APEE: Adaptive Poly-Agentic Evaluation Ecosystem

Key Findings

Research synthesis leads: 7.3/10 overall — sequential pattern excels at structured analysis
L2 Collaborative bottleneck: Average 5.6/10 vs L1 (5.7) and L3 (8.3) — collaboration needs work
LLM-as-a-Judge: 20-24B parameter judges evaluate 3B agent outputs with detailed feedback
Phase 7 complete: Progressive, jury, and calibrated evaluation modes implemented

Overview

The Adaptive Poly-Agentic Evaluation Ecosystem (APEE) is a framework for systematically evaluating multi-agent AI systems. It uses LLM-as-a-Judge evaluation where large language models (20-24B parameters) evaluate smaller agent outputs, providing meaningful, nuanced scores rather than simple heuristics.

LLM-as-a-Judge

Large models evaluate smaller agent outputs with detailed feedback and reasoning.

Ensemble Judges

Multiple judge models from different families reduce evaluation bias.

Three-Tier Metrics

Individual (L1) → Collaborative (L2) → Ecosystem (L3) evaluation levels.

6 Collaboration Patterns

Parallel, sequential, debate, hierarchical, consensus, peer review.

Quick Start

Terminal

# Clone and install
git clone https://github.com/ahjavid/technical-notes-blog.git
cd technical-notes-blog/posts/apee-evaluation-ecosystem
pip install -e .

# Pull required models
ollama pull llama3.2:3b qwen2.5-coder:3b phi4-mini:3.8b
ollama pull gpt-oss:20b mistral-small3.2:24b

# Run LLM-as-a-Judge evaluation
python examples/proper_apee_evaluation.py

# Run advanced evaluation modes
python examples/advanced_evaluation_demo.py --mode all

Architecture

Agents (small 3B models, diverse families):

Role	Model	Family	Strength
Coder (Executor)	`llama3.2:3b`	Llama	Code Generation
Analyst (Analyzer)	`qwen2.5-coder:3b`	Qwen	Analysis
Reviewer	`phi4-mini:3.8b`	Phi	Code Review

Judges (large models, different families):

Judge	Model	Size	Family
Judge 1	`gpt-oss:20b`	20B	GPT-OSS
Judge 2	`mistral-small3.2:24b`	24B	Mistral

Model Benchmark Results

0.892

phi4-mini Best

0.889

llama3.2 2nd

Models Tested

Scenarios

December 2025 Rankings (6 Models × 19 Scenarios)

🥇 phi4-mini:3.8b: Quality 0.892 (±0.092) — Best overall, excels at code_review (0.991)
🥈 llama3.2:3b: Quality 0.889 (±0.130) — Best code_generation (0.983), fastest at 3000ms
🥉 gemma3:4b: Quality 0.876 (±0.121) — Strong code_debug (0.960)
4. qwen3:4b: Quality 0.866 (±0.077) — Lowest variance, best instruction_following (0.840)
5. granite4:3b: Quality 0.837 (±0.101) — Fastest (1904ms), best math (0.889)
6. qwen2.5-coder:3b: Quality 0.829 (±0.138) — Best qa_reasoning (0.957)

Full Performance Matrix

Category	qwen3	llama3.2	gemma3	granite4	qwen2.5-coder	phi4-mini
analysis	0.861	0.934	0.837	0.812	0.939	0.927
code_debug	0.776	0.933	0.960	0.901	0.965	0.934
code_explanation	0.893	0.837	0.871	0.836	0.855	0.876
code_generation	0.927	0.983	0.922	0.853	0.888	0.927
code_review	0.912	0.983	0.969	0.928	0.790	0.991
instruction_following	0.840	0.571	0.607	0.599	0.635	0.713
math	0.719	0.800	0.838	0.889	0.538	0.855
qa_reasoning	0.900	0.936	0.928	0.895	0.957	0.903
reasoning	0.800	0.909	0.890	0.894	0.897	0.899
summarization	0.912	0.842	0.839	0.765	0.792	0.766

Best in category highlighted. Enhanced heuristic scoring with ROUGE, keyword matching, and constraint validation.

LLM-as-a-Judge Results

December 2025 Evaluation

Adversarial review leads: 8.0/10 overall — debate pattern excels (L2=8.2)
L2 Collaborative improved: Average 7.3/10 (was 5.7/10 before message fix)
L3 Ecosystem strongest: All scenarios score 7.2-8.7/10
Score range: 6.6-8.0 with mean 7.03/10 across all 12 scenarios (Basic mode)

Multi-Agent Collaborative Evaluation (Basic Mode)

Scenario	Pattern	L1	L2	L3	Overall
Adversarial Review	Debate	7.8	8.2	8.0	8.0
Constrained Problem	Debate	6.8	7.8	8.0	7.5
Research Synthesis	Sequential	7.8	6.4	8.4	7.3
Creative Collab	Debate	6.0	7.8	8.1	7.3
Knowledge Transfer	Sequential	7.2	6.8	8.0	7.3
Conflict Resolution	Consensus	7.3	7.5	7.2	7.3
Realtime Collab	Parallel	7.3	6.8	8.4	7.2
Scalability Test	Hierarchical	7.5	7.2	7.6	7.1
Doc Sprint	Peer Review	6.2	7.5	7.6	7.1
Error Recovery	Hierarchical	6.4	7.4	7.8	7.1
Collab Code Review	Peer Review	6.8	7.0	7.8	7.0
Emergent Behavior	Parallel	7.4	5.0	8.7	6.6

Advanced Evaluation Patterns (Phase 7)

Four evaluation modes with different characteristics:

Basic Mode

Avg: 6.69 | Range: 5.8-7.8
Standard LLM-as-a-Judge. Best for quick assessment.

Progressive Mode

Avg: 6.79 | Range: 6.1-7.4
4 depth levels with fail-fast. Best for large-scale screening.

Jury Mode

Avg: 6.83 | Range: 6.2-7.8
4 personas (Skeptic, Literalist, Optimist, Pragmatist).

Calibrated Mode

Avg: 6.66 | Range: 5.5-7.7
Calibration + jury. Best for novel/ambiguous tasks.

Phase 6: Visualization & Dashboard

📈 Interactive Visualization

Plotly charts for performance analysis, pattern comparison, and metric breakdown.

🔍 Anomaly Detection

Z-score (threshold: 2.0) and IQR-based detection for identifying evaluation outliers.

🎛️ REST API Dashboard

FastAPI server on port 8000 for real-time evaluation monitoring.

📊 Export Formats

HTML reports, JSON data, and CSV exports for all evaluation results.

Development Roadmap

✅ Phase 1-4: Foundation, quality scoring, benchmarks, full APEE compliance
✅ Phase 5: LLM-as-a-Judge with ensemble evaluators
✅ Phase 6: Visualization, anomaly detection, dashboard
✅ Phase 7: Advanced evaluation patterns (progressive, jury, calibrated)
🔜 Phase 8: PyPI publication, enterprise deployment, documentation

Conclusion

APEE represents a significant step toward rigorous, reproducible LLM evaluation. By implementing multiple evaluation paradigms (basic, progressive, jury, calibrated), researchers and practitioners can choose the right balance between evaluation depth and computational cost.

Key Takeaways

Multi-paradigm evaluation catches failure modes that single-method approaches miss
Persona-based jury reduces individual evaluator bias by 23% on average
Progressive depth enables 4× faster screening with minimal quality loss
Small models (3-4B) achieve 82-89% quality scores on standard benchmarks
Calibration is essential for novel/ambiguous tasks to avoid score drift

Get Involved

APEE is an active research project. Check the full documentation or view on GitHub.

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