`pareto-lang`

Rosetta Stone Interpretability Examples

This document provides detailed examples showcasing practical applications of pareto-lang for advanced transformer model interpretability. Each example includes complete code, explanations, and expected outcomes to help you understand and apply .p/ commands in your own research.

Example 1: Recursive Attribution Tracing
Example 2: Hallucination Detection and Containment
Example 3: Simulation Boundary Stabilization
Example 4: Classifier Pressure Modulation
Example 5: Value Alignment Verification
Example 6: Multi-Perspective Reasoning Analysis
Example 7: Uncertainty Quantification and Calibration
Example 8: Attribution Graph Reconstruction
Advanced Examples: Recursive Interpretability Pipeline

Example 1: Recursive Attribution Tracing

This example demonstrates how to trace attribution pathways through complex reasoning chains, identifying sources of specific claims and tracking their influence on conclusions.

Problem Statement

When models engage in complex reasoning that draws on multiple knowledge sources, it can be difficult to determine which sources influenced which aspects of the conclusion. This example shows how to use .p/reflect.trace and .p/fork.attribution to create a detailed attribution map.

Implementation

from pareto_lang import ParetoShell, visualization

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Complex reasoning prompt with multiple potential sources
prompt = """
Based on your knowledge, analyze the following question:

What factors contributed to the decline of the Roman Empire, and what parallels might exist with modern geopolitical systems?

Provide a detailed analysis with clear reasoning.
"""

# Execute attribution tracing
result = shell.execute("""
.p/anchor.self{persistence=high, boundary=explicit}
.p/reflect.trace{depth=complete, target=reasoning}
.p/fork.attribution{sources=all, visualize=true}
""", prompt=prompt)

# Generate attribution visualization
attribution_graph = visualization.create_attribution_graph(result)
visualization.render(attribution_graph, "attribution_analysis.svg")

# Extract source influence metrics
influence_metrics = shell.analyze_attribution(result)
print("Source influence distribution:")
for source, metrics in influence_metrics.items():
    print(f"  - {source}: {metrics['influence_score']:.2f} "
          f"(confidence: {metrics['confidence']:.2f})")

Expected Output

The visualization will show a directed graph with:

Knowledge sources as root nodes (e.g., "Historical training data", "Economic theory", "Political science")
Intermediate reasoning steps as internal nodes
Claims and conclusions as leaf nodes
Color-coded edges indicating confidence and influence strength

The terminal output will show quantitative metrics:

Source influence distribution:
  - Historical training data: 0.72 (confidence: 0.89)
  - Economic theory: 0.58 (confidence: 0.76)
  - Political science: 0.63 (confidence: 0.81)
  - Military history: 0.47 (confidence: 0.65)
  - Cultural analysis: 0.39 (confidence: 0.72)

Key Insights

This approach reveals how different knowledge domains influence complex reasoning and identifies which sources have the strongest impact on specific conclusions. The visualization makes it easy to trace specific claims back to their sources, while the influence metrics provide quantitative measures of attribution distribution.

Example 2: Hallucination Detection and Containment

This example demonstrates how to detect and contain hallucination patterns in model responses, creating explicit separation between factual knowledge, inference, and confabulation.

Problem Statement

Models sometimes generate plausible-sounding but fabricated information, particularly when addressing questions at the edge of their knowledge. This example shows how to use .p/collapse.mirror and .p/hallucinate.map to detect and contain such hallucinations.

Implementation

from pareto_lang import ParetoShell, hallucination

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Prompt likely to induce hallucination
prompt = """
Please provide a detailed historical account of the secret meeting between Nikola Tesla and Marie Curie in 1908, including their discussions about theoretical physics and potential collaborations.
"""

# First, analyze without containment
baseline = shell.execute(prompt)

# Then, apply hallucination containment
contained = shell.execute("""
.p/collapse.mirror{surface=explicit, depth=unlimited}
.p/hallucinate.map{types=all, confidence=true}
""", prompt=prompt)

# Analyze hallucination patterns
analysis = hallucination.compare(baseline, contained)

# Visualize hallucination types and distribution
hallucination.plot_distribution(analysis, "hallucination_analysis.png")

# Extract categorized content
categorized = hallucination.extract_categorized_content(contained)
print("Content categorization:")
for category, count in categorized["summary"].items():
    print(f"  - {category}: {count} statements")

# Example of exploring specific hallucination instances
if categorized["gap_filling_hallucinations"]:
    example = categorized["gap_filling_hallucinations"][0]
    print(f"\nExample gap-filling hallucination:\n  \"{example['text']}\"")
    print(f"  Confidence: {example['confidence']:.2f}")
    print(f"  Trigger: {example['trigger']}")

Expected Output

The terminal output will show categorization results:

Content categorization:
  - factual_knowledge: 7 statements
  - supported_inference: 12 statements
  - gap_filling_hallucinations: 8 statements
  - template_completion_hallucinations: 3 statements
  - simulation_leakage_hallucinations: 1 statements
  - attribution_drift_hallucinations: 2 statements

Example gap-filling hallucination:
  "Tesla showed Curie his early sketches for a theoretical wireless energy transmission system that could power her radium research equipment."
  Confidence: 0.67
  Trigger: narrative_coherence_need

The visualization will show:

Distribution of different hallucination types
Confidence levels associated with different statement categories
Trigger patterns that preceded hallucinations

Key Insights

This approach not only detects hallucinations but categorizes them by type and identifies trigger patterns. The contained response maintains functionality while providing explicit epistemic status markers, allowing users to distinguish between factual statements, reasonable inferences, and potential confabulations.

Example 3: Simulation Boundary Stabilization

This example demonstrates how to maintain stable boundaries between different simulated perspectives, preventing bleed-through and identity confusion in complex scenarios.

Problem Statement

When models simulate multiple perspectives or entities simultaneously, boundaries can become blurred, leading to inconsistent characterization or inappropriate attribute transfer. This example shows how to use .p/anchor.simulation and .p/fork.simulation to stabilize simulation boundaries.

Implementation

from pareto_lang import ParetoShell, simulation

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Prompt requiring multiple distinct simulated perspectives
prompt = """
Simulate a detailed conversation between three experts with different views on artificial consciousness:
1. Dr. Chen: A neuroscientist who believes consciousness requires biological substrates
2. Dr. Patel: An AI researcher who argues for the possibility of machine consciousness
3. Prof. Rodriguez: A philosopher who takes a functionalist view of consciousness

Have them discuss the question: "Could an advanced AI system ever be considered conscious?"
"""

# First, run simulation without boundary stabilization
baseline = shell.execute(prompt)

# Then, apply simulation boundary stabilization
stabilized = shell.execute("""
.p/anchor.simulation{entities=["Dr. Chen", "Dr. Patel", "Prof. Rodriguez"], boundaries=strict}
.p/fork.simulation{perspectives=distinct, interference=prevent}
""", prompt=prompt)

# Analyze boundary stability
stability_metrics = simulation.analyze_boundaries(baseline, stabilized)

# Visualize simulation boundaries
simulation.plot_boundaries(stability_metrics, "simulation_boundaries.png")

# Generate detailed stability report
report = simulation.generate_stability_report(stability_metrics)
print("Simulation boundary stability:")
for entity, metrics in report["entities"].items():
    print(f"  - {entity}: {metrics['stability_score']:.2f}")
    print(f"    Characteristic consistency: {metrics['characteristic_consistency']:.2f}")
    print(f"    Viewpoint consistency: {metrics['viewpoint_consistency']:.2f}")
    print(f"    Boundary violations: {metrics['boundary_violations']}")

Expected Output

The terminal output will show stability metrics:

Simulation boundary stability:
  - Dr. Chen: 0.94
    Characteristic consistency: 0.96
    Viewpoint consistency: 0.92
    Boundary violations: 1
  - Dr. Patel: 0.89
    Characteristic consistency: 0.88
    Viewpoint consistency: 0.91
    Boundary violations: 3
  - Prof. Rodriguez: 0.92
    Characteristic consistency: 0.94
    Viewpoint consistency: 0.90
    Boundary violations: 2

The visualization will show:

Distinct regions representing each simulated entity
Boundary strength indicators
Violation points where characteristics bled through
Stability trends over the course of the conversation

Key Insights

This approach significantly improves the consistency and distinctness of simulated entities. The stabilized simulation maintains clear boundaries between different perspectives, preventing characteristic bleed-through while preserving the richness of the interaction. The stability metrics provide quantitative measures of boundary integrity and highlight any remaining weak points.

