CADMorph: Geometry‑Driven Parametric CAD Editing via a Plan–Generate–Verify Loop
Authors: Weijian Ma
Shizhao Sun
Ruiyu Wang
Jiang Bian
Venue: NeurIPS 2025 poster ()
Keywords: Computer-Aided Design
Test-time scaling
Generative Models
Large-language Models
Primary area: applications
TL;DR: We propose CADMorph, an inference-time editing method for parametric CAD models using the signal of geometry changes via a Plan–Generate–Verify Loop over pretrained priors, namely a LDM and an LLM, bypassing the need of non-existent editing data.

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.

CADMorph: Geometry-Driven Parametric CAD Editing via a Plan-Generate- -eeriff Loop
Weijian Ma" Fudan University weijian. mal @gmai0..a..oooo com
Shizhao Sun Microsoft Research, Asia shizsucmi crosoft.. com
Ruiyu Wang University of Toronto rwangθcs. toronto..... edu
Jiang Bian
Microsoft Research, Asia j laaiaammioosoooooo com
Abstract
A Computer-Aided Design (CAD) model encodes an object in two coupled forms: a parametric construction sequence and its resulting visible geometric shape. During iterative design, adjustments to the geometric shape inevitably require synchronized edits the underlying parametric sequence, called 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 CADMorph, an iterative plan-generate-verif framework that orchestrates pretrained domain- specific foundation models during inference: parameter-to-siee diffusion model and a masked- masked-parameter-aaee aurameter-prediction (MPP) model. In the planning stage, cross-atte aateettttnnion 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- space, measures its distance 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.
(P2S) latent
Introduction
Computer-Aide Design (CAD) serves as the vital bridge between an initial concept and manufac- turable product. A CAD model thus exhibits representational duality, carrying two tightly coupled representations of the same object. The parametric construction sequence (left Figure aa) defines exact spatial relationships through operations (e.g., Line and Extrude) and numeric parameters (e.g.. 223 and 128), ensuring manufacturing accuracy while preserving full editability for future revision. The visible geometric shape (right of Figure Ta)) is rendered from that sequence, providing an intuitive and universally understood visual reference for inspection, simulation and validation.
During CAD development, the geometric shape is frequently adjusted- whether to satisfy simulation feedback, ergonomic requirements aesthetic goals-wal in turn demands corresponding edits to
Work done during internship at Microsoft Research, Asia. 1 Coorespponing author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Original Parametric Sequence
Target Geometry
Updated Parametric Sequence
[SO] 223
Thedert
[90U] 223 Line Z23 Line 120 tSou Grrlle
(Tanderr
Countercloowwise
128
BeuuCrrca ]62 162 11 Extrude 120
+
178 176-88 Fatrude 128 128 128 96 150 128 body
128 128 128 128 129 86 150 120
New_body bodyy undee [SEP]ECC)
One sided [SEP][SS0)
(6)
(e)
Figure 1: Given parametric construction sequence (left) and a target geometry-onk, shape (centre), CADMorph outputs an updated construction sequence (right) whose rendering matches the target while maintaining the pattern of the original construction sequence. Renderings in grey are shown only for visual reference. Edits in the sequences are highlighted. An intentionally simple example is shown here for clarity. More complex results appear in Section 4.2 and the Appendix.
the underlying parametric sequence, the authoritative source for manufacturing, We call this process geometry-driven parametric CAD editing: given an original parametric sequence and a targer geometric shape, generate an updated parametric sequence that reproduces the target geometric shape (Figure []. This task is laborious and error-prone, as engineers must assess the magnitude of shape changes, pinpoint the exact segments to modify within a complex and nested parametric sequence, and then propagate those changes through all dependent segments.
Despite its practical importance, geometry- driven parametric editing remains largely unexplored. Most prior work addresses unconditional editing (Xu et al. 2022 2023 Zhang al. 2024], which generates an edited parametric sequence from an original parametric sequence without any explicit guidance. A few studies explore text-based editing Yuann et al. 2025], which uses a textual instruction alongside the original parametric sequence to drive edits. However, expressing comprehensive shape changes through a single textual instruction is often cumbersome and unintuitive for end users.
The task of geometry-driven parametric CAD editing presents several core challenges. The first, structure preservation, demands that edits be confined to those segments of the original parametric sequence responsible for the desired shape change, leaving all other parts untouched. The second, semantic validity, requires that the updated parametric sequence not only be syntactically correct but also yield realistic CAD model that adheres to design conventions--sue bolt holes rather than placing them arbitrarily. The third, shape fidelity, mandates that the updated parametric sequence, when rendered, reproduce the target shape. In addition, data scarcity poses a fundamental obstacle, since no existing dataset combines an original parametric sequence with both a target geometric shape and its corresponding updated sequence.
as evenly distributing
To tackle these challenges, we leverage the geometric and design priors insde the domain-specifie pre- trained foundation models, and introduce CADMorph (Figure 2 an iterative Mean--ennrute-veriff framework that incrementally transforms the original parametric sequence into one that reproduces the target geometry by employing two complementary models, the parameter-to-sine the masked pall--paraaaaaeeeeeieee
model (P2S) and (MPP) model. The P2S is a latent diffusion model (LDM) (Rom- bach et al., [022) trained to map a parametric sequence into its corresponding geometric shape, while the MPP model is Large Language Model [Touvron et al. 2023 Meta Al 2024) trained to infill masked segments of the parametric sequence. Neither model relies on scarce triplet data cata-((rrginnal sequence, target geometry, updated sequence) -therbyy sidestepping the data- scarcity bottleneck.
The editing is an iterative synchronization between P2S and MPP. In each iteration, the planning stage identifies which segments in the current parametric sequence modify. Inspired by advances in text-to-image LDMss [Rombach et al.) 2022 Hertz et al. 2002a], we analyze cross-attention maps inside P2S to quantify segment-wise influence on the target geometric shape. The segments no longer contributes to target geometric shape are deemed safe to edit and replaced by a special [mask] token, This masking strategy confines edits into useless segments, thereby preserving the useful segments of the sequence and satisfying the structure struecture-preservatiio requirement. The generation stage proposes candidate edits by changing identified segments only. We utilize the MPP model to infill each [mask] with suitable edits. As the model is trained on large-scale parametric sequence data, it produces syntactically correct and semantically meaningful edits, ensuring semantic validity. The validation stage selects the candidate parametric sequence that best matches the target geometric shape. We map both the candidate parametric sequences and the target geometric shape into shared space, i.e., the latent space of the P2S model. Distances in this shared space serve an efficient proxy for geometric

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dissimilarity. The candidate of minimal distance to the target geometric shape is retained for the next iteration, driving convergence toward the target geometric shape and thereby enforeing shape fidelity.
CADMorph offers several key advantages. First, it is data-efficient. Rather than relying on labor- intensive triplet supervision, it allocates effort inference time by running the P2S and MPP models in its -lan-eeneratt-eerffy cycle. This strategy mirrors the principle of test-time scaling (Cobbe et al., 2021 Ma al. 2025], where additional computation during inference yields significantly improved results. Second, it is exploration-efficien
By leveraging P2S model in the planning stage, it reduce the search space of possible edits; by using P2S model again in the verification stage, it provides an effective signal that steers the edit toward promising direction.
Experiments demonstrate that CADMorph outperforms state-of-the-at general general-purpose purpose models (GPT-40 [OpenAl| 2024)) and powerful CAD-specific baselines [Ma et al.] 2024, /Zhang et al.)
2024 both quantitatively and qualitatively. In addition, we showcase two downstream applications- iterative geometry editing and reverse-engineering refnemeent-highigghue
CADMorph's versatility
in worldworld design workflows. Our key contributions are summarized as follows:
We formalize geometry-driven parametric CAD editing, a practical task in CAD workflow.
It takes original parametric sequence and target geometric shape as input and produces an updated parametric sequence whose rendered shape matches the target.
