AI is getting good enough at mathematics that the harder problem is deciding which problems deserve its compute and a mathematician's attention.

So this paper shifts AI mathematics from "solve this conjecture" to "find which conjectures are actually worth trying."

Makes a lot of sense because, AI can attempt far more mathematical problems than experts can review.

So this paper builds the missing ranking layer between those 2 stages.

FAR starts with a research direction instead of a hand-picked conjecture, searches the literature for open problems, attempts them at scale, then filters the outputs so expert mathematicians only see the most promising cases.

In its combinatorics pilot, the pipeline narrowed 51,110 papers to 4,717 apparently open, attemptable conjectures, produced 1,050 claimed new resolutions, passed 598 through automated judging, and recommended 77 for expert review.

The authors manually checked 15 selected artifacts and found all 15 mathematically correct, including proofs, counterexamples, and answers to open questions, although 1 had already been solved elsewhere.