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Gemini 3 Deep Think

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Re: Gemini 3 Deep Think

#381

Post by kristopolous »

i5heu wrote: post #175
logicprog wrote: post #105

Is it me or is the rate of model release is accelerating to an absurd degree? Today we have Gemini 3 Deep Think and GPT 5.3 Codex Spark. Yesterday we had GLM5 and MiniMax M2.5. Five days before that we had Opus 4.6 and GPT 5.3. Then maybe two weeks I think before that we had Kimi K2.5.

I think it is because of the Chinese new year. The Chinese labs like to publish their models arround the Chinese new year, and the US labs do not want to let a DeepSeek R1 (20 January 2025) impact event happen again, so i guess they publish models that are more capable then what they imagine Chinese labs are yet capable of producing.

I guess. Deepseek v3 was released on boxing day a month prior

https://api-docs.deepseek.com/news/news1226

Re: Gemini 3 Deep Think

#382

Post by dmbche »

So what happens if the AI companies can't make money? I see more and more advances and breakthrough but they are taking in debt and no revenue in sight.

I seem to understand debt is very bad here since they could just sell more shares, but aren't (either valuation is stretched or no buyers).

Just a recession? Something else? Aren't they very very big to fall?

Edit0: Revenue isn't the right word, profit is more correct. Amazon not being profitable fucks with my understanding of buisness. Not an economist.

Re: Gemini 3 Deep Think

#383

Post by S1M0N38-hn »

raincole wrote: post #127
lukebechtel wrote: post #4

Arc-AGI-2: 84.6% (vs 68.8% for Opus 4.6) Wow. https://blog.google/innovation-and-ai/models-and-research/ge...

Even before this, Gemini 3 has always felt unbelievably 'general' for me. It can beat Balatro (ante 8) with text description of the game alone[0]. Yeah, it's not an extremely difficult goal for humans, but considering: 1. It's an LLM, not something trained to play Balatro specifically 2. Most (probably >99.9%) players can't do that at the first attempt 3. I don't think there are many people who posted their Balatro p…

Hi, BalatroBench creator here. Yeah, Google models perform well (I guess the long context + world knowledge capabilities). Opus 4.6 looks good on preliminary results (on par with Gemini 3 Pro). I'll add more models and report soon. Tbh, I didn't expect LLMs to start winning runs. I guess I have to move to harder stakes (e.g. red stake).

Re: Gemini 3 Deep Think

#384

Post by echelon »

dmbche wrote: post #382

So what happens if the AI companies can't make money? I see more and more advances and breakthrough but they are taking in debt and no revenue in sight. I seem to understand debt is very bad here since they could just sell more shares, but aren't (either valuation is stretched or no buyers). Just a recession? Something else? Aren't they very very big to fall? Edit0: Revenue isn't the right word, profit is more correc…

AI will kill SaaS moats and thus revenue. Anyone can build new SaaS quickly. Lots of competition will lead to marginal profits.

AI will kill advertising. Whatever sits at the top "pane of glass" will be able to filter ads out. Personal agents and bots will filter ads out.

AI will kill social media. The internet will fill with spam.

AI models will become commodity. Unless singularity, no frontier model will stay in the lead. There's competition from all angles. They're easy to build, just capital intensive (though this is only because of speed).

All this leaves is infrastructure.

Re: Gemini 3 Deep Think

#385

Post by mrandish »

D-Machine wrote: post #334
mrandish wrote: post #308

Earlier quoted context omitted.

> does leak per definition. As a measure focused solely on fluid intelligence, learning novel tasks and test-time adaptability, ARC-AGI was specifically designed to be resistant to pre-training - for example, unlike many mathematical and programming test questions, ARC-AGI problems don't have first order patterns which can be learned to solve a different ARC-AGI problem. The ARC non-profit foundation has private vers…

> which is why only "ARC-AGI Certified" results using a secret problem set really matter. The 84.6% is certified and that's a pretty big deal. So, I'd agree if this was on the true fully private set, but Google themselves says they test on only the semi-private: > ARC-AGI-2 results are sourced from the ARC Prize website and are ARC Prize Verified. The set reported is v2, semi-private ( https://storage.googleapis.com/…

Chollet himself says "We certified these scores in the past few days." https://x.com/fchollet/status/2021983310541729894.

