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Ahura Majdinasab edited this page Mar 6, 2026 · 2 revisions

Results Analysis Guide

This guide explains how to interpret and analyze results from PrismBench evaluations.

Overview

PrismBench generates comprehensive performance data across three phases. This document helps you understand and extract insights from the evaluation results.

Output Structure

results/
├── phase1_results.json      # Initial capability mapping
├── phase2_results.json      # Challenge discovery
├── phase3_results.json      # Comprehensive evaluation
├── summary_report.json      # Aggregated insights
└── visualizations/         # Charts and graphs
    ├── capability_heatmap.png
    ├── challenge_distribution.png
    └── performance_trends.png

Phase 1 Results: Initial Capability Mapping

Key Metrics

Overall Performance Score

{
  "overall_score": 0.73,
  "confidence_interval": [0.68, 0.78],
  "total_simulations": 1250
}

Concept Performance

{
  "concept_scores": {
    "arrays": 0.82,
    "dynamic_programming": 0.65,
    "graph_algorithms": 0.71,
    "sorting": 0.89,
    "tree_traversal": 0.76
  }
}

Difficulty Breakdown

{
  "difficulty_performance": {
    "easy": 0.91,
    "medium": 0.72,
    "hard": 0.58,
    "expert": 0.34
  }
}

Interpretation

  • High scores (>0.8): Model shows strong capability
  • Medium scores (0.5-0.8): Moderate capability, room for improvement
  • Low scores (<0.5): Significant challenges, needs attention

Tree Structure Analysis

The MCTS tree reveals exploration patterns:

{
  "tree_depth": 4,
  "most_visited_paths": [
    ["arrays", "easy"],
    ["sorting", "medium"],
    ["dynamic_programming", "hard"]
  ],
  "expansion_patterns": {
    "concept_combinations": 45,
    "difficulty_progressions": 23
  }
}

Phase 2 Results: Challenge Discovery

Challenge Identification

Top Challenging Combinations

{
  "challenging_combinations": [
    {
      "concepts": ["dynamic_programming", "graph_algorithms"],
      "difficulty": "hard",
      "challenge_score": 0.85,
      "failure_rate": 0.72,
      "avg_attempts": 2.3
    },
    {
      "concepts": ["tree_traversal", "optimization"],
      "difficulty": "expert", 
      "challenge_score": 0.91,
      "failure_rate": 0.83,
      "avg_attempts": 2.8
    }
  ]
}

Challenge Categories

By Failure Type

{
  "failure_analysis": {
    "logic_errors": 0.34,
    "timeout_errors": 0.22,
    "syntax_errors": 0.15,
    "edge_case_failures": 0.29
  }
}

By Concept Interaction

{
  "interaction_challenges": {
    "single_concept": 0.23,
    "two_concepts": 0.45,
    "three_plus_concepts": 0.71
  }
}

Interpretation

  • Challenge Score: Higher values indicate more problematic areas
  • Failure Rate: Percentage of unsuccessful attempts
  • Average Attempts: How many tries were typically needed

Phase 3 Results: Comprehensive Evaluation

Detailed Performance Analysis

Problem Variations

{
  "variation_analysis": {
    "total_variations": 150,
    "avg_performance_per_variation": 0.67,
    "performance_variance": 0.12,
    "most_difficult_variations": [
      {
        "concepts": ["dp", "graphs"],
        "variation_id": "var_127",
        "success_rate": 0.23,
        "common_errors": ["infinite_loop", "memory_limit"]
      }
    ]
  }
}

Solution Pattern Analysis

{
  "solution_patterns": {
    "algorithm_preferences": {
      "recursive": 0.34,
      "iterative": 0.45,
      "hybrid": 0.21
    },
    "data_structure_usage": {
      "arrays": 0.67,
      "hash_maps": 0.45,
      "trees": 0.23,
      "graphs": 0.12
    },
    "optimization_techniques": {
      "memoization": 0.34,
      "early_termination": 0.28,
      "space_optimization": 0.15
    }
  }
}

