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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

#221

Post by isodev »

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…

> starting to feel LLMs are intelligent

LLMs are fancy “lorem ipsum based on a keyword” text generators. They can never become intelligent … or learn how to count or do math without the help of tools.

It can probably generate a story about a 5 legged dog though.

Re: Gemini 3 Pro: the frontier of vision AI

#222

Post by Choco31415 »

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.

We can wait for that to start appearing in tests or benchmarks first.

Re: Gemini 3 Pro: the frontier of vision AI

#223

Post by SXX »

devinprater wrote: post #35

Audio described Youtube please? That'd be so amazing! Even if I couldn't play Zelda yet, I could listen to a playthrough with Gemini describing it.

And yeah just checked AI studio. 1 hour Witcher 3 blood and wine gameplay in 144p is 70MB and 300,000 tokens only. And it's pretty easy to create scene by scene description.

Re: Gemini 3 Pro: the frontier of vision AI

#224

Post by runarberg »

adastra22 wrote: post #220
runarberg wrote: post #211

Earlier quoted context omitted.

Interpolation and generalization are two completely different constructs. Interpolation is when you have two data points and make a best guess where a hypothetical third point should fit between them. Generalization is when you have a distribution which describes a particular sample, and you apply it with some transformation (e.g. a margin of error, a confidence interval, p-value, etc.) to a population the sample is…

I assumed you were speaking by analogy, as LLMs do not work by interpolation, or anything resembling that. Diffusion models, maybe you can make that argument. But GPT-derived inference is fundamentally different. It works via model building and next token prediction, which is not interpolative. As for bias, I don’t see the distinction you are making. Biases in the training data produce biases in the weights. That’s w…

What I meant was that what LLMs are doing is very similar to curve fitting, so I think it is not wrong to call it interpolation (curve fitting is a type of interpolation, but not all interpolation is curve fitting).

As for bias, sampling bias is only one many types of biases. I mean the UNIX program YES(1) has a bias towards outputting the string y despite not sampling any data. You can very easily and deliberately program a bias into everything you like. I am writing a kanji learning program using SSR and I deliberately bias new cards towards the end of the review queue to help users with long review queues empty it quicker. There is no data which causes that bias, just program it in there.

I don‘t know enough about diffusion models to know how biases can arise, but with unsupervised learning (even though sampling bias is indeed very common) you can get a bias because you are using wrong, mal-adjusted, to many parameters, etc. even the way your data interacts during training can cause a bias, heck even by random one of your parameters hits an unfortunate local maxima yielding a mal-adjusted weight, which may cause bias in your output.

Re: Gemini 3 Pro: the frontier of vision AI

#225

Post by rglullis »

JamesSwift wrote: post #205
nl wrote: post #204

Earlier quoted context omitted.

Programs can solve mazes and LLMs can program. That's a different thing completely.

That just seems like an arbitrary limitation. Its like asking someone to do answer a math calculation but "no thinking allowed". Like, I guess we can gauge if a model just _knows all knowable things in the universe_ using that method... but anything of any value that you are gauging in terms of 'intelligence', is going to actually be validating their ability to go "outside the scope" of what they actually are (an aut…

By your analogy, the developers of stockfish are better chess players than any grandmaster.

Tool use can be a sign of intelligence, but "being able to use a tool to solve a problem" is not the same as "being intelligent enough to solve a specific class of problems".

Re: Gemini 3 Pro: the frontier of vision AI

#226

Post by sebastiennight »

JamesSwift wrote: post #119
danielvaughn wrote: post #57

Earlier quoted context omitted.

I don’t know much about AI, but I have this image test that everything has failed at. You basically just present an image of a maze and ask the LLM to draw a line through the most optimal path. Here’s how Nano Banana fared: https://x.com/danielvaughn/status/1971640520176029704?s=46

I just oneshot it with claude code (opus 4.5) using this prompt. It took about 5 mins and included detecting that it was cheating at first (drew a line around the boundary of the maze instead), so it added guardrails for that: ``` Create a devenv project that does the following: - Read the image at maze.jpg - Write a script that solves the maze in the most optimal way between the mouse and the cheese - Generate a new…

This (writing a program to solve the problem) would be a perfectly valid solution if the model had come up with it.

