Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation
TLDR
ProCAD proactively clarifies ambiguous text prompts before generating parametric CAD programs, improving robustness and reducing errors.
Reasoning
The paper introduces a novel proactive agentic framework that addresses ambiguity in text-to-CAD generation, showing strong empirical gains. However, it focuses narrowly on CadQuery and does not evaluate on other CAD representations or tasks like reconstruction.
Read-first score
Read-first score 67.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 78.
Field roles
Rank sensitivity
Stability: volatile; rank range: 17.
Keyword Scores
Deep Analysis
Innovations
- Proactive clarifying agent that audits prompts and asks targeted clarification questions only when necessary
- Agentic framework pairing a clarifying agent with a CAD coding agent to resolve specification issues before code synthesis
- Fine-tuning the coding agent on a curated high-quality text-to-CadQuery dataset and training the clarifying agent via agentic SFT on clarification trajectories
Methodology
ProCAD pairs a proactive clarifying agent that audits the prompt and asks targeted clarification questions to produce a self-consistent specification, with a CAD coding agent that translates the specification into an executable CadQuery program. The coding agent is fine-tuned on a curated text-to-CadQuery dataset, and the clarifying agent is trained via agentic supervised fine-tuning on clarification trajectories.
Key Results
Proactive clarification significantly improves robustness to ambiguous prompts with low interaction overhead; ProCAD reduces mean Chamfer distance by 79.9% and lowers invalidity ratio from 4.8% to 0.9% compared to Claude Sonnet 4.5.