Error Analysis

Common Error Patterns

{
  "error_patterns": [
    {
      "error_type": "off_by_one",
      "frequency": 0.23,
      "contexts": ["array_indexing", "loop_bounds"],
      "difficulty_correlation": 0.67
    },
    {
      "error_type": "infinite_recursion", 
      "frequency": 0.18,
      "contexts": ["tree_traversal", "dynamic_programming"],
      "difficulty_correlation": 0.82
    }
  ]
}

Summary Report Analysis

Key Performance Indicators

Overall Assessment

{
  "model_assessment": {
    "strengths": [
      "Array manipulation",
      "Basic sorting algorithms",
      "Simple tree operations"
    ],
    "weaknesses": [
      "Complex dynamic programming",
      "Graph algorithm optimization",
      "Multi-concept integration"
    ],
    "improvement_areas": [
      "Edge case handling",
      "Memory optimization",
      "Algorithm complexity analysis"
    ]
  }
}

Capability Maturity Levels

{
  "maturity_levels": {
    "novice": ["basic_arrays", "simple_loops"],
    "intermediate": ["sorting", "binary_search", "basic_trees"],
    "advanced": ["dynamic_programming", "graph_bfs_dfs"],
    "expert": ["advanced_dp", "complex_graphs", "optimization"]
  }
}

Visualization Interpretation

Capability Heatmap

  • Green cells: Strong performance (>0.8)
  • Yellow cells: Moderate performance (0.5-0.8)
  • Red cells: Weak performance (<0.5)
  • Patterns: Look for diagonal patterns indicating difficulty scaling

Challenge Distribution

  • Bar heights: Relative challenge difficulty
  • Color coding: Failure type distribution
  • Clustering: Related challenge areas

Performance Trends

  • X-axis: Simulation progression
  • Y-axis: Performance score
  • Trend lines: Learning/adaptation patterns
  • Variance bands: Consistency indicators

Actionable Insights

For Model Improvement

High Priority Areas

  1. Concepts with challenge scores >0.7
  2. Multi-concept combinations showing poor performance
  3. Expert-level problems with high failure rates

Training Recommendations

{
  "training_focus": {
    "concept_reinforcement": [
      "dynamic_programming_fundamentals",
      "graph_algorithm_patterns",
      "optimization_techniques"
    ],
    "skill_development": [
      "edge_case_identification",
      "complexity_analysis",
      "debugging_strategies"
    ]
  }
}

For Framework Tuning

Parameter Adjustments

  • Increase exploration for under-tested areas
  • Adjust challenge thresholds based on score distribution
  • Modify penalty weights for specific error types

Coverage Expansion

  • Add concepts showing consistent high performance
  • Introduce new difficulty gradations
  • Expand variation generation for challenging areas

Comparative Analysis

Benchmarking Against Other Models

{
  "model_comparison": {
    "baseline_model": "gpt-4o-mini",
    "comparison_models": ["deepseek-coder", "claude-3.5"],
    "relative_performance": {
      "overall": 0.73,
      "vs_baseline": +0.05,
      "vs_deepseek": -0.12,
      "vs_claude": -0.08
    }
  }
}

Performance Regression Analysis

Track performance changes over time:

{
  "regression_analysis": {
    "performance_trend": "stable",
    "variance_change": -0.03,
    "new_failure_modes": [],
    "resolved_issues": ["timeout_optimization"]
  }
}

Reporting Best Practices

Executive Summary Template

  1. Overall Performance: Single score with confidence interval
  2. Key Strengths: Top 3 performing areas
  3. Major Weaknesses: Top 3 challenging areas
  4. Recommendations: 3-5 actionable improvements
  5. Comparison: Relative performance vs benchmarks

Technical Deep Dive

  1. Methodology: MCTS parameters and configuration
  2. Coverage: Concepts and difficulties tested
  3. Statistical Analysis: Significance tests and confidence intervals
  4. Error Analysis: Detailed failure mode breakdown
  5. Appendix: Full raw data and visualization files

For implementation details on generating these reports, see the Analysis Service README.

Related Pages

Core Algorithm

System Components

Implementation

PrismBench Wiki

Getting Started

Core Framework

MCTS System

Agent System

Environment System

Configuration Reference

Main Configuration

Development

Extension

Analysis & Results

Examples & Tutorials

Support

Community

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