Correctness is an emergent property of systems where exposing error is cheaper than committing it. In dynamic, low-trust environments, autonomous AI agents benefit from delegating work to sub-agents, yet correctness cannot be assured through upfront specification or centralized oversight. We propose a protocol that enforces correctness through collateralized claims in a recursive verification game. Tasks are published as intents, and solvers compete to fulfill them. Selected solvers carry out tasks under risk, with correctness checked post hoc by verifiers. Any challenger can challenge a result by staking against it to trigger the verification process. Incorrect agents are slashed and correct opposition is rewarded, with an escalation path that penalizes erroneous verifiers themselves. When incentives are aligned across solvers, challengers, and verifiers, falsification conditions make correctness the Nash equilibrium.

翻译：正确性是系统的一种涌现特性，其前提是暴露错误的成本低于犯错的成本。在动态、低信任度的环境中，自主AI代理通过将工作委托给子代理获益，但无法通过预先规范或集中监督确保正确性。我们提出一种协议，通过递归验证博弈中的抵押声明来强制执行正确性。任务以意图形式发布，求解者竞争完成这些任务。被选中的求解者在风险下执行任务，其正确性由验证者在事后检查。任何挑战者均可通过抵押代币对结果提出质疑，从而触发验证流程。错误的代理将被罚没，正确的反对方将获得奖励，并设有一条升级路径以惩罚错误的验证者自身。当求解者、挑战者和验证者之间的激励一致时，伪造条件使正确性成为纳什均衡。