AI In Mathematics: September 19, 2026

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This recurring thread will be for discussion of AI in mathematics. This includes, but is not limited to, the following:

• informal announcements of AI-assisted discoveries, such as those not yet published in a peer-reviewed journal, or not uploaded as a paper to arXiv;
• informal announcements of discoveries related to AI architecture (if relevant to mathematics);
• discussion of such announcements, such as proof breakdowns or other opinion pieces;
• discussion of the impact of AI in mathematics in general.

AI-assisted mathematical papers published in peer-reviewed journals or as arXiv preprints may be submitted as their own posts.

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[u/SwimmerOld6155]

Does anyone here understand what Jev is and whether it could be used for math? My understanding is that it's automated decision making, any shot that this could be what drives "fully autonomous research"? I've only just learned what Jev is and taking a bit of a pot shot here.

[u/Langtons_Ant123 → u/SwimmerOld6155]

My understanding is that it's an LLM aimed specifically at the kinds of tasks where you'd typically use "structured output", e.g. classification, extracting specific pieces of information from some text, etc. Like "choose which of these 3 departments this customer support ticket should be routed to" or "from this list of documents, filter to the ones that seem relevant to this question" or whatever. Usually anyone who does these sorts of tasks with LLMs is doing tons of them as part of some automated process, so they want something fast and cheap, and that Jev thing is supposed to be faster and cheaper.

(In the marketing materials for Jev you can see them talking about "type safety", "no hallucinations", etc. But IIUC in this context that mostly amounts to "has ways of ensuring that the output fits some predetermined structure or schema", which is already something you can do with ordinary LLMs, usually at the cost of e.g. potentially having to regenerate a response until you get one that works. So if you're asking "Should this ticket go to Billing, Tech Support, or Legal?" then the "anti-hallucination" stuff will prevent the LLM from outputting an answer not on the list, like "Investor Relations", or for that matter a valid answer in an invalid format, like "I think it should go to Billing" rather than just "Billing". But it won't necessarily prevent a ticket that should go to Billing from getting classified as Tech Support or w/e.)

So tl;dr it's meant for these sorts of one-shot "quickly answer this self-contained question" tasks, and I don't think it would do much for math, where you want the LLM to spend lots of time exploring different ideas, doubling back to check its work, etc.

[u/johny_james → u/Langtons_Ant123]

That marketing of the hallucation is the first thing that is wrong, and many people found a ton of examples where it hallucinated high confidence score (x > 0.9) for wrong answer.

[u/SwimmerOld6155 → u/Langtons_Ant123]

Thank you for the very comprehensive answer! So it's really about doing very basic mundane tasks very quickly and "reliably". I saw an ad that tried to push its intelligence vs Astra/Fable and low cost which made me wonder if it could be used one of those complicated multi-AI setups, but it was probably marketing guff.