Comment by saberience
10 hours ago
Because no one cares about optimizing for this because it's a stupid benchmark.
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.
+1 to "it's a stupid benchmark".
You can always suggest a new one ;)
Why stupid? Vector images are widely used and extremely useful directly and to render raster images at different scales. It’s also highly connected with spacial and geometric reasoning and precision, which would open up a whole new class of problems these models could tackle. Sure, it’s secondary to raster image analysis and generation, but curious why it would be stupid to persue?
I suspect there is actually quite a bit of money on the table here. For those of us running print-on-demand workflows, the current raster-to-vector pipeline is incredibly brittle and expensive to maintain. Reliable native SVG generation would solve a massive architectural headache for physical product creation.
It shows that these are nowhere near anything resembling human intelligence. You wouldn't have to optimize for anything if it would be a general intelligence of sorts.
Here's a pencil and paper. Let's see your SVG pelican.
So you think if would give a pencil and a paper to the model would it do better?
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.
This exactly. I don't understand the argument that seems to be, if it were real intelligence, it would never have to learn anything. It's machine learning, not machine magic.
1 reply →