Pelican generated via OpenRouter: https://gist.github.com/simonw/cc4ca7815ae82562e89a9fdd99f07...
Solid bird, not a great bicycle frame.
Solid bird, not a great bicycle frame.
Context for the unaware: https://simonwillison.net/tags/pelican-riding-a-bicycle/
Do electric pelicans dream of touching electric grass?
That would be shocking news to me.
I'm guessing both humans and LLMs would tend to get the "vibe" from the pelican task, that they're essentially being asked to create something like a child's crayon drawing. And that "vibe" then brings with it associations with all the types of things children might normally include in a drawing.
It's just an experiment on how different models interpret a vague prompt. "Generate an SVG of a pelican riding a bicycle" is loaded with ambiguity. It's practically designed to generate 'interesting' results because the prompt is not specific.
It also happens to be an example of the least practical way to engage with an LLM. It's no more capable of reading your mind than anyone or anything else.
I argue that, in the service of AI, there is a lot of flexibility being created around the scientific method.
For the last generation of models, and for today's flash/mini models, I think there is still a not-unreasonable binary question ("is this a pelican on a bicycle?") that you can answer by just looking at the result: https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/
I've worked one an RLHF project for one of the larger model providers, and the instructions provided to the reviewers were very clear that if there was no objective correct answer, they were still required to choose the best answer, and while there were of course disagreements in the margins, groups of people do tend to converge on the big lines.
It is unreasonable to expect pelicans to ride human bikes, they have different anatomy.
Draw a pelican on a bicycle ergonomically designed for pelicans.
For reasons, I have tried to get Stable Diffusion to put parrots into spacesuits. Always ended up with the beak coming out where the visor glass should've been, either no wings at all or wings outside the suit, legs and torso just human-shaped.
ChatGPT got the helmet right, but their wings and tail (and sometimes claws) were exposed to vacuum, still very much closer to a human in either a normal or scifi space suit that happens to also be wearing a parrot head inside the space suit, and has tacked some costume wings on the outside.
Essentially, it's got the same category of wrong as fantasy art's approach to what women's armour should look like: aesthetics are great, but it would be instantly lethal if done for real.
> Generate an SVG of a California brown pelican riding a bicycle. The bicycle must have spokes and a correctly shaped bicycle frame. The pelican must have its characteristic large pouch, and there should be a clear indication of feathers. The pelican must be clearly pedaling the bicycle. The image should show the full breeding plumage of the California brown pelican.
This pattern of considering 90% accuracy (like the level we've seemingly we've stalled out on for the MMLU and AIME) to be 'solved' is really concerning for me.
AGI has to be 100% right 100% of the time to be AGI and we aren't being tough enough on these systems in our evaluations. We're moving on to new and impressive tasks toward some imagined AGI goal without even trying to find out if we can make true Artificial Niche Intelligence.
As far as I can tell for AIME, pretty much every frontier model gets 100% https://llm-stats.com/benchmarks/aime-2025
https://matharena.ai/?view=problem&comp=aime--aime_2026
As for MMLU, is your assertion that these AI labs are not correcting for errors in these exams and then self-reporting scores less than 100%?
As implied by the video, wouldn't it then take 1 intern a week max to fix those errors and allow any AI lab to become the first to consistently 100% the MMLU? I can guarantee Moonshot, DeepSeek, or Alibaba would be all over the opportunity to do just that if it were a real problem.
You'd have to put it in an agentic loop to perform corrections otherwise.
You don't need to take my word for it, try playing MMLU yourself.
https://d.erenrich.net/are-you-smarter-than-an-llm/index.htm...
Its not MMLU-Pro btw, which is considerably harder.
We need a new, authentic scenario.
I don't think there's a good description anywhere. https://youtube.com/@t3dotgg talks about it from time to time.
1. Take the top ten searches on Google Trends
(on day of new model release)
2. Concatenate
3. SHA-1 hash them
4. Use this as a seed to perform random noun-verb
lookup in an agreed upon large sized dictionary.
5. Construct a sentence using an agreed upon stable
algorithm that generates reasonably coherent prompts
from an immensely deep probability space.
You can generate a few such sentences for more samples.
Alternatively, take the top ten F500 stock performers. Some easy signal that provides enough randomness but is easy to agree upon and doesn't provide enough time to game.
It's also something teams can pre-generate candidate problems for to attempt improvement across the board. But they won't have the exact questions on test day.