LLMs, Symbolic Computation and the Future of Mathematical Discovery
“The cat’s out of the bag,” said the mathematician Andrew Granville, reflecting on the rapid improvement of AI systems. His phrase captures the mood of the moment: by 2025-26, large language models (LLMs) had become powerful enough to move from impressive demonstrations to serious mathematical and scientific use. AI systems reached gold-medal level at the International Mathematical Olympiad, while newer research workflows began using LLMs together with symbolic tools to explore large mathematical spaces and even help resolve some open problems [1-5]. Many mathematicians now see this as a turning point: AI is becoming ready for “prime time” as a research companion in mathematics, physics, and related sciences – helping researchers test ideas rapidly and discover connections that might otherwise remain hidden.
