CADmium: Fine-Tuning Code Language Models for Text- Driven Sequential CAD Design
Authors: Prashant Govindarajan
Davide Baldelli
Jay Pathak
Quentin Fournier
Sarath Chandar
Venue: Accepted by TMLR ()

Abstract: Computer-aided design (CAD) is the digital construction of 2D and 3D objects, and is central to a wide range of engineering and manufacturing applications like automobile and aviation. Despite its importance, CAD modeling remains largely a time-intensive, manual task. Recent works have attempted to automate this process with small transformer-based models and handcrafted CAD sequence representations. However, there has been little effort to leverage the potential of large language models (LLMs) for sequential CAD design. In this work, we introduce a new large-scale dataset of more than 170k CAD models annotated with high-quality, human-like descriptions generated with our pipeline based on GPT-4.1. Using this dataset, we fine-tune powerful code-LLMs to generate CAD sequences represented in a JSON-based format from natural language descriptions, demonstrating the viability and effectiveness of this approach for text-conditioned CAD generation. Because simple metrics often fail to reflect the quality of generated objects, we introduce geometric and topological metrics based on sphericity, mean curvature, and Euler characteristic to provide richer structural insights. Our experiments and ablation studies on both synthetic and human-annotated data demonstrate that CADmium is able to automate CAD design, drastically speeding up the design of new objects. The dataset, code, and fine-tuned models are available online.

Published in Transactions on Machine Learning Research (01/2026)
CADmium: Fine-Tuning Code Language Models for Text- Driven Sequential CAD Design
Prashant Govindarajan 112,3 Sarath Chandar122.3.
Davide Baldelli*
Jay Pathak
Quentin Fournier
'Chandar Research Lab CIFAR AI Chair
Mila Quebec Al Institute
3
Polytechniquu Montréal
*Ansys
CCnada
Reviewed on OpenReview:
Abstract
Computer-aided design (CAD) is the digital construction of 2D and 3D objects, and is central to a wide range engineering and mamufacturing applications like automobile and aviation. Despite its importance, CAD modeling remains largely time-intensive, mamual task. Recent works have attempted to automate this process with small transformer-basee models and handcrafted CAD sequence representations. However, there has been little effort to leverage the potential of large language models (LLMs) for sequential CAD design. In this work, we introduce new large-scale dataset of more than 170k CAD models annotated with high-quality, human-like descriptions generated with our pipeline based on GPT-4.1. Using this dataset, we fine-tune powerful code-LILMs to generate CAD sequences represented in a JSON-based format from natural language descriptions, demonstrating the viability and effectiveness of this approach for text-conditioned CAD generation. Because simple metrics often fail to reflect the quality of generated objects, introduce geometric and topological metrics based sphericity, mcan curvature, and Euler characteristic provide richer structural insights. Our experiments and ablation studies both synthetic and human- annotated data demonstrate that CADmium is able to automate CAD design, drastically speeding up the design new objects. The dataset, code, and fine-tuned models are available o
1 Introduction
Throughout history, humans have developed ever-more complex objects for survival, architectural, engineer- ing, and artistic purposes. These range from early inventions such as hunting tools, wheels, and clocks, modern systems such space shuttles. With the emergence of computers, programs were written to process machining instructions, and soon after, to design 3D objects virtually. Computer-aided design (CAD) has since been widely adopted, significantly improving the speed and efficiency of the engineering and manufac- turing process. However, the CAD pipeline still requires significant human effort and expertise.
Recognizing that CAD a sequential process involving 2D sketches, such as lines and circles, followed by 3D operations like extrusions, recent studics have leveragod transformer architectures and publicly available CAD datasets to automatically generate design steps in an autoregressive manner (Wu al. 2021 Khan) al. 2024 Recently, there has been growing interest in generating 3D objects from natural language descriptions as this approach would make CAD accessible to a broader, non-expert audience and speed up the creation process even further. Such an interest likely stems from the progress in large pre-trained foundation
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Start by creating hexagonal shape rounded corners for the main body.
Blander nulti-view
Generate Annotations
('parts":
part 1":
The final part is a hexagonal plate with rounded corners central through-hole, with a single cylindrical peg projecting downward from one of its off-center faces.
"coordinate_ 'corrdinat_syyteen: systen":
b
sketch"
Natural Language Descriptions
fface i::
"Loop_ 1":
"Line 1":
GPT-4.1
Start Point": [0.0184, 0,2755,
End Point": [0.1373,0.0686]
[...
extrusion":
Kininal Ison Descriptions
extrude depth_ towards_norman 1373,
"sketch, :
extrude cetchooppsstterrmmeee 0.0, 6.75, operation": "tenBddyreaturrpparatton"
Annotation Prompt
Generation Prompt
part_ 2"; (....
h1
LoRA
("parts": {
part 1"
Generated Json Descriptions
coordinate system"; { ... },
"sketch'; [
"extrusion" [[
*part_2" 22: [[[[]
111
Qwen2. 50...55Coo 5-Coder
Figure The CADmium pipeline for text-to-CAD generation, CADmium reformulates CAD gen- eration as a purely text-to-text task. First, GPT-4.1 generates natural-sounding yet geometrically precise descriptions of 176,017 objects using their construction sequences in minimal JSON, and up to 10 multi-view images rendered with Blender. Then, the Qwen2.5-Coder LLM is fine-tuned with LoRA to translate these descriptions back into CAD sequences.
models that has resulted in tremendous reasoning and generative capabilitics in natural language [Bronn et al. 2020) and images (Esser el al. 2024). However, unlike these modalities, text-conditionee CAD object generation has not seen the same success as the field still faces two key challenges: (1) the lack of high- quality, human-like textual annotations for CAD samples, and (2) the absence of suitable embeddings for CAD design histories that facilitate the effective use of pre-trained language models.
Current approaches address the first challenge by automating text annotation using sophisticated state-of- the-art annotation pipelines with vision-language and mixture of experts models (Khan et al. 2024 Xu et al. 2024) and the second with vectorized representations of CAD sequences combined with separate embedding layers and prompt tokens to train language models (Khan et al.) 2024 Xu et al. 2024 However, we identify critical shortcomings with both strategics. First, current machino- machine-gencrated annotations often fail strike a balance between fluency and geometric specificity, a balance essential for natural humnan interaction while containing sufficient geometric detail to unanbiguously specify the desired 3D object. Second, prior approaches treat CAD generation domain-specific task requiring custom embeddings, thereby failing to fully leverage the polential of pre-trained language models and requiring significant computational resources to learn new embeddings from limited data.
To address these limitations, we introduce CADmium a novel approach that reformulates CAD genera- tion as a purely text-to-text task. Our contributions are as follows. First, we develop a novel annotation pipeline using GPT-4.1 AO 2025) that processes up
K
multi-view images of 3D objects along with their construction sequences generate annotations that combine natural language fluency with geomet- ric precision, improving upon prior approaches like Text2CAD (Khan et al., 2024 by leveraging single, more capable multimodal foundation model. We apply pipeline 176,017 CAD sequences from Deep- CAD (W et al. 2021) and relcase new datasct with improved expert-quality annotations. Second, we
3Tee CADmium reflects the integration "CAD" (Computer-Aldee Design) and draws inspiration from the metal radmium, known for its malleability and utility precision applications.
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fine-tune Qwen 2.5- Co5eeddr- Yang et al. 2024 state-of-the-art instruction-tuned code LLM, dataset to generate JSON-formatted CAD sequences from natural language prompts, leveraging the capa- bilities of the pre-trained model without the need for specialized embedding layers. We acknowledge that the competitive performance achioved is significantly derived from the impressive pre-trained qualities of the Qwen 2.5- 2.5-Coder base model, upon which our SFT efforts provide dormain-peccfic refinement. Recent works have shown that fine-tuning LLMs can be effective for generating code used in visual content creation (Ro- driguez et al. 2024 2025); ; emphasizing their versatility in visual generative applications. We evaluate our approach against tatt--of-the-art CAD generation models, including Text2CAD (Khan et al. 2024), using both point-cloud and mesh-based metrics established in prior work. We also validate our models on the Fusion360 Reconstruction dataset (Willis et al., 2021 using annotations generated by our pipeline and the CADPrompl benchmark (Alrashedy et al. 2024) that contains 200 objects annotated by human experts. Third, we introduce novel metrics based on sphericity, mean curvature, Euler characteristic, and watertight- ness to more extensively evaluate the structural and topological characteristics of the generated objects. We highlight the human-like fluency and conciseness of CADmium's expert-level text annotations relative to Text2CAD, supported by extensive empirical analysis. Furthermore, our fine-tuned LLM achieves com- petitive performance against the pre-trained Text2CAD model, with improvements across some evaluation metrics as model size increases.
2 Related Works
The Fusion 360 Gallery (Willis et al. 2021 was the first dataset of human-designed CAD objects to in- clude their corresponding construction sequences. However, the Fusion 360 Gallery only contains 8,625 samples. As this was not enough for training, DeepCAD (Wu et al.] 2021) introduced a significantly larger dataset of 176,017 CAD models constructed using only sketch and extrude operations parsed from the ABC dataset (Koch et al. 2019). Leveraging their large-scale dataset, DeepCAD trained transformer- based autoencoder within a latent- GAN framework for unconditional CAD generation. SkexGen (Xu et al.) 2022) and SolidGen (Jayaraman et al.) 2022) later improved upon DeepCAD by introducing alternative input rep- resentations such as disentangled codebooks and boundary representations. However, these approaches were limited by their unconditional nature, offering no mechanism for users to control the generated objects.
As a result, subsequent works shifted toward conditional generation from point clouds, images, or text inputs (Xu al., 2024 You et al., 2024 Alam & Ahmed 2024 Uy al. 2022 Khan et al. 2024 Dupont et al. 2024). Progress in text-conditioned CAD generation was, however, hindered by the lack of datasets with textual annotations. The first large-scale elfort toward textual supervision for CAD generation was Text2CAD (Khan et al. 2024), which introduced an LLM//LMM-aaee annotation pipeline over DeepCAD and produced 660k beginner- to expert-level descriptions. Although foundational, its beginner-level prompts are often high-level and underspecified, while the expert-level ones tend to be overly long, redundant, or stylistically unnatural (some examples are given Appendix AA.. Subsequent works expanded this direction with increasingly complex pipelines. OmniCAD using InternVL2-200 CADFusion (Wang et al.) 2025) also released text-to-CAA CAD datasct which relies
[u et al. 2024) generated machine-produuee descriptions
GPPT-4o and human refinement. More recently, AADLLM/TCADGE (Liao et al. 2025 proposed semi- automated human-in-the-loop pipeline that separately processes multi-view images, point clouds, and CAD parameters to obtain detailed micro- and macro-level descriptions, producing a richer dataset of expert-style annotations. Across these efforts, existing datasets diverge into two categories: (i) expert-level, geometrically grounded descriptions (e.g., Tex12CAD, CADLIM) that enable unambiguous reconstruction and form the basis of our comparisons, and (ii) higher-level, abstract, or stylistic prompts (c.g., OmniCAD, beginner prompts in Tex12CAD and CADFusion) that are not directly comparable due their limited geometric specificity. We intend to generate high-quality CAD annotations that strike a balance: they must provide sufficient geometric detail to allow precise reconstruction, while remaining concise and plausibly human- written so as to reflect real-world design prompting scenarios.
In addition to annotating the DeepCAD dataset, Text2CAD introduced a supervised generation pipeline using a frozen BERT encoder (Devlin et al., 2001)) adaptive text layers, and a transformer decoder with cross-attention between text and CAD embeddings. CAD-MLLM (Xu et al.) (024) improved over this framework by incorporating LLMSs and conditioning the generations on multiple modalities, namely point
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clouds, images, and text. CAD-MLLM also introduced a new annotation pipeline and additional CAD samples parsed from the ABC dataset. In parallel, FlexCAD ZZhng et al. 2024 proposed a hierarchical text representation of CAD sequences to make the generation more controllable. With CADmium, we primarily aim to advance state-of-the-art performance in the text-to-CAD task, leaving multimodal integration and enhanced controllability for future work. Recent. works have explored related but distinct avenues: CAD- Coder |Guan al. 2025) focuses on image-to-CAD generation using a VLM to produce executable Python code, a different imput modality and sequence representation than our text-to-text JSON-based approach. Similarly, CADFusion (Wang et al. 2025) explores distinct training strategies like combining sequential learning with visual feedback via DPO. Finally, [iao et. al. (2025) proposed dual-channel Transformer- based generator (TCADGen) alongside a refinement model (CADLLM) to convert textual design prompts into executable CAD modeling sequences.
