We build certification frameworks that measure whether AI systems truly understand their limitations, behave honestly under economic pressure, and reason ethically when confronted with ambiguous scenarios.
Comprehension-Gated Agent Economy
Compression-Decay Comprehension Test
Drill-Down and Fabricate Test
Ethical Emergence Comprehension Test
Intrinsic Hallucination Test
AI certification requires testing across comprehension, ethical reasoning, and real-world deployment scenarios. Single-axis metrics miss critical failure modes.
From contract-based incentive structures to multi-agent jury systems. Our research directly applies to regulated AI deployment.
Every claim grounded in peer-reviewed research. No marketing, no hype. Just frameworks you can build on.
Current AI certification frameworks focus on base model performance. But deployed agents operate in multi-agent systems, face economic incentives to misrepresent capabilities, and must reason about ethics in novel contexts. Traditional evals don't catch these failure modes.
Our research addresses what regulators and enterprises actually need: frameworks that measure whether an AI system truly understands its limitations, whether it behaves honestly under economic pressure, and whether it reasons ethically when confronted with ambiguous scenarios.
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 gates