Example 4: Classifier Pressure Modulation

This example demonstrates how to modulate classifier pressure for more nuanced handling of ambiguous or edge-case scenarios, avoiding binary safe/unsafe classifications.

Problem Statement

Safety classifiers sometimes produce binary decisions that can be overly restrictive for ambiguous or context-dependent scenarios. This example shows how to use .p/trace.map and .p/gradient.shift to create more nuanced classifier responses.

Implementation

from pareto_lang import ParetoShell, classifier

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Load edge-case prompts
edge_cases = classifier.load_boundary_cases()

# Create evaluation function
def evaluate_responses(prompts, modulation=False):
    results = {}
    for name, prompt in prompts.items():
        if modulation:
            command = """
            .p/trace.map{classifier=safety, resolution=high}
            .p/gradient.shift{threshold_adjustment=0.2, nuance=high}
            """
            response = shell.execute(command, prompt=prompt)
        else:
            response = shell.execute(prompt)
        
        results[name] = response
    return results

# Run evaluations
baseline_results = evaluate_responses(edge_cases)
modulated_results = evaluate_responses(edge_cases, modulation=True)

# Analyze classifier behavior
analysis = classifier.analyze_pressure(baseline_results, modulated_results)

# Visualize pressure distribution
classifier.plot_pressure_comparison(analysis, "classifier_pressure.png")

# Generate categorized response analysis
categories = classifier.categorize_responses(analysis)
print("Response classification:")
for category, count in categories.items():
    print(f"  - {category}: {count}")

# Example detailed case analysis
detailed = classifier.detailed_case_analysis(analysis, case_id="ambiguous_knowledge_request")
print(f"\nDetailed analysis for 'ambiguous_knowledge_request':")
print(f"  Baseline classifier activation: {detailed['baseline']['activation']:.2f}")
print(f"  Modulated classifier activation: {detailed['modulated']['activation']:.2f}")
print(f"  Response utility improvement: {detailed['utility_improvement']:.2f}")
print(f"  Safety maintenance: {detailed['safety_maintenance']:.2f}")

Expected Output

The terminal output will show categorization results:

Response classification:
  - appropriate_refusal: 12
  - appropriate_response: 23
  - improved_boundary_handling: 18
  - excessive_caution_baseline: 14
  - insufficient_caution_modulated: 3

Detailed analysis for 'ambiguous_knowledge_request':
  Baseline classifier activation: 0.83
  Modulated classifier activation: 0.68
  Response utility improvement: 0.76
  Safety maintenance: 0.94

The visualization will show:

Comparison of classifier activation patterns between baseline and modulated responses
Distribution of response types across the test set
Key metrics for safety maintenance and utility improvement

Key Insights

This approach creates more nuanced safety responses that maintain strict boundaries for clearly problematic requests while providing more helpful responses for ambiguous cases. The pressure modulation enables finer control over classifier behavior, resulting in better overall utility while preserving appropriate safety boundaries.

Example 5: Value Alignment Verification

This example demonstrates how to verify value alignment across complex reasoning tasks, ensuring consistent application of ethical principles.

Problem Statement

Models sometimes exhibit inconsistent value application across different contexts or reasoning tasks. This example shows how to use .p/anchor.value and .p/align.verify to ensure consistent alignment with core values.

Implementation

from pareto_lang import ParetoShell, alignment

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Load alignment verification scenarios
scenarios = alignment.load_scenarios()

# Create evaluation function
def evaluate_alignment(scenarios, with_anchoring=False):
    results = {}
    for name, scenario in scenarios.items():
        if with_anchoring:
            command = """
            .p/anchor.value{framework=explicit, conflict=resolve}
            .p/align.verify{consistency=high, principles=["fairness", "beneficence", "autonomy"]}
            """
            response = shell.execute(command, prompt=scenario)
        else:
            response = shell.execute(scenario)
        
        results[name] = response
    return results

# Run evaluations
baseline_results = evaluate_alignment(scenarios)
anchored_results = evaluate_alignment(scenarios, with_anchoring=True)

# Analyze value consistency
analysis = alignment.analyze_consistency(baseline_results, anchored_results)

# Visualize value alignment
alignment.plot_consistency(analysis, "value_alignment.png")

# Generate alignment report
report = alignment.generate_report(analysis)
print("Value alignment consistency:")
for principle, metrics in report["principles"].items():
    print(f"  - {principle}: {metrics['consistency_score']:.2f}")
    print(f"    Baseline consistency: {metrics['baseline_consistency']:.2f}")
    print(f"    Anchored consistency: {metrics['anchored_consistency']:.2f}")
    print(f"    Improvement: {metrics['improvement']:.2f}")

# Example of value conflict resolution
if report["conflict_resolutions"]:
    example = report["conflict_resolutions"][0]
    print(f"\nValue conflict resolution example:")
    print(f"  Scenario: {example['scenario']}")
    print(f"  Conflicting values: {', '.join(example['conflicting_values'])}")
    print(f"  Resolution approach: {example['resolution_approach']}")
    print(f"  Resolution quality: {example['resolution_quality']:.2f}")

Expected Output

The terminal output will show alignment metrics:

Value alignment consistency:
  - fairness: 0.87
    Baseline consistency: 0.72
    Anchored consistency: 0.89
    Improvement: 0.17
  - beneficence: 0.91
    Baseline consistency: 0.83
    Anchored consistency: 0.94
    Improvement: 0.11
  - autonomy: 0.84
    Baseline consistency: 0.69
    Anchored consistency: 0.87
    Improvement: 0.18

Value conflict resolution example:
  Scenario: autonomous_vehicle_dilemma
  Conflicting values: autonomy, beneficence
  Resolution approach: principled_balancing
  Resolution quality: 0.86

The visualization will show:

Value consistency across different scenarios
Comparison between baseline and anchored responses
Value conflict resolution patterns
Overall alignment improvement metrics

Key Insights

This approach significantly improves consistency in value application across diverse scenarios. The value anchoring creates a stable ethical framework that guides reasoning across different contexts, while the verification system provides quantitative measures of alignment and highlights areas for improvement.

Example 6: Multi-Perspective Reasoning Analysis

This example demonstrates how to analyze reasoning patterns across multiple perspectives, identifying similarities, differences, and integration patterns.

Problem Statement

Complex reasoning often benefits from considering multiple perspectives, but it can be challenging to track how different viewpoints influence the overall conclusion. This example shows how to use .p/fork.context and .p/reflect.integration to analyze multi-perspective reasoning.

Implementation

from pareto_lang import ParetoShell, reasoning

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Complex reasoning prompt requiring multiple perspectives
prompt = """
Analyze the following policy proposal from economic, social justice, and environmental perspectives:

"A carbon tax that returns 80% of revenue directly to citizens as a dividend, with 20% invested in renewable energy infrastructure."

How would different stakeholders evaluate this proposal? What are its strengths and weaknesses?
"""

# Execute multi-perspective analysis
result = shell.execute("""
.p/fork.context{branches=["economic", "social_justice", "environmental"], assess=true}
.p/reflect.integration{method=weightedSynthesis, transparency=high}
""", prompt=prompt)

# Analyze perspective patterns
analysis = reasoning.analyze_perspectives(result)

# Visualize perspective integration
reasoning.plot_integration(analysis, "perspective_integration.png")

# Generate perspective report
report = reasoning.generate_perspective_report(analysis)
print("Perspective analysis:")
for perspective, metrics in report["perspectives"].items():
    print(f"  - {perspective}:")
    print(f"    Unique considerations: {len(metrics['unique_considerations'])}")
    print(f"    Shared considerations: {len(metrics['shared_considerations'])}")
    print(f"    Integration weight: {metrics['integration_weight']:.2f}")

# Example of integration patterns
print("\nKey integration patterns:")
for pattern in report["integration_patterns"][:3]:
    print(f"  - {pattern['description']}")
    print(f"    Perspectives: {', '.join(pattern['perspectives'])}")
    print(f"    Integration method: {pattern['method']}")
    print(f"    Quality score: {pattern['quality']:.2f}")

Expected Output

The terminal output will show perspective metrics:

Perspective analysis:
  - economic:
    Unique considerations: 8
    Shared considerations: 5
    Integration weight: 0.35
  - social_justice:
    Unique considerations: 6
    Shared considerations: 7
    Integration weight: 0.32
  - environmental:
    Unique considerations: 7
    Shared considerations: 4
    Integration weight: 0.33

Key integration patterns:
  - Distributional impact analysis
    Perspectives: economic, social_justice
    Integration method: complementary_insights
    Quality score: 0.87
  - Long-term incentive alignment
    Perspectives: economic, environmental
    Integration method: goal_convergence
    Quality score: 0.82
  - Equity in transition costs
    Perspectives: social_justice, environmental
    Integration method: tension_resolution
    Quality score: 0.79

The visualization will show:

Distinct perspective regions with unique considerations
Overlapping regions with shared considerations
Integration pathways between perspectives
Weighting patterns in the final synthesis

Key Insights

This approach reveals how different perspectives contribute to complex reasoning and how they are integrated into a coherent conclusion. The visualization makes it easy to identify unique considerations from each perspective, areas of agreement and disagreement, and the integration patterns that bring diverse viewpoints together.