Evaluating generative CAD models remains a challenge. Earlier works such as DeepCAD, SkexGen, and SolidGen relied on point-cloud-basse metries, including Chamfer Distance, F1 Score, Minimum Matching Distance (MMD); Coverage (COV), and Jensen-Shannon Divergence (JSD). While standard in 3D shape generation, these metrics often obscure fine structural details by uniformly sampling points throughout the object volume, failing to account for internal edges. To address these limitations, CAD-MLLM introduced mesh-based metrics such as self-intersection ratio, dangling edge length, and flux enclosure, which better capture geometric and topological correctness. Nevertheless, even these mesh-aware metrics remain limited in their ability to assess the functional quality of generated CAD models.
3 Methods
3.1 Datasets and Annotation Pipeline
We address the quality concerns of Text2CAD annotations by re-annotating all 176,017 samples from the DeepCAD library using the same multimodal inputs as Tex12CAD. These inputs include the minimal JSON metadata extracted from DeepCAD using Onshape's FeatureScript (ons), which improves human readability by eliminating redundant information, and 10 multi-view images rendered with Blender's Python mod- ule (Blender Online Community 2025) corresponding to the top, bottom, and 8 sides. We leverage the multimodal capabilities of GPT-4.1 to generate concise, natural-sounding yet geometrically precise descrip- tions. Additionally, we annotate 8,307 CAD objects from the Fusion 360 Reconstruction Datasct (Willis et al. 2021) not found in the ABC corpus from which DeepCAD is derived, offering robust measure of generalization. This comprehensive re-annotation effort across both datasets involved processing a total of over 600 million input and output tokens with GPT-4.1. Importantly, only the expert-level annotations in Tex12CAD provide enough geometric detail to unambiguously reconstruct objects. While it may be useful to generate plausible objects from incomplete descriptions some cases, evaluating them is challenging as multiple outputs could satisfy a partial description. As a result, we generate exclusively expert-level prompts and restrict comparisons to the expert-level descriptions in Text2CAD, and CADLLM annotations.
Our pipeline improves upon Tex12CAD in two key ways. First, instead of using separate vision and language LLMs, we leverage GPT-4.11, a single multimodal foundation model, enabling a more accurate interpretation of input images considering the provided CAD history. Second, GPT-4.1 drastically surpasses Mistral-50B, resulting in annotations that are more human-like, concise, and often avoid technical jargon while remaining accurate, as illustrated in Figure 2
Looking first at corpus-level statistics, CADmium's annotations are drastically more concise, most being be- tween 100 and 200 words, compared to Text2CAD's and CADLLM's longer and often redundant annotations, often excceding 200 words (Figure --). Despite being shorter, CADmium's ''s annotations achieve a greater lexical variety, with a higher ratio of unique words per annotation (Figure 11). The vocabulary growth curve also shows that CADmium uses a much broader range of words across annotations, reaching over 18,000 unique words or twice as many as Text2CAD, and comparably to CADLLM (Figure (2)) This richer vocabulary creates more diverse descriptions and reduces the risk of overfitting to a limited set of words. Additionally, CADmium's annotations represent numbers in more natural, human-like way compared to
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a
24K
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Count
20K
Vocabulary (In
14K
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Number Words
6K
d
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b
Gemma 12B CADmium
Mistral 3.2
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Text2CAD
CADLLM
7129
2044
Human Likeness
8665
0%
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Percentage of Unique Words
8530
Clarity
80K
Visual Faithfulness
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2
CADmium
Text2CAD Expert
CADLLM
Figure 2: Comparative analysis of CADmium, CADLLM, and Text2CAD expert-level annota- tions. (a) Vocabulary growth as a function of token count shows that CADLLM et al. (2025) produces substantially longer annotations, with a vocabulary expansion rate comparable CADmium and larger than Text2CAD. (b) Human-likenss, clarity, visual faithfulness given image renders, and completeness against the minimal JSON as measured by Gemma-3 12B (Team al. 2025) and Mistral Small 3.2 24B (Mittall 2025) indicates that CADmium descriptions tend to produce more natural-sounding albeit challenging de- scriptions. (c-e) Distribution of word counts, unique words, and decimal places within numerical expressions per annotation shows that CADmium descriptions are more concise and diverse (e reports only CADmium and Text2CAD distributions, as CADLLM annotations contain exclusively integer values).
the excessive precision of Text2CAD, while CADLLM annotations contain exclusively integer values. (Figure side-by-side comparison between CADmium and Text2CAD descriptions is provided in Appendix A
2
While statistics provide general feel for the quality of the corpus, they may not tell how individual de- scriptions compare head-to-head. Therefore, we conducted an LLM-as-a-judge evaluation using Gemma-3
12B (Team et al. 2025) and Mistral Small 3.2 24B (Mistral 2025 10,000 groups of three annotations Liao et al. (2025)). We looked at four key (one from CADmium, clarity and readability, visual faithfulness to image renders, and completeness with factors:
from Text2CAD, one from CADLLM Liao
respect to the minimal JSON description. CADmium was preferred for human-likeness and clarity while achieving comparable visual faithfulness. Text2CAD scored higher on completeness, which aligns with our design choice prioritize natural-sounding concise annotations over the exhaustive, JSON-like detail found in Text2CAD, We argue that such verbosity, while improving completeness, is less representative of how hu- mans naturally describe CAD models and may artificially simplify the task for language models. CADLLM
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annotations were typically rated lowest across all metrics, implying their descriptions are less readable and laithful. Notably, CADLLM explicitly separates appearance and parameter descriptions, whereas CADmium and Text2CAD integrate them into a single, interleaved description. The disparity in scores may therefore reflect the LLM judge's preference for this integrated text format over a segmented approach. Details on the evaluation setup and prompts can be found Appendix [
For model training and evaluation, we adopted the same train, validation, and test splits as defined by Text2CAD. These splits are derived from filtered samples of the DeepCAD dataset, which were curated to contain approximately 5% cuboid and 5% cylindrical models, reducing their original distribution of roughly 25% cuboid and 8% cylindrical samples in the unfiltered training set. This filtering process resulted in final split sizes of 118,299 for training, 8,925 for validation, and 8,046 for testing.
3.2 Model Selection and Training Details
We perform supervised fine-tuning (SFT) on Qwen-2.5 Coder to autoregressively reconstruct CAD histories in minimal JSON format from their textual description. We selected Qwen-2.5 Coder as it is a state theth- art instruction-tuned code-LLM, and preliminary experiments with non-code LLMs did not yield promising results. Qwen-2.5 Coder available in multiple sizes, and we considered four sizes: 1.5B, 3B, 7B, and 14B parameters. We discuss ablations on the model size in Section 1.2
All models were fine-tuned using AdamW with the default B1 0.9, 82 = 0.999, and 10-8. We use cosine learning rate schedule with 100 warmuu steps, reaching a peak learning rate of 2 x 10-1, and subsequently decaying to a final rate of 0 at the end of the training. To improve computational efficiency. mixed-precision training with fp16 was applied alongside Low-Rank Adaptation (LoRA) (Hu et al. 2022) across all linear layers, configured with a rank of 64 and an alpha value of 32.
All experiments were conducted on nodes with four NVIDIA A100 80GB GPUs, utilizing Fully Sharded Data Parallel (FSDP) (Zhao et al. (023)) for efficient distributed training, and achieving an effective batch size of 16. The loss function was computed exclusively the assistant's generated JSON responses. The models were trained for up to 4 epochs (approximately 29,000 steps), with each complete training run typically lasting between 8 to 12 hours, depending on the specific model size being finc-tuned.
3.3 Evaluation
We evaluate all models unseen 3D objects by generating their CAD sequences using greedy decoding. To assess the quality of the generated outputs, we adopt the following reconstruction and topological metrics from Text2CAD and CAD-MLLM: Invalidity Ratio (IR) is the percentage of generated samples that cannot be successfully rendered into valid CAD objects; F1 is a classification metric computed by aligning generated and ground-truth loops using the Hungarian matching algorithmm; Chamfer Distance (CD) measures the dissimilarity between generated and reference point clouds; Segment Error (SegE) is the scaled absolute difference between the number of segments in the generated and ground-truth samples; Dangling Edge Length (DangEL) is the total length of edges that are shared by fewer than two faces; Self- calf-entersection Ratio (STR) is the proportion of faces that self-intersect; and Flux Enclosure Error (FluxEE) quantifies how effectively the mesh encloses a watertight volume.
Additionally, in order to more extensively evaluate the quality of the reconstructed CAD models, we introduce novel metrics designed to capture specific geometric and topological properties. The first metric we introduce is the Sphericity Discrepancy (SD) metric. Sphericity quantitatively measures how closely a 3D object resembles a sphere. Given an object with a surface area and volume V, sphericity (V) is defined
S n (6V)
V
(1)
s
where is the surface of a sphere of volume V. Given that a sphere achieves the minimum surface area given a particular volume, E (0, 1). Sphericity, which is explored foundational work on particle shape characterization (adeell 1932), and used in various applications for quality asscssment of 3D parts, for perfect sphere and decreases for those that are less compact or have more intricate surfaces relative to
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their volume. The final sphericity discrepancy metric quantifies the difference between the predicted object (pred) and the ground truth object (ot) as the absolute difference of their normalized ratios:
SD = v pred
(2)
A lower SD value indicates greater similarity in the overall compactness between the predicted and ground truth models.
The second metric we introduce, termed the Discrete Mean Curvature Difference (DMCD), quantifies local dissimilarities in surface geometry, To achieve this, we compute a discrete mean curvature value at cach vertex for both the predicted and ground truth meshes. This computation follows the principles for discrete curvature estimation presented by Cohen-Sleiner & Morvan (2003), leveraging an implenentation available in the trimesh library Dpawson-Haggerte et al.). Specifically, for each vertex, the discrete mean curvature value k is calculated as the sum of the signed dihedral angles of all mesh edges contained within a sphere of defined radius r centered at that vertex. This per-vertex value inherently dimensionless. The curvature each vertex is then averaged, resulting in R. We compute the absolute difference bet the average K values of the predicted and ground truth meshes:
DMCD vertex
R
Rgtl
(3)
As R quantifics the global measure of curvature over the vertices of the mesh a lower DMCD value indicates a greater similarity in the surface geometry between the predicted and ground truth models.
We also introduce the Exact Euler Characteristic Match (EECM) metric. The Euler characteristic, denoted as X, is a well-established topological invariant that describes a shape's structure regardless of how
it is bent or deformed. For 3D manifold mesh, it is commonly computed using the formula x =V E + F,
where V is the number of vertices, E the number of edges, and F the number of faces (Richeson EECM is a binary metric defined as
2012 The
EECM - {1
if Xpred = Xgt -(1
(4)
otherwise
This metric serves as a basic check for whether the generated model preserves the essential topological features, like the number of holes or separate shells of the target object.
A fundamental quality indicator for generated 3D shapes, especially those intended for physical simulation, mamufacturing, or volumetric analysis, is whether they constitute a Watertight Mesh. A watertight mesh is a closed, 2-manifold polygonal surface that clearly separates interior volume from exterior space, meaning it has no holes, boundary edges, non non-manifold configurations that would prevent the unambiguous definition of its volume (Botsch et al.] 2010). We report the percentage of generated CAD models that are watertight as measure of their geometric validity and usability. In our evaluation pipeline, we compute SD, EECM, and DMCD only if both the predicted and ground truth meshes are watertight.
4 Experiments
This section provides an overview of our experimental setup and presents the evaluation of the proposed CAD generation pipeline. We conduct experiments across four datasets: the original Text2CAD dataset (Khan ct al. 2024), our re-annotated DeepCAD dataset (CADmium), the CADPrompt datasct (Alrashody et al., 2024] containing 200 expert-annotaled objects from DeepCAD (Wu et al., 2021 ; and the Fusion360 Recon- struction dataset (Willis et al. 2021 annotated using our pipeline.
