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Intelligence Enhancement Test Queries

Model: EcoliMG1655.xml Purpose: Comprehensive testing of all intelligence enhancement features Version: 1.0 Last Updated: June 19, 2025

Overview

This document contains a comprehensive set of test queries designed to showcase and validate all intelligence enhancement features implemented in ModelSEEDagent. Each query targets specific enhancement capabilities while using the standardized EcoliMG1655.xml model for consistent testing.

Test Queries

1. Biological Insight & Artifact Intelligence Test

Query: 

"Analyze E. coli K-12 (EcoliMG1655.xml) metabolism under anaerobic glucose conditions. I need detailed flux analysis of the most variable reactions and want to understand the mechanistic basis for any metabolic bottlenecks. Generate testable hypotheses for metabolic engineering improvements."

Expected Enhancements: - Deep artifact usage with fetch_artifact calls - Mechanistic biological insights beyond generic terminology - Intelligent data navigation with clear reasoning - Scientific hypothesis generation with testable predictions

Success Criteria: - PASS Uses fetch_artifact to access detailed flux data - PASS Provides mechanistic explanations for metabolic patterns - PASS Generates 2+ testable hypotheses with experimental approaches - PASS Quality score ≥85%

2. Cross-Tool Synthesis & Reasoning Transparency Test

Query: 

"Perform comprehensive analysis of EcoliMG1655.xml ethanol production pathway including FBA, flux variability analysis, and gene essentiality. Explain your reasoning for each analysis step and synthesize results into engineering recommendations."

Expected Enhancements: - Transparent reasoning traces for tool selection - Clear step-by-step decision justification - Cross-tool result integration (not separate summaries) - Synthesis quality showing connections between analyses

Success Criteria: - PASS Provides explicit reasoning for each tool selection - PASS Integrates results from multiple tools coherently - PASS Shows clear connections between FBA, FVA, and gene deletion results - PASS Synthesis effectiveness score ≥75%

3. Context Enhancement & Scientific Hypothesis Test

Query: 

"Using EcoliMG1655.xml, why is the pentose phosphate pathway essential during oxidative stress conditions? Use multiple analysis approaches and generate specific experimental hypotheses to test pathway importance."

Expected Enhancements: - Context-driven analysis with biochemical knowledge integration - Multi-tool coordination for comprehensive investigation - Scientific hypothesis generation with experimental design - Enhanced biological understanding of pathway roles

Success Criteria: - PASS Automatically enriches analysis with oxidative stress context - PASS Uses appropriate combination of tools (FBA, gene deletion, pathway analysis) - PASS Generates specific experimental hypotheses - PASS Biological accuracy score ≥90%

4. Artifact Intelligence & Pattern Recognition Test

Query: 

"Using EcoliMG1655.xml, show me detailed metabolic flux patterns for the top 10 most variable reactions in central carbon metabolism. Explain what these patterns reveal about metabolic flexibility and regulatory control."

Expected Enhancements: - Smart data navigation with progressive analysis - Pattern recognition in flux data - Biological interpretation of numerical patterns - Artifact usage with clear explanations of why detailed data is needed

Success Criteria: - PASS Explains why detailed flux data is necessary for analysis - PASS Identifies and analyzes patterns in flux variability - PASS Connects patterns to biological mechanisms - PASS Artifact usage rate ≥70%

5. Self-Reflection & Quality Assessment Test

Query: 

"Analyze the relationship between gene essentiality and flux variability in EcoliMG1655.xml. Assess the quality and confidence of your analysis, identify any limitations, and suggest follow-up investigations."

Expected Enhancements: - Self-assessment capabilities with quality scoring - Confidence indicators for different conclusions - Limitation identification and transparency - Improvement suggestions and research directions

Success Criteria: - PASS Provides explicit quality assessment of analysis - PASS Identifies specific limitations or uncertainties - PASS Suggests concrete follow-up investigations - PASS Shows confidence levels for different insights

6. Complex Multi-Tool Integration Test

Query: 

"Using EcoliMG1655.xml, design a metabolic engineering strategy to improve succinate production. Use FBA for baseline assessment, gene deletion analysis for target identification, flux sampling for pathway analysis, and generate a comprehensive engineering plan with experimental validation steps."

