CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing
Authors: Yu Yuan
Shizhao Sun
Qi Liu
Jiang Bian
Venue: ICML 2025 poster (2025)
Keywords: Computer Aided Design
Generative Models
Text-based Editing
Large Language Models
Primary area: applications->computer_vision
TL;DR: This paper introduces CAD-Editor, a novel generative model that enables precise text-based editing of CAD designs.

Abstract: Computer Aided Design (CAD) is indispensable across various industries. 
\emph{Text-based CAD editing}, which automates the modification of CAD models based on textual instructions, holds great potential but remains underexplored.
Existing methods primarily focus on design variation generation or text-based CAD generation, either lacking support for text-based control or neglecting existing CAD models as constraints.
We introduce \emph{CAD-Editor}, the first framework for text-based CAD editing. 
To address the challenge of demanding triplet data with accurate correspondence for training, we propose an automated data synthesis pipeline. 
This pipeline utilizes design variation models to generate pairs of original and edited CAD models and employs Large Vision-Language Models (LVLMs) to summarize their differences into editing instructions.
To tackle the composite nature of text-based CAD editing, we propose a locate-then-infill framework that decomposes the task into two focused sub-tasks: locating regions requiring modification and infilling these regions with appropriate edits. 
Large Language Models (LLMs) serve as the backbone for both sub-tasks, leveraging their capabilities in natural language understanding and CAD knowledge.
Experiments show that CAD-Editor achieves superior performance both quantitatively and qualitatively.

A Locate-thennInfill Framework with Automated Training Data Synthesis for Text-Based CAD Editing
CAD-Editor:
Yu Yuan
Shizhao Sun 2 Qi Liu'
Jiang Bian
2
Abstract
1. Introduction
In the modern digital era, Computer- Aided Design (CAD) has become indispensable across industries. Most modern CAD tools follow the Sketch-and -eeech--nd-Extrude (SE) Opera- tions paradigm (Shahin, 2008; Camba et al., 2016), where designers sketch 2D curves define the outer and inner boundaries of profiles, extrude them into 3D shapes, and combine these shapes to create complex models.
Computer Aided Design (CAD) is indispensable across various industries. Text-based CAD editing, which automates the modification of CAD models based on textual instructions, holds great poten- tial but remains underexplored. Existing methods primarily focus on design variation generation or text-based CAD generation, either lacking support for text-based control or neglecting existing CAD models constraints. We introduce CAD-Editor, the first framework for text-based CAD editing. To address the challenge of demanding triplet data with accurate correspondence for training, we propose an automated data synthesis pipeline. This pipeline utilizes design variation models to generate pairs of original and edited CAD mod- els and employs Large Vision-Language Models (LVLMs) to summarize their differences into edit- ing instructions. To tackle the composite nature of text-based CAD editing, we propose a locate- then-infill framework that decomposes the task into two focused sub-tasks: locating regions re- quiring modification and infilling these regions with appropriate edits. Large Language Models (LLMs) serve the backbone for both sub-tasks, leveraging their capabilities in natural language understanding and CAD knowledge. Experiments show that CAD-Editor achieves superior perfor- mance both quantitatively and qualitatively. The code is available at https: / /////gttubb// com/ microsoft/CAD-EAi
CAD model creation is an iterative process, where an initial draft undergoes multiple modifications until it aligns with user requirements. Natural language plays a crucial role throughout this process, serving as a key medium of com- munication. For non-experts, it offers the most intuitive way to express their needs, while for professionals, it enables fast, detailed, and precise instructions. Consequently, sys- tem capable of automatically editing CAD models based on textual instructions - known as text-based CAD edit- ing (Figure 1) 1)-hae has the potential to revolutionize the entire CAD design workflow. Such a system could significantly accelerate the development of CAD models and empower a broader range of individuals, especially those with limited design expertise, create CAD models more effectively.
While important, text-based CAD editing receives limited attention. Some studies explore design variation generation, where new CAD models are generated by randomly alter- ing components of an existing model (Wu al., 2021; ; Xu et al., 2022; 2023; Zhang et al., 2024b). However, these ap- proaches lack support for text-based control over the appear- ance of the generated CAD models, limiting their practical usability. Another line of research makes initial attempts at text-based CAD generation, focusing on generating new CAD models directly from textual descriptions (Khan et al., 2024b; Li al., 2024). Nonetheless, these methods do not incorporate an existing CAD model as input, which pre- vents them from leveraging the original design's context and constraints.
Work done during the internship at Microsoft Research Asia. Open-source research project starts from March 2024. Yu Yuan: <yyhappier@ waw...aa..eeei.....mm.. astc.edu.cn>> Qi Liu: <qlliuqlustc..dd.cog @uwt..edu.ct>.. Jiang Bian: <jiabia@ @ sijabia@micrsoff..comm microsoft.com.> 'University of Science and Technology of China *Microsoft Research Asia. Correspondence to: Shizhao Sun <shizsu microsoft.com>>.
bast- CAD editing presents several distinct challenges. First, training for this task requires triplet data with accu- rate correspondence among an original CAD model, an editing instruction, and an edited CAD model. However, such data does not naturally exist, and manually collection
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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CAD-Editor
Transform the rectangular panel into circular one.
Add smaller prisms on the top.
Increase the height of the entire shape.
Drill four smaller holes through corners.
Remove the cylinder.
Figure 1. Text-based CAD editing achieved by EAD-Editor Each sub-figure shows the editing instruction at the top, the original CAD model on the left, and the edited CAD model on the right. The rendered image is shown for better comprehension. The actual editing occurs on skelch-and-extrusion (SE) operations of a CAD model to provide editability and reusability.
both costly and difficult to scale. Second, text-based CAD editing is inherently composite problem. It demands com- prehensive understanding of diverse textual instructions and geometric concepts, the ability to locate the corresponding parts within the intricate structure of the CAD's SE opera- tions, and the capability to generate concrete modifications to these SE operations.
ter align with real-world user needs.
We propose an automated data synthesis pipeline that combines design variation models and LVLMs to gen- erate triplet data with accurate correspondence, ad- dressing a critical challenge in training.
We develop a locate-then-infi then-infill framework that decom- poses the task into focused sub-tasks, and leverage LLMs the backbone, effectively handling the com- posite nature of text- CAD editing.
In this work, we introduce CAD-Editor, the first frame- work for text-based CAD editing. We frame the task as senueecc-to-sqqueneee (seq2seq) generation problem, where the input combines an editing instruction and the sequence representation of the original CAD model, and the output is the sequence of the edited CAD model (Figure 2). To address the need for triplet data with accurate correspon- dence, we propose an automated data synthesis pipeline that leverages existing design variation models and Large Vision-Language Models (LVLMs) (Figure 3). Starting from an existing CAD model, design variation models gen- erate edited CAD models by randomly altering parts while keeping others unchanged, producing pairs of original and edited CAD models. LVLMs then summarize the differ- ences between these CAD models into editing instructions, resulting in triplets with strong correspondence. To tackle the composite nature of text-based CAD editing, we de- compose the task into specialized sub-tasks: locating and infilling, each addressing a specific aspect of the editing process (Figure 4). For both sub-tasks, Large Language Models (L.LMs) serve as the backbone, leveraging their strong natural language understanding capabilities and ba- sic CAD-related knowledge, including SE operations and geometric concepts (Makatura et al., 2023). In the locat- ing stage, LLMs identify regions requiring modification by generating a masked CAD sequence, where special tokens <mask> indicate the regions to be modified. In the infilling stage, LLMS generate appropriate edits for these masked regions, using the masked CAD sequence from the locating stage as context.
We conduct extensive experiments, demonstrating that our approach outperforms baselines in generation va- lidity, eex alignment, and overall quality.
2. Related Works
CAD Generation. Parametric CAD (Shahin, 2008; Camba et al., 2016), defined by its sketch-and-extruuee operations, is central to mechanical design due to its ability to retain modeling history, which facilitates both editing and manu- facturing. Recent large-scale CAD datasets (Wu et al., 2021) have fueled the development of generative models. Wu et al. (2021) explored unconditional generation, where a random latent vector is used as input to generate CAD models. Xu et al. (2022; 2023); Zhang al. (2024b) focused on design variation generation, which randomly modifies specific part of an existing CAD model. Recently, Khan et al. (2024a); Li et al. (2024) studied text-based CAD generation, which transforms textual descriptions into CAD models. Our work differs in two key aspects. First, we target distinct task, unlike prior work, which either lacks text-based control (Wu et al., 2021; Xu al., 2022; 2023; Zhang et al., 2024b) or disregards existing CAD models as constraints (Khan et al., 2024a; Li et al., 2024). Second, we introduce a novel locate-then-infill framework based on LLMs to handle the composite nature of text-based CAD editing. Previous ap- proaches either rely on VAE-based (Wu al., 2021; Xu et al., 2022; 2023) or transformer-based (Khan al., 2024a; Li et al., 2024) architectures, or apply LLMS without ac- counting for task-specific needs (Zhang et al., 2024b).
