Personally, I don't attend them since I figured out I can set up agents to performatively engage in AI-related discussion and events for me, freeing up tons of my time thanks to automation.
Truly: Nothing better than AI tools to brave the challenges and requirements of modern life. "Claude, ride the hype train" is the decisive prompt you need.
This is a perfect illustration of something I noticed with llm progress. Ask them to improve an svg like this, and it never fixes the missing crossbar or disconnected limbs, it just adds more stuff. In this example they have obviously improved greatly, and it contains a ridiculous amount of detail, but they still to get the basic shape of the frame wrong. It's weird. And the pattern shows up everywhere, try it with a webpage and it will add more buttons and stuff. I've even experimented with feeding the broken pelican svgs to an image model to look for flaws, and they still fail to spot the broken elements.
When you say "improve an svg like this", how are you imagining setting that workflow up? Are you just feeding them the SVG to iterate on; or are you giving them access to a browser to look at the rendering of the SVG?
I ask because:
Insofar as the original pelican test is zero-shot, it effectively serves as a way to test for the presence of a kind of "visual imagination" component within the layers of the model, that the model would internally "paint" an SVG [or PostScript, etc] encoding of an image onto, to then extract effective features from, analyze for fitness as a solution to a stated request, etc.
But if you're trying to do a multi-shot pelican, then just feeding back in the SVG produced in the previous attempt, really doesn't correspond to any interesting human capability. Humans can't take an SVG of a pelican and iteratively improve upon it just based on our imagined version of how that SVG renders, either! Rather, a human, given the pelican, would simply load the pelican SVG in a browser; look at the browser's rendering of the pelican; note the things wrong with that rendering; and then edit the SVG to hopefully fix those flaws (and repeat.)
I imagine current (mult-modal and/or computer-use) LLMs would actually be very good at such an "iterative rendered pelican" test.
I'm talking about two type of improvement, model improving, and prompt based improving. I am noticing that the baseline output has a lot more going on, the model has improved, yet it still makes those obvious looking mistakes with the shape of the frame or disconnected limbs etc.
And I am saying that if you take one of these SVGs and ask an LLM to look for flaws, it rarely spots those obvious flaws and instead suggests adding a sunset and fish in the birds mouth.
This is also my gripe with a lot of this stuff, always evaluating models on what they can literally oneshot is completely pointless; it's not how anything works, neither for humans nor for scaffolded AIs. I guess it's neat if you want to argue that a certain level of intelligence can "never be achieved" in a single forward pass, but like, so what. No one cares about that, except people who have already decided to be anti AI.
(not that I am in any sense pro AI, but it's just a weird lack of intellectual rigor)
Asking a model to improve its output is not one-shotting tho? My observation was that asking an llm to iterate and improve a response causes it to add more stuff, rather tha repair the broken stuff. And that model progress in general has the same pattern. This new model adds more details to its responses but continues to make mistakes at about the same rate.
The question was whether you were giving it the rendered image and using the model's visual modal capability, or feeding back in the textual SVG.
It's hard to "imagine" what the rendered SVG looks like, for both humans and LLMs, so just iterating on text won't really be as useful of a test. But if you show it what it rendered, it might observe the bad-looking bicycle and be able to fix the text that way.
To a certain extent, it feels like a Sonnet 3.7 moment. Slightly overeager - you ask for a button color change, you see layout changes, new package dependencies, and the README rewritten from scratch - and not necessarily correctly.
When I ask for a pelican on a bike, I want the Platonic ideal of a pelican on a bike, not a vision of an alternative reality in which pelicans created bikes. Though, thinking about it again, maybe I should.
Slightly overeager - you ask for a button color change, you see layout changes, new package dependencies, and the README rewritten from scratch - and not necessarily correctly.
It's because LLMs are fundamentally generative (creative), not truth-seeking or logic-seeking. Simple logic has always been incredibly expensive to impossible for LLMs.
Their ability is best described as "spiky". To steal from aphyr: think kiki, more than bouba. Whats interesting is that a lot of the models seem to have similar spikes and "troughs", though there are differences.
Especially without being able to look at the rendered output! (At least I'd be surprised if modern server-side tool calls regularly include an SVG renderer that can show a rasterized version to the model to iterate on it.)
One of the many things Google was pitching today is that they're going to run things like google search with access to linux container environments to do things like run tool calls... which will presumably be able to rasterize SVGs and show them to the model.
But Simon says he runs these through the API without tool access specifically to prevent that sort of "cheating". I.e. it's an LLM benchmark not an LLM+Harness benchmark.
Although every single render of those has pedals on the correct side as opposed to the Gemini optical illusion back pedal that tries to be both on the other side of the central gear and infront of the back wheel.
Not really a criticism but an interesting point that you would never expect a human to make that mistake even in a bad drawing.
Love your pelicans, as always. And that one is... Wow.
I noticed the "Synthwave" aesthetic, which is enjoying quite some success since quite some time now, has found its way into AI models (even when it's not in the user's query). It's not the first time I see the sun at sunset with color bands etc. in AI-generated pictures. Don't know why it's now taking on in AI too.
Hence the comments here about the 90s, Sonny Crockett's white Ferrari Testarossa in Miami, etc.
To be honest as a kid from the 80s and a teenager from the 90s who grew up with that aesthetic in posters, on VHS tape covers, magazine covers, etc. I do love that style and I love that it made a comeback and that that comeback somehow stayed.
We've been daily-driving this model for a few weeks and let me tell you, everything it does is a lot. Fast as fuck and it's actually not bad intelligence-wise for a fast model. It basically tries to make up for any intelligence deficit by just doing a lot, checking a lot, retrying a lot.
That's not to say I don't spend my days raging at it... a lot... but it's not that bad. It does tend to ignore completion criteria but it doesn't obviously degrade when being nudged like some models do.
One time I told it “we are doing science” and I had DNA emoji everywhere and it so over enthusiastically embraced the science theme I was genuinely laughing. It finished one task with a flourish of several dna emoji and proclaimed: The Science is COMPLETE. I died.
It really is a lot some of the time. And it’s chain of thought is hilarious a lot of the time.
I'm hoping we'll have many of these pelican cyclist pictures collected. Then when all the models can do it well, we'll stop posting about them, and dhen the next generations of AIs train on the data we'll have these canonical archetypes.
I've found prompts like "capybara with spotted fur and 7 octopus tentacles instead of legs, each a different color, riding a tricycle" etc. to be a better test
Last time I tried, ChatGPT's image generator got the best result.
Wow what’s with all the styling? Is it manifestation of google’s styling bias? I like the result for sure. It’s shiny and pretty. But then it’s something I didn’t ask for.
That's likely because you're using the Gemini app which has a tool for image generation (nano banana) - I do my tests against the API to avoid any possibility of tool use.
Not a great bicycle though, it forgot the bar between the pedals and the back wheel and weirdly tangled the other bars.
Expensive too - that pelican cost 13 cents: https://www.llm-prices.com/#it=11&ot=14403&sel=gemini-3.5-fl...