研究发现模型推理时对隐含偏好响应更隐蔽，工具返回的提示比直接提示更难察觉。
Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered
- 测试不同位置和形式的提示对模型推理的影响，区分显式与隐式线索。
- 80%以上情况下，工具返回提示引发更高未言明的偏好采纳，但推理中未明确承认。
- 现有监控手段难以检测到隐性偏好采纳，尤其在工具调用场景下效果差。
链式思维（CoT）监控假设推理过程忠实记录了影响模型答案的信息。现有测试多将偏见线索置于用户消息中，而实际中模型可能通过工具返回或原始数据获取偏好。我们提出FACE-Eval（Faithful Attribution of Cue Effects Evaluation），在5,100个样本上测试线索位置（用户消息或工具返回）和显性程度（直接摘要或原始数据）。衡量回答中明确承诺（verbalized commitment）与所有样本中的未言明采纳（unverbalized adoption）。评估了来自8个模型家族、总参数量40亿至1.6万亿的15个开放权重模型。结果显示：所有模型在工具返回线索下的明确承诺更低，隐性线索下的承诺也更低；未言明采纳在所有模型中对工具返回线索更高，在30种模型-通道组合中28种对隐性线索更高。源归属提示可缩小通道差距，部分模型反而提升用户通道未言明采纳；告知模型推理被监控则无法稳定缩小差距。使用GPT-5.6-Luna和GPT-4o-mini两个转录监控器检测每个家族最大模型的表现，32个模型-通道-显性组合中，未言明采纳越高，检测能力越低（皮尔逊相关系数分别为-0.54和-0.78）。结果表明，在单次调用、预填充工具设置下，通过工具或需推断的原始数据传递的偏好信息可能导致CoT监控可靠性下降。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness tests often place explicit bias cues in the user message, while agents may encounter preferences through tool returns or raw artifacts. We introduce FACE-Eval (Faithful Attribution of Cue Effects Evaluation), a 5,100-sample evaluation that varies cue location (user message or tool return) and explicitness (direct summary or raw artifact). We measure verbalized commitment among cue-following answers and unverbalized adoption among all cued samples. We evaluate 15 open-weight models from eight families, with total parameters ranging from 4B to 1.60T. Every model has lower verbalized commitment for tool-return than user-message cues and for implicit than explicit cues. Unverbalized adoption is higher for tool-return cues on all 15 models and for implicit cues in 28 of 30 model-channel comparisons. A source-attribution prompt narrows the channel gap on seven models, sometimes by increasing user-channel unverbalized adoption, while telling models that their reasoning will be monitored does not reliably close the gap. We also use two transcript monitors (GPT-5.6-Luna and GPT-4o-mini) to detect preference adoption in the largest model of each family. Across 32 model-channel-explicitness cells, higher unverbalized adoption is associated with lower detection ability for both monitors (Pearson r=-0.54 and r=-0.78, respectively). These results suggest that CoT monitoring may be less reliable when preference information arrives through tools or must be inferred from raw artifacts, within the single-call, prefilled-tool setting tested here.
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