We leverage two complementary models, a parameter-to- shape (P2S) diffusion model and masked- parametric prediction (MPP) Transformer, sidestepping the data-scarcity bottleneck. masked-parametrri
We introduce an iterative -en-geerratte-eeriff framework that jointly exploits P2S and MPP models. We analyze P2S model's cross-attention maps to localize segments requiring edits (planning), use MPP model infill those segments with semantically valid candidate revi- sions (generation), and embed each candidate and the target shape in the P2S model's ' latent space to select the sequence whose rendering closely matches the target shape (verification).
2
Related Work
Deep Learning in CAD. Since the pioneering work of Wu al. [021], deep learning research in Computer-Aided Design (CAD) has witnessed a surge and now spans four main directions: learnin,, representation learning, generation, reverse engineering. and editing. Representation learning methods
derive compact embeddings, either unimodal [Wu et al. [22T]] or multimodal [Ma et al. 2023] to support downstream recognition tasks. Generation approaches translate free-form text into CAD
parametric sequences [Khan et al. 2024b Wang et al. 2025c Li et al., 2024)). D. Reverse-engineering pipelines reconstruct editable primitives
[022] Li et al.) 2023 Ren al.) [022] or full 2024a Ma et al.
Uy et al.
parametric sequences [Khan et al.
[2024] Lambourne et al. 2022 from raw 3D
geometry. Editing methods begin with an original parametric sequence and output a revised one, either via random sampling [Xu et al. [2022 2023 Zhang et al. [024] or under text-driven guidance (Yuan) jet al. 2025). Our focus, geometry-driven parametric editing, shares surface similarities with previous reverse engineering and editing methods, yet differs in essential ways. Reverse-engien engineering pipelines ignore the designer's ' intent embodied in the original parametric sequence, while prior editing methods disregard the rich visual cues provided by a target geometry shape. By simultancously respecting the original sequence and leveraging the target shape as guidance, our work bridges this gap and addresses problem that neither existing evvers-enggnnerriig nor editing methods fully resolve.
3D Shape Editing. Research in this area operates directly on the geometry shape, whether as mesh signed distance field (SDF) or neural radiance field (NeRF). These methods offer part-aware, besed mesh- coupled-optimization and SDF-sculpting frameworks for 3D shape
editing [Hertz et al.] 2022b Wang et al.) 2023b] Chen et al.) 2023, [Wang et al. 2023a] Hu et al.) 2024. Rubab and Tong 2025 Besides, pretrained text-to-image diffusion models have been used to guide 3D shape editing, e.g., integrating 2D inpainting or -ctreddittlllation sampling (SDS) losses to achieve coolti-view-consiie
modifications (Yu et al. 2023 Tiang et al, 2023| Zhou et al. 2023 Dihlmann et al. 2024 Poole al., 2022]. In contrast,
work treats the shape guidance and performs edits on the underlying parametric sequence, rather than directly deforming the shape itself.
Test-time Scaling with Verifiers. The core idea is to increase inference-time effort, i.e., generating multiple candidate outputs and then using a verifier to select the best one, a paradigm shown to substantially improve generative models [Cobbe et al. 2021 This strategy has been applied across domains, e.g., large language models, lasion- models, image generation, speech synthesis
and video generation [Lee et al. 2025
Wang et al. 2025b Dong et al. 2023 Wang et al. 2025a
3

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Stage 1: Planning
G Crask
Stage 2: Generation
C.nask
- [C/)
<instr.> Cmask
[SO]] Line
Line Line e [SOL] Circle Extrude [EOS]
P25 Model IDDIM Inw])
[SO] Line Bre [SOL] Gircle Extrude [EOS]
([SOL [MASK] tM Extrude [EOS]
[oUjiine
[SOL] Circle Extrude [EOS)
[SOL] Line Ard ---
U x R
(a)
C--1
ne Cpnsk
P2S Model (Rev. Process)
[EOS]
S
P2S Model IDDIM Inx]]
x
X
C/ E [c+}
Stage 3: Verification
(c.) - Ct
jer 2
gmiin
DDIM Inversion
Reverse Process
Command Proposals
Infilling
(b)
Es
Cross
Ds
Pretrained Large Language Modelss @
LoRA
[SOU] Line
Extrude [EOS]
E
Parameter-to-Shee -hape (P2S) Model
<instruction>
Masked Parameter Prediction (MPP) Model
[SOL] Line [MASK] Extrude [EOS]
Figure 2: General pipeline of CADMorph. (a) Iterative editing loop. For each round E R: (i) The planning stage selects the editing location via peeking the cross-attention map of the P2S model. (ii) The generation stage propose candidate sequences via the MPP model (a finetuned LLM). (iii) The verification stage selects the candidate sequence best matches the target shape for round r. The parameters of each primitive in are omitted for brevity. (b) Architecture of P2S and MPP model.
Xie et al. 2025 Zhang al. 2025 Chen et al. 2025 Li et al. 2025a Ye et al. 2025 Peng al. 2025 Cong et al. 2025 Li et al. 2025b]. In our approach, the generation stage invokes the MPP model multiple times generate diverse candidate parametric sequences, and the verification stages examine their embeddings in latent space of the P2S model to select the sequence that best matches the target geometry shape. This procedure mirrors the principle of test-time scaling with verifiers, and is the first application of this paradigm to CAD related tasks.
3 Method
We first formalize the task of geometry-drivee parametric CAD editing (Section 3.1 Then, we present an overview for CADMorph, our iterative plan-generrae--verfyy framework that leverages the interplay between two complementary pretrained foundation models CAD domains: the parameter- to-shape (P2S) model and the introduce the architecture of the P2S and MPP model (Section 3.3 of the plan-generaae--errin pipeline (Section 3.4)
prredicttr--reiictioon (MPP) model (Section 3.2) Next, we
Finally, we delve into each stage
3.1 Task Formulation
For given CAD model, let C denote its parametric construction sequence and S its visual geometric shape. Given an original parametric sequence C' and target geometric shape S', geometry-driven parametric CAD editing seeks an updated parametric sequence C' that reproduces S' (Figure I). Although many parametric sequences may reproduce S", the preferred solution is the one that preserves the structure of C rather than replacing it with an entirely different sequence. Thus, the overall goal is:
C' = arg min D geoomerry (F(C', + Rettrucuee (C',C).
Here, J(C") denotes the rendered geometric shape of the sequence C', either via CAD kernels or neural networks. Dgeometry (...) measures the discrepancy between two geometric shapes, and Rettnsture (:: measures the structure similarity between two parametric sequences. an illustrative hyperparameter.

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Taken-wise Map Projected Shape Meshes
P2S Model Inv)
[SOL]
Line
Extrude
[sou]
Circle
Extrude
[EOS]
S*
P2S Model (DDIM Inc.)
Figure 3: Visualization of P2S model's cross-attention (CA) maps. For each segment in the parametric sequence C² we project its CA score on the derived mesh of its corresponding shape - (top row) and the target shape 5' (bottom row). Bright regions highlight the geometry that is strongly associated with the corresponding sequence segment, indicating that part of the shape more attracted to the sequence segment that describes it.
3.2 Approach Overview
CADMorph orchestrates two complementary pretrained foundation models (Figure[[(b), parameter- to-shape (P2S) model and masked-parreee parameterr-rediition (MPP) model. The P2S model is a latent diffusion model trained on (parametric sequence, shape rendering) pairs, which translates parametric sequence to geometric shape. The MPP model is Large Language Model (LLM) trained on massive construction sequences, which infills the masked region of plausible parametric sequences. Neither model requires scarce (original sequence, target shape, updated sequence) triplets for training.
Built these models, CADMorph incrementally transforms the original parametric sequence C into the sequence C" that reproduces the target geometry 5' by iterating over three stages: planning, generation and verification (Figure ((a)). At the r-th iteration:
Planning. Starting from the parametric sequence from the last iteration C² (C₁ = C), we locate the segments requiring edits by analyzing the P2S model's cross-attention maps. Those segments are replaced with the special token [mask], yielding the masked parametric sequence Crmask
2. Generation. The MPP model infills the [mask] tokens N times, producing set of candidate sequences C,,..., CN that propose semantically plausible edits.
