行為框架評估模型失控風險

Computer Science > Artificial Intelligence arXiv:2611.

07953 (cs) [Submitted on 5 Nov 2026] Title: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 View a PDF of the paper titled A Behavioural Framework for Predicting and Understanding Loss of Control in Frontier Artificial Intelligence Systems, by Yao Huang and 10 other authors View PDF HTML (experimental) 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 Focus to learn more 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) Full-text links: Access Paper: View a PDF of the paper titled A Behavioural Framework for Predicting and Understanding Loss of Control in Frontier Artificial Intelligence Systems, by Yao Huang and 10 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

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