The contributions of this work are summarized as follows:
We introduce a new task, text-based CAD editing, en- abling precise edits through textual instructions to bet-
Large Language Models (LLMs). Recently, LLMs like GPT (Achiam et al., 2023; OpenAl, 2023) and aMA (Tou-
2

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CAD-Editor
INPUT
Original CAD Sequence:
To address the need for training data with good corre- spondence among I, Corig. and Codie. denoted as D Rendered CAD Model
CAD Sequence
(Original CAD sequence)
Instruction:
1ine, 19 <εot> 31,7, <EoC> 1ine,52, 19 <EoC> 1i,e,52,43 <Eot> 31, <EoC> line, 10, <EoC> <EoL> <EoF> <Eo5> 33, 0,, <εoε> 3,, <BoS> 2.,.333,,,,113333,,14..80..0.11
Increase the cylinder's height and reduce its diameter centrally.
Edited CAD Sequence:
<EOC> <EOL> <EOF>
OUTPUT
<EoE>
[Edited sequence]
Figure 2. Left: Example input and output for CAD-Edice The input combines the original CAD sequence with the editing instruction. and the output is the edited CAD sequence. The specific CAD sequence is shortened to '[Original (or Edited) CAD Sequence]' to save space. Right: An illustration for specific CAD sequence and its rendered CAD model.
vron et al., 2023) have distinguished themselves from smaller models, particularly through advanced prompt- ing (Brown et al., 2020; Wei et al., 2022b; Kojima et al., 2022) or fine-tuning methods (Wei et al., 2022a). Beyond excelling in natural language processing (Yue et al.. 2024; Zhan et al., 2025; Zhao et al., 2025), LLMS have trans- form generative tasks in other domains, e.g. motion gener- ation (Zhang et al., 2024a) and material generation (Gru- ver et al., 2024). These advancements inspire us adopt LLMs the backbone for subtasks in our locate-then-infill framework. Moreover, LLMs and LVLMs are increasingly utilized for data synthesis to enhance training (Xu et al., 2024; Yu et al., 2024). Specifically, Khan et al. (2024b) leverage LLMs/LVLMS to synthesize data for text-based CAD generation. However, our task of text-based CAD editing presents distinct challenges. First, unlike Khan al. (2024a), who generates two-tuple data text prompt and CAD model), our task involves creating triplet data: edit- ing instruction, an original CAD model, and an edited CAD model. We address this by combining design variation mod- els with LLMss/LLLMs. Second, while Khan et al. (2024a) focus on captioning single CAD models, our task requires summarizing differences between two CAD models. We handle this by introducing a stepwise captioning strategy.
generates Cedn as output, i.e., Cedit = f(I, Corig).
We formulate text-based CAD editing as sequence-to- sequence (seq2seq) generation problem. To achieve this, both the editing instruction and the CAD models are rep- resented as sequences of textual tokens. The editing in- struction I naturally consists of textual tokens. For the CAD models Corig and Cedit. we adopt the sequence format introduced by Zhang et al. (2024b), which abstracts all prim- itives in SE operations, including numerical and categorical parameters, into textual tokens (Figure 2).
{ (I, Cong. Cean) (where N is the data size), we introduce an automated data synthesis pipeline (Section 4). First, we obtain Codit from Corig by leveraging existing design varia- tion models (Xu al., 2023) randomly modify certain parts of the original CAD model while keeping others un- changed. Next, we generate the corresponding I by utilizing LVLMs to summarize the differences between the paired CAD models. Finally, we assemble these components into triplets.
Based on seq2seq formulation and triplet-correspondence dataset, we propose locate-then-infill framework to tackle the composite nature of text-based CAD editing (Section 5), Specifically, we decompose the problem into two focused sub-tasks: locating and infilling, each handling a more manageable aspect of the overall problem. In the locat- ing stage, we predict a masked CAD sequence Cmask by inserting special <mask> tokens into Corig. indicating the re- gions that require modification, i.e., Cmask flucate (I, Corig). In the infilling stage, We generate Codn by filling in pre- cise modifications within the masked regions, i.e., Cedi fuan (I, Corig. C.ask). In both stages, LLMs serves as the backbone, leveraging their natural lauguage understanding and CAD knowledge acquired during the pre-training.
Test- Editing in Other Domains. Text-based editing has been widely explored across various domains, e.g., 3D editing (Mikaeili et al., 2023), image editing (Meng et al., 2021; Brooks et al., 2023), and video editing (Chai et al., 2023; Ceylan et al., 2023). enables users to specify and modify particular objects or attributes with precision and flexibility. Inspired by these advancements, we introduce text-based editing in the CAD domain for the first time.
3. Approach Overview
Let denotes the editing instruction, Corig the original CAD model and Cedit the edited CAD model. Here, the CAD (SE) opera-
model is represented using tions, this representation preserves the modeling history, making it easier to edit. The goal of text-based CAD editing is to learn a function ff that takes I and Coni as inputs and
4. Automated Data Synthesis Pipeline
Figure 3 illustrates our data synthesis pipeline, comprising three key steps introduced below.
3

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CAD-Editor
(a) Paired CAD Models Generation
(c) Assembling
"Replace cylinder roctangular prisn."
Design Variation Generation
line ... line <EoE> line <EDE>
circle <EdE>>>>>>>>
(b) Editing Instruction Generation
"Insert cylindrical rod through central hole."
"Decrease the size of the cylinder's radius."
Multi-modal Representation
Visual
Stepwise Captioning
line arc
line art circle <EoE> circle <EoE>
cirde <EoE>
Textual
"Reduce the radius of the cylinder by units."
line <Eoε> 31533,31,33319,
line <EoE> 3,,,3111, 43,31,19,31 <o.xx
53,31,9,31 <..E>>>>>
Figure 3. Illustration of automated data synthesis pipeline.
Paired CAD Models Generation. In this step, create paired CAD models, Cariy and Cedi. by starting with an ex- isting CAD model and applying design variation models to create its variations. We source CAD models from the DeepCAD dataset (Wu et al., 2021) and use Hnc-CAD (Xu al., 2023) our design variation model'. Given an ex- isting CAD model Co. use Hnc-CAD's auto-completion to generate variants CI...., CK. We then create paired CAD models by: 1) treating Co as the original and C₁ (k E [J) as the edited CAD model; 2) reversing the roles, considering C₁ as the original and Co as the edited CAD model; and 3) using two generated variants, Ck, and Cs2 (k,, 22 E [1, K], kn # 12), as the original and edited CAD models. This approach captures common CAD editing operations, including addition, deletion, and replacement.
model pair, we follow these steps: 1) describing each CAD model - this involves analyzing geometric properties such as component types, quantities, sizes, and spatial relationships; 2) identifying differences using the detailed descriptions from the previous step alongside CAD models, this stage extracts the necessary modifications between CAD models; and 3) compressing instructions the final step refines the editing instructions into concise yet precise form.
Assembling. Finally, we assemble CAD pairs (Corig and Coda) from the first step and editing instruction (Z) from the second step into triplets, constructing the training dataset D = {II, Corig J Note that the visual modality only used in the second stage generate diverse editing instructions. In the final dataset, all CAD models are rep- resented as SE operations, aligning with the focus of this work text-based CAD editing in the SE domain.
Editing Instruction Generation. In this step, generate editing instructions, I. by summarizing the difference be- tween Curig and Cedi using LVLMs. To ensure both diversity and accuracy, we introduce two key techniques.
5. Locate-then-Infil Framework
Figure illustrates our framework. We decompose text- based CAD editing by explicitly introducing a masked CAD sequence, Cuask, to indicate potential modification regions:
First, we represent CAD models in multiple modalities: the visual and sequence modalities. The intuition is that high-level structural changes (e.g., changing cylinder to cube) are more easily observed in the visual modality, while low-level numerical changes (e.g., doubling the height) are better captured in the sequence modality. Additionally, the sequence modality provides a detailed representation of operations, ensuring that information is lost. our imple- mentation, we use rendered images for the visual modality and SE operations for the sequence modality.
P(Cedit | Corig: I)
P(Cmask Carig, I): P(Codit Corigy I, Cmask)
where P(-) denotes the probability. Here, P(Cmask Corig represents locating stage, while P(Ceti Corig, I, Cmack) cor- responds to the infilling stage.
Second, we propose stepwise captioning strategy to break down the complex task of difference summarization into three sub-tasks, enhancing generation quality of LVLMs. For each representation (visual or sequence) of a CAD
5.1. Locating Stage
This stage aims generate masked CAD sequence, Cmusk- where regions requiring modification are marked by special tokens <mask> while unchanged parts are copied from the original CAD sequence Conig We adopt LLMs as the backbone and autoregressively predict tokens in Causk using
'ee choose Hinc-AAD for its well-developed spen-source im- plementation. Other design variation models (Xu et al., 2022: Zhang al., 2024b) also applicable.

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CAD-Editor
Locating Stage
(b) Infilling Stage
Examples Input and Output
Masked CAD Secuence
Edited CAD Sequence
Original
carcle 20,53 21,90,,,,,,,,1
ttts <tats
Masied
LLMs
LLMs
Sequence
cireee 4 2,,0,,,, 20,20,
(ESS 4828>
Original CAD Editing instruction
Criginal CAD Sequence
Editing Instruction
Masked CAD Sequence
Eciting Instruction
legends
Friting Instruction Token
Original CAD
Masked CAD
Modified CAD Taken
Figure 4. (a)-(b): Overview of Locate- soecate-hen-aae Infill framework. (c): Examples of input and output, where the left column shows abstracted representations using legends, the middle column displays conerete sequences and the right column presents rendered visual objects.
