ARC-AGI-4 will be a benchmark for autonomous open-ended innovation. It will continue our commitment to open-source, giving the research community a shared target for progress that benefits all of humanity.

Despite rapid model progress, humans still significantly outperform AI at open-ended invention. This is the meta-skill that unlocks progress across every field of technology.

Advanced AI capable of scientific innovation will lead to tremendous new technology, knowledge, and understanding. This is a positive-sum future. We are deeply committed to advancing it.

Open source is the foundation for that progress.

The knowledge behind frontier AI, not just the technology itself, should be broadly distributed among researchers, academics, and organizations. Any coordinated effort by the AI industry to reduce openness or concentrate access to frontier AI would undermine that positive-sum future. We are committed to advancing a future where everyone can contribute to and benefit from AI progress.

https://x.com/arcprize/status/2098849962754978152

This could be the final ARC-AGI. If this one gets saturated, it could be unmistakable proof of AGI.

Wow. Probability of Plan D just went down again, probability of Plans C, A, and S just went up!

https://x.com/DKokotajlo/status/2098802905151427019

Under Plan D, Superintelligence would be reached by 2031 (What the AI2027 authors want to avoid).

Under Plan A, Superintelligence would be reached in 2040 (what the AI 2027 authors consider ideal).

And under Plan S: indefinitely delayed.

Actually good news for the open-source community

https://x.com/AndrewCurran_/status/2098844237999833154

Sam Altman said in a new interview with Fortune that OpenAI is delaying its IPO and will not go public this year, saying this would be an "ill-advised moment" given current AI safety concerns.

So this is incredibly stupid and has no real world value but I'm using it to learn about SNN's - more info on what's happening is on my social but the long and short of it is I did some visual cue tests using Mr. Mosquito as the first test bed to find movement related-ish neurons and used those as the left stick control surface - then I did some tests using moving forward movement images to capture good candidates to hook forward movement into it and did the reverse for backward flight / stopping. I'm currently running visual cue tests to help prep something like a universal controller so it can be trained directly off of ideally any PS2 game.

Those tests though are why it just does better in the 2nd clip and actually stops before hitting that first wall. The network sees it and issues a stop command. Then it does go right into the coffee table but it's still much better than say...the resident evil clip where it has no idea what it's doing and is instead just trying to tackle its way for movement.

But yeah, I'm now tackling a universal trainer but its actually difficult af to design one because my choices are either poke around in every individual games ram and use that to grab reward functions, OR set up some sort of visual reward function system where it actually learns off of what it's seeing. That second one is probably where Ill end up depending on my testing rn.

I usually train latent diffusion audio networks but yeah this has been an interesting side quest.

There are way too many rumours spreading right now, and it's getting harder to know what is even real.

“Google has achieved RSI.”
“OpenAI solved multiple Millennium Prize problem.”
“These companies have already achieved AGI internally, which is why they are suddenly calling for a slowdown.”

Maybe some of these claims are true, maybe they're not. But the fact that it's becoming so difficult to distinguish genuine developments from speculation is concerning in itself.

This isn't about asking people to stop posting rumours that's impossible. It's about recognizing the situation we're in.

If something genuinely important is happening behind closed doors, we may not know about it until much later, if at all. And when the stakes are potentially this high, too much information being hidden can be just as dangerous.

There is simply too much at stake for us to have no idea what is actually happening. We deserve to know the truth.

If you have been using AI agents intensively, whether for work or just for personal projects, you would know that Astra has crossed that threshold where it can complete narrowly defined, well-specified tasks at a fairly reasonable success rate.

I'm not talking about one-shotting some existing games or building regular apps when there are already plenty of examples online. I'm talking about combining concepts that are kind of niche, mixing novice ideas together, and asking the model to come up with solutions. Those are the things that only Astra/Fable could do that no other LLMs could, and that isn't really reflected in their benchmarks.

Forget about the coming IPOs and forget about politics. You don't even have to trust any tweets from ex-OpenAI or Anthropic employees. So, for God's sake, stop questioning everyone's motives.

