It's a problem similar to "give me a random word" always returning "zephyr" and "luminous", or guessing the numbers 42/27.

It also affects writing, if you ask for a story with a minmal prompt something very similar comes out of all of them. Often fixated on clocks.

The tension is that most users want something differently generic when they provide a short or vague prompt for creative work. They want to treat the AI like an ideal creative consultant, who not only knows the ideas that are played out and tired but also the ones that they themselves have delivered a dozen times already. They don't want to specify deeper constraints or survey all the cliches and tropes, but they also don't want to generate something that they -- or the people they share the output with -- will immediately recognize as cliche themselves.

But LLM's are structured to do exactly the opposite. They discover specific commonalities and tropes and most-frequently-sufficient wells or ley lines that they gravitate towards from whatever their starting point it. You can nudge them away from the most generically cliche tropes by being more detailed in your prompt, but they'll still just find the nearest most right cliche from there.

It's what they do and the same quality that makes them tiring and predictable for creative output makes them very useful for things like business communication or coding where landing on such cliches is ideal.

To get more variety out, it can be useful to use Perchance-like text-randomization techniques to shuffle combinatoric variations on a prompt before sending it to the model. But that technique is best made available through some kind of special purpose harness for the creative task, not basic chat or direct curl calls to the API.

It would be nice if there was something like the "temperature" parameter (which many closed models like Claude now disable) to add more randomness to the final product without completely destroying the output and giving you a non-functional SVG, so that you could "roll" more pelicans and pick one that you liked.

If I were to design something to accomplish this now, I'd probably attempt to introduce a pipeline that emitted random thematic words from some non-LLM source to feed into the pelican prompt, but even then, I'd expect that some of these models that rely on churning through CoT tokens for extended periods of time would still spend so much time thinking about pelicans wearing scarfs or where the sun would be that it would risk undoing some of my randomization pipeline.

I once tried something similar on an older model of Claude, telling it to "contemplate" a random word before answering my prompts, but at times it seemed to trigger the Claude model's suspicion that there was a jailbreak attempt when I would inject things like "Contemplate sea otters for one sentence before answering"

(I didn't read the linked LLM creativity paper to see if it covers any of this yet)

I've started asking for "Generate an SVG of a pelican riding a bicycle in the style of Henri Cartier-Bresson," which is a hint I want something like his famous Hyères, France photo [1].

The Hyères photo has the bike going in the opposite direction and from above. It's also tricky because it's a focal point for the eye, but not the center of the photo. The other elements of the frame direct your eye, but that visual behavior is not obvious to an AI. Lastly the image must capture the unquantifiable "decisive moment" that made Cartier-Bresson so famous.

So far I haven't seen an AI that really gets this prompt. Adding specificity will improve the results to a degree, but I haven't seen an AI that really gets what makes a Cartier-Bresson photo.

1 - https://en.wikipedia.org/wiki/Hy%C3%A8res,_France

Interestingly I gave the same prompt to Sol 6.1 Medium.

“Generate an SVG of a pelican riding a bicycle. Be creative”

And it replied:

“I’ll draw a seaside pelican on a vintage bicycle, with a fish-filled basket and a scarf catching the breeze.”

So it came up with exactly the same fish basket and seaside idea. No lighthouse though. Maybe these ideas are just cliche though.

> I'm not clear on what the claim is here.

> But, if prompted, "Generate an SVG of a pelican riding a bicycle," with no additional details or specification, then I would _expect_ something generic

I believe the claim is that without a detailed specification, you see very similar outcomes. Every pelican is going left to right, every picture has similar sky background, every picture seems to be drawn at the same time of the day (position of the sun), every picture has a few clouds... I don't have data on this, but I imagine the same question being asked to a group of people would result in more varied outcomes.

This matches my experience with Opus 5.5. If asked to compose a synthwave song in csound, it produces something fairly generic but recognizably synthwave. If I add light guidelines on avoiding generic sound and being creative, the results are often weird and interesting.

The curious thing is that trying to prompt it by steering towards some specific goal indirectly gives much worse results than just stating the goal itself.

imagine you ask the LLM for a joke. the biggest models, multi-trillion-parameter (don't quote me) astras and fables and argons, only know like 5 jokes. obviously bullshit, but it is what it is.

same goes for pelicans. the canonical pelican riding a bicycle is riding on a sunny day on an asphalt road going through some lawns or praires, left to right.

but ask it to tell a joke about something specific, turns out it knows five jokes about those specific things.

you could say the LLMs are quite aware of the zeitgeist they're in, by construction (their RL frying automated feedback loops burn the zeitgeist right into the weights.)

But, if prompted, "Generate an SVG of a pelican riding a bicycle," with no additional details or specification, then I would _expect_ something generic: a side view, nondescript terrain or perhaps terrain associated with a pelican (i.e., beach), little clothing (because birds don't wear clothes) with the possible exception of something strongly associated with bicyclists (a helmet).

