AIM-CAD: Causality-Inspired Auditing of Historical Edits for Training-Free CAD Editing

Language-guided parametric CAD editing requires realizing user-specified modifications while preserving unaffected content and maintaining a valid, editable model. However, obtaining large-scale, high-quality editing supervision is costly, while task-specific fine-tuning requires additional model-specific adaptation. This motivates training-free editing that reuses historical edits as external experience. Such reuse remains challenging: new requests may differ from available examples in instruction or CAD structure, and historical traces may contain redundant or context-specific operations. Moreover, plausible edit plans may not translate faithfully into executable CAD operations. We propose Audited Intent–Operation Memory for CAD Editing (AIM-CAD), an experience-guided, training-free framework for reusing historical edits. AIM-CAD builds an audited memory by organizing historical edits as intent–operation graphs and applying causality-inspired interventions to check instruction-level executable sufficiency and prune redundant operation attributions. These interventions assess functional support within a fixed CAD program. Relevant audited experience is then retrieved across instruction semantics and CAD structure. A Graph-Guided Dual-Output Generator produces both structural and executable realizations, whose consistency is checked by Graph–Script Consistency Validation before output. Experiments on adapted CAD-Editor and BenchCAD benchmarks across multiple LLM backbones demonstrate the effectiveness of AIM-CAD. Code will be available at https://anonymous.4open.science/r/aim-cad-4F2B/.