Corig and the editing instruction I as context:
we strive to ensure high-quality synthetic data, achieving absolute accuracy impossible. To further improve perfor- mance, we introduce selective dataset curated with human annotations. Rather than having human annotators create editing instructions or CAD models from scratch, we adopt more efficient approach - inviting them to select the best option from generated candidates. This significantly reduces human effort and accelerates dataset construction. Specifically, we first fine-tune LLMs on synthetic data and use them to generate multiple edited CAD sequences, These sequences then rendered into visual objects, and hu- man annotators select the best one. The chosen sequence, along with its corresponding original CAD model and tex- tual instruction, is added to the selective dataset. Finally, we further fine-tune LLMs using this selective dataset.
P(Cmask Corig I) II
P(cmaak Corig I, miss)) 1=1
(2)
where T is the sequence length.
Creating Ground-Trutth Masked CAD Sequence via LCS. To finetune LLMs for the locating task, we require ground- truth masked CAD sequences, denoted as Cgt-mak as super- vision signals. We obtain them using the Longest Common Subsequence (LCS) algorithm. Let Corig {c ...., e²) and Cedit {č'..... ..... δ²' denote the tokenized original and edited CAD sequences respectively. The LCS algorithm computes the longest subsequence CLOS = fct c**) such that CLcs S Corig and CLos S Cedit. where the indices 21, .2. ig represent the positions of matching tokens in Corig. Using CLcs, we construct Cgt-mask as follows: 1) for each token c¹ E Corig, if c' E CLOS, retain cf in Cgt-mask and if at & CLCs, replace ct with the placeholder token <ma sk 2) for tokens in Codit that do not appear in CLas (representing insertions), insert <mask> tokens at the corresponding posi- tions in Cgt-mask; 3) consecutive <mask> tokens are merged into a single <mask> token for simplicity.
5.3. Training and Inference
Training. In the locating stage, we fine-tune LLMs us- ing Low-Rank Adapters (LoRA) (Hu et al., 2022) with the ground-truth masked CAD sequence constructed via LCS. For the infilling stage, we first fine-tune LLMs using LoRA with the synthetic dataset introduced in Section 4. We then further refine the model by fine-tuning it with LoRA on the selective dataset introduced in Section 5.2.
5.2. Infilling Stage
Inference. During inference, the locating and the infilling stage operates sequentially. The locating stage generates Crask using Corig and I as input. The infilling stage generates Cedn by using Cmusk from the locating stage along with Corig and I input.
This stage focuses on generating the final edited sequence Cedn by precisely filling in the masked regions while preserv- ing the unmodified parts. We employ LLMs as the backbone, autoregressively predicting tokens Codit using Cmask from the locating stage along with Corig and I as inputs:
6. Experiments
P(Cedil Cinask Carig, I)
6.1. Experimental Setup
T
II P(c Cmasks Corig I, C... 1=1
Datasets. We use the DeepCAD dataset (Wu et al., 2021) which contains 178k CAD models. We split into 90% training, 5% validation, and 5% testing segments. We fol- low the same strategy in existing work (Xu et al., 2022; 2023) to remove duplication. For the synthetic dataset used
Improving Performance with Selective Data. To fine-tune LLMs for the infilling task, we use the dataset DD synthe- sized through the pipeline introduced Section While
5

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CAD-Editor
Input
GPT-4o-Basic
GPT-4o-IC
Input
Ours
Go--ic-Basic
GPT-4o-IC
for training, generate 120k examples using the method introduced in Section 4. For the test set, randomly ple 2k examples from the original test segment, generate the initial version following Section 4, and manually examine them to ensure the correctness. To compare the performance of different methods, we generate outputs for each exam- ple the test set, yielding 10k CAD models for evaluation. (1) 'Attach an annular groove around the central hole."
(5) 'Remove the central vertical rectangular prism"
1
(2) 'Add horizontal sylinder centrally beneath the arch structure."
(6) "Extend length, remove extension, add two cubcid extensions on end."
(3) 'Enlarge the central hale."
(7) 'Attach hexagonal prism to one sylinder end, add recess."
(4) 'Add cylinder with central circular hole. aligning with first hole."
(8)
rectangular prisms."
Figure 5. Qualitative results from CAD-Editor, GPT-4o-Basic and GPT-4o-IC.
Basic, which provides only an explanation of CAD opera- tion sequences; and 2) GPT-4o-IC: which includes three in-context (IC) examples retrieved based on cosine simi- larity between editing instructions, in addition to the basic explanation. Notably, existing CAD design variation mod- els and text-based CAD generation models do not support text-based CAD editing. We include them as baselines to show that our model can generate CAD models with com- parable superior validity and quality while addressing more complex task.
Implementation Details. During data synthesis, GPT-4o utilized for the visual modality, while LLaMA-3-70B han- dles the sequence modality. For training, we adopt Llama- 3-8B-Instruct as the backbone model, fine-tuning it over 70 epochs using PyTorch's s Distributed Data Parallel (DDP) framework on 4 NVIDIA A800-80GB SMX GPUs. The initial learning rate is set to le-4 with a maximum token length of 1024. We employ LoRA with rank of 32. During the inference, set the temperature as 0.9 and top-p as 0.9 to generate varied results in each trial.
6.2. Metrics
As this work is the first to address text-based CAD editing, we propose evaluating the task from three key aspects.
(1) Validity. The generated CAD sequence must be valid, that is, it can successfully parsed and rendered into a 3D visual object. We denote this as Valid Ratio (VR).
Baselines. We compare our results with three types of baselines: 1) design variation generation models, including SkexGen (Xu al., 2022), Hne-CAD (Xu et al., 2023) and FlexCAD (Zhang et al., 2024b); 2) text-based CAD generation models such as Text2CAD (Khan al., 2024a) and 3) foundation models that not specifically designed for CAD generation but have acquired some CAD know]- edge during pre-training. For the third category, we select one the most powerful foundation models, GPT-4o, as our baseline. We use two prompting strategies: 1) GPT-40-
(2) Realism. The generated CAD models should realis- tic and similar to ground-truth CAD models. To measure this, adopt the Ceasen-Shannon Divergence (JSD) from prior work (Wu et al., 2021; Xu et al., 2022; 2023). JSD quantifies the similarity between two probability distribu- tions, evaluating how often the ground-trutt point clouds occupy similar positions as the generated point clouds.
(3) Edit Consistency. The generated edits should faith- fully reflect the provided textual instructions. We assess
6

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CAD-Editor
rectangular prism to form trapezoids."
"Draw and extrude smaller star inside original prism."
"Move the hole from edge to center enlarge
"Hatten arch, add circle stop a base.
H
"Subtract concentric sylinder to form annular ring.
"Attach four cuboids perpendicularly to vertical edge.
"Add smaller sylinder vertically larger cylinder.'
sharter intersecting prism, keeping the longer one intact."
Figure 6. Additional results from CAD-Edito with various editing instructions. In cach sub- figure, the left image shows the original CAD model, the right image displays the edited CAD model and the text below provides the editing instruction.
"Add a 44-unit diameter circuler hols."
"Attach small cube mid-hhiigh a vertical face."
"Drill three amaller equidistant holes on top."
"Reduce the cylinder's height by half."
Figure CAD-Editor can deal with parametrie instructions, including explicit numeric expressions and implicit parametric cues.
consistency at both the point cloud and image levels:
Table 1. Quantitative evaluations. SkexGen, Hac-CAD, FlexCAD and Text2CAD do not support bast- editing. only their generation quality compared. JSD. CD, and D-CLIP values are scaled by 10 the higher the better, the lower the better.
Point Cloud Level. We use the Chamfer Distance (CD) reconstruction metric as used in (Khan et al., 2024b)
to evaluate geometric alignment between the edited and ground-truth CAD model pairs. While CD captures shape similarity, it may not fully reflect semantic consistency, as the goal of editing is not exact replication but meaningful transformation aligned with the instruction.
Method
VR
JSD +
CD
D-CLIP
H-Eval 1
SkexGen
74.3
1.94
Hne-CAD
77.4
1.77
Image Level. We adapt Directional CLIP Score (D- CLIP) from the image editing domain (Gal al., 2022; Brooks et al., 2023) as the metric. Building upon CLIP score (Radford et al., 2021; Sohn et al., 2023), D-CLIP evaluates how well the change between the image for the edited CAD model and the image for the original CAD model aligns with the editing instruction:
FlexCAD
82.1
1.72
Text2CAD
84.8
2.39
1.91
GPT-4o-Basic
63.2
1.10
2,30
- 1.088
7.22
GPT-4o-IC
84.5
0.70
1.55
-0.11
15.6
CAD-Editor
0.65
1.18
AI- AT
D-CLIP
(4)
6.3. Main Results
AT
Quantitative Evaluation. Table reports the average scores across runs. Notably. CAD-Editor achieves high Valid Ratio of 95.6%, significantly surpassing other meth- ods and indicating greater proportion of valid and high- quality CAD generations. In terms of CD and D-CLIP, which measure alignment with editing instructions, CAD- Editor achieves scores of 1.18 and 0.1 respectively, repre- senting substantial improvements over both GPT-4o-Band and GPT-4o-IC. These results underscore CAD-Fditor's ef-
AT Er (feda) Er (forig), AI Ey (Jedn) F, (fwriy)
where E, and Er are CLIP's image and text encoders, re- spectively, forig is neutral text "c.g.."hiis is a 3D shape."), fedit is the concatenation of forig and the textual instruction. eddtt and bonig are the images for the edited and original CAD model, respectively.