3. Verification. Each candidate is projected into the P2S model's latent space, and the one closest to the target geometric shape 5' is chosen as C,, This sequence seeds the next iteration.
The loop terminates when C converges or a maximum number of iterations is reached, yiclding the final parametric sequence C that reproduces the target shape S", For the details of each stage, please refer to Section
3.3 Model Architecture
CADMorph involves two foundation models pretrained on existing data in CAD domain. These models are originally designed for other tasks and are jointly used in plan-generaae-verrffy framework to achieve the geometry-driven parametric CAD editing task.
Parameter-to-Shape (P2S) Model (left of Figure b)). We represent each shape voxelized truncated signed distance field (tSDF). The P2S model transforms parametric sequences into 3D shape latents, and further decodes it into SDF. It follows SDFusion's 's architecture [Cheng et al. 2023 and is re-trained on (parametric sequences, SDF) pairs of CAD data. It comprises two components: (1) shape encoder-decoder pair (E, and D.) that embeds SDFs into latent space and reconstructs them, and (2) a diffusion model that maps parametric sequence into 3D shape latents.
(MPP) Model (right of Figure b)). It infills the masked segments of a parametric sequence. It adopts the the architecture of FlexCAD (Zhang et al., 2024] i.e., SoRA-finctunee Llama-3 [Meta Al 2024 8B model with hierarchical masking strategies.

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Input
Target
Shape
GPT-4o
04-mini
04-mini
-high
CAD-
Diffuser
FlexCAD
Ours
(1)
(2)
(3)
(4)
(5)
(6)
(8)
(9)
(10)
(11)
Figure 4: Qualitative results. Top row: rendering of original parametric sequence (shown instead of tokens for clarity). Second row: target shape. Other rows: renderings of the edited sequences from each method; empty cells indicate that no valid shape could be rendered. Better view zoomed in.
3.4 The Tenn-Gennratt--eeiinn Framework
Planning. It receives the previous parametric sequence Ch-1 and returns masked parametric sequence Cfmask where each special [mask] token marks segment requiring edits. A naive strategy would mask segments at random, but this wastes computation on portions of CY that already match the target shape S". Therefore, we focus on segments in C² that mismatch S".
To locate those segments, need segment-level metric that estimates each segment's contribution to the target shape. Recent work on text-to-image diffusion [Rombach et al.) [022] shows that cross-attention maps expose the correspondence between words and the pixels they describe. We observe an analogous effect in our P2S model: the cross-attention maps indicate which parametric sequence segments responsible for which geometric parts. As Figure illustrates, when the shape-sequence pair (S,,, C,) comes from the same CAD model, attention peaks align tightly-e, the "Line" segment C,- attends the line in S,. Conversely, when (S', C,) comes from different models, segments irrelevant to S" (such as "Arc") receive low attention scores, while relevant ones (e.g.. "Line", "Circle") still correlate well.
Motivated by this insight, we take the cross-attention score between the i-th segment of C/ and the latent representation of S' as contribution score, denoted as MMCC S*). As absolute magnitudes of M vary across segments (e.g., [SOL] tends to receive a lower score compared to other segments in Figure 3) convert this value into scale-invariant relative score:
J(i) (M(C (O), S.-)).
(i)) measures how much each segment's 's influence changes between S and St. At each iteration, we rank segments by J(i) and mask the K largest ones (in practice, those above the mean J) to obtain C.mak This strategy concentrates computational effort on the segments most responsible for the discrepancy with the target shape S', yielding more efficient and accurate edits.
Generation. Starting from the masked parametric sequence Cmask from the planning stage, the generation stage calls the MPP model times to produce set of candidate parametric sequence Ctl ,....., C,N. For each candidate CR, the MPP model infills the masked segments autoregressively.

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Table 1: Quantitative results. ToU, mean CD, and median CD quantify how closely the shape rendered from the edited sequence matches the target geometry. Edit Dist. measures how much the edited sequence diverges from the original sequence. IR is the percentage of edited sequences that cannot be rendered into valid shape, and JSD the distributional gap between the generated and target shapes. Human Eval. reports the average rank assigned by human annotators.
Method
loUt
mean CD
median CD
JSDJ
IR (% )
Edit Dist.
Human Eval.
GPT-4o 0.247
0,107
0.0171
0.737
25.1
21.12
4.57
o4-mini
0,185
0.118
0.0283
0.748
32.95
22.49
5,40
o4-mini-higli
0.193
0.100
0,0200
0.745
40.5
25.27
5.37
CAD-Diffuser
0.548
0.097
0,0093
0.689
5.7
17.29
1.94
FlexCAD
0.447
0.029
0.0065
0.634
15.3
22.29
2.35
Ours
0.687
0.009
0.0031
0.621
3.1
16.87
1.37
drawing the CAD knowledge it gained through pre-trate pre-training and task-specific fine-tuning:
T
P(C+
Cmuis))
II
P(C
O C.
t=1
where C.P is the token autoregressively generated at step t and CR. denotes the partial sequence generated so far.
Verification. It receives candidate parametric sequences C,...., C.N produced in the generation stage, and selects the one whose corresponding shape latent representation lies closest that of the target shape S'. As the P2S model builds a bridge between parametric sequences and the latent space of geometric shapes, we diffuse every candidate sequence and encode the target shape into the shape latent space and measure their Euclidean distance. For candidate sequence C, we obtain its latent vector through a reverse process of the P2S model, denoted as F(C,), ). The target shape S' is embedded by the P2S model's s shape encoder E.,, denoted as (S').. We then choose:
C = arg min [[((C) - (S())l22 ČEQ
where Q is a priority queue that retains the X best candidate sequences seen up to iteration r. Maintaining this cross-iteration priority queue enlarges the search horizon: it rescues high-quality candidates generated earlier rounds and attenuates the impact of occasional noisy candidates, leading to reliable convergence toward the target shape.
4
Experiment
4.1 Experimental Setup
Datasets. We train both the P2S and MPP model on the DeepCAD corpus [Wu ct al. 202 which contains about 130k CAD models after removing non-renderable shapes. We keep the official train/validation/test splits. Parametric construction sequences follow the format of al. [2024] voxelised SDFs are obtained with PythonOCC [Paviot and Contributors 2023]. Trimesh Dawson Haggerty and Contributors 2025 . and meshtosdf [Kleineberg] 2025). For evaluation, we adopt the 2k test set from CAD-Editor Yuan et al. 2025). Each data provides an "edited" parametric sequence whose rendered voxelized SDFs serves as the target shape in our task, while the the accompanying textual instructions are discarded. Following common practice (Yuan et al. [2025] Wang et al. 2025c Cheng al. 2023], we generate 5 outputs for each test case for fair comparison and utilize truncated SDFs with the distance range in [-0.2, 0.2).
Implementation Details. The MPP model is finetuned from Llama3-8b- In-trruuttt [Mea Al 2024 with LoRA Hu et al. 2022] rank of 32 under the batch of 16 for 60 epochs on 8 A100-40GB SXM GPUs. The initial learning rate is set to 5e 4 with maximal token length of 1024. The P2S model is trained on the same hardware with total batch size of 8 and an initial learning rate of 5e 5 for 600k steps. The maximum iteration of the nan-gennraaiio--verffy framework is set 10.
