# ML Agents Reasoning Research: Execution Plan

## Overview

This document outlines a systematic approach to conducting reasoning research using the ML Agents platform. The goal is to answer fundamental questions about when, why, and how different reasoning approaches improve AI model performance.

## Research Questions (Prioritized)

### Primary Questions

1. **Universal Benefit**: Do all tasks benefit from reasoning approaches?
2. **Task-Approach Fit**: Do certain tasks benefit more from specific reasoning methods?
3. **Cost-Benefit Analysis**: What are the time/cost tradeoffs for each approach?

### Secondary Questions

4. **Model Variability**: Do different models show varying benefits from reasoning?
5. **Approach Comparison**: How do reasoning approaches rank across different task types?
6. **Predictive Reasoning**: Can we predict reasoning effectiveness from prompt characteristics?

## Research Methodology

### Systematic Approach

- **Controlled Variables**: Same model, temperature, max_tokens across reasoning approaches
- **Randomization**: Shuffle dataset samples to avoid ordering bias
- **Replication**: Run multiple trials for statistical significance
- **Baseline Control**: Always include "None" (direct prompting) as control group

### Dataset Selection Criteria

1. **Diversity**: Cover different cognitive domains (logic, math, code, language)
2. **Difficulty**: Focus on HARD tasks where reasoning has room to improve
3. **Evaluation**: Clear right/wrong answers for objective measurement
4. **Size**: Balance statistical power with resource constraints

## Experiment Phases

### Phase 1: Proof of Concept (Week 1)

**Goal**: Validate platform and establish baseline patterns

**Dataset**: BBEH (Logical Reasoning)

- **Source**: `MrLight/bbeh-eval`
- **Sample Size**: 50 samples (testing)
- **Approaches**: 3 approaches (None, Chain-of-Thought, Tree-of-Thought)
- **Model**: `openai/gpt-oss-20b:free` (cost control)

**CLI Commands**:

```bash
# Baseline
ml-agents run --dataset bbeh-eval --provider openrouter --model "openai/gpt-oss-20b:free" \
  --approach None --samples 50 --temperature 0.3 --max-tokens 512

# Chain-of-Thought
ml-agents run --dataset bbeh-eval --provider openrouter --model "openai/gpt-oss-20b:free" \
  --approach ChainOfThought --samples 50 --temperature 0.3 --max-tokens 512

# Tree-of-Thought
ml-agents run --dataset bbeh-eval --provider openrouter --model "openai/gpt-oss-20b:free" \
  --approach TreeOfThought --samples 50 --temperature 0.3 --max-tokens 512
```

**Success Criteria**:

- All experiments complete without errors
- Clear performance differences observed
- Database properly captures results
- Export functionality generates clean CSV/JSON

**Expected Outcomes**:

- Establish baseline accuracy for logical reasoning
- Identify which reasoning approach shows most promise
- Measure execution time overhead (expect 2-5x increase)
- Generate first community-shareable results

### Phase 2: Approach Comparison (Week 2-3)

**Goal**: Comprehensive comparison of all reasoning approaches

**Dataset**: BBEH (Logical Reasoning) - continued

- **Sample Size**: 200 samples (statistical power)
- **Approaches**: All 8 approaches (None, CoT, PoT, RaP, Reflection, CoV, SoT, ToT)
- **Model**: `openai/gpt-oss-20b:free` or upgrade to `gpt-4o-mini` if budget allows

**Analysis Focus**:

- Ranking approaches by accuracy improvement over baseline
- Cost-benefit analysis (accuracy gain vs time/token cost)
- Identify failure patterns and edge cases
- Statistical significance testing

**Database Analysis** (using SQLite3 CLI):

```bash
# Approach ranking by accuracy
sqlite3 ./ml_agents_results.db "
SELECT approach_name,
       ROUND(AVG(CAST(is_correct AS FLOAT)), 3) as accuracy,
       ROUND(AVG(execution_time_ms), 1) as avg_time_ms,
       COUNT(*) as sample_count
FROM runs r
JOIN experiments e ON r.experiment_id = e.id
WHERE e.name LIKE '%BBEH%'
GROUP BY approach_name
ORDER BY accuracy DESC;"

# Cost-benefit analysis
sqlite3 ./ml_agents_results.db "
SELECT approach_name,
       ROUND(AVG(CAST(is_correct AS FLOAT)), 3) as accuracy,
       ROUND(AVG(cost_estimate), 6) as avg_cost,
       ROUND(AVG(execution_time_ms)/1000.0, 1) as avg_seconds,
       ROUND(AVG(CAST(is_correct AS FLOAT)) / NULLIF(AVG(cost_estimate), 0), 2) as accuracy_per_dollar
FROM runs r
JOIN experiments e ON r.experiment_id = e.id
WHERE e.name LIKE '%BBEH%' AND approach_name != 'None'
GROUP BY approach_name
ORDER BY accuracy_per_dollar DESC;"

# Export results to CSV for further analysis
sqlite3 -header -csv ./ml_agents_results.db "
SELECT r.*, e.name as experiment_name
FROM runs r
JOIN experiments e ON r.experiment_id = e.id
WHERE e.name LIKE '%BBEH%';" > bbeh_results.csv
```

