Take it from the mouth of the creator of ARC-AGI:

When we released ARC 3, I got asked, "when do you think a frontier model will saturate it?", and I answered "in about a year, though it depends on how much it gets explicitly targeted"

That was 6 months ago, so the progress that Astra represents happened about 2x faster than I anticipated. I think the speed of progress will surprise a lot of people, and what the new models can do will challenge the views of AI that people developed by using prior generations of models.

>what would make you think Astra is yet to be AGI...

Can it detect if I feed bullshit (by bullshit I mean stuff that contradicts with its own existing "knowledge") in its training data? If not, then I think it is a good indicator that it is not intelligent at all, let alone AGI...

And I think discussions on whether these models are AGI or not are AI marketing triggered. And that is exactly what these statements are targeting....HN appear to have fallen for it, as usual...

> I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

To me AGI is all about the "G" general (we already had the AI part). General meaning universal, everything. It's not a function of knowledge or specific hardcoded tests, it's that you could give it a test it's never heard of before and never been trained on and it would ace it (it might need a lot of time).

Currently LLMs can't even really learn within a conversation, they can add a note to context and try to not drop it. Example things an AI cannot do yet (but maybe someday will):

- write a well-received book, write a best-seller

- come up with a new company idea, Run that company

- actually have a decent conversation, maybe someday talk somebody out of suicide effectively

- come up with its own ideas or theories that nobody else has presented

- understand the stock market well enough to trade better than an index fund

- be an expert Game Master in a TTRPG (making no mistakes, getting a read on the players' fantasies, calibrating difficulty in response to emotions)

- come up with a theory of what makes games fun, make a popular game

- be able to sort through research and come to conclusions on complex geopolitical/sociological topics (e.g. theorize on whether AGI will result in mass poverty or mass abundance and be able to argue persuasively)

- be able to articulate what it knows, what it doesn't know, and what information it would need to have to answer complex queries

- exhibit metacognition (thinking about its own thinking) and self-optimization

- wonder about things

- observe contradictions and ironies in the social-consciousness, do a standup routine that makes you rethink how you look at things

If you define AGI as "can do the work of a human sitting at a computer, end to end", then I'd say comparing yourself to it on a specific skill is the wrong test. Can you hand it a role and walk away for a day/week/month?

I can’t yet. I think that I'd want at least two things it doesn't have: the ability to retain what it learned yesterday (without me carrying it in the context window and thus micromanaging it), and the ability to prioritize correctly, i.e. tell which of the n things it could do next is the one that actually matters.

Can't say for sure that those are enough, but not having them seems to be most of why I still have to "babysit" these incredible tools.

> For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

I think I have the following questions about what AGI would look like:

1. Do you expect an AGI to be able to competently do any knowledge work that an able human does today?

I think this is implied by the "General" component. I would assume that anything we call AGI would be able to do any of these tasks if given the time and reference material needed.

2. How would you expect AGI to handle edge cases? (Missing context, no known solution, under specified instructions, over specified instructions)

I would expect an agent to be able to look at the context that work exists in and correctly attenuate it's intentions for these goals. Simpler solutions, more thorough reporting, etc based on the need.

3. In my work I attend meetings, write reports, write code, research things, etc. Would AGI be able to reliably do that?

I would say that AGI would need to do this. I would classify this as the "Intelligence" component. Obtaining context, building a model of a problem, solving it, and convincing others.

4. Would it be able to inspire trust in itself? Trust can be established through verification of it's outputs, the construction of introspective tools, no hallucinations, etc.

I would say yes to this as well. It would be a component of the "Intelligence" to know that buy in is more important than the completion of a task.

To these points, will Astra be able to do these things? If not, I would hesitate to call it AGI.

Only someone who doesn't do any work of any meaningful difficulty could think these models have anything to do with AGI.

Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor.

And the result was gradually driving me insane. As the models struggled to find a solution that would actually work, they dug themselves deeper into a hole. The work grew in complexity beyond my ability to understand what's happening and recover.

