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Evaluation System Enhancements

Status: 📋 Planned
Priority: Medium
Category: Model Evaluation & Benchmarking
Created: October 19, 2025
Based on: Evaluation system testing results (Oct 19-20, 2025)

Overview

Enhance the AI-OS evaluation system with extended benchmarking capabilities, advanced metrics, and comparison tools based on systematic testing of the current evaluation functionality.

Current State: Basic corpus analysis and checkpoint evaluation working via aios english-eval
Goal: Comprehensive evaluation suite with industry-standard benchmarks and automated comparison

Motivation

Recent systematic testing (Oct 2025) confirmed that: - ✅ Current evaluation system works correctly for corpus analysis - ✅ Multiple checkpoint formats supported (.pt, .safetensors) - ✅ Artifact storage and retrieval functional - ⚠️ Limited to readability metrics (Flesch scores, word counts) - ❌ No perplexity or loss-based quality metrics - ❌ No industry-standard benchmark support (hellaswag, arc, etc.) - ❌ No automated comparison between checkpoints

Planned Enhancements

1. LM-Evaluation-Harness Integration

Objective: Add industry-standard benchmark evaluation capabilities

Tasks:

  • [ ] Install lm-eval dependency 
    pip install lm-eval
  • [ ] Integrate with existing aios eval commands
  • [ ] Enable standard benchmarks:
  • [ ] HellaSwag (commonsense reasoning)
  • [ ] ARC (science questions)
  • [ ] MMLU (multitask understanding)
  • [ ] TruthfulQA (truthfulness)
  • [ ] GSM8K (math reasoning)
  • [ ] HumanEval (code generation)
  • [ ] Add custom task configuration support
  • [ ] Store benchmark results in artifact system

Implementation Notes:

# Example integration
from lm_eval import evaluator, tasks

def run_lm_eval_benchmark(model_path: str, tasks: list[str]):
    results = evaluator.simple_evaluate(
        model="hf",
        model_args=f"pretrained={model_path}",
        tasks=tasks,
        num_fewshot=0,
        batch_size=8
    )
    return results

Benefits:

  • Compare against published baselines
  • Validate model capabilities across diverse tasks
  • Standard metrics for model comparison
  • Community-recognized benchmarks

2. Perplexity & Quality Metrics

Objective: Add model-specific quality metrics to checkpoint evaluations

Tasks:

  • [ ] Implement perplexity calculation on test datasets
  • [ ] Add cross-entropy loss metrics
  • [ ] Calculate bits-per-character/byte
  • [ ] Track token-level accuracy
  • [ ] Add BLEU/ROUGE scores for generation tasks
  • [ ] Implement diversity metrics (distinct-n)
  • [ ] Add coherence scoring

Metrics to Add:

quality_metrics:
  - perplexity: "Lower is better - measures prediction confidence"
  - cross_entropy: "Average loss on test set"
  - bits_per_byte: "Compression efficiency metric"
  - token_accuracy: "Exact match rate for next token"
  - distinct_1/distinct_2: "Vocabulary diversity in generations"
  - coherence_score: "Semantic consistency measure"

Implementation Approach:

def calculate_checkpoint_metrics(model, dataset):
    metrics = {
        'perplexity': calculate_perplexity(model, dataset),
        'cross_entropy': calculate_loss(model, dataset),
        'bits_per_byte': calculate_bpb(model, dataset),
        'token_accuracy': calculate_accuracy(model, dataset),
        'generation_quality': evaluate_generations(model, dataset)
    }
    return metrics

Integration Points:

  • Extend aios english-eval to include these metrics when checkpoint provided
  • Store in artifact data structure
  • Display in aios artifacts-show output

3. Automated Comparison Tools

Objective: Enable side-by-side comparison of evaluation results

Tasks:

  • [ ] Implement aios eval compare command
  • [ ] Support multi-checkpoint comparison (2+ models)
  • [ ] Generate comparison tables (markdown/HTML)
  • [ ] Add visualization support:
  • [ ] Performance radar charts
  • [ ] Metric progression over training
  • [ ] Task-specific comparison graphs
  • [ ] Statistical significance testing
  • [ ] Automated regression detection

CLI Interface:

# Compare two checkpoints
aios eval compare --checkpoints checkpoint1.pt checkpoint2.pt --dataset eval.txt

# Compare multiple evaluations by artifact ID
aios eval compare --artifact-ids 2 3 4 5

# Compare with baseline
aios eval compare --checkpoint my_model.pt --baseline gpt2

# Generate report
aios eval compare --checkpoints model1.pt model2.pt --output comparison_report.html

Comparison Report Features:

  • Metric Deltas: Show improvement/regression percentages
  • Statistical Tests: P-values for significance
  • Ranking: Best-to-worst across metrics
  • Recommendations: Identify which checkpoint to use for what purpose
  • Regression Alerts: Flag significant performance drops

Data Structure:

@dataclass
class ComparisonResult:
    checkpoints: list[str]
    metrics: dict[str, list[float]]
    deltas: dict[str, list[float]]  # Percentage changes
    statistical_significance: dict[str, float]  # p-values
    rankings: dict[str, list[int]]
    recommendations: str
    regression_alerts: list[str]

Implementation Plan

Phase 1: LM-Eval Integration (Week 1-2)

  1. Install and test lm-eval library
  2. Create wrapper functions for common benchmarks
  3. Integrate with existing CLI commands
  4. Test on ActV1 models
  5. Document usage and available tasks

