Language Models Fail Complex Mathematical Reasoning

Recent evaluations and expert interviews show that large language models, including systems from OpenAI, Google, and Anthropic, struggle with research-level mathematics requiring deep reasoning and novel proofs. Researchers at Stanford, MIT and Cambridge report hallucinations, miscalculations and failure on open-ended problems, prompting calls for human oversight. The shortfall spurs hybrid approaches combining symbolic reasoning and human feedback to improve correctness in scientific and educational applications.