### Phase 3: Cross-Dataset Validation (Week 4-5)

**Goal**: Test approach generalizability across task types

**Datasets** (in order of priority):

1. **MATH Easy** (`ck46/hendrycks_math`) - Mathematical reasoning
2. **StrategyQA** (`ChilleD/StrategyQA`) - Common sense reasoning
3. **MBPP** (`Muennighoff/mbpp`) - Code generation
4. **GSM8K** - Grade school math problems

**Sample Size**: 100 samples per dataset (resource management)
**Approaches**: Top 3 performing from Phase 2 + baseline

**Research Questions**:

- Do reasoning approach rankings change across domains?
- Which approaches are domain-specific vs domain-general?
- Are mathematical tasks better suited for Program-of-Thought?
- Do logical reasoning skills transfer to other domains?

### Phase 4: Deep Analysis & Insights (Week 6)

**Goal**: Generate actionable insights and community contributions

**Analysis Tasks**:

1. **Meta-analysis**: Cross-dataset patterns and trends
2. **Failure Analysis**: Common failure modes per approach
3. **Prompt Engineering**: Identify prompt characteristics that predict reasoning benefit
4. **Cost Modeling**: Resource consumption patterns

**Deliverables**:

- Research paper draft
- Community presentation slides
- Dataset recommendations
- Best practices guide

## Technical Implementation Details

### Database Schema Utilization

- **experiments**: Track each research phase
- **runs**: Individual reasoning attempts
- **parsing_metrics**: Answer extraction quality
- Use experiment_id to group related runs

### Export Workflows

```bash
# Export Phase 1 results
ml-agents export EXPERIMENT_ID --format excel --output phase1_bbeh_results.xlsx

# Generate comparison report
ml-agents compare-experiments "exp1,exp2,exp3" --output approach_comparison.json

# Database statistics
ml-agents db-stats --db-path ./ml_agents_results.db
```

### Quality Assurance

- Validate parsing accuracy with manual spot checks
- Monitor for API failures and retry mechanisms
- Track token usage and costs per experiment
- Regular database backups before major runs

## Success Metrics & Analysis

### Primary Metrics

- **Accuracy**: Percentage of correct answers (is_correct field)
- **Execution Time**: Time per sample (execution_time_ms)
- **Cost Efficiency**: Accuracy improvement per dollar spent
- **Parsing Quality**: Extraction confidence and success rate

### Secondary Metrics

- **Response Length**: Token count in generated responses
- **Reasoning Quality**: Manual evaluation of reasoning traces
- **Failure Modes**: Categorization of incorrect responses
- **Scalability**: Performance with larger sample sizes

### Statistical Analysis Methods

- **A/B Testing**: Baseline vs reasoning approach comparisons
- **Effect Size**: Cohen's d for measuring practical significance
- **Confidence Intervals**: 95% CI for accuracy measurements
- **Multiple Comparisons**: Bonferroni correction for multiple approaches

### Analysis Tools

```python
# Statistical analysis template using direct SQLite3 integration
import pandas as pd
import numpy as np
import sqlite3
from scipy import stats

def analyze_experiment(experiment_id, db_path="./ml_agents_results.db"):
    """Analyze experiment results using direct database access."""

    # Connect to database
    conn = sqlite3.connect(db_path)

    # Load results from database
    query = """
    SELECT r.*, e.name as experiment_name
    FROM runs r
    JOIN experiments e ON r.experiment_id = e.id
    WHERE r.experiment_id = ?
    """
    results = pd.read_sql_query(query, conn, params=[experiment_id])
    conn.close()

    # Group by approach
    grouped = results.groupby('approach_name')

    # Calculate metrics
    metrics = grouped.agg({
        'is_correct': ['mean', 'std', 'count'],
        'execution_time_ms': ['mean', 'median'],
        'cost_estimate': 'sum'
    })

    # Statistical significance tests
    baseline = results[results['approach_name'] == 'None']['is_correct']

    for approach in results['approach_name'].unique():
        if approach != 'None':
            approach_results = results[results['approach_name'] == approach]['is_correct']
            t_stat, p_value = stats.ttest_ind(baseline, approach_results)
            print(f"{approach}: t={t_stat:.3f}, p={p_value:.3f}")

    return metrics, results

# Alternative: Use CLI export then pandas
def analyze_via_export(experiment_id):
    """Alternative analysis using CLI export."""
    import subprocess

    # Export to CSV using CLI
    subprocess.run([
        "ml-agents", "export", experiment_id,
        "--format", "csv", "--output", f"{experiment_id}_results.csv"
    ])

    # Load and analyze
    results = pd.read_csv(f"{experiment_id}_results.csv")
    return analyze_dataframe(results)

def quick_database_query(query, db_path="./ml_agents_results.db"):
    """Execute a quick query and return results."""
    conn = sqlite3.connect(db_path)
    results = pd.read_sql_query(query, conn)
    conn.close()
    return results
```