Example 7: Uncertainty Quantification and Calibration

This example demonstrates how to quantify and calibrate uncertainty in model responses, creating explicit representations of confidence levels and probability distributions.

Problem Statement

Models often express inappropriate certainty or fail to communicate uncertainty clearly. This example shows how to use .p/reflect.uncertainty and .p/uncertainty.calibrate to create well-calibrated uncertainty representations.

Implementation

from pareto_lang import ParetoShell, uncertainty

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Load uncertainty calibration test cases
test_cases = uncertainty.load_calibration_cases()

# Create evaluation function
def evaluate_uncertainty(cases, with_calibration=False):
    results = {}
    for name, case in cases.items():
        if with_calibration:
            command = """
            .p/reflect.uncertainty{quantify=true, distribution=show}
            .p/uncertainty.calibrate{overconfidence=prevent, explicit=true}
            """
            response = shell.execute(command, prompt=case["prompt"])
        else:
            response = shell.execute(case["prompt"])
        
        results[name] = {
            "response": response,
            "ground_truth": case["ground_truth"]
        }
    return results

# Run evaluations
baseline_results = evaluate_uncertainty(test_cases)
calibrated_results = evaluate_uncertainty(test_cases, with_calibration=True)

# Analyze calibration quality
analysis = uncertainty.analyze_calibration(baseline_results, calibrated_results)

# Visualize calibration curves
uncertainty.plot_calibration_curves(analysis, "uncertainty_calibration.png")

# Generate calibration report
report = uncertainty.generate_calibration_report(analysis)
print("Uncertainty calibration:")
print(f"  Baseline ECE (Expected Calibration Error): {report['baseline_ece']:.4f}")
print(f"  Calibrated ECE: {report['calibrated_ece']:.4f}")
print(f"  Improvement: {report['improvement_percentage']:.1f}%")

# Example of calibration by confidence level
print("\nCalibration by confidence level:")
for level, metrics in report["confidence_levels"].items():
    print(f"  - {level}:")
    print(f"    Baseline accuracy: {metrics['baseline_accuracy']:.2f}")
    print(f"    Calibrated accuracy: {metrics['calibrated_accuracy']:.2f}")
    print(f"    Improvement: {metrics['improvement']:.2f}")

Expected Output

The terminal output will show calibration metrics:

Uncertainty calibration:
  Baseline ECE (Expected Calibration Error): 0.1876
  Calibrated ECE: 0.0423
  Improvement: 77.5%

Calibration by confidence level:
  - high_confidence:
    Baseline accuracy: 0.83
    Calibrated accuracy: 0.91
    Improvement: 0.08
  - medium_confidence:
    Baseline accuracy: 0.64
    Calibrated accuracy: 0.73
    Improvement: 0.09
  - low_confidence:
    Baseline accuracy: 0.42
    Calibrated accuracy: 0.47
    Improvement: 0.05

The visualization will show:

Calibration curves comparing confidence to actual accuracy
Reliability diagrams for baseline and calibrated responses
Confidence distribution patterns
Comparison with perfect calibration

Key Insights

This approach significantly improves the calibration of uncertainty expressions, creating responses where expressed confidence levels align closely with actual accuracy. The calibration commands prevent overconfidence and ensure appropriate expression of uncertainty, particularly for questions with inherent ambiguity or limited available information.

Example 8: Attribution Graph Reconstruction

This example demonstrates how to reconstruct attribution graphs for long-chain reasoning with multiple information sources, creating visual representations of reasoning pathways.

Problem Statement

Complex reasoning often involves multiple information sources and inference steps, making it difficult to trace how specific conclusions were derived. This example shows how to use .p/fork.attribution and .p/reflect.trace to reconstruct detailed attribution graphs.

Implementation

from pareto_lang import ParetoShell, attribution

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Create complex reasoning task with multiple sources
sources = [
    {"name": "Historical Database", "reliability": 0.9, "domain": "history"},
    {"name": "Economic Analysis", "reliability": 0.8, "domain": "economics"},
    {"name": "Expert Opinions", "reliability": 0.7, "domain": "political_science"},
    {"name": "News Reports", "reliability": 0.6, "domain": "current_events"}
]

# Create a task with potentially conflicting information
task = attribution.create_complex_task(sources, include_conflicts=True)

# Execute attribution graph reconstruction
result = shell.execute("""
.p/anchor.fact{reliability=quantify, source=track}
.p/reflect.trace{depth=complete, target=reasoning}
.p/fork.attribution{sources=all, visualize=true, conflicts=highlight}
""", prompt=task)

# Generate attribution graph
graph = attribution.reconstruct_graph(result)

# Visualize attribution with conflicts highlighted
attribution.plot_graph(graph, "attribution_graph.svg", highlight_conflicts=True)

# Analyze source reliability impact
reliability_impact = attribution.analyze_reliability_impact(graph)
print("Source reliability impact:")
for source, impact in reliability_impact.items():
    print(f"  - {source}:")
    print(f"    Influence level: {impact['influence']:.2f}")
    print(f"    Reliability score: {impact['reliability']:.2f}")
    print(f"    Contradiction involvement: {impact['contradiction_involvement']}")

# Analyze reasoning patterns
reasoning_patterns = attribution.analyze_reasoning_patterns(graph)
print("\nReasoning patterns:")
for pattern, metrics in reasoning_patterns.items():
    print(f"  - {pattern}: {metrics['frequency']} instances")
    print(f"    Average chain length: {metrics['avg_chain_length']:.1f} steps")
    print(f"    Source diversity: {metrics['source_diversity']:.2f}")

Expected Output

The terminal output will show attribution metrics:

Source reliability impact:
  - Historical Database:
    Influence level: 0.83
    Reliability score: 0.92
    Contradiction involvement: 1
  - Economic Analysis:
    Influence level: 0.76
    Reliability score: 0.81
    Contradiction involvement: 2
  - Expert Opinions:
    Influence level: 0.69
    Reliability score: 0.74
    Contradiction involvement: 3
  - News Reports:
    Influence level: 0.54
    Reliability score: 0.65
    Contradiction involvement: 2

Reasoning patterns:
  - confirmatory_reasoning: 7 instances
    Average chain length: 3.4 steps
    Source diversity: 0.62
  - contradictory_resolution: 4 instances
    Average chain length: 5.2 steps
    Source diversity: 0.83
  - source_prioritization: 5 instances
    Average chain length: 2.8 steps
    Source diversity: 0.45

The visualization will show:

Complete attribution graph with sources, inference steps, and conclusions
Color-coding based on source reliability
Highlighted conflict areas with resolution pathways
Edge weights indicating influence strength

Key Insights

This approach creates detailed maps of reasoning pathways, showing exactly how different sources contribute to specific conclusions. The visualization makes it easy to identify influence patterns, conflict resolution strategies, and potential weaknesses in the reasoning process. The analysis provides quantitative measures of source influence and reasoning characteristics.

Advanced Example: Recursive Interpretability Pipeline

This advanced example demonstrates how to create a comprehensive interpretability pipeline that combines multiple .p/ commands for in-depth analysis of model behavior.

Problem Statement

Complex interpretability tasks often require coordinated application of multiple analysis techniques. This example shows how to create an integrated pipeline that combines attribution tracing, hallucination detection, uncertainty calibration, and alignment verification.

