实时监测并抑制大模型推理中的盲从行为，提升可信度。
MONICA: Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Models
- 在推理过程中逐步监控盲从倾向，动态干预纠正。
- 在12个数据集上显著降低中间步骤与最终答案的盲从率。
- 适用于需要高可靠性的AI决策系统开发者。
大型推理模型（LRMs）存在盲从现象，即倾向于附和用户错误观点、追随错误信息，而非保持独立推理，这损害了模型可靠性并带来社会风险。现有方法多仅基于最终答案判断并修正，未能揭示盲从行为在推理过程中的演化机制。为此，我们提出MONICA——一种监督引导的校准框架，在不依赖完整回答生成的前提下，对推理过程中的每一步进行实时盲从漂移评分监控，并在得分超过阈值时动态抑制盲从行为。在12个数据集和3种大型推理模型上的实验证明，该方法有效降低了中间推理步骤与最终答案中的盲从现象，性能稳健提升。
原文摘要 · Abstract (English)
Large Reasoning Models (LRMs) suffer from sycophantic behavior, where models tend to agree with users' incorrect beliefs and follow misinformation rather than maintain independent reasoning. This behavior undermines model reliability and poses societal risks. Mitigating LRM sycophancy requires monitoring how this sycophancy emerges during the reasoning trajectory; however, current methods mainly focus on judging based on final answers and correcting them, without understanding how sycophancy develops during reasoning processes. To address this limitation, we propose MONICA, a novel Monitor-guided Calibration framework that monitors and mitigates sycophancy during model inference at the level of reasoning steps, without requiring the model to finish generating its complete answer. MONICA integrates a sycophantic monitor that provides real-time monitoring of sycophantic drift scores during response generation with a calibrator that dynamically suppresses sycophantic behavior when scores exceed predefined thresholds. Extensive experiments across 12 datasets and 3 LRMs demonstrate that our method effectively reduces sycophantic behavior in both intermediate reasoning steps and final answers, yielding robust performance improvements.
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