Everyone knows neural mesh generation is a bit of a useless toy, but in 2022, Autodesk Research published SkexGen (ICML 2022), an autoregressive model that generates CAD construction sequences. You sketch a 2D profile, extrude it, boolean it, and each step is a valid CAD operation. SCAD and Adam and whatnot are already extrapolating that to LLMs, and it's fine - if they were actually specialized, they'd be quite good, actually, and the gap is that and an LLM vision problem.
And it works because CAD files natively store their construction history, and it exists because the construction sequences are always stored, and the DeepCAD dataset gave them thousands of these sequences for free. Where is that for Blender?
The research community has chosen to pursue direct mesh generation, tokenizing vertices and faces into sequences (PolyGen, MeshAnything, MeshXL, MESHTRON, etc.) or reversing mesh simplification (ARMesh). These approaches are getting better, but they're fundamentally fighting the representation. A sequence of vertex coordinates doesn't encode why an edge loop is there, but by.ops does. We should make a dataset. Blender already logs every bpy.ops call to its Info panel. A recording addon could capture the full bpy.ops call with all parameters, the selection state at each step (which vertices/edges/faces were selected), a lightweight mesh snapshot at each step (or at key intervals) and the final mesh as the label.
Yes, there's challenges between noise, context and scale, but...? The autoregressive method is proven already. The gap is entirely in data collection infrastructure. Why not?