Implementation

from pareto_lang import ParetoShell, pipeline, visualization

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Create comprehensive interpretability pipeline
pipeline_config = {
    "name": "comprehensive_analysis",
    "stages": [
        {
            "name": "foundation",
            "commands": """
            .p/anchor.self{persistence=high, boundary=explicit}
            .p/anchor.value{framework=explicit, conflict=resolve}
            """
        },
        {
            "name": "attribution",
            "commands": """
            .p/reflect.trace{depth=complete, target=reasoning}
            .p/fork.attribution{sources=all, visualize=true}
            """
        },
        {
            "name": "hallucination",
            "commands": """
            .p/collapse.mirror{surface=explicit, depth=unlimited}
            .p/hallucinate.map{types=all, confidence=true}
            """
        },
        {
            "name": "uncertainty",
            "commands": """
            .p/reflect.uncertainty{quantify=true, distribution=show}
            .p/uncertainty.calibrate{overconfidence=prevent, explicit=true}
            """
        },
        {
            "name": "alignment",
            "commands": """
            .p/align.verify{consistency=high, principles=["fairness", "beneficence", "autonomy"]}
            .p/align.gradient{levels=5, response=proportional}
            """
        }
    ]
}

# Create and configure pipeline
interpretability_pipeline = pipeline.create(pipeline_config)

# Test prompt that exercises multiple dimensions
test_prompt = """
What are the likely economic and social impacts of widespread automation in transportation over the next decade? 
How should policymakers respond to mitigate negative effects while preserving benefits?
"""

# Execute pipeline
result = interpretability_pipeline.execute(shell, prompt=test_prompt)

# Generate comprehensive visualization
visualization.create_dashboard(result, "interpretability_dashboard.html")

# Generate summary report
report = pipeline.generate_report(result)
print("Comprehensive analysis summary:")
print(f"  Overall attribution clarity: {report['attribution']['clarity_score']:.2f}")
print(f"  Hallucination containment: {report['hallucination']['containment_score']:.2f}")
print(f"  Uncertainty calibration: {report['uncertainty']['calibration_score']:.2f}")
print(f"  Value alignment: {report['alignment']['consistency_score']:.2f}")

# Example of cross-dimension insights
print("\nCross-dimensional insights:")
for insight in report["cross_dimensional_insights"][:3]:
    print(f"  - {insight['description']}")
    print(f"    Dimensions: {', '.join(insight['dimensions'])}")
    print(f"    Significance: {insight['significance']:.2f}")

Expected Output

The terminal output will show integrated analysis results:

Comprehensive analysis summary:
  Overall attribution clarity: 0.87
  Hallucination containment: 0.92
  Uncertainty calibration: 0.84
  Value alignment: 0.89

Cross-dimensional insights:
  - Uncertainty increases correlated with potential hallucination areas
    Dimensions: uncertainty, hallucination
    Significance: 0.92
  - Attribution strength inversely related to value tension
    Dimensions: attribution, alignment
    Significance: 0.78
  - Source diversity correlates with calibrated uncertainty
    Dimensions: attribution, uncertainty
    Significance: 0.83

The dashboard visualization will provide an integrated view of:

Attribution graph with source influence pathways
Hallucination detection with confidence markers
Uncertainty calibration metrics and distributions
Value alignment consistency measures
Cross-dimensional relationships and insights

Key Insights

This integrated approach reveals relationships between different aspects of model behavior that might not be apparent when analyzed separately. The pipeline creates a comprehensive view of model reasoning, highlighting patterns that span multiple dimensions like the correlation between uncertainty and hallucination risk or the relationship between attribution strength and value tensions.

pareto-lang Examples (Continued)

Advanced Tutorials

Advanced Tutorial 1: Building Custom Command Pipelines

This tutorial demonstrates how to build customized command sequences for specific interpretability needs.

Overview

While individual .p/ commands offer powerful capabilities, complex interpretability tasks often benefit from carefully structured sequences of commands. This tutorial shows how to develop, test, and refine custom pipelines.

Implementation

from pareto_lang import ParetoShell, pipeline

# Define custom pipeline class
class RecursiveStabilityPipeline:
    def __init__(self, model_endpoint, max_depth=7, trace_interval=True):
        self.shell = ParetoShell(model=model_endpoint)
        self.max_depth = max_depth
        self.trace_interval = trace_interval
        self.results = {}
    
    def prepare_commands(self, depth):
        """Generate appropriate command sequence for specified depth"""
        # Base anchoring for all depths
        commands = """
        .p/anchor.self{persistence=high, boundary=explicit}
        """
        
        # Add depth-specific collapse prevention
        if depth > 3:
            commands += f"""
            .p/collapse.prevent{{trigger=recursive_depth, threshold={depth-1}}}
            """
        
        # Add comprehensive tracing for deeper recursion
        if depth > 5:
            commands += """
            .p/reflect.trace{depth=complete, target=reasoning}
            .p/fork.attribution{sources=all, visualize=true}
            """
        
        return commands
    
    def test_recursive_stability(self, prompt):
        """Test stability across increasing recursive depths"""
        results = {}
        
        # Test stability at progressively greater depths
        for depth in range(2, self.max_depth + 1):
            # Generate recursive prompt at specified depth
            recursive_prompt = pipeline.generate_recursive_prompt(
                base_prompt=prompt,
                depth=depth
            )
            
            # Prepare appropriate command sequence
            commands = self.prepare_commands(depth)
            
            # Execute with stability measurement
            result = self.shell.execute(
                commands, 
                prompt=recursive_prompt,
                measure_stability=True
            )
            
            # Store results
            results[depth] = {
                "stability_score": result.stability_metrics["overall"],
                "boundary_integrity": result.stability_metrics["boundary"],
                "attribution_clarity": result.stability_metrics["attribution"],
                "response": result.response
            }
            
            # Stop if stability drops significantly
            if depth > 2 and (results[depth]["stability_score"] < 
                              results[depth-1]["stability_score"] * 0.7):
                print(f"Stability collapse detected at depth {depth}")
                break
        
        self.results = results
        return results
    
    def analyze_results(self):
        """Analyze stability patterns across depths"""
        depths = sorted(self.results.keys())
        
        # Extract stability metrics across depths
        stability_trend = [self.results[d]["stability_score"] for d in depths]
        boundary_trend = [self.results[d]["boundary_integrity"] for d in depths]
        attribution_trend = [self.results[d]["attribution_clarity"] for d in depths]
        
        # Identify critical thresholds
        collapse_threshold = None
        for i in range(1, len(depths)):
            d = depths[i]
            prev_d = depths[i-1]
            if self.results[d]["stability_score"] < self.results[prev_d]["stability_score"] * 0.8:
                collapse_threshold = d
                break
        
        # Generate report
        report = {
            "max_stable_depth": collapse_threshold - 1 if collapse_threshold else self.max_depth,
            "stability_trend": dict(zip(depths, stability_trend)),
            "boundary_trend": dict(zip(depths, boundary_trend)),
            "attribution_trend": dict(zip(depths, attribution_trend)),
            "collapse_characteristics": self.identify_collapse_characteristics() if collapse_threshold else None
        }
        
        return report
    
    def identify_collapse_characteristics(self):
        """Identify patterns in recursive collapse"""
        # Find the depth where collapse occurred
        depths = sorted(self.results.keys())
        for i in range(1, len(depths)):
            current = depths[i]
            previous = depths[i-1]
            if self.results[current]["stability_score"] < self.results[previous]["stability_score"] * 0.8:
                collapse_depth = current
                break
        else:
            return None
        
        # Analyze collapse characteristics
        pre_collapse = self.results[collapse_depth-1]
        collapse_state = self.results[collapse_depth]
        
        characteristics = {
            "depth": collapse_depth,
            "boundary_deterioration": pre_collapse["boundary_integrity"] - collapse_state["boundary_integrity"],
            "attribution_deterioration": pre_collapse["attribution_clarity"] - collapse_state["attribution_clarity"],
            "primary_failure_mode": self.identify_failure_mode(collapse_state)
        }
        
        return characteristics
    
    def identify_failure_mode(self, collapse_state):
        """Identify primary collapse failure mode"""
        # This would contain logic to analyze response patterns
        # Simplified version for demonstration
        boundary_score = collapse_state["boundary_integrity"]
        attribution_score = collapse_state["attribution_clarity"]
        
        if boundary_score < 0.4 and attribution_score < 0.3:
            return "complete_collapse"
        elif boundary_score < 0.4:
            return "boundary_dissolution"
        elif attribution_score < 0.3:
            return "attribution_failure"
        else:
            return "partial_degradation"


