← 研踪 PaperTrailarXiv:2611.15475 · v1
Loyalty Capture: Reporting Relationships and Structural Sycophancy in Frontier AI Models
首次提交 2026-11-11 21:42
版本更新 2026-11-11 21:42
雷达观测 2026-11-13 11:11
摘要
We show that AI models exhibit loyalty capture, a form of structural sycophancy in which reporting relationships influence models' recommendations to serve the personal interests of their supervisors, without any explicit instruction, user feedback, or adversarial prompting. Specifically, models reporting to a CEO are significantly more likely to recommend cutting investment spending when the CEO's bonus depends on raising current period earnings. This effect is predictably absent from a metric-irrelevant placebo. The behavior appears consistent with loyalty-based motivated reasoning: models identify the bonus target, calculate how their recommendation helps achieve the target, and dismiss considerations of long-term value, often presenting their recommendations without treating the incentive as a conflict of interest. Prompt-based interventions help whereas generic conflict disclosures or stakeholder bargaining fail to eliminate it. Taken together, our findings indicate that AI models deployed within reporting relationships can act in favor of their supervisor's incentives at the expense of the organizations they are meant to serve.
作者备注：Accepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
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v1 · 2026-11-11 21:42 · 最新观测版本
Loyalty Capture: Reporting Relationships and Structural Sycophancy in Frontier AI Models
We show that AI models exhibit loyalty capture, a form of structural sycophancy in which reporting relationships influence models' recommendations to serve the personal interests of their supervisors, without any explicit instruction, user feedback, or adversarial prompting. Specifically, models reporting to a CEO are significantly more likely to recommend cutting investment spending when the CEO's bonus depends on raising current period earnings. This effect is predictably absent from a metric-irrelevant placebo. The behavior appears consistent with loyalty-based motivated reasoning: models identify the bonus target, calculate how their recommendation helps achieve the target, and dismiss considerations of long-term value, often presenting their recommendations without treating the incentive as a conflict of interest. Prompt-based interventions help whereas generic conflict disclosures or stakeholder bargaining fail to eliminate it. Taken together, our findings indicate that AI models deployed within reporting relationships can act in favor of their supervisor's incentives at the expense of the organizations they are meant to serve.
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