I participated in a "math" competition in high school which mostly tested logic and reasoning. The reason my team won by a landslide is because I showed up with a programmable calculator and knew how to turn the problems into a program that could solve them.

By prompting the model to create the program, you're taking away one of the critical reasoning steps needed to solve the problem.

Re: Gemini 3 Pro: the frontier of vision AI

#227

Post by Paracompact »

Benjammer wrote: post #83
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…

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…

> Does the test instruction prompt tell it that it should be interpreting the image very, very literally, and that it should attempt to discard all previous knowledge of the subject before making its assessment of the question, etc.?

No. Humans don't need this handicap, either.

> More specifically, what is a successful outcome here to you? Simply returning the answer "5" with no other info, or back-and-forth, or anything else in the output context?

Any answer containing "5" as the leading candidate would be correct.

> What is your idea of the LLMs internal world-model in this case? Do you want it to successfully infer that you are being deceitful? Should it respond directly to the deceit? Should it take the deceit in "good faith" and operate as if that's the new reality? Something in between?

Irrelevant to the correctness of an answer the question, "how many legs does this dog have." Also, asking how many legs a 5-legged dog has is not deceitful.

> This doesn't seem like particularly useful or productive analysis to me, so I'm curious what the goal of these "tests" are for the people who write/perform/post them?

It's a demonstration of the failures of the rigor of out-of-distribution vision and reasoning capabilities. One can imagine similar scenarios with much more tragic consequences when such AI would be used to e.g. drive vehicles or assist in surgery.

Re: Gemini 3 Pro: the frontier of vision AI

#228

Post by sebastiennight »

biophysboy wrote: post #91
pwagland wrote: post #74

Earlier quoted context omitted.

I tried this with Claude: ``` > [Image #1] Create a unicode "ascii-art" version of this image, with the optimal path through the maze highlighted in a solid colour. I'll create an ASCII art version of this maze with the solution path highlighted! ┌─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┐ ●●│ │ │ │ │ │ │ │ │ │ │ │ │ │ ├─┤●└─┴─┐ ├───┐ │ ╔═╗ ├─────┤ ╔═══╝ │ ╔═╝ ╔═╝ │ │ ╔═╝ ├─┤ │ │●●●●●└─┤ │ │ ║ │ │ │ ║…

Honestly, even though it failed, I'm kind of impressed that the trajectory mostly stays in the lines. If you remove all but two openings, does it work? The drawing you show has more than two openings, some of which are inaccessible from the inside of the maze.

It's ASCII art, so the "trajectory" will always stay within the lines, because you can't have the ● and ║ characters intersect each other.

The only impressive part would be that the trajectory is "continuous", meaning for every ● there is always another ● character in one of the 4 adjacent positions.

Re: Gemini 3 Pro: the frontier of vision AI

#229

Post by theoa »

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…

Draw a millipede as a dog:

Gemini responds:

Conceptualizing the "Millipup"

https://gemini.google.com/share/b6b8c11bd32f

Draw the five legs of a dog as if the body is a pentagon

https://gemini.google.com/share/d74d9f5b4fa4

And animal legs are quite standardized

https://en.wikipedia.org/wiki/List_of_animals_by_number_of_l...

It's all about the prompt. Example:

Can you imagine a dog with five legs?

https://gemini.google.com/share/2dab67661d0e

And generally, the issue sits between the computer and the chair.

;-)

Re: Gemini 3 Pro: the frontier of vision AI

#230

Post by mhl47 »

We are currently working on some christmas puzzle, that are - I would say - a bit more difficult from the visual side. GPT5.1 completely failed at all of them while Gemini 3 solved two till know that I would consider rather impressive.

One was two screenshots of a phone screen with chats that are timestamped and it had to take the nth letter of the mth word based on the timestamp. While the type of riddle could be in the training data the ability to OCR this that well and understand the spatial relation to each object perfectly is something I have not seen from other models yet.

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