Axiom Math

“Recently, I think like a couple of weeks ago, we just announced that action prover solved these four research level open problems.”

“So from Putnam Perfect Score, that was four months in, then two months later was the four research conjectures, and then, you know, during this middle, we also have tested something that is transfer learning from math to code verification, so another evaluatio…”

“And I think that we shouldn't do pre-training. We shouldn't try to just only train from scratch. I think we're kind of focusing on post-training reinforcement learning can potentially get us better performance gain.”

“We actually have actually a few more research conjectures that's being proven every week just by the supply of mathematicians you know, from the world, and we try to put those problems into use.”

“So a lot of the sort of like verify, verify proof is actually, you know, one of our prover tools that's about to be released, and that's actually a hundred times faster than The other counterparts that are the open source, like effort, cloud comparator.”

“Our kind of very broad vision is that we are going to have a conjecture. We're going to have a prover. And then there is knowledge base.”

“We are actually gonna release them on a public, like, API on these, all these dozen of pools. Very, very soon. Beginning of March.”

“We still feel like we cannot fully elaborate and emphasize the thing that we are seeing that is the next frontier of AI. That is a generation and verification loop. That is the discovery of verified knowledge.”

“So on the easy end of the pun-end problem, we have 40 nodes. On the hard end of research questions in-house, we currently have a research problem with thousands of nodes.”

“So we actually analyzed all 12 problem solutions of the Putnam exam, and we found that a lot of the solutions actually differ from the human solution.”