Difficulty in validating complex mathematical proofs due to transcription errors and lack of expertise in specific mathematical areas

Signals over time

2 observed signals across 1 sources, tracked for 4 days. Confidence: low.

What people are saying Observed

• “On the other side, I’ve never gone to college, yet I was able to take an extremely important and complicated equation and decompose it into its constituent parts, animating each with data visualizations and animated tables of values. [0] This was several frontier model iterations ago. The LLM model was able to break down and express math in such a simple way that I could understand and follow the training of the LLM model itself! Are the math and computation accurate? I don’t know, and likely there are significant errors. Nonetheless, if I had a little more time -- not infinite time -- I would be able to prove whether they are or not. Likely this is the path forward for understanding the mechanisms of medicine, and since most humans learn by doing and interacting with an environment, interacting like this will become how we use AI for learning in the near future. [0] https://adamsohn.com/grpo/”

  Hacker News · question

• “1) yes (but it was mostly claude) 2) yes 3) Here's what 2b4m can show: - simulate the convention error as a mistranscribed previous result (convention error and mistranscription are, AFAIK, equivalent constructions) - assuming the mistranscribed previous result, the entire proof is logically consistent - if you "correctly transcribe" the previous result, the proof fails in the expected position 3) no (like lean, I do not have the tower of math results that Weil classes needs) 4) yes. the "fake proof" is the proof that OpenAI submitted. 5) that's the general idea, Lean proofs are human readable, but they aren't naively readable. I can't hand a random math major that has no lean training a proof and expect them to validate it. The tradeoff is tediousness. 2b4m proofs are crazy tedious. 6) it has its own engine, so yes, the kernel may have errors. Transciption to lean is a planned feature. 7) Yep, I'm not an expert in Weil-classes, so it's possible that the AI mistranscribed the math proofs in the literature. It's also possible that the math proofs in the literature have been incorrectly validated by the community. 8) no. Strictly”

  Hacker News · frustration