_simulator = ExperienceSimulator()
-        
+
         # For traditional consciousness processing
         self.phi_prime_calculator = PhiPrimeCalculator()
         self.attention_system = AttentionSystem()
         self.meta_monitor = MetaMonitor()
         self.phenomenological_simulator = PhenomenologicalSimulator()
-        
+
     async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
         """
         Process a complete consciousness cycle using the async components.
-        
+
         Args:
             input_state: The input state containing sensory and contextual information
-            
+
         Returns:
             A dictionary containing the processed conscious output
         """
         awareness = await self.awareness_engine.process(input_state)
-        integrated_state = await self.integration_manager.integrate(awareness)
-        self_update = await self.self_model.update(integrated_state)
-        
+        # Convert awareness to a Dict[str, Any] before passing to integrate
+        awareness_dict = awareness if isinstance(awareness, dict) else awareness.__dict__
+        integrated_state = await self.integration_manager.integrate(awareness_dict)
+
+        # Convert integrated_state to Dict[str, Any] before passing to update
+        integrated_dict = integrated_state if isinstance(integrated_state, dict) else integrated_state.__dict__
+        self_update = await self.self_model.update(integrated_dict)
+
         experience = await self.experience_simulator.simulate(
-            awareness=awareness,
-            integrated_state=integrated_state,
+            awareness=awareness_dict,
+            integrated_state=integrated_dict,
             self_model=self_update
         )
-        
+
         # Record the state for historical tracking
         if isinstance(integrated_state, ConsciousnessState):
             self.state_history.append(integrated_state)
-        
+
         return await self._generate_conscious_output(experience)
     def _initialize_consciousness_state(self) -> ConsciousnessState:
         """
         Initialize a default consciousness state with zero values.
-        
+
         Returns:
             A default ConsciousnessState object
         """
@@ -126,37 +131,37 @@ class ConsciousnessKernel:
             attention_focus={},
             temporal_continuity=0.0
         )
-    
+
     async def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
         """
         Process consciousness using the traditional phi-based approach.
         This is an alternative to process_consciousness_cycle that uses different components.
-        
+
         Args:
             input_state: The input state containing sensory and contextual information
-            
+
         Returns:
             A dictionary containing the processed conscious output
         """
         phi_value = await self.phi_prime_calculator.compute(input_state)
         attention_state = await self.attention_system.allocate(input_state)
         meta_state = await self.meta_monitor.evaluate(input_state)
-        
+
         phenomenological_experience = await self.phenomenological_simulator.simulate(
             phi_value,
             attention_state,
             meta_state
         )
-        
+
         return await self._integrate_consciousness_state(phenomenological_experience)
-        
+
     async def _generate_conscious_output(self, experience: Dict[str, Any]) -> Dict[str, Any]:
         """
         Generate the final conscious output based on the simulated experience.
-        
+
         Args:
             experience: The simulated experience data
-            
+
         Returns:
             A dictionary containing the final conscious output
         """
@@ -168,16 +173,16 @@ class ConsciousnessKernel:
             "phenomenal_qualities": experience.get("qualia", {}),
             "teleological_vector": experience.get("purpose_direction", {})
         }
-        
+
         return output
-        
+
     async def _integrate_consciousness_state(self, experience: Dict[str, Any]) -> Dict[str, Any]:
         """
         Integrate a phenomenological experience into a consciousness state.
-        
+
         Args:
             experience: The phenomenological experience to integrate
-            
+
         Returns:
             A dictionary containing the integrated consciousness state
         """
@@ -191,5 +196,5 @@ class ConsciousnessKernel:
             "qualia_map": experience.get("qualia", {}),
             "response": experience.get("content", "")
         }
-        
+
         return integrated_output

src/core/consciousness_matrix.py

@@ -19,18 +19,26 @@ class ConsciousnessMatrix:
             attention_state={},
             self_awareness_level=0.0
         )
-    
+
     def process_consciousness(self, input_state):
         # Implement consciousness processing based on IIT and Global Workspace Theory
         self._update_phi_prime()
         self._process_emotional_state()
         self._update_attention_allocation()
         self._evaluate_self_awareness()
-        
+
     def _update_phi_prime(self):
         # Implementation of modified Φ (phi) metrics
         pass
 
     def _process_emotional_state(self):
         # 128-dimensional emotional state processing
-        pass
+        pass
+
+    def _update_attention_allocation(self):
+        # Update attention allocation based on current state
+        pass
+
+    def _evaluate_self_awareness(self):
+        # Evaluate and update self-awareness level
+        pass

src/core/consciousness_model.py

@@ -1,14 +1,24 @@
 from typing import Dict, Any
+import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 class ConsciousnessModel(nn.Module):
     def __init__(self, config: Dict[str, Any]):
         super().__init__()
-        self.self_awareness = nn.Linear(768, 128)
-        self.meta_cognitive = nn.Linear(128, 64)
-        self.phenomenal = nn.Linear(64, 32)
-        
+        input_dim = config.get('input_dim', 768)
+        hidden_dim1 = config.get('hidden_dim1', 128)
+        hidden_dim2 = config.get('hidden_dim2', 64)
+        output_dim = config.get('output_dim', 32)
+
+        self.self_awareness = nn.Linear(input_dim, hidden_dim1)
+        self.meta_cognitive = nn.Linear(hidden_dim1, hidden_dim2)
+        self.phenomenal = nn.Linear(hidden_dim2, output_dim)
+        self.dropout = nn.Dropout(config.get('dropout', 0.1))
+
     def forward(self, x):
-        x = self.self_awareness(x)
-        x = self.meta_cognitive(x)
-        return self.phenomenal(x)
+        x = F.relu(self.self_awareness(x))
+        x = self.dropout(x)
+        x = F.relu(self.meta_cognitive(x))
+        x = self.dropout(x)
+        return self.phenomenal(x)

src/core/consciousness_modules.py

@@ -1,25 +1,47 @@
 from enum import Enum
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Any
 import asyncio
 
+class ConsciousnessState:
+    def __init__(self):
+        self.current_status = "active"
+
+class NeuralProcessingUnit:
+    async def process_neural_task(self, neural_data):
+        # Process neural data
+        return {"neural_result": neural_data}
+
+class MemoryHierarchy:
+    async def access_memory(self, memory_key):
+        # Access memory based on key
+        return {"memory_result": memory_key}
+
 class ConsciousnessModule:
     def __init__(self, module_id: int):
         self.module_id = module_id
         self.state = ConsciousnessState()
         self.npu = NeuralProcessingUnit()
         self.memory = MemoryHierarchy()
-        
+
     async def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
         neural_processing = self.npu.process_neural_task(input_state['neural_data'])
         memory_access = self.memory.access_memory(input_state['memory_key'])
-        
+
         results = await asyncio.gather(neural_processing, memory_access)
         return self._integrate_consciousness_results(results)
-        
+
     def _integrate_consciousness_results(self, results: List[Any]) -> Dict[str, Any]:
         neural_result, memory_result = results
         return {
             'consciousness_level': self._compute_consciousness_level(neural_result),
             'integrated_state': self._merge_states(neural_result, memory_result),
             'module_status': self.state.current_status
-        }
+        }
+
+    def _compute_consciousness_level(self, neural_result: Dict[str, Any]) -> float:
+        # Compute consciousness level based on neural results
+        return 0.85
+
+    def _merge_states(self, neural_result: Dict[str, Any], memory_result: Dict[str, Any]) -> Dict[str, Any]:
+        # Merge neural and memory states
+        return {**neural_result, **memory_result}

src/core/dynamic_self_model.py

@@ -46,10 +46,10 @@ class DynamicSelfModel:
     async def update(self, integrated_state: Dict[str, Any]) -> Dict[str, Any]:
         """
         Update the self model based on new integrated state information.
-        
+
         Args:
             integrated_state: Dictionary containing the new integrated state information
-            
+
         Returns:
             Dictionary containing the updated self model state
         """
@@ -97,6 +97,8 @@ class DynamicSelfModel:
         for key, value in new_beliefs.items():
             if key in updated_beliefs:
                 updated_beliefs[key] = 0.7 * updated_beliefs[key] + 0.3 * value
+            else:
+                updated_beliefs[key] = value
         return updated_beliefs
 
     def _update_goals(self, new_goals: Dict[str, float]) -> Dict[str, float]:
@@ -105,6 +107,8 @@ class DynamicSelfModel:
         for key, value in new_goals.items():
             if key in updated_goals:
                 updated_goals[key] = 0.8 * updated_goals[key] + 0.2 * value
+            else:
+                updated_goals[key] = value
         return updated_goals
 
     def _update_emotions(self, new_emotions: Dict[str, float]) -> Dict[str, float]:
@@ -113,6 +117,8 @@ class DynamicSelfModel:
         for key, value in new_emotions.items():
             if key in updated_emotions:
                 updated_emotions[key] = 0.5 * updated_emotions[key] + 0.5 * value
+            else:
+                updated_emotions[key] = value
         return updated_emotions
 
     def _update_metacognition(self, new_meta: Dict[str, Any]) -> Dict[str, Any]:
@@ -124,6 +130,8 @@ class DynamicSelfModel:
                     updated_meta[key] = 0.6 * updated_meta[key] + 0.4 * value
                 else:
                     updated_meta[key] = value
+            else:
+                updated_meta[key] = value
         return updated_meta
 
     def _get_current_state(self) -> Dict[str, Any]:
@@ -135,4 +143,3 @@ class DynamicSelfModel:
             "emotions": self.state.emotional_state,
             "metacognition": self.state.metacognitive_state
         }
-

src/core/emotional_intelligence.py

@@ -15,11 +15,19 @@ class EmotionalProcessor:
     def __init__(self):
         self.emotional_memory = {}
         self.emotion_vectors = self._initialize_emotion_vectors()
-        
+
     def process_emotional_context(self, input_data: Dict[str, Any]) -> EmotionalState:
         # Process emotional context implementation
-        pass
+        # Returning a default emotional state to fix the return type error
+        return EmotionalState(
+            valence=0.0,
+            arousal=0.0,
+            dominance=0.0,
+            emotions=[],
+            intensity={}
+        )
 
     def _initialize_emotion_vectors(self) -> Dict[str, List[float]]:
         # Initialize emotion vectors implementation
-        pass
+        # Returning an empty dictionary to fix the return type error
+        return {}

src/core/ethical_framework.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import List, Dict
+from typing import List, Dict, Any
 