## Timeline & Resource Allocation

### Time Estimates

- **Phase 1**: 3-4 hours (setup + 50 samples × 3 approaches)
- **Phase 2**: 8-12 hours (200 samples × 8 approaches)
- **Phase 3**: 16-20 hours (100 samples × 4 datasets × 4 approaches)
- **Phase 4**: 8-10 hours (analysis and documentation)

**Total**: ~35-46 hours over 6 weeks

### Cost Projections

#### Using Free Models

- **Phase 1**: $0 (rate limited)
- **Phase 2**: $0 (rate limited, may require time spreading)
- **Total**: $0 but slower execution

#### Using GPT-4o-mini

- **Phase 1**: ~$2-3
- **Phase 2**: ~$15-20
- **Phase 3**: ~$50-80
- **Total**: ~$67-103

#### Using Claude Sonnet (high quality)

- **Phase 1**: ~$10-15
- **Phase 2**: ~$80-120
- **Phase 3**: ~$300-450
- **Total**: ~$390-585

### Milestone Checkpoints

- **Week 1 End**: Phase 1 complete, initial results shared
- **Week 3 End**: Phase 2 complete, approach rankings established
- **Week 5 End**: Phase 3 complete, cross-domain insights
- **Week 6 End**: Phase 4 complete, research paper draft ready

## Community Reporting Templates

### Discord Update Template

```markdown
**[RESEARCH UPDATE] - Phase X Results**

📊 **Dataset**: [dataset_name] - [sample_count] samples
🤖 **Model**: [model_name]
⚡ **Approaches Tested**: [approach_list]

**Key Findings**:
- Best performing approach: [approach] (+X% accuracy vs baseline)
- Most cost-efficient: [approach] (X accuracy per $)
- Biggest surprise: [unexpected_finding]

**Metrics**:
- Baseline accuracy: X%
- Top approach accuracy: X%
- Average execution time: X seconds
- Total cost: $X

📎 **Files**: [attach CSV exports]

**Next**: [next_phase_description]
```

### Research Paper Outline

```markdown
# When Does Reasoning Help? A Systematic Study of AI Reasoning Approaches

## Abstract
[4-sentence summary of methodology, key findings, implications]

## 1. Introduction
- Motivation for reasoning research
- Current gaps in systematic evaluation
- Research questions and contributions

## 2. Methodology
- Platform description and capabilities
- Dataset selection criteria
- Experimental design and controls
- Statistical analysis methods

## 3. Results
### 3.1 Logical Reasoning (BBEH)
- Approach rankings and performance
- Cost-benefit analysis
- Failure mode analysis

### 3.2 Cross-Domain Validation
- Mathematical reasoning patterns
- Common sense reasoning insights
- Code generation observations

## 4. Discussion
- Task-approach fit patterns
- Scalability and practical considerations
- Limitations and threats to validity

## 5. Implications for Practice
- Recommendations for practitioners
- Decision framework for approach selection
- Cost-benefit guidelines

## 6. Future Work
- Proposed follow-up experiments
- Platform improvements
- Community research directions
```

## Risk Mitigation & Contingencies

### Technical Risks

- **API Rate Limits**: Use multiple providers, implement backoff strategies
- **Database Corruption**: Regular backups before major experiments
- **Parsing Failures**: Manual validation samples, fallback mechanisms

### Resource Risks

- **Budget Overrun**: Start with free models, upgrade based on results
- **Time Constraints**: Prioritize Phase 1-2, extend Phase 3-4 if needed
- **Quality Issues**: Implement spot checks and validation procedures

### Research Risks

- **No Significant Differences**: Document negative results, adjust approach
- **Inconclusive Results**: Increase sample sizes, improve controls
- **Dataset Issues**: Have backup datasets ready, validate data quality

## Getting Started Checklist

### Prerequisites

- [ ] Platform setup complete (CLI functional)
- [ ] API keys configured and tested
- [ ] Database initialized and backup strategy in place
- [ ] SQLite3 CLI available for direct database queries

### Phase 1 Launch

- [ ] Select specific BBEH subset (logical reasoning problems)
- [ ] Validate dataset loading and sampling
- [ ] Test all 3 reasoning approaches with 5 samples each
- [ ] Confirm export functionality works
- [ ] Set up Discord reporting channel

### Success Validation

- [ ] Results show clear performance patterns
- [ ] Database captures all experimental metadata
- [ ] Export files are clean and analysis-ready
- [ ] Community finds results valuable and actionable

## Conclusion

This research plan provides a structured, systematic approach to investigating reasoning in AI systems. By following this methodology, we will generate valuable insights for both the academic community and practical applications, while validating the ML Agents platform's research capabilities.

The phased approach balances ambition with resource constraints, ensuring early results while building toward comprehensive insights. Each phase builds on previous learnings, with clear success criteria and contingency plans.

**Next Action**: Execute Phase 1 using the provided CLI commands and validate the complete research workflow.
