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GPT-6 Astra

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Re: GPT-6 Astra

#721

Post by NothingAboutAny »

dingdong2026 wrote: post #555
intenex wrote: post #272

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

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…

yeah for me it's "I wanna add this new thing to an existing system" and the AI responds "we should just add some arbitrary state here to facilitate this feature". The real issue is the existing system needs to change entirely to facilitate, I know this, Good developers know this, The AI however knows the shitty solution would solve the immediate problem because it's been trained on shitty solutions. the problem could simply be the AI doesn't have all nebulous loose context I have about the goals of the project and future plans, but I would have to write a novel to give it that context.

Re: GPT-6 Astra

#722

Post by ShinyLeftPad »

driverdan wrote: post #271
abixb wrote: post #247

I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…

> If this is truly AGI (subject to one's definition of AGI still) Scoring well in a benchmark that's called AGI does not make an LLM AGI.

talking about self proclaimed, it's about as much AGI as openAI is open.

Re: GPT-6 Astra

#723

Post by dgunay »

uludag wrote: post #334
intenex wrote: post #272

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

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…

This is a big reason why I feel like even though LLMs are _effectively_ AGI in some regard, they also are a hack around what most people figured AGI would look like before the advent of LLMs. Humans can do metacognition, output multimodally at the same time (verbal _and_ physical intelligence go together to produce an expressive face while one talks), have a good sense for what they do and don't know, continuously take in and respond to the world around them in a (mostly) uninterrupted fashion without "turns", learn knew knowledge and retain it for their whole lives, etc. When you reduce a human to a text generator, yes obviously SOTA LLMs perform way better, but rather than invent something that can operate as an always-running "being", we've grafted a harness around an intelligence that is bound purely to speak only when spoken to. Maybe organic intelligence is already that, playing out at a super high refresh rate, but I don't know.

Re: GPT-6 Astra

#724

Post by m0zzie »

XCSme wrote: post #354
GodelNumbering wrote: post #297

The most interesting part, even more than ARC 3 score, to me is that this is the first model I recall seeing that scores lower on Max than High reasoning effort on some coding benchmarks: Terminal-Bench 4.0: High (57.9%), Max (56.7%) DeepSWE: High (73.3%), Max (71.5%) It _loses_ 1-2% performance going to High from Max

That's quite common with many models, after "High" reasoning, over-thinking starts occurring and the model skips over the right solution by convincing itself otherwise.

I find this very amusing, given we humans are also highly susceptible to this.

Re: GPT-6 Astra

#725

Post by swingboy »

fnordpiglet wrote: post #638
dalemhurley wrote: post #312

OpenAI is killing it now that they are more focused. Killing projects like Sora et al have seen it go from irrelevant to level footing with Anthropic. Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive). Codex is slightly better than Claude Code. Good on Sam Altman getting back to basics and turning OpenAI around.

Codex is missing a few things that Claude code has had for some time like defined plugin subagents and a few other things. But overall it’s fairly capable. The biggest gripe I have is that codex really restricts context window sizes and compaction leads to a lot of grounding work, and overall codex GPT is too literal in many situations - it’s follows direction slavishly, and when subagent reviewers are used, they ten…

You can enable the 1 million token context window and adjust when it compacts in your config.

> model_context_window = 1000000

> model_auto_compact_token_limit = 900000

I believe it does consume your usage a bit faster though.

Re: GPT-6 Astra

#726

Post by deepfriedbits »

drivebyhooting wrote: post #607

For people skeptical of AGI. Consider the following: 15 years ago if you were the sole proprietor of these models, would you be able to hold a dozen remote junior engineer jobs? Maybe even more? These models could certainly pass all interviews with flying colors and even survive independently in a company role. I think sole ownership of AI 15 years ago could be worth north of $10 million per year. Just as rank-and-fi…

Yeah, I can't believe all of the skepticism. If we're not at textbook AGI, we're awfully darn close.

The demo video showed Astra create a drawing of a rocket ship from an audio prompt, take the drawing to blender, and ended with the gentleman 3D printing the rocket ship. Maybe I'm a bit older than the average HN commenter, but that's damn near magic and a great many here are kind of just taking it for granted.

Re: GPT-6 Astra

#727

Post by SmirkingRevenge »

billypilgrim wrote: post #459
XCSme wrote: post #372

It's fun, but every new model release makes me even less interested to create cool stuff. Like, what's the point, if the next AI can do it in 5 seconds?

„The depressing thing about tennis is that no matter how good I get, I'll never be as good as a wall.“ -Mitch Hedberg

those things are fscking relentless

Re: GPT-6 Astra

#728

Post by hyperpape »

Cu3PO42 wrote: post #58

Just two days ago, a preprint by Julia Stadlmann went up on arXiv [0] improving the prime gap from 246 to 240. Now OpenAI announces Astra has shown a gap of 186 [1]. That must really blow. [0] https://arxiv.org/abs/2608.31126 [1] https://cdn.openai.com/pdf/51126fac-1b68-4128-9666-c908bcc16...

Don't think everything is just "who can produce the biggest/smallest number": https://mathstodon.xyz/@tao/117208619314517025.

Re: GPT-6 Astra

#729

Post by Fizz43 »

intenex wrote: post #272

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

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