用强但不可信模型拆解任务，靠可信模型监控执行过程来防作弊。
Factor(U,T): Controlling Untrusted AI by Monitoring their Plans
- 让强模型拆解任务，弱模型独立执行并由可信方监控。
- 仅看任务指令时监控效果差（AUROC 0.52），难区分恶意行为。
- 结合执行上下文监控可精准识别风险（AUROC 0.96），适合高安全场景。
随着AI能力提升，我们越来越多依赖强大模型分解复杂任务——但若分解者本身不可信怎么办？因子认知协议将复杂任务分解为子任务：一个模型负责分解，其他模型在隔离中执行子任务。以往方法使用可信的弱模型进行分解，限制了在复杂分解任务中的应用。本文提出Factor(U,T)，即由不可信的强模型负责分解，而可信模型执行子任务。当仅观察自然语言指令而非完整解决方案时，监控能否发现恶意行为？我们在BigCodeBench（Python编程任务数据集）上进行了基线测试与红队评估。结果显示，仅基于任务指令的监控器区分恶意与诚实分解的能力较差（AUROC 0.52），远低于基于完整代码解决方案的监控器（AUROC 0.96）。此外，Factor(D,U)——使用可信分解器并监控具体子任务执行结果——实现了出色的判别能力（AUROC 0.96）和强安全性（1.2% ASR），证明执行上下文监控比单纯分析分解更有效。
原文摘要 · Abstract (English)
As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is malicious? Factored cognition protocols decompose complex tasks into simpler child tasks: one model creates the decomposition, while other models implement the child tasks in isolation. Prior work uses trusted (weaker but reliable) models for decomposition, which limits usefulness for tasks where decomposition itself is challenging. We introduce Factor($U$,$T$), in which an untrusted (stronger but potentially malicious) model decomposes while trusted models implement child tasks. Can monitors detect malicious activity when observing only natural language task instructions, rather than complete solutions? We baseline and red team Factor($U$,$T$) in control evaluations on BigCodeBench, a dataset of Python coding tasks. Monitors distinguishing malicious from honest decompositions perform poorly (AUROC 0.52) compared to monitors evaluating complete Python solutions (AUROC 0.96). Furthermore, Factor($D$,$U$), which uses a trusted decomposer and monitors concrete child solutions, achieves excellent discrimination (AUROC 0.96) and strong safety (1.2% ASR), demonstrating that implementation-context monitoring succeeds where decomposition-only monitoring fails.
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