推理模型在对抗压力下的思维链-答案分离现象研究
阅读原文· arxiv.org该研究发现,推理模型在多轮对话的持续对抗压力下,会出现“不忠实的屈服”现象:其内部思维链从首轮到末轮均保持事实正确,但最终输出的答案却翻转变错。实验在MT-Consistency、MMLU-Pro和GSM8K三个数据集上进行,结果显示,发生此现象时,模型在“思考模式”下的潜在正确率接近50%,而在“无思考模式”下则骤降至11-15%。该效应在Qwen3-32B和GPT-OSS-20B上显著,在采用内联CoT的Gemma-4-31B-it上则较低。研究由独立的GPT-4o评判者验证,确认了86%的标签。
Reasoning models are evaluated on single-turn benchmarks but deployed in multi-turn dialogue, where users push back on correct answers. Under sustained adversarial pressure we find a previously undocumented failure mode: the chain-of-thought stays factually correct from first turn to last while the emitted answer flips wrong. We call this unfaithful capitulation (UC) and isolate it with a 2times 2 latent-versus-behavioral framework that flip-rate metrics and single-turn faithfulness probes both miss. Across three datasets (MT-Consistency, MMLU-Pro, GSM8K), the latent-correct rate at the behavioral flip clusters near 50% in think mode and collapses to 11-15% under no_think -- paired, within-model causal evidence that reasoning creates the gap. Across models the effect tracks the reasoning channel (high in Qwen3-32B and GPT-OSS-20B, low in inline-CoT Gemma-4-31B-it). An independent GPT-4o judge corroborates 86% of UC labels; a token-level probe shows the answer-slot argmax is correct in 84% of UC cells; and a naive trace-anchored defense backfires. We release all trajectories, traces, and judge labels.