Axiom Math

“Like that's over 50 companies, somewhere between a hundred K and one million where you kind of get in a seed round and the companies that have graduated out of that have been open router, whisper, axiom math.”

“We are like a seven, eight months old company, so it definitely means a lot to us. It's a really cool milestone. We're currently about like 30 people now, right?”

“If you have more structured and formal data, it's going to be a lot more horizontal than the specific vertical we are tackling.”

“No, that's not the TEM. The TEM is all code. The TEM is a right of first refusal on all AI-generated code. Like, right of first refusal, meaning, you know, you get to choose whether you want to verify it.”

“I think that we found scaling inference to have almost no wall recursively decomposing you know, approved goal into many sub goals and then learning to backtrack as well.”

“We have good performance, you know, having solved open research questions and number theory, commutative algebra, algebraic geometry, some discrete math that come into Rx and probability.”

“I think a Lean-based system will struggle in those very creative places, which is why we at Axiom actually also invest on something called mathematical discovery.”

“We have some major news in the coming weeks, basically open sourcing entire code bases of mathematical discovery coming up.”

“Francois Charton, a member of technical staff at Axiom, and he previously have done Patent Boost and End-to-end, you know, settle this proof, a thirty-year-old conjecture by finding a counterexample found the solution to a one-hundred-and-thirty-year-old probl…”

“And we actually recently, with no modification to the Putnam system, we saw a 99% out of the 189 problems, we saw a 187, we missed only two code-wisp-proof.”

“I think this is the future of coding. Yes, I think this is the future of coding. And I think this is where, you know, this is where I think even if we are supposed, like given the assumption that everything can be formally verified, you know, like studying sor…”

“Currently, actually, you know, for each line of code written, there could be like 20 lines of proof.”

“We have seen it scale from 40 notes to 4000 notes.”

“At one point we're gonna get to there because, you know, I think the conjecture will probably depend on what, you know, will probably depend on what we mean by taste, elegance. Feels like an alignment problem to me, you know? Like, you know, who gets to say wh…”

“Because when we realize the dream, the company's gonna be worth ten billion.”

“So actually what happened was our competitor, Harmonic, decided to publicize that they have solved unsolved problems, Erdos number one two four and four 81, and then we trusted their literature review, believing that these problems are really, truly unsolved. …”

“So we just released AXLE, A-X-L-E, stands for Axiom Lean Engine. And it's really a set of, kind of, proof validation and manipulation tools that are built for Lean in the language of Lean. So it's a bunch of metaprogramming tools.”

“And without it, we couldn't have solved it with I think the eight problems within the time limit. Definitely not, not within the time limit.”

“The DNA of the company is math. We think that verification is the best first market.”

“I expect fragmentation to start to happen as Axiom and Harmonic establish category leadership.”

“Potentially trying to prove commercial value is going to distract significantly from the core capability improvement.”

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

“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 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.”

“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.”

“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.”

“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.”

“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…”

“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.”

“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.”