# Phase 14: Reasoning Approaches Comparison Study (Planned)

## Strategic Context

**Purpose**: Evaluate and compare multiple reasoning approaches against the local pop-os API to measure their effectiveness relative to the None baseline established in Phase 13.

**Foundation**: Building on Phase 13's successful local API integration with 0.93s baseline response time and 170 token average usage.

**Research Questions**:

- How do different reasoning approaches impact response quality?
- What is the token usage overhead for each reasoning method?
- Which approaches provide the best quality/performance tradeoff?
- How do reasoning approaches scale with larger datasets?

**Status**: 📋 **PLANNED** - Awaiting implementation

## Implementation Plan

### **Phase 14.1: Reasoning Approaches Testing**

**Objective**: Test three primary reasoning approaches against the same prompts used in Phase 13

**Test Matrix**:

- **Approaches**:
  - ChainOfThought (CoT)
  - TreeOfThought (ToT)
  - ReasoningAsPlanning (RAP)
- **Model**: Qwen/Qwen2.5-1.5B-Instruct (consistent with Phase 13)
- **Endpoint**: <http://pop-os:8000/v1>
- **Temperature**: 0.3 (same as baseline)

**Implementation Steps**:

1. Create test scripts for each reasoning approach
2. Use identical test questions from Phase 13 for direct comparison
3. Capture detailed metrics for each approach
4. Compare results against None baseline

### **Phase 14.2: Comparative Analysis**

**Objective**: Analyze effectiveness and efficiency across reasoning approaches

**Metrics to Compare**:

- **Response Quality**:
  - Answer correctness rate
  - Response completeness
  - Reasoning clarity
- **Performance Metrics**:
  - Response time (vs 0.93s baseline)
  - Token usage (vs 170 baseline)
  - Cost efficiency (tokens per correct answer)
- **Scaling Characteristics**:
  - Performance degradation with complexity
  - Token usage growth patterns

**Analysis Framework**:

```python
comparison_metrics = {
    "approach": str,
    "avg_response_time": float,
    "avg_tokens": int,
    "correctness_rate": float,
    "time_overhead": float,  # vs None baseline
    "token_overhead": float,  # vs None baseline
    "quality_score": float   # 0-1 normalized
}
```

### **Phase 14.3: Scale Testing**

**Objective**: Evaluate reasoning approaches at scale with larger datasets

**Test Configuration**:

- **Dataset Sizes**: 100, 250, 500 samples
- **Test Types**:
  - Sequential processing (current)
  - Parallel processing (with rate limiting)
- **Resource Monitoring**:
  - Memory usage tracking
  - CPU utilization
  - Request queue depth

**Stress Test Scenarios**:

1. **Sustained Load**: 100 requests at 1 req/sec
2. **Burst Load**: 50 requests as fast as possible
3. **Memory Test**: Monitor memory growth over 500 requests
4. **Parallel Test**: Concurrent requests (respecting rate limits)

## Test Design

### **Standard Test Suite**

Using Phase 13's test questions plus extended reasoning challenges:

```python
# Basic questions (from Phase 13)
basic_questions = [
    "What is 2 + 2?",
    "What is the capital of France?",
    # ... etc
]

# Reasoning-intensive questions
reasoning_questions = [
    "If all roses are flowers and some flowers fade quickly, can we conclude that some roses fade quickly?",
    "A train travels 60 miles in 1 hour. How far will it travel in 2.5 hours at the same speed?",
    "What is the next number in the sequence: 2, 6, 12, 20, 30, ?",
    "If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?",
    "Mary has twice as many apples as John. John has 3 more apples than Sarah. If Sarah has 5 apples, how many does Mary have?"
]
```

### **Evaluation Rubric**

| Criterion | Weight | Description |
|-----------|--------|-------------|
| Correctness | 40% | Did the approach reach the right answer? |
| Reasoning Quality | 30% | Was the reasoning logical and clear? |
| Efficiency | 20% | Token usage and response time |
| Robustness | 10% | Handling of edge cases and ambiguity |