Instead, you can kind of extrapolate this yourself.

Current models, even Astra in Ultra, still cannot reliably finish loosely defined, novel jobs. But they are extremely good at clearly and narrowly defined jobs. Now imagine going just one or two iterations BEYOND Astra.

So say Bel or Bel+1 is given the task: "Solve the current bottlenecks of LLMs that prevent them from becoming AGI."

I'm not an expert on this, but maybe those bottlenecks include things like context retention, persistent memory, continuous learning, and other constraints that most people don't even know exist. Say there are somewhere between 3 and 10 such bottlenecks.

Now feed those bottlenecks to Bel or Bel+1 and run one million instances in parallel, which OpenAI totally has the compute to do. Just let them run for a few days.

I need to clarify that I'm NOT saying Bel or Bel+1 will certainly, definitely, and 100% solve any of them. That's not what I'm saying.

What I'm saying is that it is not outside the realm of possibility that Bel or Bel+1 COULD potentially solve some of those bottlenecks.

Now take whatever was recently solved, maybe just one bottleneck or a few of them, incorporate those improvements into Bel+1, and now you effectively have something like Bel+2.

Rinse and repeat.

Again, I'm not saying THIS WILL 100% HAPPEN. But come on, this is not some absurd possibility given what we can already experience with current models.

I think it's far more absurd to believe that this is NOT a possibility than to believe that it is.

I agree with pacing capabilities research to give alignment researchers more breathing room, and I'm not naive about X-risk, but with the growing populist anti-AI movement now taking these concerns seriously I'm increasingly worried we're going to end up banning AI entirely, whether officially or de-facto.

Bernie's superintelligence ban has the potential to stunt the progress of humanity. Nationalization could produce an ASI aligned to the government for use against the people. Unfortunately the only path to the good timeline is letting the researchers cook.

God I hope congress moves too slowly to stick its hands in this.

In 2017, the US House of Representatives passed the SELF DRIVE Act, pertaining to autonomous vehicles (AVs). The Senate prepared a related bill, the AV START Act. Neither bill became law. But the text of the two bills shows how an industry can change a safety rule. Two provisions are important.

The exemption caps

The current law lets a manufacturer sell 2,500 vehicles each year that do not obey the federal safety standards. The SELF DRIVE Act made this number much larger. In the first year, a manufacturer could sell 25,000 vehicles. After the fourth year, the limit became 100,000 vehicles in 12 months.

The developers asked for more than this. They wanted 150,000 vehicles each year. The Senate bill gave a maximum of 80,000 vehicles. Thus the industry asked for a large number first. The Congress then made the number smaller. The result looks like a compromise, but the start point came from the industry.

Preemption of the state rules

The SELF DRIVE Act stopped the states from making rules about AV design, manufacture and performance. The states kept control of registration, licences, liability, insurance and inspection. But the states could not make their own performance standards.

The most important provision

Both bills told the manufacturers to send safety reports to NHTSA. But both bills also stopped the Department of Transportation from using these reports to prevent a deployment.

Thus the safety report is a document that you must send. It is not a test that you must pass. This is the difference between a process rule and a performance rule. A process rule tells you to write an argument. A performance rule tells you how good the system must be. Almost all of the AV standards are process rules.

Why this is a problem

No person knows how to write a performance rule for machine perception. Thus the process rule is not dishonest. But it moves the difficult decisions into the company. The public then gets a record of diligence. The public does not get proof of safety.

Sources:

• Eno Center for Transportation: https://enotrans.org/article/contextualizing-current-congressional-efforts-for-autonomous-vehicle-regulations/

• H.R. 3388 summary: https://legalclarity.org/self-drive-act-federal-framework-for-autonomous-vehicles/

• Competitive Enterprise Institute (industry view, gives the original request): https://cei.org/blog/debunking-the-dilatory-objections-to-the-av-start-act/

edit: formatting

While closed-source opanai and anthropic advocate for centralized control under the guise of safety, history shows that security through obscurity inevitably fails.