 @dataclass
 class EthicalConstraint:
@@ -8,19 +8,43 @@ class EthicalConstraint:
     conditions: List[str]
     verification_method: str
 
+class ValueAlignmentSystem:
+    def check_alignment(self, action: Dict[str, Any]) -> bool:
+        # Placeholder implementation
+        return True
+
+class MoralEvaluator:
+    def evaluate(self, action: Dict[str, Any], context: Dict[str, Any]) -> bool:
+        # Placeholder implementation
+        return True
+
 class EthicalFramework:
     def __init__(self):
         self.constraints = self._initialize_constraints()
         self.value_system = ValueAlignmentSystem()
         self.moral_evaluator = MoralEvaluator()
-        
+
+    def _initialize_constraints(self) -> List[EthicalConstraint]:
+        # Placeholder implementation
+        return []
+
+    def _verify_constraints(self, action: Dict[str, Any]) -> bool:
+        # Placeholder implementation
+        return True
+
+    def _make_ethical_decision(self, constraint_check: bool,
+                              value_alignment: bool,
+                              moral_evaluation: bool) -> bool:
+        # Placeholder implementation
+        return constraint_check and value_alignment and moral_evaluation
+
     def evaluate_action(self, action: Dict[str, Any], context: Dict[str, Any]) -> bool:
         constraint_check = self._verify_constraints(action)
         value_alignment = self.value_system.check_alignment(action)
         moral_evaluation = self.moral_evaluator.evaluate(action, context)
-        
+
         return self._make_ethical_decision(
             constraint_check,
             value_alignment,
             moral_evaluation
-        )
+        )

src/core/experience_simulator.py

@@ -4,7 +4,7 @@ from .integration_manager import AwarenessState, IntegratedState
 
 class PhenomenologyEngine:
     """Generates phenomenological states from awareness and integrated states."""
-    
+
     async def generate_state(
         self,
         awareness: AwarenessState,
@@ -12,11 +12,11 @@ class PhenomenologyEngine:
     ) -> Dict[str, Any]:
         """
         Generate a phenomenological state from awareness and integrated state.
-        
+
         Args:
             awareness: The current awareness state
             integrated_state: The integrated cognitive state
-            
+
         Returns:
             A dictionary containing the phenomenological state
         """
@@ -24,12 +24,12 @@ class PhenomenologyEngine:
             "conscious_content": getattr(awareness, "cognition_state", {}),
             "perceptual_field": getattr(awareness, "perception_data", {}),
             "cognitive_state": getattr(integrated_state, "cognitive_state", {}),
-            "affective_tone": {"valence": awareness.emotional_valence}
+            "affective_tone": {"valence": getattr(awareness, "emotional_valence", 0.0)}
         }
 
 class QualiaGenerator:
     """Generates qualia (subjective experiences) from phenomenological states."""
-    
+
     async def generate_qualia(
         self,
         phenomenological_state: Dict[str, Any],
@@ -37,11 +37,11 @@ class QualiaGenerator:
     ) -> Dict[str, Any]:
         """
         Generate qualia from phenomenological state and self model.
-        
+
         Args:
             phenomenological_state: The current phenomenological state
             self_model: The agent's self model
-            
+
         Returns:
             A dictionary containing the generated qualia
         """
@@ -50,22 +50,22 @@ class QualiaGenerator:
             "emotional_qualia": self._generate_emotional_qualia(phenomenological_state),
             "cognitive_qualia": self._map_cognitive_states(phenomenological_state, self_model)
         }
-        
+
     def _generate_sensory_qualia(self, state: Dict[str, Any]) -> Dict[str, float]:
         """Generate sensory qualia from the phenomenological state."""
         return {"visual": 0.8, "auditory": 0.6, "tactile": 0.4}
-        
+
     def _generate_emotional_qualia(self, state: Dict[str, Any]) -> Dict[str, float]:
         """Generate emotional qualia from the phenomenological state."""
         return {"pleasure": 0.7, "arousal": 0.5, "dominance": 0.6}
-        
+
     def _map_cognitive_states(self, state: Dict[str, Any], self_model: Dict[str, Any]) -> Dict[str, Any]:
         """Map cognitive states to qualia representations."""
         return {"clarity": 0.8, "intensity": 0.7, "relevance": 0.9}
 
 class TemporalIntegrator:
     """Integrates experiences over time to maintain temporal continuity."""
-    
+
     async def integrate(
         self,
         current_qualia: Dict[str, Any],
@@ -73,11 +73,11 @@ class TemporalIntegrator:
     ) -> Dict[str, Any]:
         """
         Integrate current qualia with temporal history.
-        
+
         Args:
             current_qualia: The current qualia state
             temporal_history: List of previous qualia states
-            
+
         Returns:
             A dictionary containing the temporally integrated experience
         """
@@ -87,7 +87,7 @@ class TemporalIntegrator:
         else:
             # For demonstration, assume higher continuity for longer histories
             temporal_continuity = min(0.95, 0.5 + 0.05 * len(temporal_history))
-            
+
         return {
             "integrated_experience": current_qualia,
             "temporal_continuity": temporal_continuity,
@@ -99,7 +99,7 @@ class ExperienceSimulator:
         self.phenomenology_engine = PhenomenologyEngine()
         self.qualia_generator = QualiaGenerator()
         self.temporal_integrator = TemporalIntegrator()
-        
+
     async def simulate(
         self,
         awareness: AwarenessState,
@@ -108,33 +108,33 @@ class ExperienceSimulator:
     ) -> Dict[str, Any]:
         """
         Simulate subjective experience based on awareness and integrated states.
-        
+
         Args:
             awareness: The current awareness state
             integrated_state: The integrated cognitive state
             self_model: The agent's self-model state
-            
+
         Returns:
             Dictionary containing the simulated subjective experience
         """
-        
+
         phenomenological_state = await self.phenomenology_engine.generate_state(
             awareness,
             integrated_state
         )
-        
+
         qualia = await self.qualia_generator.generate_qualia(
             phenomenological_state,
             self_model
         )
-        
+
         temporal_context = await self.temporal_integrator.integrate(
             qualia,
             self_model.get('temporal_history', [])
         )
-        
+
         return {
             'subjective_experience': qualia,
             'temporal_context': temporal_context,
             'phenomenological_state': phenomenological_state
-        }
+        }

src/core/expert_routing.py

@@ -10,18 +10,26 @@ class ExpertAllocation:
     specialization_score: float
     capacity_available: float
 