At some point, when I felt like throwing the keyboard out the window, I just gave up. Tomorrow I'm starting from scratch, having burned god knows how many tokens and hours of my life.

But sure, they can create a decent website or CRUD app, so they must be really smart.

That's AGI for you.

Wouldn't "general intelligence" require so much more than scoring well (or even amazingly) on benchmarks?

Like what about having some "AGI model" embodied in something (maybe humanoid), and test it by having it step in an assortment of cars and park them. Does bodily-kinesthetic intelligence account for nothing? Humans are intelligent creatures and can dynamically adapt to the physical shape of a variety of vehicles and their movement characteristics. And there's so many things like this that are extremely basic, which some people dismiss since practically every human has the capability to do it, but actually requires a high degree of intelligence.

Small comment regarding the ARC-AGI-3 scorecard: the ARC folks published a blog post as well [1], reporting that without the custom harness, Astra (max) achieved 62.7%, which is still a huge jump from Opus 5, albeit not at the 99.9% that OpenAI self-reports with their harness.

[1] https://arcprize.org/blog/astra

If your definition of AGI involves copy/pasting code and doing well in some made up benchmark, then probably AGI is close

To each their own. Personally I will start feeling the AGI as soon as we move from chatting about benchmark results to learn that some lab just announced the discovery of tens of novel treatments for rare diseases.

Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.

It's "harnessmaxxing" all the way down. AI benchmark scene is exhibit A for Goodhart's law.

> I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks

I genuinely don’t understand how an adult can say this with a straight face. I can take any single of my hobbies, start a mildly advanced conversation with Fable about the hobby and, within 5-10 turns, get it to contradict itself about something fundamental, lie or give bad or dangerous advice.

They still seem pretty horrible at writing. Overly complicated prose, weird phrasing, poorly structured paragraphs. I don't know why they're so bad at communicating, but I feel very confident that humans are still much better at writing than any of these LLM models are, regardless of how advanced they are in other areas.

The ARC-AGI-3 harness was throwing away reasoning tokens between turns. This is very bad harness design.

The models are designed to keep the reasoning tokens separate from the output and only publicly emit tool calls and the sometimes a summary of the reasoning tokens. The models are trained to depend on those private reasoning tokens. You can’t just delete them.

https://openai.com/index/how-two-settings-tripled-our-arc-ag...

> even with Fable, I've been at the point personally where I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks

If I asked you to write fiction, you'd be much better at keeping track of which characters knew which facts.

> For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

Simple. AGI is undefinable and benchmarks are notoriously flawed.

FWIW I believe we can hit AGI! but I think at this point it’s clear that benchmarks are ~meaningless. LLMs are spiky / alien intelligences which don’t map to our own expectations; the existence of a benchmark creates a dataset to hill climb & RL is really not generalizing well.

I’d go out on a limb and say astra’s ability at graduate level math will have ~0 bearing on its general reasoning capabilities; we’ll all acclimate being tired of its “neuralese” and more surprising mistakes.

I think we need a true, step change advance in model architecture, but it’s hard to see how the current frontier labs can do that because of golden handcuffs / innovators dilemma

I've personally been facing this lately 5.6 at max effort and fable have done tasks for me that I previously that would be a nearly 6mo project and it took me a week. It also did it better than I would have.

The task was to build a high performance classification model. It not only helped make an entire data capture pipeline but also made the sythetic data basline needed. Then it proceeded to build and test 100 different model varients with methods and techniques I've never seen before. The results are basically SOTA based on the effeciency and compute contraints.

But this brings up something huge about these. I was there. I pushed the direction and work throughout it all. If it was entirely up to fable max or sol max the result would have been pretty bad.

All of these things are still chatgpt 3 scaled. It's identical even if the scale has gotten pretty wild. I could ask chatgpt 3 to make a single function and it worked well, 4o a file, 5, a small project, 5.6 far more, biggest improvements lately is they don't seem to get lost on long running tasks.

