# 快慢对齐：面向长时程轨迹和持续部署的控制论监督框架

> 全文为英语

**Published:** 2026年11月9日
**Author:** Stephen Peng, Vineet Jagadeesan Nair, Noah Broestl, Anne Friederike Kleppe, Steven Mills, Adi Zolotov
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/aligning-fast-and-slow-a-control-theoretic-oversight-framework-for-long

arXiv:2611.08781v1 公告类型：新 摘要：AI智能体可以在单个任务上通过检查，同时保留会改变其后续行为的内存或代码变更。我们引入了一个控制论框架，将控制建议动作的快速回路与慢速回路耦合…

arXiv:2611.08781v1 Announce Type: new Abstract: AI agents can pass checks on individual tasks while retaining memory or code changes that alter their later behavior. We introduce a control-theoretic framework that couples a fast loop controlling proposed actions with a slow loop reviewing persistent changes and reassessing configurations after model or tool updates. When measurement errors and changes between checks are bounded and controllers can enforce their decisions, we derive a deployment-level bound and a rule for dividing review effort. In simulation, dividing a fixed review budget between both loops reduces mean maximum departure from intended behavior roughly twelvefold compared with review confined to individual runs. In coding stress tests with authored memories and five matched seeds, combined controls reduce mean severity on a 0-3 scale from 0.325 without gates to 0.117, resolving 33 of 120 issues versus 35 without gates; in trading stress tests, combined controls reduce mean shortfall in low-emission purchases from 0.892 without gates to 0.107. The overall framework helps developers and auditors compare and enforce oversight designs, while the agent studies test component controls rather than the full deployment-level guarantee.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08781). 查看原文.*

**标签：** #arXiv

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