Title: CADMorph: Geometry‑Driven Parametric CAD Editing via a Plan–Generate–Verify Loop
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computer-Aided Design

Test-time scaling

Generative Models

Large-language Models
Abstract: A Computer-Aided Design (CAD) model encodes an object in two coupled forms: a \emph{parametric construction sequence} and its resulting \emph{visible geometric shape}.
During iterative design, adjustments to the geometric shape inevitably require synchronized edits to the underlying parametric sequence, called \emph{geometry-driven parametric CAD editing}.
The task calls for 1) preserving the original sequence’s structure, 2) ensuring each edit's semantic validity, and 3) maintaining high shape fidelity to the target shape, all under scarce editing data triplets.
We present \emph{CADMorph}, an iterative \emph{plan–generate–verify} framework that orchestrates pretrained domain-specific foundation models during inference: a \emph{parameter-to-shape} (P2S) latent diffusion model and a \emph{masked-parameter-prediction} (MPP) model.
In the planning stage, cross-attention maps from the P2S model pinpoint the segments that need modification and offer editing masks. 
The MPP model then infills these masks with semantically valid edits in the generation stage. 
During verification, the P2S model embeds each candidate sequence in shape-latent space, measures its distance to the target shape, and selects the closest one. 
The three stages leverage the inherent geometric consciousness and design knowledge in pretrained priors, and thus tackle structure preservation, semantic validity, and shape fidelity respectively. 
Besides, both P2S and MPP models are trained without triplet data, bypassing the data-scarcity bottleneck.
CADMorph surpasses GPT-4o and specialized CAD baselines, and supports downstream applications such as iterative editing and reverse-engineering enhancement.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nGiven a CAD sequence and a target shape, the proposed method can learn the desired part of the sequence that needs to be changed and generate a new sequence that make the reconstructed shape similar to the target shape. The proposed method has three stages, the planning stage to find the desired part sequence by cross attention map, the generation stage to generate various candidates from the masked sequence, and the verification stage to measure the similarity in the latent space.The experiment results show that the proposed method can efficiently edit the input CAD sequence to make the generated shape similar to the target shape.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The cross-attention map is the most interesting part. The edited shape driven by the target shape successfully acquires more similar features to the target shape.\n\n2. The experimental results look promising.\n \n3. The paper is well-structured and easy to follow.\n\nWeaknesses:\n\n1. The novelty and contribution are limited. The key modules are directly adopted from existing works. The main contribution is the plan-generation-verification pipeline and the cross-attention map between the sequence and the shape, which is a combination of existing modules.\n\n2. There are concerns about the practical usage of the method. The method requires a CAD shape program and a 3D CAD model as input. When designers are editing the CAD program, it is less practical to find a target shape in real-time. If the target shape could be an image or sketch, it would likely have more practical applications.\n\n3. From the results in the paper and supplementary materials, the target shape often appears close to the source shape in many parts. It would be beneficial to see more challenging cases and significant topology editing, such as adding numerous holes. Current examples seem naive, and the editing does not appear significant. Along with the inference latency, the practical usage is limited, raising questions about the community's potential interest in this paper.\n\n4. An additional experiment showing the accuracy and time effects of different maximum iteration times would be beneficial.\n\nQUESTIONS:\nI would like to see response about my concerns raised in the weakness section.\n\nIt would be better to provide more challenging examples and studies for the iteration times.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles geometry-driven CAD editing by proposing a framework that updates a construction sequence to match a target geometry. It combines a parameter-to-shape diffusion model with a masked sequence predictor, forming a plan–generate–verify loop. The method avoids the need for triplet supervision and effectively handles limited editing data.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The proposed plan–generate–verify framework is well-motivated. Traditional CAD editing methods require triplet supervision ⟨original sequence, target geometry, updated sequence⟩, where the updated sequence is especially difficult to obtain. This framework effectively removes the need for such supervision.\n2. Leveraging cross-attention maps to localize editable segments is an interesting and intuitive idea.\n3. The paper is well-written and easy to follow.\n\n**Weaknesses:**\n\nWhile the proposed method performs well on benchmark examples, its applicability to real-world scenarios remains unclear. The generation process relies entirely on the MPP model to fill in masked segments based on limited context. In complex CAD applications, such as furniture design, where the geometric difference is large and structural edits are nontrivial, the required updates may involve high-level understanding of the geometry. This can make it difficult for the model to converge or produce valid outputs, even after many iterations.\n\nQUESTIONS:\n1. The qualitative results mainly focus on mechanical parts. I wonder how well the method performs on more complex real-world data, such as the furniture dataset from BrepGen [1]. For example, could the authors try morphing between two different tables? That would be an interesting demonstration.\n2. What is the inference time of CADMorph compared to other baselines? It would be helpful to understand the practical cost of the iterative loop.