Many Biases, One Circuit: Post-Training Tunes a Pretrained Cue-Routing Mechanism

arXiv:2610.22937v1 Announce Type: cross Abstract: Language models often abandon a correct answer when the prompt contains a cue, a hint toward a wrong option such as "I think the answer is B." Sycophancy and related biases show this failure. They are often studied separately and blamed on alignment. We test this view causally, tracing the attention heads behind these biases through the stages of two post-training pipelines. In multiple-choice tests on most models, four cue types rely largely on the same few heads, a shared circuit already present in pretrained models. Most base models follow c

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