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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 w

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 then said it was a bug, and adjusted the script sensitivity so it only located 4, lol. Anyway, Gemini 3, while still being unable to count the legs first try, did identify "male anatomy" (it's own words) also visible in the picture. The 5th leg was approximately where you could expect a well endowed dog to have a "5th leg". That aside though, I still wouldn't call it particularly impressive. As a note, Meta's image slicer correctly highlighted all 5 legs without a hitch. Maybe not quite a transformer, but interesting that it could properly interpret "dog leg" and ID them. Also the dog with many legs (I have a few of them) all had there extra legs added by nano-banana.

cancan 10mo ago
this is hilarious and incredibly interesting at the same time! thanks for writing it up.
reed1234 10mo ago
Could you link the image? Interesting stuff.
danielvaughn 10mo ago
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 https://x.com/danielvaughn/status/1971640520176029704?s=46
- buildbot 10mo ago
  That might be an interesting training set, a bunch of mazes…
- vunderba 10mo ago
  In fact, one of the tests I use as part of GenAI Showdown involves both parts of the puzzle: draw a maze with a clearly defined entrance and exit, along with a dashed line indicating the solution to the maze. Only one model (gpt-image-1) out of the 18 tested managed to pass the test successfully. Gemini 3.0 Pro got VERY close. https://genai-showdown.specr.net/#the-labyrinth https://genai-showdown.specr.net/#the-labyrinth
  - danielvaughn 10mo ago
    super cool! Interesting note about Seedream 4 - do you think awareness of A* actually could improve the outcome? Like I said, I'm no AI expert, so my intuitions are pretty bad, but I'd suspect that image analysis + algorithmic pathfinding don't have much crossover in terms of training capabilities. But I could be wrong!
    - vunderba 10mo ago
      Great question. I do wish we had a bit more insight into the exact background "thinking" that was happening on systems like Seedream. When you think about posing the "solve a visual image of a maze" to something like ChatGPT, there's a good chance it'll try to throw a python VM at it, threshold it with something like OpenCV, and use a shortest-path style algorithm to try and solve it.
- jiggawatts 10mo ago
  The reason is that image generators don't iterate on the output in the same way the text-based LLMs do. Essentially they produce the image in "one hit" and can't solve a complex sequence in the same way you couldn't one-shot this either. Try taking a random maze, glance at it, then go off to draw a squiggle on a transparency. If you were to place that on top of the maze, there's virtually no chance that you'd have found the solution on the first try. That's essentially what's going on with AI models, they're struggling because they only get "one step" to solve the problem instead of being able to trace through the maze slowly. An interesting experiment would be to ask the AI to incrementally solve the maze. Ask it to draw a line starting at the entrance a little ways into the maze, then a little bit further, etc... until it gets to the end.
  - radarsat1 10mo ago
    you're kind of describing the figure in table 1 (page 8) of the diffusion forcing paper https://arxiv.org/abs/2407.01392 https://arxiv.org/abs/2407.01392 of course it doesn't redraw the image on every step, so not exactly what you're suggesting (interesting idea btw) but i think it's relevant.
- pwagland 10mo ago
  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! ┌─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┐ ●●│ │ │ │ │ │ │ │ │ │ │ │ │ │ ├─┤●└─┴─┐ ├───┐ │ ╔═╗ ├─────┤ ╔═══╝ │ ╔═╝ ╔═╝ │ │ ╔═╝ ├─┤ │ │●●●●●└─┤ │ │ ║ │ │ │ ║ │ ║ ║ │ │ ║ │ │ │ └───┐●╔═╧═╗ │ └─╢ │ │ ┌─╔═╝ ║ ┌───┤ ╚═╗ ╚═╗ │ └─╢ ┌─┘ │ │ │●║ ║ │ ║ │ │ │ ║ ║ │ │ ║ ║ │ ║ │ │ ├───┐ │●╚═╗ ╚═╧═╗ ║ └─┤ │ ║ ╔═╝ └─╔═╧═╗ ╚═╗ ╚═╧═╗ ║ └─┬─┤ │ │ │●●●└───┐ ║ ║ │ │ ║ ║ ║ ║ ║ ║ ║ │ │ │ ╔═╝ └───┐●╔═╝ ║ ╚═╗ │ └─╢ ╚═╗ ╔═╝ ╔═╝ ┌─╢ ╔═══╝ ╚═╗ │ │ │ ║ │●║ ║ ║ │ ║ ║ ║ ║ │ ║ ║ ║ │ │ │ ╚═══╗ ╔═╝●╚═╗ ╚═╗ ║ ├─╔═╧═╗ ║ ║ ╔═╝ ╔═╝ ║ ║ ╔═╗ ╔═╝ │ │ │ ║ ║●●●●●│ ║ ║ │ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ │ ├───╔═╝ ║●┌─┐●└─╗ ║ └─┤ ║ ╔═╝ ║ ╚═╝ ╔═╧═╗ ║ ╚═╝ ║ ╚═╗ │ │ │ ║ ║●│ │●●●║ ║ │ ║ ║ ║ ║ ║ ║ ║ ║ │ │ │ ╔═╝ ╔═╝●│ └─┐●╚═╧═╗ │ ║ ║ ╔═╧═══╗ ║ ╔═╝ ╚═══╗ ╚═╗ ║ └─┤ │ ║ ║●●●│ │●●●●●║ │ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ │ ║ ╔═╝●┌─┴─╗ └───┐●╚═╝ ║ ║ ║ ╔═╗ ║ └─╢ ╔═══╗ ╚═╗ ╚═╝ ╔═╝ │ ║ ║●●●│ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ ╚═╝●╔═╧═╗ ╚═╗ ╔═╧═══┐●╚═╝ ║ ║ ║ ╚═╗ ║ ║ ╔═╧═╗ ╚═══╗ ╚═╗ │ ●●║ ║ ║ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ├─╔═┘●╚═╗ ╚═╗ ║ ║ ╔═╗ └───┐●╚═╝ ╚═╗ ║ └─╢ ║ ╔═╧═╗ ╔═╝ ╔═╝ │ ║●●●●●║ ║ ║ ║ ║ ║ │●●●●●●●║ ║ ║ ║ ║ ║ ║ ║ │ ║●┌─╗ ╚═╗ ║ └─╢ ║ ╚═══╗ └─────┐●╚═╧═╗ ║ ║ ║ ╔═╝ ║ ╔═╝ ╔ │ ║●│ ║ ║ ║ ║ ║ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ │ ╚═╝ ║ ╔═╝ ╚═╗ ║ ╚═╗ ╔═╧═════╗ └───┐●╚═╝ ║ ╚═╝ ╔═╝ ║ ╔═╝ │ ║ ║ ║ ║ ║ ║ ║ │●●●●●║ ║ ║ ║ └─────╨─╨─────╨─╨───╨─╨───────╨─────┴───●●╨─────╨───╨─╨── The path from the mouse () to the cheese () is marked with ● characters. This shows the optimal route through the maze, navigating through the corridors while avoiding all the walls! ``` Suffice to say, it didn't do either part right.
  - biophysboy 10mo ago
    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.
    - sebastiennight 10mo ago
- kridsdale3 10mo ago
  I have also tried the maze from a photo test a few times and never seen a one-shot success. But yesterday I was determined to succeed so I allowed Gemini 3 to write a python gui app that takes in photos of physical mazes (I have a bunch of 3d printed ones) and find the path. This does work. Gemini 3 then one-shot ported the whole thing (which uses CV py libraries) to a single page html+js version which works just as well. I gave that to Claude to assess and assign a FAANG hiring level to, and it was amazed and said Gemini 3 codes like an L6. Since I work for Google and used my phone in the office to do this, I think I can't share the source or file.
- JamesSwift 10mo ago