True. You see a lot of the same criticisms when someone asks for a one-shot game or 3D demo and the model responds with something bland and generic. That's exactly what I prefer, it's my job to specify the details. Extrapolation may be the ultimate sign of intelligence, and it might be needed when using AI to tackle open math problems where humans have failed, but it's not usually what I want if I'm doing anything creative or artistic.

Argon is pretty good at getting the distinction right from what I've seen. Ask for something generic, you get something generically functional without a lot of ornamentation. Follow up with requests for specific enhancements, and the model complies effectively and tastefully. If anything, it overcooks the pelicans when given the standard open-ended prompt.

> then I would _expect_ something generic

All going in the same direction? With a sun? With a full mouth (pelicans dont look like that, but a cartoon one might - why no reference to realism). Most of them have clouds.

There are helmets (makes sense) but just as many scarves.

A shocking number have chains.

If we asked 1000 people to do this same exercise would we have this level of convergence? If they were split between men and women would we see this many chains? (https://www.messynessychic.com/2016/04/20/making-bicycles-fr... )

Would they even look like bikes?

Is any one going to draw a gray pelican? Does it matter?

The question is designed to be as generic as possible, and were converging on the most generic and safe answer ... the question is WHY (more so when humans would have much higher diversity, even if wrong). We're asking the generic question because no one wants to repeat the "German solders from 1940's" fiasco: https://archive.is/5l66U (archive link to skip pay wall NYT).

This is what "alignment" looks like? Are we reaching some "ground truth", some platonic form around pelicans on bikes? Are we just back feeding the best output into the training data and this is the convergence we ended up at? Is someone who LOVES pelicans deeply offended that all of them have full mouths and it's just not anatomically accurate and we are all missing it?

I realize that some of this seems over the top, but there are foundational conversations to be had around this without the conversation devolving into someone accusing someone else of having an "-ism" style bias.

It's not really hard to understand.

Why do a lot of people like Fruit of the Loom logos had cornucopias in them?

This isn't generic, it's the same thing. Why would you expect the same thing? There are limitless ways to picture a bird on a bike. White pelican? Brown pelican? Wings out for balance? Wings anthropomorphized into hands on handle bars? Bmx bike? Mountain bike? Riding towards you, away from you, on a race course, etc.

Do a Google image search for pelican, you get a diverse set of sub species of pelicans and different poses and angles for them. Go on a writing prompt forum, you get many diverse ideas from the same simple prompt. Hell, ask your friends at a dinner party to describe a pelican riding a bike and see what happens.

Now, there are a lot of images of bikes from that side angle facing right on the web. Why? Because that's how they're staged on e-commerce websites. Does this say something about AI?

This a huge feature for coding.

I don’t want creative code. I want boring code following generally agreed upon best practices.

If you need more creativity it can come from the user or the harness.

Nah, second system effect will kick in and you’ll get a non functioning clusterfuck embodying their traumas from the first system.

> [prompt:] Now as if a professional graphic designer has added typography to a poster paint drawing by his young child.

This manages to be so much more joyless, bleak, and tone-deaf than any of the example slop. Impressive.

Why would you ask for a pelican riding a bicycle in the first place? This is a nonsense question: nobody would ask this.

But step back, someone did ask it, perhaps that person is you. That person has reasons. Whatever those reasons are should be reflected in the image. Of course those reasons need to expressed in the prompt, but the bug here is the prompt doesn't give the details. If you can't figure out details (beyond "be creative") then give up. If I'm illustrating a Harry Potter book and just want to show far out magic things that better should a very different style from illustrating a science fiction novel where pelicans have been genetically engineers such that they now do ride bicycles - likely the anatomy changes mean they can't fly...

This is important. I recently asked AI to take an outline and turn it into slides. I got some good slides out of the outline except that once in a while it decided to generate an image which looked cool, had the correct corporate colors - but otherwise had nothing to do with either any product we make or my presentation. I deleted the images but maybe I should have refined them to something meaningful?

Why would you even want a completely random pelican scene every time? It would break nearly everyone's expectations if these things didn't do what they were asked for.

> most of the images show the pelican riding from left-to-right

Much of that is a pre-AI bias: A majority of bike-pictures are from an angle which reveals the bike's gears to the audience, which are usually on the rider's right-hand side.

Most photos of bicycles are in that orientation to show the drivetrain.

maybe someone can create an equivalent of psychedelic drugs for llms? I guess that would probably correspond to raising the temperature in whatever sampling alogirthm one is using.

Well, even the bicycle might be difficult. A famous online post on the subject (near 20 years from inception now): https://www.behance.net/gallery/35437979/Velocipedia

This is the kind of thing that happens all the time in elementary schools. My mom taught elementary school for a while and her favorite thing was seeing all the students put their own spin on the art assignment(“prompt”, if you will).

Is every kid a Picasso? No, but there definitely is creative diversity among these kids who all have relatively similar shared backgrounds (neighborhood, social class, religion, etc)

I've pointed this out so many times. Even if you crank the temperature of the LLM up to the point where the oceans boil off into space, at the end of the day, generic in => generic out where prompting is concerned.

