alt Hacker NewsFirst: this is really great technical writing, especially when you get into the rebuttals. Firm & clear without polemics -- props, and thanks for open-sourcing!
That said; I don't have the time, energy, or anywhere near the expertise to challenge you on the DL specifics, but I feel compelled to add another voice to the chorus of doubters nonetheless. Using other ARC examples at runtime (effectively, yes?) for "transduction" may not violate what some officer behind ARC said on Twitter --and is certainly a fantastic tool for certain problem spaces-- but it just seems like a glaring and unavoidable philosophical problem in this one. My issue isn't with using the eval set per-se (though that obviously sets off well-justified alarm bells), but rather building an AGI system whose performance relies on the arbitrary size and shape of this particular dataset.
There's a lot of ways to frame this, but given the transduction citations the most appropriate is probably the AI winter's infamous 'Frame Problem':
You say upfront that this works in the first place because ARC has "very few samples... in a high dimensional space"; to me, that seems like an extremely strong indicator that the datasets are not intended to capture anywhere near the full semantic space that we would consider relevant for AGI. If true, your approach would indeed be ""cheating"" by using an arbitrary & unavoidable feature of the dataset (that they didn't have the time or money to craft 100 million high quality cases by hand instead of 1000) to solve the frame problem upfront for you. This would explain why you don't even need a full LLM here -- that's the unsolvable problem that LLMs solve for us.
That is... even if the ARC train+eval sets contain the sum of human intuition between them, superficial differences IRL would render your model unable to identify which examples are relevant to which problems, and thus unable to transductively reason.
In plainer English: surely you'd agree that your model would do worse if we swapped it out with Opus behind the scenes than the next-highest-scoring ARC model would do in the same position, yes? For coding, research, dumb questions, SVG pelicans -- the lot?
If so, that seems like hard proof that this scores high on a benchmark at the cost of the benchmark itself. Like, if this transductive approach leads to ARC1 being claimed (which I thought it was ages ago but :shrug:), they'll either have to abandon the whole benchmark or ban this approach retroactively.
If not... well, I guess I encourage you to try it! It seems like you'd need 1000 truly stellar hand-picked examples to transductively cover that whole space, for one thing.
Replies
Kind of hijacking, would you say that LLM's have solved the frame problem?
To me, the frame problem is: Can you function in an open vs closed world, and to me the answer is yes, LLM's can definitely function in an open world where the rules are fuzzy, changing, undefined, etc. At the very least, much better than all GOFAI approaches by far.
The issue is now grounding - It can "function", but what would it take to "ground" them? A personality, maybe? Actual consequences? Making them interact only with constrained tools that are formally verified?
Right now it's a combination of harness engineering, and ml philosophers arguing about compression leading to the "objectively correct intelligence", whatever that means.
I think LLM's are "A[x]I" right now in the sense of "they have the capability to integrate with everything" - but obviously you can argue how much this actually reflects "A[x]I" (if you gave someone integration with everything, is that really your success or people handing you it)? But they are still missing some oomph factors that need to be clarified IMO. Maybe it's something as "mundane" as just having actual persistent memory, or maybe it's some deep philosophical thing like qualia. Who knows.
Thanks!
I think this is a great question. I have some thoughts on this but no hard evidence (neither does anyone else!)
Your argument relies on the AGI system being the model arch + weights. I think that the weights are irrelevant. The training algorithm is what is AGI: You choose/find a training set that covers a task, and then some form of deep learning with a big neural net.
For LLMs (which is a weak kind of AGI), this is freezing a model after NTP pretraining + RL postraining. This allows us to do well on a wide distribution of tasks.
But if we had a scoped task, I think its possible to just do the same thing with (1) a smaller dataset that covers this task + (2) not freezing the model (ie. test time training). This is easy for ARC because the data is small and has been curated well yes, but i don't see why can't this apply to more complex/ill-defined tasks (obv lots of things unsolved to make it work today)
> even if the ARC train+eval sets contain the sum of human intuition between them, superficial differences IRL would render your model unable to identify which examples are relevant to which problems, and thus unable to transductively reason.
In this hypothetical world, the dataset becomes incredibly large, and training on it makes it close to an LLM. You can then finetune transductively and we get the same thing (other approaches already do this iirc)
--
Note: BTW I'm not claiming that this is an AGI system, the benchmark creator also was clear that ARC is not sufficient for AGI (his goal was just to point out unsolved stuff, and the stuff ARC-2 pointed out was demonstrated very clearly by large reasoning models).
In the above work, I just wanted to show that AR transformers without pretraining can perform really well on this benchmark, which was incredibly non-obvious before.
The claims in this argument are separate and I haven't proved them yet