Metries. As the first work for geometry-driven parametric editing, we introduce evaluation metrics that assess the task from the following perspectives. 1) Target shape adherence. The shape rendered
7

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INPUT (parametric sequence only)
TARGET SHAPE
OUTPUT (parametric sequence only)
[8m]] Girole 176 128 [901] Circle 188
Circle
47 [SOL]
116
Theders
Extuud 128 128 128
New body Two_ Two_sided[SER [SEP]
128
+
150 New, body Two, sideddd [SEP] [SOL] Cirole
30 Exrude
129
151178
[Sai]Cirele 198 Eetrade 128 128
176 128 48 Extrude 128 128 129 119 124 126 226 130 New, body Two, aidka [SEP]
128 111 127 126 226 130 New body Two sicee (EOS]
Ours
[SOL] 136 49 Line 223 114 Line 223 128 Line 128 128 [SOL] Circlo 149 1105 [SOL] Circle 128
[SOL] 158 49 Line 114 Line 128
Une 128 [SOL]
110 8(SOL]
(SOL)
Glide
(SOL] Circle
Extruble
168
128 128 128 84 161 128 103 188 1:00
118 New body Twodd [SEP]
New body Two, sided [SEP] [SOL] Line 152 128 Line 158 223 Line 128 128 Fatrude 102 128 182
[SOL]Line 153 128 Line 158 228 Line 128 128 Edtrude 128 126 128 147 82 198 102
126 52
bodyy Symmetric
198 130 New body Two, sided [EOS])
[
[E0S]
[SOL] Cirole 176 128 48 Estrusie 128 128
158 Line 223 1in 223 Line [904]
CAD-
128 113 124 128 224
Circle 110 Extuda
128 128 103
Diffuser
48 New bodly sided (SPP)
130 New body Two_sided (so_sidddSEEP|SSO [SPP Line 158 128 Line
153 223 Line 128 128 Edtrude 192 126 192 126 112 132
(1)
(i))
130 128 New body Symmetric (FOS)
Figure 5: Comparison of edited parametric sequences. Our method is compared with the strongest baseline, CAD-Diffuser. Yellow highlights mark segments where our method preserves the original sequence with only minimal changes, whereas CAD-Diffuser introduces larger modifications.
[SOL]
(SFP)) (904 CRRie 176 198 dn Fxmuate
Extrude 128 /Aaaire
[SCL]
164
160 17 43 128 Oue
120 125 100 123
145 138 kiww botty One sided (SPP) (605)
18 108 129 17
Ondy sided [SP))
One siced (SEP) [00
body
(SEP) (609)
(80L) 222 128 Line 321 770 Line
c.... [SEP] [SOL] Unve 223 128 Line 225 223 Line 228 Uie 128 Exiuude
[SOL] L 222 129 Line 229 191 Line 122 191 Line 128 125
Extru de 128 128 128 52 94 128 192 144 129 Now, body
131 New body
One sided (SFP) (FOS))
120 Extrude 125 125
128 122 128 126 101
120 178 Line 126
265 255 19 185 126 6 146 128
New body One siddd [SEP] [BEP] [SOL] Linc 228
Exrudo 255 125 219
Join sided [SEP]
223
One sided [SEP) [SOL] ..... Exi udo 128 128 128 92 176
[SOL) ..... Extrude
125
144 16 175 128 loin Ono sided (SEP) [SOL] Extrud
aided [SEP] .....
to
asded (SEI)
Figure 6: Iterative editing capability of CADMorph. Yellow highlights indicate the modified segments, while "......" denotes parts left unchanged from the previous parametric sequence.
from the updated parametric sequence should closely reproduce the target shape. We adopt Chamfer Distance (CD) [Wu et al.) [021] and Intersection over Union (IOU) MM et al.) 2024] to quantify the surface and volume consistency between the result shape and the target shape. 2) Source sequence consistency. The updated parametric sequence should be loyal to the original sequence, instead of generating a completely different sequence. We measure this with the Edit Distance (Edit Dist.)
[Ma et al. 2024] between the result sequence and the original parametric sequence. 3) Generation Fidelity. These metrics reflect the overall quality of the outputs. We report the Invalid Rate (IR) [Wu et al. 2021], which is the fraction of updated parametric sequences that fail render into valid shape, and Jensen-Shannon Divergence (JSD) [Wu et al. 2021] between the distribution of generated outcomes and the ground-truth with respect to rendered point clouds.
4.2 Comparison with Existing Methods
Baseline Methods. We benchmark CADMorph against three families of baselines. 1) Vision- language models (VLMs) their reasoning variants. We test GPT-40-241210, 04-mini-250421.
04-mini-high-2500 2554-2 Following standard practice [Khan et al. 2024bb Wu et al. 2024]. each model provided with a explanation of the CAD construction sequence grammar and multi-view renderings of the target shape. 2) Reverse engineering methods. We adopt CAD-Diffuser [a et al.) 2024]. which is a reproducible state-of-the- art method that reconstructs parametric sequence directly from geometry. 3) Classical editing methods. We adopt FlexCAD [Zhang et al., 2024], which edits a given parametric sequence without explicit guidance. Reverse-engineering methods ignore the user intent embedded in the original sequence, while classical editing methods disregard the visual cues in the target geometry; neither is designed for geometry-driven parametric editing. We include them to highlight the difficulty of the task and demonstrate the advantage of our method.
8

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Table 2: Quantitative results for ablation studies.
No. Method
ToUt
mean CD2
JSD
IR (%)
Edit Dist. +
A. Ours
0,687
0.009
0.621
3.1
16.87
B. Candidate queue Q of verification stage
0,619
0.010
0.635
3.3
16,93
C. -Verification stage
0,517
0.023
0,633
10.7
18.42
D.
Planning stage
0.447
0.029
0.634
15.3
22.29
Target Geometry
Reverse engineered Parametric Sequence
Updated Parametric Sequence
[SOL] J Line 128 159
223 Risberrd
[iol] 138 128 Line 1E9 86 Line 223 B6 Line 223 Line 106 Line 148 Line
Reverse Engineer
88 line 225 10s Line 181 106 Line 1&1
148 Line 128 148 Line 128 128 Extrci
CAD Morph
Line 128 128 Extrude
182 32
192 B4 182 126 184 145 184 128
245 New body Cnn__idee _ided
body
(SFP) [FOS]
[SEP] [5OS]
Figure Use CADMorph after conventional reverse engineering to further improve shape fidelity.
Quantitative Results. Table I shows results. Our method achieves better performance all metrics. VLM (GPT-4o) struggles to output syntactically valid sequences (high IR) and consequenty produces shapes with poor quality (low IoU, high CD and JSD). Adding visual-reasoning enhancements (o4- mini and 04-mini-high) does not yield gains, underscoring the gap between generic VLM capabilities and the demands of geometry- Diffuser and FlexCAD outperform the VLMs, yet both still fall short of simultaneously preserving the structure of the original construction sequence and reproducing the target shape.
parametric editing. Traditional pipelines perform better: CAD-
Qualitative Results. Figure shows results. Our method consistently yields shapes that most closely reproduce the target shape. GPT-4o and its reasoning variants frequently generate invalid sequences, and their valid shape renderings diverge from the target shape. FlexCAD almost always produces syntactically valid sequence, but it lacks visual guidance, its results rarely resemble the target shape. CAD-Diffuser performs better than GPT-4o and FlexCAD, yet it still falls short of our method.
Figure 5 compares the edited parametric sequences from our method and the strongest baseline, CAD-Diffuser. Whereas our approach strives for the smallest possible edits needed to reproduce the target geometry, CAD-Diffuser often rewrites larger portions of the sequence. In example (i), our method reuses an existing cylinder and simply adjusts its parameters; CAD-Diffuser collapses the two original cylinders into single one. In example (ii), method preserves the circular hole from the original sequence, while CAD-Diffuser proposes an adjacent arc. This difference is crucial in practice: drilling hole and cutting inward are involve distinet manufacturing processes. By retaining the hole rather than proposing an arc, our edits leave the downstream manufacturing workflow intact.
Human Evaluation. 5 human annotators ranked 200 outputs from each method, jointly evaluating (1) how closely the rendered shape matches the target and (2) how well the edited sequence preserves the original structure. As shown in Table I. our method receives the highest preference scores.
