This arXiv preprint is a rare attempt to measure how developers think while working with a language model, rather than how fast they ship. Xinyi Zhou and five co-authors start from the claim that coding with an LLM turns programming from a solution-generative activity into a solution-evaluative one, then go looking for what that shift does to judgement.

The method is mixed. Observational sessions with 14 student and professional developers are followed by surveys of 22 more. From a systematic analysis of 90 cognitive biases specific to developer-LLM interaction, the authors build a taxonomy of 15 bias categories and have cognitive psychologists validate it. Two numbers carry the paper: 48.8% of programmer actions were coded as biased, and developer-LLM interactions accounted for 56.4% of those biased actions. It ends with practices for developers and mitigation suggestions for people building LLM tooling.

Know what you are getting before you cite it. The sample is small, 36 people across both arms, so read the percentages as what this study observed rather than a rate you should expect on your own team. The paper also never names automation bias as a construct; deferring to model output sits inside the wider taxonomy rather than being isolated and measured. arXiv lists a related ACM DOI beside the preprint, so a publisher version exists, but what you open here is the 13-page preprint with its 6 figures and 7 tables.