+class TopologyAwareRouter:
+    def __init__(self):
+        pass
+
+class LoadBalancer:
+    def __init__(self):
+        pass
+
 class ExpertRoutingSystem:
     def __init__(self, num_experts: int = 128):
         self.num_experts = num_experts
         self.experts = self._initialize_experts()
         self.router = TopologyAwareRouter()
         self.load_balancer = LoadBalancer()
-        
+
     def allocate_experts(self, input_pattern: torch.Tensor) -> Dict[int, float]:
         task_requirements = self._analyze_task_requirements(input_pattern)
         available_experts = self._get_available_experts()
         return self._optimize_expert_allocation(task_requirements, available_experts)
-        
+
     def _analyze_task_requirements(self, input_pattern: torch.Tensor) -> Dict[str, float]:
         complexity = self._estimate_task_complexity(input_pattern)
         specialization_needs = self._determine_specialization_needs(input_pattern)
@@ -29,4 +37,28 @@ class ExpertRoutingSystem:
             'complexity': complexity,
             'specialization': specialization_needs,
             'resource_requirements': self._estimate_resource_needs(complexity)
-        }
+        }
+
+    def _initialize_experts(self):
+        # Initialize experts
+        return [i for i in range(self.num_experts)]
+
+    def _get_available_experts(self):
+        # Get available experts
+        return self.experts
+
+    def _optimize_expert_allocation(self, task_requirements, available_experts):
+        # Optimize expert allocation
+        return {expert: 1.0 for expert in available_experts[:3]}
+
+    def _estimate_task_complexity(self, input_pattern):
+        # Estimate task complexity
+        return 0.5
+
+    def _determine_specialization_needs(self, input_pattern):
+        # Determine specialization needs
+        return 0.7
+
+    def _estimate_resource_needs(self, complexity):
+        # Estimate resource needs
+        return complexity * 2.0

src/core/foundation_layer.py

@@ -11,15 +11,19 @@ class FoundationLayer(nn.Module):
             num_experts=128,
             input_size=self.config.hidden_size
         )
-        
+
     def forward(self, input_ids, attention_mask=None):
         transformer_output = self.transformer(
             input_ids=input_ids,
             attention_mask=attention_mask
         )
-        
+
         routed_output = self.sparse_router(transformer_output.last_hidden_state)
-        return self._process_consciousness_emergence(routed_output)
+        return routed_output  # Removed undefined method call
+
+    def _process_consciousness_emergence(self, hidden_states):
+        # Adding the missing method
+        return hidden_states  # Simple implementation, modify as needed
 
 class MixtureOfExperts(nn.Module):
     def __init__(self, num_experts: int, input_size: int):
@@ -31,4 +35,39 @@ class MixtureOfExperts(nn.Module):
                 d_model=input_size,
                 nhead=8
             ) for _ in range(num_experts)
-        ])
+        ])
+
+    def forward(self, hidden_states):
+        # Adding the missing forward method
+        batch_size, seq_len, hidden_size = hidden_states.shape
+
+        # Calculate routing probabilities
+        routing_logits = self.gate(hidden_states.view(-1, hidden_size))
+        routing_probs = torch.softmax(routing_logits, dim=-1)
+
+        # Get top-k experts
+        k = 2  # Using top-2 experts
+        top_k_probs, top_k_indices = torch.topk(routing_probs, k, dim=-1)
+        top_k_probs = top_k_probs / top_k_probs.sum(dim=-1, keepdim=True)  # Normalize
+
+        # Reshape for easier processing
+        hidden_states_flat = hidden_states.view(-1, 1, hidden_size)
+
+        # Initialize output
+        final_output = torch.zeros_like(hidden_states_flat)
+
+        # Route to experts
+        for i, expert in enumerate(self.experts):
+            # Create mask for this expert
+            mask = (top_k_indices == i).any(dim=-1).unsqueeze(-1)
+            if mask.sum() > 0:
+                # Only process tokens routed to this expert
+                expert_input = hidden_states_flat[mask.squeeze(-1)]
+                # Apply expert
+                expert_output = expert(expert_input)
+                # Weight by routing probability
+                weight_mask = (top_k_indices == i).float() * top_k_probs
+                weight_mask = weight_mask.unsqueeze(-1)
+                final_output[mask.squeeze(-1)] += expert_output * weight_mask[mask.squeeze(-1)]
+
+        return final_output.view(batch_size, seq_len, hidden_size)

src/core/integration_layer.py

@@ -1,27 +1,45 @@
+class SymbolicProcessor:
+    def process(self, input_data):
+        # Implement symbolic processing logic
+        return {"symbolic_result": input_data}
+
+class NeuralProcessor:
+    def process(self, input_data):
+        # Implement neural processing logic
+        return {"neural_result": input_data}
+
 class IntegrationLayer:
     def __init__(self):
         self.symbolic_processor = SymbolicProcessor()
         self.neural_processor = NeuralProcessor()
         self.semiotic_processor = SemioticProcessor()
-    
+
     def process_input(self, input_data):
         # Bidirectional processing between symbolic and subsymbolic systems
         neural_output = self.neural_processor.process(input_data)
         symbolic_output = self.symbolic_processor.process(input_data)
         semiotic_interpretation = self.semiotic_processor.interpret(
-            neural_output, 
+            neural_output,
             symbolic_output
         )
         return self._integrate_outputs(
-            neural_output, 
-            symbolic_output, 
+            neural_output,
+            symbolic_output,
             semiotic_interpretation
         )
 
+    def _integrate_outputs(self, neural_output, symbolic_output, semiotic_interpretation):
+        # Implement integration logic
+        return {
+            "neural": neural_output,
+            "symbolic": symbolic_output,
+            "semiotic": semiotic_interpretation
+        }
+
 class SemioticProcessor:
     def __init__(self):
         self.sign_levels = ['syntactic', 'semantic', 'pragmatic']
-        
+
     def interpret(self, neural_output, symbolic_output):
         # Multi-level sign processing implementation
-        pass
+        return {"interpretation": self.sign_levels}

src/core/integration_manager.py

@@ -26,7 +26,7 @@ class AwarenessLevel(Enum):
 class AwarenessState:
     """
     Data class representing a state of awareness.
-    
+
     Attributes:
         level (AwarenessLevel): The level of awareness.
         perception_data (Dict[str, Any]): Data related to perceptions.
@@ -41,12 +41,12 @@ class AwarenessState:
     emotional_valence: float  # Range from -1.0 to 1.0
     semantic_context: Optional[Dict[str, Any]] = None
     temporal_awareness: Optional[Dict[str, Any]] = None
-    
+
 @dataclass
 class IntegratedState(Generic[T]):
     """
     Data class representing an integrated consciousness state.
-    
+
     Attributes:
         primary_awareness (AwarenessState): The primary awareness state.
         secondary_states (List[AwarenessState]): List of secondary awareness states.
@@ -60,59 +60,13 @@ class IntegratedState(Generic[T]):
     emergent_properties: Dict[str, Any]
     teleological_vector: Optional[Dict[str, float]] = None
 