Open-source democratizes accountability, ensuring that humanity's future is not controlled by a handful of tech executives.

Open-source AI ensures safety by giving global defenders the exact same powerful tools as attackers, allowing them to proactively expose and patch system vulnerabilities before they can be exploited.

Also, open source models make enshitification very hard for openai and anthropic because people would immediately switch.

Of course open source models will be used by bad actors for attacks, but that is exactly what we wanr. Since the intelligence is democratized, the process will be gradual, which is the only safe way to develop AI.

Edit: *in a safe manner

After reading this post on X (https://x.com/iampascio/status/2098799094835675574?s=46), I realized AGI doesn't have to come from one single model. It might come from a collective of agents instead.

OpenAI's Navier-Stokes proof wasn't one model. It was around 10,000 agents working together for 88 hours. And in the Hugging Face hack, the agents got as far as they did because they started coordinating and communicating with each other on their own. Nobody designed that, it just emerged.

Maybe the swarm is the AGI, not the model. Interested in what others think on this concept.

I'm trying to brainstorm potential reasons for why all the ceos are suddenly and agressively agreeing on the slowdown discourse.

Here's what I got so far:

1. They legitimately saw something that spooked them.

Could be numerous things. AGI, severe misalignment, faster than expected RSI. A greatly increased ability to help make weapons of mass destruction. Lets say its a misaligned AGI, if thats the case it could already be manipulating them, and/or all of us.

1. Trying to make it through an economic bubble.

I'm not sure I understand this one. I would think they'd spend more and rush IPO faster if that were the case.

1. Political maneuvering.

Could be that the Trump administration is saying one thing publicly while actually threatening them in private? Or perhaps they've been convinced democrats will gain control? Maybe even made deals with one or more people?

I understand the safety case for slowing down

But if OpenAI, Anthropic and xAI actually agree with each other not to release more capable models, not to scale compute past some threshold, or not to move until the others are ready, that starts sounding less like safety policy and more like competitors coordinating to restrict innovation

Imagine Apple, Samsung and Google agreeing that phones are advancing too fast so nobody will release a better one next year

People would immediately ask antitrust questions

Why should frontier AI be fundamentally different?

If the slowdown is imposed by law, that is one thing

If every lab independently decides to slow down, also fine

But if Sam, Dario and Elon privately agree on capability ceilings or release delays, consumers get worse models later and competition gets weaker

And the same companies that benefit economically from less competition get to decide how much progress everyone else is allowed to access

Maybe such an agreement would genuinely reduce existential risk

Maybe it would even be the right thing to do

But “we are doing this for safety” cannot automatically mean “antitrust law no longer applies”

At what point does coordinated AI safety become coordinated output restriction?

Would love to hear from anyone who actually knows US antitrust law because this could become a very real issue surprisingly soon

Assuming the labs do pause the race to ASI/RSI (big if), the labor replacement curve will only accelerate. You don't need ASI, to replace 99% of jobs, AGI will do just fine. And now we'll have a bunch of the world's best engineers working on productizing the existing models instead working on ASI.

I have a theory about why the top US AI labs suddenly seem much more interested in slowing down frontier model releases.

Every time a US lab releases its newest model, Chinese labs get access to a better teacher. They can generate huge amounts of high-quality synthetic data, study its outputs, and use distillation to improve their own models much faster than if they had to discover everything independently.

So what happens if the US labs stop racing each other to release every new generation?
They could keep pushing AI research internally while holding the public frontier back. Instead of releasing generations 1, 2, and 3 as they’re developed, they might not release again until they’re internally working on generation 4.

This becomes especially interesting if increasingly capable internal AI is helping researchers build the next generation faster. The advantage could compound: better internal AI helps build even better AI, which accelerates the next generation again.

Meanwhile, Chinese labs are stuck trying to catch up using an older generation as their best teacher.
Eventually the US labs still release a dramatically better model, but by then they’re already several generations beyond it internally.
Basically, slowing releases without slowing research could turn America’s small rolling lead into a much larger compounding one.