Text to CAD doesn't need papers. You can literally just try it and see that it works well with the frontier models. If you want reification/meshing I recommend [1] which is what Godot uses. You can throw the results in a physics engine in an afternoon and see for yourself.
This wasn't obvious a year ago, but today CAD literally reduces to Simon Wilson's pelican test, since CAD is largely a matter of functional CSG, and CSG is really not that different from SVG. It's just one more dimension, which it turns out is not a problem.
LLMs consistently one-shot CSG based video game levels with interesting physics puzzles (citing myself). Given this I'm willing to conclude that the frontier models are good at automated CAD if given the correct harness. But I guess a lot of people don't know this yet.
Personally I can complain to no end about arXiv papers being insufficiently rigorous, especially in the text classification area that I've been interested in since 2005 or so.
Show me the blog posts where people talk about the results they got "vibe coding" and those arXiv papers look great in comparison!
There is the insidious thing with LLMs is that they can get the general shape of something right but that thing will not be useful if the last 1% is wrong. It might be that the operator sees the problem and fixes it, but it may also be that the LLM hypnotizes the operator into not seeing the errors and gaps.
I know there are many sorts of problems where I've had good experiences with LLMs but I know other people have had bad experiences and some of it might be my skill but some of it is just plain luck.
I don't think I've ever heard a mechanical engineer say "torus" in my life unless they're talking about the car. When you are doing feedback with a human operator you use terms like "make this thicker" or "rotate that this way" while pointing at them. Text does not have this.
Text to CAD doesn't need papers. You can literally just try it and see that it works well with the frontier models. If you want reification/meshing I recommend [1] which is what Godot uses. You can throw the results in a physics engine in an afternoon and see for yourself.
This wasn't obvious a year ago, but today CAD literally reduces to Simon Wilson's pelican test, since CAD is largely a matter of functional CSG, and CSG is really not that different from SVG. It's just one more dimension, which it turns out is not a problem.
LLMs consistently one-shot CSG based video game levels with interesting physics puzzles (citing myself). Given this I'm willing to conclude that the frontier models are good at automated CAD if given the correct harness. But I guess a lot of people don't know this yet.
[1] https://github.com/elalish/manifold
Personally I can complain to no end about arXiv papers being insufficiently rigorous, especially in the text classification area that I've been interested in since 2005 or so.
Show me the blog posts where people talk about the results they got "vibe coding" and those arXiv papers look great in comparison!
There is the insidious thing with LLMs is that they can get the general shape of something right but that thing will not be useful if the last 1% is wrong. It might be that the operator sees the problem and fixes it, but it may also be that the LLM hypnotizes the operator into not seeing the errors and gaps.
I know there are many sorts of problems where I've had good experiences with LLMs but I know other people have had bad experiences and some of it might be my skill but some of it is just plain luck.
I don't think I've ever heard a mechanical engineer say "torus" in my life unless they're talking about the car. When you are doing feedback with a human operator you use terms like "make this thicker" or "rotate that this way" while pointing at them. Text does not have this.