And that distinction matters in practice. If getting slightly better answers means using 5–10× more tokens or a bunch of external calls, the costs add up fast. That doesn’t scale well in the real world. It’s hard to call something a breakthrough when quality goes up but the bill and latency go up just as much.
I also think we should be careful about reading too much into benchmarks. A lot of them reward clever prompting and tool orchestration more than actual general intelligence. Once you factor in reliability, speed, and cost, the story often looks less impressive.
It doesn't mean anything. No frontier lab is trying hard to improve the way its model produces SVG format files.
I would also add, the frontier labs are spending all their post-training time on working on the shit that is actually making them money: i.e. writing code and improving tool calling.
The Pelican on a bicycle thing is funny, yes, but it doesn't really translate into more revenue for AI labs so there's a reason it's not radically improving over time.
I don't think SVG is the problem. It just shows that models are fragile (nothing new) so even if they can (probably) make a good PNG with a pelican on a bike, and they can make (probably) make some good SVG, they do not "transfer" things because they do not "understand them".
I do expect models to fail randomly in tasks that are not "average and common" so for me personally the benchmark is not very useful (and that does not mean they can't work, just that I would not bet on it). If there are people that think "if an LLM outputted an SVG for my request it means it can output an SVG for every image", there might be some value.
Current-gen LLMs might be able to do that with in-context learning, but if limited to pretraining alone, or even pretraining followed by post-training, would one book be enough to impart genuine SVG composition and interpretation skills to the model weights themselves?
My understanding is that the answer would be no, a single copy of the SVG spec would not be anywhere near enough to make the resulting base model any good at SVG authorship. Quite a few other examples and references would be needed in either pretraining, post-training or both.
So one measure of AGI -- necessary but not sufficient on its own -- might be the ability to gain knowledge and skills with no more exposure to training material than a human student would be given. We shouldn't have to feed it terabytes of highly-redundant training material, as we do now, and spend hundreds of GWh to make it stick. Of course that could change by 5 PM today, the way things are going...
You could try to rasterize the SVG and then use an image2text model to describe it, but I suspect it would just “see through” any flaws in the depiction and describe it as “a pelican on a bicycle” anyway.
Incredible work anyways!
I imagine the Alibaba infra is being hammered hard.