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Gemini 3 Deep Think

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Re: Gemini 3 Deep Think

#311

Post by mrandish »

WarmWash wrote: post #234
mapontosevenths wrote: post #204

Earlier quoted context omitted.

> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI. That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Thats said, I'm reminded of the impossible voting tests…

>because we can no longer find tasks that are feasible for normal humans but unsolved by AI. "Answer "I don't know" if you don't know an answer to one of the questions"

I've been surprised how difficult it is for LLMs to simply answer "I don't know."

It also seems oddly difficult for them to 'right-size' the length and depth of their answers based on prior context. I either have to give it a fixed length limit or put up with exhaustive answers.

Re: Gemini 3 Deep Think

#312

Post by tl »

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…

Per BalatroBench, gemini-3-pro-preview makes it to round (not ante) 19.3 ± 6.8 on the lowest difficulty on the deck aimed at new players. Round 24 is ante 8's final round. Per BalatroBench, this includes giving the LLM a strategy guide, which first-time players do not have. Gemini isn't even emitting legal moves 100% of the time.

Re: Gemini 3 Deep Think

#313

Post by rfoo »

r2vcap wrote: post #294
i5heu wrote: post #175

Earlier quoted context omitted.

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.

[flagged]

For another example, Singapore, one of the "many Asian countries" you mentioned, list "Chinese New Year" as the official name on government websites. [0] Also note that both California and New York is not located in Asia.

And don't get me started with "Lunar New Year? What Lunar New Year? Islamic Lunar New Year? Jewish Lunar New Year? CHINESE Lunar New Year?".

[0] https://www.mom.gov.sg/employment-practices/public-holidays

Re: Gemini 3 Deep Think

#314

Post by ankit219 »

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…

Agreed. Gemini 3 Pro for me has always felt like it has had a pretraining alpha if you will. And many data points continue to support that. Even as flash, which was post trained with different techniques than pro is good or equivalent at tasks which require post training, occasionally even beating pro. (eg: in apex bench from mercor, which is basically a tool calling test - simplifying - flash beats pro). The score on arc agi2 is another datapoint in the same direction. Deepthink is sort of parallel test time compute with some level of distilling and refinement from certain trajectories (guessing based on my usage and understanding) same as gpt-5.2-pro and can extract more because of pretraining datasets.

(i am sort of basing this on papers like limits of rlvr, and pass@k and pass@1 differences in rl posttraining of models, and this score just shows how "skilled" the base model was or how strong the priors were. i apologize if this is not super clear, happy to expand on what i am thinking)

Re: Gemini 3 Deep Think

#315

Post by zzrush »

r2vcap wrote: post #294
i5heu wrote: post #175

Earlier quoted context omitted.

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.

[flagged]

I didn't expect language policing has reached such level. This is specifically related to China and DeepSeek who celebrates Chinese new year. Do you demand all Chinese to say happy luner new year to each other?

Re: Gemini 3 Deep Think

#316

Post by [deleted] »

mNovak wrote: post #115
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 excited for the big jump in ARC-AGI scores from recent models, but no one should think for a second this is some leap in "general intelligence". I joke to myself that the G in ARC-AGI is "graphical". I think what's held back models on ARC-AGI is their terrible spatial reasoning, and I'm guessing that's what the recent models have cracked. Looking forward to ARC-AGI 3, which focuses on trial and error and explorin…

[deleted]

Re: Gemini 3 Deep Think

#317

Post by amelius »

colordrops wrote: post #157
mNovak wrote: post #115

Earlier quoted context omitted.

I'm excited for the big jump in ARC-AGI scores from recent models, but no one should think for a second this is some leap in "general intelligence". I joke to myself that the G in ARC-AGI is "graphical". I think what's held back models on ARC-AGI is their terrible spatial reasoning, and I'm guessing that's what the recent models have cracked. Looking forward to ARC-AGI 3, which focuses on trial and error and explorin…

Wouldn't you deal with spatial reasoning by giving it access to a tool that structures the space in a way it can understand or just is a sub-model that can do spatial reasoning? These "general" models would serve as the frontal cortex while other models do specialized work. What is missing?

They should train more on sports commentary, perhaps that could give spatial reasoning a boost.

Re: Gemini 3 Deep Think

#319

Post by octoberfranklin »

imiric wrote: post #287
raybb wrote: post #159

Earlier quoted context omitted.

OpenRouter is pretty great but I think litellm does a very good job and it's not a platform middle man, just a python library. That being said, I have tried it with the deep think models. https://docs.litellm.ai/docs/

Part of OpenRouter's appeal to me is precisely that it is a middle man. I don't want to create accounts on every provider, and juggle all the API keys myself. I suppose this increases my exposure, but I trust all these providers and proxies the same (i.e. not at all), so I'm careful about the data I give them to begin with.

Unfortunately that's ending with mandatory-BYOK from the model vendors. They're starting to require that you BYOK to force you through their arbitrary+capricious onboarding process.

Re: Gemini 3 Deep Think

#320

Post by koolala »

nutjob2 wrote: post #155
Ozzie_osman wrote: post #130

Earlier quoted context omitted.

Peacetime Google is not like wartime Google. Peacetime Google is slow, bumbling, bureaucratic. Wartime Google gets shit done.

OpenAI is the best thing that happened to Google apparently.

Next they compete on ads...
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