I tried an experiment like this a while back (for the GPT-5 launch) and was surprised at how ineffective it was.
This is a better version of what I tried but suffers from the same problem - the models seem to stick close to their original shapes and add new details rather than creating an image from scratch that's a significantly better variant of what they tried originally.
I feel like I’ve seen this with code too, where it’s unlikely to scrap something and try a new approach a more likely to double down iterating on a bad approach.
For the svg generation, it would be an interesting experiment to seed it with increasingly poor initial images and see at what point if any the models don’t anchor on the initial image and just try something else
Thanks for the answer. OK, yes. That makes a lot more sense. I am context greedy ever since I read that Adobe research paper that I shared with you months ago. [0]
The whole "context engineering" concept is certainly a thing, though I do dislike throwing around the word "engineer" all willy-nilly like that. :)
In any case, thanks for the response. I just wanted to make sure that I was not missing something.
Maybe there’s a bias towards avoiding full rewrites? An “anti-refucktoring” bias
I’d be curious if the approach would be improved by having the model generate a full pelican from scratch each time and having it judge which variation is an improvement. Or if something should be altered in each loop, perhaps it should be the prompt instead
This is a better version of what I tried but suffers from the same problem - the models seem to stick close to their original shapes and add new details rather than creating an image from scratch that's a significantly better variant of what they tried originally.