Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling

• URL: http://arxiv.org/abs/2606.31252v1
• Date: Tue, 30 Jun 2026 07:31:01 GMT
• Title: Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
• Abstract summary: We present Embodied CAD, solver-grounded LLM agents for parametric B-Rep assembly modeling.<n>The framework combines action grammar constraints, deterministic parameter resolution, and solver-derived rewards for supervised warm-up and GRPO-style refinement.<n>We evaluate Embodied CAD on multi-step mechanical, industrial equipment, and mold-oriented assembly tasks using solver-aligned metrics.
• Score: 7.918861077775763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry. We present Embodied CAD, solver-grounded LLM agents for parametric B-Rep assembly modeling. Instead of generating a complete script in one pass, the agent iteratively selects actions from a stratified L0-L4 CAD skill library, resolves them into typed geometric operations, executes them in a CAD backend, and uses solver feedback to plan, repair, and learn. The framework combines action grammar constraints, deterministic parameter resolution, and solver-derived rewards for supervised warm-up and GRPO-style refinement. We evaluate Embodied CAD on multi-step mechanical, industrial equipment, and mold-oriented assembly tasks using solver-aligned metrics: executable rate, skill accuracy, operation-family accuracy, exact policy accuracy, and task completion success. The results show that solver-grounded planning executes all strong-planner workflows in the current benchmark, while learned controllers reach high executable rates and expose the remaining gap between valid tool calls and exact long-horizon policy prediction.

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