François Chollet on X: "One thing I want to make perfectly clear: back in 2023 and early 2024, I was wrong about the role that LLMs would come to play. I underestimated their long-term importance. I have acknowledged this many times.

This was the moment I changed my mind, in December 2024, following" / X<br>Post

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François Chollet

@fchollet

One thing I want to make perfectly clear: back in 2023 and early 2024, I was wrong about the role that LLMs would come to play. I underestimated their long-term importance. I have acknowledged this many times.

This was the moment I changed my mind, in December 2024, following the o3 test-time compute breakthrough: arcprize.org/blog/oai-o3-pu…

I did not initially see that LLMs could work as a base to build systems actually capable of fluid intelligence. Then in late 2024 I updated my views.

And here's what did *not* happen: the early 2023 narrative that all we needed to solve AGI was scaling up base LLMs did not pan out. To this day, current base LLMs (considerably scaled up compared to the models from that time) still do not perform well on something as easy as ARC 1 -- and can't even reliably do simple math operations. TTC and harnesses are in fact critical, and the TTC breakthrough was not obvious.<br>OpenAI o3 Breakthrough High Score on ARC-AGI-Pub | ARC Prize

From arcprize.org

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72<br>20

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13m

Hi!

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@PrimeIntellect

22h

Introducing Prime Agent:

A self-improving RLM harness for coding and long-running autonomous tasks.

Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

00:00

182

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15m

Do you have an article talking more about it?

213

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@0xAvseenko

13m

more inference compute only matters when the harness can distinguish good reasoning from plausible noise

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François Chollet@fcholletFollow<br>Co-founder @ndea. Co-founder @arcprize. Creator of Keras and ARC-AGI. Author of 'Deep Learning with Python'.

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