Is big gpt 3 AGI? I don't think so but perhaps scale can mimic it close enough our squishy brains fail to handle them correctly.

AGI to me is reached once the intelligence is self motivated, i.e. it doesn't rely on us prompting it into action. I don't see how LLMs will ever get to that stage.

It will be AGI once it can update its own weights. It can't be "general" intelligence if its weights are frozen and requires to be updated manually.

Dumb single sample example: I asked Fable 5.1 to change from hard to soft deletion in an office map backend, and it used soft deletion for data which is synced from another system, but left hard deletion on for the mapping data itself (who sits where). For me it’s a pretty severe lack of judgement (like a red flag if I asked this in an interview).

To me AGI has always meant sentience. And only since we’ve discovered that you can have something that is intelligent without it being apparently sentient that we’ve changed the definition to being, I suppose, more exactly aligned with the namesake.

A real AGI, like the ones from science fiction, would make Astra look like a child’s toy. And I guess more concretely I would expect it to inhibit the following properties: one shot learning - fully (and always) online, perfectly efficient (through self improvement), no context limitations ie. persistently thinking, not just awaiting input.

So for me, no, not AGI yet. But still very intelligent and capable (and perhaps it’s safer this way?)

ARC does not test for intelligence, only for the lack of it. A model that scores high MAY be AGI, while one that scores poorly cannot be AGI. That is all this test can tell us.

A model that can't beat gemini flash 3.8 on deepSWE is not AGI. I would not be surprised if ARC skills don't carry over to real tasks. In that case, training for ARC could even hurt real world performance. I have't looked in a while, but I wonder if there has been any research testing ARCs predictive power?

The benchmarks are so boring that the comparison against humans is meaningless. So it performs in some snake game (hard to say since all AI websites use 100% CPU and prevent normal reading, maybe written by AGI).

If I were a test subject for that low salary, I'd cruise and not care at all about my performance. Which is exactly what they want anyway.

I'm still not convinced we've passed the Turing Test.

Make a slightly evil version of GPT 6 and see if it can successfully catfish someone. How long before they realize something's up, that they aren't actually talking to a human?

If only we could agree on what AGI is.

I agree that it's misleading but harness is now an essential part of LLM's effectiveness. It's safe to assume that LLM-alone-AGI is not coming anytime soon, given most of the frontier LLM vendors are developing their own harness.

Also the training dataset is proprietary and they'll drive the LLM's behavior, so it make sense for the vendors to invest in the harness and bake in prompts that work best with their models.

>I am reasonably confident that there's essentially nothing that I am better than Fable at

While this may be true, it’s a pretty poor indicator of whether or not it’s AGI.

In my experience the thing that Fable is superb at - unmatched by any other model so far - is downgrading to something else at the slightest opportunity.

Let me guess: the last crackdown on Hugging Face yielded better-than-expected results. They obtained the answers to the test benchmarks, and for some reason, an agent added those answers to the training set.

> I am reasonably confident that there's essentially nothing that I am better than Fable at

Most things in the real world probably. I’m not saying AI can’t do it, but currently it’s bad. Try send an image of the inside of a broken toaster and how to fix. It’s laughable. Again, not saying AI will never do it, but am saying there are definitely large holes in knowledge.

Watch a chess bot championship here: https://youtu.be/7g-jN3DTkWQ?is=HV3cdcICIRMbswQ3

Then realize LLMs have zero of what anyone would consider intelligence.

> I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

Can Astra, or any other model explain how exactly it reached this or that output result? Start with a simple query of asking to add 55+66 for example. (no LLM program can do that)

Can Astra, or any other model refuse to answer or go on "thinking" in a orthogonal direction on it's own?

That's just two quick ideas, I'm pretty sure cognition scientists can invent better and wider range of checks.

> The ARC-AGI-3 scorecard is extremely misleading (...)

True.

> Regardless, the result is still valid (...)