It's the reason that if you ask a random child to draw a house, you'll end up with the classic “house with a pitched roof”: a square plus a triangular roof, one door, two windows, and sometimes a chimney.

That would actually be quite interesting. But then there's something sad about the thought that the results, if shared online, would likely be used to train future models.

SVGs are mostly used for icons and polygonal images, because that's what they're good at. The entire point of using vector graphics over raster is to take advantage of the fact that for low-poly objects, SVGs are much smaller. Icony-ness is definitely implied by the specification of format here.

At least when I think SVG I think stylized and simple to reduce the size of the file and keep scaling. What first comes to mind for me is you don't want photorealism or you'd just ask for a picture.

I think you designing a logo for your van is the most surprising aspect of this entire discussion.

With this test being an interesting case to evaluate various aspects at once: https://fenderog.com/art-crit/page.html?p=ceate-an-svg-of-an...

LLMs can be pre-trained on LLM outputs, and there have been a few recent interesting implementations of this.

- https://arxiv.org/abs/2609.37891

- https://arxiv.org/pdf/2508.10975

- https://www.datologyai.com/blog/eating-the-rainbow-a-primer-...

As well as recent model releases that used large fractions of synthetic data (Arcee Trinity):

- https://arxiv.org/pdf/2602.17004v1

It's mostly a matter of increasingly complex and sophisticated sampling methodologies to sift for the valuable synthetic tokens.

There have also been some interesting results about models that use the same data converging to very similar behavior, but I can't find the specific reference at the moment. That one tracks intuitively with the notion that the model is building a latent representation of everything it sees.

> LLMs cannot be pre-trained on LLM outputs because LLM output diversity is low.

And with ever-more LLM usage both in labs and the real world, the task of finding new/“creative” out-of-average-distribution material will only continue to increase in difficulty. Meaning if this continues, in the future, all output becomes the equivalent of the color brown?

Llms are trained on llm output all the time ...

It's going to be interesting to see what happens as LLMs get long running context too. If it has a few gig of history, how would its design decisions change?

They already do this, if you ask for a pelican on a bicycle you will get a pelican on a bicycle. However your simple prompt has very little semantic capacity and underspecifies the rest. This phenomenon makes the underspecified part devolve into annoying repetitive patterns that are the same for everyone and even in the same generation.

well, what you asked for is not completely specified so maybe there should be some variation (or more variation than there currently is) in the output (with a pelican on a bike being the common part). Just because you asked for a pelican riding a bicycle, why shouldn't it have a top hat or be on the moon? Just some common sense reasoning?

The lot of us don't mind that it is a mostly deterministic tool. It's just that this throws a shank in the whole "replace humans" thingy

If you asked me I'd say the pelican is nowhere in particular, it's no particular time of day, it's not facing in any particular direction, the bike isn't any particular colour and the pelican isn't wearing or carrying anything in particular. I'm imagining a pelican on a bicycle. That is all.

That's why I'm not an artist.

Because there's a difference between explicitly targeting some outcome and attempts at that outcome being in the training data. There would be no reasoning to explicitly aim for this rather than, say, general SVG reasoning, especially since it would be possible for targeting this over other SVG creation to undermine general SVG ability. It's not impossible that labs are targeting pelicans on bicycles specifically, but every attempt at discerning whether they are has said that similar requests for SVGs that show other things (e.g., other animals using other modes of transportation, or just other SVGs in general) show similar SVG ability.

I feel like in the beginning it was a way to highlight the fidelity of the models as they struggled to produce good latex and svgs figures. But especially now with the iterative loops that is no longer such a good benchmark. And honestly these pelicans are pretty much the same, so yeah it feels totally benchmaxxed.

Chollet, Google and Alexander Wang both admitted as such

1) Chollet stated that RL:Pre-training is 9:1.

2) Google said argon was trained on SVGs

3) Wang said that Muse Wonder was not trained on 3D models

What is most charming about this benchmark is that by now they do train on past results, and thus commit all the same errors over and over.

I got downvoted[0] heavily for even mentioning the possibility. But I'll say that even if it isn't, there are plenty of pelicans in the training set for new models. People saying "well then why does it also work for flamingos???" don't understand how LLMs work.

[0] https://news.ycombinator.com/item?id=49526966

What exactly do you imagine is happening that is forcing them to fake the benchmark results? You think they're afraid their IPO will fail if they don't make a convincing pelican, and are hiding an illicit attempt at manipulating said result? And getting caught trying to trick people wouldn't tank the price?

This might be heresy to say on HN, but this dinky forum and its recurring comments aren't actually the center of the known universe. Faking a pelican isn't worth ruining a 200 billion dollar business. They have bigger fish to fry.

Moreover, there has been a literal massive cross-model experiment showing there is no scientific evidence for improved pelican results over any other similar prompt. If y'all could kindly consider hard evidence instead of conspiracy theories, we could move on to more interesting topics.

There goes the autonomous agent future then

But if you ask human creators you get the variety from the same prompt.

I believe you have missed the point entirely.