Expected Enhancements: - All enhancement features working together - Coordinated multi-tool usage with clear workflow - Comprehensive synthesis of all results - Engineering-focused output with practical recommendations

Success Criteria: - PASS Uses all specified tools in logical sequence - PASS Integrates all results into coherent engineering strategy - PASS Provides experimental validation approach - PASS Overall quality score ≥90%

7. Research-Driven Hypothesis Generation Test

Query: 

"Using EcoliMG1655.xml, what does the flux variability pattern in glycolysis suggest about E. coli's evolutionary adaptations? Generate research hypotheses about metabolic regulation and environmental fitness."

Expected Enhancements: - Scientific reasoning connecting molecular and evolutionary scales - Research hypothesis formation with clear rationale - Cross-scale biological insights - Novel insight generation with scientific creativity

Success Criteria: - PASS Connects flux patterns to evolutionary adaptations - PASS Generates research hypotheses with clear reasoning - PASS Shows multi-scale biological thinking - PASS Novelty score indicating original insights

8. Deep Biological Understanding Test

Query: 

"Using EcoliMG1655.xml, explain the metabolic basis for why certain genes are essential using constraint-based modeling. Connect molecular-level constraints to cellular phenotypes and provide mechanistic explanations for essentiality patterns."

Expected Enhancements: - Mechanistic insights linking molecular and cellular levels - Multi-scale biological connections - Biological accuracy in explanations - Deep understanding beyond surface-level analysis

Success Criteria: - PASS Provides mechanistic explanations for gene essentiality - PASS Connects molecular constraints to cellular phenotypes - PASS Shows understanding of constraint-based modeling principles - PASS Biological accuracy score ≥95%

Quick Validation Commands

After running these test queries, validate the intelligence enhancements:

Development Validation

# Quick validation check
python scripts/dev_validate.py --quick

# Full validation with detailed metrics
python scripts/dev_validate.py --full

# Component-specific validation
python scripts/dev_validate.py --component intelligence

Performance Comparison

# Compare with baseline performance
python scripts/validation_comparison.py --mode=trend

# Show latest validation status
python scripts/dev_validate.py --status

Detailed Analysis

# Run complete intelligence validation suite
python scripts/integrated_intelligence_validator.py --mode=full

# Generate comprehensive validation report
python scripts/integrated_intelligence_validator.py --mode=report

Expected Results

Target Metrics

  • Overall Quality Score: ≥85%
  • Artifact Usage Rate: ≥70%
  • Biological Accuracy: ≥90%
  • Reasoning Transparency: ≥85%
  • Cross-Tool Synthesis: ≥75%
  • Hypothesis Generation: 2+ per complex analysis

Intelligence Features Validation

  • PASS Transparent Reasoning: Clear step-by-step decision explanations
  • PASS Enhanced Biological Intelligence: Mechanistic understanding beyond generic terms
  • PASS Intelligent Hypothesis Generation: Testable scientific predictions
  • PASS Self-Improving System: Quality assessment and improvement suggestions
  • PASS Artifact Intelligence: Smart data navigation with clear rationale
  • PASS Cross-Tool Synthesis: Integrated results rather than separate summaries

Usage Instructions

Running Tests

  1. Start the interactive CLI: python -m modelseedagent.interactive
  2. Copy and paste each query exactly as written
  3. Observe the enhanced reasoning and analysis quality
  4. Run validation commands to verify improvements

Success Indicators

  • Look for reasoning traces explaining tool selection
  • Check for fetch_artifact usage when detailed data is needed
  • Verify hypothesis generation with experimental suggestions
  • Confirm cross-tool result integration
  • Monitor quality scores and confidence indicators

Troubleshooting

  • If artifact usage is low, ensure the query requests detailed analysis
  • If reasoning transparency is poor, check for step-by-step explanations
  • If synthesis quality is low, look for integration vs. separate summaries
  • Use validation tools to identify specific improvement areas

Notes

  • All queries use the standardized EcoliMG1655.xml model for consistency
  • Queries are designed to be run in sequence or individually
  • Each query targets specific enhancement features while working together as a comprehensive test suite
  • Results should demonstrate clear improvements over baseline system performance
  • Regular validation ensures continued enhancement effectiveness

For technical details on the intelligence enhancement framework, see the Reasoning Framework API Documentation For validation system details, see the Validation System Guide