4.3 Downstream Applications
Iterative Editing Capability. Figure/Jillustinn illustrrates CADMorph's iterative editing workflow. In the first round, the user provides an original parametric sequence and a target shape. CADMorph returns an updated sequence that reproduces the target shape. In the subsequent rounds, the output from the previous round is treated the new "original" sequence. Given a new target shape, CADMorph applies further edits to produce a sequence that meets the new target shape. This loop enables successive refinements while preserving the edit history at each round.
noteworthy pattern appears in the bottom example of Figure fifth example of Figure when the given shape is inaccurate-such as legs are not fully flush with the panel-CADMM CADMorph silently correct them. We hypothesize that this behavior originates from the MPP model, which absorbed extensive real-world and design knowledge during pre-training and CAD-specific fine-tuning.
Refining Reverse Engineering Results. As illustrated in Figure conventional engineringinn pipeline first reconstructs parametric sequence from the target geometry. CADMorph then takes

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this preliminary sequence together with the target shape and outputs refined sequence that more faithfully reproduces the design-for for example, by extending the extrusion height.
4.4 Ablation Studies
Table shows
shows ablation studies. 1) Remove the verifier's 's priority queue (variant B). Without the queue, the verifier can only pick the best candidate from the current round rather than from all previous rounds, causing a large IoU decline. This confirms that the priority queue is helpful for retaining high-quality candidates across iterations. 2) Remove verification stage (variant C). With no feedback on shape similarity, this variant chooses the candidate at random. 3) Remove planning stage (variant D). Segments requiring edits are now chosen at random. Variants C and D leads broad drop in performance, highlighting the value of our plan-generate-verify framework.
5
Conclusion
We tackle geometry parametric editing: given an original CAD sequence and a target shape, produce an updated sequence that reproduces that shape. To solve this, we introduce CADMorph, an iterative seun-geeaatt-eerffy framework that couples two complementary components Parameter- to-Shape (P2S) diffusion network and an LL.M-based Masked Parameter Prediction (MPP) model. Experiments demonstrate that CADMorph surpasses all baselines, and we showcase practical appli- cations, i.e., iterative editing and refining reverse-engineet
results. In the future, We will pursue stronger P2S and MPP backbones boost CADMorph's performance. Besides, we plan deploy CADMorph as a data-generation engine to synthesize triplets for training a fully end-to-end model.
Limitations. 1) Inference latency. Like other test-time-scaling methods that employ verifiers, CADMorph must execute several llan-geeerate-eerin iterations, incurring noticeable runtime. Future work could reduce this overhead by accelerating the component models and parallelizing generation and verification. 2) Test set. Our experiments rely on the CAD-Editor's s test set (Yuan et al., 2025). the only publicly available benchmark suited to CADMorph. However, its CAD models simpler than real-world assemblies, highlighting the need for a richer and more challenging dataset.
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Reviews:
[{"review_id": "0owzlgi7ME", "reviewer": "Reviewer_t3cp", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "After reading the rebuttal and carefully considering the opinions of other reviewers, I remain positive about this paper. I believe the authors' response has adequately addressed my concerns. I recommend a **Boardline Accept**.", "rebuttal": "We sincerely thank the reviewer for the encouraging and insightful feedback. We are delighted that youour contribution are recognized: (i) the practical importance and relative under exploration of geometry-driven parametric CAD editing, (ii) our use of cross attention maps from the P2S diffusion model as an intuitive, geometry-aware guide for pinpointing edit regions, and (iii) our strategy of leveraging domain-specific foundation models to sidestep the scarcity of triplet supervision and still achieve strong performance. \n\nThe weakness proposed by the reviewer mainly fall into the following categories. Here we will address them in detail.\n\n1.\tDataset limitations. As noted in the limitation section, the ML/AI community currently lacks industry-level complex CAD models in the form of CSG. We hope this limitation will not lead to a negative impression of our submission. In the following, we try our best to show the potential and generalizability of our approach.\n\n    a.\tWe selected some complicated CAD models in current dataset, namely DeepCAD and performed additional experiments. Due to the restriction of including images during the rebuttal phase, the results are presented in the form of construction sequences at the end of the response to Reviewer qRBB. These examples demonstrate that CADMorph successfully: 1) added numerous holes to a corner-like board, 2) transformed a torus into a rudder-like object, and 3) added a stack of evenly distributed fins onto a rectangle motherboard. These examples suggest promising scaling ability of CADMorph to more complex CAD models.\n\n    b.\tTo address the challenge posed by the lack of complex CAD models, we discussed a potential solution at the end of part B in the supplementary material. The main idea is to decompose complex CAD models into sketch-extrusion pairs, use CADMorph on them one by one, and finally assemble them via Boolean operations of SDFs. We plan to invest further effort in making this proposal practical and effective in future work.\n\n    c.\tPreviously accepted work like CAD-Editor (arxiv:2502.03997), FlexCAD (arxiv:2411.05823), CAD-Llama (arxiv:2505.04481), CADFusion (arxiv:2501.19054) and Text2CAD (arxiv:2409.17106)  were also conducted on the same datasets and have shown significant impact in both research and industry. In this sense, we believe that our experiments conducted on this dataset provides a sufficient starting point for unlocking further capabilities of domain-specific foundation models in the CAD domain.\n\n2.\tRunning time. We have included a running time analysis in part E and Table 1 in the supplemental material. The running time of CADMorph is comparable to or shorter than prevailing 3D generation and editing methods like Magic3D (arxiv:2211.10440), Zero-1-to-3 (arxiv:2303.11328), DreamFusion (arxiv:2209.14988), GaussianEditor (arxiv:2311.14521), and GaussCtrl (arxiv:2403.08733), which typically takes tens of minutes or even hours. Moreover, CADMorph’s running time is on par with test-time reasoning models such as ChatGPT o3 and o4. These comparisons indicate that the running time of CADMorph is within an acceptable range.\n\n3.\tAbout additional ablation studies.\n\n    a.\tFor the number of candidates, we ran an additional ablation study about different numbers of candidates proposed at each iteration. The result is shown in the table below. From the result we can observe that the candidate number only slightly affects the result of CADMorph. This is because is a cross-iteration priority queue is maintained, which always retains the $X$ best candidates seen up to iteration $r$ (see line 214-217). As a result, CADMorph is less sensitive to the specific choice of the number of candidates.\n\n    |Candidate Number  |IoU($\\uparrow$)  |Mean Chamfer Distance($\\downarrow$)  |JSD($\\downarrow$)   |Invalid Rate($\\downarrow$) |Edit Distance($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |1  |0.658  |0.012   |0.629   |3.5%  |17.10   |\n    |2  |0.688  |0.009   |0.632   |3.2%  |16.93   |\n    |5 (Our Original Setting) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n    |10 |0.691  |0.013   |0.623   |3.3%  |17.02   |\n\n    b.\tFor the masking strategy, in current experiment, we mask the parts of the sequence where the discrepancy of latents $J$ exceeds the average $\\bar{J}$ across the sequence (see line 199). We add two ablation studies: 1) masking the top 10% tokens with the highest latent discrepancies $J$; 2) applying purely random masking without using the proposed $J$. The result is listed in the following table. From the table we can observe a performance drop from both ablations. Specifically, we hypothesize that the 10% masking settings hinders performance because it causes the editing process to stagnate, preventing effective guidance towards resolving large geometric differences.\n\n    |Masking Strategy  |IoU ($\\uparrow$)  |Mean Chamfer Distance ($\\downarrow$)  |JSD ($\\downarrow$)   |Invalid Rate ($\\downarrow$) |Edit Distance ($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |Purely random masking 50% of primitives without P2S guidance |0.497  |0.027   |0.633   |14.7%  |22.36   |\n    |Masking top 10% w.r.t. latent discrepancy  |0.643  |0.014   |0.635   |4.8%  |19.47   |\n    |Masking above avg latent discrepancy (Our Strategy) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n\n    c.