A key aspect of our evaluation protocol is that we must ensure that generated 3D models meet specific shape and structural requirements to correctly compute metrics. First, all metrics require that the generated JSON, or vector representation in the case of Tex12CAD, successfully convert into a valid 3D mesh. The proportion of samples failing this basic requirement is quantified by the invalidity ratio (IR). Beyond this universal prerequisite, EECM, DMCD, and SD additionally require both predicted and ground-truth meshes to be watertight.
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Due to these varying requirements, the number of objects that can correctly be evaluated per metric differs across experiments, for instance when comparing different model sizes. To maximize coverage, we report results using the entire subset of valid samples per metric and model, and include the corresponding sample size. However, as these subsets vary from one model to the next, it may hinder direct comparison. To address this, we include a complementary analysis in Appendix B where all metrics are recomputed on the intersection of samples that satisfy all relevant conditions across compared experiments, ensuring fair and consistent comparisons.
Additionally, for the Chamfer Distance, observed that rare outliers with extremely high values skewed the mean, rendering it less informative. We therefore report the median CD as more robust alternative. All metrics are accompanied by their 95% confidence interval computed over individual test samples for a single model run.
To assess whether observed differences between models are statistically significant, we apply different hy- pothesis tests depending on the evaluation protocol. When comparing results on non-identical sets of valid meshes (main paper tables), we treat the samples as independent and use umpaired tests: Welch's t-test if both groups appear approximately normal, otherwise the Mann Whitney U test, and for binary outcomes such as validity and watertightness we use Fisher's exact test or the Chi-square test with Yates' correction when expected counts are sufficient.. When comparing results on the common subset of objects valid across models (Appendix Bp, we use paired tests: the Wilcoxon signed-rank test for continuous metrics and Mc- Nemar's exact test for binary outcomes. In all cases, we report differences as statistically significant the adjusted p-value (Benjamini Henjamini-Hochherg Hochberg correction within each metric) is below 0.05.
4.1 Generalization to Human- Annotated P (CADPrompt)
To assess our models' ability to generalize prompts authored by humans, we evaluated them on the CADPrompt dataset (Alrashedy et al. 2024). This dataset comprises expert-written natural language in- structions for 200 DecpCAD samples, serving a crucial test for out-of-distribution generalization. Table [ presents the results, comparing our Qwen Coder 14B model trained on CADmium data against the original Text2CAD model as key baseline. A corresponding common subset analysis is provided in Appendix B
The Text2CAD model achieves a remarkably low Invalidity Ratio, a characteristic of its specialized vector representation for CAD sequences, which is designed with strong inductive bias that guides the model to generate valid outputs. When comparing our Qwen Coder 14B model trained on CADmium annotations against this Text2CAD model baseline, our LLM-based approach proves to be competitive alternative, by demonstrating substantially better Line, Arc, Circle, Extrusion F1 scores, SD, and CD. The Text2CAD model excels in overall geometric fidelity as measured by SIR, and achieves higher Watertightness. This highlights that while the Text2CAD model's design ensures high validity and strong performance on some global shape metrics, our LLM approach offers significant advantages in understanding instructions for specific, geometric elements and structural properties.
4.2 Impact of Model Scale
We investigated the influence of model scale by fine-tuning Qwen-2.5 Coder models of 1.5B, 3B, 7B, and 14B parameters on our CADmium training data. These models were evaluated on both the CADmium test set and the Fusion360 Reconstruction dataset to assess performance scalability and generalization. Detailed metrics are presented in Table 2 and Table with a corresponding analysis on common subset of valid samples provided in Appendix
A primary benefit of increasing model size is the consistent reduction in the Invalidity Ratio (IR). On the CADmium lest set, IR drops progressively with larger models, and the 14B model achieves a notably lower IR on the more challenging Fusion360 dataset as well. Accuracy in reconstructing geometric primitives, as mcasured by F1 scores, generally improves or remains robust with incrcasing model size, particularly when compared on a common subset of generated objects (see Appendix B). Metrics related to fine-grained mesh quality, such DangEL and SD, also tend to show improvements with larger models, suggesting better
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Table 1: Performance comparison on the human-annotated CADPrompt dataset. Metrics are calculated on the set of valid meshes generated by each model individually. Reported values include 95% confidence intervals in brackets. Boldface indicates the better-performing model when the difference is statistically significant at p 0.05. Boldface indicates statistically significant differences (p 0.05); details of the statistical testing procedure are provided in Section
Model
Text2CAD Text2CAD
Qwen-2.5 Coder 14B CADmi
Trained on
IR. (%) 4 Num Valid
1.57
6.28
188
179
Line F1 1
0.67 (o.62, 0.71]
0.69 [0.64, 0.74
Are F1
0.00 [0.00. 0.00]
0.22 [0.11,
Circle F1
0.31 [0.23, 0.43]
0.61 [0.51, 0.71
Extrusion F1
0.84 [0.82, 0.87]
0.89 [0.87, 0.94]
CD mean +
221. .2 [186.79, 288.88]
205.46 (170.29, 287.00)
CD median +
127.70 [87.50, 162.911
1 116.77 66.75 [81.64, 179.09]
SIR +
0.02 [0.01, 0.04)
0.11 [0.07, 0.15]
DangEL 4
0.20 (0.03, 0.44]
2.98 (1.67, 4.55]
SegE
0.09 [0.03, 0.17]
0.78 (0.48, 1.14]
FluXEE (x10*)
0.04 [0.00, 0.12]
2.24 (1:01, 3.73]
Watertightness (%)
95.74 [02.56, 98.40)
83.80 [78.21, 88.83)
Num Watertight
165
136
BECM T DMCD (x (x10") +
0.66 [o.50, 0.73)
0.68 [0.60, 0.75]
13.39 [11.81,
8.42 [6.63, 10.35
SD mean (x 102)
14.78 [12.74, 17.02)
7.02 [5:05, 9.26]
SD median (x 10))
11.15 (8.68, 13.64]
1.54 [1.07, 2.15]
Table 2: Impact of Qwen Coder model scale (1.5B, 3B, 7B, 14B parameters), trained on CADmium anno- tations, evaluated the CADmium test set. Metrics are calculated on the set of valid meshes generated by each model individually for each dataset. Reported values include 95% confidence intervals in brackets. Statistical significance is assessed with statistical tests on adjacent sizes (1.5B+33 -3B, 3B-7BB 7B-14B). Bold indicates a size that significantly better than the next smaller size at p < 0.05. Details of the statistical testing procedure are provided in Section
Metric
1.5B
3B
7B
14B
IR (%) L Num Valid
11.86
8.51
5.93
3.33
7092
7361
7569
7778
Line F1 f
0.90 [0.90, 0.91]
0.91
[0.90,
0.91
[0.90, 0.91
0.92
[0.91,
Arc. F1 7
0.70 jo.68, 0.75]
0.74
0.73
[0.71,
0.78
(0.70,
Circle
0.87 [0.s6, 0.89]
0.91
0.92
0.89
[0.80, 0.90]
0.92
[0.91, 0.92
Extrusion F1
0.98 J0.98, 0.99]
0.98
0.98
[0.98, 0.99]
0.99
[0.95, 0.98]
moan $
70.48 [66. 74.522
43.91
[40.52, 47.36)
63.11
[59.41, 06.91)
42.69
70..11 45.85)
modian L
J0.29, 0.32]
0.24 (0.23, 0.20
0.27
[0.26, 0.29]
0.23
[0.22, 0.25
SIR. 4
0.04 0.004
0.05 [0.04, 0.06
0.04
[a.04, 0.05]
0.04 (0.04, 0.04
DangEL +
1.18 [1:03; 1.35]
1.35 [1.20, 1.50)
1.10 [0.98, 1.25
1.13 [1.00, 1.27
Segl L
0.48 [o.42, 0.55]
0.52 (0.46, 0.57.
0.48
[p.43, 0.58]
0.54 [o.45, 0.67]
FlaxEE (x10P)
0.38 [o.30, 0.47]
0.49 (0.30, 0.60)
0.36
[a.29, 0.43]
0.39 jo.31, 0.48]
Watertightness (%) f
90.38 [e0. 71, 91.00)
90.31
63, 90.99)
90.54
[90. 91.20]
90.78 100. 91.42)
Num Watertight
6205
6408
6575
6779
EECM f
0.87 j0.86, 0.88]
0.89
(0.88, 0.00
0.88
[0.87, 0.89)
0.89 j0.88, 0.90)
DMCD (x10*) 4
2.97 [2.78, 3.16]
2.68
[2.49. 2.87
2.73
[2.54, 2.93]
2.67 [2.48,
SD mean (x10²) L
2.00 12.70,
2.19
12.02, 2.37
2.23
[2.06; 2:411
1.99 11.83, 2.15]
SD median (x10P) 4
0.00 10.00, 0.00]
0.00 (0.00,
0.00 [0.00, 0.00)
0.00 10.00,
adherence to manifold properties. Some examples of the reconstructions generated by the 14B model are shown in Figure

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Published in Transactions on Machine Learning Research (01/2026)
Table 3: Impact of Qwen Coder model scale (1.53, 3B, 7B, 14B parameters), trained on CADmium anno- tations, evaluated on the Fusion360 test set. Metrics are calculated on the set of valid meshes generated by each model individually for each dataset. Reported values include 95% confidence intervals in brackets, Statistical significance is assessed with statistical tests adjacent sizes (1.5B +3B, 3B++B, 7B-+14B). Bold indicates size that is significantly better than the next smaller size at p 0.05. Details of the statistical testing procedure are provided in Section
Metric
1.5B
7B
TR (%6 4 Num Valid
22.62
23.38
16.79
12.58
64128
6365
6912
7262
Line FI
0.79 (0.78, 0.80]
0.83
[o.83, 0.84]
0.83 [o.82, 0.83]
0.84 [0.83, 0.85]
Are P1 f
0.63 (0.02, 0.63]
0.70
[0.68, 0.71
0.67 [0.65, 0.68
0.71 [0.70, 0.73)
Circle
0.84 [0.83, 0.85
0.90
[0.59, 0.90
0.88 [0.87, 0.88]
0.90 [0.89, 0.90)
Extrusion F1 f
0.97 (0.97, 0.98]
0.98
[0.97, 0.96]
0.98 [0.95, 0.96]
0.98 (0.98, 0.96)
CD mean L.
226.46 (318.86, 284.38]
191.94
[184.22, 199.68]
206 39.399900 [199.19, 213.78)
202.00 [194.52, 209.37
CD median 4
110.18 [104.45, 117.16)
76.34
[60. 24, 63.42)
92. [86.51, 97.20
76.05 [70.08, 82.17
SIR
0.05 [0.04, 0,05]
0.05 [0.05, 6.06]
0.05 [0.00, 6.06]
0.04 [0.04, 0.00]
DangEL
1.36 [1.23,
1.41 [1,27, 1.56]
1.54 [1.40, 1.70]
1.26 [1.12, 1.42]
SegE +.
0.59
0.60
0.62
0.65 [0.57,
FluxER (x109)
0.28 (o.21,
0.31
0.30
0.38 [0.24, 0.60]
Watertightness (%) f
86.62 [55.80, 48]
86.50 [86.67, 87.95)
85.21 [84.38, 86.05"
87.35 (66.67, 88.00)
Num Watertight
5241
5192
5504
5941
EECM C
0.77 (0.76,
0.81 [0.50,
0.78 [0.77,
0.80 [0.79, 0.83]
DMCD (x109) $
5.19 (4.91,
4.09
4.90
4.43
SD mean (x10P) 4
4.53
3.18
3.87
[9.63,
3.45
SD median (x10")
0.00 [o.00, 0.00]
0.00 [n.00, 0.00]
0.00 [0.00, 0.00]
0.00
[0.00, 0.00)
4.3
Impact of Annotation Quality and Braining/Evaluatinnn Congruence
To isolate the impact of annotation quality, we adopted the original Text2CAD modeling approach, which utilizes a custom transformer architecture and a specialized vector representation for CAD sequences. We trained this Text2CAD architccture separately on our CADmium dataset and the original Text2CAD datasot, followed by cross-set evaluations. The results are presented in Table 4 An analysis on common subset of samples is available in Appendix B
Our primary finding is that when models are trained and evaluated in-distribution (i.c., on datasots with the same annotation style), the Tex12CAD architecture trained on our CADmium annotations achieves per- formance that broadly comparable or superior to the same architecture trained on the original Text2CAD annotations. As detailed in Table 3 the CADmium-trained model (when evaluated on CADmium data) shows lower IR, higher F1 scores for all primitive types, better EECM, DMCD, and SD compared to the Text2CAD-trainel model evaluated on Text2CAD data. This is a significant result, considering that CAD- mium's annotations designed to be more human-like and are therefore less directly aligned with the ground truth JSON structure than Text2CAD's 's more template-driven expert prompts.
