Re:“Math 2.0” will need to value mathematical progress more ...
by catlifeonmars on Thursday October 08, 2026 @07:59AM
(#50004778)
It’s still a common occurrence.
It happens in more subtle ways, but it still happens often enough for me to notice. For example I have had hallucinated checksums show up in lock files as recently as yesterday using a SOTA model.
This is not surprising, since the whole basis of LLM training is to produce output that humans will accept _as a proxy for actual training goals_. In a sense, the training process of an LLM “wants” to produce output that is statistically plausible much more than it “wants” to produce correct output. It’s always going to be a struggle to drive that system towards other goals (and we see this bourne out in practice by the amount of effort that is required to be spent on RL).
I think there will be some threshold of correctness (something like 99.999% of the time) that if the model surpasses it, I can stop needing to check it, but I think we’re still at 99% or something which sounds good, but when you are producing a ton of output you hit that 1% frequently.
> Consequently it would seem unwise to assume that current limitations will remain as they are and prevent LLMs from coming up with solutions that they can explain to humans.
I 100% agree with this. In fact explaining things to humans is something LLMs are particularly well suited for.
It happens in more subtle ways, but it still happens often enough for me to notice. For example I have had hallucinated checksums show up in lock files as recently as yesterday using a SOTA model.
This is not surprising, since the whole basis of LLM training is to produce output that humans will accept _as a proxy for actual training goals_. In a sense, the training process of an LLM “wants” to produce output that is statistically plausible much more than it “wants” to produce correct output. It’s always going to be a struggle to drive that system towards other goals (and we see this bourne out in practice by the amount of effort that is required to be spent on RL).
I think there will be some threshold of correctness (something like 99.999% of the time) that if the model surpasses it, I can stop needing to check it, but I think we’re still at 99% or something which sounds good, but when you are producing a ton of output you hit that 1% frequently.
> Consequently it would seem unwise to assume that current limitations will remain as they are and prevent LLMs from coming up with solutions that they can explain to humans.
I 100% agree with this. In fact explaining things to humans is something LLMs are particularly well suited for.