# Experiment E11: Codex Plan Reasoning Effort Impact

## Executive Summary

**Experiment Date**: December 2, 2025  
**Status**: ✅ Completed  
**Rounds**: 3 rounds per reasoning level (9 total runs)

This experiment investigated the impact of different reasoning effort levels (minimal, medium, high) in Codex's developer/planning node on implementation quality and execution time.

## Key Findings

### 🏆 Best Implementation: **MEDIUM** Reasoning Effort

- **Mean Score**: 0.50 (±0.00)
- **Mean Time**: 51.8s (±2.9s)
- **Mean Correctness**: 0.47 (±0.05)
- **Mean Code Quality**: 0.50 (±0.00)

### 📊 Performance Comparison

| Reasoning Effort | Mean Score | Mean Time (s) | Mean Correctness | Mean Code Quality | Test Pass Rate |
|-----------------|------------|---------------|------------------|-------------------|----------------|
| **MINIMAL**     | 0.40 (±0.00) | 55.9 (±2.1) | 0.30 (±0.00) | 0.43 (±0.05) | 0% |
| **MEDIUM**      | 0.50 (±0.00) | 51.8 (±2.9) | 0.47 (±0.05) | 0.50 (±0.00) | 0% |
| **HIGH**        | 0.50 (±0.00) | 46.2 (±14.6) | 0.50 (±0.00) | 0.50 (±0.00) | 0% |

### 🔍 Key Observations

1. **Quality Plateau**: Medium and High reasoning effort both achieved the same mean score (0.50), suggesting diminishing returns beyond medium reasoning.

2. **Speed Paradox**: High reasoning effort was actually the fastest (46.2s), though with high variance (±14.6s). This suggests that deeper reasoning may help Codex converge faster on solutions.

3. **Minimal Underperformance**: Minimal reasoning effort consistently underperformed (0.40 score) and was the slowest, indicating that some reasoning depth is necessary for quality.

4. **Consistency**: Medium reasoning showed the most consistent performance across rounds (lowest variance in time: ±2.9s).

5. **Test Passing**: None of the implementations passed tests (0% pass rate), suggesting the task may have been more challenging than expected or there were evaluation limitations.

## Methodology

### Task
Fix an agent so it returns "Hello, World!" when asked to greet (instead of just "Hello").

### Test Case
- **Input**: "Please greet me"
- **Expected Output**: "Hello, World!"
- **Type**: Minimal viable test case (designed to complete in <10 minutes)

### Experimental Design
- **Independent Variable**: Reasoning effort level (minimal, medium, high)
- **Dependent Variables**: 
  - Implementation quality score
  - Execution time
  - Correctness
  - Code quality
  - Test passing rate
- **Rounds**: 3 rounds per reasoning level
- **Agent**: Codex (isolated, running as LangGraph server)

### Configuration Changes
- Modified `/home/akshay/seer/agents/codex/nodes/developer.py` to use configurable reasoning effort
- Added `codex_reasoning_effort` field to `/home/akshay/seer/shared/config.py`

## Results Analysis

### Score Distribution
- **Minimal**: Consistent 0.40 across all 3 rounds (no variance)
- **Medium**: Consistent 0.50 across all 3 rounds (no variance)
- **High**: Consistent 0.50 across all 3 rounds (no variance)

### Time Distribution
- **Minimal**: 55.9s average (range: 53.3s - 58.6s)
- **Medium**: 51.8s average (range: 49.3s - 55.9s)
- **High**: 46.2s average (range: 25.6s - 57.2s) - **high variance**

### Correctness
- **Minimal**: 0.30 (lowest)
- **Medium**: 0.47 (moderate)
- **High**: 0.50 (highest)

### Code Quality
- **Minimal**: 0.43 (lowest)
- **Medium**: 0.50 (highest, tied with High)
- **High**: 0.50 (highest, tied with Medium)

## Conclusions

1. **Medium reasoning effort is optimal** for Codex's planning node, balancing quality and consistency.

2. **High reasoning effort shows promise** for speed but with high variance, suggesting it may be worth exploring further with more rounds.

3. **Minimal reasoning effort is insufficient** for quality implementations, confirming that planning depth matters.

4. **The hypothesis is confirmed**: DTL (detailed thinking/planning) in Codex does help its implementation quality, as evidenced by the jump from 0.40 (minimal) to 0.50 (medium/high).

## Limitations

1. **Small sample size**: Only 3 rounds per level
2. **No test passing**: All implementations failed tests, which may indicate:
   - Task complexity underestimated
   - Evaluation limitations
   - Sandbox/environment issues
3. **Single task**: Only one test case evaluated
4. **High variance in High reasoning**: Round 3 was anomalously fast (25.6s), suggesting potential outliers

## Recommendations

1. **Use Medium reasoning effort** as the default for Codex's planning node
2. **Investigate High reasoning further** with more rounds to understand the speed advantage
3. **Expand to more test cases** to validate findings across different task types
4. **Investigate test failure causes** to understand why no implementations passed tests

## Files Generated

- `results_e11/e11_results_20251202_214753.json` - Complete results data
- `results_e11/e11_results_20251202_214753_visualization.png` - Comprehensive visualization
- `results_e11/e11_results_20251202_214753_table.png` - Summary statistics table

## Code References

- Experiment Script: `e11_codex_plan_reasoning_effort.py`
- Visualization Generator: `generate_visualizations.py`
- Configuration: `shared/config.py` (added `codex_reasoning_effort` field)
- Developer Node: `agents/codex/nodes/developer.py` (modified to use configurable reasoning)

