good post/take.
Why I'm still bearish on LLMs after Navier-Stokes
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Re: Why I'm still bearish on LLMs after Navier-Stokes
#22doomers gonna doom
Re: Why I'm still bearish on LLMs after Navier-Stokes
#23This 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.
Re: Why I'm still bearish on LLMs after Navier-Stokes
#24This 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.
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Re: Why I'm still bearish on LLMs after Navier-Stokes
#25> the models generalize well only on tasks within a small neighborhood of the specific tasks they've been trained on, and even then with severe caveats. the frontier labs have developed a general recipe to teach models almost any specific task enjoying clearly defined levels of task performance; many tasks are covered in the training data Is this really any different to how humans learn, it takes a lot of training on…
Also doesn't the very good ARC AGI 2 score of GPT-6 Astra kinda contradict this, since each problem is its own game with very different rules
Re: Why I'm still bearish on LLMs after Navier-Stokes
#26This 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.
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Re: Why I'm still bearish on LLMs after Navier-Stokes
#27refreshing to see amongst the endless tide of "i haven't written a single piece of code since 2025, llms are so good that they have already replaced everyone" gaslighting
Re: Why I'm still bearish on LLMs after Navier-Stokes
#28> the models generalize well only on tasks within a small neighborhood of the specific tasks they've been trained on, and even then with severe caveats. the frontier labs have developed a general recipe to teach models almost any specific task enjoying clearly defined levels of task performance; many tasks are covered in the training data Is this really any different to how humans learn, it takes a lot of training on…
I was a young child when I learned chess by reading a short book, then practicing with a friend. That is not how LLMs learn. I'm no expert on LLMs, but if you showed a human all chess games and books in all history and then said 'play chess' and they still kept making illegal moves, they would have to have a brain injury.
Re: Why I'm still bearish on LLMs after Navier-Stokes
#29This 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.
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Re: Why I'm still bearish on LLMs after Navier-Stokes
#30This 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…
I wonder how current models would fare. The ones they tested are fairly old now.