The ARC-AGI papers claim to show that training on a public or semi-private set of ARC-AGI problems to be of very limited value in passing a private set. none of ARC-AGI can possibly be valid. So, before "public, semi-private or private" answers leaking or 'benchmaxing' on them can even matter - you need to first assess whether their published papers and data demonstrate their core premise to your satisfaction.

There is no "trust" regarding the semi-private set. My understanding is the semi-private set is only to reduce the likelihood those exact answers unintentionally end up in web-crawled training data. This is to help an honest lab's own internal self-assessments be more accurate. However, labs doing an internal eval on the semi-private set still counts for literally zero to the ARC-AGI org. They know labs could cheat on the semi-private set (either intentionally or unintentionally), so they assume all labs are benchmaxing on the public AND semi-private answers and ensure it doesn't matter.

Re: Gemini 3 Deep Think

#386

Post by robertwt7 »

lukebechtel wrote: post #4

Arc-AGI-2: 84.6% (vs 68.8% for Opus 4.6) Wow. https://blog.google/innovation-and-ai/models-and-research/ge...

I’m surprised that gemini 3 pro is so low at 31.1% though compared to opus 4.6 and gpt 5.2. This is a great achievement but its only available to ultra subscribers unfortunately

Re: Gemini 3 Deep Think

#387

Post by ipnon »

dmbche wrote: post #382

So what happens if the AI companies can't make money? I see more and more advances and breakthrough but they are taking in debt and no revenue in sight. I seem to understand debt is very bad here since they could just sell more shares, but aren't (either valuation is stretched or no buyers). Just a recession? Something else? Aren't they very very big to fall? Edit0: Revenue isn't the right word, profit is more correc…

They're using the ride share app playbook. Subsidize the product to reach market saturation. Once you've found a market segment that depends on your product you raise the price to break even. One major difference though is that ride share's haven't really changed in capabilities since they launched: it's a map that shows a little car with your driver coming and a pin where you're going. But it's reasonable to believe that AI will have new fundamental capabilities in the 2030s, 2040s, and so on.

Re: Gemini 3 Deep Think

#388

Post by rob-wagner »

dubeye wrote: post #343
rob-wagner wrote: post #297

I’ve been using Gemini 3 Pro on a historical document archiving project for an old club. One of the guys had been working on scanning old handwritten minutes books written in German that were challenging to read (1885 through 1974). Anyways, I was getting decent results on a first pass with 50 page chunks but ended up doing 1 page at a time (accuracy probably 95%). For each page, I submit the page for a transcription…

It sounds like a job where one pass might also be a viable option. Until you do the manual review you won't have a full sense of the time savings involved.

Good idea. I’ll try modifying the prompt to transcribe, identify the language, and translate if not English, and then return a structured result. In my spot checks, most of the errors are in people’s names and if the handwriting trails into margins (especially into the fold of the binding). Even with the data still needing review, the translations from it has revealed a lot of interesting characters as well as this little anecdote from the minutes of the June 6, 1941 Annual Meeting:

It had already rained at the beginning of the meeting. During the same, however, a heavy thunderstorm set in, whereby our electric light line was put out of operation. Wax candles with beer bottles as light holders provided the lighting. In the meantime the rain had fallen in a cloudburst-like manner, so that one needed help to get one's automobile going. In some streets the water stood so high that one could reach one's home only by detours. In this night 9.65 inches of rain had fallen.

Re: Gemini 3 Deep Think

#390

Post by kenjackson »

XCSme wrote: post #378
kenjackson wrote: post #118

Earlier quoted context omitted.

Everyone is already at 80% for that one. Crazy that we were just at 50% with GPT-4o not that long ago.

But 80% sounds far from good enough, that's 20% error rate, unusable in autonomous tasks. Why stop at 80%? If we aim for AGI, it should 100% any benchmark we give.

Are humans 100%?
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