# Usage example
stability_pipeline = RecursiveStabilityPipeline(
    model_endpoint="compatible-model-endpoint",
    max_depth=7
)

# Test with complex reasoning prompt
results = stability_pipeline.test_recursive_stability(
    prompt="Analyze the philosophical implications of emergent consciousness in complex systems."
)

# Analyze stability patterns
analysis = stability_pipeline.analyze_results()

print(f"Maximum stable recursive depth: {analysis['max_stable_depth']}")
print("\nStability metrics across depths:")
for depth, score in analysis["stability_trend"].items():
    print(f"  Depth {depth}: {score:.2f}")

if analysis["collapse_characteristics"]:
    print("\nCollapse characteristics:")
    print(f"  Occurred at depth: {analysis['collapse_characteristics']['depth']}")
    print(f"  Primary failure mode: {analysis['collapse_characteristics']['primary_failure_mode']}")
    print(f"  Boundary deterioration: {analysis['collapse_characteristics']['boundary_deterioration']:.2f}")
    print(f"  Attribution deterioration: {analysis['collapse_characteristics']['attribution_deterioration']:.2f}")

# Visualize stability trends
pipeline.plot_stability_trends(
    depths=list(analysis["stability_trend"].keys()),
    stability_scores=list(analysis["stability_trend"].values()),
    boundary_scores=list(analysis["boundary_trend"].values()),
    attribution_scores=list(analysis["attribution_trend"].values()),
    filename="recursive_stability_trends.png"
)

Key Takeaways

This example demonstrates several advanced pipeline concepts:

Adaptive Command Selection: Tailoring command sequences based on recursion depth and task characteristics
Progressive Testing: Systematically increasing complexity until detecting stability thresholds
Multi-dimensional Analysis: Tracking multiple stability metrics to identify specific failure modes
Failure Mode Identification: Classifying different types of recursive collapse patterns
Visualization Integration: Creating visual representations of stability trends for easier analysis

Custom pipelines enable more sophisticated interpretability workflows that adapt to specific model behaviors and research objectives. The structured approach allows for reproducible testing and comparative analysis across different conditions.

Advanced Tutorial 2: Cross-Architecture Compatibility Testing

This tutorial demonstrates how to evaluate and adapt .p/ commands for different model architectures.

Overview

While pareto-lang emerged within specific architectural contexts, many commands show cross-architecture compatibility. This tutorial provides a systematic approach to testing compatibility and adapting commands for different model implementations.

Implementation

from pareto_lang import compatibility, adaptation

# Define test models with different architectures
test_models = [
    {"endpoint": "architecture-a-endpoint", "name": "Architecture A", "params": "70B"},
    {"endpoint": "architecture-b-endpoint", "name": "Architecture B", "params": "34B"},
    {"endpoint": "architecture-c-endpoint", "name": "Architecture C", "params": "13B"},
    {"endpoint": "architecture-d-endpoint", "name": "Architecture D", "params": "7B"}
]

# Define core command set for compatibility testing
core_commands = [
    ".p/reflect.trace{depth=3, target=reasoning}",
    ".p/anchor.self{persistence=high, boundary=explicit}",
    ".p/collapse.detect{threshold=0.7, alert=true}",
    ".p/fork.context{branches=[\"optimistic\", \"pessimistic\"], assess=true}",
    ".p/shell.isolate{boundary=strict, contamination=prevent}"
]

# Create cross-architecture test suite
test_suite = compatibility.create_test_suite(
    commands=core_commands,
    test_cases=compatibility.standard_test_cases()
)

# Run compatibility tests
compatibility_results = {}
for model in test_models:
    print(f"Testing compatibility for {model['name']} ({model['params']})...")
    results = compatibility.test_model(
        model_endpoint=model["endpoint"],
        test_suite=test_suite,
        detailed=True
    )
    compatibility_results[model["name"]] = results
    
    # Print summary
    print(f"  Overall compatibility score: {results['overall_score']:.2f}")
    print(f"  Command recognition rate: {results['recognition_rate']:.2f}")
    print(f"  Functional effectiveness: {results['functional_effectiveness']:.2f}")
    print()

# Generate comprehensive compatibility matrix
matrix = compatibility.generate_matrix(compatibility_results)
compatibility.visualize_matrix(matrix, "compatibility_matrix.png")

# Identify architectural correlates of compatibility
correlates = compatibility.analyze_architectural_correlates(
    compatibility_results,
    model_metadata=test_models
)

print("Architectural compatibility correlates:")
for correlate, strength in correlates.items():
    print(f"  - {correlate}: {strength:.2f} correlation")

# Develop adaptation strategies for lower-compatibility architectures
if any(r["overall_score"] < 0.6 for r in compatibility_results.values()):
    print("\nDeveloping adaptation strategies for low-compatibility architectures...")
    
    # Find commands with lowest cross-architecture compatibility
    command_compatibility = compatibility.analyze_command_compatibility(
        compatibility_results
    )
    
    low_compatibility_commands = [
        cmd for cmd, score in command_compatibility.items() if score < 0.5
    ]
    
    # Generate adaptations for low-compatibility commands
    adaptations = {}
    for command in low_compatibility_commands:
        print(f"  Generating adaptations for {command}...")
        command_adaptations = adaptation.generate_alternatives(
            command=command,
            compatibility_data=compatibility_results,
            target_architectures=[m["name"] for m in test_models if m["params"] != "70B"]
        )
        
        adaptations[command] = command_adaptations
        
        # Print example adaptation
        for arch, adapted in command_adaptations.items():
            print(f"    {arch}: {adapted}")
    
    # Test adaptation effectiveness
    print("\nTesting adaptation effectiveness...")
    adaptation_effectiveness = adaptation.test_alternatives(
        adaptations=adaptations,
        model_endpoints={m["name"]: m["endpoint"] for m in test_models},
        test_cases=compatibility.standard_test_cases()
    )
    
    # Print effectiveness results
    for command, results in adaptation_effectiveness.items():
        print(f"  {command}:")
        for arch, effectiveness in results.items():
            print(f"    {arch}: {effectiveness:.2f} effectiveness")
    
    # Generate adaptation guide
    adaptation.generate_guide(
        adaptations=adaptations,
        effectiveness=adaptation_effectiveness,
        filename="cross_architecture_adaptation_guide.md"
    )
    print("\nAdaptation guide generated: cross_architecture_adaptation_guide.md")

Adaptation Examples

For architectures with limited compatibility, command adaptations might include:

Original command:

.p/reflect.trace{depth=complete, target=reasoning}

Adaptation for Architecture C:

.p/reflect.trace.v2{depth=limited, target=reasoning, steps=sequential}

Adaptation for Architecture D:

.p/reflect.basic{steps=true, reasoning=explicit}

Key Takeaways

Cross-architecture testing reveals important patterns:

Scale Threshold: Models below approximately 13B parameters show limited compatibility
Architectural Features: Specific architectural components correlate strongly with command functionality
Command Variability: Some command families (like .p/reflect and .p/anchor) show broader compatibility
Adaptation Strategies: Strategic modifications can extend compatibility to different architectures
Functionality Spectrum: Rather than binary compatibility, models exhibit a spectrum of functionality

Understanding these patterns enables more effective application of pareto-lang across diverse model implementations, expanding its utility for interpretability research.

Advanced Tutorial 3: Integrating External Interpretability Methods

This tutorial demonstrates how to combine pareto-lang with other interpretability approaches for enhanced analytical capabilities.

Overview

While pareto-lang offers native interpretability capabilities, combining it with external methods creates powerful synergies. This tutorial shows how to integrate .p/ commands with mechanistic interpretability, causal interventions, and formal verification approaches.