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CAD-Editor
"Add a hollow cylinder on top."
"Add hexagonal prism on top."
rectangular
Input
prism on top."
Input
"Add smaller prisms on the top."
Input
'Subtract cylinder shell from a solid cylinder.'
"Cut centered hexagon through the cylinder."
"Drill one large central hole four smaller holes."
Input
"Deepen the inward curvature of the sides."
Figure 9. Given the same CAD model and instruction, CAD-Editor produces diverse outcomes.
Figure 8. Given one CAD model and various instructions, CAD-
Editor produces different outcomes.
Table 2. Ablation studies. CAD-Editor-mnii trained on small set with 10k examples.
fectiveness in adhering to user instructions. Additionally, CAD-Editor outperforms all baselines on the point cloud evaluation metric JSD, demonstrating good generation qual- ity. Overall, the results indicate that CAD-Editor not only enable precise text-based CAD editing, which better align- ment with user instructions, but also achieve higher validity and better quality of the generated CAD designs.
Method
VR f
JSD 4
CD
D-CLIP
CAD-Editor-mini w/ Basic
88.8
0.78
1.25
0.12
CAD-Editor-mini Step
90.1
0.70
1.22
0.07
CAD-Editor w/ Direct
86.1
0.77
1.26
0.19
Qualitative Evaluation. Figure 5, we qualitatively com- pare our method with GPT-4o-Ban -assic-Bssiccaa and GPT-4o-IC. We observe that GPT-4o-Band often generates irrelevant edits (case 6), unrealistic shapes (case 3), or fails to make any changes (cases ,4 and 7). Additionally, it struggles with distinguishing shape types (case 2) and locating specific positions (case 5). It performs better with dynamic few-shot prompting (i.e., GPT-4o-IC), highlighting the high quality of our synthetic data. GPT-4o-IC can detect specific shapes reasonably well but still struggles with precise localization (case? 2 and 4) and object count (case 8). In contrast, our model successfully executes many challenging edits, in- cluding modifying sizes, shapes, and positions, as well as replacing, adding, and removing objects.
CAD-Editor w/
0.67
1.13
CAD-Editor w/ L-1 HS
95.6
0.65
1.18
0.11
models from the full set of generated results. Each pair of original CAD model and edited CAD model was indepen- dently rated by five crowd workers. For each pair, a score of is assigned the generated data is deemed successful, and otherwise. Success is defined by two criteria: alignment with the text and sufficiently high visual quality. The results, denoted H-Eval, are presented in Table 1. CAD-Editor outperforms baselines, indicating that crowd workers fre- quently found CAD models generated by baselines to be misaligned with the instructions or of lower quality, whereas our method demonstrated superior performance.
Besides, we present more results from CAD-Editor. Fig- ure shows how CAD-Editor handles a variety of editing instructions and different CAD models. Figure 7 shows that CAD-Editor has the ability interpret parameterized instructions, including explicit numeric expressions such as "add 44-unit diameter circular hole" and implicit paramet- ric cues like "reduce the cylinder's height by half". Figure 8 illustrates that, given the same original CAD model, CAD- Editor applies different modifications based on the provided editing instructions. Figure shows that when the edit- ing instruction is vague, CAD-Editor generates multiple CAD models, all aligning with the user's intent. Figure 10 highlights the iterative editing capability of CAD-Editor, allowing users to refine a CAD model through successive instructions until a satisfactory result is achieved.
6.4. Ablation Studies
Stepwise Captioning Strategy. As introduced in Section 4, we propose stepwise captioning strategy to decompose the complex task of generating editing instructions. To evaluate its effectiveness, we conduct an experiment where LVLMs are directly queried to generate editing instructions, denoted as E-D-Editor w/ Basic. Our approach, which incorporates stepwise captioning. is referred to CAD-Editor w/ Step. Due to resource constraints, we compare these methods on subset of 10k examples. Table 2 shows results. CAD- Editor w/ Step outperforms CAD-Editor w/ Basic across all metrics, highlighting the importance of stepwise captioning strategy in ensuring accurate editing instruction generation.
Human Evaluation. We randomly sampled 2,000 CAD
8

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CAD-Editor
"Remove top cylinder, keeping the central hole intact."
"Transform the hexagonal one, and extrude cylinder from the hole."
"Replace circular hole with an elongated slot."
"Add larger rectangular base."
"Fill smaller holes."
"Transform the circular hole into hexagonal cutout."
"Fill cutout." "
"Round the
"Addl four cylindrical legs."
Figure 10. Apply CAD-Edite iteratively to edit a CAD model until it meets user requirements.
Locate-then-Inie Framework. Our approach consists of two stages: the locating and infilling stages, each focused on specialized sub-task of text-based CAD editing. A more straightforward approach would be to treat the task whole, without decomposing it, and directly fine-tune LLMs. We refer to this as CAD-Editor w/ Direct and com- pare it with our method, CAD-Editor w/I.-I. As shown in Table 2, CAD-Editor w/L-I outperforms CAD-Editor w/ Di- rect, showing the effectiveness of our two-stage approach in addressing the composite nature text-based CAD editing.
Editor outperforms other methods. In the future, we aim to enhance our data synthesis pipeline to make it more cost- effective and efficient. We also plan to develop an advanced benchmark that better reflects practical user scenarios.
Acknowledgements
We thank Ruiyu Wang for his assistance in conducting one of the baseline experiments. We also appreciate the con- structive comments from the anonymous reviewers.
Selective Dataset. In Section 5.2, we propose improving the performance of the infilling stage with selective data curated with human annotation. We conduct experiments both with or without such human selection, denoted as CAD-Editor w/ L-I & HS and CAD-Editor L-I Table 2 shows that this strategy results in the greatest improvement in both JSD and D-CLIP scores, demonstrating its effectiveness in enhancing generation quality and better aligning editing instructions with the edited CAD model.
Impact Statement
This work presents novel task: text-based CAD editing, along with an automated data annotation pipeline and locate-then seate-tten-innfeee infill framework to effectively address it. By streamlining the CAD model development process, our ap- proach has the potential to significantly accelerate design workflows and make CAD modeling more accessible to broader audience, especially those with limited design ex- pertise. Rather than replacing human designers, goal is to augment creativity and productivity, empowering in- dividuals to bring their ideas to life more efficiently and effectively.
7. Limitation
While CAD-Editor shows promising results, it has limita- tions. First, its data synthesis pipeline relies on LVLMs, which are costly and struggle with processing multiple im- ages simultancously. Second, as an LLM-based system, it faces challenges with long contexts and generating extended sequences, limiting its ability to handle highly complex CAD models and their corresponding edits.
Ethics Statement
The data used in this work is tailored for creating and modi- fying CAD models. Due to its specialized nature, the mis- use risk is naturally minimized, ensuring that the developed methods primarily benefit design and engineering tasks.
8. Conclusion
We introduced CAD-Editor, the first framework for text- based CAD editing. We proposed data synthesis pipeline to address the need of triplet data with accurate correspon- dence, and locate-then-infi framework handle the com- posite nature of the task. Experiments showed that CAD-
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CAD-Editor
A. CAD-Editor Dataset Preprocessing
The DeepCAD dataset exhibits severe data imbalance. Among deduplicated 137,012 training models, over 91. % have three or fewer SE operations, while only 6.2% have 4-5 SEs. Models with more than 5 SEs make up less than 3%, and those exceeding 10 SEs account for just 0.4%. To mitigate the impact of this long-tail distribution, we limit our dataset to models with at most 3 SE pairs.
Additionally, the design variation generation model can sometimes introduce noise by altering CAD model too drastically or not at all. To ensure data quality, we implement a data filtering strategy. We filter out CAD pairs with too many changes by excluding examples with more than three editing instructions or more than five <mask> tokens. We also filter out pairs with no significant changes by excluding instructions containing phrases like "no transformation needed"..
B. Stepwise Captioning Strategy
To implement stepwise captioning, we utilize GPT-4o four times per CAD pair to generate the final image-level editing instructions. For sequence-level, we employ LLaMA-3-70BB The detailed prompts are illustrated in Figure 11.
Visual Modality
Step
Please take look at the first of two 3D shapes we'll be examining. Please provide detailed description, focusing its geometric properties, including the type and number of elements it features, the proportions of its size, its positional relationships between elements, and any additional details that stand out.
Step 2:
Now, let's turn our attention the second 3D shape. Please provide detailed description, focusing on its geometric properties, including the type and number of elements features, the proportions of size, positional relationships between elements, any additional dotails that stand out.
Step 3:
Please provide detailed Instructions for transforming the first 3D shape into the second.