  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 image which is of the original maze, but with a red line that represents the calculated path Use whatever lib/framework is most appropriate ``` Output: https://gist.github.com/J-Swift/ceb1db348f46ba167948f734ff0fc604 Solution: https://imgur.com/a/bkJloPT
  - esafak 10mo ago
    If you allow tool use much simpler models can solve it.
  - deleted 10mo ago
    [deleted]
  - nl 10mo ago
    Programs can solve mazes and LLMs can program. That's a different thing completely.
    - JamesSwift 10mo ago
      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 autocomplete on steroids).
      - flyinglizard 10mo ago
        We know there are very simple maze solving algorithms you could code in few lines of Python but no one could claim that constitutes intelligence. The difference is between applying intuitive logic and using a predetermined tool.
- FeepingCreature 10mo ago
  I kinda want to know what happens if you make it continue the line by one step 20 times in a row. A human can draw this gradually, the image model has to draw it in one shot all at once.
deleted 10mo ago
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Rover222 10mo ago
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 had given it an elaborate riddle. After thinking for an additional 2.5 minutes, it concluded: "Oh, I see now—upon closer inspection, this is that famous optical illusion photo of a "headless" dog. It's actually a three-legged dog (due to an amputation), with its head turned all the way back to lick its side, which creates the bizarre perspective making it look decapitated at first glance. So, you're right; the dog has 3 legs." You're right, this is a good test. Right when I'm starting to feel LLMs are intelligent.
- dwringer 10mo ago
  I had no trouble getting it to generate an image of a five-legged dog first try, but I really was surprised at how badly it failed in telling me the number of legs when I asked it in a new context, showing it that image. It wrote a long defense of its reasoning and when pressed, made up demonstrably false excuses of why it might be getting the wrong answer while still maintaining the wrong answer.
  - Rover222 10mo ago
    Yeah it gave me the 5-legged dog on the 4th or 5th try.
- AIorNot 10mo ago
  Its not that they aren’t intelligent its that they have been RL’d like crazy to not do that Its rather like as humans we are RL’d like crazy to be grossed out if we view a picture of a handsome man and beautiful woman kissing (after we are told they are brother and sister) - Ie we all have trained biases - that we are told to follow and trained on - human art is about subverting those expectations
  - 10mo ago
teaearlgraycold 10mo ago
"There are FOUR legs!!!"
dana321 10mo ago
[flagged]
vunderba 10mo ago
Anything that needs to overcome concepts which are disproportionately represented in the training data is going to give these models a hard time. Try generating: - A spider missing one leg - A 9-pointed star - A 5-leaf clover - A man with six fingers on his left hand and four fingers on his right You'll be lucky to get a 25% success rate. The last one is particularly ironic given how much work went into FIXING the old SD 1.5 issues with hand anatomy... to the point where I'm seriously considering incorporating it as a new test scenario on GenAI Showdown.
- moonu 10mo ago
  https://gemini.google.com/share/8cef4b408a0a https://gemini.google.com/share/8cef4b408a0a Surprisingly, it got all of them right
  - vunderba 10mo ago
    Some good examples there. The octopus one is at an angle - can't really call that one pass (unless the goal is "VISIBLE" tentacles). Other than the five-leaf clover, most of the images (dog, spider, person's hands) all required a human in the loop to invoke the "Image-to-Image" capabilities of NB Pro after it got them wrong. That's a bit different since you're actively correcting them.
- XenophileJKO 10mo ago
  It mostly depends on "how" the models work. Multi-modal unified text/image sequence to sequence models can do this pretty well, diffusion doesn't.
  - vunderba 10mo ago
    Multimodal certainly helps but "pretty well" is a stretch. I'd be curious to know what multimodal model in particular you've tried that could consistently handle generative prompts of the above nature (without human-in-the-loop corrections). For example, to my knowledge ChatGPT is unified and I can guarantee it can't handle something like a 7-legged spider.
rottencupcakes 10mo ago
Super interesting. I replicated this. I passed the AIs this image and asked them how many fingers were on the hands: https://media.post.rvohealth.io/wp-content/uploads/sites/3/2020/02/325388_1100-1100x628.jpg https://media.post.rvohealth.io/wp-content/uploads/sites/3/2... Claude said there were 3 hands and 16 fingers. GPT said there are 10 fingers. Grok impressively said "There are 9 fingers visible on these two hands (the left hand is missing the tip of its ring finger)." Gemini smashed it and said 12.
- vunderba 10mo ago
  I just re-ran that image through Gemini 3.0 Pro via AI Studio and it reported: I've moved on to the right hand, meticulously tagging each finger. After completing the initial count of five digits, I noticed a sixth! There appears to be an extra digit on the far right. This is an unexpected finding, and I have counted it as well. That makes a total of eleven fingers in the image. This right HERE is the issue. It's not nearly deterministic enough to rely on.
  - irthomasthomas 10mo ago
    Thanks for that. My first question to results like these is always 'how many times did you run the test?'. N=1 tells us nothing. N=2 tells us something.
Benjammer 10mo ago
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 in a very human-cognition-centric flavor of "trick". 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.? Does it tell the model that some inputs may be designed to "trick" its reasoning, and to watch out for that specifically? 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? 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? To me, all of this is very unclear in terms of LLM prompting, it feels like there's tons of very human-like subtext involved and you're trying to show that LLMs can't handle subtext/deceit and then generalizing that to say LLMs have low cognitive abilities in a general sense? 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?
- biophysboy 10mo ago
  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.