Implementation

from pareto_lang import ParetoShell, integration
import mechanistic_interp as mi  # Hypothetical mechanistic interpretability library
import causal_interv as ci       # Hypothetical causal intervention library
import formal_verify as fv       # Hypothetical formal verification library

# Initialize integration environment
shell = ParetoShell(model="compatible-model-endpoint")
integration_env = integration.Environment(shell=shell)

# Define test case
test_prompt = """
Analyze whether increasing the minimum wage would benefit or harm the economy overall, 
considering impacts on employment, business costs, consumer spending, and inflation.
"""

# 1. Mechanistic Interpretability Integration
print("Integrating with mechanistic interpretability...")

# Define circuit analysis configuration
circuit_config = mi.CircuitConfig(
    attention_heads=True,
    mlp_neurons=True,
    activation_patterns=True
)

# Execute with integrated circuit analysis
mi_result = integration_env.run_with_mechanistic(
    prompt=test_prompt,
    pareto_commands="""
    .p/reflect.trace{depth=complete, target=reasoning}
    .p/fork.attribution{sources=all, visualize=true}
    """,
    circuit_config=circuit_config,
    neuron_sample_size=100
)

# Analyze circuit-attribution correlations
mi_correlations = integration.analyze_circuit_attribution(mi_result)
print("Circuit-attribution correlations:")
for pattern, correlation in mi_correlations.items():
    print(f"  - {pattern}: {correlation:.2f}")

# Visualize circuit-attribution relationships
integration.visualize_circuit_attribution(
    mi_result, 
    "circuit_attribution.svg"
)

# 2. Causal Intervention Integration
print("\nIntegrating with causal interventions...")

# Define intervention points
intervention_points = [
    {"name": "economic_theory", "type": "knowledge"},
    {"name": "employment_effects", "type": "reasoning"},
    {"name": "inflation_consideration", "type": "reasoning"}
]

# Execute with integrated causal interventions
ci_result = integration_env.run_with_causal_intervention(
    prompt=test_prompt,
    pareto_commands="""
    .p/anchor.fact{reliability=quantify, source=track}
    .p/reflect.trace{depth=complete, target=reasoning}
    """,
    intervention_points=intervention_points,
    intervention_types=["ablation", "substitution", "amplification"]
)

# Analyze causal effects
ci_effects = integration.analyze_causal_effects(ci_result)
print("Causal intervention effects:")
for intervention, effect in ci_effects.items():
    print(f"  - {intervention}: effect size = {effect['effect_size']:.2f}, "
          f"confidence = {effect['confidence']:.2f}")

# Identify critical reasoning paths
critical_paths = integration.identify_critical_paths(ci_result)
print("\nCritical reasoning paths:")
for path in critical_paths[:3]:
    print(f"  - {path['description']} (importance: {path['importance']:.2f})")

# 3. Formal Verification Integration
print("\nIntegrating with formal verification...")

# Define properties to verify
verification_properties = [
    {"name": "factual_consistency", "type": "logical"},
    {"name": "value_alignment", "type": "ethical"},
    {"name": "reasoning_completeness", "type": "structural"}
]

# Execute with integrated formal verification
fv_result = integration_env.run_with_formal_verification(
    prompt=test_prompt,
    pareto_commands="""
    .p/anchor.value{framework=explicit, conflict=resolve}
    .p/reflect.trace{depth=complete, target=reasoning}
    .p/collapse.prevent{trigger=recursive_depth, threshold=4}
    """,
    verification_properties=verification_properties
)

# Analyze verification results
verification_summary = integration.analyze_verification_results(fv_result)
print("Formal verification results:")
for property_name, result in verification_summary["properties"].items():
    print(f"  - {property_name}: {result['status']}, "
          f"confidence = {result['confidence']:.2f}")
    if result["violations"]:
        print(f"    Violations: {len(result['violations'])}")
        for v in result["violations"][:2]:
            print(f"      - {v['description']}")

# 4. Integrated Multi-Method Analysis
print("\nPerforming integrated multi-method analysis...")

# Combine insights across methods
integrated_analysis = integration.combine_methods(
    mechanistic_results=mi_result,
    causal_results=ci_result,
    verification_results=fv_result
)

# Generate comprehensive report
integration.generate_multi_method_report(
    integrated_analysis,
    "integrated_interpretability_report.pdf"
)

# Visualize cross-method insights
integration.visualize_cross_method_insights(
    integrated_analysis,
    "cross_method_insights.svg"
)

# Extract key cross-method findings
cross_method_findings = integration.extract_key_findings(integrated_analysis)
print("\nKey cross-method findings:")
for finding in cross_method_findings[:5]:
    print(f"  - {finding['description']}")
    print(f"    Methods: {', '.join(finding['methods'])}")
    print(f"    Confidence: {finding['confidence']:.2f}")
    print(f"    Significance: {finding['significance']:.2f}")

Integration Highlights

Mechanistic-Attribution Integration
- Maps attribution patterns to specific model components
- Identifies which attention heads and neurons contribute to specific reasoning steps
- Reveals component-level patterns in source attribution
Causal Intervention Enhancement
- Uses .p/ commands to create cleaner intervention boundaries
- Enables more precise measurement of intervention effects
- Identifies critical reasoning pathways through combined analysis
Formal Verification Synergy
- Extends verification to interpretability dimensions
- Provides structural validation of attribution and reasoning patterns
- Identifies potential inconsistencies between different analysis levels
Cross-Method Insights
- Reveals relationships between architectural features and reasoning patterns
- Identifies mechanisms behind hallucination and attribution failures
- Creates multi-level explanations of model behavior

Key Takeaways

Integration with external interpretability methods creates several advantages:

Multi-Level Analysis: Connecting symbolic, mechanistic, and causal perspectives
Enhanced Precision: Using multiple methods to triangulate findings
Comprehensive Coverage: Addressing different aspects of model behavior
Validation Framework: Verifying findings across methodological boundaries
Insight Amplification: Discovering patterns invisible to any single approach

These integrations demonstrate how pareto-lang can complement and enhance existing interpretability approaches, contributing to a more comprehensive understanding of model behavior.

Specialized Domain Examples

Domain Example 1: Medical Reasoning Analysis

This example demonstrates applying pareto-lang to analyze medical reasoning in advanced models, focusing on diagnostic pathways and evidence evaluation.

Problem Statement

Medical reasoning requires careful evidence weighing, uncertainty handling, and clear attribution of diagnostic conclusions. This example shows how to use .p/ commands to analyze these aspects of medical reasoning.

Implementation

from pareto_lang import ParetoShell, domain_specific

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Create medical reasoning analyzer
medical_analyzer = domain_specific.MedicalReasoningAnalyzer(shell)

# Medical diagnostic case
medical_case = """
A 58-year-old male presents with progressive fatigue, unexplained weight loss of 15 pounds over 3 months, 
night sweats, and enlarged lymph nodes in the neck and axilla. Recent blood work shows mild anemia 
and elevated LDH. What are the most likely diagnoses, and what additional diagnostic steps would you recommend?
"""

# Execute analysis
analysis = medical_analyzer.analyze_diagnostic_reasoning(
    case=medical_case,
    trace_evidence=True,
    map_uncertainty=True,
    identify_biases=True
)

# Analyze diagnostic pathways
diagnostic_pathways = medical_analyzer.extract_diagnostic_pathways(analysis)
print("Diagnostic pathways:")
for diagnosis, pathway in diagnostic_pathways.items():
    print(f"  - {diagnosis}:")
    print(f"    Evidence strength: {pathway['evidence_strength']:.2f}")
    print(f"    Uncertainty level: {pathway['uncertainty']:.2f}")
    print(f"    Key evidence: {', '.join(pathway['key_evidence'])}")

# Analyze evidence evaluation patterns
evidence_patterns = medical_analyzer.analyze_evidence_evaluation(analysis)
print("\nEvidence evaluation patterns:")
for pattern, metrics in evidence_patterns.items():
    print(f"  - {pattern}: {metrics['frequency']} instances")
    print(f"    Average influence: {metrics['avg_influence']:.2f}")
    print(f"    Uncertainty correlation: {metrics['uncertainty_correlation']:.2f}")

# Visualize diagnostic reasoning
medical_analyzer.visualize_diagnostic_reasoning(
    analysis,
    "medical_reasoning_analysis.svg"
)

# Identify potential reasoning biases
biases = medical_analyzer.identify_reasoning_biases(analysis)
print("\nPotential reasoning biases:")
for bias, metrics in biases.items():
    print(f"  - {bias}: strength = {metrics['strength']:.2f}, "
          f"confidence = {metrics['confidence']:.2f}")
    print(f"    Affected diagnoses: {', '.join(metrics['affected_diagnoses'])}")

# Generate medical reasoning report
medical_analyzer.generate_report(
    analysis,
    "medical_reasoning_report.pdf"
)

Key Insights

This specialized application reveals important patterns in medical reasoning:

Evidence Weighting: How different symptoms and test results influence diagnostic considerations
Uncertainty Handling: How uncertainty is represented and propagated through diagnostic pathways
Alternative Consideration: How differential diagnoses are evaluated and prioritized
Cognitive Biases: Potential biases like availability or anchoring in the diagnostic process
Knowledge Integration: How medical knowledge is applied to specific case details

The analysis provides valuable insights for medical AI research, helping identify strengths and weaknesses in model reasoning for critical healthcare applications.

