Semantics; it’s obviously not equivalent to physical violence. 

I expect this to start not happening right away.

So at least we’ll see who’s right soon.

It seems that Erdos problem 897 was already resolved, with an essentially identical solution, in the literature: https://www.erdosproblems.com/forum/thread/897

See the comment thread between KoishiChan and Terrence Tao. 

So, this is yet another example of an "original insight" from an LLM turning out not to hold up under scrutiny. 

It has been less than one year since I posted my model of what is going on with LLMs: https://www.lesswrong.com/posts/vvgND6aLjuDR6QzDF/my-model-of-what-is-going-on-with-llms

It already does not seem to hold up very well. I underestimated how far reasoning models based on CoT could scale. Generally, I’m now more convinced that labs can consistently convert money into capabilities by training transformers. In particular, I was previously very skeptical that RL would “just happen” to be solved with the same techniques as sequence prediction / text generation. It does seem that sequential decision making (eg Pokémon) continues to be the biggest weakness (by far relative to other capabilities), and agents are not very useful outside of coding, but this may be a transient issue with perception or just training focus (though I still think it’s not clear, I would bet that way). 

I think progress is very hard to predict and trying is not very constructive (at least for me). In particular, I’ve updated that my area of expertise (eg AIXI) may not be very helpful for making such predictions, as compared to more direct experience with models. Also, I think perhaps my reasoning has been corrupted by optimism which prevented me from viewing the labs as competent adversaries (equipped to overcome many of the obstacles I hoped would stop them).

There still have not been very convincing original insights from LLMs, and it’s possible that I’m updating too far and early-2025 me will turn out to be more right than late-2025 me. One problem is that there really is a great deal of hype / advertising around LLMs, and incentives to blow up the significance of their accomplishments so it’s hard to say exactly how good they are at things like math except through first-hand experience (until they start one-shotting seriously hard problems). But even from first-hand experience alone, IF I HAD TO BET, I’d say the writing is on the wall.

Here’s the paper: https://arxiv.org/abs/2512.17086

Well, guess I was wrong.

(I’ll probably lose my bet with @Daniel Kokotajlo that horizon lengths plateau before 8 hours)

That’s very good!

Yeah - I don’t really like that the word “prosaic” has no connection to technical aspects of the currently prosaic models. 

I don’t want to start referring to “the models previously known as prosaic” when new techniques become prosaic.

This is reasonable, but includes “transformer” which seems a bit too narrow. 

The problem with “neuro-symbolic AI” is that Gary Marcus types use it to refer to something distinct from the current paradigm. Even though it is ironically a pretty good description of the current paradigm.