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Getting 50% (SoTA) on Arc-AGI with GPT-4o

(redwoodresearch.substack.com)
394 points tomduncalf | 1 comments | | HN request time: 0.23s | source
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whiplash451 ◴[] No.40715123[source]
The article jumps to the conclusion that "Given that current LLMs can perform decently well on ARC-AGI" after having used multiple hand-crafted tricks to get to these results, including "I also did a small amount of iteration on a 100 problem subset of the public test set" which is hidden in the middle of the article and not mentioned in the bullet list at the top.

Adding the close-to ad-hominem attack on Francois Chollet with the comics at the beginning (Francois never claimed to be a neuro-symbolic believer), this work does a significant disservice to the community.

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z7 ◴[] No.40715887[source]
>Francois never claimed to be a neuro-symbolic believer

His response:

"This has been the most promising branch of approaches so far -- leveraging a LLM to help with discrete program search, by using the LLM as a way to sample programs or branching decisions. This is exactly what neurosymbolic AI is, for the record..."

"Deep learning-guided discrete search over program space is the approach I've been advocating, yes... there are many different flavors it could take though. This is one of them (perhaps the simplest one)."

https://x.com/fchollet/status/1802773156341641480

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1. YeGoblynQueenne ◴[] No.40715928[source]
That kind of neuro-symbolic AI is a bit like British cuisine: place two different things next to each other in the same plate, like bangers and mash, and call it "a dish".

Nope. This is neurosymbolic AI:

Abductive Knowledge Induction From Raw Data

https://www.doc.ic.ac.uk/~shm/Papers/abdmetarawIJCAI.pdf

That's a symbolic learning engine trained in tandem with a neural net. The symbolic engine is learning to label examples for the neural net that learns to label examples for the symbolic engine. I call that cooking!

(Full disclosure: the authors of the paper are my thesis advisor and a dear colleague).