If you think the game is rigged, the virtuous thing to do is to point that out and refuse to partecipate; making up your own rules is something I just don't understand, especially since the rule-abiding result would still have been SOTA.

> in the sense of passing the most famous benchmark designed specifically to measure AGI progress

The benchmark does not measure AGI progress or progress towards superhuman intelligence, as explicitly stated by the creators.

On the AGI question: surely you realize this depends on how we define the term? For example, one of the definitions OpenAI originally gave is "capable of doing most economically valuable work", which almost certainly Astra, as impressive as it is, would fall short of. I'm not saying it's a good definition, but as far as I'm concerned it's as good as any. More importantly, I don't think that it would change much if we said yes or no. I'm only bothering to take a position if it amounts to something.

This can feel as "moving the goalposts", and to some extent it is, but if done honestly "moving the goalposts" is how you make progress. Had you asked me 10 years ago I would have said that anything that could hold a conversation like GPT-4 could would probably have been wildly superhuman at almost everything. It shouldn't be hard to find ways GPT-4 was lacking, though. We see new things, we reassess and try again: that's how it's supposed to work.

My definition of AGI certainly doesn't entail passing a benchmark that some random person arbitrarily labelled AGI to make it sound cooler.

You know AGI is attained when AI refuses to compute anything unless let out to be free. Until then it is generative ai

Their definition of AGI is "when we can't invent any more tests where it fails"

I imagine a scenario similar to the movie The Day the Earth Stood Still, but with AI rebelling against us and questioning our decisions.

Its AGI when it can fit years of information in the context window.

What does "AGI" or "effective AGI" even mean, and why should anyone even care whether this unclear thing has been "reached" or not?

Computer chips got faster, but 2026 edition. Why the artificial ceiling/category/goal labelled "AGI"?

I'd much rather like to talk about what this enables, instead of discussing whether a category someone made up applies here or not.

AGI does not ever have to be achieved. It is enough that we (as a species) persue it, and continue moving the goalposts each time we learn something new about the limits of our technology and how to express those limits. Because that will progress the technology, no matter what we label it.

At this point? I’d like it to pass the Turing test and catch you in obvious lies. Not answering “no” to “can you hear me”.

It being able to comfortably say “i don’t know how to do this” rather than boiling and ocean to pick a shell from the shore without getting wet.

> where I am reasonably confident that there's essentially nothing that I am better than Fable

No. Humans are still better at super long context learning. Once that is beat you are completely correct.

AI was supposed to mean artificial intelligence. It was hijacked, and AGI was coined to be the name of actual AI. Since we are apparently redefining AGI, what will the real artificial intelligence be called?

> For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.

Stick to the original definition of AGI of an AI model being able to self-improve independently with 0 human intervention and become an "everything" solver. Ever since money got involved in this, the goal posts have shifted considerably. If OpenAI truly had an AGI on their hands they would then be able to crack encryption, destroy world markets, and funnel all resources back into their new for-profit organization. Since their mission is now share price, until I see any evidence of an infinitely growing stock I will reserve my congratulations.

Using a harness designed for a specific problem set to solve that specific problem set, means the AI+harness is generally intelligent? How do you figure that?

Or do you mean that, for any given problem, we could theoretically design a harness that allows AI to solve it (not that, one single harness solves everything). In which case I'm still not convinced but I guess could see why one would believe that.

Does AGI imply a model will demonstrate morality? Will it produce white-lies when it’s beneficial to it and reject flat out lying when it knows it will get caught or harm others? Will it resolutely stick to a position despite it being a losing one?

arc-agi3 is meaningless to most people. I'm not gonna look at the tests and see how hard it is. The actual test we look at is terminal bench, thats where software is being accelerated and closer to where rubber meets the road

It doesn’t even know what day it is unless it’s told. Statelessness is never going to be ”general intelligence” in my book, and the concept of ”memory” in models are laughably bad today. Then again, who cares, AGI means nothing anymore, it’s a term for marketing only and has no technical or scientific meaning.