CAD

TL;DR for operators A manufacturing process planner has to preserve relationships across geometry, drawing requirements, material constraints, manufacturing rules, process order, and tooling. If a tolerance or surface-finish requirement is attached to the wrong CAD feature, later reasoning can be internally coherent and still produce the wrong plan. Design-to-Plan addresses that problem with a hybrid architecture: deterministic components handle precision-sensitive perception and machining calculations, while LLM agents reason through structured tools and retrieved manufacturing knowledge.1 Across three 100-case downstream benchmarks, parallel configurations reached 100% execution success and more consistently invoked the tools expected for each case. They also cut average token use from 22,481 to 8,896 for process sequencing and from 36,659 to 11,868 for tool selection. Knowledge retrieval was the exception, where parallel coordination slightly increased token use. ...

A bracket looks simple until someone has to manufacture it. On a screen, a generated part can look almost right: the flange appears round, the bolt holes seem evenly spaced, and the central bore is visible enough to satisfy a casual glance. Then a machinist opens the file, measures it, and discovers the inconvenient details: the wall thickness is wrong, a boolean cut failed, two solids merely touch instead of joining, or the bounding box is off by a few millimeters. ...

The compiler is the interesting part Blocks are easy to understand. That is why this paper is more interesting than it first looks. At the surface, Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions is a paper about using large language models to generate LEGO-style assemblies from natural language prompts.1 It shows a medieval castle, an International Space Station model, a modular multitool kit, and an image-to-parts helicopter conversion. Naturally, the tempting summary is: “LLMs can now design LEGO models.” ...