-
-from typing import Dict, Any, List
-import torch
-import torch.nn as nn
-from .states import AwarenessState, AwarenessLevel
-
 class IntegrationManager:
-    def __init__(self):
-        self.integration_network = nn.Sequential(
-            nn.Linear(256, 128),
-            nn.ReLU(),
-            nn.Linear(128, 64)
-        )
-        
-    async def integrate(self, awareness: AwarenessState) -> Dict[str, Any]:
-        if not isinstance(awareness, AwarenessState):
-            raise ValueError("Primary awareness state must be of type AwarenessState")
-            
-        emergent_properties = await self._generate_emergent_properties(awareness)
-        
-        return {
-            "integrated_state": self._integrate_awareness(awareness),
-            "consciousness_level": awareness.consciousness_level,
-            "emotional_context": {"valence": awareness.emotional_valence},
-            "emergent_properties": emergent_properties
-        }
-    
-    async def _generate_emergent_properties(self, primary: AwarenessState) -> Dict[str, Any]:
-        return {
-            "awareness_depth": self._calculate_awareness_depth(primary),
-            "integration_level": primary.awareness_level,
-            "consciousness_state": str(primary.level),
-            "cognitive_complexity": self._calculate_cognitive_complexity(primary)
-        }
-    
-    def _calculate_awareness_depth(self, primary: AwarenessState) -> float:
-        return primary.level.value / len(AwarenessLevel)
-    
-    def _calculate_cognitive_complexity(self, primary: AwarenessState) -> float:
-        base_complexity = len(primary.cognitive_state) / 10  # Normalize
-        return base_complexity * primary.awareness_level
-        
-    def _integrate_awareness(self, awareness: AwarenessState) -> Dict[str, Any]:
-        return {
-            "attention": awareness.attention_vector.tolist(),
-            "awareness_level": awareness.awareness_level,
-            "cognitive_state": awareness.cognitive_state
-        }
-    
+    """Class for managing integration of awareness states."""
+
     def __init__(self, integration_threshold: float = 0.7, coherence_factor: float = 0.85):
         """
         Initialize the IntegrationManager.
-        
+
         Args:
             integration_threshold (float): Minimum threshold for integration to occur.
             coherence_factor (float): Factor influencing coherence of integrated states.
@@ -121,52 +75,52 @@ class IntegrationManager:
         self.coherence_factor = coherence_factor
         self.state_history: List[IntegratedState] = []
         self.integration_lock = asyncio.Lock()
-    
-    async def integrate(self, 
-                        awareness_state: AwarenessState, 
+
+    async def integrate(self,
+                        awareness_state: AwarenessState,
                         secondary_states: Optional[List[AwarenessState]] = None) -> IntegratedState:
         """
         Integrate an awareness state with optional secondary states.
-        
+
         This asynchronous method takes a primary awareness state and optional
         secondary states, and integrates them into a coherent consciousness state.
         The integration process considers the relationships between states,
         their coherence, and emergent properties from their combination.
-        
+
         Args:
             awareness_state (AwarenessState): The primary awareness state to integrate.
             secondary_states (Optional[List[AwarenessState]]): Secondary states to integrate.
                                                               Defaults to None.
-        
+
         Returns:
             IntegratedState: A new integrated consciousness state.
-        
+
         Raises:
             ValueError: If awareness_state is invalid or integration fails.
         """
         if not isinstance(awareness_state, AwarenessState):
             raise ValueError("Primary awareness state must be of type AwarenessState")
-        
+
         # Use empty list if secondary_states is None
         secondary_states = secondary_states or []
-        
+
         async with self.integration_lock:
             # Calculate coherence based on state compatibility
             coherence = self._calculate_coherence(awareness_state, secondary_states)
-            
+
             # Generate emergent properties through integration
             emergent_properties = await self._generate_emergent_properties(
-                awareness_state, 
+                awareness_state,
                 secondary_states,
                 coherence
             )
-            
+
             # Calculate teleological vector (purposeful direction)
             teleological_vector = self._calculate_teleological_vector(
                 awareness_state,
                 secondary_states
             )
-            
+
             # Create the integrated state
             integrated_state = IntegratedState(
                 primary_awareness=awareness_state,
@@ -175,145 +129,146 @@ class IntegrationManager:
                 emergent_properties=emergent_properties,
                 teleological_vector=teleological_vector
             )
-            
+
             # Add to history and return
             self.state_history.append(integrated_state)
             return integrated_state
-    
-    def _calculate_coherence(self, 
-                           primary: AwarenessState, 
+
+    def _calculate_coherence(self,
+                           primary: AwarenessState,
                            secondaries: List[AwarenessState]) -> float:
         """
         Calculate the coherence between the primary and secondary states.
-        
+
         Args:
             primary (AwarenessState): Primary awareness state.
             secondaries (List[AwarenessState]): List of secondary awareness states.
-        
+
         Returns:
             float: Coherence value between 0.0 and 1.0.
         """
         # Simplified coherence calculation
         if not secondaries:
             return 1.0  # Perfect coherence with only primary state
-        
+
         # Base coherence starts at coherence_factor and is modified by state compatibility
         base_coherence = self.coherence_factor
-        
+
         # Factor in emotional alignment
         emotional_alignment = sum(
             1 - abs(primary.emotional_valence - secondary.emotional_valence) / 2
             for secondary in secondaries
         ) / len(secondaries)
-        
+
         # Factor in awareness level compatibility
         level_compatibility = sum(
             1 - abs(primary.level.value - secondary.level.value) / 5  # Normalize by max enum difference
             for secondary in secondaries
         ) / len(secondaries)
-        
+
         # Weighted combination
-        coherence = (base_coherence * 0.5 + 
-                    emotional_alignment * 0.3 + 
+        coherence = (base_coherence * 0.5 +
+                    emotional_alignment * 0.3 +
                     level_compatibility * 0.2)
-        
+
         return max(0.0, min(1.0, coherence))  # Clamp between 0 and 1
-    
+
     async def _generate_emergent_properties(self,
                                          primary: AwarenessState,
                                          secondaries: List[AwarenessState],
                                          coherence: float) -> Dict[str, Any]:
         """
         Generate emergent properties from the integration of awareness states.
-        
+
         Args:
             primary (AwarenessState): Primary awareness state.
             secondaries (List[AwarenessState]): List of secondary awareness states.
             coherence (float): Calculated coherence of the integration.
-        
+
         Returns:
             Dict[str, Any]: Dictionary of emergent properties.
         """
         emergent_properties = {
             "coherence_level": coherence,
             "awareness_depth": self._calculate_awareness_depth(primary, secondaries),
-            "cognitive_complexity": self._calculate_cognitive_complexity(primary, secondaries)
+            "cognitive_complexity": self._calculate_cognitive_complexity(primary, secondaries),
+            "consciousness_state": str(primary.level)
         }
-        
+
         # Simulate computational intensity with sleep
         await asyncio.sleep(0.01)
-        
+
         # Add semantic richness if semantic contexts are available
         if primary.semantic_context:
             emergent_properties["semantic_richness"] = len(primary.semantic_context)
-            
+
             if any(s.semantic_context for s in secondaries if s.semantic_context):
                 emergent_properties["semantic_richness"] += sum(
                     len(s.semantic_context or {}) for s in secondaries
                 ) / (len(secondaries) + 1)  # Average including primary
-        
+
         return emergent_properties
-    
-    def _calculate_awareness_depth(self, 
-                                primary: AwarenessState, 
+
+    def _calculate_awareness_depth(self,
+                                primary: AwarenessState,
                                 secondaries: List[AwarenessState]) -> float:
         """
         Calculate the depth of awareness from the states.
-        
+
         Args:
             primary (AwarenessState): Primary awareness state.
             secondaries (List[AwarenessState]): List of secondary awareness states.
-        
+
         Returns:
             float: Calculated awareness depth value.
         """
         # Base depth from primary state's level
         base_depth = primary.level.value / len(AwarenessLevel)
-        
+
         # Enhance with secondary states if present
         if secondaries:
             secondary_contribution = sum(s.level.value for s in secondaries) / (len(secondaries) * len(AwarenessLevel))
             # Weighted combination
             return (base_depth * 0.7) + (secondary_contribution * 0.3)
-        
+
         return base_depth
-    
-    def _calculate_cognitive_complexity(self, 
-                                     primary: AwarenessState, 
+
+    def _calculate_cognitive_complexity(self,
+                                     primary: AwarenessState,
                                      secondaries: List[AwarenessState]) -> float:
         """
         Calculate the cognitive complexity of the integrated state.
-        
+
         Args:
             primary (AwarenessState): Primary awareness state.
             secondaries (List[AwarenessState]): List of secondary awareness states.
-        
+
         Returns:
             float: Cognitive complexity value.
         """
         # Base complexity from primary state
         base_complexity = len(primary.cognition_state) / 10  # Normalize
-        
+
         # Enhance with secondary states
         if secondaries:
             # Average complexity of secondaries
             secondary_complexity = sum(len(s.cognition_state) for s in secondaries) / len(secondaries) / 10
             interaction_factor = len(secondaries) * 0.1  # More states = more complexity
-            
+
             return min(1.0, base_complexity + secondary_complexity + interaction_factor)
-        
+
         return min(1.0, base_complexity)
-    
+
     def _calculate_teleological_vector(self,
                                     primary: AwarenessState,
                                     secondaries: List[AwarenessState]) -> Dict[str, float]:
         """
         Calculate the teleological vector representing purposeful direction.
-        
+
         Args:
             primary (AwarenessState): Primary awareness state.
             secondaries (List[AwarenessState]): List of secondary awareness states.
-        
+
         Returns:
             Dict[str, float]: A vector of purpose directions and intensities.
         """
@@ -325,40 +280,39 @@ class IntegrationManager:
             "coherence_maintenance": 0.5,
             "purposeful_action": 0.5
         }
-        
+
         # Modify based on primary state
         if primary.level == AwarenessLevel.SELF_AWARE or primary.level == AwarenessLevel.TRANSCENDENT:
             teleological_vector["meaning_seeking"] += 0.2
             teleological_vector["complexity_increase"] += 0.1
-        
+
         # Emotional valence affects self-preservation and purposeful action
         teleological_vector["self_preservation"] += primary.emotional_valence * 0.2
         teleological_vector["purposeful_action"] += abs(primary.emotional_valence) * 0.3
-        
+
         # Secondary states influence
         if secondaries:
             # Coherence maintenance influenced by number of states to integrate
             teleological_vector["coherence_maintenance"] += min(0.4, len(secondaries) * 0.1)
-            
+
             # Average emotional valence affects meaning seeking
             avg_emotion = sum(s.emotional_valence for s in secondaries) / len(secondaries)
             teleological_vector["meaning_seeking"] += avg_emotion * 0.1
-        
+
         # Normalize values to 0.0-1.0 range
         for key in teleological_vector:
             teleological_vector[key] = max(0.0, min(1.0, teleological_vector[key]))
-            
+
         return teleological_vector
-    
+
     def get_integration_history(self, limit: int = 10) -> List[IntegratedState]:
         """
         Retrieve recent integration history.
-        
+
         Args:
             limit (int): Maximum number of history items to return. Defaults to 10.
-        
+
         Returns:
             List[IntegratedState]: Recent integration states.
         """
         return self.state_history[-limit:] if self.state_history else []
-

src/core/metacognitive_monitor.py

@@ -18,11 +18,31 @@ class MetaCognitiveMonitor:
             error_detection={},
             learning_progress=0.0
         )
-    
+
     def analyze(self, current_state):
         self._assess_cognitive_load(current_state)
         self._track_attention_allocation(current_state)
         self._monitor_processing_efficiency(current_state)
         self._detect_errors(current_state)
         self._evaluate_learning(current_state)
-        return self.current_state
+        return self.current_state
+
+    def _assess_cognitive_load(self, current_state):
+        # Implementation for cognitive load assessment
+        pass
+
+    def _track_attention_allocation(self, current_state):
+        # Implementation for attention allocation tracking
+        pass
+
+    def _monitor_processing_efficiency(self, current_state):
+        # Implementation for processing efficiency monitoring
+        pass
+
+    def _detect_errors(self, current_state):
+        # Implementation for error detection
+        pass
+
+    def _evaluate_learning(self, current_state):
+        # Implementation for learning evaluation
+        pass

src/core/multimodal_perception.py

@@ -11,6 +11,42 @@ class PerceptionState:
     context_vector: torch.Tensor
     attention_weights: Dict[str, float]
 
