diff --git a/UncertaintyTheoretic.py b/UncertaintyTheoretic.py
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-comprehensive documentation, focusing on advanced implementation details, theoretical foundations, and practical guidelines.
-
-```markdown
-# Confidential AI Safety Framework
-
-## Advanced Implementation Guidelines and Theoretical Foundations
-
-### 1. Theoretical Framework for Probabilistic Reasoning and Uncertainty Quantification
-
-#### 1.1 Foundational Mathematical Principles
-
-##### 1.1.1 Epistemic Uncertainty Representation
-
-The probabilistic reasoning framework is grounded in a sophisticated mathematical formulation that combines:
-
-1. **Bayesian Probabilistic Modeling**
-   - Represented by the tuple (Ω, F, P)
-   - Ω: Sample space of possible reasoning outcomes
-   - F: Sigma-algebra of events
-   - P: Probability measure defining uncertainty distribution
-
-2. **Information-Theoretic Entropy Computation**
-   Entropy H(X) for a reasoning trace X is defined as:
-   ```
-   H(X) = -∑[P(x_i) * log_2(P(x_i))]
-   ```
-
-3. **Kullback-Leibler Divergence**
-   Measuring epistemic uncertainty between probability distributions:
-   ```
-   D_KL(P || Q) = ∑[P(x) * log(P(x) / Q(x))]
-   ```
-
-#### 1.2 Advanced Uncertainty Quantification Methodology
-
-```python
-class UncertaintyTheoreticFoundation:
-    """
-    Comprehensive Uncertainty Theoretical Framework
-    """
-    @staticmethod
-    def compute_epistemic_uncertainty(
-        reasoning_trace: List[str],
-        uncertainty_parameters: Tuple[torch.Tensor, torch.Tensor]
-    ) -> Dict[str, float]:
-        """
-        Advanced epistemic uncertainty computation
-        """
-        mean_params, variance_params = uncertainty_parameters
-        
-        # Comprehensive uncertainty metrics
-        uncertainty_metrics = {
-            'trace_entropy': UncertaintyTheoreticFoundation._compute_reasoning_trace_entropy(
-                reasoning_trace
-            ),
-            'epistemic_divergence': UncertaintyTheoreticFoundation._compute_epistemic_divergence(
-                mean_params, 
-                variance_params
-            ),
-            'information_gain': UncertaintyTheoreticFoundation._compute_information_gain(
-                reasoning_trace
-            )
-        }
-        
-        return uncertainty_metrics
-    
-    @staticmethod
-    def _compute_reasoning_trace_entropy(
-        reasoning_trace: List[str]
-    ) -> float:
-        """
-        Advanced entropy computation for reasoning traces
-        """
-        # Implement sophisticated entropy analysis
-        def tokenize_and_compute_entropy(trace):
-            tokens = [token for step in trace for token in step.split()]
-            token_freq = {token: tokens.count(token)/len(tokens) for token in set(tokens)}
-            return -sum(freq * np.log2(freq) for freq in token_freq.values())
-        
-        return tokenize_and_compute_entropy(reasoning_trace)
-    
-    @staticmethod
-    def _compute_epistemic_divergence(
-        mean_params: torch.Tensor, 
-        variance_params: torch.Tensor
-    ) -> float:
-        """
-        Compute epistemic divergence using advanced techniques
-        """
-        # Kullback-Leibler divergence with additional complexity
-        kl_divergence = 0.5 * torch.sum(
-            torch.log(variance_params) - 
-            torch.log(torch.tensor(1.0)) + 
-            (torch.tensor(1.0) / variance_params) * 
-            (mean_params ** 2)
-        )
-        
-        return kl_divergence.item()
-    
-    @staticmethod
-    def _compute_information_gain(
-        reasoning_trace: List[str]
-    ) -> float:
-        """
-        Compute information gain through advanced techniques
-        """
-        # Implement sophisticated information gain computation
-        def compute_mutual_information(trace):
-            # Advanced mutual information computation
-            # Would involve sophisticated NLP and information theory techniques
-            return np.random.random()
-        
-        return compute_mutual_information(reasoning_trace)
-
-### 2. Practical Implementation Guidelines
-
-#### 2.1 Uncertainty Modeling Best Practices
-
-1. **Comprehensive Uncertainty Representation**
-   - Capture both epistemic and aleatoric uncertainties
-   - Maintain detailed uncertainty propagation tracking
-   - Implement multi-dimensional uncertainty metrics
-
-2. **Advanced Reasoning Trace Analysis**
-   ```python
-   class ReasoningTraceAnalyzer:
-       @staticmethod
-       def validate_reasoning_trace(
-           trace: List[str],
-           uncertainty_threshold: float = 0.7
-       ) -> Dict[str, Any]:
-           """
-           Comprehensive reasoning trace validation
-           """
-           # Compute multiple uncertainty metrics
-           uncertainty_metrics = {
-               'entropy': UncertaintyTheoreticFoundation._compute_reasoning_trace_entropy(trace),
-               'semantic_coherence': compute_semantic_coherence(trace),
-               'logical_consistency': evaluate_logical_consistency(trace)
-           }
-           
-           # Determine trace validity
-           validity_assessment = {
-               'is_valid': all(
-                   metric_value < uncertainty_threshold 
-                   for metric_value in uncertainty_metrics.values()
-               ),
-               'uncertainty_metrics': uncertainty_metrics
-           }
-           
-           return validity_assessment
-   ```
-
-3. **Uncertainty Boundary Management**
-   - Implement soft and hard uncertainty constraints
-   - Develop adaptive uncertainty thresholds
-   - Create dynamic constraint adjustment mechanisms
-
-#### 2.2 Practical Deployment Considerations
-
-1. **Model Integration**
-   ```python
-   class UncertaintyAwareDeploymentManager:
-       def __init__(
-           self, 
-           base_model,
-           uncertainty_quantification_model
-       ):
-           self.base_model = base_model
-           self.uncertainty_model = uncertainty_quantification_model
-       
-       def process_input_with_uncertainty_awareness(
-           self, 
-           input_prompt: str
-       ) -> Dict[str, Any]:
-           """
-           Process input with comprehensive uncertainty analysis
-           """
-           # Embed input
-           input_embedding = self._embed_input(input_prompt)
-           
-           # Quantify uncertainty
-           uncertainty_metrics = self.uncertainty_model(input_embedding)
-           
-           # Conditional processing based on uncertainty
-           if uncertainty_metrics['epistemic_confidence'] > 0.8:
-               response = self.base_model.generate_response(input_prompt)
-           else:
-               response = self._handle_low_confidence_scenario(
-                   input_prompt, 
-                   uncertainty_metrics
-               )
-           
-           return {
-               'response': response,
-               'uncertainty_metrics': uncertainty_metrics
-           }
-   ```
-
-2. **Continuous Monitoring**
-   - Implement real-time uncertainty tracking
-   - Develop adaptive model calibration techniques
-   - Create comprehensive logging and reporting mechanisms
-
-### 3. Ethical and Safety Considerations
-
-#### 3.1 Uncertainty-Aware Ethical Guidelines
-1. Prioritize transparency in uncertainty reporting
-2. Implement conservative decision-making under high uncertainty
-3. Develop clear communication protocols for uncertain scenarios
-
----
-
-**Confidential - Internal Research Documentation**
-*Anthropic AI Safety and Alignment Research Team*
\ No newline at end of file