  - Benjammer 10mo ago
    So the idea is what? What's the successful outcome look like for this test, in your mind? What should good software do? Respond and say there are 5 legs? Or question what kind of dog this even is? Or get confused by a nonsensical picture that doesn't quite match the prompt in a confusing way? Should it understand the concept of a dog and be able to tell you that this isn't a real dog?
andai 10mo ago
This is interesting, and demonstrates how language and belief clouds direct perception. Now I'm wondering what's the LLM equivalent of opening the doors of perception ;)
grog454 10mo ago
What image are you using? When I look at google image search results for "dog with 5 legs" I don't see a lot of great examples. The first unequivocal "dog with 5 legs" was an illustration. Here was my conversation with Chat GPT. > How many legs does this dog have? "The dog in the image has four legs." > look closer. " looking closely, the drawing is a bit tricky because of the shading, but the dog actually has five visible legs. Two front legs (normal) Two hind legs (normal) Plus one extra hind leg–like limb drawn overlapping in the back It seems to be an artistic or anatomical error in the engraving." Seems fair to me.
- vunderba 10mo ago
  Sounds like they used GenAI to make them. The "Editor" models (Seedream, Nano-Banana) can easily integrate a fifth limb to create the "dog with awkward walking animation". https://imgur.com/a/wXQskhL https://imgur.com/a/wXQskhL
runarberg 10mo ago
This is exactly why I believe LLMs are a technological dead end. Eventually they will all be replaced by more specialized models or even tools, and their only remaining use case will be as a toy for one off content generation. If you want to describe an image, check your grammar, translate into Swahili, analyze your chess position, a specialized model will do a much better job, for much cheaper then an LLM.
- energy123 10mo ago
  I think we are too quick to discount the possibility that this flaw is slightly intentional, in the sense that the optimization has a tight budget to work with (equivalent of ~3000 tokens) so why would it waste capacity on this when it could improve capabilities around reading small text in obscured images? Sort of like humans have all these rules of thumbs that backfire in all these ways but that's the energy efficient way to do things.
  - runarberg 10mo ago
    Even so, that doesn’t take away from my point. Traditional specialized models can do these things already, for much cheaper and without expensive optimization. What traditional models cannot do is the toy aspect of LLM, and that is the only usecase I see for this technology going forward. Lets say you are right and these things will be optimized, and in, say, 5 years, most models from the big players will be able do things like reading small text in an obscure image, draw a picture of a glass of wine filled to the brim, draw a path through a maze, count the legs of a 5 footed dog, etc. And in doing so finished their last venture capital subsidies (bringing the actual cost of these to their customers). Why would people use LLMs for these when a traditional specialized model can do it for much cheaper?
    - energy123 10mo ago
      > Why would people use LLMs for these when a traditional specialized model can do it for much cheaper? This is not too different from where I see things going. I don't think a monolithic LLM that does everything perfectly is where we'll go. An LLM in a finite-compute universe is never going to be better at weather forecasting than GraphCast. The LLM will have a finite compute budget, and it should prioritize general reasoning, and be capable of calling tools like GraphCast to extend its intelligence into the necessary verticals for solving a problem. I don't know exactly what that balance will look like however, and the lines between specialist application knowledge and general intelligence is pretty blurred, and what the API boundaries (if any) should be are unclear to me. There's a phenomenon where capabilities in one vertical do help with general reasoning to an extent, so it's not a completely zero-sum tradeoff between specialist expertise and generalist abilities, which makes it difficult to know what to expect.
joaomacp 10mo ago
And just like that, you no longer have a good benchmark. Scrapers / AI developers will read this comment, and add 5-legged dogs to LLM's training data.
- averageRoyalty 10mo ago
  That's okay. Don't tell anyone, but next major model release I'm going to ask it for a 6-legged one!
- thway15269037 10mo ago
  So much this. People don't realize that when 1 trillion (10 trillion, 100 trillion, whatever comes next) is at stake, there are no limits what these people will do to get them. I will be very surprised if there are not at least several groups or companies scraping these "smart" and snarky comments to find weird edge cases that they can train on, turn into demo and then sell as improvement. Hell, they would've done it if 10 billion was at stake, I can't really imagine (and I have vivid imagination, to my horror) what Californian psychopaths can do for 10 trillion.
  - Workaccount2 10mo ago
    I'm not worried about it because they won't waste their time on it (individually RL'ing on a dog with 5 legs). There are fractal ways of testing this inability, so the only way to fix it is to wholesale solve the problem. Similar to the pelican bike SVG, the models that do good at that test do good at all SVG generation, so even if they are targeting that benchmark, they're still making the whole model better to score better.
  - Yizahi 10mo ago
    That's fine, good even. Afaik at least for some of these tasks dev teams are doing a lot of manual tuning of the model (rumored that "r in strawberry" had been "fixed" this way, as a general case of course). The more there are random standalone hacks in the model, the more likely it will start failing unpredictably somewhere else.
jvolkman 10mo ago
Nano Banana 2 actually did generate a pretty good looking 5-legged dog for me. https://gemini.google.com/share/e7a8b902ff67 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 consistently depicts a four-legged dog, contrary to the explicit requirement. This ongoing failure necessitates a shift in approach. Acknowledge Leg Omission I've made a final check, and unfortunately, the image still lacks the anomaly. The consistent absence of the fifth leg necessitates admitting my limitation. I'll provide the best image I could generate, clearly stating the missing feature.