Step 4:
Condense your instructions to one sentence, 10 words maximum.
Sequence Modality
## Task
You senior Computer-Aided Design (CAD) engineer. Your task is to provide clear and concise editing instruction (10 words fewer) for editing sketch-and-owee red-extrude CAD model. Your response should include:
1. Description of the Original CAD Model: Analyze the CAD operation sequence and describe the resulting geometry. Include element
types (e.g., cylinder. prism, hole), quantities, proportions, spatial relationships, and any notable details.
2. Description the Edited CAD Model: Analyze the operation sequence describe the resulting goometry. Include element
types (e.g., cylinder, prism, hole), quantities, proportions, spatial relationships, and any notable details.
3. Change Analysls:
Geometric Changes: Describe added, removed, or modified elements, including types (e.g., cylinder, prism, hole) and quantities (e.g. two rectangles). Use spatial or geometric features (o.g.. "upper triangular face", "smaller rectangular prism", "central circular hole")
instead of unintuitive terms like "first" "second."
Proportions Dimensions: Note changes size, scaling, or relative proportions.
Positional Relationships: Explain spatial alignment and relationships between elements. Other Notable Details: Highlight any additional observations.
Purpose: Suggest the intent behind the edit (e.g., "add central hole", "remove the smaller prism", or "Increase length by 8 units").
4. Editing Instruction: Provido a concise instruction (max 10 words) describing the modification.
Sketch-and-Exttee Model Overview
## Your Task
Original CAD Sequence: oiriginal CAD sequence]
Edited CAD Sequence: [odited CAD sequence]
Let's think step by step. Your output should be the following json format:
"Description of the Original CAD Model": your description here.
"Description of the Edited CAD Model":
your description here.
"Change Analysis": your change analysis here.
"Editing Instruction": the final editing instruction here (10 words maximum).
Figure 11. Detailed prompt used for stepwise captioning.
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CAD-Editor
Furthermore, Figure 12 presents comparison between the basic captioning approach and our proposed stepwise captioning method.
Basic Caption:
Basic Caption:
Basic Caption:
Fill the rectangular cutout and left circular hole.
Add a cylinder and rectangular prism to the surface.
Drill three holes into the rectangular prism.
Stepwise Caption: Fill cutout, remove hole, center remaining hole on top.
Stepwise Caption:
Stepwise Caption: Drill two vertical holes top, horizontal hole through side.
Attach cylinder and semi-
cylindrical prism to opposite sides.
Figure 12. Comparison between the basic captioning method our stepwise captioning method.
C. Locate-then-Infil Framework
We adopt task-specific prompts for both the locating and infilling stages. The detailed prompt designs for each task shown in Table 3.
Table Prompt for Locate then-the--eeee Infill framework.
Locating Prompt
Infilling Prompt
Below is a Computer-Aided Design (CAD) oper-
Below is the original Computer-Aided Design (CAD) operation sequence.
ation sequence. Replace the parts that need to be modified with the string "<mask>" according to the editing instruction.
Original CAD Operation Sequence:
[Original CAD sequence]
Original CAD Operation Sequence:
The parts that need be modified according the editing in-
[Original CAD sequence]
struction have been replaced by the string "<mask>".
Editing Instruction:
Editing Instruction:
[Textual editing instruction]
(Textual editing instruction]
Masked CAD Operation Sequence:
Masked CAD Operation Sequence:
[Masked CAD sequence]
(CAD sequence with "<mask>")
Generate the edited CAD sequence that could replace " "<mask>"
in the CAD model:
D. Additional Qualitative Results
We present qualitative comparisons between the directly fine-tuned LLM and our proposed Locate-then-Infill framework in Figure 13. Compared the direct fine-tuning approach, our framework improves generation quality, enhances text-CAD alignment, and ensures greater output stability.
Additional qualitative comparisons between CAD-Editor and other baseline methods are shown in Figure 14, demonstrating the superior performance of CAD-Editor under various editing conditions.
13

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CAD-Editor
"Add smaller coaxial cylinders each end."
'Remove the top face of the solid rectangular prism.'
"Increase the height of each cylinder."
'Add horizontal cylinder centrally beneath the arch structure."
Input
Direct
Locate-then-Infill
Figure 13. The qualitative comparison between the directly fine-tuned LLM and our Locate-then- (heate-then--nfll framework.
GPT-4o
-Basic
"Drill smaller hooes through maintaining symmetry alignment."
"Add raised hesagon with
"Cut bottom rectangle from face."
horizontally duplicate cube by one edge length."
GPT-4o -IC
"Align extend smaller cylinder from one base."
porpendicularly
"Add protruding from one disk buse."
"Mignn and evenly space three holes in straight line."
the horizontal base."
CAD- Editor
"Ous the central connecting prism to separate prisms."
'Attach small cube holght on vertical face."
"Subtract concentric cylinder to form annular ring."
"Add symmetrical cylindrical
protrusions to one long restangulor face."
Figure 14. Additional qualitative comparison results between CAD-Editor and baseline methods.
14

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CAD-Editor
E. Scaling & Generalization
Due to the severe data imbalance DeepCAD, we constrain the SE length during training. To evaluate the scalability of our method, we conduct an additional experiment in which we artificially balance the dataset across different SE lengths. Specifically, we construct training set containing 4,000 samples for each SE length from 1 to 5, ensuring a uniform distribution. The model is then evaluated on separate test sets corresponding to each SE length. As shown in Table 4, CAD-Editor consistently outperforms the baselines across all SE lengths. Model performance declines slightly as SE length increases. Since generating longer sequences is inherently more challenging, this result demonstrates that our method generalizes well to complex CAD structures given sufficient training data.
To further assess generalization, we evaluate CAD-Editor's generalization by testing a model trained on DeepCAD directly on Fusion 360 dataset (Willis et al., 2021). As shown in Table 5, CAD-Editor outperforms baselines, confirming its generalization ability datasets with different shape distributions.
We present additional results generated by CAD-Editor in Figure 15, including cases with more than three SEs and examples sourced from the Fusion 360 dataset.
Table Quantitative evaluation across different numbers of sketch-extrude (SE) operations. We construct dataset of 20,000 examples with a uniform distribution over SE counts from to ISD and D-CLIP values are scaled by 10 t: higher is better, &: lower is better.
Method
JSD
D-CLIP 1
VR
3
4
5
2
4
1
2
3
4
5
GPT-4o-Basic
5.98
2.86
3.68
3.82
3.95
-0.31
-0.90
-0.47
-0.97
56.5
64.2
59.4
61.5
58.3
GPT-4o-IC
5,42
1.75
2.29
2.40
3.67
0.39
-0.06
-0.50
-0.11
-0.38
76.2
85.0
76.5
75.5
73.1
CAD-Editor
4.08
1.46
2.01
2.35
3.15
0.41
-0.23
-0.06
-0.02
-0.29
84.3
91.6
87.5
79.3
79.5
Table 5. Quantitative evaluation on the dataset generated from the Fusion 360 Gallery. Lower JSD and higher D-CLIP and VR values indicate better performance. The results demonstrate that our method exhibits strong generalization ability.
Method
JSD4
D-CLIPT
VR
GPT4o-Basic
4.58
-0.45
57.7
GPT4o-IC
2.59
-0.39
81.5
CAD-Editor
2.34
0.41
93.9
Increase the vertical tori without changing positions.
Remove Inner section, add two smaller concentric cylinders top.
Combine every two small cylinders into one large cylindeer.
Drill symmetria holes through one flat cylinder disk.
Add vertical cylinders to existing outer protrusions of the shape.
Add a triangular roof to the top.
Remove the smallest prism.
Add a centered semiciroular cutout above the two circular holes.
Figure 15. Extended results from CAD-Editor complex scenarios, including examples with more than three SEs and examples sounced from Fusion 360 datasets.
15

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CAD-Editor
F. Baseline Editing Methods
This section presents the detailed prompt used in the GPT-4o-IC baseline, as illustrated Figure 16. The GPT-to-Basic prompt follows the same format, excluding the in-context examples.
GPT-4o-IC
Modify the original Computer-Aided (esign(CAD) operation sequence according to the instruction: ## Instructions for sketch-and-extruue model
sketch-and-
model consists of multiple extruded-sketches.
# Sketch
A sketch consists of multiple faces
face consists of multiple loops.
loop consists multiple curves.
A curve is either a line, an arc, or circle.
A circle is defined by four points with four geometry tokens.
arc is defined by three points but with two tokens, where third point specified by next curve (or the first curve when a loop is closed).
A line is defined by start point.
A point is represented by two integers which stands for the and y coordinate, respectively.
loop
circle can not contain additional curves since already a closed path.
When a face consists of multiple loops, the first loop defines the external boundary, and the remaining loops define
internal loops (l.e., holes).
An andiiiimitive token appears at the end of each primitive (curve, line, face, loop sketch).
Extrude
Each sketch will be followed by an extrude, which is represented by 18 parameters: BWITTRRARARRRRO.. B represents of the three Boolean operations: add, cut or intersect. It occupies parameter
indicates the displacements of the top the bottom planes from the reference plane which sketch is extruded to form a solid. It occupies parameters.