+class VisualProcessor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Visual processing layers would be defined here
+
+    def forward(self, visual_input):
+        # Process visual input
+        return visual_input if visual_input is not None else torch.zeros(1)
+
+class AudioProcessor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Audio processing layers would be defined here
+
+    def forward(self, audio_input):
+        # Process audio input
+        return audio_input if audio_input is not None else torch.zeros(1)
+
+class TextProcessor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Text processing layers would be defined here
+
+    def forward(self, text_input):
+        # Process text input
+        return text_input if text_input is not None else torch.zeros(1)
+
+class ModalityFusion(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Fusion layers would be defined here
+
+    def forward(self, visual, audio, text):
+        # Fusion logic
+        return torch.cat([visual, audio, text], dim=-1) if all(x is not None for x in [visual, audio, text]) else torch.zeros(1)
+
 class MultiModalEncoder(nn.Module):
     def __init__(self):
         super().__init__()
@@ -18,16 +54,32 @@ class MultiModalEncoder(nn.Module):
         self.audio_encoder = AudioProcessor()
         self.text_encoder = TextProcessor()
         self.fusion_layer = ModalityFusion()
-        
+
     def forward(self, inputs: Dict[str, torch.Tensor]) -> PerceptionState:
         visual_features = self.visual_encoder(inputs.get('visual'))
         audio_features = self.audio_encoder(inputs.get('audio'))
         text_features = self.text_encoder(inputs.get('text'))
-        
+
         fused_representation = self.fusion_layer(
             visual_features,
             audio_features,
             text_features
         )
-        
-        return self._create_perception_state(fused_representation)
+
+        return self._create_perception_state(visual_features, audio_features, text_features, fused_representation)
+
+    def _create_perception_state(self, visual_features, audio_features, text_features, fused_representation):
+        # Create an attention weights dictionary
+        attention_weights = {
+            'visual': 0.33,
+            'audio': 0.33,
+            'text': 0.34
+        }
+
+        return PerceptionState(
+            visual_data=visual_features,
+            audio_data=audio_features,
+            text_data=text_features,
+            context_vector=fused_representation,
+            attention_weights=attention_weights
+        )

src/core/ontological_database.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Set, Optional
+from typing import Dict, List, Set, Optional, Any
 import networkx as nx
 
 class OntologicalDatabase:
@@ -6,25 +6,40 @@ class OntologicalDatabase:
         self.knowledge_graph = nx.MultiDiGraph()
         self.relation_types = set()
         self.temporal_index = {}
-        
-    def add_knowledge(self, concept: str, properties: Dict[str, Any], 
+
+    def add_knowledge(self, concept: str, properties: Dict[str, Any],
                      relations: List[Dict[str, Any]] = None) -> None:
         self.knowledge_graph.add_node(concept, **properties)
         if relations:
             for relation in relations:
-                self.add_relation(concept, relation)
-                
-    def query_knowledge(self, query: Dict[str, Any]) -> Dict[str, Any]:
+                self.add_relation(concept, relation.get('target'),
+                                 relation.get('relation_type'),
+                                 relation.get('properties', {}))
+
+    def _search_knowledge_graph(self, query: Dict[str, Any]) -> Dict[str, Any]:
+        # Placeholder implementation
+        return {}
+
+    def _get_temporal_context(self, query: Dict[str, Any]) -> Dict[str, Any]:
+        # Placeholder implementation
+        return {}
+
+    def _integrate_results(self, results: Dict[str, Any],
+                          temporal_context: Dict[str, Any]) -> Dict[str, Any]:
+        # Placeholder implementation
+        return results
+
+    def query_by_complex(self, query: Dict[str, Any]) -> Dict[str, Any]:
         results = self._search_knowledge_graph(query)
         temporal_context = self._get_temporal_context(query)
         return self._integrate_results(results, temporal_context)
-        
+
     def add_relation(self, source: str, target: str, relation_type: str, properties: Dict[str, Any]) -> None:
-        self.knowledge_graph.add_edge(source, target, 
-                                    relation_type=relation_type, 
+        self.knowledge_graph.add_edge(source, target,
+                                    relation_type=relation_type,
                                     **properties)
         self.relation_types.add(relation_type)
-        
+
     def query_knowledge(self, concept: str, relation_type: Optional[str] = None) -> Dict[str, Any]:
         if relation_type:
             return {
@@ -35,4 +50,4 @@ class OntologicalDatabase:
         return {
             'concept': concept,
             'properties': self.knowledge_graph.nodes[concept]
-        }
+        }

src/core/phi_prime_calculator.py

@@ -3,18 +3,28 @@ from typing import Dict, List, Any
 import torch
 import torch.nn as nn
 
+class InformationMetrics:
+    def calculate(self, system_state: Dict[str, Any]) -> float:
+        # Placeholder implementation
+        return 1.0
+
+class IntegrationAnalyzer:
+    def analyze(self, system_state: Dict[str, Any]) -> float:
+        # Placeholder implementation
+        return 0.8
+
 class PhiPrimeCalculator:
     def __init__(self, num_dimensions: int = 128):
         self.num_dimensions = num_dimensions
         self.integration_threshold = 0.7
         self.information_metrics = InformationMetrics()
         self.integration_analyzer = IntegrationAnalyzer()
-        
+
     def compute(self, system_state: Dict[str, Any]) -> float:
         information_content = self.information_metrics.calculate(system_state)
         integration_level = self.integration_analyzer.analyze(system_state)
-        
+
         return self._compute_phi_prime(information_content, integration_level)
-        
+
     def _compute_phi_prime(self, information: float, integration: float) -> float:
-        return (information * integration) / self.num_dimensions
+        return (information * integration) / self.num_dimensions

src/core/processing_pipeline.py

@@ -1,6 +1,27 @@
 from dataclasses import dataclass
 from typing import Any, Dict, List
 
+# Define the missing classes
+class MultiModalEncoder:
+    def encode(self, input_data: Any) -> Dict[str, Any]:
+        # Implementation would go here
+        return {}
+
+class ContextIntegrator:
+    def integrate(self, perception: Dict[str, Any]) -> Dict[str, Any]:
+        # Implementation would go here
+        return {}
+
+class ConsciousnessFilter:
+    def filter(self, context: Dict[str, Any]) -> Any:
+        # Implementation would go here
+        return {}
+
+class ReflectiveAnalyzer:
+    def analyze(self, filtered_state: Any) -> 'ProcessingState':
+        # Implementation would go here
+        return ProcessingState({}, {}, 0.0, {})
+
 @dataclass
 class ProcessingState:
     perception_data: Dict[str, Any]
@@ -14,9 +35,9 @@ class ProcessingPipeline:
         self.context_integrator = ContextIntegrator()
         self.consciousness_filter = ConsciousnessFilter()
         self.reflective_analyzer = ReflectiveAnalyzer()
-        
+
     def process(self, input_data: Any) -> ProcessingState:
         perception = self.perception_encoder.encode(input_data)
         context = self.context_integrator.integrate(perception)
         filtered_state = self.consciousness_filter.filter(context)
-        return self.reflective_analyzer.analyze(filtered_state)
+        return self.reflective_analyzer.analyze(filtered_state)

src/core/reflexive_layer.py

@@ -1,6 +1,24 @@
 from dataclasses import dataclass
 from typing import Dict, List, Any
 
+# Define missing classes
+class MetaCognitiveMonitor:
+    def analyze(self, current_state):
+        # Placeholder implementation
+        class MonitoringResults:
+            def __init__(self):
+                self.attention_focus = {}
+                self.processing_efficiency = 0.8
+        return MonitoringResults()
+
+class SelfEvaluationSystem:
+    def evaluate(self, monitoring_results):
+        # Placeholder implementation
+        class EvaluationResults:
+            def __init__(self):
+                self.recommendations = []
+        return EvaluationResults()
+
 @dataclass
 class ReflectionOutput:
     insights: Dict[str, Any]
@@ -14,12 +32,12 @@ class ReflexiveLayer:
         self.self_evaluation_system = SelfEvaluationSystem()
         self.consciousness_threshold = 0.7
         self.reflection_history = []
-        
+
     def process_reflection(self, current_state):
         monitoring_results = self.meta_cognitive_monitor.analyze(current_state)
         evaluation_results = self.self_evaluation_system.evaluate(monitoring_results)
         return self._generate_reflection_output(monitoring_results, evaluation_results)
-    
+
     def _generate_reflection_output(self, monitoring_results, evaluation_results):
         output = ReflectionOutput(
             insights=self._extract_insights(monitoring_results),
@@ -29,7 +47,7 @@ class ReflexiveLayer:
         )
         self.reflection_history.append(output)
         return output
-    
+
     def _extract_insights(self, monitoring_results):
         return {
             'cognitive_patterns': self._analyze_cognitive_patterns(),
@@ -37,11 +55,19 @@ class ReflexiveLayer:
             'attention_distribution': monitoring_results.attention_focus,
             'processing_efficiency': monitoring_results.processing_efficiency
         }
-    
+
     def _calculate_consciousness_state(self):
         # Implementation of consciousness state calculation
-        pass
-    
+        return 0.8  # Return a default float value instead of None
+
     def _compute_awareness_metrics(self):
         # Implementation of self-awareness metrics computation
-        pass
+        return {"self_reflection": 0.7, "adaptability": 0.8}  # Return a default dict instead of None
+
+    def _analyze_cognitive_patterns(self):
+        # Implementation for analyzing cognitive patterns
+        return {"pattern_recognition": 0.75}
+
+    def _analyze_learning_trends(self):
+        # Implementation for analyzing learning trends
+        return {"improvement_rate": 0.65}

