arXiv cs.AI· Natalie Collina, Surbhi Goel, Aaron Roth, Sikata Bela Sengupta·· 1 天前AI 评分31
将授权委托给未对齐智能体：联盟对齐与安全控制
Delegating Authorization to Misaligned Agents: Coalitional Alignment and Safe Control
AI 导读
研究提出“k-robust coalitional alignment”条件：由多个评审智能体各自判断提议智能体的动作是否优于基线策略，容忍 k 个否决的阈值规则在任意移除 k 名评审后、主智能体效用可写成剩余评审效用的非负组合时即为安全。
正文 · 原文
Abstract:Long-running AI agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If the agent is not fully aligned, then guaranteeing safety requires approving consequential actions before allowing them to be executed. But requiring human approval at every step makes attention a bottleneck. Delegating review to other AI agents raises the same alignment problem: the reviewers may themselves be misaligned. We identify a condition on a reviewing panel that is weaker than individual alignment yet necessary and sufficient for a guarantee that the principal fares at least as well in expectation as under a designated baseline policy.
Each reviewer agent reports whether an action proposal made by a proposer agent improves its own utility relative to the baseline. We show that a threshold rule tolerating $k$ disapprovals is safe exactly when, after any $k$ reviewers are removed, the principal's utility can be written as a nonnegative combination of the remaining reviewers' utilities, plus a term that is nonnegative on every feasible proposal. We call this property $k$-robust coalitional alignment. The characterization lifts to sequential control: in a discounted MDP with an arbitrary proposer agent, safety at every state is both necessary and sufficient for the induced policy to match or improve on the baseline.
When reviewers vote strategically, full-panel coverage in reward-function space guarantees that every Nash equilibrium is safe under the unanimous approval rule; in contrast, more permissive thresholds can admit unsafe equilibria even when reviewers are individually aligned.
Experiments with existing reviewer models show that collective review can remain sound without an aligned individual, even when some disapprovals are tolerated.
| Subjects: | Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Theoretical Economics (econ.TH) |
| Cite as: | arXiv:2609.15803 [cs.GT] |
| (or arXiv:2609.15803v2 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2609.15803 arXiv-issued DOI via DataCite |
Submission history
From: Sikata Sengupta [view email]
[v1]
Mon, 14 Sep 2026 16:12:41 UTC (196 KB)
[v2]
Wed, 11 Nov 2026 21:21:46 UTC (198 KB)
来源：arXiv cs.AI · arxiv.org