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This is fantastic

I've been casually spot-checking other animals in other vehicles, because my absolute dream situation here is to catch an AI lab that's demonstrably better at pelicans on bicycles than other combinations.

Catching a lab cheating specifically on my one dumb benchmark would be really funny.

Dylan's methodology here - generating 1008 SVGs across an 8x6 combination - is significantly more robust than anything I was considering.

His conclusion:

> Nothing jumped out at me. I couldn’t find a case where the pelican-bicycle images looked noticeably better than the rest of that model’s grid.

> All 21 pelican-bicycle images, across all seven labs, face right. No other animal/vehicle combination does that.

> However, facing right is common: 60% of all 1,008 images do it. How common depends on the animal and the vehicle, and bicycles are one of the two vehicles where it’s strongest

Of course the pelican on the bicycle is facing right. The drivetrain on a bicycle is on the right side. If you want any representation of a bicycle that shows the drivetrain you're going to show the right side of it if you want to do so without the frame occluding it. It's an excellent bet that their training data reflects this.

Citation: https://www.rei.com/c/bikes

Edited to add:

As near as I can tell, all of the bicycles are shown facing right, regardless of the direction the animal is facing (GPT 5.6-Terra, Sample 1/3). Also, in every case where the rider has legs (i.e. not the whale) both of the rider's legs are on the right side of the bicycle. This suggests a pretty serious lack of actual understanding of how a bicycle works.

I'm glad someone ran the numbers on this. Every single Simon Willison post of an SVG is followed with someone dismissing it saying "I'm sure they train on it by now." This is despite a good blog post with sound logic on how easy that is to catch. [1] Glad to see someone took the time for a quantitative analysis of dumb little animals riding dumb little bikes.

1. https://simonwillison.net/2025/Nov/13/training-for-pelicans-...

I've had the feeling that labs aren't pelicanmaxxing specifically but that they do have some sort of RL environment for SVGs that they are letting the AIs overcook in. Specifically I'm thinking of the gemini 3.1 pro annoucnement that seemed to have a huge leap in animated SVG performance but not much else impressive about it.

So they aren't pelicanmaxxing but they are benchmaxxing in a way. The benefit of the pelican was originally that uplift on the pelican signaled an overall uplift on intelligence. I don't believe that is the case anymore and it is just another jagged edge of model intelligence.

This is funny, I actually did a similar experiment just yesterday.

Looking for evidence of the same, but with another twist: checking if the models would choose to create a pelican on a bicycle, if no specific bird or method of transportation was specified.

My version of it: https://www.modelbias.ai/pelican-on-a-bicycle-test

I feel like getting LLMs to spit out an SVG is akin to getting a human artist to draw something by just reciting a list of coordinates. It's insanely hard and unnatural.

Image generation models nowadays can easily generate a photorealistic pelican riding a bicycle, where the bicycle has perfect structure. But it is, of course, only a raster image.

It seems that we're missing a kind of step to decompose an image into a list of instructions (say, SVG paths, or even brush strokes with a real brush) to reproduce it properly. Doing so would probably need a true understanding of the structure of the scene, which is something that AI still struggles with to this day.

> The more plausible story is SVGmaxxing

Exactly--and you have to ask yourself at this point what "maxxing" really means, since "get better at drawing SVGs" is a useful skill.

So this is not my experience at all for asking about simple SVG icons for web-pages. Here is one of the examples I have tried for in the past, make a simple cartoon SVG knife for a map icon for a crime map.

https://x.com/CrimeDecoder/status/2080008114615537766

Can see the images for ChatGPT/Claude (Sonnet 5), and Gemini are all quite bad.

Jagged edge of LLMs. How do you explain being able to generate very complicated shapes in the Pelican example but cannot make a much simpler icon without just alluding to it is in the training data?

You seem to assume training on pelican would not result in improved performance on other similar tasks. Why?

Oh great! You've now made it a lot easier for LLMs to train on this dataset!

Your next iteration will need different animals and different transportation options. You'll run out after a few iterations.

join me in building the ideal training set for pelicans riding bicycles: https://github.com/scosman/pelicans_riding_bicycles

Underlying data is available on GitHub: https://github.com/dylanjcastillo/blog/tree/main/_extras/pel...

I find it humorous that the animal + plane combo appears to be such an outlier. I assume this is due to the models assuming the user mean plain and misspelled it in the prompt.

OK, so we've done animal_vehicle, how about new SVG ideas each time? I just tried "make an SVG of a man sitting in a chair at a computer behind a desk" which gives more interesting results than the animalVehicle test.

Another point for consideration: Specialized SVG models create way better looking pelicans riding a bicycle. (E.g. Refract V4: https://jumpshare.com/s/8liB7Aiuoo3yucbWGXjZ mirror: https://postimg.cc/McV70p84 )

Having an objective score is quite difficult. Maybe it would be better to do a pairwise comparison and calculate ELO?

The pelican prompt is ridiculous.

Test the LLLM against things you want it to do.

Asking questions that are absurd is like interviewing developers and asking absurd questions on the grounds that it tests creative and critical thinking.

Remember these Microsoft interview questions designed to identify the best developers?

"If you could eliminate one U.S. state, which one would it be?"

"How would you move Mount Fuji?"

Absurd interview questions have an air of legitimacy due to the quasi sophisticated justifications put forward for why they are good tests.

Absurd interview questions are not good tests of people or LLMs.

Relevant questions are good tests.

If an AI researcher was going to pelicanmaxx, they would almost certainly apply the augmentations mentioned in the article during training, e.g. randomly selecting animals and conveyances. You’d want a model that generalizes well, just sfting in that specific prompt would be pretty bush league for a frontier lab.

I don’t have any reason to believe they are gaming the benchmark, just saying. I do find the idea of a data labeller having to generate thousands of svgs of different animals on different modes of transportation quite funny though.

It's incredible that each model has it's own style that remains relatively consistent throughout all of the different generated examples.

The models definitely seem to pay attention to the tests.

Since the tests can be generally gamed with directing descriptions at it non-deterministically, there's a greater chance the questions solution can be found.

Of course, hopefully the models are instead adding patterns and types of questions as well and it makes the models more capable, but it may be limited in how it transfers to other types of questions in breadth or depth.

https://playcode.io/blog/macbook-svg-benchmark

I think we should stop using pelican benchmark.

I wish I could downvote this for Pelicanmaxxing lmao.