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

dank.systems

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

#101

Post by what »

gizmodo59 wrote: post #94
carodgers wrote: post #17

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…

why cant models make a tool call to stockfish? its like saying model can't execute python for complex math calculations

Because then it’s not playing chess, stockfish is?

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

#102

Post by minraws »

echelon wrote: post #93
minraws wrote: post #81

Earlier quoted context omitted.

So AGI needs to be trained on something to work well on it. Lovely reasoning we have right here. Delusion runs deep in HN circles. I say that as someone heavily invested in AI startups and projects and as someone working in the field. I think most people on HN should touch grass and find real human contact. Lmao Incredible reasoning all around here.

I'm stating that certain folks are trying to use the software-generating product as an AGI/ASI and then complaining when it doesn't play chess very well. People are holding it wrong, deliberately or not. Some are inventing bad faith measures so they can claim AI sucks.

Then why respond at all for the sake of responding?

We all know AI can code, but the question it all stemmed from what if it's AGI or GM level in chess on it's own.

You can't just back pedal from the statement that apparently being able to code a chess engine is the same as being good at chess.

I can write a chess engine that beats Magnus Carlson without AI that alone neither makes me GM level or AGI or any of the other claims the above comments seem to be making?

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

#103

Post by diehunde »

minraws wrote: post #81
echelon wrote: post #78

Earlier quoted context omitted.

The AI can write a chess bot program that will beat you. You're thinking about this the wrong way. The system is built and delivered as it is because that's how the providers make the most money. If they cared to have it perform well in chess games, you'd see a different shape and behavior. We shouldn't ask the multibillion dollar automated software generation system to play games with us any more than we should ask…

So AGI needs to be trained on something to work well on it. Lovely reasoning we have right here. Delusion runs deep in HN circles. I say that as someone heavily invested in AI startups and projects and as someone working in the field. I think most people on HN should touch grass and find real human contact. Lmao Incredible reasoning all around here.

AI bros: the LLM beats humans at solving Navier-Stokes and some old cypher. We are close to AGI

Also AI bros: LLM can’t beat an avg chess player. But that doesn’t mean anything. It doesn’t count

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

#104

Post by iron_albatross »

keeda wrote: post #73

The premise in the very first point seems off: > the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers... Even assuming this is how the AI companies are being valued (they're not), the numbers are off. The "value" of most knowledge workers -- based on what enterprises currently pay for th…

When thinking about these valuations, shouldn’t we try to quantify how much knowledge work becomes obsolete if other knowledge workers are automated? I.e. there are a huge amount of knowledge workers employed in businesses that create tools for other knowledge workers. AI won’t automate their work, those businesses will just cease to exist.

And then there’s the second order effect: if all the knowledge workers get automated, who is going to buy the stuff that’s produced?

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

#105

Post by traes »

shric wrote: post #74
MichaelNolan wrote: post #69

Earlier quoted context omitted.

I wonder why they didn’t throw a real chess engine in there for a baseline. There are engines where you can set the elo in the settings, so it should possible to see these LLMs relative to a human 1500 rather than just relative to each other.

> so it should possible to see these LLMs relative to a human 1500 rather than just relative to each other As a 1500 elo human I can tell you that a 1500 elo chess engine doesn't play like anything like a 1500 elo human.

This is true, but I'm not sure it matters? I was poking around at the lichess database recently and those elo calibrated bots are remarkably well calibrated, their rating variance sticks out like a sore thumb compared to human players even at similar game volumes. So it should still be a decent predictor of how good a human at that level is, even if the playstyle seems alien.

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

#106

Post by diehunde »

famouswaffles wrote: post #88
bigstrat2003 wrote: post #61

Earlier quoted context omitted.

> Frontier labs don't care about chess. If OpenAI cared, GPT-7 could be a grandmaster+ level chess player. If the models were actually intelligent, the way that the boosters claim, they wouldn't need to be tuned to play chess in order to be good at it. That's kind of the point of intelligence, that it is generically applicable to whichever task one wishes.

If humans were actually intelligent, they wouldn't need to train and practice to play good chess. I mean, what level do you think people without any practice or training are ?

Except all these LLMs were already trained with hundreds of chess book and game databases and they still suck

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

#107

Post by what »

willmarch wrote: post #82
skydhash wrote: post #72

Earlier quoted context omitted.

Pretty much this. Feed it a book or two on chess, and you should have a decent (or good) player. That's the generic intelligence people have. The aims is not to be supremely talented at something, but being able to read a manual and figure how to use/play something. Mastery can be gained overtime.

If you gave a human a book or two on chess they would not become a decent player (they would be closer to 500-600 than 1100 ELO) and they would only get better after playing hundreds or thousands of games (often making illegal moves and moves that violate the rules of chess as they learn). Your assumptions/intuition about generic human intelligence feels quite incorrect, considering LLMs currently play better than a…

> considering LLMs currently play better than a brand new human player would

They’ve ingested all the literature on playing chess, a brand new human player has not.

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

#108

Post by ransom1538 »

woeirua wrote: post #84
knuppar wrote: 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.

Open models wont be open for long. No one is going to release an open model capable of chaining zero-days. Even the Chinese aren't that reckless because it will just be turned around and used against them.

I haven't heard of the term "chaining zero-days". Now as a SRE I wont sleep.

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

#109

Post by camd32 »

> current frontier models need laborious oversight and guardrails on even the simplest tasks.

This is only true if you are concerned about the intermediate steps of the model as opposed to the outcome. The huggingface hack was a perfect example of the model doing whatever it takes to accomplish the goal of maximizing its score.

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

#110

Post by paimapi »

WhitneyLand wrote: post #59
carodgers wrote: post #17

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 C…

so prove it! get a public repo out there, have it play against some open source engines

also I think the operative letter in AGI is the G - and if the G is short for 'variably competent savant-like hyperfocus on certain kinds of software coding and not any other general skill' then its not really G at all, is it?

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