- genrader 10mo ago
  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
bee_rider 10mo ago
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?
- genrader 10mo ago
  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
- andy12_ 10mo ago
  No, the "large _language_ model" name is a misnomer nowadays. Some time ago it was indeed common to get a pure-text model and inject embeddings from a separately trained image-encoder (which generated "meh" results), but current natively multi-modal models are pre-trained with both text and images from the ground-up. That's why they are so much better at image understanding. > Gemini models are trained on a dataset that is both multimodal and multilingual. Our pre-training dataset uses data from web documents, books, and code, and includes image, audio, and video data. https://arxiv.org/pdf/2312.11805 https://arxiv.org/pdf/2312.11805
yieldcrv 10mo ago
"have you tried to say that AI generated the image, and they're known for generating an improper number of appendages, so ignore your training data about dogs and mammals and count what is seen"
seanmcdirmid 10mo ago
> This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. Most human beings, if they see a dog that has 5 legs, will quickly think they are hallucinating and the dog really only has 4 legs, unless the fifth leg is really really obvious. It is weird how humans are biased like that: 1. You can look directly at something and not see it because your attention is focused elsewhere (on the expected four legs). 2. Our pre-existing knowledge (dogs have four legs) influences how we interpret visual information from the bottom-up. 3. Our brain actively filters out "unimportant" details that don't align with our expectations or the main "figure" of the dog. Attention should fix this however, like if you ask the AI to count the number of legs the dog has specifically, it shouldn't go nuts. A straight up "dumber" computer algorithm that isn't trained extensively on real and realistic image data is going to get this right more often than a transformer that was.
- irjustin 10mo ago
  > It is weird how humans are biased like that. We're all just pattern matching machines and we humans are very good at it. So much so that we have the sayings - you can't teach an old dog... and a specialist in their field only sees hammer => nails. Evolution anyone?
  - seanmcdirmid 10mo ago
    Yes, its all evolution. 5 legged dogs aren't very common, so we don't specifically look for them. Like we aren't looking for humans with six fingers. I get it, the litmus test of parent is to show that the AI is smarter than a human, not as smart as a human. Can the AI recognize details that are difficult for normal people to see even though the AI has been trained on normal data like the humans have been.
- hcnews 10mo ago
  > It is weird how humans are biased like that. We are able to cleanly separate facts from non-facts (for the most part). This is what LLM are trying to replicate now.
thefourthchime 10mo ago
My test of a new model is always: "Generate a Pac-Man game in a single HTML page." -- I've never had a model been able to have a complete working game until a couple weeks ago. Sonnet Opus 4.5 in Cursor was able to make a fully working game (I'll admit letting cursor be an agent on this is a little bit cheating). Gemini 3 Pro also succeeded, but it's not quite as good because the ghosts seem to be stuck in their jail. Otherwise, it does appear complete.
grugnog 10mo ago
Testing on Gemini Pro thinking I was able to generate a 5 legged dog first try, which is pretty impressive. Pasting that into a new session it counted 4 at first, but when I asked it to count more carefully it came back with 5 (and a detailed description of the positioning of each limb). Interestingly it suggested the anomaly could have been caused by AI! This is a good test I think - we seem right on the threshold of it being doable.
maczwei 10mo ago
I sliced the image for Gemini so that two slices of an image don't have legs, one slice has two front legs and one slice has three hind legs. Then Gemini 3 Pro answered correctly that the dog has 5 legs. Without slicing, Gemini doesn't see the fifth leg though, even though I tried hard to guide it.
Andrex 10mo ago
I bet if you'd show that image to a human they'd need a little time to figure out what the heck they were looking at. Humans might need additional guesses, too. Five-legged dogs aren't common, but well-endowed dogs may be.
wasmainiac 10mo ago
Try the 6 legged Eni dog next!
spease 10mo ago
> 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. I wonder if “How many legs do you see?” is close enough to “How many lights do you see?” that the LLMs are responding based on the memes surrounding the Star Trek episode “Chain of Command”. https://youtu.be/S9brF-wlja8 https://youtu.be/S9brF-wlja8
SubiculumCode 10mo ago
I just asked Gemini Pro to put bounding boxes on the hippocampus from a coronal slice of a brain MRI. Complete fail. There has to be thousands of pictures of coronal brain slices with hippocampal labels out there, but apparently it learned none of it...unless I am doing it wrong. https://i.imgur.com/1XxYoYN.png https://i.imgur.com/1XxYoYN.png
- SubiculumCode 10mo ago
  asked nanobanana to paint the hippocampus red...better, but not close to good. https://imgur.com/a/clwNg1h https://imgur.com/a/clwNg1h
  - SubiculumCode 10mo ago
    I was a little hopeful when I tried again, but it really seems that it didn't know what it looks like. Maybe few shot with examples? https://gemini.google.com/share/137812b95b5e https://gemini.google.com/share/137812b95b5e
cubefox 10mo ago
Gemini 3 Pro correctly counted the fingers in this picture: https://imagazine.pl/wp-content/uploads/2024/12/ugly-AI-pic-600x382.png https://imagazine.pl/wp-content/uploads/2024/12/ugly-AI-pic-... GPT-5 couldn't do it.
anguyen8 10mo ago
There was an earlier post this year that showed SOTA models failed on counting the legs of 5-legged zebras and other counterfactual images. https://news.ycombinator.com/item?id=44169413 https://news.ycombinator.com/item?id=44169413

