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Gemini 3 Pro: the frontier of vision AI

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Re: Gemini 3 Pro: the frontier of vision AI

#161

Post by varispeed »

Rover222 wrote: post #64
Workaccount2 wrote: post #48

Well It is the first model to get partial-credit on an LLM image test I have. Which is counting the legs of a dog. Specifically, a dog with 5 legs. This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. In fact GPT5 wrote an edge detection script to see where "golden dog feet" met "bright green grass" to prove to me that there were only 4 legs. The script found 5, and GPT-5 the…

I just tried to get Gemini to produce an image of a dog with 5 legs to test this out, and it really struggled with that. It either made a normal dog, or turned the tail into a weird appendage. Then I asked both Gemini and Grok to count the legs, both kept saying 4. Gemini just refused to consider it was actually wrong. Grok seemed to have an existential crisis when I told it it was wrong, becoming convinced that I ha…

Do 7 legged dog. Game over.

Re: Gemini 3 Pro: the frontier of vision AI

#162

Post by ftcHn »

knollimar wrote: post #86
amorzor wrote: post #47

Earlier quoted context omitted.

Can you give an example of the sort of harness you used for that? Would love to play around with it

I've been using pyrevit inside revit so I just threw a basic loop in there. There's already a building model and the coworkers are just placing and wiring outlets, switches, etc. The harness wasn't impressive enough to share (alos contains vibe coded UI since I didn't want to learn XAML stuff on a friday night). Nothing fancy; I'm not very skilled (I work in construction) I gave it some custom methods it could call,…

I'm going to try to get it to generate extrusions in Revit based on images of floor plans. I've tried doing this in bunch of models without success so far.

Re: Gemini 3 Pro: the frontier of vision AI

#164

Post by JacobAsmuth »

hodder wrote: post #36
minimaxir wrote: post #28

Earlier quoted context omitted.

Gemini 3 Pro is not Nano Banana Pro, and the image generation/model that decodes the generated image tokens may not be as robust. The thinking step of Nano Banana Pro can refine some lateral steps (i.e. the errors in the homework correction and where they are spatially in the image) but it isn't perfect and can encounter some of the typical pitfalls. It's a lot better than Nano Banana base, though.

As a consumer I typed this into "Gemini". The behind the scenes model selection just adds confusion. If "AI" trust is the big barrier for widespread adoption to these products, Alphabet soup isn't the solution (pun intended).

It works fine for me. https://imgur.com/a/MKNufm1

Re: Gemini 3 Pro: the frontier of vision AI

#165

Post by jeffbee »

tills13 wrote: post #156
levocardia wrote: post #132

Earlier quoted context omitted.

This is a really interesting "data flywheel" -- better model >> more usable data >> even better model

surely there's an upper limit to this though with models literally eating themselves.

When a human students learns to read more carefully we don't consider that a negative.

Re: Gemini 3 Pro: the frontier of vision AI

#166

Post by genrader »

jvolkman wrote: post #112
Workaccount2 wrote: post #48

Well It is the first model to get partial-credit on an LLM image test I have. Which is counting the legs of a dog. Specifically, a dog with 5 legs. This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. In fact GPT5 wrote an edge detection script to see where "golden dog feet" met "bright green grass" to prove to me that there were only 4 legs. The script found 5, and GPT-5 the…

Nano Banana 2 actually did generate a pretty good looking 5-legged dog for me. https://gemini.google.com/share/e7a8b902ff67 But it didn't actually think it had done so, aligning with your observations. The last bits of its thinking were pretty sad (for it): Analyzing Missing Anomaly I'm focusing on the discrepancy - the persistent absence of the fifth leg in the generated images. Despite multiple attempts, the output…

Google's summary of the thinking process isn't as detailed or accurate as what is actually being thought out.

For instance, look at what other reasoning models show you about their thinking. You can tell Gemini is not giving it all to you, so I don't put full trust in it unless it routinely is giving me an issue that I can catch in there.

For instance, it doesn't show you all the tool use that it can use nor distinguish the different types where I really wish it would. Especially because I force it to in a Gemini personal context instruction as well as many prompts and it does do them

Re: Gemini 3 Pro: the frontier of vision AI

#167

Post by genrader »

biophysboy wrote: post #88
Benjammer wrote: post #83

Earlier quoted context omitted.

It always feels to me like these types of tests are being somewhat intentionally ignorant of how LLM cognition differs from human cognition. To me, they don't really "prove" or "show" anything other than simply - LLMs thinking works different than human thinking. I'm always curious if these tests have comprehensive prompts that inform the model about what's going on properly, or if they're designed to "trick" the LLM…

I thought adversarial testing like this was a routine part of software engineering. He's checking to see how flexible it is. Maybe prompting would help, but it would be cool if it was more flexible.

You're correct, however midwit people who don't actually fully understand all of this will latch on to one of the early difficult questions that was shown as an example, and then continued to use that over and over without really knowing what they're doing while the people developing the model and also testing the model are doing far more complex things

Re: Gemini 3 Pro: the frontier of vision AI

#168

Post by genrader »

bee_rider wrote: post #118
Workaccount2 wrote: post #48

Well It is the first model to get partial-credit on an LLM image test I have. Which is counting the legs of a dog. Specifically, a dog with 5 legs. This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. In fact GPT5 wrote an edge detection script to see where "golden dog feet" met "bright green grass" to prove to me that there were only 4 legs. The script found 5, and GPT-5 the…

Naive question, but what is Gemini? I wonder if a lot of these models are large language models that have had image recognition and generation tools bolted on? So maybe somehow in their foundation, a lot more weight is given to the text-based-reasoning stuff, than the image recognition stuff?

Go watch some of the more recent Google developer, Google AI, and Google deepmind videos, they're all separate channels at YouTube but try to catch some from the last 6 months with some of these explanatory topics on the developer side that are philosophical/ mathematical enough to explain this to you without going into the gritty details and should answer your question

Re: Gemini 3 Pro: the frontier of vision AI

#169

Post by hackeruser741 »

It's fascinating how these models struggle with simple counting or novel configurations like a 5-legged dog or a 13-hour clock, despite excelling at complex language tasks. It highlights the difference between learning patterns from vast datasets and true conceptual understanding.

Re: Gemini 3 Pro: the frontier of vision AI

#170

Post by genrader »

TheAceOfHearts wrote: post #56

Since I think it's interesting to highlight the jagged intelligence, I have a simple word search puzzle [0] that Nano Banana Pro stills struggles to solve correctly. Gemini 3 Pro with Code Execution is able to one-shot the problem and find the positions of each word (this is super impressive! one year ago it wasn't possible), but Nano Banana Pro fails to highlight the words correctly. Here's the output from two tests…

If you're using for instance the Gemini web app there may be a preference in the system prompt to immediately favor the fact that you said to create an image when in fact it may have been better to initially start with a regular chat prompt, making sure you're on Gemini 3 Pro thinking, and then give it exactly what you usually would. You can tell it that after it has an answer to the question then to create an image for it.

This may even work if you tell it to do all that prior to figuring out what to create for the image,

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