T represents 3D translation applied to the extruded solid. It occupies 3parameters.
R represents 3D rotation of the extrusion direction. It occupies 6 parameters.
represents the uniform scaling factor. occupies parameter.
O represents the center of scaling as a 2D coordinate. It occupies parameters. # Note
Note that every number integer.
## Examples for editing aketch-and-extrudde model
[The 3 most similar editing instructions and their corresponding CAD pairs]
## Your task
Original CAD Command Sequence: [original sequence]
Instruction: [editing instruction]
Your output should be of the following json format:
"edited sequence": your modified CAD sequence here.
Figure 16. Prompt employed baseline methods for CAD model editing.
16


Reviews:
[{"review_id": "Adgkwxb7kj", "reviewer": "Reviewer_keVC", "summary": "Authors propose a novel task for text-based CAD editing of sketch-and-extrude models. They demonstrate that LVLMs can be used to annotate the editing instructions. A synthesis CAD editing dataset is proposed based on DeepCAD dataset. Finally a locate-then-infill framework is proposed to generate the edited CAD sequence from original and text instructions. Overall task is well-motivated and the proposed method is solid.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## 1. More Details on Using HNC-CAD to Generate Design Variations\n\nWe follow the original implementation of HNC-CAD when setting the retained part of the original model and the sampling threshold.\n\n- Only one sketch-extrude (SE) component from the original model is retained, while all other SE components are autocompleted by HNC-CAD.\n- For top-p sampling, p is set to 0.95.\n  For completion diversity, please see Figure 9&10 in the original HNC-CAD paper. In short, HNC-CAD can generate variations that differ in the number of SE components, curve types (e.g., lines, arcs, circles), geometric scale and proportions, and spatial relationships.\n\n## 2. Complexity of Editing Instructions\n\nWe address this concern by highlighting that our paper already presents several complex editing instructions and providing new qualitative results:\n\n- **Task Complexity.** Text-based CAD editing is inherently challenging. As shown in Fig.1, instructions cover deletion (1st case), addition (5th case), local (2nd/3rd cases) and global changes (4th case). These require understanding not only geometry (e.g., holes, corners) but also the underlying sketch-and-extrusion (SE) structure—e.g., identifying which SE operation controls the “inward curvature” in the 2nd case.\n- **Coupled Edits.** While we do not explicitly model parametric constraints, our framework can implicitly handle coupled changes. For instance, in Fig.1 (4th case), increasing the wall and overall thickness leads to coordinated changes in diameters and height of four holes. In Fig.9 (3rd case), when two holes are removed, the remaining one is automatically enlarged and centered, showing learned structural adaptation. \n- **Complex Editing.** Our current approach supports complex edits through stepwise decomposition, inspired by chain-of-thought reasoning. For example, the 3rd case in Fig.9 could be summarized as: “Remove all cutouts, round the corners, and add four cylindrical legs.” This complex goal is achieved through a series of simpler instructions. In the future, we will explore single-step approaches to handling complex editing instructions.\n- **Additional Results.** See added examples in **Figure 1 in the below link**.  \n\n## 3. Inference Speed\n\nIt is 0.69s for the locating stage and 1.31s for the infilling stage per sample, measured on a single NVIDIA A800 GPU.\n\n## 4. 3-Steps of Sketch and Extrude and Scaling Issue\n\nThis stems from **severe data imbalance** in the DeepCAD dataset. Among deduplicated 137,012 training models, over 91.1% have three or fewer SE operations, while only 6.2% have 4–5 SEs. Models with more than 5 SEs make up less than 3%, and those exceeding 10 SEs account for just 0.4%.\nTo show the scalability of our method, we conducted an experiment where we artificially balanced the dataset across SE lengths. Specifically, we constructed a training set with 4,000 samples for each SE length from 1 to 5, ensuring uniform distribution. The model was then evaluated on separate test sets for each SE length. As shown in **Table 1 in the below link**:\n\n- CAD-Editor consistently outperforms the baselines across all SE lengths.\n- Model performance declines slightly as SE length increases. Since generating longer sequences is inherently more challenging, this result demonstrates that our method generalizes well to complex CAD structures given sufficient training data.\n\nFurthermore, Fig. 2 includes examples with more than three SEs, further showcasing our model’s generalization ability.\n\n## 5. Quantized Parameters\n\nDiscretization is a common technique in geometry modeling to prevent excessively long sequences and improve training efficiency [1,2]. Moreover, DeepCAD dataset has already been quantized, and this format has been widely adopted in recent CAD research [3,4,5].\n\n## 6. Novelty of Generated Results\n\nIn **editing tasks**, novelty is not always required; the primary goal is to **faithfully follow user instructions**—even if the result resembles existing designs.\n\nConsidering your concern, we evaluate the **novelty** and **uniqueness** of generated CAD sequences using the metrics in SkexGen.\n\n- **Novelty**: The percentage of generated CAD sequence that does not appear in the training set.\n- **Unique**: The percentage of generated data that appears once in the generated set.\n\nAs shown in **Table 2 in the below link** , our model achieves slightly higher novelty and uniqueness compared to baselines, indicating diverse generation. More importantly, as shown in Table 1 of our main paper, our method significantly outperforms others in **quality**, **instruction alignment**, and **validity**—key factors in editing tasks.\n\n**Link**\n\n https://anonymous.4open.science/r/CAD-Editor-MoreResult-DBDC\n\n*References*  \n\n[1] LayoutTransformer, CVPR 2021.  \n\n[2]LLaMA-Mesh, arXiv:2411.09595, 2024.\n\n[3] SkexGen, ICML 2022\n\n[4] Hnc-CAD, ICML 2023\n\n[5] FlexCAD, ICLR 2024", "claims_and_evidence": "The two main contributions of this paper are well-supported. Dataset collection pipeline is clearly explained in section 4 and the supplementary. Table 2 results also demonstrate that locate-then-infill framework is better than directly generating the edited CAD sequence.", "methods_and_evaluation_criteria": "Finetuning LLM with LoRA and using two-stage approach kind of like CoT makes sense for this problem.  In terms of evaluation, both D-CLIP and user evaluations confirm the advantage of the proposed method.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "Line 148 “we use hnc-cad autocomplete to generate variants”. It is a bit unclear how to use hnc-cad to generate design variations. Which part of the original model is kept, and which are autocompleted? What sampling threshold is used, how diverse or related the completed results are w.r.t the original?", "relation_to_broader_scientific_literature": "CAD generation is important for manufacturing design. Been able to control generated result with editing prompts is a next step towards AI-driven content creation. This should have big impact for ML / HCI researchers working on CAD generation.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "A  concern is the lack of complexity for the editing instructions. From the figures, most of them are either 1) adding / removing a extruded solid from the original model, or 2) modifying a single or few loop parameters. Those are very simple instructions that don’t require professional understanding of how different parts of a CAD model relates to each other. Something more useful and commonly find in real-world CAD editing is when I edit one part of the model, another part will change its diameters along with it, something like a fully-constraint model would do but with the use of generative model. \n\nOverall, the paper is a good step in the right direction, but the result quality is very simple and mostly demonstrate understanding of geometry shape, which very likely come from the use of LVLMs (e.g. gpt-4o).", "other_comments_or_suggestions": "N/A", "questions_for_authors": "It will be great if authors can demonstrate more complicated editing ability using this approach. A few other questions I want to ask are\n\n1 ) What is the inference speed of the model?\n\n2) Why only constraint to 3-steps of sketch and extrude, those really limites the CAD complexity. Is there potential scaling issue with this approach?\n\n3) Why use quantized parameteres instead of float numbers (figure 2) if everything is just text tokens? Is it because the sequence length becomes too long and untrainable?\n\n4) How novel are the generated results? I know there there are overalp between train/val deepcad data, and deduplication is only done for within training set. That means if the text instruction is similiar then the model can just overfit and remember the results. This is a concern expecially when the training data is small. Some metrics or results that demonstrate the novelty will be nice to have.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "P7YujXlQbn", "reviewer": "Reviewer_PgA5", "summary": "This paper introduces a text-based CAD editing framework. The authors propose an automated data synthesis pipeline that generates triplet data with VLMs and variation models. They designed a locate-then-infill framework to perform the editing process.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## 1. Edit the Starting of a CAD Sequence\n\n1. To support edits at diverse positions, we adopt a reversible annotation strategy as described in Sec.4: for each training pair, we also include a version with edited sequence and original sequence swapped, encouraging the model to generalize across editing directions. \n2. We construct a dataset with 200 cases, each requiring modifications at the beginning of the CAD sequence. Test results show that 75.5% of the generated outputs successfully include edits at the start. We attribute this capability to the strong generalization ability of LLMs.\n\n## 2. Direct Editing vs. Text-based Editing\n\nWe agree that direct editing is simpler for scaling CAD models, which is why we did not focus on such cases (as shown in the qualitative results). Text-based editing is more beneficial when modifying sketch-extrude components and spatial relationships.\n\n## 3. LCS-based Mask Generation\n\n1. Our work focuses on **semantically meaningful and structurally coherent edits** (e.g., feature addition, deletion, or modification), which typically preserve the **relative order of most operations**. In such cases, LCS provides a practical and effective way to generate ground-truth masks. Empirically, this method performs robustly, and many real-world edits also fall into this category.