## Expected Results

### **Hypotheses**

1. **ChainOfThought**:
   - 2-3x token usage
   - 20-30% improvement in correctness
   - 1.5x response time

2. **TreeOfThought**:
   - 3-5x token usage
   - 30-40% improvement in correctness
   - 2-3x response time

3. **ReasoningAsPlanning**:
   - 2-4x token usage
   - 25-35% improvement in correctness
   - 2x response time

### **Success Metrics**

- At least one reasoning approach shows >25% improvement in correctness
- Response times remain under 3 seconds for basic questions
- Token usage scales linearly with question complexity
- Memory usage remains stable over extended runs

## Implementation Commands

### **Individual Approach Testing**

```bash
# ChainOfThought testing
ml-agents eval run LOCAL_TEST ChainOfThought --samples 25 \
  --provider local-openai \
  --model "Qwen/Qwen2.5-1.5B-Instruct" \
  --api-base "http://pop-os:8000/v1"

# TreeOfThought testing
ml-agents eval run LOCAL_TEST TreeOfThought --samples 25 \
  --provider local-openai \
  --model "Qwen/Qwen2.5-1.5B-Instruct" \
  --api-base "http://pop-os:8000/v1"

# ReasoningAsPlanning testing
ml-agents eval run LOCAL_TEST ReasoningAsPlanning --samples 25 \
  --provider local-openai \
  --model "Qwen/Qwen2.5-1.5B-Instruct" \
  --api-base "http://pop-os:8000/v1"
```

### **Comparative Analysis**

```bash
# Compare all approaches
ml-agents eval compare --approaches None,ChainOfThought,TreeOfThought,ReasoningAsPlanning \
  --dataset LOCAL_TEST \
  --samples 100 \
  --provider local-openai \
  --model "Qwen/Qwen2.5-1.5B-Instruct" \
  --api-base "http://pop-os:8000/v1"

# Export comparison results
ml-agents results compare "exp1,exp2,exp3,exp4" --format excel
```

### **Scale Testing**

```bash
# Large dataset test
ml-agents eval run LOCAL_TEST_LARGE ChainOfThought --samples 500 \
  --provider local-openai \
  --model "Qwen/Qwen2.5-1.5B-Instruct" \
  --api-base "http://pop-os:8000/v1" \
  --parallel-requests 2

# Memory profiling
/usr/bin/time -v ml-agents eval run LOCAL_TEST ChainOfThought --samples 100 ...
```

## Risk Mitigation

**Technical Risks**:

- **Memory Exhaustion**: Implement batch processing for large datasets
- **API Timeouts**: Increase timeout limits for complex reasoning
- **Rate Limiting**: Respect local API capacity with proper throttling

**Research Risks**:

- **Model Limitations**: 1.5B model may not fully utilize advanced reasoning
- **Prompt Engineering**: May need approach-specific prompt optimization
- **Evaluation Bias**: Ensure diverse question types for fair comparison

## Success Criteria

**Phase 14.1 Success**:

- [ ] All three reasoning approaches tested successfully
- [ ] Metrics captured for direct comparison with baseline
- [ ] No system stability issues

**Phase 14.2 Success**:

- [ ] Comprehensive comparison report generated
- [ ] Clear performance/quality tradeoffs identified
- [ ] Recommendations for approach selection

**Phase 14.3 Success**:

- [ ] Scale tests completed without failures
- [ ] Resource usage patterns documented
- [ ] Production readiness assessment complete

## Deliverables

1. **Test Scripts**: Extended versions for each reasoning approach
2. **Results Database**: All experiments stored in ml_agents_results.db
3. **Comparison Report**: Detailed analysis with visualizations
4. **Performance Profile**: Resource usage and scaling characteristics
5. **Recommendation Guide**: When to use each reasoning approach

## Next Steps

Following Phase 14 completion:

- **Phase 15**: Implement custom reasoning approaches optimized for local models
- **Phase 16**: Create automated approach selection based on prompt analysis
- **Phase 17**: Develop reasoning approach ensemble methods

This phase will establish the scientific foundation for understanding reasoning effectiveness in resource-constrained environments.