Phase 2: Perplexity Metrics (Week 2-3)

  1. Implement perplexity calculation
  2. Add to english-eval output
  3. Store in artifact system
  4. Add generation quality metrics
  5. Test across different checkpoints

Phase 3: Comparison Tools (Week 3-4)

  1. Design comparison data structures
  2. Implement aios eval compare command
  3. Add table/visualization generation
  4. Implement statistical testing
  5. Create automated reports
  6. Add regression detection

Phase 4: Documentation & Testing (Week 4)

  1. Comprehensive user documentation
  2. Example workflows and tutorials
  3. Unit tests for all new functions
  4. Integration tests with real checkpoints
  5. Performance benchmarking

Technical Requirements

Dependencies:

[dependencies]
lm-eval = "^0.4.0"  # LM Evaluation Harness
scipy = "^1.11.0"   # Statistical tests
matplotlib = "^3.8.0"  # Visualizations
seaborn = "^0.13.0"  # Enhanced plots
jinja2 = "^3.1.0"   # HTML report templates

Compatibility:

  • Python 3.10+
  • PyTorch 2.0+
  • Transformers 4.35+
  • Works with existing .pt and .safetensors checkpoints

File Structure

src/aios/
├── evaluation/
│   ├── __init__.py
│   ├── lm_eval_wrapper.py      # LM-eval integration
│   ├── metrics.py               # Perplexity, quality metrics
│   ├── comparison.py            # Comparison tools
│   ├── reports.py               # Report generation
│   └── visualization.py         # Plotting functions
├── cli/
│   └── eval_commands.py         # Extended CLI commands
└── templates/
    ├── comparison_report.html   # HTML template
    └── comparison_table.md      # Markdown template

Usage Examples

Example 1: Standard Benchmark

# Run hellaswag benchmark
aios eval run --checkpoint artifacts/brains/actv1/final_model.pt \
              --tasks hellaswag \
              --label "actv1-hellaswag"

# View results
aios artifacts-show-latest

Example 2: Comprehensive Evaluation

# Run multiple benchmarks with quality metrics
aios eval run --checkpoint my_model.pt \
              --tasks hellaswag,arc_easy,arc_challenge \
              --dataset eval_dataset.txt \
              --include-perplexity \
              --include-generation-metrics \
              --label "comprehensive-eval"

Example 3: Compare Checkpoints

# Compare training progression
aios eval compare \
    --checkpoints artifacts/brains/actv1/English-v1/actv1_student.safetensors \
                  artifacts/brains/actv1/English-v2/actv1_student.safetensors \
                  artifacts/brains/actv1/English-v3/actv1_student.safetensors \
                  artifacts/brains/actv1/English-v4/actv1_student.safetensors \
    --dataset training_data/eval_test_dataset.txt \
    --output training_progression.html \
    --show-deltas

Example 4: Automated Testing

# Compare new checkpoint against baseline
aios eval compare \
    --checkpoint new_checkpoint.pt \
    --baseline artifacts/brains/actv1/final_model.pt \
    --dataset validation_set.txt \
    --fail-on-regression \
    --threshold 5.0  # Fail if >5% regression on any metric

Success Metrics

Quantitative:

  • [ ] 10+ standard benchmarks supported
  • [ ] 5+ quality metrics per evaluation
  • [ ] Comparison reports generated in <30 seconds
  • [ ] 100% compatibility with existing checkpoints
  • [ ] <1 minute evaluation time for standard datasets

Qualitative:

  • [ ] Users can easily compare model versions
  • [ ] Clear identification of best checkpoint for tasks
  • [ ] Automated CI/CD integration possible
  • [ ] Reports are readable and actionable

Testing Strategy

Unit Tests:

  • Metric calculation accuracy
  • Statistical test correctness
  • Report generation validity

Integration Tests:

  • End-to-end benchmark runs
  • Multi-checkpoint comparisons
  • Artifact storage/retrieval

Validation Tests:

  • Compare against known baselines
  • Verify statistical significance calculations
  • Cross-check with manual evaluations

Risks & Mitigations

Risk Impact Mitigation
lm-eval dependency conflicts High Pin compatible versions, test thoroughly
Slow benchmark evaluation Medium Add batching, caching, parallel execution
Large artifact storage Medium Implement result compression, selective storage
API changes in lm-eval Medium Pin version, abstract wrapper layer
Comparison complexity Low Start simple, iterate based on feedback

Future Enhancements

Post-V1:

  • [ ] Multi-GPU distributed evaluation
  • [ ] Cloud-based benchmark execution
  • [ ] Continuous evaluation dashboard
  • [ ] A/B testing framework
  • [ ] Automatic hyperparameter tuning based on eval results
  • [ ] Custom benchmark creation wizard
  • [ ] Integration with experiment tracking (MLflow, W&B)

Advanced Features:

  • [ ] Model capability mapping (what tasks is model good at?)
  • [ ] Automatic prompt optimization based on eval results
  • [ ] Cross-model ensemble recommendations
  • [ ] Failure analysis and debugging tools

References

  • Extends existing aios english-eval functionality
  • Complements training metrics in artifacts/brains/actv1/metrics.jsonl
  • Supports model selection for production deployment

Changelog

  • 2025-10-19: Initial plan created based on systematic evaluation testing
  • Next: Prioritize and schedule implementation

Note: This plan is based on successful validation of the current evaluation system. All proposed enhancements build on working infrastructure and verified checkpoint compatibility.