\n2. We are not entirely certain about the precise meaning of \"structural reordering\" as mentioned by the reviewer. Based on our understanding, it may refer to scenarios such as:\n\n- **Swapping the order of two sketch-extrude (SE) pairs** with unchanged content. Here, LCS can still match one SE pair and identify the other as edited, resulting in a masked segment that is then infilled.\n- Or, consider a case like a *flat plate with two holes*. If the editing instruction is: *\"use two separate 2D sketches followed by extrusions to create the holes, instead of creating a single 2D sketch and performing two cut operations\"*, then the entire sequence $C\\_{edit}$ differs from $C\\_{orig}$ . In this case, our LCS-based mask generation still applies, as it correctly identifies that all tokens need to be masked and regenerated. \n\n## 4. Reconstruction Metrics\n\n1. We have included JSD scores in our evaluation, measuring the similarity between generated results and ground truth at the distribution level. This serves as a distribution-level reconstruction metric.\n2. Following your suggestion, we will incorporate sample-level metrics like Chamfer Distance (CD) in the revised version to better assess the fidelity of individual edits. As shown in **Table 3** in https://anonymous.4open.science/r/CAD-Editor-MoreResult-DBDC , CAD-Editor outperforms baselines.\n\n## 5. D-CLIP Metric\n\nWe would like to address your concerns as follows.\n\n1. In our experiments, all CAD models are rendered using a same protocol: views are automatically scaled, centered, and captured from a fixed camera angle, consistent across all methods and samples. This ensures a fair comparison under identical visual conditions.\n2. In practice, our test set contains over **10,000 diverse examples**, and we observe that aggregating results over such a large dataset helps **mitigate viewpoint-specific bias**. Importantly, all methods are evaluated under the **same rendering protocol**, ensuring that D-CLIP remains a **fair and comparable** metric. Finally, the D-CLIP scores align with our **human evaluations (H-Eval)**, which directly assess the alignment between the editing instruction and the resulting CAD model.\n3. We acknowledge that any single viewpoint may fail to capture certain edited regions due to occlusion. On the other hand, naïvely averaging D-CLIP scores across multiple random viewpoints can introduce **semantic bias**, as some views may be unrelated to the editing instruction and show no meaningful change, thereby diluting the score. Ideally, the viewpoint should be selected adaptively based on both the **editing instruction** and the **3D geometry**, so as to maximize visibility of the modified region. However, It's hard to reliably achieve this.\n\n## 6. Assess and Mitigate Errors Introduced by LVLMs in Data Synthesis Pipeline\n\n1. Assessment\n\n- In the early development stages, we relied on human evaluation to assess errors introduced by LVLMs. For example, manually reviewing 100 samples showed that stepwise captioning improved correctness from 65% (basic) to 81%. Qualitative comparisons are provided in Appendix B.\n- In the later stages, we established a test set where editing instructions were human-verified. Evaluation metrics on this set (e.g., VR, JSD, D-CLIP) indicate whether the training data generated by LVLMs contain significant errors when the model is fixed.\n\n2. Mitigation\n   LVLM-induced errors are mitigated through a stepwise captioning strategy (Sec. 4) and a selective dataset (Sec. 5.2), as demonstrated by ablation studies on the human-verified test set (Table 2).", "claims_and_evidence": "1. Automated data synthesis pipeline: It is derived based on HNC-CAD's auto completion, therefore, the differences always come from the latter part of the sequences. I am wondering if the method can handle cases where the start of the CAD sequences needs to be edited.\n\n2.Locate-then-infill, This claim seems reasonable and supported by ablation studies.", "methods_and_evaluation_criteria": "The proposed method makes sense to a certain extent where we need to add or replace some parts of the CAD modules. However, some simpler cases where users use text prompt to just scale the CAD models are not reasonable to me since direct editing could be simpler than describing in text.\n\nThe evaluation criteria are reasonable but could be improved, please see Experimental Designs Or Analyses for details.", "theoretical_claims": "For the locate-then-infill decomposition, the LCS-based mask generation assumes token-level correspondence between C_ori and C_edit, which may fail for edits involving structural reordering. The paper’s focus on simpler edits mitigates this, but it could be a problem in complex scenarios.", "experimental_designs_or_analyses": "The experiments evaluated the framework across multiple dimensions (validity, realism, edit consistency) with VR, JSD, D-CLIP. However, adding some reconstruction metrics may better illustrate the effectiveness of the method. For example, you can generate the text descriptions to some ground truth target CAD sequences and ask the model to edit the source to the target and calculate metrics like CD, EMD, COV, etc.", "relation_to_broader_scientific_literature": "This work can contribute to the industrial design community to accelerate the design process for CAD models.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "The D-CLIP metric, adapted from image editing, may not fully capture the nuances of CAD editing. How are the images rendered? what if the edited region is occluded in certain camera poses?", "other_comments_or_suggestions": "None", "questions_for_authors": "1. How do you assess and mitigate errors introduced by LVLMs in the data synthesis pipeline?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hHnVyVG3f0", "reviewer": "Reviewer_QCF8", "summary": "This paper introduces CAD-Editor, the first framework for text-based CAD editing. The authors frame the problem as a sequence-to-sequence generation task and propose a locate-then-infill approach that decomposes editing into two sub-tasks: locating regions requiring modification and infilling these regions with appropriate edits. To address the lack of training data, they develop an automated data synthesis pipeline combining design variation models with Large Vision-Language Models. Experimental results demonstrate that CAD-Editor outperforms baseline methods in validity, text-CAD alignment, and generation quality.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## 1. Ambiguous Editing Instructions\n\nWe agree that handling ambiguous editing instructions is a critical challenge. However, ambiguity in natural language is a long-standing issue in NLP research [1]. As the first work on natural language-driven CAD editing, our focus is on establishing a complete and effective framework. Comprehensive ambiguity resolution remains an avenue for future work.\nConsidering your concerns, we conduct a systematic analysis based on the phenomenon shown in Figure 8. Specifically:\n\n- (a) Dataset Construction: We used LLMs to identify ambiguous editing instructions from our dataset. We selected the top 100 instructions with the highest ambiguity scores as judged by GPT4-o.\n\n- (b) Inference Diversity: For each ambiguous instruction, we ran our model three times, resulting in 300 generated outputs in total.\n\n- (c) Human Evaluation: We asked three human annotators to assess whether each generated result aligned with the intended instruction. For each instruction, we recorded how many of the 3 outputs were judged as \"semantically consistent\". The distribution was as follows:\n\n  | Correct Outputs (out of 3) | Percentage (%) |\n  | -------------------------- | -------------- |\n  | 0                          | 5              |\n  | 1                          | 16             |\n  | 2                          | 47             |\n  | 3                          | 32             |\n\n## 2. Alignment with Human Expectations\n\nAs detailed in Sec. 6.2, we have conducted human evaluation, which is specifically designed to assess both the alignment with textual instructions and the overall visual quality of the edits. Importantly, human raters were instructed to take into account how well the output aligns with human expectations and common design standards as part of their evaluation criteria.\n\n## 3. Complex CAD Models\n\nWe address this concern by highlighting that our paper already presents several complex editing instructions and providing new qualitative results. Please refer to **Complexity of Editing Instructions** for Reviewer keVC.\n\n## 4. More Details about D-CLIP\n\nThe D-CLIP was originally proposed in the image generation domain to measure whether the semantic direction in CLIP space between two images aligns with the direction between two corresponding text prompts (e.g., “a face”(source) → “a smiling face” (target)). In our work, we adapt this idea to the CAD editing setting as follows:\n\n- We render the original and edited CAD models into images.\n- We define a neutral base text (e.g., “This is a 3D shape”) and concatenate it with the editing instruction to form the target text, mimicking the (source → target) text pair in the original D-CLIP formulation.\n- We then compute the CLIP-space direction between the image embeddings of the original and edited shapes and compare it to the direction between the text embeddings of the neutral and edited instruction texts.\n  This adaptation allows us to measure whether the visual change in the CAD model aligns with the semantic intention expressed in the editing instruction. We will update the metric description in the revised version to improve clarity.\n\n*References* \n\n[1] Promptify, UIST 2023.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "There is no theoretical claim.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "As an application paper, this work is related to the CAD field.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "**Strengths**\n- The paper addresses a novel and practical problem (text-based CAD editing) that has significant real-world applications.\n- The automated data synthesis pipeline is clever and well-designed, leveraging existing design variation models and LVLMs to generate paired data with editing instructions.\n- The locate-then-infill framework offers an intuitive decomposition of the editing task that aligns with how humans might approach CAD editing.\n-The qualitative results are impressive, showing the system's capability to perform a variety of complex editing operations based on natural language instructions.\n\n**Weaknesses**\n- The paper lacks a detailed discussion of how the system handles ambiguous or imprecise editing instructions. While Figure 8 shows some examples of diverse outcomes for vague instructions, a more systematic analysis would be valuable to show its stability and robustness.