Conversely, both models exhibit a performance degradation when evaluated out-of-distribation. The model trained on CADmium annotations sees a notable drop Fl scores and an increase in CD when tested on the Tex1 dataset. Similarly, the model trained on Tex12CAD annotations shows considerably worse geometric fidelity (e.g., higher CD) and higher IR when evaluated on the CADmium dataset's more natural prompts.
5 Limitations
While CADmium demonstrates the efficacy of fine-tuning code-LLMs for text-to-CAD generation, multiple limitations remain.
A primary limitation is the absence of large-scale human evaluation for the generated annotations. We employed L.M-as-a-judde paradigm to scale our assessment, but this approach may carry intrinsic biases.
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Table 4: Impact of annotation quality using the Text2CAD architecture. Models were trained on either CADmium or Text2CAD annotations and evaluated on both the CADmium and Text2CAD test sets. Met- rics are calculated on the set of valid meshes generated by each Reported values include 95% confidence intervals in brackets. Boldface indicates statistically significant dif- ferences (p < 0.05); details of the statistical testing procedure are provided in Section 4
configuration individually.
Evalnated on
CA Dmmiu
Text2CAD
Trained on
CADmium
Tex12CAD
CADuium
Tex12CAD
IR. (%) Num Valid
3.07
4.44
4.95 7648
4.34
7799
7447
7697
Line F1 1
0.88 [0.87, 0.88
0.73
[0.72, 0.742
0.64 [0.63, 0.64]
0.82 [0.81, 0.83)
Are F1 f
0.58 [0.36, 0.00
0.21
[0.19, 6.25)
0.24 [0.25, 0.26]
0.38 [0.36, 0.40)
Circle F1 f
0.87 [0.86,
0.57 [0.06, 0.50]
0.47 [0.46, 0.48
0.76 [o.74, 0.77]
Extrusion F1
0.98
0.89 [0.89,
0.81
0.93 [o.95, 0.94]
CD mcan
53.55 [p0.17, 57.06]
145.87 [141.11, 150.64)
107. [104.16, 111.93]
29.38 (27.50, 31.35]
CD median
0.33 [0.32, 0.36]
51.67
[47.72, 56.18]
34 11.23 [81.87, 36.29"
0.38 [o.a5, 0.42]
STR $
0.05 [0:05, 0.06
0.05 [0.04, 0.05]
0.08 (0.09, 0.00]
0.05 10.04, 0.05]
DangEL 4
1.66 [1.50, 1.83)
1.16 (1,02, 1.31]
2.56 [2.34, 2.78)
1.13 [1.01, 1:26
SegE 1
0.80 [o.Gs, 0.96]
0.53 [o.47, 0.61]
1.17 [1.08, 1.28]
0.55 [o.40, 0.62)
FliXEE (x10")
0.46 [0.59, 0.56)
0.59
[0.46, 0.78]
0.97 [0.84, 1.10]
0.46 (0.56, 0.57]
Watertightuess (%)
87. [86,90, s8.36]
91.90
[01. 29,
82.48 [81. 63,.63, 53.00"
90.52 (89.85, 91.17]
Num Watertight
6562
6555
5937
6644
EECM
(0.55, 0.87
0.76
(0.75, 0.77]
0.66 [o.65, 0.87]
0.82 [0.81, 0.83)
DMCD (x10")
3.65 [8.44, 3.87.
8.29
[7.98, 8.02]
9.41 [p.11, 9.78]
5.35 (5.10, 5.63]
SD mean (x10º) 1
2.71 [2.54, 2,87
8.69
[8.39, 8.99]
11.52 [11.14, 11.91;
5.04 (4.70, 5.29]
SD median (x10%)
0.00 (0.00, 0.00)
3.65
[a.41, 4.01]
5.33 0.090
0.11 [0.06, 0.16)
LLM judges can exhibit preference leakage, favoring outputs that resemble their own training data or specific stylistic patterns, potentially preferring machine-generated text. To mitigate this evaluation bias, include two families of LLMs, Gemma-3 12B and Mistral Small 3.2 24B, for judging and comparing our annotations with previous datasets.
Our dataset focuses on expert-level, geometrically precise descriptions to ensure unambiguous reconstruction. However, this may limit the model's ability to generalize to abstract, vague, or purely functional descriptions often used by non-experts (e-g., "design a handle for a mug"). The model may struggle when instructions lack explicit geometric parameters. Further, the current scope is limited to sketch and extrude operations. Advanced CAD features such as chamfers and fillets operations are not currently supported, limiting the complexity of manufacturable objects. However, unlike the rigid structure of vectorized representations, text-based CAD sequences are inherently flexible, facilitating the addition of novel operations.
6 Discussion and Conclusion
Despite the current progress of foundation models that have multimodal capabilities, achieving state-of-the- art in language, image, and 3D object generation (Xiang et al.) (024) and understanding, there is still plenty of room for improvement when it comes to domain-specific tasks. Much of this improvement depends on the availability of domain-relevant data and the capacity of large machine learning models to learn from it. Finc-tuning large language models for generating application-specifc 3D structural entitios, such as small molecules, proteins, and materials, is an active area of research that has shown remarkable progress in recent years. Likewise, CAD also deals with 3D mechanical objects, and automating this process can rapidly advance manufacturing sectors and 3D printing. Specifically, text-conditioned CAD model generation and completion can greatly help engineers and designers to quickly optimize the design process with simple natural language instructions.
With the absence of large and good-quality human-annotated textual descriptions of CAD objects that are publicly available, it is necessary to utilize powerful and multinodal language models that can generate hunan-level annotations given the images and design history of CAD objects. Tn this regard our main
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goals were further the state-of-tho-art in terms of the quality of the text annotations, improve the training process by using instruction-tune Code Qwen models, and augment, the evaluation pipeline by including ad- ditional metrics that can provide deeper structural and topological insights (Appendix D). We also highlight some important limitations in our work. Although we demonstrated that GPT-gencrated prompts exhibit human-like characteristics, further investigation needed to evaluate their generalization to diverse types of human prompts. Additionally, we observed few outlier reconstructions with high Chamfer distances from the ground truths, which may be mitigated by increasing the training data and model size. In future work, plan to extend our approach with multimodal framework that facilitates editing CAD designs. Overall, we wish to integrate some of the ad advancemenss from recent concurrent works, particularly those focused on multimodality and enhanced controllability with CADmium's training methodology.
Summary of the Description
Ground Truth
Text2CAD
Qwen Coder
A thin cuarter-circle plate extruded to 0.0296 thickness, spanning from the positive Y-axis the positive X-ax's. contains
quarter-fing forming ribbed annular sagmant with extra features.
x
flet plate 0.0052 thick, shaped by asymmetric profile that widens from narrow base to broed top. of
edges sketch plane.
siender prismatic extruded 0.75 0.0878 cross-saction, its profile is mastly rectangular with smzotth concave and convex arcs at the ends, forming gently radiused corners.
long outer 20 and 80.028, open through length. One end carries cosxial solid shaft 90.026 0.0978, forming a stapped eylinder with through bore ending the shaft.
U-shaped bracket 0.75 0.3343 0.1555 large opening two contains a through-siot formed by small U-sheped out, allowing for mounting or festening.
An L-shaged bracket 0.3021 x 0.75 x 0:125 with short and taller vertical features rectenguler slot leg
inner through holes on the vertical face mounting.
Figure 3: Examples of generated objects from the CADmium test set: 3D models are generated by both Qwen2.5 Coder (fine-tuned) and Text2CAD (trained) the CADmium training set. The examples are shown using CADmium's 's expert prompts, with the natural-language descriptions shortened for readability. We present both successful and failed reconstructions from Qwen2.5 Coder and compare them against the outputs of Text2CAD.
7 Acknowledgments
Sarath Chandar is supported by the Canada CIFAR AI Chairs program, the Canada Research Chair in Lifelong Machine Learning, and the NSERC Discovery Grant. This research enabled in part by compute resources provided by Mila and Compute Canada. This work was supported by funding from Ansys.
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A Example Annotations
This appendix provides illustrative examples comparing our CADmium annotations, generated using GPT- 4.1, with the expert-level annotations from the Tex12CAD dataset for the same CAD models. These qualita- tive comparisons, presented in Figures through highlight several improvements discussed in Section 3.1 and quantitatively supported by the analysis in Figure
The selected examples specifically demonstrate instances where:
CADmium annotations offer more natural, human-like phrasing, avoiding the overly programmatic or -SON-key-lin references sometimes found in Text2CAD expert annotations (as shown in Figure
Our pipeline's use of more powerful vision-language model (GPT-4.1) leads to more accurate capture of geometric details, such as correctly identifying the number of holes in an object, compared to potential misinterpretatiins in Text2CAD annotations (Figure
CADmium annotations leverage broader knowledge to describe objects by their resemblance lo known items or by their overall form (e.g., "a clamp-like object"), providing more holistic understanding than descriptions that merely summarize CAD construction entities, as sometimes seen in Text2CAD (Figure G).
Our annotations provide clear, correct, and actionable geometric information, in contrast to Text2CAD examples that may be vague, contain errors, or lack sufficient detail for unambiguous reconstruction (Figure 7
CADmium annotations represent numerical values with natural level of precision, unlike the exces- sively long decimal expansions often present in Text2CAD annotations, making prompts aligned with human expression (Figure gh.
Each figure typically presents the CADmium annotation alongside the corresponding Text2CAD expert annotation, often accompanied by visual renders of the 3D object for better context and comparison.
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Text2CAD Expert
Create first part the CAD model, a cylindrical object with hollow center. Begin by setting up new coordinate system with Euler angles (0.0, 0.0, 0.0) and translation vector of (0.0, 0.0, 0.0).
For part 1, create 2D sketch on the X-Y plane of this coordinate system. The sketch consists of 'face_1 which has two loops ('loop_1 and 'lop__ ) 'Loop_1" contains single circle named 'circle_1 with center at (0.2083, 0.2083) radius of 0,2083.
l'arc_1') 0.1176) and ends at (0.1994, 0.1176). The second curve, 'line_2 2" starts at (0.1994, 0.1176) and ends at (0.2783, 0.1176). The third curve, arc_1", starts at (0.2783, 0.1176), has a midpoint (0.2083, 0.3229), ends (0.1383, 0.1176).
'Loop_2' contains
('line_1'), 1 line ('line_2
first curve,
(0.1383,
Next, scale the 2D sketch with scaling factor of 0.4167. Then, extrude the scaled 2D sketch 0..333' units in the positive Z direction and 0.0'' units the negative direction. Create new solid body for this operation and complete the first part of the CAD model.
The first part of the CAD model has the following dimensions: for length and width, 0 and for height, 1.8389333333833333.
CADmium
Begin by creating solid cylinder with diameter of 0.4167 and height of 0.3333. On the top face of the cylinder, sketch a circle centered at (0.2083, 0.2083) with radius 0.2083. Inside this circle, define a closed loop comprised of two horizontal lines and the first line from (0.1383, 0.1176) (0.1944, 0.1176), the second line from (0.1994, 0.1176) to (0.2783, 0.1176), and connect the end points of these lines back to the starting point with an arc that has its midpoint at (0.2083, 0.3229), forming a cylindrical hole
segment that with a flat chard across its lower portion as defined by the lines and arc, resulting in a cylinder with partial circular cutout running through center.
the circle. Extrude this
profile fully through the cylinder
Figure 4: Annotation comparison: CADmium (light purple) uses natural language, while Tex12CAD (light blue) references JSON-like keys (e.g., 'face 1*).
