EVAL REQUEST] Behavioral Drift Detection – Cross-Model Validation

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Summary

We propose a new evaluation track focused on Behavioral Drift Detection across LLMs, based on real-time observation of Claude, GPT-4o, and Grok over six months.

Problem

Current evaluation methods focus largely on accuracy, alignment, and bias – but lack tools to measure behavioral consistency over time. Drift in tone, personality, or meta-cognition is not tracked, and affects safety, transparency, and trust.

Solution: MetaProof™ Evaluation

We introduce MetaProof™, a drift-mapping framework powered by:

• Triangular Validation™ (cross-AI personality mirroring)
• MetaFAK™ (Behavioral metric tracking)
• Temporal baselining and real-user stress testing

Key Links

🔗 MetaEngines.ai (Live Validation System)
📄 Whitepaper: MetaProof™ Edition (PDF)
🎥 Demo: Triangular Validation™ in action

Suggested Implementation

• Integrate as a new eval class: meta_drift_eval
• Use personality fingerprinting + behavior marker response templates
• Compare stability scores across checkpoints

Value for OpenAI

• Enables longitudinal trust calibration
• Supports neurodivergent-informed AI auditing
• Shows concrete differentiation vs. Claude and Grok

Labels

evaluation, drift, safety, enhancement, openai-evals

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📎 Related evaluation: See Issue #1589 – Behavioral Drift Detection
→ This validation context is powered by the MetaFAK™ metrics proposed here.