A Protocol for Trustless Verification Under Uncertainty

TL;DR

This paper introduces a protocol enabling trustless verification of AI agent outputs through collateralized claims, recursive verification, and economic incentives, ensuring correctness as a Nash equilibrium in dynamic environments.

Contribution

It proposes a novel recursive verification protocol with economic incentives that enforce correctness without upfront specifications or centralized oversight.

Findings

Correctness is achieved as a Nash equilibrium under aligned incentives.

The protocol uses collateralized claims and recursive verification to ensure system integrity.

Economic incentives effectively motivate truthful behavior among agents.

Abstract

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.