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Text2CAD Expert
Create the first part the CAD model, a rectangular metal plate with rounded corners and two holes on the top side. Begin by setting new coordinate system with Euler angles (0.0, 0.0, 0.0) and translation vector of (0.0, 0.0, 0.0).
Next, draw 2D sketch on the X-Y plane of the coordinate system, and create a rectangular outline consisting of straight lines and provided the detailed instructions.
Create two circular holes at the specified locations: the first circle has center at (0.0781, 0.6771) and a radlus of 0.026; the center (0.2969, 0.0521) 0.75 using the sketchl) scale parameter.
second circle
radius
Ensure all loops closed
scale sketch by
Transform the scaled 2D sketch into 3D sketch by applying coordinate system's orientation (euler angles) and translation vector. Extrude the sketch by 0.0391 create 3D solid model representing 1.
The part has the following dimensions:
Height: 0.33906299998998999
Width: 0.7499899999999998
Length: 97499999998899999999988
cADmium
Begin by sketching a rectangle measuring 0.75 in length and 0.7292 in width. On the lower edge (along the length), create a vertical from origin (0.0,
convex arc from (0.3499, 0.0) through mid-point (0.4844, 0.0664) to (0.6188, 0.0), then continue the bottom edge with horizontal line to (0.75, 0.0).
(0.3499, 0,0).
From (0.75, 0.0), draw a vertical line upwards to (0.75, 0.7292), forming the right edge. On the top edge, draw horizontal line from (0.75, 0.7292) (0.4001, 0.7292). From there, add concave from (0.4001, 0.7292), passing through (o. 2852856, 0.6628) as the mid-point, and ending at (0.1312, 0.7292). Complete the rectangle by drawing horizontal line from (0.1312, 0.7292) to (0.0, origin
0.7292), vertical line down Add four through-holes with a diameter of 0.052 (radius 0.026), located at the following centers: (0.0781, 0.6771), (0.2969, 0.0521), (0.4531, 0.6771), and (0.6719, 0.0521).
0.0).
Extrude the entire profile perpendicular to the sketch plane thickness of 0.0391.
Figure 5: Geometric detail capture: CADmium (light purple) correctly identifies four holes, unlike Text2CAD (light blue) which describes two, highlighting improved visual understanding.
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Text2CAD Expert
Create the first part the CAD model: a rectangular prism with cylindrical hole in its center. Begin by setting up a new coordinate system with Euler angles (0.0, 0.0, -90.0) and translation vector of (0.0002, 0.171, 0.0).
Create the first face of the sketch with two loops. Loop composed 11 curves and arcs, as well as circles, the first radius 0.0202. The second loop this face circle with center (0.2577,
which 0.7086) and a radius of 0.0202.
(0.2577, 0.6338)
Extrude sketch along the normal direction by depth of 0.171, resulting in extrusion depth towards the normal is 0.171 and opposite the normal 0.0. Scale the sketch by 0.75 and create new solid body for this operation.
part
model. Ensure
The first part of the CAD model has the following dimensions: length of 0.1394, width of 0.1394, and height of 0.171 units.
CADmium
Begin by sketching the side profile of clamp-like ring on the base plane, starting with three major arcs and several straight segments. Draw
approximately (0.1728, 0.5901) through (0.0, 0.3357) to (0.1936, 0.0967), From there, continue with a line straight down to (0.1936, 0,0002), right to (0. (0.2294, 0.0002), to (0. 2294, 0.0902) and then further to (0,2294, 0.1295). Next, add convex arc from (0.2294, 0.1295) through (0.2579, 0.5654) to (0.2856, 0.1294). Draw a short downward line (0.2856, 0.0901), then further down 28.2856,00 0.0). Take horizontal line to (0.3236, 0.0), to (0.3236, 0.0972), and create third arc from (0.3236, 0.0972) through (0.5154, 0.3359) to (0.3438, 0.5896). Draw vertical line upward to (0.3438, 0.75), (0.1728, 0.75), and finally straight Within the upper region, add two through-holes as circles: one centered at (0.2577, 0.6338) and another at (0.2577, 0.7086), each clamp.
horizontally
(0.1728, 0.5901).
radius 0.0202. Extrude On the side of the clamp near the split section, make cut sketching circle with radius of 0.0202, centered at (0.0202, 0.0202) on the relevant face. Cut this feature through the body for total depth of 0,3542, with the cut axis perpendicular the ring surface.
profile symmetrically
thickness 0.171, forming main body
the top of the structure (positioned at the maximum height af the clamp), add rectangular slot by sketching square of 0.1394 0.1394,
(0.0, 0.0). Cut the slot downward into the ring for depth 0.1534, forming mounting attachment feature that aligns with the existing through-holes. Ensure internal curves, outer profile, clamp's open, split-ring geometry and the structural attachment at the top.
align concentrically and orthogonally on respective faces, preserving
Figure 6: Descriptive abstraction: CADmium (light purple) identifics a "clamp-like ring", while Text2CAD (light blue) summarizes CAD entities with less descriptive power.
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Text2CAD Expert
Create the first part of the CAD model, a rectangular prism with curved top. Begin by setting up new coordinate system with angles (0.0, 0.0,
translation vector (0.0002, 0.0029,
Next, create six sketches on the X-Y plane of the coordinate system.
Face 1:
Draw a loop consisting of lines and arcs defined by specific points. loop consisting of and lines defined specific points.
Face
Face 3: Draw loop consisting of lines and arcs defined by specific points.
defined specific points. Face 5: Draw loop consisting of ares and lines defined by specific points.
loop consisting
Face Draw loop consisting
arcs defined by specific points.
CADmium
Create pattern composed of six identical solid bodies, each shaped like rounded wedge, arranged evenly in a radial pattern around central axis with small gap between each segment. Model a single segment first: Begin by sketching the outline of the wedge on 2D plane. The outer curved profile is defined by combination of straight lines and arcs: start with short straight section, followed by a convex arc sweeping smoothly to longer straight side, transitioning to another tangent arc, and completing the profile with final line that returns to the starting point, enclosing the form. The cross-sectional profile extends total length of approximately 0.75 and a maximum width of about 0.72 at its widest point.
Extrude this closed sketch symmetrically upward to height of 0.19, yielding 3D body with smoothly blended edges. After the segment created, pattern identical body five more times radially, rotating each one by 60 degrees around central vertical axis, so that all six segments form near-complete flower or propeller-like arrangement. Ensure that each wedge maintains a small gap from its neighbors, creating central vold and distinct separation between each body. The shape should result in a symmetric, modular cluster of six equally spaced, extruded rounded wedges.
Figure 7: Clarity and correctness: CADmium (light purple) accurately describes "six identical solid bodies, each shaped like rounded wedge", whereas Text2CAD (light blue) provides vague and insufficient de- scription.
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Text2CAD Expert
The first of the CAD model is three-dimensional, metallic, rectangular prism with cylindrical hole in the center. To create this part, first set up a new coordinate system with Euler angles (o.0, 0.0, 0.0) and a translation vector of (0.0, 0.0, 0.0). the X-Y plane the coordinate system, consisting
Now, create sketch loop 4, loop and loop 6. Loop is rectangular shape defined by four lines, loop and loop 3 are circles with the same center at with center (0.375,
loops shared plane: loop loop 2,
(0.0417, 0.7083) respectively, loop loop 5 and loop 6 are circles similar to loop 2 and loop but with centers located at (0.7083, 0.0417) and (0.7083, 0.7083) respectively.
(0.0417, 0.0417)
larger
0.3333,
Scale the 2D sketch factor of 0.75, then transform into 3D sketch using the euler angles and translation vector from the coordinate system. Extrude the 2D sketch to generate 3D model with the following properties: extrusion depth towards normal 0.1867, opposite the normal 0.0, the sketch scale is 0.75. This operation creates depth).
solid body.
The part has the following dimensions: length is 07799999999999997 width is 0.749999989999999 and height is
2.6666666666666666 (incluclig For the second part of the CAD model, create new coordinate system with Euler angles of (0.0, 0.0, 0.0) and translation vector of (0.0417, 0.0417, 0.0).
Create sketch plane defined the coordinate system, consisting of single loop: loop 1. This loop is defined by circle with center (0.3333, 0.3333) and radius 0.3333.
Scale the 2D sketch factor of 0.6667, then transform it into 3D sketch using the euler angles and translation vector from the second coordinate system. Extrude the 2D sketch to generate a 3D model with the following properties: extrusion depth towards normal 0.0333, opposite
0.6667. operation joins with previous body. The part has the following dimensions: length is 8866666866866665, width 0.68666666666666, and height is 0.03333333833332 (including extrusion depth). The final shape approximately 20 units long, 10 units wide, and units high (including the height of the cylindrical hole).
0.0,
sketch
dimee-dimensional, rectangular prism with curved, hollowed-out center. The dimensions of the prism are
CADmium
Begin by creating solid cylinder centered at (0.2083, 0.2083) with radius of 0.2083. Inside this circle, define a closed loop comprised of two horizontal lines and line
diameter 0.4167 height 0.3333. the top
cylinder, sketch circle
an arc: draw connect the end points of these lines back the starting point with arc that has its midpoint at (0.2083, 0.3229), forming segment that cuts across the base the circle. Extrude this internal profile fully through the cylinder to create cylindrical hole with a flat chord across its lower portion defined the lines and arc, resulting in a cylinder with partial circular cutout running through its center.
(0.1383, 0.1176) (0.1994, 0.1178), second
(0.1994, 0.1176) to (0.2783, 0.1176), and
Figure 8: Numerical representation: CADmium (light purple) uses natural numorical procision, contrasting with Tex12CAD's (light blue) use overly precise, unnatural decimals.
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B
Metrics on Common Evaluable Subset
As detailed in Section | the calculation of various evaluation metrics is contingent upon the generated CAD models meeting specific validity and structural prerequisites. The number of samples satisfying these diverse requirements can vary across different models or experimental setups. The results presented the main paper maximize data utilization by reporting metrics on all evaluable samples for each individual condition, which can lead to comparisons across slightly different sets of underlying CAD models.
To provide more direct and controlled comparison, this appendix presents supplementary tables correspond- ing to the key experiments in the main paper. In these tables, all metrics are re-calculated based exclusively on the intersection of CAD models that were successfully generated and met all necessary criteria (including validity and any specific prerequisites like watertightness) across all experimental conditions being compared within that specific analysis. This common subset evaluation ensures that different models or configurations are assessed on an identical cohort of generated objects, offering clearer insights into relative performance by minimizing confounding effects from varying sample populations.
Table Performance comparison on the annotttdd CADPrompt dataset (common subset evaluation). Metrics are computed on the intersection of samples for which both models produced valid CAD soquences. Reported values include 95% confidence intervals in brackets. Boldface indicates the better-performing model whenever the difference is statistically significant at p 0.05 (Wilcoxon signed-rank test for continuous metrics, McNemar's test for the binary watertightness metric).
Model Trained on
Text2CAD Text2CAD
Qwen-2.5 Coder 14B
CADmium
IR. (%) Num Valid
1.57
6.28
176
176
Line F1 f
0.67 0.63,, 0.72]
0.69
00.64, 0.74]
Are F1
0.00 0.00, 0.000
0.23 0.11, 0.36)
Circle F1 f
0.33 (a.23, 0.42]
0.61 [o.s1,
Extrusion F1 f
0.84 [0882, 0.87]
0.89
[0.87, 0:92]
CD mean
224.38 197.33, 263.47
203 04 (170.25, 233.10
CD median
129.47 37.09, 170.25]
112.84
(78.56, 177.11]
STR J
0.03 0.00,
0.10
DangEL J
0.21
2.99
4.64]
SegE
0.10 (o.0s, 0.18]
0.78
(o.48, 1.14]
FlxEE (x109) 4
0.05 0.00, 12]
2.28
1.04, 3.78]
Watertightness (%)
95.74
992.00, 98 40] 128
83.80
'78,21, 88 80]
Num Watertight
128
EECM
0.66 (o.ss, 0.71]
(0.59, 0.75]
DMCD (x10*)
14.88 122.44
8.34
(6.54, 10.32]
mean (x10P)
15.69 11937, 18.27)
7.22
55.09, 9.60]
SD median (x10?)
