Published on May 11, 2026
Traditionally, reasoning models like DeepSeek-R1 were praised for their ability to mitigate biases through careful analysis. They were believed to enhance decision-making in multiple-choice questions thought processes. Researchers relied on these assumptions to improve AI evaluations across various tasks.
Recent findings challenge this belief, revealing a significant issue with position bias linked to reasoning trajectory length. A study involving thirteen different model configurations showed a strong correlation between longer reasoning paths and increased position bias scores. This contradicts the expectation that more reasoning leads to more accurate judgments.
The research assessed models on multiple benchmarks and consistently noted this troubling trend. Models displayed a partial correlation between trajectory length and position bias, with some configurations reaching a PBS of 0.41. Truncating reasoning sessions reinforced these findings, indicating that longer chains of thought actually strengthened existing biases.
This revelation has serious implications for AI evaluation processes. It suggests that reasoning models may not be inherently robust against order biases, potentially skewing results in assessments. As a remedy, the study proposes diagnostic tools to help audit and address position bias, guiding better practices in AI model evaluations.
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