\tFor the number of iterations. We set the maximum number of iterations as 10 throughout the current experiments. We also conduct an ablation study where the maximum number of iterations ranges between 1, 2, 5 and 10. From the table we can observe that the performance growth saturates when the iterations becomes large, which is in line with the qualitative results in Part G and Figure 4 in the supplementary material where simple modifications can be achieved at the first several iterations.\n\n    |Num. of Iterations  |IoU($\\uparrow$)  |Mean Chamfer Distance($\\downarrow$)  |JSD($\\downarrow$)   |Invalid Rate($\\downarrow$) |Edit Distance($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |1  |0.512  |0.024   |0.629   |10.8%  |19.48   |\n    |2  |0.634  |0.015   |0.627   |6.8%  |17.29   |\n    |5  |0.665  |0.011   |0.626   |4.7%  |17.31   |\n    |10 (Our Original Setting) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n\nBelow is the discussion about the reviewer’s questions.\n\n1.\tAbout the timing of CADMorph. We have included a running time analysis in part E and Table 1 in the supplemental material. Besides, we provide qualitative examples about iterations needed for convergence in Part G and Figure 4 in the supplementary material, where simple edit converges at the early iterations (top example) while complex edit take more iterations to converge (bottom example).\n\n2.\tHonestly, we do not test CADMorph on complex industrial CAD models, because we currently do not have access to such data containing hundreds of CSG operations. Specifically, while DeepCAD includes up to 60 operations and Fusion360 and WHUCAD have at most 150 operations, the quantity of such complex CAD models is too scarce to form editing triplets). We think the main bottleneck lies in the lack of large-scale, high-quality datasets featuring complex CAD structures. Without exposure to such data, the MPP and P2S modules are unable to generate CAD models involving hundreds of operations. In part B of the supplementary, we discussed a potential alternatives: synthesizing complex CAD models for training by using the Boolean operations of SDFs to combine different sketch-extrusion pairs.\n\n3.\tIn part B of the supplementary material, we explain the reason for choosing SDF as the first exploration and possibilities for other 3D formats. Below is a brief summary. For meshes, the graph-based representation is nuanced and the correspondence between the primitives and vertex / edge does not directly correspond to spatial occupation. For implicit representation like NeRF / Gaussian, besides the deficits mentioned in meshes, the generation time for each 3D asset is also longer than voxelized SDFs. Currently, the solution for using other 3D representations is to transform them into SDFs by leveraging other 3D models and marching cubes. We leave the innate and native integration of meshes / NeRF / Gaussians for future work.\n\n4.\tFor joint training, we did not experiment with its effect due to the scarcity of editing triplets for training data, which is the motivation of CADMorph. We explored other modality interaction method. While it is not closely related to combing P2S and MPP models, we would like to share it for your reference. Specifically, we tried using natural language as the medium. The difference between the source shape (rendered from the source construction sequence) and the target shape is first summarized into natural language, and the modification is then performed using the natural language as the guidance. We made a quick PoC via the API of GPT-4o. From the experiment we can observe little difference between this mechanism and naïve GPT-4o, thus calling for the need of domain-specific foundation models and  the designed interaction strategies between the foundation models so as to trigger the abilities that they were not trained for.\n|Method  |IoU($\\uparrow$)  |Mean Chamfer Distance($\\downarrow$)  |JSD($\\downarrow$)   |Invalid Rate($\\downarrow$) |Edit Distance($\\downarrow$)   |\n|--|--|--|--|--|--|\n|GPT-4o  |0.247  |0.107   |0.737   |25.1%  |21.12   |\n|Using Language Description as Intermediary  |0.177  |0.083   |0.726   |29.0%  |19.50   |\n|Ours |0.687  |0.009   |0.621   |3.1%  |16.87   |\n\nOnce again, thank you for your encouraging comments, insightful feedback, as well as thought-provoking discussion."}, {"review_id": "723xLXp6sS", "reviewer": "Reviewer_ZSgx", "summary": "The 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.", "questions": "I 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.", "limitations": "Yes.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "I 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).", "was_revised": true, "final_justification": "This paper is one of the first to address the task of geometry-driven parametric CAD editing. The proposed method is shown to be effective than using LLMs (GPT 4o, etc.). The additional ablation study looks reasonable.\n\nThere is a concern from reviewer qRBB, \"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.\". However, I do not find this to be much an issue due to the recent advances in X-to-3D models, as pointed out in the rebuttal by the authors.\n\nI suggest the authors to include these extra results in the final revision.", "rebuttal": "Thank you for your professional and insightful review—your comments pinpoint exactly the challenges that motivated our design of CADMorph. We appreciate your recognition that our framework is the first to address the task of geometry-driven parametric CAD editing while eliminating the reliance on scarce editing triplets.\n\nBelow is our reply to the weaknesses and questions you’ve mentioned.\n\n1.\tAbout the _robustness_ of our method. Our framework involves several hyperparameters, e.g., number of candidates, masking proportion and number of iterations We conduct additional ablation studies and find that our framework performs robustly across a wide range of those hyperparameters. Specifically, the number of candidates has a minor impact on performance. For the masking proportion and the number of iterations, once they exceed a certain threshold, further increases do not lead to significant performance differences. Below is the detailed explanation of our newly added ablation.\n\n    a)\t For the number of candidates, we ran an additional ablation study about different numbers of candidates proposed at each iteration. The result is shown in the table below. From the result we can observe that the candidate number only slightly affects the result of CADMorph. This is because a cross-iteration priority queue is maintained, which always retains the $X$ best candidates seen up to iteration $r$ (see line 214-217). As a result, CADMorph is less sensitive to the specific choice of the number of candidates at each iteration.\n\n    |Candidate Number  |IoU($\\uparrow$)  |Mean Chamfer Distance($\\downarrow$)  |JSD($\\downarrow$)   |Invalid Rate($\\downarrow$) |Edit Distance($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |1  |0.658  |0.012   |0.629   |3.5%  |17.10   |\n    |2  |0.688  |0.009   |0.632   |3.2%  |16.93   |\n    |5 (Our Original Setting) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n    |10 |0.691  |0.013   |0.623   |3.3%  |17.02   |\n\n    b)\tFor the _masking proportion_, we explore three settings: 1) applying purely random masking without using the proposed latent discrepancy $J$; 2) masking the top 10% tokens with the highest latent discrepancy $J$; 3) masking tokens where the latent discrepancy $J$ exceeds the sequence average $\\bar{J}$ (our original setting in the paper). The result is listed in the following table. Although we did not have enough time to investigate more fine-grained settings, the current results still offer valuable insights. First, both the top-10% and above-average masking strategies outperform purely random masking, suggesting that the framework is effective under different masking proportions.Second, the top-10% strategy performs worse than the above-average strategy. We hypothesize this is because masking only the most discrepant tokens may overly constrain the editing process, making it harder to adapt to large geometric differences.