RA-CAD：学习执行后批判的状态感知文本到CAD生成模型
RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
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中文总结 AI 辅助
该研究针对文本到CAD生成的反馈利用不足问题，提出RA-CAD智能体，通过生成-执行-批判-重写循环结合CCB、FAO优化，在CADFusion和Text2CAD上实现最优性能。
中文摘要 AI 辅助
文本到CAD生成是将自然语言设计意图转换为可编辑、可执行的参数化计算机辅助设计（CAD）代码，可减少手动建模所需的专业知识和工作量。现有方法采用固定的、外部提供的、由提示诱导的或单独优化的批判机制来优化生成过程，但不一定能优化整个生成过程中对反馈的解释及转换为有效修正动作的方式。为弥合这一反馈利用差距，本文提出RA-CAD（CAD的ReAct智能体），这是一种状态感知智能体，通过“生成-执行-批判-重写”循环与CAD环境交互。在每次迭代中，RA-CAD执行当前代码并观察其结果；基于设计指令、当前代码和执行反馈，智能体随后生成明确的执行后批判作为中间策略动作，该批判要么验证当前结果以终止，要么提供面向修正的指导以约束下一次重写。CAD代码自举（CCB）首先通过监督微调建立基础的参数化CAD编码能力；反馈驱动的智能体优化（FAO）随后对策略生成的代码和批判序列应用轨迹级组相对策略优化，为完整交互轨迹分配终端F1值和Chamfer距离奖励。该公式将批判转化为与结果对齐的可学习策略决策，而非未优化的辅助输出。在CADFusion和Text2CAD上的实验表明，与现有方法及强大的专有语言模型相比，RA-CAD实现了最先进的执行有效性和几何质量，证明了所提出的状态感知文本到CAD智能体的有效性。
英文摘要
Text-to-CAD generation translates natural-language design intent into editable and executable parametric computer-aided design (CAD) codes, reducing the expertise and effort required for manual modeling. Existing methods incorporate fixed, externally supplied, prompt-induced, or separately optimized critique mechanisms to optimize the generation process, but they do not necessarily optimize how feedback is interpreted and translated into effective corrective actions throughout the generation process. To bridge this feedback-utilization gap, we present RA-CAD (ReAct Agent for CAD), a state-aware agent that interacts with the CAD environment through a Generate--Execute--Critique--Rewrite loop. At each iteration, RA-CAD executes the current code and observes its outcome. Conditioned on the design instruction, current code, and execution feedback, the agent then generates an explicit post-execution critique as an intermediate policy action. This critique either validates the current result for termination or provides revision-oriented guidance that conditions the next rewrite. CAD Code Bootstrapping (CCB) first establishes fundamental parametric CAD coding capabilities through supervised fine-tuning. Feedback-Driven Agent Optimization (FAO) subsequently applies trajectory-level Group Relative Policy Optimization to both policy-generated code and critique sequences, assigning terminal F1 and Chamfer Distance rewards to the complete interaction trajectory. This formulation makes critique an outcome-aligned, learnable policy decision rather than an unoptimized auxiliary output. Experiments on CADFusion and Text2CAD show that RA-CAD achieves state-of-the-art execution validity and geometric quality compared with existing methods and strong proprietary language models, demonstrating the effectiveness of the proposed state-aware text-to-CAD agent.