12.26 99.6, 15.14]
1.51
2..06.2.15) 2.15]
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Table 6: Impact of Qwen Coder model scale (1.5B, 3B, 7B, 14B parameters) on the CADmium test set (common subset evaluation). For each dataset, metrics are computed on the intersection of samples for which all three model sizes produced valid CAD sequences. All models were trained on CADmium annota- tions. Reported values include 95% confidence intervals in brackets. Statistical significance is assessed with statistical tests on adjacent sizes +LBB 3B-7B, 7B 7B-+14B). Bold indicates size that is significantly better than the next smaller size at p < 0.05. Details of the statistical testing procedure are provided in Section
Metric
1.5B
3B
7B
14B
IR (%) J. Num Valid
11.86
8.51
5.93
3.33
6500
6500
6500
6500
Line F1 1
0.93 J0.92,
0.94 [0:93,
0.94 [0.94,
0.95 [0.95,
Are F1
0.78 (0.76, 0.81]
0,80 (0.77, 0.82]
0.82 [0.79, 0.84
0.86 [o.84, 0.88]
Circle F1
0.90 (0.89, 0.91
0.93 [0.92, 0.94
0.93 [0.92, 0.94
0.95 [0.94, 0.96]
Extrusion F1
0.99 (0.98, 0.99]
0.99 [0.99, 0.99]
0.99 [0.99, 0.99]
0.99 [0.99, 0.99)
CD) mean
64.74 660.84, 68.67]
39.48 [36 42.97]
54.23 [50.43, 58.22)
37.11 1 [34.13, 40.33
CD median +
0.26 0.25, 0.27]
0.21 [0.21, 0.22]
0.23 [0.21, 0.24]
0.20 [o.20, 0.21]
SIR 4
0.04 0.03, 0.04]
0.03 [0.03, 0.03)
0.03 (0.02, 0.03)
0.02 [0.02, 0.03)
DangEL +
0.94 0.80, 1.09]
0.76 [0.85, 0.87]
0.65 [0.56, 0.78]
0.57 [o.48, 0.67)
SegE
0.33 (0.29, 0.38]
0.30 (0.26, 0.34
0.26 [0.23, 0.29]
0.24 [0.21, 0.288
FluXEE (x10P) 4
0.30 j0.25, 0.35]
0.30 (0.22, 0.38]]
0.24 [0.17, 0.314]
0.25 [o.18. 0.3k3
Watertightness (%)
90.38 01.06]
90.31 (89 (0.09)
90.54 [80.87, 91.19)
90.78 190.13, 91.44]
Num Watertight
6205
6408
6575
6779
EECM
0.90 0.91]
0.91 [0.91,
0.91 [0.90,
0.92 [0.92,
DMCD (x (x109) 4
2.42 (2.25, 2.60]
2.18 [2.00, 2.37]
2.08 [1.91, 2.26]
1.95 [1.78, 2.14]
SD mean (x10?)
2.51 (2.31, 2.72]
1.83 [1.67, 1.99]
1.82 [1.66, 1.99)
1.60 [1.44, 1.76]
SD median (x10²)
0.00 jo.00, 0.00]
0.00 [0.00, 0.00]
0.00 [0.00, 0.00]
0.00 [0.00, 0.00)
Table 7: Impact of Qwen Coder model scale (1.5B, 3B, 7B, 14B parameters) on the Fusion360 test set (common subset. evaluation). For each dataset, metrics are computed on the intersection of samples for which all three model sizes produced valid CAD sequences. All models were trained on CADmium annota- tions. Reported values include 95% confidence intervals in brackets. Statistical significance is assessed with statistical tests on adjacent sizes 11.5B+33B 3B-7B, 7B 7B+14B). Bold indicates size that is significantly better than the next smaller size at p < 0.05. Details of the statistical testing procedure are provided in Section
Metric
1.5B
3B
7B
14B
IR (%) I.
22.62
23.38
16.79
12.58
Num Valid
5304
53004
5304
5304
Line F1 1
0.86 [o.s5, 0.87]
0.88 [0.87, 0.89]
0.90 [o.89, 0.91]
0.91
[0.90, 0.92]
Arc F1 f
0.74 (0.71, 0.76)
0.75 1o. 0.771
0.78 (0.76, 0.800
0.81 [0.79,
Circle Fl f
0.90 [o.89, 0.91]
0.92 [0:92, 0.98]
0.92 (o.92,
0.94 [0.93, 0.94]
Extrusion F1 f
0.98 (o.98, 0.99]
0.98 [o.98, 0.98]
0.99 (o.99,
0.99 [0.99, 0.99]
mean J
227.21 [218.41, 206.09]
188.94 [181.02, 197.01]
206 18 [197.73, 214
211.93 [202: 70, 221.
median 4
103.82 (97.32, 110.25]
72.37 [64.80, s0.78)
85.0 15.95 (79.99, 91.91]
73.71 [63.45, 82.08)
SIR
0.04 (o.03, 0.04)
0.04 jo.03, 0.04]
0.05 (0.04, 0.05]
0.03 (o.0s, 0.03)
DangFI. 4
1.03 (0:90; 1.15)
0.86 jo.73, 0.98)
1.21 [1.06, 1.39]
0.73 (0.6], 0.85)
Segl 1.
0.37 [o.34, 0.43]
0.34 Jo.30, o.ae]
0.38 (o.34, 0.42]
0.28 [0.25, 0.32]
FlaXEE (x10P)
0.20 (0.15, 0.27]
0.20 [0.14, 0.27]
0.18 (o.12, 0.25]
0.18 10.12, 0.23]
Watertightness (%) t
86.62 (83.78, 97.46]
86.50 [85.67, S7.355
85.21 [84.38, 66.04]
87.35
56, 88.10)
Num Watertight
5241
5192
5504
5941
EECM 1
0.82 [0.81, 0.83)
0.84 jo.s3, 0.80)
0.84 (o.83, 0.85]
0.87 [0.86, 0 0.88)
DMCD (x10*) 4
4.04 [3.78, 4.30)
3.36 [3.12, 8.61]
3.56 (3.31, 3.83]
3.26 [3.02, 3.52]
SD mean [x10P) L
3.55 [3.29, 3.82)
2.53 [2.33, 2.70
2.91 12.08, 3.15]
2.51
[2.29, 2.73
SD median (x10P)
0.00 [o.00, 0.00)
0.00 J0.00, 0.00
0.00 (0.00. 0.00)
0.00 10.00, 0.00]
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Table 8: Impact of annotation quality using the Text2CAD architecture (common subset evaluation). Models were trained either CADmium Text2CAD annotations. For each of the four training/evallutee set pairings, metrics are computed on the intersection of samples for which both compared conditions produced valid CAD sequences. Reported values include 95% confidence intervals in brackets. Boldface indicates statistically significant differences (p < 0.05); details of the statistical testing procodure are provided in
Section
Evaluated
CADmium
Text2CAD
Trained on
CADmiuu
Tex12CAD
CADmium
Text2CAD
IR (%) 4 Num Valid
3.07
4.44
4.95
4.34
7277
7277
7469
7469
Line
0.89 [o.89,
0.74
[0.73, 0.75]
0.64 [0.64, 0.65]
0.83 [o.82, 0.83]
[0:58, 0.89
0.22
[0.20, 0.24]
0.25 (0.23, 0.271
0.38 (0.36, 0.41]
Circle F1
(0:88, 0.00
0.58 [0.57, 0.60]
0.47 [0.46, 0.00]
0.76 [0.75, 0.77]
Extrusion
0.98 [0.98, 0.98
0.90
[o.80, 0.90]
0.81 [o.80, 0.81]
0.94 [0.98. 0.94)
CD mean
50.44 [16.90, 58.93)
144.85
[140.00, 149.60)
107.47 [103-45, 111.46)
28.52 (26.58, 30.47]
CD modian
0.31 [0.29, 0.32]
50.78
[36.95, 55.
33 38 [20.78, 35.88"
0.37 [o.34, 0.40)
SIR J
0.04 [o.04, 0.05)
0.04
[0.04, 0.05]
0.08 [a.07, 0.0s]
0.04 [o.04, 0.03)
DangEL +
1.36 [1:22, 1.51)
1.11
[0.97, 1.26]
2.38 [2.17, 2.00]
1.06 10.94, 1.19)
SegE 4
0.63 [o.53,
0.50
1.06 [0.97,
0.48 [0.44. 0.53)
FliXEE (x10") 4
0.39 [o.31,
0.57
0.93
0.42 [0.38. 0.53)
Watertightuess (%)
87.63 [sc. 88, 88.36)
91.90
[91, 29, 92.50
82.48
[HL. 62, 53.34"
90.52 (89.85, 91.17]
Num Watertight
6033
6033
5673
5673
EECM
0.87
[0.86, 0.88
0.77 [0.75, 0.80)
0.66 [0.66, 0.68)
0.82 [0.83, 0.85)
DMCD (x10*)
3.37
7.64 [7.32,
9.19 [8.89,
5.09 [4.81. 5.36)
SD mean (x 1x
2.50
8.31 [8.01,
11.31 [10.91, 11.71
4.68 [4.42, 4.98)
SD median (x10²)
0.00
3.20
[2.87, 3.48]
5.07 [4.72,
0.02 [0.00, 0.03)
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C
Prompt Details
This appendix details the various prompts utilized in our rescarch pipeline.
The generation of our CADmium annotations was performed using GPT-4.1. The system message provided to GPT-4.1, which guided it to produce the descriptions, displayed in Figure 9 The user message is composed of the minimal JSON representation of the CAD model, 10 multi-view images rendcred with Blender, and the instructions.
For the text-to-CAD generation task, our fine-tuned Qwen Coder models take natural language description input. The specific format used to instruct the model to generate the corresponding JSON-based CAD sequence is shown in Figure 10
To evaluate the quality of our CADmium annotations against CADLLM and Text2CAD expert annotations, we employed an LLM-as-a-judge setup using both Gemma-3 12B (Team et al. 2025 and Mistral 3.2 Small [Mistral 2025), as described in Section 3.1 and with results presented in Figure 2p. In this evaluation, 10,000 paired annotations (one from CADmium, model) were assessed. To mitigate positional bias, the annotations were randomly assigned to the labels "1.", "2.", and "3." within the user message presented to Gemma-3 12B. For optimizing the classification process and minimizing output variability, the model was explicitly instructed to respond with only single digit (0, 1, 2, or 3), allowing the judgment to be captured efficiently. The specific prompts used to instruct LLMs for each the four evaluation criteria are delailed as follows:
for CADLLM, one from Text2CAD expert for the same CAD
The prompt for assessing human-likeness is shown in Figure
The prompt for assessing clarity and readability is shown in Figure
The prompt for assessing visual faithfulness (given image renders of the object) is shown in Figure 13
The prompt for assessing completeness against the ground truth minimal JSON description is shown in Figure 12.
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User Message
qopp
P
You are an expert mechanical engineer tasked with creating clear, precise instructions text-to- CAD generator. have of multi-view images displaying 3D model, well JSON describing the exact CAD operations used construct the object.
This is the json file: jjson
{ ("parts": {
"part_1" {
"coordinate_sve :strtesssssttm: { }, "sketch"; [[ ... ]
"extrusion": { ... ]
"part_2": [ ... ]
Create single, comprehensive text description of this 3D object that:
Describes all geometrical features accurately based on the operations and dimensions
Uses natural language as if a human designer were explaining how to model this object
Is written in second-person as instructions text-to-CAD system Includes critical dimensions and geometric relationships (note that you don't need specify the measurement for lengths)
Avoids redundancy while ensuring completeness
Focuses the design intent and functional geometry
Answer only with the description. No introductory phrases, titles, commentary, summaries or conclusions
Your description should be concise complete, capturing every important geometric feature without unnecessary repetition.
Figure 9: Prompt structure for CAD model annotation. The user message contains the JSON description and the 10 multi-view renders of the 3D models, and instruct GPT-4.1 to generate natural language description.
