DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents

DeciCAD introduces a domain‑native harness for CAD agents that defines semantic CAD actions, decision‑oriented observations, environment feedback, and persistent state transitions. Using this harness, the authors develop a learning pipeline that extracts causal decision supervision from interaction trajectories and refines the policy through supervised fine‑tuning and geometry‑grounded online reinforcement learning. Across four CAD benchmarks, DeciCAD outperforms a general‑purpose coding harness, achieving higher F1 and IoU scores while reducing model‑output tokens by 45–88% and improving geometry availability from 66.5% to 73.5%.

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• llms
• agents
• reinforcement-learning
• fine-tuning
• benchmarks