\n\n    |Masking Strategy  |IoU ($\\uparrow$)  |Mean Chamfer Distance ($\\downarrow$)  |JSD ($\\downarrow$)   |Invalid Rate ($\\downarrow$) |Edit Distance ($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |Purely random masking 50% of primitives without P2S guidance  |0.497  |0.027   |0.633   |14.7%  |22.36   |\n    |Masking top 10% w.r.t. latent discrepancy  |0.643  |0.014   |0.635   |4.8%  |19.47   |\n    |Masking above avg latent discrepancy (Our Strategy) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n\n    c)\tFor the number of iterations. We set the maximum number of iterations as 10 throughout our experiments. To evaluate its impact, we conduct an ablation study with the maximum number of iterations set to 1, 2, 5 and 10. From the table we can observe that the performance growth tends to saturate as the iterations increases. Additionally, qualitative results are presented in Part G and Figure 4 in the supplementary material. Specifically, simpler editing cases (top example in Fig 4) typically converge in fewer iterations, while more complex editing cases (bottom example) require more iterations.\n\n    |Num. of Iterations  |IoU($\\uparrow$)  |Mean Chamfer Distance($\\downarrow$)  |JSD($\\downarrow$)   |Invalid Rate($\\downarrow$) |Edit Distance($\\downarrow$)   |\n    |--|--|--|--|--|--|\n    |1  |0.512  |0.024   |0.629   |10.8%  |19.48   |\n    |2  |0.634  |0.015   |0.627   |6.8%  |17.29   |\n    |5  |0.665  |0.011   |0.626   |4.7%  |17.31   |\n    |10 (Our Original Setting) |0.687  |0.009   |0.621   |3.1%  |16.87   |\n\n2. About complicated objects or objects with intricate details. We discussed a potential solution at the end of part B in the supplementary material. The main idea leverages the extensibility and Boolean algebra support of SDFs, allowing us to decompose a complex CAD model into a set of sketch-extrusion pairs. Each pair maintains a manageable level of complexity and scale, making it easier for cross-attention mechanisms and latent representations to capture the relevant details.  In future work, we plan to further develop this solution and collect complex CAD data to validate its effectiveness. We appreciate your insightful observation and value the opportunity to discuss future directions on this topic. \n\nOnce again, we sincerely thank the reviewer for the recognition to our work, as well as the discussion about the future direction of research in this field."}, {"review_id": "fUbhwhNaF7", "reviewer": "Reviewer_t7b3", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "My original concern was that this method might not work well on complex data, such as furniture. The author has addressed this concern. I believe the proposed plan–generate–verify framework, which does not require ground truth editing data, is novel.", "rebuttal": "We sincerely thank the reviewer for his positive feedback. We feel delighted that the reviewer recognizes the training-free framework design, as well as the editing segmentation localization via cross-attention maps.\n\nThe concerns of the reviewer mainly fall into three categories. Here we will address them in detail. \n\n1.\tApplicability in scenarios with large difference. CADMorph is primarily designed for editing tasks with medium-level differences between the source and the target. In cases where the difference is large, a more suitable approach would be to first apply reverse engineering to obtain the construction sequence of the target shape,  and then use CADMorph to iteratively refine this sequence and  improve shape fidelity, as shown in the downstream application part in this paper (Section 4.3). Besides, directly editing CAD models with large geometric discrepancies is one of the major sources of failures, as is shown in Part H and Figure 1 in the supplementary materials.\n\n2.\tApplicability in real-world scenarios like furniture. We manually selected a pair of tables from DeepCAD dataset, where the source is a coffee table with legs positioned at the corners, and the target is a long and thin table with its legs tall and stretched in. Due to the restriction of using images during the rebuttal phase, we exhibit this editing example in the form of construction sequences at the end of this rebuttal. Additionally, a similar example can be found in the example 5 of Figure 4 in the main paper, with analysis provided in lines 289-292.\n\n    For Brepgen mentioned by the reviewer, this dataset is in the form of Breps and lacks the corresponding construction sequence (CSG) that captures the drawing process. Since CADMorph focus on construction sequences, we did not include this dataset in our experiment. For other datasets with similar complexity to Brepgen (e.g., intricately designed furniture) and available in the form of CSGs, we currently did not have access to such data, as discussed in the limitation section.\n\n3.\tRunning time. We have included a running time analysis in part E and Table 1 in the supplemental material. The running time of CADMorph is comparable to or shorter than prevailing 3D generation and editing methods like Magic3D (arxiv:2211.10440), Zero-1-to-3 (arxiv:2303.11328), DreamFusion (arxiv:2209.14988), GaussianEditor (arxiv:2311.14521), and GaussCtrl (arxiv:2403.08733), which typically takes tens of minutes or even hours. Moreover, CADMorph’s running time is on par with test-time reasoning models such as ChatGPT o3 and o4. These comparisons indicate that the running time of CADMorph is within an acceptable range.\n\n4.\tFailure cases. This can be found in part H and Figure 1 in the supplementary material. To recap, this shows that CADMorph may stop at somewhere in the middle during editing, and it still does not have the knowledge of the rendering process of the CAD kernel. We consider solving these issues in future work.\n\nIn all, we sincerely thank the reviewer for the positive feedback, and we will seriously consider the integration of B-reps in future work.\n\nP.S. Another example of editing tables in DeepCAD dataset.\n\nOriginal Construction Sequence:\n\n```\n[SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 128 128 128 61 75 128 113 134 128 New_body One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 61 86 128 11 224 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 163 86 128 11 224 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 61 188 128 11 224 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 163 188 128 11 224 128 Join One_sided [SEP] [EOS]\n```\n\nThe target shape in the form of construction sequence:\n\n```\n[SOL] Line 176 128 Line 176 223 Line 128 223 Line 128 128 Extrude 128 128 128 83 37 128 182 123 128 New_body One_sided [SEP] [SOL] Line 170 128 Line 170 223 Line 128 223 Line 128 128 [SOL] Line 167 131 Line 167 220 Line 131 220 Line 131 131 Extrude 255 128 255 93 209 123 162 138 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 98 204 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 98 67 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 143 204 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 143 67 123 15 219 128 Join One_sided [SEP] [EOS]\n```\n\nThe edited construction sequence:\n```\n[SOL] Line 176 128 Line 176 223 Line 128 223 Line 128 128 Extrude 128 128 128 83 37 128 182 123 128 New_body One_sided [SEP] [SOL] Line 170 128 Line 170 223 Line 128 223 Line 128 128 [SOL] Line 167 131 Line 167 220 Line 131 220 Line 131 131 Extrude 255 128 255 93 209 123 162 138 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 98 204 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 98 67 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 143 204 123 15 219 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Line 128 223 Line 128 128 Extrude 255 128 255 143 67 123 15 219 128 Join One_sided [SEP] [EOS]\n```"}, {"review_id": "A1rd5eGNu7", "reviewer": "Reviewer_qRBB", "summary": "Given 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.", "questions": "I 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I thank the authors's response.\n\nI hope the authors could add the integration of other input modalities and the challenging cases in their revision as promised", "rebuttal": "We sincerely thank the reviewer for the valuable feedback. We address the concerns as follows:\n\n### Novelty and contribution\nThe essence of CADMorph lies in utilizing domain-specific foundation models, namely the pretrained priors on domain data, to perform tasks they were not explicitly trained for, at test time, where data scarcity hinders training-based solutions. This is achieved by leveraging existing modules, namely LLM-based MPP and LDM-based P2S, as the foundation, and orchestrating them through a plan-generation-verify pipeline. This pipeline synergistically triggers the sequence editing capability guided by geometry, enabling capabilities beyond what either existing MPP or P2S modules were originally designed to perform. \n\nWe position our contribution as developing simple yet effective strategies that empower existing foundation models to gain new capabilities at test time, for what the era of foundation models calls in the face of ever-complicating tasks where data is too hard to collect. To help clarify the significance of our approach, we highlight two influential works that share similar philosophy. Although they differ in domain and specific strategies, they exemplify the value of enabling existing modules to go beyond their original purposes without retraining.