Note that the claims are about the kernel not the web demo: While the kernel is formally verified, the UI and glue code (that call the kernel) are not. I once hit a bug that looked like there were holes in the resulting mesh: I found that it was an overflow in the glue code (now fixed) that converts the exact rationals coordinates of the output mesh, for which we prove the specification, to floats before sending them to the GPU. All geometry is happening inside the kernel, for which we know the specification holds.

How does it compare against https://github.com/elalish/manifold in performance and zero corruption?

I spent a lot of time making elalish/manifold work in Godot Engine and it is now a method in Blender too.

https://manifoldcad.org

If it helps, feel free to use the apache2 licensed unit tests that were generated in manifold development https://github.com/elalish/manifold/tree/master/test

This is really neat. I think CSG is one of the more intuitive ways to build 3d environments (especially 'indoor' ones). No geometry is ever destroyed in a proper CSG workflow. A hierarchy of brushes makes it possible to very rapidly iterate parameters like how long a hallway is or the time it takes to get from one objective to another. Often, you only have to apply a transform to a single brush to affect something that would take a non-CSG workflow an entire afternoon to recover from.

The current story with CSG around commercial game engines is pretty awful. Unity has some 3rd party options that can work well but they're also glitchy and don't integrate well with the other parts of the ecosystem. For a while I thinking about building my own CSG implementation but the exact things this project solved were what was stopping me. The EditorWindow integration stuff is not as scary to me (although it is painful). The advanced geometry and set operations is what kept me from trying. It appears that dragon has been (at least partially) slain.

> A human reviewer only needs to read 93 lines of formal specification and run the Lean checker to certify the correctness of the kernel, skipping the intricate 1000+ lines of AI-written implementation. To prove correctness, AI autonomously wrote over 60,000 lines of Lean proofs, which also never have to be inspected by a human.

I am curious about the approach as I find claims like this hard to believe. There is a theory behind proof kernels that they must be small because they have to be trusted and verifiable by a human. Otherwise the whole system breaks.

How does one trust an LLM generated kernel is proving the right things?

The issue I have with all these formally verified numerical algorithms is that, at the end of the day, I need this implemented using actual floating point. It's great that it's verified, but not for the case that is practically meaningful. Unless it's implemented using hardware accelerated floating point, most of these algorithms are not sufficiently performant to be practically useful.

Congrats with this achievement. Do you see it possible that this project can be integrated into, for example, FreeCAD?