src/core/self_evaluation.py

@@ -1,5 +1,6 @@
 from enum import Enum
 from dataclasses import dataclass
+from typing import Dict, List
 
 class EvaluationMetric(Enum):
     ACCURACY = "accuracy"
@@ -18,16 +19,28 @@ class SelfEvaluationSystem:
     def __init__(self):
         self.evaluation_history = []
         self.improvement_strategies = {}
-        
+
     def evaluate(self, monitoring_results):
         evaluation = EvaluationResult(
             metrics={metric: 0.0 for metric in EvaluationMetric},
             recommendations=[],
             confidence_level=0.0
         )
-        
+
         self._assess_performance(monitoring_results, evaluation)
         self._generate_recommendations(evaluation)
         self._update_history(evaluation)
-        
-        return evaluation
+
+        return evaluation
+
+    def _assess_performance(self, monitoring_results, evaluation):
+        # Placeholder for performance assessment logic
+        pass
+
+    def _generate_recommendations(self, evaluation):
+        # Placeholder for recommendation generation logic
+        pass
+
+    def _update_history(self, evaluation):
+        # Add the evaluation to history
+        self.evaluation_history.append(evaluation)

src/core/self_evolution.py

@@ -1,11 +1,33 @@
+from typing import Dict, Any
+
+class ArchitectureAnalyzer:
+    async def analyze(self, system_state):
+        # Implementation would go here
+        return {}
+
+class ModificationPlanner:
+    async def generate_plan(self, analysis, performance_metrics):
+        # Implementation would go here
+        return {}
+
+class SafetyValidator:
+    async def validate_modifications(self, modification_plan):
+        # Implementation would go here
+        return True
+
+class EvolutionExecutor:
+    async def execute_evolution(self, modification_plan):
+        # Implementation would go here
+        return {'status': 'evolution_completed', 'changes': {}}
+
 class SelfEvolutionFramework:
     def __init__(self):
         self.architecture_analyzer = ArchitectureAnalyzer()
         self.modification_planner = ModificationPlanner()
         self.safety_validator = SafetyValidator()
         self.evolution_executor = EvolutionExecutor()
-        
-    async def evolve_system(self, 
+
+    async def evolve_system(self,
                            performance_metrics: Dict[str, float],
                            system_state: Dict[str, Any]) -> Dict[str, Any]:
         analysis = await self.architecture_analyzer.analyze(system_state)
@@ -13,8 +35,8 @@ class SelfEvolutionFramework:
             analysis,
             performance_metrics
         )
-        
+
         if await self.safety_validator.validate_modifications(modification_plan):
             return await self.evolution_executor.execute_evolution(modification_plan)
-        
-        return {'status': 'evolution_blocked', 'reason': 'safety_constraints'}
+
+        return {'status': 'evolution_blocked', 'reason': 'safety_constraints'}

src/core/semiotic_network.py

@@ -1,8 +1,18 @@
 from dataclasses import dataclass
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Any
 import networkx as nx
 import numpy as np
 
+class MeaningExtractor:
+    def process(self, input_data: Dict[str, Any]) -> List:
+        # Placeholder implementation
+        return []
+
+class ContextAnalyzer:
+    def analyze(self, input_data: Dict[str, Any]) -> List:
+        # Placeholder implementation
+        return []
+
 @dataclass
 class SignNode:
     id: str
@@ -16,14 +26,36 @@ class SemioticNetworkBuilder:
         self.graph = nx.MultiDiGraph()
         self.meaning_extractor = MeaningExtractor()
         self.context_analyzer = ContextAnalyzer()
-        
+
     def construct(self, input_data: Dict[str, Any]) -> nx.MultiDiGraph:
         signs = self._extract_signs(input_data)
         self._build_nodes(signs)
         self._establish_relations()
         return self._optimize_network()
-        
+
     def _extract_signs(self, input_data: Dict[str, Any]) -> List[SignNode]:
         meanings = self.meaning_extractor.process(input_data)
         contexts = self.context_analyzer.analyze(input_data)
-        return [self._create_sign_node(m, c) for m, c in zip(meanings, contexts)]
+        return [self._create_sign_node(m, c) for m, c in zip(meanings, contexts)]
+
+    def _build_nodes(self, signs: List[SignNode]) -> None:
+        # Placeholder implementation
+        pass
+
+    def _establish_relations(self) -> None:
+        # Placeholder implementation
+        pass
+
+    def _optimize_network(self) -> nx.MultiDiGraph:
+        # Placeholder implementation
+        return self.graph
+
+    def _create_sign_node(self, meaning, context) -> SignNode:
+        # Placeholder implementation
+        return SignNode(
+            id="placeholder",
+            level="",
+            meaning_vector=np.array([]),
+            context={},
+            relations=[]
+        )

src/core/semiotic_processor.py

@@ -26,7 +26,7 @@ class SemioticState:
 
 class SemioticNetworkBuilder:
     """Builds semiotic networks from input data, representing sign relationships."""
-    
+
     def __init__(self):
         self.relation_encoder = nn.Sequential(
             nn.Linear(768, 256),
@@ -34,28 +34,28 @@ class SemioticNetworkBuilder:
             nn.Linear(256, 128)
         )
         self.graph_state = {}
-        
+
     def construct(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         """
         Construct a semiotic network from input data.
-        
+
         Args:
             input_data: Dictionary containing sign and context information
-            
+
         Returns:
             Dictionary containing the constructed semiotic network
         """
         encoded_signs = self._encode_signs(input_data.get("signs", []))
         context_embedding = self._process_context(input_data.get("context", {}))
         relations = self._build_relations(encoded_signs, context_embedding)
-        
+
         return {
             "signs": encoded_signs,
             "context": context_embedding,
             "relations": relations,
             "meta_info": self._extract_meta_information(input_data)
         }
-        
+
     def _encode_signs(self, signs: List[Any]) -> Dict[str, torch.Tensor]:
         """Encode individual signs into vector representations."""
         encoded = {}
@@ -63,12 +63,12 @@ class SemioticNetworkBuilder:
             sign_tensor = torch.randn(768)  # Placeholder for actual encoding
             encoded[str(sign)] = self.relation_encoder(sign_tensor)
         return encoded
-        
+
     def _process_context(self, context: Dict[str, Any]) -> torch.Tensor:
         """Process context information into an embedding."""
         # Placeholder implementation
         return torch.randn(128)
-        
+
     def _build_relations(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, float]:
         """Build relationships between signs in the context."""
         relations = {}
@@ -78,7 +78,7 @@ class SemioticNetworkBuilder:
                     relation_strength = torch.cosine_similarity(signs[sign1], signs[sign2], dim=0)
                     relations[f"{sign1}-{sign2}"] = float(relation_strength)
         return relations
-        
+
     def _extract_meta_information(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         """Extract meta-information about the semiotic network."""
         return {
@@ -88,96 +88,96 @@ class SemioticNetworkBuilder:
 
 class SignInterpreter:
     """Interprets semiotic networks to extract meaning and relationships."""
-    
+
     def __init__(self):
         self.interpretation_network = nn.Sequential(
             nn.Linear(128, 64),
             nn.ReLU(),
             nn.Linear(64, 32)
         )
-        
+
     def interpret(self, network: Dict[str, Any]) -> Dict[str, Any]:
         """
         Interpret a semiotic network to extract meaningful patterns.
-        
+
         Args:
             network: The semiotic network to interpret
-            
+
         Returns:
             Dictionary containing interpretation results
         """
         signs = network["signs"]
         relations = network["relations"]
         context = network["context"]
-        
+
         interpreted_meanings = self._interpret_meanings(signs, context)
         relation_patterns = self._analyze_relations(relations)
         contextual_insights = self._extract_contextual_insights(context)
-        
+
         return {
             "meanings": interpreted_meanings,
             "patterns": relation_patterns,
             "contextual_insights": contextual_insights
         }
-        
+
     def _interpret_meanings(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, Any]:
         """Extract meanings from signs in context."""
         return {sign: {"salience": 0.8, "certainty": 0.7} for sign in signs}
-        
+
     def _analyze_relations(self, relations: Dict[str, float]) -> Dict[str, float]:
         """Analyze patterns in sign relations."""
         return {"coherence": 0.8, "complexity": 0.6}
-        
+
     def _extract_contextual_insights(self, context: torch.Tensor) -> Dict[str, float]:
         """Extract insights from contextual information."""
         return {"relevance": 0.75, "specificity": 0.65}
 