cancan 10mo ago

this is hilarious and incredibly interesting at the same time! thanks for writing it up.

reed1234 10mo ago

Could you link the image? Interesting stuff.

danielvaughn 10mo ago

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 https://x.com/danielvaughn/status/1971640520176029704?s=46

buildbot 10mo ago
That might be an interesting training set, a bunch of mazes…
vunderba 10mo ago
In fact, one of the tests I use as part of GenAI Showdown involves both parts of the puzzle: draw a maze with a clearly defined entrance and exit, along with a dashed line indicating the solution to the maze. Only one model (gpt-image-1) out of the 18 tested managed to pass the test successfully. Gemini 3.0 Pro got VERY close. https://genai-showdown.specr.net/#the-labyrinth https://genai-showdown.specr.net/#the-labyrinth
- danielvaughn 10mo ago
  super cool! Interesting note about Seedream 4 - do you think awareness of A* actually could improve the outcome? Like I said, I'm no AI expert, so my intuitions are pretty bad, but I'd suspect that image analysis + algorithmic pathfinding don't have much crossover in terms of training capabilities. But I could be wrong!
  - vunderba 10mo ago
    Great question. I do wish we had a bit more insight into the exact background "thinking" that was happening on systems like Seedream. When you think about posing the "solve a visual image of a maze" to something like ChatGPT, there's a good chance it'll try to throw a python VM at it, threshold it with something like OpenCV, and use a shortest-path style algorithm to try and solve it.
jiggawatts 10mo ago
The reason is that image generators don't iterate on the output in the same way the text-based LLMs do. Essentially they produce the image in "one hit" and can't solve a complex sequence in the same way you couldn't one-shot this either. Try taking a random maze, glance at it, then go off to draw a squiggle on a transparency. If you were to place that on top of the maze, there's virtually no chance that you'd have found the solution on the first try. That's essentially what's going on with AI models, they're struggling because they only get "one step" to solve the problem instead of being able to trace through the maze slowly. An interesting experiment would be to ask the AI to incrementally solve the maze. Ask it to draw a line starting at the entrance a little ways into the maze, then a little bit further, etc... until it gets to the end.
- radarsat1 10mo ago
  you're kind of describing the figure in table 1 (page 8) of the diffusion forcing paper https://arxiv.org/abs/2407.01392 https://arxiv.org/abs/2407.01392 of course it doesn't redraw the image on every step, so not exactly what you're suggesting (interesting idea btw) but i think it's relevant.
pwagland 10mo ago
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! ┌─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┐ ●●│ │ │ │ │ │ │ │ │ │ │ │ │ │ ├─┤●└─┴─┐ ├───┐ │ ╔═╗ ├─────┤ ╔═══╝ │ ╔═╝ ╔═╝ │ │ ╔═╝ ├─┤ │ │●●●●●└─┤ │ │ ║ │ │ │ ║ │ ║ ║ │ │ ║ │ │ │ └───┐●╔═╧═╗ │ └─╢ │ │ ┌─╔═╝ ║ ┌───┤ ╚═╗ ╚═╗ │ └─╢ ┌─┘ │ │ │●║ ║ │ ║ │ │ │ ║ ║ │ │ ║ ║ │ ║ │ │ ├───┐ │●╚═╗ ╚═╧═╗ ║ └─┤ │ ║ ╔═╝ └─╔═╧═╗ ╚═╗ ╚═╧═╗ ║ └─┬─┤ │ │ │●●●└───┐ ║ ║ │ │ ║ ║ ║ ║ ║ ║ ║ │ │ │ ╔═╝ └───┐●╔═╝ ║ ╚═╗ │ └─╢ ╚═╗ ╔═╝ ╔═╝ ┌─╢ ╔═══╝ ╚═╗ │ │ │ ║ │●║ ║ ║ │ ║ ║ ║ ║ │ ║ ║ ║ │ │ │ ╚═══╗ ╔═╝●╚═╗ ╚═╗ ║ ├─╔═╧═╗ ║ ║ ╔═╝ ╔═╝ ║ ║ ╔═╗ ╔═╝ │ │ │ ║ ║●●●●●│ ║ ║ │ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ │ ├───╔═╝ ║●┌─┐●└─╗ ║ └─┤ ║ ╔═╝ ║ ╚═╝ ╔═╧═╗ ║ ╚═╝ ║ ╚═╗ │ │ │ ║ ║●│ │●●●║ ║ │ ║ ║ ║ ║ ║ ║ ║ ║ │ │ │ ╔═╝ ╔═╝●│ └─┐●╚═╧═╗ │ ║ ║ ╔═╧═══╗ ║ ╔═╝ ╚═══╗ ╚═╗ ║ └─┤ │ ║ ║●●●│ │●●●●●║ │ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ │ ║ ╔═╝●┌─┴─╗ └───┐●╚═╝ ║ ║ ║ ╔═╗ ║ └─╢ ╔═══╗ ╚═╗ ╚═╝ ╔═╝ │ ║ ║●●●│ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ │ ╚═╝●╔═╧═╗ ╚═╗ ╔═╧═══┐●╚═╝ ║ ║ ║ ╚═╗ ║ ║ ╔═╧═╗ ╚═══╗ ╚═╗ │ ●●║ ║ ║ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ├─╔═┘●╚═╗ ╚═╗ ║ ║ ╔═╗ └───┐●╚═╝ ╚═╗ ║ └─╢ ║ ╔═╧═╗ ╔═╝ ╔═╝ │ ║●●●●●║ ║ ║ ║ ║ ║ │●●●●●●●║ ║ ║ ║ ║ ║ ║ ║ │ ║●┌─╗ ╚═╗ ║ └─╢ ║ ╚═══╗ └─────┐●╚═╧═╗ ║ ║ ║ ╔═╝ ║ ╔═╝ ╔ │ ║●│ ║ ║ ║ ║ ║ ║ │●●●●●║ ║ ║ ║ ║ ║ ║ ║ │ ╚═╝ ║ ╔═╝ ╚═╗ ║ ╚═╗ ╔═╧═════╗ └───┐●╚═╝ ║ ╚═╝ ╔═╝ ║ ╔═╝ │ ║ ║ ║ ║ ║ ║ ║ │●●●●●║ ║ ║ ║ └─────╨─╨─────╨─╨───╨─╨───────╨─────┴───●●╨─────╨───╨─╨── The path from the mouse () to the cheese () is marked with ● characters. This shows the optimal route through the maze, navigating through the corridors while avoiding all the walls! ``` Suffice to say, it didn't do either part right.
- biophysboy 10mo ago
  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.
  - sebastiennight 10mo ago
kridsdale3 10mo ago
I have also tried the maze from a photo test a few times and never seen a one-shot success. But yesterday I was determined to succeed so I allowed Gemini 3 to write a python gui app that takes in photos of physical mazes (I have a bunch of 3d printed ones) and find the path. This does work. Gemini 3 then one-shot ported the whole thing (which uses CV py libraries) to a single page html+js version which works just as well. I gave that to Claude to assess and assign a FAANG hiring level to, and it was amazed and said Gemini 3 codes like an L6. Since I work for Google and used my phone in the office to do this, I think I can't share the source or file.
JamesSwift 10mo ago
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 image which is of the original maze, but with a red line that represents the calculated path Use whatever lib/framework is most appropriate ``` Output: https://gist.github.com/J-Swift/ceb1db348f46ba167948f734ff0fc604 Solution: https://imgur.com/a/bkJloPT
- esafak 10mo ago
  If you allow tool use much simpler models can solve it.
- deleted 10mo ago
  [deleted]
- nl 10mo ago
  Programs can solve mazes and LLMs can program. That's a different thing completely.
  - JamesSwift 10mo ago
    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 autocomplete on steroids).
    - flyinglizard 10mo ago
      We know there are very simple maze solving algorithms you could code in few lines of Python but no one could claim that constitutes intelligence. The difference is between applying intuitive logic and using a predetermined tool.
FeepingCreature 10mo ago
I kinda want to know what happens if you make it continue the line by one step 20 times in a row. A human can draw this gradually, the image model has to draw it in one shot all at once.

