GPT-5 Uncovers 60-Year-Old Mathematical Solution, Highlighting AI's Role in Scientific Literature Retrieval

Sebastien Bubeck, a prominent researcher, recently clarified the capabilities of advanced AI models like GPT-5 in scientific discovery, emphasizing their role in navigating and connecting existing knowledge rather than generating new results. Bubeck addressed previous "unnecessary confusion" by detailing how GPT-5 successfully surfaced the solution to a decades-old mathematical problem, Erdős' problem #1043, which had been largely overlooked by human experts. This demonstration underscores AI's potential to significantly accelerate research by making scientific literature "come alive."

The core of Bubeck's explanation centered on Erdős' problem #1043, posed in 1958, concerning the width of sets defined by complex polynomials. While the problem was solved by Christian Pommerenke in 1961, the solution was obscurely placed within his paper [Po61] and not explicitly linked to problem #1043 in subsequent academic databases like Mathscinet. GPT-5's ability to identify this hidden solution, buried as an "off-hand comment" between theorems, showcased its "super-human search" capabilities that surpass traditional search indexes and even previous LLM generations.

Bubeck further highlighted GPT-5's advanced understanding by noting its capacity to translate and explain complex mathematical proofs from a German paper cited in Pommerenke's work. "This is very important, because in math it's not so much about the result itself but rather about the understanding that comes with it," Bubeck stated, emphasizing the AI's ability to provide deeper comprehension. This translation and explanation capability is seen as a significant accelerator for researchers.

The incident serves as a powerful example of how AI can bridge gaps in scientific understanding, connecting disparate fields and forgotten results. Industry experts and various reports suggest that large language models are transforming scientific discovery by accelerating data analysis, hypothesis generation, and literature review, identifying patterns and suggesting new research directions. Bubeck believes this capability is a "game changer for the scientific community," fostering greater context and continuity in scientific progress.