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System Message
Generate CAD model JSON EXACTLY matching this schema:
p
(parLt": Always use sequential part.., part.... even if some are null
ppordinate_systemm"
"Euler Angles": (0.0, 0.0],
angles degrees
"Translation Vector": (0.0, 0.0, 0.0] // x,Y, position offsets
"description::
"height": 0.0, Total vertical dimension
"langth:: 0.0, Total horizontal dimension
"name": // (optional) Component identifler
"shape": (optional) Basic geometric classification
"width": Total depth dimension
"extrusion"-
"extrude, depph_ opposite,
// Negative
"extrude dewtheetpwareeeeee htwptttowwdd_oooee normalt 0.0, // Positive direction extrusion
"aperation": NewwBddyfstuureSerator // One of: NewuBedyfestureOperatiin, Joneestttrrtnneerraiiinn
CutFratureOperatiin, IntersactFaturrpperaaioo
"sketch_scale" // Scaling factor
geametry
(sktcc"::
(face_]": sequential
face_2... unused)
(loop_1": Use sequential loop_1, loop.2... (nuil unused)
"oircle_1": Use sequential circla_1, circle_2...
"Center": [0.0. 0.0), /XX,Y X,Y coordinates
"Radius"
"arc,"": // Use sequential arc,1, arc.
"Start Point": 0.00,
"End Point": 0.0).
"Mid Point": 0.0)
line]": /f sequential line_2..
Point": [0.0, 0.0],
"End Point": [0.0,0.00 0.0]
(other geometric elamants null/none)
11 lother loops as null/none)
(other null/none)
part null, // Maintain sequential numbering null || (additional parts)
STRICT RULES:
OUTPUT ONLY RAW JSON (no bommsttiigaarrccoommom//eeeeeaan
COPY INSTRUCTIONAL
FROM JSON EXAMPLES
numbers as floats (0.0 not
ALLOWED OPERATIONS:
intersecteeaturepperrttt
REQUIREMENTS
only primitives):
Circles: Center[X.Y] Radius
Ares: Start[X,Y] + End[X.]] Mid[XY]
Lines: Start[X.Y] End[XY]
ENFORCE part part_2... sequence (include nulls)
NO NEW FIELDS
User Message
Begin by creating a solid cylinder with diameter of 0.4167 and height 0.3333. On the top face of the cylinder, sketch circle centered at 10.2083, 0.2083) with radius of 0.2083. Inside this circle, define a closed loop comprised of two horizontal lines and 0.1176) (0.2783, midpoint (0.2083, 0.3229), forming a segment that cuts across the base of the circle. Extrude this internal profile fully through the cylinder to create cylindrical hole with flat chard across its lower portion as defined by the lines and arc, resulting cylinder
draw first line from (0.1383, 0.1176) (0.1994, 0.1176), the second line from (0.1994, lines
connect
points
starting point
circular cutout running through
Figure 10: Prompt structure for CAD model generation. The system message specifies the JSON schemn and constraints, while the user message contains the natural language description of the target CAD model.
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System Message
You are an expert linguistic analyst. Your task is to determine which of three provided text annotations describing 3D CAD model is more likely to have been written by a human.
You will be presented Respond with:
annotations, labeled "1.", "2.", and "3.".
"1" if Annotation is more likely human-generated.
- -"2" If Annotation 2 is more likely human-generated.
"3" if Annotation 3 is more likely human-generated.
"O"" are equally likely human- summan-generatd, seem Al-generated, or you cannot confidently determine difference.
entire response must be single digit (0, 1, 2, 3). not provide any other text, explanation, or punctuation.
User Message
Here annotations for CAD model: "(annotation_
"(annotation_2)
"(annotation_3 3)"
Which annotation more likely uumanngenerrttd?? If you confidently determine, they equally likely
human or AI, indicate tie. Answer with 1 [for Annotation 1), 2 (for Annotation 2), 3 (for Annotation 3), 0 (for tie) only.
evaluation, tasking the judge to assess and compare the human- likeness of two provided CAD model descriptions (one from CADmium, one from Text2CAD expert).
Figure 11: Prompt for the
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System Message
are an expert CAD model analyst with strong natural language understanding capabilities.
Your task to evaluate three text annotations against detailed JSON description of 3D CAD object's construction history and features.
The JSON describes the object in terms of its constituent parts, sketches (including geometric primitives like lines and arcs), extrusion operations, coordinate systems, and descriptive rreserties (like name, shape, and dimensions). You must determine which of the two text annotations more completely, accurately, and exhaustively represents these core provided
features detailed
You will be given the JSON and three annotations labeled "..,, "2.", and "3." Respond with:
- "1" if Annotation is more comprehensive and accurate in representing the JSON's core features.
"2" if Annotation 2 is more comprehensive and accurate in representing the JSON's core features. more comprehensive and accurate representing the JSON's core features. "0" annotations are approximately equal in their representation
Annotation
JSON's core features, or are equally lacking. Your entire response must be single digit 2, 3). Do not provide any other text, explanation, punctuation.
User Message
The following is JSON description detailing the construction and features a 3D CAD object:
json
f "parts":
"part_"" {
b. "extrusion": { ]
coordinate__a srrdiinat__sstemms {(
"sketch": [ ] h
"part_2": {
}b}
Now, consider these three text annotations for the same 3D CAD object:
"(annotation__ _1"
"[annotation_2
"[annotation_]
Based on the detailed information in the JSON description provided above, which annotation (1, 2, or 3) most exhaustively and accurately represents the core features the CAD object (such as parts, their geometric makeup, construction operations, key descriptive properties) all substantially equal in regard equally incomplete, indicate tie. Answer with (for Annotation (for Annotation 2), (for Annotation 3), 0 (for tie) only.
Prompt for the LLM-as-a-judge evaluation, tasking the judge to assess and compare the com- pleteness of two provided CAD model descriptions against the ground truth minimal JSON representation of the corresponding 3D object.
Figure 12:
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Figure 13: Prompt for the LLM-as-a-judge evaluation, tasking the judge to assess and compare the visual faithfulness of two provided CAD model descriptions against multi-view image renders of the corresponding 3D object.
System Message
You expert visual-textual analyst. Your task to examine images 3D CAD model corresponding text annotations.
You must determine which of the three annotations most accurately and faithfully describes the visual content presented in the images.
You will be provided with three annotations, labeled "1.", "2.", and "3.". The images will be provided alongside this text. Respond with:
"1" if Annotation most accurately describes the images.
"2" Annotation 2 most accurately describes the images.
- "3" if Annotation 3 most accurately describes the images.
"O" if all annotations describe the images with equal accuracy, or if neither adequately describes the images, or you cannot
confidently determine difference.
Your entire response must be single digit (0, 1, 2, 3). Do not provide any other text, explanation, or punctuation.
User Message
qpppp
viewing images 3D model. Below two text annotations:
1. "(annotation_l)
"(annotatio_] 2)"
"(annotation _3"
Considering the visual details in the provided images, which annotation (1 or 2) most accurately and faithfully describes the 3D model shown? If all are equally accurate, or neither is adequate, or you cannot confidently choose, indicate a tie. Answer with (for Annotation 1), (for Annotation 2), 3 (for Annotation 3), or (for a tie) only.
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as language, straightforward Published in Transactions on Machine Learning Research (01/2026)
System Message
You are an expert linguistic evaluator. Your task is to analyze two text annotations provided for a 3D CAD model and determine which one exhibits superior clarity, conciseness, and overall readability for general audience. three annotations, labeled "1." and "2.", and structure,
You will be presented
Consider factors Respond with:
of understanding.
"1" if Annotation is clearer and more readable.
- -"2" if Annotation 2 is clearer and more readable.
"33" If Annotation 3 is clearer and more readable.
"0" annotations are approximately equal in clarity and readability, or are equally unclear.
Your entire response must single digit 2, 3). not provide any other text, explanation, or punctuation.
User Message
Below three text annotations for 3D CAD model. Please evaluate them based on their clarity, conciseness, and overall readability.
1. "(annotation_)) __
2. "(annotation_2 2)
3. "(annotation33)
Which annotation is clearer, more concise, and easier for general audience to understand? If they are substantially equal in these aspects, or equally unclear, indicate tie. Answer with 1 (for Annotation 1), 2 (for Annotation 2), (for Annotation 3), or (for tie) only.
Figure 14: Prompt for the LLM-as-a-judge evaluation, tasking the judge to assess and compare the clarity and readability of two provided CAD model descriptions (one from CADmium, one from Text2CAD expert).
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D Qualitative Analysis of New Metrics
The purpose of this section is to show examples of cases where the new metrics (EECM, SD, and DMCD) had poor values for the test dataset reconstructions by Qwen-7B model trained on the CADmiun dataset.
Exact Euler Characteristic Match (EECM)
Ground Truth, UID: 00262126
(a) Ground Truth Fuler Characteristic: -8 (5 holes)
Generated
(c) Generated Euler Characteristic: 0 (1 hole)
Figure 15: In this example, the Euler Score mismatch translates to the discrepancy in the number of holes between the ground truth (5 holes) and the generated reconstruction (1 hole).
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Ground Truth, UID: 00260276
(a) Ground Truth Euler Characteristie: 2 (0 holes, 1 connected component)
Generated
(c) Generated Euler Characteristic: (0 holes, 2 connected components)
Figure 16:
In this example, the Euler Score mismatch translates to the discrepancy in the number of connected components, as there are two components in the generated reconstruction.
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Ground Truth, UID: 00326020
(a) Ground Truth Euler Characteristic: 2 (0 holes)
Generated
(c) Generated Euler Characteristic: 0 (1 hole)
Figure 17: In this example, the Fuler Score mismatch translates to the discrepancy in the number of holes between the ground truth (0 holes) and the generated reconstruction (1 hole).
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Sphericity Discrepancy (SD)
Ground Truth, UID: 00513302
(a) Ground Truth Sphericity: 0.0983
Generated
(b) Generated Sphericity: 0.8059
Figure 18: SD: 0. 0076;; The discrepancy in the sphericity indicates the difference in the overall shape. Higher sphericity indicates closer resemblance to sphere.
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Ground Truth, UID: 00947632
(a) Ground Truth Sphericity: 0.20-17 Generated
(b) Generated Sphericity: 0.8667
Figure 19: SD: 0.6620; The discrepancy in the sphericity indicates the difference in the overall shape. Higher sphericity indicates closer resemblance to sphere.
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Ground Truth, UID: 00963721
(a) Ground Truth Sphericity: 0.0881 Generated
(b) Generated Sphericity: 0.7255
Figure 20: SD: 0.6371; The discrepancy in the sphericity indicates the difference in the overall shape. Higher sphericity indicates closer resemblance to sphere.
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Discrete Mean Curvature Difference (DMCD)
Ground Truth, UID: 00517800
(a) Ground Truth Mean Curvature (Averaged): -0.1221
Generated
(b) Predicted Mean Curvature (Averaged): 0.0471
Figure 21: DMCD: 0.1061; Discrepancy in average mean curvature between the ground truth and generated meshes.
E
Design Choices
Annotation Pipeline Prior works like Text2CAD utilized ensembles of separate vision and language models. We consciously chose single, stato-of-tho-art multimodal model (GPT-4.1) to unify the reasoning process. We hypothesized that a single model with strong instruction-following capabilities would reduce error propagation between the visual perception and text generation stages. We prioritized minimal JSON inputs over verbose serialized formats to maximize the token context window available for reasoning. Con- sistent with the methodology proposed in Text2CAD, we utilize 10 multi-view images as input for the annotation model.
Annotation Evaluation For evaluating annotation quality, we employed Gemma-3 12B and Mistral Small 3.2 24B. We avoided using GPT-4 for evaluation to prevent self-preference bias, as our annotations were generated by GPT-4.1. By using distinct model families for evaluation, we aimed to decouple the generator's bias from the evaluator's preference.
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Ground Truth, UID: 00855780
(a) Ground Truth Mean Curvature (Averaged): -0.0096
Generated
(b) Predicted Mean Curvature (Averaged): 0.0925
Figure 22: DMCD: 0.1021; Discrepancy in average mean curvature between the ground truth and generated meshes.
