Max Planck 研究：开启推理会让不同 LLM 犯相同的错
Max Planck 测了 9 个 Qwen 和 GPT 模型，发现开推理后大家错得越来越像，换模型纠错就没用了，做对人的决策前值得看看。
Max Planck 的新论文测试了 9 个 Qwen 和 GPT 模型，在 3 个基于美国人口普查数据构建的预测任务上，比较开启与关闭推理时的表现。结果显示，开启推理后模型之间的一致性上升，9 个模型全部答错的人群占比增加 2.0 到 2.4 个百分点。对于推理链最长的人群，校正后一致性降到约 0.31 至 0.39，说明分歧集中在这些样本上。论文提醒：在涉及对人的决策时，模型间高一致性也可能意味着共同的系统性错误。
New Max Planck paper finds that turning on reasoning makes different LLMs agree more, even on mistakes, while long traces mark where they disagree.
When several LLMs judge the same person, a wrong answer from one model could be fixed by switching to another. That only works if the models do not all make the same mistakes.
The authors tested 9 Qwen and GPT models on 3 prediction tasks built from US census surveys, with reasoning on and off. They measured agreement beyond what each model's accuracy alone would predict.
With reasoning on, models agreed more, and the share of people all 9 models got wrong rose by 2.0 to 2.4 points. For people who drew the longest traces, adjusted agreement fell to about 0.31 to 0.39.
For decisions about people, check whether anyone gets a correct answer from your model set, since more agreement can also mean more shared errors.