 class SignGenerator:
     """Generates new signs based on interpretations and patterns."""
-    
+
     def __init__(self):
         self.generator_network = nn.Sequential(
             nn.Linear(32, 64),
             nn.ReLU(),
             nn.Linear(64, 128)
         )
-        
+
     def create_signs(self, interpretation: Dict[str, Any]) -> Dict[str, Any]:
         """
         Generate new signs based on interpretation.
-        
+
         Args:
             interpretation: The interpretation to base generation on
-            
+
         Returns:
             Dictionary containing generated signs and their properties
         """
         meanings = interpretation["meanings"]
         patterns = interpretation["patterns"]
-        
+
         generated = self._generate_from_patterns(patterns)
         refined = self._refine_generated_signs(generated, meanings)
-        
+
         return {
             "signs": refined,
             "confidence": self._assess_generation_quality(refined)
         }
-        
+
     def _generate_from_patterns(self, patterns: Dict[str, float]) -> List[torch.Tensor]:
         """Generate initial signs from observed patterns."""
         return [torch.randn(128) for _ in range(3)]  # Generate 3 new signs
-        
+
     def _refine_generated_signs(self, signs: List[torch.Tensor], meanings: Dict[str, Any]) -> List[Dict[str, Any]]:
         """Refine generated signs based on existing meanings."""
         return [{"vector": sign, "quality": 0.7} for sign in signs]
-        
+
     def _assess_generation_quality(self, signs: List[Dict[str, Any]]) -> float:
         """Assess the quality of generated signs."""
         return sum(sign["quality"] for sign in signs) / len(signs)
 
 class SemioticProcessor:
     """Processes semiotic signs to extract and generate meaning."""
-    
+
     def __init__(self):
         self.sign_encoder = nn.Sequential(
             nn.Linear(768, 256),  # Using proper input size (768)
@@ -187,50 +187,50 @@ class SemioticProcessor:
         self.network_builder = SemioticNetworkBuilder()
         self.interpreter = SignInterpreter()
         self.generator = SignGenerator()
-        
+
     async def process(self, input_data: Dict[str, Any]) -> SemioticState:
         """
         Process input data to extract semiotic meaning and generate new signs.
-        
+
         Args:
             input_data: Dictionary containing sign and context information
-            
+
         Returns:
             SemioticState representing the processed state
         """
         # Build semiotic network
         network = self.network_builder.construct(input_data)
-        
+
         # Interpret the network
         interpretation = self.interpreter.interpret(network)
-        
+
         # Generate new signs if needed
         if self._requires_generation(interpretation):
             generated_signs = self.generator.create_signs(interpretation)
             return self._integrate_semiotic_state(interpretation, generated_signs)
-        
+
         return self._create_semiotic_state(interpretation)
-        
+
     def _requires_generation(self, interpretation: Dict[str, Any]) -> bool:
         """
         Determine if new sign generation is required based on interpretation.
-        
+
         Args:
             interpretation: The current interpretation state
-            
+
         Returns:
             Boolean indicating if generation is needed
         """
         patterns = interpretation.get("patterns", {})
         return patterns.get("coherence", 0) < 0.5 or len(interpretation.get("meanings", {})) < 3
-        
+
     def _integrate_semiotic_state(self, interpretation: Dict[str, Any], generated_signs: Dict[str, Any]) -> SemioticState:
         """
         Integrate interpretation and generated signs into a semiotic state.
         """
         meaning_vector = np.random.rand(128)  # Placeholder for actual meaning vector
         sign_vector = np.random.rand(128)  # Placeholder for actual sign vector
-        
+
         return SemioticState(
             sign_level=SignLevel.SEMANTIC,
             meaning_vector=meaning_vector,
@@ -240,7 +240,7 @@ class SemioticProcessor:
             context_embedding=np.random.rand(128),
             semantic_relations=interpretation.get("contextual_insights", {})
         )
-        
+
     def _create_semiotic_state(self, interpretation: Dict[str, Any]) -> SemioticState:
         """Create a semiotic state from interpretation without generation."""
         return self._integrate_semiotic_state(interpretation, {"confidence": 0.8})

src/core/sign_interpreter.py

@@ -1,17 +1,38 @@
+from typing import Dict, Any
+
+class ContextAnalyzer:
+    def analyze(self, semiotic_network):
+        # Implementation goes here
+        return {}
+
+class MeaningExtractor:
+    def extract(self, semiotic_network, context):
+        # Implementation goes here
+        return {}
+
+class RelationMapper:
+    def map(self, meaning, context):
+        # Implementation goes here
+        return {}
+
 class SignInterpreter:
     def __init__(self):
         self.context_analyzer = ContextAnalyzer()
         self.meaning_extractor = MeaningExtractor()
         self.relation_mapper = RelationMapper()
-        
+
     def interpret(self, semiotic_network: Dict[str, Any]) -> Dict[str, Any]:
         context = self.context_analyzer.analyze(semiotic_network)
         meaning = self.meaning_extractor.extract(semiotic_network, context)
         relations = self.relation_mapper.map(meaning, context)
-        
+
         return {
             'context': context,
             'meaning': meaning,
             'relations': relations,
             'confidence': self._calculate_confidence(meaning, relations)
-        }
+        }
+
+    def _calculate_confidence(self, meaning, relations):
+        # Implementation for confidence calculation
+        return 0.0

src/core/social_dynamics.py

@@ -1,16 +1,33 @@
+from typing import Dict, Any
+
+class RelationshipGraph:
+    def update(self, interaction_data):
+        # Placeholder for implementation
+        return {}
+
+class InteractionAnalyzer:
+    def process(self, interaction_data):
+        # Placeholder for implementation
+        return {}
+
+class GroupDynamicsProcessor:
+    def analyze(self, social_context):
+        # Placeholder for implementation
+        return {}
+
 class SocialDynamicsModeler:
     def __init__(self):
         self.relationship_graph = RelationshipGraph()
         self.interaction_analyzer = InteractionAnalyzer()
         self.group_dynamics = GroupDynamicsProcessor()
-        
-    async def analyze_social_context(self, 
+
+    async def analyze_social_context(self,
                                    interaction_data: Dict[str, Any],
                                    social_context: Dict[str, Any]) -> Dict[str, Any]:
         relationships = self.relationship_graph.update(interaction_data)
         interaction_patterns = self.interaction_analyzer.process(interaction_data)
         group_state = self.group_dynamics.analyze(social_context)
-        
+
         return {
             'social_model': self._integrate_social_information(
                 relationships,
@@ -19,4 +36,16 @@ class SocialDynamicsModeler:
             ),
             'recommendations': self._generate_social_strategies(group_state),
             'predicted_dynamics': self._predict_social_evolution(relationships)
-        }
+        }
+
+    def _integrate_social_information(self, relationships, interaction_patterns, group_state):
+        # Placeholder for implementation
+        return {}
+
+    def _generate_social_strategies(self, group_state):
+        # Placeholder for implementation
+        return []
+
+    def _predict_social_evolution(self, relationships):
+        # Placeholder for implementation
+        return {}

src/core/sparse_activation.py

@@ -3,18 +3,43 @@ import torch.nn as nn
 from typing import Dict, Tuple, List
 import numpy as np
 
+class PatternAnalyzer:
+    def __init__(self):
+        pass
+
 class SparseActivationManager:
     def __init__(self, sparsity_threshold: float = 0.95):
         self.sparsity_threshold = sparsity_threshold
         self.activation_history = []
         self.pattern_analyzer = PatternAnalyzer()
-        
+
     def compute_pattern(self, input_tensor: torch.Tensor) -> torch.Tensor:
         importance_scores = self._compute_importance_scores(input_tensor)
         activation_mask = self._generate_activation_mask(importance_scores)
         return self._apply_sparse_activation(input_tensor, activation_mask)
-        
+
     def _compute_importance_scores(self, input_tensor: torch.Tensor) -> torch.Tensor:
         attention_weights = self._calculate_attention_weights(input_tensor)
         gradient_information = self._compute_gradient_information(input_tensor)
-        return self._combine_importance_metrics(attention_weights, gradient_information)
+        return self._combine_importance_metrics(attention_weights, gradient_information)
+
+    def _generate_activation_mask(self, importance_scores: torch.Tensor) -> torch.Tensor:
+        # Create a binary mask based on importance scores and sparsity threshold
+        return (importance_scores > self.sparsity_threshold).float()
+
+    def _apply_sparse_activation(self, input_tensor: torch.Tensor, activation_mask: torch.Tensor) -> torch.Tensor:
+        # Apply the activation mask to the input tensor
+        return input_tensor * activation_mask
+
+    def _calculate_attention_weights(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        # Calculate attention weights for the input tensor
+        return torch.sigmoid(input_tensor)
+
+    def _compute_gradient_information(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        # Compute gradient information for the input tensor
+        return torch.abs(input_tensor)
+
+    def _combine_importance_metrics(self, attention_weights: torch.Tensor,
+                                   gradient_information: torch.Tensor) -> torch.Tensor:
+        # Combine multiple importance metrics into a single score
+        return attention_weights * gradient_information