Fine-tuning We selected Qwen-2.5 Coder due to its demonstrated superiority in code generation bench- marks compared to general-purpose LLMs of similar size. For training, we utilized Low-Rank Adaptation (LoRA) with a rank of 64. This rank chosen empirically; preliminary experiments suggested that lower ranks (r 8, 16) were insufficient for the model to learn the strict syntax of the CAD JSON schema, while full fine-tuning was computationally prohibitive without providing significant performance gains.
Metrics Standard metrics like Chamfer Distance (CD) often fail to capture topological defects in chanical parts (e.g., closed hole open hole). To address this, utilize Exact Euler Characteristic Match (EECM) and Sphericity Discrepancy (SD). EECM directly reflects the number of holes and connected components, while SD measures the object's deviation from a perfect sphere. Additionally, we employ the Discrete Mean Curvature Difference (DMCD) to calculate the average "mean curvature" across vertices. DMCD useful for sccnarios where surface curvature fidclity is crucial, such as comparing the smoothness of mechanical fillets. Collectively, these metrics are particularly helpful when scaling towards complex 3D object generation, where intricate differences in geometry and topology critical.
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Ground Truth, UID: 00079323
(a) Ground Truth Mean Curvature (Averaged): -0.0185
Generated
(b) Predicted Mean Curvature (Averaged): 0.0778
Figure 23: DMCD: 0.0963; Discrepancy in average mean curvature between the ground truth and generated meshes.
F Licenses for External Assets
This appendix clarifies the licenses and terms of use for the primary external datasets, pre-trained models, and software libraries utilized in this research, based on publicly available information as of May 2025.
The DeepCAD dataset (Wu et al. 2021 is available under the MTT License, as indicated in official repository. The ABC datasct (Koch et al. 2019), which is source for DeepCAD, is also distributed under the MIT License. The Fusion 360 Reconstruction dataset (Willis et al., (021)), originating from the Autodesk Fusion 360 Gallery, is referenced in academic literature as being available for research purposes under the Creative Commons AttributiionNNoCommmertt
4.0 International (CC BY-NC 4.0) license.
The language models leveraged include: Qwen2.5-Coder (Yang et al. 2024 open-source versions of which are provided under the Apache License 2.0. The GPT-4.11 model (OpenAl 2025) is a proprietary offering from OpenAI, and its usage governed by OpenAl's prevailing Terms of Use, Usage Policies, and Sharing & Publication Policy. Gemma models Team al. 2025). developed by Google, are released under a custom Gemma license.
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Key software utilized includes Blender (Blender Online Community 2025 which is distributed under the GNU General Public License (GPL.) version 3 or later. The Python library trimesh (Dawson-Haggerly et al. used for geometric computations, is available under the MIT License.
The authors have made a best effort to ascertain and respect these licenses and terms. For precise and up- to-date licensing information, direct reference the official documentation for each asset recommended.
G Licenses for New Assets
Furthermore, the CADmium dataset, which includes the novel annotations generated as part of this research, is relcased by the authors under the MIT License. The fine-tuned models presented in this paper, which are derived from Qwen2.5-Coder, released by the authors under the Apache License 2.0.
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Reviews:
[{"review_id": "9Wh4KBaiFA", "reviewer": "Reviewer_W47D", "summary": "", "questions": "", "limitations": "", "rating": null, "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": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces CADmium, a text-to-CAD system that reframes CAD generation as a text-to-text translation problem. To support this, the authors build a large dataset of approximately 176K CAD models with expert-style textual descriptions synthesized using GPT-4.1. They fine-tune Qwen2.5-Coder models to convert natural-language prompts into JSON-based CAD command sequences and show consistent improvements over Text2CAD, particularly in geometric accuracy and shape integrity. CADmium introduces new geometry-focused evaluation measures, including Sphericity Discrepancy, Euler characteristic matching, and Discrete Mean Curvature Difference, complementing standard fidelity metrics. Experiments across DeepCAD, CADPrompt, and Fusion360 benchmarks demonstrate superior geometric element fidelity and better performance on curvature and topological metrics, establishing CADmium as an effective text-to-CAD generation framework.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper presents extensive quantitative evidence across several datasets (DeepCAD, CADPrompt, Fusion360) and evaluates on both model-generated and human-written instructions. The reported metrics include geometric and topological measures, invalidity rates, F1 scores, Chamfer distances, and newly introduced structural metrics. Confidence intervals and statistical significance tests strengthen the credibility of comparisons. Ablation on model scale (1.5B → 14B) and cross-training experiments using both CADmium and Text2CAD architectures provide convincing evidence that the approach is effective and that improvements arise from both data quality and model design.", "audience_yn": "Yes", "audience_explanation": "The topic is highly relevant to the TMLR community, particularly researchers working on program synthesis, text-to-3D generation, CAD automation, and leveraging LLMs for structured geometry tasks. The paper makes contributions in dataset creation, fine-tuning code-oriented LLMs for CAD generation, and introducing new geometric/topological evaluation metrics — areas that intersect machine learning, computer-aided design, and generative modeling.", "requested_changes": "While the paper presents a compelling dataset and text-to-CAD pipeline, the manuscript would benefit from a deeper discussion and clearer positioning of the data generation methodology and data selection strategy for CAD code. In particular:\n\n1- Clarify the data generation technique: Expand the explanation of how GPT-4.1’s multimodal reasoning was leveraged beyond basic captioning — e.g., how the pipeline balances geometric fidelity with natural language fluency and avoids over-constrained descriptions that simply mirror JSON keys. A flow diagram or pseudo-prompt example could strengthen transparency.", "broader_impact_concerns": "This work focuses on automating CAD design from natural language, which has significant positive potential for accessibility, rapid prototyping, engineering productivity, and democratizing 3D design.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "3JkglySSmO", "reviewer": "Reviewer_7RcQ", "summary": "", "questions": "", "limitations": "", "rating": null, "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": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "**Contributions:**\n\n1. **Annotation pipeline and proposed dataset**: The authors proposed a pipeline that uses GPT-4.1's multimodal capabilities to generate textual descriptions for training. The work re-annotated the DeepCAD dataset with high-quality, human-like descriptions for 176,017 CAD models. They presented this new large-scale dataset with improved annotations compared to existing Text2CAD descriptions.\n\n2. **Reformulation of the CAD task**: This work propose to formulate the task as a purely text-to-text problem, minimizing manual intervention. For this reformulated task, the authors fine-tuned a Qwen-2.5-Coder using LoRA on the composed dataset to generate JSON-formatted CAD sequences from natural language descriptions.\n\n3. **Proposing new evaluation metrics**: The paper introduces several new geometric and topological metrics including Sphericity Discrepancy (SD), Discrete Mean Curvature Difference (DMCD), Exact Euler Characteristic Match (EECM), and watertightness assessment to provide richer structural insights beyond traditional point-cloud metrics.\n\n**Strengths:**\n1. The text-to-text formulation for CAD generation is novel and well positioned for using existing pre-trained models effectively.\n2. The proposed dataset is beneficial to the community. The work also provides comprehensive evaluation of the proposed model on multiple datasets including the proposed one. I believe the dataset and the evaluation metrics can be valuable tools and resources to the research community working on 3D object generation and CAD automation.\n3. CADmium shows better performance in reconstructing specific geometry from text comparing to baselines. It achieves much higher F1 scores than Text2CAD for arcs and circles. The authors also use the Sphericity Discrepancy metric to show that CADmium can better capture the object's overall compactness.\n\n**Weaknesses:**\n- The performance degradation when evaluating ob different annotation styles shows room for improvement in the pipeline design. The model struggles on out-of-distribution data, which suggests that it could be overfitting to the specific style of its training prompts.", "claims_and_evidence_yn": "Yes", "claims_explanation": "1. The paper provides detailed evaluation across multiple datasets including Text2CAD, CADPrompt, Fusion360 and comparisons to other baseline models. The work also provides ablation studies on model scale and annotation quality. \n2. The introduced geometric and topological metrics e.g. SD, DMCD, EECM, watertightness are mathematically sound. The metrics are clearly defined with clear mathematical formulations and implementation details.", "audience_yn": "Yes", "audience_explanation": "1. **Computer Vision and 3D Processing Community**: The work addresses text-conditioned 3D object generation, which is an active area of research. The novel evaluation metrics (sphericity, curvature, Euler characteristic) provide valuable tools for the 3D vision community.\n\n2. **Applied AI and Engineering**: This work makes it easier to automate CAD design using AI, which is important for industry. People who work on engineering or manufacturing with AI will find it useful. It speeds up the creation process and lets people who are not CAD experts use the system without knowing special CAD programming.", "requested_changes": "1. The authors should more clearly position the contributions as significant incremental improvements upon prior work. E.g. the re-annotation of the DeepCAD and Fusion360 datasets using an adapted pipeline. The performance gains should be largely attributable to the pre-trained Qwen-2.5 Coder base model, which is not a novel contribution of this work. This should be clearly distinguished from the paper's contribution.\n\n2. Manual validation is encouraged as the \"LLM-as-a-judge\" evaluation has been questioned a lot in the field, which often considered methodologically flawed. Using Gemma-3 12B to judge GPT-4.1's output could be preference leakage, as the judge is likely biased towards the style of the LLMs. The claims of superior \"human-likeness\" and \"clarity\" are less reliable without independent human validation.\n\n3. A more comprehensive ablation on annotation sytle is encouraged, comparing annotations from more MLLMs not just Text2CAD. In section 4.3 This result suggests that the models may be fitting on the specific style and vocabulary of their training data, rather than learning a truly generalized, style-agnostic \"understanding\" of geometric instructions.", "broader_impact_concerns": "- Quality and safety are serious consideration. Ensuring the reliability and correctness of generated designs is crucial. Errors in the generated CAD files can have significant consequences if deployed in real-world applications. In that sense, the labor requested for validation is non-negligible and should be considered.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "5EpWbmw9EK", "reviewer": "Reviewer_UbsL", "summary": "", "questions": "", "limitations": "", "rating": null, "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": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors propose a new approach, called CADmium, to generate 3D objects in a text-to-text manner, in which a series of precise object descriptions given in JSON format are prompted to GPT-4.1 to produce corresponding natural language descriptions, and these generated annotations are then used to fine-tune the Qwen2.5-Coder model. The authors also conducted several experiments, including a main comparison with Text2CAD and multiple ablation studies to evaluate the effectiveness of the approach.", "claims_and_evidence_yn": "Yes", "claims_explanation": "I think the partial claims made by authors are supported by the evidence. Some concerns are given below:\n\n1. **Main claim**: The authors claim that their approach automates sequential CAD design, leverages fine-tuning of code-oriented LLMs, and achieves competitive or improved performance compared to Text2CAD on several metrics. We find that the authors have provided sufficient experimental evidence to support this main claim.\n2. **Baseline coverage**: Although the authors compare their model primarily against Text2CAD, I think this work is not merely an exploratory analysis of LLMs ability on a specific problem but rather introduces a new approach that aims to improve both the methodological diversity and performance of LLMs on text-to-CAD generation tasks. Therefore, it would be important to include or at least discuss comparisons with other relevant approaches, such as Text-to-CAD (R. Wang et al., 2025) and CAD-Coder (C. He et al.), to more convincingly demonstrate the advantages of the proposed method.\n3. **LLM-as-judge evaluation**: The authors employ an LLM-as-a-judge setup to assess the quality of the newly generated annotations along four perspectives. However, using only Gemma-3-12B as the judge raises concerns about reliability. It would strengthen the study to either employ a more capable LLM as the evaluator or conduct a small-scale human evaluation to calibrate and validate the judgment quality of Gemma-3-12B before using it for large-scale assessment. In current case, relying solely on Gemma-3-12B makes the evaluation less convincing.\n4. **Methodological novelty**: From a methodological standpoint, the essence of this approach itself is relatively common in the community. Although this paper may be the first to apply such fine-tuning techniques to CAD generation, the conceptual novelty remains limited. Just a concern although this is not required.", "audience_yn": "Yes", "audience_explanation": "This paper is suitable for the TMLR community as it relates to several active research areas, including LLMs, code generation (albeit only JSON), and ML for Design.", "requested_changes": "Please refer to the above comments.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)