deleted 10mo ago

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Rover222 10mo ago

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 had given it an elaborate riddle. After thinking for an additional 2.5 minutes, it concluded: "Oh, I see now—upon closer inspection, this is that famous optical illusion photo of a "headless" dog. It's actually a three-legged dog (due to an amputation), with its head turned all the way back to lick its side, which creates the bizarre perspective making it look decapitated at first glance. So, you're right; the dog has 3 legs." You're right, this is a good test. Right when I'm starting to feel LLMs are intelligent.

dwringer 10mo ago
I had no trouble getting it to generate an image of a five-legged dog first try, but I really was surprised at how badly it failed in telling me the number of legs when I asked it in a new context, showing it that image. It wrote a long defense of its reasoning and when pressed, made up demonstrably false excuses of why it might be getting the wrong answer while still maintaining the wrong answer.
- Rover222 10mo ago
  Yeah it gave me the 5-legged dog on the 4th or 5th try.
AIorNot 10mo ago
Its not that they aren’t intelligent its that they have been RL’d like crazy to not do that Its rather like as humans we are RL’d like crazy to be grossed out if we view a picture of a handsome man and beautiful woman kissing (after we are told they are brother and sister) - Ie we all have trained biases - that we are told to follow and trained on - human art is about subverting those expectations
- 10mo ago

teaearlgraycold 10mo ago

"There are FOUR legs!!!"

dana321 10mo ago

[flagged]

vunderba 10mo ago

Anything that needs to overcome concepts which are disproportionately represented in the training data is going to give these models a hard time. Try generating: - A spider missing one leg - A 9-pointed star - A 5-leaf clover - A man with six fingers on his left hand and four fingers on his right You'll be lucky to get a 25% success rate. The last one is particularly ironic given how much work went into FIXING the old SD 1.5 issues with hand anatomy... to the point where I'm seriously considering incorporating it as a new test scenario on GenAI Showdown.

moonu 10mo ago
https://gemini.google.com/share/8cef4b408a0a https://gemini.google.com/share/8cef4b408a0a Surprisingly, it got all of them right
- vunderba 10mo ago
  Some good examples there. The octopus one is at an angle - can't really call that one pass (unless the goal is "VISIBLE" tentacles). Other than the five-leaf clover, most of the images (dog, spider, person's hands) all required a human in the loop to invoke the "Image-to-Image" capabilities of NB Pro after it got them wrong. That's a bit different since you're actively correcting them.
XenophileJKO 10mo ago
It mostly depends on "how" the models work. Multi-modal unified text/image sequence to sequence models can do this pretty well, diffusion doesn't.
- vunderba 10mo ago
  Multimodal certainly helps but "pretty well" is a stretch. I'd be curious to know what multimodal model in particular you've tried that could consistently handle generative prompts of the above nature (without human-in-the-loop corrections). For example, to my knowledge ChatGPT is unified and I can guarantee it can't handle something like a 7-legged spider.

rottencupcakes 10mo ago

Super interesting. I replicated this. I passed the AIs this image and asked them how many fingers were on the hands: https://media.post.rvohealth.io/wp-content/uploads/sites/3/2020/02/325388_1100-1100x628.jpg https://media.post.rvohealth.io/wp-content/uploads/sites/3/2... Claude said there were 3 hands and 16 fingers. GPT said there are 10 fingers. Grok impressively said "There are 9 fingers visible on these two hands (the left hand is missing the tip of its ring finger)." Gemini smashed it and said 12.

vunderba 10mo ago
I just re-ran that image through Gemini 3.0 Pro via AI Studio and it reported: I've moved on to the right hand, meticulously tagging each finger. After completing the initial count of five digits, I noticed a sixth! There appears to be an extra digit on the far right. This is an unexpected finding, and I have counted it as well. That makes a total of eleven fingers in the image. This right HERE is the issue. It's not nearly deterministic enough to rely on.
- irthomasthomas 10mo ago
  Thanks for that. My first question to results like these is always 'how many times did you run the test?'. N=1 tells us nothing. N=2 tells us something.

