A Behavioural Framework for Predicting and Understanding Loss of Control in Frontier Artificial Intelligence Systems

Authors: Yao Huang , Yichi Zhang , Yifan Wang , Yitong Sun , Haoyu Dong , Jifan Ma , Jinzhe Tu , Xiaojian Li , Sören Mindermann , Wei Xu , Yinpeng Dong

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Abstract: Frontier artificial intelligence (AI) systems are increasingly operating over longer horizons with diminishing human oversight, raising the risk of loss of control: systems acting beyond effective human control with potentially irreversible consequences. Because such failures may become observable only after containment is difficult, they must be assessed prospectively. We introduce a behavioural framework that decomposes loss-of-control risk into three factors: misaligned motive, harm-enabling capability, and monitoring evasion. We operationalise these factors through 13 measurable aspects and 29 evaluation tasks, whose scores are combined into a single loss-of-control (LoC) score. Across 13 frontier models, the LoC score shows strong prospective validity, correlating with failure rates in contained long-horizon risk scenarios (Pearson r = 0.84). Risk varies systematically across models and cannot be explained by capability alone. We further show that the framework can guide targeted mitigations: fine-tuning and monitoring directed at diagnosed vulnerabilities reduce failure rates while largely preserving general performance. These results provide a practical basis for predicting, understanding, and mitigating loss-of-control risk before it materialises.

Comments:

39 pages, 5 figures

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:

arXiv:2611.07953 [cs.AI]

(or

arXiv:2611.07953v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07953

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yifan Wang [ view email ]

[v1]

Thu, 5 Nov 2026 12:49:20 UTC (4,287 KB)

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