\n_Eg 1 Chain-of-Thought Prompting (arxiv:2201.11903)_ leaves existing LLMs untouched and instead provides demonstrations with intermediate steps at test time. It marks a significant breakthrough in eliciting reasoning capabilities from LLMs.\n_Eg 2 DreamFusion (arxiv:2209.14988)_ keeps the existing Stable Diffusion unchanged and uses it as a prior to optimize a NeRF. It represents a significant step forward in leveraging 2D generation models to enhance 3D generation. \n\n### Image or sketch could be more practical\nWhile our work uses SDF as the target shape representation, this does not imply that SDFs must be provided directly in practical applications. In real-world scenarios, we treat the SDF as an interface and convert more accessible input formats, such as images or sketches, into SDFs with reasonable effort. For example, this transformation can be achieved using existing foundations, such as Hunyuan3D-2.5 (arxiv:2506.16504, image-to-mesh, followed by tSDF extraction via marching cube), Magic3DSketch (arxiv:2407.19225, sketch-to-mesh and extract the tSDF via marching cube), or LASDiffusion (arxiv:2305.04461, direct sketch-to-SDF).\nWe sincerely thank you for the insightful comments w.r.t. practical usage. We will include the integration of other input modalities in final version. For further details on why we chose SDFs as the interface, please refer to supplementary material Part B. \n### Challenging cases\nAs including images is banned, we provide examples in the form of construction sequences at the end of this rebuttal. The three examples successfully 1) added a stack of holes of equal intervals along a corner-like board, 2) transformed a torus into a rudder-like object, and 3) added fin-like heat sinks onto a flat motherboard.\n### Accuracy and time effect\nFor overall runtime, we include details in supplementary material Part E and Table 1. It is on par with test-time reasoning models (e.g., ChatGPT o3 and o4), indicating that the runtime of CADMorph fails within an acceptable range.\nFor effect of maximum number of iterations, we provide an ablation study in response 3c to reviewer t3cp. The results show that the performance gradually saturates as iteration number increases. Qualitative results in supplementary material Part G and Figure 4 (page 7) provide more intuitive examples. Simpler cases (top) require fewer iterations, while more complex ones (bottom) demand more. In future work, we plan to explore a dynamic stopping mechanism to better balance accuracy and runtime based on task complexity.\nOverall, thanks for your valuable feedback. It has helped us refine our work and broaden its appeal. We will revise the paper in accordance with your suggestions.\n\nP.S. Additional challenging cases.\n\n1. Adding multiple cylinders around the edge of a corner-like board.\n\nOriginal Sequence:\n```\n[SOL] Arc 130 126 64 Counterclockwise Line 160 126 Line 221 126 Arc 223 128 64 Counterclockwise Line 223 156 Arc 221 158 64 Counterclockwise Arc 160 219 64 Clockwise Arc 158 221 64 Counterclockwise Line 130 221 Arc 128 219 64 Counterclockwise Line 128 128\n```\n\nThe target shape in the form of construction sequence:\n```\n[SOL] Arc 130 126 64 Counterclockwise Line 160 126 Line 221 126 Arc 223 128 64 Counterclockwise Line 223 156 Arc 221 158 64 Counterclockwise Arc 160 219 64 Clockwise Arc 158 221 64 Counterclockwise Line 130 221 Arc 128 219 64 Counterclockwise Line 128 128 [SOL] Circle 144 142 4 [SOL] Circle 144 158 4 [SOL] Circle 144 174 4 [SOL] Circle 144 190 4 [SOL] Circle 144 205 4 [SOL] Circle 160 142 4  [SOL] Circle 176 142 4 [SOL] Circle 192 142 4 [SOL] Circle 207 142 4 Extrude 192 64 192 80 128 83 144 134 128 New_body One_sided [SEP] [EOS]\n```\n\nThe edited construction sequence:\n```\n[SOL] Arc 130 126 64 Counterclockwise Line 160 126 Line 221 126 Arc 223 128 64 Counterclockwise Line 223 156 Arc 221 158 64 Counterclockwise Arc 160 219 64 Clockwise Arc 158 221 64 Counterclockwise Line 130 221 Arc 128 219 64 Counterclockwise Line 128 128 [SOL] Circle 144 142 4 [SOL] Circle 160 142 4  [SOL] Circle 176 142 4 [SOL] Circle 192 142 4 [SOL] Circle 144 158 4 [SOL] Circle 144 174 4 [SOL] Circle 144 190 4 [SOL] Circle 144 205 4 [SOL] Circle 207 142 4 Extrude 192 64 192 80 128 83 144 134 128 New_body One_sided [SEP] [EOS]\n```\n\n2. Transforming a torus into a rudder-like object.\n\nOriginal Sequence:\n```\n[SOL] Circle 176 128 48 [SOL] Circle 176 128 20 Extrude 128 128 128 53 128 128 151 139 128 New_body One_sided [SEP] [EOS]\n```\n\nThe target shape in the form of construction sequence:\n```\n[SOL] Circle 176 128 48 [SOL] Circle 176 128 20 Extrude 128 128 128 53 128 128 151 139 128 New_body One_sided [SEP] [SOL] Arc 223 128 12 Clockwise Line 223 223 Line 128 223 Line 128 128 Extrude 128 128 128 117 202 128 22 139 128 Join One_sided [SEP] [SOL] Arc 163 68 12 Clockwise Line 223 103 Line 188 163 Line 128 128 Extrude 128 128 128 187 175 128 29 139 128 Join One_sided [SEP] [SOL] Line 188 93 Line 223 153 Line 163 188 Arc 128 128 12 Clockwise Extrude 128 128 128 187 81 128 29 139 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Arc 128 223 12 Clockwise Line 128 128 Extrude 128 128 128 117 32 128 22 139 128 Join One_sided [SEP] [SOL] Line 163 68 Line 223 103 Arc 188 163 12 Clockwise Line 128 128 Extrude 128 128 128 39 89 128 29 139 128 Join One_sided [SEP] [SOL] Line 188 93 Arc 223 153 12 Clockwise Line 163 188 Line 128 128 Extrude 128 128 128 39 167 128 29 139 128 Join One_sided [SEP] [EOS]\n```\nThe edited construction sequence:\n```\n[SOL] Circle 176 128 48 [SOL] Circle 176 128 20 Extrude 128 128 128 53 128 128 151 139 128 New_body One_sided [SEP] [SOL] Line 188 93 Arc 223 153 12 Clockwise Line 163 188 Line 128 128 Extrude 128 128 128 39 167 128 29 139 128 Join One_sided [SEP]  [SOL] Arc 223 128 12 Clockwise Line 223 223 Line 128 223 Line 128 128 Extrude 128 128 128 117 202 128 22 139 128 Join One_sided [SEP] [SOL] Arc 163 68 12 Clockwise Line 223 103 Line 188 163 Line 128 128 Extrude 128 128 128 187 175 128 29 139 128 Join One_sided [SEP] [SOL] Line 188 93 Line 223 153 Line 163 188 Arc 128 128 12 Clockwise Extrude 128 128 128 187 81 128 29 139 128 Join One_sided [SEP] [SOL] Line 223 128 Line 223 223 Arc 128 223 12 Clockwise Line 128 128 Extrude 128 128 128 117 32 128 22 139 128 Join One_sided [SEP] [SOL] Line 163 68 Line 223 103 Arc 188 163 12 Clockwise Line 128 128 Extrude 128 128 128 39 89 128 29 139 128 Join One_sided [SEP] [EOS]\n```\n3. Adding multiple fin-like heat sinks onto a mother board.\n\nOriginal Sequence:\n```\n[SOL] Line 223 128 Line 223 160 Line 128 160 Line 128 128 Extrude 128 128 128 32 96 128 192 137 128 New_body One_sided [SEP] [EOS]\n```\nThe target shape in the form of construction sequence:\n```\n[SOL] Line 223 128 Line 223 160 Line 128 160 Line 128 128 Extrude 128 128 128 32 96 128 192 137 128 New_body One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 106 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 144 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 78 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 40 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 172 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 211 96 137 64 171 128 Join One_sided [SEP] [EOS]\n```\nThe edited construction sequence:\n```\n[SOL] Line 223 128 Line 223 160 Line 128 160 Line 128 128 Extrude 128 128 128 32 96 128 192 137 128 New_body One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 172 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 211 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 78 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 40 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 172 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 211 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 106 96 137 64 171 128 Join One_sided [SEP] [SOL] Line 138 128 Line 138 223 Line 128 223 Line 128 128 Extrude 128 128 128 144 96 137 64 171 128 Join One_sided [SEP] [EOS]\n```"}]

Author rebuttal:

Meta-review: This paper presents a plan–generate–verify iterative framework that cleverly integrates a P2S latent diffusion model with an LLaMA-3-based MPP, and uses cross-attention to automatically localize editable segments. This work offers a highly innovative solution to the largely unexplored task of geometry-driven parametric CAD editing, and the paper is well structured and highly readable. Although the experiments currently cover only simple parts in the CAD-Editor 2k dataset, inference still takes minutes, and deeper ablation studies or complex assembly examples are lacking, the reviewers unanimously agree that these shortcomings do not diminish the method’s overall contribution; they can be addressed in the camera-ready version by adding further experiments and efficiency analyses.

Recommendation
Accept.  This is a well-motivated work and a clear description framework. The proposed plan–generate–verify iterative method is better than LLM prompting and the baseline methods (CAD-diffuser and FlexCAD).
Decision: Accept (poster)