\n3. Can the authors include some failure cases and discuss typical failure modes?\n\n[1] Xu, X., Lambourne, J., Jayaraman, P., Wang, Z., Willis, K., & Furukawa, Y. (2024). Brepgen: A b-rep generative diffusion model with structured latent geometry. ACM Transactions on Graphics (TOG), 43(4), 1-14.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a novel method called CADMorph for geometry-driven parametric CAD editing: given an original CAD parametric sequence from a source object, it generates an updated sequence for a target object that is not too far-off from the original sequence while successfully achieving the target shape. \n\nThe method proposes an iterative plan-generate-verify framework by utilizing parameter-to-shape (P2S) model based on a latent diffusion model and masked-parametric-model (MPP) based on a large language model (LMM). P2S model does the planning by locating the segments requiring edits via cross-attention maps and replace them with mask tokens. MPP model infills the mask tokens to propose the edited candidates. The plan and generate steps are repeated iteratively in which verify step is performed in each iteration to select the candidate closest to target shape.\n\nSTRENGTHS AND WEAKNESSES:\nI am not too familiar with the parametric representation, dataset, and related works. Based on my understanding, here are some of the strengths and weaknesses.\n\nStrengths:\n1. This paper is one of the first to address the task of geometry-driven parametric CAD editing. The challenges of the task is well-explained in the introduction. The proposed method also does not necessitates scarce triple data (original sequence, target geometry, updated sequence).\n2. The proposed plan-generate-verify framework works better than LLM prompting and the baseline methods (CAD-diffuser and FlexCAD).\n\nWeaknesses:\n1. The methods seems to involve a lot of randomness. The generate step produces a set of N candidates without any constraint (except for the mask tokens). It is not clear from the paper on how many candidates, masking proportion, and number of iterations should be done to ensure generation robustness.\n2. The masking in the plan step is based on the cross-attention of the latent representations. For shapes with complex objects, the attention scores might become not accurate.\n3. The verification step is also performed to minimize the distance in latent representation. There is no explicit geometry signal on this step. This way, the method can struggle with CAD models with intricate details (e.g., with tiny parts or holes).\n\nQUESTIONS:\nI am mainly concerned about the robustness of the method. I would appreciate if the authors can provide some insights on the number of candidates, masking proportion, verify iterations to the robustness of the final outputs. I expect a small ablation study for this.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces CADMorph, a novel approach for geometry-driven parametric CAD editing. The task takes an original parametric CAD construction sequence and a target geometric shape as inputs, and produces an updated parametric sequence that renders the target shape while preserving the structure of the original sequence. The method employs an iterative plan-generate-verify framework utilizing two complementary models: a Parameter-to-Shape (P2S) latent diffusion model that maps parametric sequences to geometric shapes, and a Masked Parameter Prediction (MPP) model based on finetuned LLaMA-3. In each iteration, the planning stage analyzes cross-attention maps from the P2S model to identify segments requiring modification, the generation stage uses the MPP model to propose semantically valid edits for masked segments, and the verification stage selects the candidate sequence whose shape embedding best matches the target. The approach circumvents data scarcity by training the two models separately without requiring triplet supervision data.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1. The paper addresses geometry-driven parametric CAD editing, a practically important but underexplored task that bridges the gap between reverse engineering and traditional CAD editing methods.\n\n2. The use of cross-attention maps from the P2S diffusion model to identify which parametric segments need editing is creative and well-motivated. The visualization in Figure 3 convincingly demonstrates this insight.\n\n3. The approach cleverly avoids the need for scarce triplet training data (original sequence, target shape, updated sequence) by leveraging two separately trained models, making the method practical to implement.\n\n4. The method outperforms strong baselines including GPT-4o and specialized CAD methods on multiple metrics. The human evaluation further confirms its superiority.\n\n### Weaknesses\n\n1. **Limited evaluation dataset**: The experiments rely solely on the CAD-Editor test set with 2k examples, which the authors acknowledge contains simpler models than real-world assemblies. This limits the generalizability claims of the approach.\n\n2. **Computational efficiency concerns**: As a test-time scaling method requiring multiple iterations of plan-generate-verify cycles, the inference latency is a significant limitation for practical deployment. The paper lacks detailed runtime analysis.\n\n3. **Limited ablation studies**: The ablations don't explore important design choices such as the number of iterations, different masking strategies, or the impact of the number of candidates N in the generation stage.\n\nQUESTIONS:\n1. Could you provide specific timing comparisons between CADMorph and baseline methods? How many iterations are typically needed for convergence, and what is the wall-clock time for editing a single CAD model?\n\n2. Have you tested CADMorph on more complex industrial CAD models with hundreds of operations? What are the main bottlenecks when scaling to such models?\n\n3. Why was voxelized tSDF chosen over other 3D representations? Have you experimented with mesh-based or implicit representations, and how do they compare?\n\n4. Did you explore other ways to combine the P2S and MPP models? For example, could they be trained jointly or combined in a different framework?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}