REAL_DATA_FETCH_STATUS.md

# Real Benchmark Data Fetch - In Progress

**Status**: ⏳ **RUNNING**  
**Started**: Now  
**ETA**: 10-15 minutes

---

## 🎯 What's Happening

We're fetching **REAL per-question success rates** from the **top 5 models** on the OpenLLM Leaderboard for MMLU.

### Models Being Queried
1. **meta-llama/Meta-Llama-3.1-70B-Instruct** (~85% MMLU)
2. **Qwen/Qwen2.5-72B-Instruct** (~85% MMLU)
3. **mistralai/Mixtral-8x22B-Instruct-v0.1** (~77% MMLU)
4. **google/gemma-2-27b-it** (~75% MMLU)
5. **microsoft/Phi-3-medium-128k-instruct** (~78% MMLU)

### Data Being Collected
- **14,042 MMLU questions** per model
- **Per-question correctness** (0 or 1)
- **Aggregated success rate** across all 5 models
- **Difficulty classification** based on real performance

---

## 📊 What We'll Get

### Per-Question Data
```json
{
  "mmlu_42": {
    "question_text": "Statement 1 | Some abelian group...",
    "success_rate": 0.60,  // 3 out of 5 models got it right
    "num_models_tested": 5,
    "difficulty_tier": "medium",
    "difficulty_label": "Moderate",
    "model_results": {
      "meta-llama__Meta-Llama-3.1-70B-Instruct": 1,
      "Qwen__Qwen2.5-72B-Instruct": 1,
      "mistralai__Mixtral-8x22B-Instruct-v0.1": 0,
      "google__gemma-2-27b-it": 1,
      "microsoft__Phi-3-medium-128k-instruct": 0
    }
  }
}
```

### Expected Distribution
Based on top model performance:
- **LOW success (0-30%)**: ~10-15% of questions (hard for even best models)
- **MEDIUM success (30-70%)**: ~25-35% of questions (capability boundary)
- **HIGH success (70-100%)**: ~50-65% of questions (mastered)

This gives us the **full spectrum** to understand LLM capability boundaries!

---

## 🔍 Why This Approach is Better

### What We Tried First
❌ **Domain-level estimates**: All questions in a domain get same score  
❌ **Manual evaluation**: Too slow, expensive  
❌ **Clustering**: Groups questions but doesn't give individual scores  

### What We're Doing Now ✅
**Real per-question success rates from top models**

**Advantages**:
1. **Granular**: Each question has its own difficulty score
2. **Accurate**: Based on actual model performance
3. **Current**: Uses latest top models
4. **Explainable**: "5 top models got this right" vs "estimated 45%"

---

## ⏱️ Timeline

| Step | Status | Time |
|------|--------|------|
| Fetch Model 1 (Llama 3.1 70B) | ⏳ Running | ~3 min |
| Fetch Model 2 (Qwen 2.5 72B) | ⏳ Queued | ~3 min |
| Fetch Model 3 (Mixtral 8x22B) | ⏳ Queued | ~3 min |
| Fetch Model 4 (Gemma 2 27B) | ⏳ Queued | ~3 min |
| Fetch Model 5 (Phi-3 Medium) | ⏳ Queued | ~3 min |
| Aggregate Success Rates | ⏳ Pending | ~1 min |
| Save Results | ⏳ Pending | <1 min |

**Total**: ~10-15 minutes

---

## 📦 Output Files

### Main Output
[`./data/benchmark_results/mmlu_real_results.json`](file:///Users/hetalksinmaths/togmal/data/benchmark_results/mmlu_real_results.json)

Contains:
- Metadata (models, fetch time, counts)
- Questions with real success rates
- Difficulty classifications

### Statistics
- Total questions collected
- Difficulty tier distribution
- Success rate statistics (min, max, mean, median)

---

## 🚀 Next Steps (After Fetch Completes)

### Immediate
1. ✅ Review fetched data quality
2. ✅ Verify difficulty distribution makes sense
3. ✅ Check for any data issues

### Then
1. **Load into vector DB**: Use real success rates
2. **Build embeddings**: Generate for all questions
3. **Test queries**: "Calculate quantum corrections..." → find similar hard questions
4. **Validate accuracy**: Does it correctly identify hard vs easy prompts?

### Finally
1. **Integrate with MCP**: `togmal_check_prompt_difficulty` uses real data
2. **Deploy to production**: Ready for use in Claude Desktop
3. **Monitor performance**: Track query speed, accuracy

---

## 💡 Key Innovation

**We're not estimating difficulty - we're measuring it directly from the world's best models.**

This means:
- ✅ **No guesswork**: Real performance data
- ✅ **Cross-model consensus**: 5 top models agree/disagree
- ✅ **Capability boundary detection**: Find questions at 30-50% success (most interesting!)
- ✅ **Actionable insights**: "Similar to questions that 4/5 top models fail"

---

## 📈 Expected Results

### Difficulty Tiers
Based on top model performance patterns:

**LOW Success (0-30%)** - ~500-1000 questions
- Graduate-level reasoning
- Multi-step problem solving
- Domain-specific expertise
- **These are the gold mine for detecting LLM limits!**

**MEDIUM Success (30-70%)** - ~2000-3000 questions  
- Capability boundary
- Requires careful reasoning
- Some models succeed, others fail
- **Most interesting for adaptive prompting**

**HIGH Success (70-100%)** - ~8000-10000 questions
- Within LLM capability
- Baseline knowledge
- Factual recall
- **Good for validation**

---

## 🎯 Success Metrics

### Data Quality
- [ ] All 5 models fetched successfully
- [ ] 1000+ questions with complete data
- [ ] Difficulty distribution looks reasonable
- [ ] No major data anomalies

### Performance
- [ ] Fetch completes in <20 minutes
- [ ] All questions have success rates
- [ ] Stratification works (low/medium/high)
- [ ] JSON file validates

### Usability
- [ ] Data format ready for vector DB
- [ ] Metadata preserved (domains, questions)
- [ ] Can be post-processed easily
- [ ] Documented and reproducible

---

**Current Status**: Script running, check back in ~15 minutes!

Run this to check progress:
```bash
tail -f <terminal_output>
```

Or check the output file:
```bash
ls -lh ./data/benchmark_results/mmlu_real_results.json
```