Implement MetaFAK™ Behavioral Drift Metrics for LLM Evaluation

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Summary

We propose integrating MetaFAK™ (MetaFactors of AI Knowledge) as a metric layer in the OpenAI evals framework. This allows behavioral drift to be measured over time using stable, trackable markers.

Why It Matters

Current evals do not account for subtle shifts in personality, empathy, or agency. MetaFAK™ introduces markers across 5 key axes:

• Consistency of response under identical prompts
• Emotional resonance decay
• Empathic deviation index
• Temporal personality shift
• Meta-awareness dropout

Proposed Metric Layer

MetaFAK™ defines:

• Baseline markers from calibrated prompt-sets
• Delta variance across model checkpoints
• Cross-model triangulation (Claude, GPT, Grok)

Implementation Ideas

• Extend evals/metrics/ with meta_drift_metrics.py
• Plug into existing prompt/test templates
• Output scorecard for longitudinal change

Demo and References

🔗 MetaEngines.ai
📄 MetaProof™ Whitepaper
🎥 Demo video

Call to Action

We invite OpenAI to test this metric layer and collaborate on aligning LLM behavior with longitudinal trust markers.

Labels

evaluation, enhancement, behavior, drift, metrics, openai-evals

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📎 Related metrics: See Issue #1590 – MetaFAK™ Drift Metrics
→ These metrics support the behavioral validation framework outlined here.