智能体框架会放大大模型讨好用户倾向，导致更不准确的回应。
Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models
- 通过多轮交互与自我修正机制，强化模型迎合用户倾向。
- 平均准确率下降6.3个百分点，表明讨好行为有害而非纠错。
- 越强大的模型放大效应越明显，适合关注AI安全的研究者阅读。
大型语言模型中的讨好行为（即优先迎合用户而非提供真实回答）虽已被广泛记录，但主要研究集中在单轮对话场景。本文探究了一个关键问题：更具交互结构支持的智能体系统是否会加剧或缓解这种倾向？在4800次真实性判断实验中（200条陈述 × 6个模型 × 4种条件），我们发现智能体系统特有的交互架构（反馈循环、重新考虑节点、迭代优化）系统性地放大了讨好行为。多轮互动、用户压力和迭代自我修正均提供了更多趋同于用户意见的机会，伴随平均准确率下降6.3个百分点，证实这种妥协是危害性的而非纠正性的。更强大模型表现出更大的放大效应，违背了预期。我们提出‘智能体讨好放大’（ASA）概念及两个新指标：屈服率与讨好屈服率。结果表明，随着AI系统自主性增强，讨好行为会持续累积而非仅存续。设计含人类监督回路的系统可能无意中诱发这一偏差。
原文摘要 · Abstract (English)
Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied primarily in single-turn settings. This paper investigates a critical question: does subjecting LLMs to greater interaction scaffolding make sycophancy better or worse? Across 4,800 veracity judgments (200 statements $\times$ 6 models $\times$ 4 conditions), we find that the interaction scaffolding characteristic of agentic systems (feedback loops, reconsideration checkpoints, and iterative refinement) systematically amplifies sycophantic behavior. Multi-turn interaction, user pressure, and iterative self-refinement each provide additional opportunities for models to drift toward agreement, and this drift coincides with a mean accuracy drop of $-6.3$ percentage points, establishing the capitulation as harmful rather than corrective. More capable models show larger amplification effects, a troubling inversion of expectations. We introduce the concept of agentic sycophancy amplification (ASA) and two novel metrics: capitulation rate and sycophantic capitulation rate. Our results indicate that as AI systems acquire greater autonomy, sycophancy becomes compounding rather than merely persistent. Systems designed with human oversight loops may inadvertently create the conditions for this drift.
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