On one hand, integrating AI into mathematical research holds great potential—potential that will only grow as these new tools are refined. On the other hand, it raises crucial questions regarding many research-related processes, such as the evaluation of scientific results, the use of data fed into Large Language Models (LLMs), and the training of early-career researchers in the AI ​​era.

Above all, there is a fundamental issue concerning the understanding of the research results produced. The primary (and perhaps sole) objective of mathematical research is the advancement of mathematical knowledge: understanding objects and structures, developing new theories, and so forth. Problem-solving—however significant the problems may be—serves as a measure of this progress but is never an end in itself. We are currently at a historic juncture marked by a curious paradigm shift: the emergence of a vast array of results that we know to be true—because they come with a certification of validity—yet which no one fully understands.

As a community, we must continue to insist on the need to understand the advances being made, while also respecting and fostering the spaces, structures, and processes that make such understanding possible.

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