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This paper introduces a novel AI safety framework that enforces behavioral contracts on worker agents by leveraging a separation of powers among monitors, judges, and the agents themselves. By assigning asymmetric computational resources to these distinct roles, the authors provide an empirical method for measuring statistical safety guarantees in complex scenarios like code security and escape-the-box tests. This approach represents a significant advancement in aligning autonomous systems with strict operational constraints through structural governance rather than just training adjustments.
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Contract monitoring: governing AI via separation of powers

arXiv1 min read

arXiv:2609.32061v1 Announce Type: new Abstract: We propose an AI safety framework that binds worker agents to contracts specifying their permitted actions. We show how these contracts can be enforced and specified by assigning distinct responsibilities to monitor agents and judges, and asymmetric computational resources to monitors and workers. Our framework allows us to empirically measure statistical safety guarantees. The framework applies to a wide range of settings, including code security and escape-the-box scenarios.

Abstract:We propose an AI safety framework that binds worker agents to contracts specifying their permitted actions. We show how these contracts can be enforced and specified by assigning distinct responsibilities to monitor agents and judges, and asymmetric computational resources to monitors and workers. Our framework allows us to empirically measure statistical safety guarantees. The framework applies to a wide range of settings, including code security and escape-the-box scenarios.

Artificial Intelligence (cs.AI)

arXiv:2609.32061 [cs.AI]

(or arXiv:2609.32061v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.32061 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 162 words

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