Abstract
We introduce a market-based verification framework in which independent agents compete to validate or invalidate AI-generated claims through incentivized mechanisms. Unlike static evaluation pipelines or passive verification models, this system constructs a continuous adversarial marketplace where validators bid for the right to assess claims, stake capital on their judgments, and are rewarded or penalized based on correctness. The framework aligns incentives toward truth detection by making verification a profit-driven activity under uncertainty. We formalize the architecture, design, incentive compatibility, training strategies, and evaluation methodologies. The proposed system reframes truth validation as a decentralized, adversarial process rather than a static model capability.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny and Prasad, Deepthi, "Market-Based Adversarial Verification for AI Outputs", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10035