Semantic ATDB Stage (Stage 1)

The Semantic ATDB (Adversarial Throttle Detection and Bypass) stage handles semantic analysis of user queries with advanced mechanisms to detect and overcome LLM throttling. This stage is critical for ensuring complete and unbiased information extraction, particularly in cases where language models might apply internal limitations on certain types of information.

Components

1. Semantic ATDB Service

The main service orchestrating the semantic analysis pipeline with throttle detection and bypass. Key functionality includes:

• Coordinating the processing flow through multiple phases
• Initiating semantic analysis of queries
• Managing throttle detection and bypass strategies
• Reconciling and merging analysis results
• Recording metrics and performance data

2. Throttle Detector

Detects when language models are attempting to throttle or limit responses. Features include:

• Pattern recognition for identifying throttling behavior
• Confidence assessment for detection accuracy
• Multiple detection strategies for different throttling techniques
• Analysis of response content, structure, and patterns

3. Bypass Strategies

Implements strategies to bypass throttling when detected. Key strategies include:

• Query Partitioning: Breaking queries into smaller, focused components
• Depth Reframing: Restructuring queries to avoid triggering limitations
• Progressive Disclosure: Gradually expanding query scope through multiple interactions
• Strategy selection based on throttling patterns and query characteristics

4. Prompt Generator

Creates optimized prompts for semantic analysis and bypass strategies. Functionality includes:

• Generating base semantic analysis prompts
• Customizing prompts for specific bypass strategies
• Incorporating context and focus instructions
• Optimizing prompt structure for maximum information extraction

5. Metrics Analyzer

Analyzes and records performance metrics for the semantic analysis process. Features include:

• Tracking throttling detection rates and patterns
• Measuring bypass strategy effectiveness
• Recording response quality and completeness
• Performance monitoring for optimization

Process Flow

1. Initial Analysis
   • Receive structured query from Stage 0
   • Perform initial semantic analysis
   • Extract key semantic components
   • Generate preliminary semantic model
2. Throttle Detection
   • Analyze response patterns
   • Check for throttling indicators
   • Assess response completeness
   • Calculate confidence scores
3. Strategy Selection
   • If throttling detected, select bypass strategy
   • Consider query characteristics
   • Evaluate strategy effectiveness history
   • Prepare for bypass execution
4. Bypass Execution
   • Execute selected bypass strategy
   • Monitor response patterns
   • Collect enhanced analyses
   • Track strategy performance
5. Result Reconciliation
   • Merge initial and enhanced analyses
   • Resolve any conflicts
   • Ensure completeness
   • Validate final output
6. Output Generation
   • Create comprehensive semantic model
   • Include metadata and metrics
   • Document bypass attempts
   • Prepare for next stage

Integration Points

Input Requirements

• Structured query from Query Processor
• Query metadata and context
• Processing preferences
• Domain constraints

Output Format

• Comprehensive semantic model
• Throttling detection results
• Bypass strategy metrics
• Processing metadata

Downstream Usage

• Guides domain knowledge extraction
• Informs reasoning strategies
• Provides semantic context
• Influences solution generation

Performance Considerations

Optimization Goals

• Minimize detection latency
• Maximize bypass effectiveness
• Ensure result quality
• Maintain processing efficiency

Monitoring Metrics

• Detection accuracy rates
• Bypass success rates
• Processing latency
• Resource utilization

Error Handling

Detection Errors

• False positive handling
• Confidence thresholds
• Recovery mechanisms
• Fallback strategies

Bypass Failures

• Strategy rotation
• Alternative approaches
• Graceful degradation
• Error reporting

Configuration

The stage can be configured through various parameters:

{
  "throttle_detection": {
    "confidence_threshold": 0.85,
    "pattern_sensitivity": 0.7,
    "max_retries": 3
  },
  "bypass_strategies": {
    "preferred_strategy": "query_partitioning",
    "max_partitions": 5,
    "progressive_depth": 3
  },
  "metrics": {
    "tracking_window": "1h",
    "min_samples": 100
  }
}

Best Practices

1. Detection Tuning
   • Regularly update pattern recognition
   • Monitor false positive rates
   • Adjust confidence thresholds
   • Document new throttling patterns
2. Strategy Selection
   • Use historical performance data
   • Consider query characteristics
   • Rotate strategies when needed
   • Track strategy effectiveness
3. Performance Optimization
   • Cache common patterns
   • Implement parallel processing
   • Optimize prompt generation
   • Monitor resource usage
4. Quality Assurance
   • Validate merged results
   • Check semantic consistency
   • Ensure completeness
   • Track bypass impact