Posted by dcastm 4 hours ago
https://x.com/CrimeDecoder/status/2080008114615537766
Can see the images for ChatGPT/Claude (Sonnet 5), and Gemini are all quite bad.
Jagged edge of LLMs. How do you explain being able to generate very complicated shapes in the Pelican example but cannot make a much simpler icon without just alluding to it is in the training data?
> Here is what Qwen3.6-35B-A3B via Openrouter provided for a sloth riding a skateboard: https://imgur.com/a/Dy8fvR5
> Like Grok 4 Fasts attempt at a mushroom in a rowboat, it is barely recognisable as anything despite both Qwen3.6-35B-A3B and Grok 4 Fast having no issue with more popular (i.e. benchmarked) examples. [...]
> And here is Opus 4.7 [which simonw claimed to provide a worse pelican vs Qwen], again via Openrouter: https://imgur.com/a/Qus1Enf
Anyone who hasn't witnessed such deltas either hasn't looked at enough examples, a sufficient variety of models, or both. And they are, unfortunately, not limited to "SVGMaxxing", but a wide range of evals.
Your next iteration will need different animals and different transportation options. You'll run out after a few iterations.
Obviously they're all a bit cartoon-y, what else do you expect from SVGs. But I'm not convinced you could find a single human on Earth over the age of 4 who would seriously give the vehicle in GPT/1/whale/plane a 5/5.
Browse through the options a bit and the rest is not that much better. Grok/2/cat/plane, one of the more accurate planes, got a 2/5. For the most part, vehicles entirely missing do get a 1/5, except for whatever it is in Gemini/1/heron/plane scoring 4. Animals inside planes get completely random vehicle scores I guess.
The cats are all orange, except for a few of the skateboard cats that are black. I'm sure there's nothing to read into there...
Well, I've convinced myself that the next effective test of multimodal models will be whether their judgments of LLM-generated SVG airplanes are anywhere close to reasonable.
He was asking the question - do we see gains across other tasks? The underlying question was: Is the additional attention given to this specific task creating a false impression of progress?
You could train a model to do something a bit different like a pencil sketch, or vector graphic sketch, of something given a photo of it, and expect that to be a generalized skill, but if you are asking the model to do it "from memory" then memorizing a pelican is no substitute for not having memorized a horse.
I'll look for a better image host in the future. I guess the economic incentives makes them all turn bad after a while.