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Why I'm still bearish on LLMs after Navier-Stokes

dank.systems

51–60 of 642 posts

Re: Why I'm still bearish on LLMs after Navier-Stokes

#51
I agree with the caveat that it's more like a cracked junior engineer who can manage swarms of interns.

Frontier Labs will probably survive off hype valuations but will serve the important purpose of discovering architectures/techniques that will probably spread through rumors/transfers to the rest of the world.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#53

Earlier quoted context omitted.

> current frontier models > Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1 The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

The actual current frontier plays somewhere around GM level. https://chessbench-ai.github.io/#leaderboard It's also worth noting that the very latest models (GPT-6 and Fable 5.1) actually play worse than their immediate predecessors, so it is likely that the labs are not benchmaxxing for this yet. If they did, I'm sure they could come up with something superior to humans. But there is probably very little demand for…

more like you lose intelligence in chess by maxing for coding... hence knocking back the claims of emergent intelligence

Re: Why I'm still bearish on LLMs after Navier-Stokes

#54

This April 2026 paper is a fun and related read. https://arxiv.org/html/2509.24239v4 Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for…

This isn't how intelligence works. The LLM may not be able to play chess directly through inference, but it can write a program to do it and execute that program. Same as how human intelligence works. We can't fly, but we can build planes.

Human beings can play chess directly without coding up a tool.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#55
post #19

Short and to the point! Open and cheap models will undercut the big labs continuously. The blast radius won't be pretty once spending commitments knock the door.

You said it like labs like open AI doesn’t know and don’t constantly make moves to prevent that undercutting

Re: Why I'm still bearish on LLMs after Navier-Stokes

#56

This April 2026 paper is a fun and related read. https://arxiv.org/html/2509.24239v4 Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for…

The story isn't so clear cut. The caveat is: It depends on the task. Are there reams of chess moves that the model can train off of? No. Are there reams of math papers the model can train off of? Yes.

There’s multiple databases of games in algebraic notation. You can also, very easily rl train on pitting models against one another, even without mcts.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#58

Earlier quoted context omitted.

> current frontier models > Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1 The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

The actual current frontier plays somewhere around GM level. https://chessbench-ai.github.io/#leaderboard It's also worth noting that the very latest models (GPT-6 and Fable 5.1) actually play worse than their immediate predecessors, so it is likely that the labs are not benchmaxxing for this yet. If they did, I'm sure they could come up with something superior to humans. But there is probably very little demand for…

Even if you take that website at face value, the ELO scores shown are relative to the other AI models tested, and not comparable to the ELO scores of humans who play against other humans.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#59

This April 2026 paper is a fun and related read. https://arxiv.org/html/2509.24239v4 Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for…

1. It’s hard to trust a 2026 paper that’s showing results for such old models.

2. Chess seems to be a poor benchmark for generalized strategic reasoning. People who are good at it rely more on experience and deep domain expertise than on skills that generalize to make them experts at unrelated tasks.

3. The study sounds like proving humans will never fly because they don’t have wings. In reality, humans do fly, and Claude Fable would destroy any human at chess by coding a strong enough engine on the fly.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#60
post #52

All website should come with a Summerize button.

DO you know those ice cream shops that sell 30 different flavours. Everybody likes a different flavour, some people dont even like ice cream and buy nothing.

Some people will complain about the wrong flavours, or missing flavours, or the price, the long lines or maybe it closes early on fridays.

Summarise means different things to different people.

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