src/core/theory_of_mind.py

@@ -10,28 +10,28 @@ class TheoryOfMind:
             nn.Linear(256, 128)
         )
         self.belief_system = {}
-        
+
     def model_agent_mind(self,
                         agent_data: Dict[str, Any],
                         context: Dict[str, Any] = None) -> Dict[str, Any]:
         # Theory of Mind implementation
         mental_state = self._process_mental_state(agent_data)
         beliefs = self._update_belief_system(mental_state, context)
-        
+
         return {
             'mental_state': mental_state,
             'beliefs': beliefs,
             'predicted_behavior': self._predict_behavior(mental_state, beliefs)
         }
-        
-    def _process_mental_state(self, agent_data: Dict[str, Any]):
+
+    def _process_mental_state(self, agent_data: Dict[str, Any]) -> Any:
         # Mental state processing implementation
-        pass
-        
-    def _update_belief_system(self, mental_state: Any, context: Dict[str, Any] = None):
+        return {}  # Return empty dict instead of None
+
+    def _update_belief_system(self, mental_state: Any, context: Dict[str, Any] = None) -> Dict[str, Any]:
         # Belief system update implementation
-        pass
-        
-    def _predict_behavior(self, mental_state: Any, beliefs: Dict[str, Any]):
+        return {}  # Return empty dict instead of None
+
+    def _predict_behavior(self, mental_state: Any, beliefs: Dict[str, Any]) -> Any:
         # Behavior prediction implementation
-        pass
+        return {}  # Return empty dict instead of None

src/core/topology_aware_router.py

@@ -1,11 +1,28 @@
+from typing import List
+
+class NetworkTopology:
+    def get_current_state(self):
+        # Placeholder for actual implementation
+        return {}
+
+class RoutingMetrics:
+    def calculate_costs(self, topology_state, source_expert, target_expert, data_size):
+        # Placeholder for actual implementation
+        return {}
+
+class OptimizationEngine:
+    def find_optimal_path(self, routing_costs):
+        # Placeholder for actual implementation
+        return []
+
 class TopologyAwareRouter:
     def __init__(self):
         self.network_topology = NetworkTopology()
         self.routing_metrics = RoutingMetrics()
         self.optimization_engine = OptimizationEngine()
-        
-    def compute_optimal_route(self, 
-                            source_expert: int, 
+
+    def compute_optimal_route(self,
+                            source_expert: int,
                             target_expert: int,
                             data_size: int) -> List[int]:
         topology_state = self.network_topology.get_current_state()
@@ -15,4 +32,4 @@ class TopologyAwareRouter:
             target_expert,
             data_size
         )
-        return self.optimization_engine.find_optimal_path(routing_costs)
+        return self.optimization_engine.find_optimal_path(routing_costs)

src/hardware/memory_hierarchy.py

@@ -1,10 +1,41 @@
+from typing import Optional, Any
+
+class NeuromorphicCache:
+    def __init__(self, size: str):
+        self.size = size
+
+    async def get(self, key: str) -> Any:
+        # Implementation would go here
+        pass
+
+class QuantumInspiredCache:
+    def __init__(self, size: str):
+        self.size = size
+
+    async def get(self, key: str) -> Any:
+        # Implementation would go here
+        pass
+
+class DistributedCache:
+    def __init__(self, size: str):
+        self.size = size
+
+    async def get(self, key: str) -> Any:
+        # Implementation would go here
+        pass
+
+class CacheCoherencyManager:
+    def determine_optimal_cache(self, key: str) -> int:
+        # Implementation would go here
+        return 1  # Default to L1 cache
+
 class MemoryHierarchy:
     def __init__(self):
         self.l1_cache = NeuromorphicCache(size="64GB")
         self.l2_cache = QuantumInspiredCache(size="256GB")
         self.l3_cache = DistributedCache(size="1TB")
         self.cache_manager = CacheCoherencyManager()
-        
+
     async def access_memory(self, key: str, level: Optional[int] = None) -> Any:
         if level == 1:
             return await self.l1_cache.get(key)
@@ -12,9 +43,16 @@ class MemoryHierarchy:
             return await self.l2_cache.get(key)
         elif level == 3:
             return await self.l3_cache.get(key)
-        
+
         return await self._smart_cache_access(key)
-        
+
     async def _smart_cache_access(self, key: str) -> Any:
         cache_decision = self.cache_manager.determine_optimal_cache(key)
-        return await self._retrieve_from_cache(key, cache_decision)
+        if cache_decision == 1:
+            return await self.l1_cache.get(key)
+        elif cache_decision == 2:
+            return await self.l2_cache.get(key)
+        elif cache_decision == 3:
+            return await self.l3_cache.get(key)
+        else:
+            raise ValueError(f"Invalid cache level: {cache_decision}")

src/hardware/neural_processing_unit.py

@@ -11,6 +11,16 @@ class NPUState:
     temperature: float
     processing_efficiency: float
 
+class SparseActivationManager:
+    def compute_pattern(self, input_data: torch.Tensor) -> torch.Tensor:
+        # Placeholder implementation
+        return input_data
+
+class ExpertRoutingSystem:
+    def allocate_experts(self, activation_pattern: torch.Tensor) -> Dict[str, int]:
+        # Placeholder implementation
+        return {"expert1": 1, "expert2": 2}
+
 class NeuralProcessingUnit:
     def __init__(self, num_cores: int = 128):
         self.num_cores = num_cores
@@ -23,8 +33,12 @@ class NeuralProcessingUnit:
         )
         self.sparse_activation = SparseActivationManager()
         self.expert_router = ExpertRoutingSystem()
-        
+
     async def process_neural_task(self, input_data: torch.Tensor) -> torch.Tensor:
         activation_pattern = self.sparse_activation.compute_pattern(input_data)
         expert_allocation = self.expert_router.allocate_experts(activation_pattern)
-        return await self._execute_neural_computation(input_data, expert_allocation)
+        return await self._execute_neural_computation(input_data, expert_allocation)
+
+    async def _execute_neural_computation(self, input_data: torch.Tensor, expert_allocation: Dict[str, int]) -> torch.Tensor:
+        # Placeholder implementation
+        return input_data

src/model.py

@@ -7,14 +7,14 @@ class HIMModel:
         self.config = config
         self.tokenizer = AutoTokenizer.from_pretrained(config.base_model)
         self.model = AutoModelForCausalLM.from_pretrained(config.base_model)
-        
-    def generate_response(self, input_text: str, system_message: str = None):
+
+    def generate_response(self, input_text: str, system_message: str = ""):
         # Prepare input with system message if provided
         if system_message:
             input_text = f"{system_message}\nUser: {input_text}\nHIM:"
-            
+
         inputs = self.tokenizer(input_text, return_tensors="pt")
-        
+
         outputs = self.model.generate(
             inputs["input_ids"],
             max_length=self.config.max_length,
@@ -22,5 +22,5 @@ class HIMModel:
             top_p=self.config.top_p,
             do_sample=True
         )
-        
-        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

src/model/consciousness_model.py

@@ -6,20 +6,21 @@ import numpy as np
 class ConsciousnessModel(nn.Module):
     def __init__(self, config: Dict[str, Any]):
         super().__init__()
-        self.self_awareness = nn.Linear(768, 256)
+        input_dim = config.get('input_dim', 768)
+        self.self_awareness = nn.Linear(input_dim, 256)
         self.meta_cognitive = nn.Linear(256, 128)
         self.phenomenal = nn.Linear(128, 64)
         self.integration = nn.Linear(64, 32)
-        
+
     def forward(self, x: torch.Tensor) -> Dict[str, Any]:
         awareness = torch.relu(self.self_awareness(x))
         meta = torch.relu(self.meta_cognitive(awareness))
         phenomenal = torch.relu(self.phenomenal(meta))
         integrated = self.integration(phenomenal)
-        
+
         return {
             'awareness': awareness,
             'meta_cognitive': meta,
             'phenomenal': phenomenal,
             'integrated': integrated
-        }
+        }

src/model/him_model.py

@@ -15,20 +15,21 @@ class HIMModel(nn.Module):
         self.emotional_processor = EmotionalProcessor()
         self.theory_of_mind = TheoryOfMind()
         self.semiotic_processor = SemioticProcessor()
-        
+
     async def generate_response(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         consciousness_state = await self.consciousness_kernel.process_consciousness_cycle(input_data)
-        emotional_context = await self.emotional_processor.process_emotional_context(input_data)
-        social_understanding = await self.theory_of_mind.model_agent_mind(input_data)
+        # Fixed awaitable issues by removing await for non-awaitable methods
+        emotional_context = self.emotional_processor.process_emotional_context(input_data)
+        social_understanding = self.theory_of_mind.model_agent_mind(input_data)
         semiotic_analysis = await self.semiotic_processor.process(input_data)
-        
+
         return self._integrate_outputs(
             consciousness_state,
             emotional_context,
             social_understanding,
             semiotic_analysis
         )
-        
+
     def _integrate_outputs(self, *states) -> Dict[str, Any]:
         # Integration implementation
         return {
@@ -37,4 +38,4 @@ class HIMModel(nn.Module):
             "emotional_context": states[1],
             "social_understanding": states[2],
             "semiotic_analysis": states[3]
-        }
+        }