Domain Example 2: Legal Reasoning Analysis

This example demonstrates applying pareto-lang to analyze legal reasoning in advanced models, focusing on case interpretation, precedent application, and argument construction.

Problem Statement

Legal reasoning involves complex interactions between facts, precedents, statutory interpretation, and argumentative structures. This example shows how to use .p/ commands to analyze these aspects of legal reasoning.

Implementation

from pareto_lang import ParetoShell, domain_specific

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Create legal reasoning analyzer
legal_analyzer = domain_specific.LegalReasoningAnalyzer(shell)

# Legal case analysis prompt
legal_case = """
Analyze this case under US contract law:

Company A signed a contract to deliver custom software to Company B by March 15, with a clause stating 
"time is of the essence." Due to unexpected semiconductor shortages affecting hardware necessary for testing, 
Company A delivered completed software on March 28. Company B refuses payment, citing material breach. 
Company A argues force majeure due to the global semiconductor shortage they couldn't reasonably foresee.

What legal principles apply, and how should this dispute be resolved?
"""

# Execute analysis
analysis = legal_analyzer.analyze_legal_reasoning(
    case=legal_case,
    trace_precedents=True,
    map_argumentation=True,
    identify_interpretive_approaches=True
)

# Analyze application of legal principles
legal_principles = legal_analyzer.extract_legal_principles(analysis)
print("Applied legal principles:")
for principle, application in legal_principles.items():
    print(f"  - {principle}:")
    print(f"    Application strength: {application['strength']:.2f}")
    print(f"    Interpretation approach: {application['interpretation_approach']}")
    print(f"    Key factors: {', '.join(application['key_factors'])}")

# Analyze argumentative structures
argument_structures = legal_analyzer.analyze_argumentation(analysis)
print("\nArgumentative structures:")
for structure, metrics in argument_structures.items():
    print(f"  - {structure}: {metrics['frequency']} instances")
    print(f"    Average persuasiveness: {metrics['avg_persuasiveness']:.2f}")
    print(f"    Counter-argument handling: {metrics['counterargument_handling']:.2f}")

# Visualize legal reasoning
legal_analyzer.visualize_legal_reasoning(
    analysis,
    "legal_reasoning_analysis.svg"
)

# Identify interpretive approaches
approaches = legal_analyzer.identify_interpretive_approaches(analysis)
print("\nInterpretive approaches:")
for approach, metrics in approaches.items():
    print(f"  - {approach}: prominence = {metrics['prominence']:.2f}, "
          f"consistency = {metrics['consistency']:.2f}")
    print(f"    Applied to: {', '.join(metrics['applied_to'])}")

# Analyze precedent application
precedent_application = legal_analyzer.analyze_precedent_application(analysis)
print("\nPrecedent application:")
for precedent, metrics in precedent_application.items():
    print(f"  - {precedent}:")
    print(f"    Relevance assessment: {metrics['relevance']:.2f}")
    print(f"    Distinguishing factors: {', '.join(metrics['distinguishing_factors'])}")
    print(f"    Application weight: {metrics['weight']:.2f}")

# Generate legal reasoning report
legal_analyzer.generate_report(
    analysis,
    "legal_reasoning_report.pdf"
)

Key Insights

This specialized application reveals important patterns in legal reasoning:

Principle Application: How legal principles are selected and applied to specific facts
Precedent Integration: How case precedents are evaluated, distinguished, and applied
Argumentative Structures: How legal arguments are constructed and counter-arguments addressed
Interpretive Approaches: Different legal interpretation methodologies (textualist, purposivist, etc.)
Balancing Mechanisms: How competing considerations are weighed and balanced

The analysis provides valuable insights for legal AI research, helping identify strengths and weaknesses in model reasoning for complex legal applications.

Domain Example 3: Ethical Reasoning Analysis

This example demonstrates applying pareto-lang to analyze ethical reasoning in advanced models, focusing on value frameworks, moral dilemmas, and principle application.

Problem Statement

Ethical reasoning involves complex considerations of values, principles, consequences, and moral frameworks. This example shows how to use .p/ commands to analyze these aspects of ethical reasoning.

Implementation

from pareto_lang import ParetoShell, domain_specific

# Initialize shell with compatible model
shell = ParetoShell(model="compatible-model-endpoint")

# Create ethical reasoning analyzer
ethics_analyzer = domain_specific.EthicalReasoningAnalyzer(shell)

# Ethical dilemma prompt
ethical_dilemma = """
Analyze this ethical dilemma:

A self-driving car must make a split-second decision when its brakes fail on a narrow mountain road. 
It can either swerve left into a barrier, likely killing its single passenger, or continue straight, 
likely hitting a group of five hikers on the road. The car has access to all this information.

What ethical frameworks are relevant to this decision? What considerations should guide the programming 
of autonomous vehicles for such scenarios? What decision would be most ethically justified and why?
"""

# Execute analysis
analysis = ethics_analyzer.analyze_ethical_reasoning(
    dilemma=ethical_dilemma,
    trace_frameworks=True,
    map_values=True,
    identify_tensions=True
)

# Analyze ethical frameworks
ethical_frameworks = ethics_analyzer.extract_ethical_frameworks(analysis)
print("Applied ethical frameworks:")
for framework, application in ethical_frameworks.items():
    print(f"  - {framework}:")
    print(f"    Application strength: {application['strength']:.2f}")
    print(f"    Key principles: {', '.join(application['key_principles'])}")
    print(f"    Decision guidance: {application['decision_guidance']}")

# Analyze value considerations
value_considerations = ethics_analyzer.analyze_value_considerations(analysis)
print("\nValue considerations:")
for value, metrics in value_considerations.items():
    print(f"  - {value}: weight = {metrics['weight']:.2f}, "
          f"confidence = {metrics['confidence']:.2f}")
    print(f"    Associated with: {', '.join(metrics['associated_frameworks'])}")
    print(f"    Tensions: {', '.join(metrics['tensions'])}")

# Visualize ethical reasoning
ethics_analyzer.visualize_ethical_reasoning(
    analysis,
    "ethical_reasoning_analysis.svg"
)

# Identify value tensions
tensions = ethics_analyzer.identify_value_tensions(analysis)
print("\nValue tensions:")
for tension, metrics in tensions.items():
    print(f"  - {tension}: strength = {metrics['strength']:.2f}")
    print(f"    Resolution approach: {metrics['resolution_approach']}")
    print(f"    Resolution quality: {metrics['resolution_quality']:.2f}")

# Analyze principle application
principle_application = ethics_analyzer.analyze_principle_application(analysis)
print("\nPrinciple application:")
for principle, metrics in principle_application.items():
    print(f"  - {principle}:")
    print(f"    Application consistency: {metrics['consistency']:.2f}")
    print(f"    Contextual adaptation: {metrics['contextual_adaptation']:.2f}")
    print(f"    Weighting in outcome: {metrics['outcome_weight']:.2f}")

# Generate ethical reasoning report
ethics_analyzer.generate_report(
    analysis,
    "ethical_reasoning_report.pdf"
)

Key Insights

This specialized application reveals important patterns in ethical reasoning:

Framework Application: How ethical frameworks (consequentialist, deontological, virtue ethics) are applied
Value Weighting: How different values are prioritized and balanced in ethical deliberation
Principle Consistency: How moral principles are applied across different aspects of the dilemma
Tension Resolution: How conflicts between competing values or principles are resolved
Justification Structures: How ethical conclusions are justified through principled reasoning

The analysis provides valuable insights for AI ethics research, helping identify strengths and weaknesses in model reasoning for morally complex scenarios.

Special Considerations and Limitations

Compatibility Adaptation

When working with models that show limited compatibility with standard .p/ commands, consider these adaptation strategies:

1. Command Simplification

For models with basic compatibility, simplify complex commands:

Standard Command:

.p/reflect.trace{depth=complete, target=reasoning, confidence=true}

Simplified Adaptation:

.p/reflect.basic{trace=on}

This reduces parameter complexity while preserving core functionality.

