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Evaluation Fixes - Fair Comparison Implementation

Date: January 11, 2026
Status: ✅ Fixes implemented, ready for re-evaluation

Executive Summary

Critical bugs were discovered in the evaluation methodology that made the monolithic vs ensemble comparison invalid. The primary issue was incorrect token counting where ensemble appeared to use "98% fewer tokens" when the actual difference was ~20%.

All previous evaluation results (in evaluation_analysis.md) are INVALID and should be discarded.

What Was Wrong

Bug #1: Ensemble Cache Had Zero Token Counts ⚠️⚠️⚠️

Evidence:

// Monolithic cache (data/cache/summaries/doc_1_summary.json)
{
  "metadata": {
    "tokens_used": 14639,  // ✓ Correct
    ...
  }
}

// Ensemble cache (data/cache/ensemble_summaries/doc_1_summary.json)
{
  "metadata": {
    "tokens_used": 0,  // ✗ BUG - Should be ~14,600
    ...
  }
}

Impact:

  • Monolithic correctly counted ~146K tokens from cached summaries (10 docs × 14.6K)
  • Ensemble counted 0 tokens from cached summaries
  • This created a false ~146K token difference

Root Cause: The process_chunk function in ensemble.py failed to extract token usage from CrewAI results and silently defaulted to 0.

Bug #2: No Validation for Invalid Cache Data

The cache loading logic in utils.py didn't warn when tokens_used was 0, making the bug invisible.

Bug #3: Inconsistent Methodology

Both agents used the same cached document summaries (map phase), but only monolithic included those tokens in its metrics. This wasn't a "bug" per se, but made the comparison unfair.

What Was Fixed

Fix #1: Improved Token Extraction (ensemble.py)

Location: ensemble.py lines 136-163

Changes:

  • Added multiple fallback methods to extract token usage from CrewAI
  • Implemented token estimation from input+output text when API data unavailable
  • Added detailed logging when extraction fails or falls back to estimation
  • Ensures tokens_used is never 0 in cache files

Code:

# Try multiple ways to extract token usage
if hasattr(result, "usage_metrics"):
    # ... extract from usage_metrics
elif hasattr(result, "token_usage"):
    # ... extract from token_usage

# Fallback: estimate if extraction failed
if not tokens_found or metrics["total_tokens"] == 0:
    logger.warning(f"No token usage found for doc {doc_idx} chunk {chunk_idx}, using estimation")
    input_tokens = estimate_tokens(chunk)
    output_tokens = estimate_tokens(summary)
    metrics["total_tokens"] = input_tokens + output_tokens

Fix #2: Cache Validation (utils.py)

Location: utils.py lines 205-211

Changes:

  • Added validation warning when cache has tokens_used=0
  • Helps catch this bug in future evaluations

Code:

tokens_used = cached['metadata'].get('tokens_used', 0)
if tokens_used == 0:
    logger.warning(
        f"⚠️  Cache for document {doc_idx} has tokens_used=0! "
        f"This may indicate a bug in cache generation. "
        f"Cache file: {cache_file}"
    )

Fix #3: Cleared All Caches

Action taken:

# Backed up old caches
data/cache/backup_20260111_111517/summaries/
data/cache/backup_20260111_111517/ensemble_summaries/

# Cleared for fresh evaluation
data/cache/summaries/ - EMPTY
data/cache/ensemble_summaries/ - EMPTY

Fix #4: Verified Model Consistency

Confirmed:

  • Monolithic: Uses OLLAMA_MODEL (defaults to qwen2.5:7b)
  • Ensemble: Uses CREWAI_MODEL (defaults to openai/qwen2.5:7b)
  • Both use the same underlying model ✓

How to Run Fair Evaluation

Option A: Full Pipeline Comparison (Recommended)

This provides the most honest comparison by measuring everything from scratch:

# Caches are already cleared, just run:
python evaluate.py

# This will:
# 1. Generate fresh document summaries for both agents (map phase)
# 2. Track tokens accurately for both
# 3. Perform synthesis (reduce/ensemble phase)
# 4. Generate fair comparison metrics

Expected results:

  • Both agents will show ~146K tokens for document summarization (map phase)
  • Monolithic will show ~151K tokens for synthesis (reduce phase with all summaries)
  • Ensemble will show ~169K tokens for ensemble work:
    • Pre-reduction: ~148K (reducing summaries from 146K to ~6K)
    • Archivist: ~8K (processing reduced summaries)
    • Drafter: ~5K
    • Critic: ~4K
    • Orchestrator: ~4K
  • Total: Monolithic ~297K vs Ensemble ~315K (ensemble uses +6% MORE, not less!)

Option B: Test Mode (Faster)

For quick verification of fixes:

python evaluate.py --test

# Uses only 1 document instead of 10
# Faster but less comprehensive

Option C: Specific Task Only

python evaluate.py --task task1

# Evaluates only task1
# Good for debugging

What to Expect in New Results

Token Usage (Corrected Estimates)

Agent Map Phase Reduce/Ensemble Phase Total % Difference
Monolithic ~146K ~151K (1 call with all summaries) ~297K baseline
Ensemble ~146K ~169K (reduction + 4 agents) ~315K +6% MORE

Key insight: Ensemble actually uses MORE tokens, not less!

  • Monolithic: One synthesis call with all summaries
  • Ensemble: Pre-reduction step + Archivist + Drafter + Critic + Orchestrator
  • The "98% fewer tokens" was completely wrong (bug in cache metadata)
  • The "20% fewer tokens" was also wrong (forgot about pre-reduction step)
  • Reality: Ensemble uses ~6% MORE tokens due to additional orchestration

Latency

Both agents load summaries fresh (no cache), so timing will be fair:

  • Map phase: Similar for both (~10-15 min for 10 docs)
  • Reduce phase: Varies based on synthesis complexity
  • Total: Expect monolithic to be slightly faster (fewer orchestration steps)

Quality Metrics

These should remain similar to before (assuming they weren't affected by the token bug):

  • ROUGE-1 F1
  • BERTScore F1
  • Judge scores (instruction, groundedness, completeness)

Verification Checklist

After running new evaluation, verify:

  • No warnings about tokens_used=0 in logs
  • Both agents show similar map-phase token counts (~146K)
  • Cache files contain valid token metadata
  • MLflow logs show all metrics
  • New analysis markdown generated

Files Modified

  1. ensemble.py - Improved token extraction with fallbacks
  2. utils.py - Added cache validation warnings
  3. evaluation_analysis.md - Added disclaimer about invalid data
  4. Cache directories - Cleared and backed up

Questions?

If you see unexpected results or have questions:

  1. Check logs for warnings about token extraction
  2. Inspect cache files: cat data/cache/summaries/doc_1_summary.json
  3. Verify token counts in MLflow UI
  4. Compare with backed-up cache in data/cache/backup_*

Ready to run fair evaluation! 🚀

python evaluate.py