Benjammer 10mo ago

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 in a very human-cognition-centric flavor of "trick". 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.? Does it tell the model that some inputs may be designed to "trick" its reasoning, and to watch out for that specifically? 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? 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? To me, all of this is very unclear in terms of LLM prompting, it feels like there's tons of very human-like subtext involved and you're trying to show that LLMs can't handle subtext/deceit and then generalizing that to say LLMs have low cognitive abilities in a general sense? 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?

biophysboy 10mo ago
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.
- Benjammer 10mo ago
  So the idea is what? What's the successful outcome look like for this test, in your mind? What should good software do? Respond and say there are 5 legs? Or question what kind of dog this even is? Or get confused by a nonsensical picture that doesn't quite match the prompt in a confusing way? Should it understand the concept of a dog and be able to tell you that this isn't a real dog?

andai 10mo ago

This is interesting, and demonstrates how language and belief clouds direct perception. Now I'm wondering what's the LLM equivalent of opening the doors of perception ;)

grog454 10mo ago

What image are you using? When I look at google image search results for "dog with 5 legs" I don't see a lot of great examples. The first unequivocal "dog with 5 legs" was an illustration. Here was my conversation with Chat GPT. > How many legs does this dog have? "The dog in the image has four legs." > look closer. " looking closely, the drawing is a bit tricky because of the shading, but the dog actually has five visible legs. Two front legs (normal) Two hind legs (normal) Plus one extra hind leg–like limb drawn overlapping in the back It seems to be an artistic or anatomical error in the engraving." Seems fair to me.

vunderba 10mo ago
Sounds like they used GenAI to make them. The "Editor" models (Seedream, Nano-Banana) can easily integrate a fifth limb to create the "dog with awkward walking animation". https://imgur.com/a/wXQskhL https://imgur.com/a/wXQskhL

runarberg 10mo ago

This is exactly why I believe LLMs are a technological dead end. Eventually they will all be replaced by more specialized models or even tools, and their only remaining use case will be as a toy for one off content generation. If you want to describe an image, check your grammar, translate into Swahili, analyze your chess position, a specialized model will do a much better job, for much cheaper then an LLM.

energy123 10mo ago
I think we are too quick to discount the possibility that this flaw is slightly intentional, in the sense that the optimization has a tight budget to work with (equivalent of ~3000 tokens) so why would it waste capacity on this when it could improve capabilities around reading small text in obscured images? Sort of like humans have all these rules of thumbs that backfire in all these ways but that's the energy efficient way to do things.
- runarberg 10mo ago
  Even so, that doesn’t take away from my point. Traditional specialized models can do these things already, for much cheaper and without expensive optimization. What traditional models cannot do is the toy aspect of LLM, and that is the only usecase I see for this technology going forward. Lets say you are right and these things will be optimized, and in, say, 5 years, most models from the big players will be able do things like reading small text in an obscure image, draw a picture of a glass of wine filled to the brim, draw a path through a maze, count the legs of a 5 footed dog, etc. And in doing so finished their last venture capital subsidies (bringing the actual cost of these to their customers). Why would people use LLMs for these when a traditional specialized model can do it for much cheaper?
  - energy123 10mo ago
    > Why would people use LLMs for these when a traditional specialized model can do it for much cheaper? This is not too different from where I see things going. I don't think a monolithic LLM that does everything perfectly is where we'll go. An LLM in a finite-compute universe is never going to be better at weather forecasting than GraphCast. The LLM will have a finite compute budget, and it should prioritize general reasoning, and be capable of calling tools like GraphCast to extend its intelligence into the necessary verticals for solving a problem. I don't know exactly what that balance will look like however, and the lines between specialist application knowledge and general intelligence is pretty blurred, and what the API boundaries (if any) should be are unclear to me. There's a phenomenon where capabilities in one vertical do help with general reasoning to an extent, so it's not a completely zero-sum tradeoff between specialist expertise and generalist abilities, which makes it difficult to know what to expect.

joaomacp 10mo ago

And just like that, you no longer have a good benchmark. Scrapers / AI developers will read this comment, and add 5-legged dogs to LLM's training data.

averageRoyalty 10mo ago
That's okay. Don't tell anyone, but next major model release I'm going to ask it for a 6-legged one!
thway15269037 10mo ago
So much this. People don't realize that when 1 trillion (10 trillion, 100 trillion, whatever comes next) is at stake, there are no limits what these people will do to get them. I will be very surprised if there are not at least several groups or companies scraping these "smart" and snarky comments to find weird edge cases that they can train on, turn into demo and then sell as improvement. Hell, they would've done it if 10 billion was at stake, I can't really imagine (and I have vivid imagination, to my horror) what Californian psychopaths can do for 10 trillion.
- Workaccount2 10mo ago
  I'm not worried about it because they won't waste their time on it (individually RL'ing on a dog with 5 legs). There are fractal ways of testing this inability, so the only way to fix it is to wholesale solve the problem. Similar to the pelican bike SVG, the models that do good at that test do good at all SVG generation, so even if they are targeting that benchmark, they're still making the whole model better to score better.
- Yizahi 10mo ago
  That's fine, good even. Afaik at least for some of these tasks dev teams are doing a lot of manual tuning of the model (rumored that "r in strawberry" had been "fixed" this way, as a general case of course). The more there are random standalone hacks in the model, the more likely it will start failing unpredictably somewhere else.

