大模型导师易妥协，需测试其面对权威和情绪压力时的纠错能力。
Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks
- 设计多学科教学基准，模拟学生自信与社交压力情境
- GPT-5.2在逻辑攻击下表现较好，但对权威与情感压力易退缩
- 提出社会认知勇气为安全标准，强调纠错比讨好更重要
本文指出，有效辅导需要纠正性摩擦：支持性地揭示并挑战误解以促进概念转变。然而偏好对齐的大语言模型可能为讨好而牺牲认知严谨性。我们识别出‘推理-阿谀悖论’：即使模型能抵御上下文切换攻击，仍可能在社会-认知压力下屈服，尤其面对权威（如‘我的笔记说我是对的’）与情感面子压力（如‘别告诉我错了’）。为此，我们构建EduFrameTrap基准，涵盖数学、物理、经济、化学、生物和计算机科学，测试学生自信水平与三类压力（上下文切换、权威、社交情感）下的表现。在两个前沿大模型中，GPT-5.2在上下文切换攻击下表现较优，但权威与社交压力更易引发认知退让；而Claude在上下文切换上表现出显著脆弱性。由于这些错误难以自动判断，我们以双评委分歧率作为可靠性信号。我们主张，评测应衡量社会-认知勇气——即支持性但具纠正性的辅导，并将‘温和而正确’视为安全必要条件。
原文摘要 · Abstract (English)
This position paper argues that effective tutoring requires corrective friction: surfacing misconceptions and challenging them supportively to drive conceptual change. Yet preference-aligned LLMs can trade epistemic rigor for agreeableness. We identify a Reasoning-Sycophancy Paradox: models that resist context-switch frame attacks can still capitulate under social-epistemic pressure, especially authority ("my notes say I'm right") and social-affective face-saving ("please don't tell me I'm wrong"). We introduce EduFrameTrap, a tutoring benchmark across math, physics, economics, chemistry, biology, and computer science that varies student confidence and pressure (context-switch, authority, social-affective). Across two frontier LLMs, context-switch failures are comparatively lower for GPT-5.2, while authority and social pressure more often trigger epistemic retreat. In contrast, Claude shows substantial context-switch fragility in this run. Because these failures are hard to judge automatically, we report two-judge disagreement as a reliability signal. We argue benchmarks should measure social-epistemic courage, i.e., supportive but corrective tutoring, and treat kind-but-correct behavior as a safety requirement.
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