Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam

A red herring, an irrelevant passage added to a problem, corrupts a language model's reasoning and, through it, its final answer, while the form of the response survives untouched. The benchmark, called the Graduate Economic Reasoning Benchmark (GERB), is sixty graduate-level microeconomics problems, each a detailed setup with a verified final answer and a step-by-step reference solution. Each problem has two versions, one with the red herring and one without, and each of those is asked in two ways, one requesting an explanation and one not. This is a within-subject $2\times2$ factorial experimental design. Thirty-eight language models answer all four versions of every problem. The clean problems (the control group) are already hard, with the models answering under sixty percent correctly on average. The red herring lowers the probability of a correct final answer by 12.3 percentage points, about a quarter of the models' mean accuracy of 0.525. The damage is largest on the problems the model rates as easy. Reasoning ability confers no protection, as the red herring's effect does not differ detectably across models with and without reasoning ability. It does change how the failure looks, since a model with no reasoning mode repeats one wrong answer across waves while a reasoning model wavers. The red herring also leads a model to rate a problem as easier than its clean version, while answering it wrong more often. Although open- and closed-weight models reach the same accuracy, the open-weight models reach it at a substantially lower cost per correct final answer. The form of the response is preserved even as its substance fails. The model still produces an explanation (explanation given), the final answer still follows from the reasoning shown (coherence), and, in the aggregate, it remains the same across waves (consistency).