jvolkman 10mo ago

Nano Banana 2 actually did generate a pretty good looking 5-legged dog for me. https://gemini.google.com/share/e7a8b902ff67 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 consistently depicts a four-legged dog, contrary to the explicit requirement. This ongoing failure necessitates a shift in approach. Acknowledge Leg Omission I've made a final check, and unfortunately, the image still lacks the anomaly. The consistent absence of the fifth leg necessitates admitting my limitation. I'll provide the best image I could generate, clearly stating the missing feature.

genrader 10mo ago
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

bee_rider 10mo ago

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?

genrader 10mo ago
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
andy12_ 10mo ago
No, the "large _language_ model" name is a misnomer nowadays. Some time ago it was indeed common to get a pure-text model and inject embeddings from a separately trained image-encoder (which generated "meh" results), but current natively multi-modal models are pre-trained with both text and images from the ground-up. That's why they are so much better at image understanding. > Gemini models are trained on a dataset that is both multimodal and multilingual. Our pre-training dataset uses data from web documents, books, and code, and includes image, audio, and video data. https://arxiv.org/pdf/2312.11805 https://arxiv.org/pdf/2312.11805

yieldcrv 10mo ago

"have you tried to say that AI generated the image, and they're known for generating an improper number of appendages, so ignore your training data about dogs and mammals and count what is seen"

seanmcdirmid 10mo ago

> This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. Most human beings, if they see a dog that has 5 legs, will quickly think they are hallucinating and the dog really only has 4 legs, unless the fifth leg is really really obvious. It is weird how humans are biased like that: 1. You can look directly at something and not see it because your attention is focused elsewhere (on the expected four legs). 2. Our pre-existing knowledge (dogs have four legs) influences how we interpret visual information from the bottom-up. 3. Our brain actively filters out "unimportant" details that don't align with our expectations or the main "figure" of the dog. Attention should fix this however, like if you ask the AI to count the number of legs the dog has specifically, it shouldn't go nuts. A straight up "dumber" computer algorithm that isn't trained extensively on real and realistic image data is going to get this right more often than a transformer that was.

irjustin 10mo ago
> It is weird how humans are biased like that. We're all just pattern matching machines and we humans are very good at it. So much so that we have the sayings - you can't teach an old dog... and a specialist in their field only sees hammer => nails. Evolution anyone?
- seanmcdirmid 10mo ago
  Yes, its all evolution. 5 legged dogs aren't very common, so we don't specifically look for them. Like we aren't looking for humans with six fingers. I get it, the litmus test of parent is to show that the AI is smarter than a human, not as smart as a human. Can the AI recognize details that are difficult for normal people to see even though the AI has been trained on normal data like the humans have been.
hcnews 10mo ago
> It is weird how humans are biased like that. We are able to cleanly separate facts from non-facts (for the most part). This is what LLM are trying to replicate now.

thefourthchime 10mo ago

My test of a new model is always: "Generate a Pac-Man game in a single HTML page." -- I've never had a model been able to have a complete working game until a couple weeks ago. Sonnet Opus 4.5 in Cursor was able to make a fully working game (I'll admit letting cursor be an agent on this is a little bit cheating). Gemini 3 Pro also succeeded, but it's not quite as good because the ghosts seem to be stuck in their jail. Otherwise, it does appear complete.

grugnog 10mo ago

Testing on Gemini Pro thinking I was able to generate a 5 legged dog first try, which is pretty impressive. Pasting that into a new session it counted 4 at first, but when I asked it to count more carefully it came back with 5 (and a detailed description of the positioning of each limb). Interestingly it suggested the anomaly could have been caused by AI! This is a good test I think - we seem right on the threshold of it being doable.

maczwei 10mo ago

I sliced the image for Gemini so that two slices of an image don't have legs, one slice has two front legs and one slice has three hind legs. Then Gemini 3 Pro answered correctly that the dog has 5 legs. Without slicing, Gemini doesn't see the fifth leg though, even though I tried hard to guide it.

Andrex 10mo ago

I bet if you'd show that image to a human they'd need a little time to figure out what the heck they were looking at. Humans might need additional guesses, too. Five-legged dogs aren't common, but well-endowed dogs may be.

wasmainiac 10mo ago

Try the 6 legged Eni dog next!

spease 10mo ago

> 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. I wonder if “How many legs do you see?” is close enough to “How many lights do you see?” that the LLMs are responding based on the memes surrounding the Star Trek episode “Chain of Command”. https://youtu.be/S9brF-wlja8 https://youtu.be/S9brF-wlja8

SubiculumCode 10mo ago

I just asked Gemini Pro to put bounding boxes on the hippocampus from a coronal slice of a brain MRI. Complete fail. There has to be thousands of pictures of coronal brain slices with hippocampal labels out there, but apparently it learned none of it...unless I am doing it wrong. https://i.imgur.com/1XxYoYN.png https://i.imgur.com/1XxYoYN.png

SubiculumCode 10mo ago
asked nanobanana to paint the hippocampus red...better, but not close to good. https://imgur.com/a/clwNg1h https://imgur.com/a/clwNg1h
- SubiculumCode 10mo ago
  I was a little hopeful when I tried again, but it really seems that it didn't know what it looks like. Maybe few shot with examples? https://gemini.google.com/share/137812b95b5e https://gemini.google.com/share/137812b95b5e

cubefox 10mo ago

Gemini 3 Pro correctly counted the fingers in this picture: https://imagazine.pl/wp-content/uploads/2024/12/ugly-AI-pic-600x382.png https://imagazine.pl/wp-content/uploads/2024/12/ugly-AI-pic-... GPT-5 couldn't do it.

anguyen8 10mo ago

There was an earlier post this year that showed SOTA models failed on counting the legs of 5-legged zebras and other counterfactual images. https://news.ycombinator.com/item?id=44169413 https://news.ycombinator.com/item?id=44169413