\n- The evaluation can benefit from more comparison with human-designed edits to assess how well the system aligns with human expectations and design standards.\n- The authors acknowledge limitations regarding complex CAD models, but it is important that the author should show whether this method has this potential, as least all the figures in the paper are not that interesting.", "other_comments_or_suggestions": "- Some figures (particularly Figure 3) are not aligned. \n- The explanation of evaluation metrics in Section 6.1 should be clearer, especially regarding how D-CLIP is adapted from the image domain to CAD models.\n- The paper can benefit from more explicit definitions of technical terms specific to CAD modeling for readers less familiar with the domain.", "questions_for_authors": "Please see the above content.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZwZHey8sxl", "reviewer": "Reviewer_owCQ", "summary": "This paper introduces CAD-Editor, a framework for text-based CAD editing that leverages large language models (LLMs) through a locate-then-infill strategy for identifying modification areas and executing edits. To be more specific, CAD-Editor breaks down the editing process into two steps. First, it identifies the areas that need changes by creating a masked CAD sequence. Second, it further fills in these masked sections with context-aware edits, ensuring seamless integration with the existing design. Besides, CAD-Editor also develops an automated pipeline that generates triplet data using design variation models and Large Vision-Language Models (LVLMs) to preparing training data. Experiments show CAD-Editor outperforms existing methods in both quantitative and qualitative evaluations.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## 1. Discussion on CAD Translator\n\nWe will include this work and other recent text-to-CAD efforts in the Related Work section. Since CAD Translator does not take existing CAD models as input, it cannot directly handle the text-based editing task discussed here. Additionally, its code is not publicly available, preventing experimental comparison.\n\n## 2. Data Representation and Training Paradigm Rely on FlexCAD\n\nWhile our work is built upon existing CAD representations and seq2seq training paradigms of FlexCAD, these are not the focus of our research. Our main novelty lies in defining a new task and developing a tailored framework to address its unique challenges. \n\n1. **New Task.** We define text-based CAD editing, enabling precise modifications via textual instructions—unlike FlexCAD on CAD variation.\n2. **Automated Data Synthesis Pipeline.** A key challenge is the lack of triplet data (original CAD, instruction, edited CAD). We propose a novel pipeline that combines design variation models with LVLMs, along with a stepwise captioning strategy to improve caption quality. This enables the creation of a high-quality dataset, which we release for future research.\n3. **Locate-Then-Infill Framework.** We decompose editing into two stages, with tailored solutions and ablation studies validating its effectiveness.\n   - Locating: Identifying modifiable regions via masked CAD sequences.  We tackle the lack of supervision signals using LCS-based mask generation.  \n   - Infilling: Generating edits for masked regions.  We enhance data quality via selective dataset.\n\n## 3. Control Parameters\n\n1. We have included parameter information in our dataset. Specifically, the sequence-level captioning approach more frequently captures parametric cues. As shown in Figure 3, parameter-related information such as \"reduce by 10 units\" is included, and the model is trained to associate such expressions with corresponding geometric changes.\n2. we analyzed the dataset and found that:\n   - 11.10% of instructions contain explicit numeric expressions,\n   - 12.85% include number words that represent quantities (e.g., one, two, first); see cases 4, 6, 8 in Figure 5;\n   - 31.33% feature implicit parametric cues such as \"half\", \"double\", \"left\", \"top\", \"center\", or \"end\" (see cases 1,2,4,6 in Figure 5).\n3. This indicates that the model receives parameter-related supervision during training.\n4. We add qualitative examples (**Figure 2** in https://anonymous.4open.science/r/CAD-Editor-MoreResult-DBDC ) demonstrating the model’s ability to interpret parameterized instructions, such as generating a correctly sized hole for \"Add a 44-unit diameter circular hole\" and producing a proportionally smaller cylinder for \"reduce the cylinder's height by half\". These examples will be included in the revised paper.\n\n## 4. Independent across Different Modalities\n\n1. Our experiments indicate that LVLMs do not struggle significantly more with either sequence or visual modality. We manually reviewed 100 samples each from the sequence and visual modalities, with correctness rates of 78% and 83%, respectively—indicating no significant difference in difficulty for LVLMs.\n2. We tested using both modalities together as input and observed a correctness rate of 86%, which was not significantly better than single-modality inputs.\n3. As clarified in Sec. 4 of the paper, our use of both modalities aims to enhance the diversity and coverage of editing instructions, rather than to improve per-sample accuracy. For example, visual modality better captures structural changes, while the sequence modality provides fine-grained numerical edits.\n\n## 5. Generalization Ability\n\n1. DeepCAD provides **a substantial amount of data** for training purposes (approximately 178k instances), whereas other datasets offer significantly less data (Fusion 360 [4] contains only about 8k instances).\n2. **Related works on generating CAD models are predominantly trained on the DeepCAD [1, 2, 3, 4], which is a standard in this field.** To our knowledge, no other datasets comparable to DeepCAD are available.\n3. **Studies have indicated that using DeepCAD is unlikely to result in overfitting** due to its extensive and cross-industry data [5]. According to the research that introduces DeepCAD [1] (supplementary E), the model trained on DeepCAD generalize well to Fusion 360, which is collected from different sources to DeepCAD.\n   We **evaluate CAD-Editor’s generalization by testing a model trained on DeepCAD directly on Fusion 360**. As shown in **Table 4** (in https://anonymous.4open.science/r/CAD-Editor-MoreResult-DBDC) , CAD-Editor outperforms baselines, confirming its generalization ability to datasets with different shape distributions.\n\n## 6.  3 Sketch-Extrude Pairs\n\nPlease see **3-Steps of Sketch and Extrude** to Reviewer keVC.\n\n*References* \n\n[1] DeepCAD, ICCV 2021. \n\n[2] SkexGen, ICML 2022.\n\n[3] Hnc-CAD, ICML 2023.\n\n[4] Text2CAD, Neurips 2024.\n\n[5] ABC, CVPR 2019.", "claims_and_evidence": "Yes, all claims in the submission are clear.", "methods_and_evaluation_criteria": "For the metrics in the submission, it would be better if COV (Coverage) and MMD (Minimum matching distance) are reported.", "theoretical_claims": "Yes, the reviewer has checked the correctness of all claims.", "experimental_designs_or_analyses": "Yes, the reviewer has checked them all. The experimental designs are reasonable.", "relation_to_broader_scientific_literature": "Text-based CAD editing is a branch of Text2CAD tasks, which has been rarely discussed previously and can promote the development of CAD community.", "essential_references_not_discussed": "Considering CAD-Editor is a branch of text2CAD task, it would be better to discuss more text2CAD efforts in the related work section.\n\nFor example, \"CAD Translator: An Effective Drive for Text to 3D Parametric Computer-Aided Design Generative Modeling. ACM MM 2024\" is also a recent text2cad task.", "other_strengths_and_weaknesses": "####Strengths####\n1. This paper is well-structured and easy to follow.\n2. Text-based CAD editing is an interesting application and has been rarely discussed previously.\n3. The paper proposes a new method for text-based CAD editing that utilizes a locate-then-infill strategy, allowing users to edit CADs via natural language commands.\n\n####Weaknesses####\n1. The data representation and training paradigm heavily rely on prior method [*] with minor improvements.\n2. The edited CAD model could be useless if the editing process fails to precisely control the parameters. The current approach does not encode the actual parameters, or at best, it only encodes a limited amount of parameter information.\n3. The proposed *stepwise captioning strategy* is independent across different modalities, meaning there is no interaction between information from them, which may lead to suboptimal results. Additionally, the current LVLMs struggle to accurately describe complex CAD models using only the sequence modality, which can cause errors to accumulate in the later steps. \n4. The model's generalization ability remains questionable, which means it could potentially underperform in real-world scenarios. Firstly, the generation of synthetic data relies on models like HNC-CAD, which are also trained on the DeepCAD dataset. This introduces a risk of overfitting to the same distribution. Secondly, all data containing more than three sketch-extrusion pairs were excluded, but DeepCAD itself already includes a lot of simple examples. By excluding these CAD models, the resulting training set may be biased toward simpler CAD models. Consequently, the model may exhibit weak generalization and limited reliability in real-world applications.\n\n[*] Zhang, Z., Sun, S., Wang, W., Cai, D., and Bian, J. Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models.", "other_comments_or_suggestions": "1. To enhance the generalization ability of proposed model, complex data like more sketch-extrusion pairs should be included in the training set.\n\n2. Although Text-based CAD editing has not been discussed yet, the inability to control command parameters still leaves current CAD-Editor far from practical applications.", "questions_for_authors": "1. Would authors please showcase more results by editing parameters in CAD commands?\n2. The reviewer is curious about why did authors choose to remove the long-tail data with more than 3 sketch-extrusion pairs? Could you please give the detailed clarifications?\n\nThe reviewer may adjust the original score based on authors' feedback.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes CAD-Editor, a novel framework for text-based CAD editing with a locate-then-infill design and a high-quality synthetic dataset. Among four reviewers, three gave weak accept recommendations (keVC, PgA5, owCQ), highlighting the novelty, practical relevance, and sound technical design. One reviewer (QCF8) gave a weak reject but acknowledged the contribution as a meaningful exploration for the CAD field and is not opposed to acceptance. The authors provided a thorough rebuttal addressing the concerns. The area chair recommends acceptance.
Decision: Accept (poster)