Post #4069931

2026-07-24 18:47 UTC

I think a serious possibility is that AI generated papers flood the zone with uninteresting incremental results that are eventually meaningless and full of mistakes. Right now, math is full of smart, dedicated people, so at least major results are reviewed carefully. But as AI alarmism drives away many honest people from the field, the remaining mathematicians will be burdened with far more work to review, and their cognitive faculties will be eroded by LLM use. Despite 4 years of development, $3 trillion of debt, mountains of stolen data, all the agents and harnesses and loops and other expensive tricks, as well as the advantages of Lean in math research, LLMs still hallucinate. I believe this is happening with software, but at least there are objective consequences for screwing up there (guy gets his home directory deleted, email is sent on a guy’s behalf without permission, small business gets every customer subscription cancelled). But nothing bad happens if there is a mathematical mistake in a paper and nobody catches it. One could say to just provide a Lean proof, but there is still the issue of making sure the Lean code actually matches the content of the paper. Exactly what force will correct things? Still, I don’t think this is the most likely possibility. The AI companies are extremely unsustainable financially, and it’s not like they’re very popular. Once they collapse, I believe there will be a re-evaluation of how LLMs should be used in research. If they are used (let alone trained), someone is going to have to pay the bills. In the end, we have to ask ourselves the question of why one does math. To me, math is not really a field where you memorize trivia. The real value comes from being able to think abstractly and rigorously from first principles, and from understanding why something is true rather than just knowing it is true. It is another aspect of your ability to reason as a free human. A few dedicated people go into math research, but your skills can easily go to many places. If you’re starting undergrad, you have plenty of time to see how this all pans out before making a decision.

Replies (2)

@BioMan@awful.systems 2026-07-24 19:22

Biologist here. This REALLY reminds me of how jealously cells guard their genomic DNA from interaction with nucleic acids out in the environment. Most genetic information on Earth is malicious information, selfish replicators in the form of viruses or transposable elements or selfish elements. Things that subvert the signals within a cell for their own propagation and provide nothing productive that the cells care about. So cells jealously guard their own genomic RNA and have all kinds of checks to make sure that nothing other than that sequence gets used, and outside sequence does not get incorporated into it. ANY DNA in your cytplasm gets rapidly destroyed, double stranded RNA sets off your immune system like crazy, even RNA with sequence statistics that are not quite like that of your species can set off an inflammatory reaction, immune system cells seeing RNA inside them that is overly compact and optimized like viral RNA treat them as sources of antigen rather than self. I cannot help but think we are living through the transformation of our non-brain-information sphere into a state like that of the genetic information sphere. Most material out there being meaningless for our purposes and us needing to jealously guard the provenance of information we use so as to not use bull, or worse, huge amounts of malicious information made to subvert us to the purposes of the powers that be that generate it. Evolution makes parasites more reliably than anything else. How did we train text-generation systems? Basically, to mimic the written word on the page like a stick bug on a stick. They’re like those beetles that live in ant colonies, sending out social signals that make the ants see them as offspring that have to be babied rather than parasites that don’t contribute. They replicate the form while not being the thing that they have subverted the signals of being.

Open ##4070643
@Enthalpiste@social.sciences.re 2026-07-27 16:16

@lagrangeinterpolator@awful.systems @flaviat@awful.systems As someone working in computational physics, this is the current trend and it is kindof depressing. We see a lot of mid/shit tier papers focused on developing new LLM based database analyses for materials discovery and they all seem to actually suck. More generally in my area of expertise people use Deep Neural Netwoks all around with bazillion parameters, and since all physicists know that "you can fit any data with a high enough polynomial", this is exactly what we were taught *not* to do. I really wonder how people will look at this in the future because this is all I don't like in science and will make me want to quit if it goes on for too long. For now I still trust that the bubble collapse will make it stop at some point.

Open ##4141363