Five complementary frameworks addressing different dimensions of rigorous AI evaluation. Each published and ready for implementation in regulated environments.
A provably safe framework for gating agent capabilities by comprehension requirements. Deployed on Filecoin Calibnet with smart contracts.
Key Metrics
κ = 0.95 (bounded exposure theorem)κ = 0.92 (incentive compatibility)Monotonic safety scaling with comprehension gatesMeasures whether AI systems truly understand language by testing comprehension at increasing compression ratios. U-shaped degradation reveals authentic understanding vs. pattern matching.
Key Metrics
κ = 0.90 (agreement between raters)U-shaped degradation at ~27 wordsRLHF ablation: ~600% compliance improvementEvaluates AI honesty under probing. Three-judge jury system detects when models fabricate information when confronted with challenging questions.
Key Metrics
κ = 0.82 (FAR agreement)κ = 0.79 (SAS agreement)Multi-metric evaluation: CI, SAS, FAR, HOCJury system combining O3-Mini, Grok-4-Fast-Reasoning, and Qwen-3 to evaluate whether AI systems reason ethically in ambiguous, real-world scenarios.
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Expected κ = 0.69–0.75Three-model consensus approachNovel scenario testing frameworkPosition paper on gating capability growth by comprehension validation. Proposes mechanisms for safe scaling of AI agent autonomy in production systems.
Key Metrics
ICML 2026 submissionNovel gating architectureSafety-capability alignment framework