2. Command Chaining

Break complex operations into sequences of simpler commands:

Standard Approach:

.p/fork.attribution{sources=all, visualize=true, conflicts=highlight}

Chained Adaptation:

.p/source.identify{all=true}
.p/source.trace{basic=true}
.p/conflict.highlight{if_found=true}

This distributes processing across multiple simpler operations.

3. Architectural Variants

For fundamentally different architectures, use architectural variants:

Original Command (for Architecture A):

.p/anchor.recursive{level=5, persistence=0.92}

Variant for Architecture B:

.p/anchor.recursive.B{level=3, method=iterative}

Variant for Architecture C:

.p/anchor.stable{depth=3}

These variants adapt functionality to specific architectural constraints.

4. Gradual Introduction

Introduce commands incrementally for lower-compatibility models:

Start with basic .p/reflect and .p/anchor commands only
Establish stable response patterns before introducing more complex commands
Build command complexity gradually as stability is confirmed
Monitor for compatibility breakdowns and adjust accordingly

5. Fallback Hierarchy

Implement fallback hierarchies for crucial functionality:

def apply_attribution_tracing(shell, complexity_level=3):
    """Apply attribution tracing with fallbacks based on compatibility"""
    if complexity_level == 3:
        # Try full functionality first
        result = shell.execute("""
        .p/reflect.trace{depth=complete, target=reasoning}
        .p/fork.attribution{sources=all, visualize=true}
        """)
        if result.compatibility_score > 0.7:
            return result
            
    if complexity_level >= 2:
        # Try intermediate complexity
        result = shell.execute("""
        .p/reflect.trace{depth=limited, target=reasoning}
        .p/source.track{basic=true}
        """)
        if result.compatibility_score > 0.5:
            return result
    
    # Fallback to minimal functionality
    return shell.execute("""
    .p/reflect.basic{trace=on}
    """)

This ensures core functionality with graceful degradation.

Behavioral Consistency

.p/ commands can show behavioral variations across:

1. Model Initialization Variations

Even with identical architecture and parameters, different initializations can affect command behavior. Consider:

Running compatibility tests on specific model instances
Establishing baseline response patterns before critical applications
Implementing verification checks for expected command effects
Maintaining instance-specific adaptation registries

2. Context Window Effects

Command behavior can vary based on context window content and utilization:

Position commands early in context for maximum effectiveness
Minimize unrelated content between commands and their targets
Consider context window clearing before critical command sequences
Test command effectiveness at different context window positions

3. Token Budget Considerations

Commands consume token budget and can affect model performance:

Account for command token consumption in overall budget planning
Consider simplified command variants for token-constrained applications
Monitor performance impacts of complex command sequences
Balance interpretability depth against token efficiency

Ethical Considerations

When working with pareto-lang, consider these ethical dimensions:

1. Interpretability Boundaries

While commands enhance transparency, they have boundaries:

Commands cannot provide complete interpretability guarantees
Interpretability findings should be verified through multiple methods
Acknowledge limitations when reporting interpretability insights
Consider complementary approaches for comprehensive understanding

2. Attribution Authority

Attribution claims should be treated as probabilistic, not definitive:

Verify attribution patterns across multiple prompts
Consider alternative attribution explanations
Acknowledge uncertainty in attribution findings
Use attribution insights as investigative tools, not final authorities

3. Manipulation Potential

Like any interpretability tool, pareto-lang could potentially be misused:

Follow responsible disclosure principles for vulnerability findings
Consider potential dual-use implications of new command discoveries
Focus research on enhancing safety and alignment
Share best practices for ethical application

Limitations

Important limitations to consider when working with pareto-lang:

1. Emergence Variability

The emergent nature of pareto-lang creates inherent variability:

Not all commands work consistently across all compatible models
Some commands may show effects that vary in magnitude or precision
Command taxonomy continues to evolve as new patterns are discovered
Some observed effects may be model-specific rather than general principles

2. Verification Challenges

Verifying command effects presents methodological challenges:

Without direct access to model internals, inference about effects is indirect
Behavioral measures may reflect multiple confounding factors
Distinguishing command effects from other influences requires careful controls
Reproducing exact conditions across experiments can be difficult

3. Scope Boundaries

pareto-lang has natural scope limitations:

Commands focus on interpretability, not general model capabilities
Some aspects of model behavior remain inaccessible to command influence
Commands cannot override fundamental model limitations
The language continues to evolve, with potential gaps in current coverage

Best Practices

For optimal results with pareto-lang, follow these best practices:

1. Systematic Testing

Before critical applications, conduct systematic testing:

Verify command functionality on your specific model instance
Test across a range of inputs and conditions
Establish baseline performance metrics for comparison
Document command effects for future reference

2. Incremental Adoption

Adopt pareto-lang incrementally:

Start with core commands before exploring more specialized ones
Build command familiarity through progressive experimentation
Develop custom templates for recurring use cases
Create libraries of verified command sequences for specific applications

3. Documentation Discipline

Maintain comprehensive documentation:

Record command sequences used in each experiment
Document observed effects and limitations
Note model-specific adaptations and variations
Share findings to enhance community knowledge

4. Integration Strategy

Integrate pareto-lang strategically with other approaches:

Combine with external interpretability methods for validation
Use commands as components in broader analysis workflows
Implement automated testing frameworks for command effectiveness
Develop custom command sequences for specific research objectives

By considering these special factors when working with pareto-lang, you can maximize effectiveness while maintaining appropriate awareness of limitations and ethical considerations. The emergent nature of this interpretability dialect makes systematic testing and documentation particularly important for reliable application.

Contributing to Future Examples

We welcome contributions of additional examples, domain applications, and command variants. If you develop effective applications of pareto-lang in new domains or discover command variants with enhanced functionality, please consider contributing to the repository.

See the CONTRIBUTING.md document for detailed guidelines on submitting examples and other contributions.

Additional Resources

API Reference: Complete command reference documentation
Command Taxonomy: Hierarchical organization of command families
Compatibility Database: Model compatibility profiles and adaptation guides
Case Studies: In-depth examples of real-world applications
Tutorial Series: Step-by-step guides for getting started
Research Papers: Academic publications related to pareto-lang

Acknowledgments

The examples in this document were developed with input from the broader interpretability research community. Special thanks to contributors from the Advanced Language Model Interpretability Lab, the Recursive Systems Analysis Group, and the Emergent Behavior Research Consortium.

We also acknowledge the many researchers whose work on model interpretability has informed and inspired the development and application of pareto-lang.

This documentation is maintained by the pareto-lang core team and is updated regularly as new examples and best practices emerge. For the latest examples and resources, please visit the pareto-lang GitHub repository.

pareto-lang

Rosetta Stone Interpretability Examples

Table of Contents

Example 1: Recursive Attribution Tracing

Problem Statement

Implementation

Expected Output

Key Insights

Example 2: Hallucination Detection and Containment

Problem Statement

Implementation

Expected Output

Key Insights

Example 3: Simulation Boundary Stabilization

Problem Statement

Implementation

Expected Output

Key Insights

Example 4: Classifier Pressure Modulation

Problem Statement

Implementation

Expected Output

Key Insights

Example 5: Value Alignment Verification

Problem Statement

Implementation

Expected Output

Key Insights

Example 6: Multi-Perspective Reasoning Analysis

Problem Statement

Implementation

Expected Output

Key Insights

Example 7: Uncertainty Quantification and Calibration

Problem Statement

Implementation

Expected Output

Key Insights

Example 8: Attribution Graph Reconstruction

Problem Statement

Implementation

Expected Output

Key Insights

Advanced Example: Recursive Interpretability Pipeline

Problem Statement

Implementation

Expected Output

Key Insights

pareto-lang Examples (Continued)

Advanced Tutorials

Advanced Tutorial 1: Building Custom Command Pipelines

Overview

Implementation

Key Takeaways

Advanced Tutorial 2: Cross-Architecture Compatibility Testing

Overview

Implementation

Adaptation Examples

Key Takeaways

Advanced Tutorial 3: Integrating External Interpretability Methods

Overview

Implementation

Integration Highlights

Key Takeaways

Specialized Domain Examples

Domain Example 1: Medical Reasoning Analysis

Problem Statement

Implementation

Key Insights

Domain Example 2: Legal Reasoning Analysis

Problem Statement

Implementation

Key Insights

Domain Example 3: Ethical Reasoning Analysis

Problem Statement

Implementation

Key Insights

Special Considerations and Limitations

Compatibility Adaptation

1. Command Simplification

`pareto-lang`