Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models

Hongguan Xiao; Jianxing Liao; Jingxian Liao; Junyan Xu; Maowen Tang; Shui Yu; Sicheng He; Yatao Sun; Yun Li

REVIEW 4 major objections 5 minor 72 references

Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that a two-stage pipeline — a Transformer draft generator and a confidence-guided LLM editor — turns text descriptions into accurate CAD modeling commands, reaching 96.6% command accuracy and 86.4% full-sequence accuracy.

desk verdict Solid two-stage text-to-CAD system with honest limitations, but the headline accuracy is measured on near-lossless synthetic text; worth peer review after adding a human-prompt test. read the letter →

arxiv 2505.19490 v1 pith:NAW2SFIN submitted 2025-05-26 cs.AI

Jianxing Liao , Junyan Xu , Yatao Sun , Maowen Tang , Sicheng He , Jingxian Liao , Shui Yu , Yun Li

show 1 more author

Hongguan Xiao

This is my paper · ORCID

classification cs.AI

keywords CADcommandsequencegenerationtext-to-CADlargelanguagemodelsTransformerdual-channelfeatureaggregationLoRAfine-tuningsemi-automatedannotationcomputer-automateddesign

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that CAD modeling during the detailed design phase can be automated by splitting the work into two stages: a compact Transformer model (TCADGen) converts parameter and appearance descriptions into a draft CAD command sequence, and a fine-tuned large language model (CADLLM) corrects that draft using the per-command confidence scores TCADGen attaches to it. A semi-automated annotation pipeline supplies the training text, generating appearance descriptions from multiple views and point clouds with vision-language models and parameter descriptions with an LLM that verifies its own output by reconstructing the ground-truth sequence. Reported results on a DeepCAD-derived benchmark put the combined pipeline at 0.966 average command accuracy, 0.864 full-sequence accuracy, and a Chamfer distance of 3.12, far below Transformer-only baselines. If those numbers hold on real user prompts, a designer could hand a detailed part specification to the system and receive an editable modeling sequence, a step toward computer-automated rather than merely computer-aided design.

What carries the argument

The central object is the CAD Command Sequence (CCS), a sketch-and-extrude program whose commands (Line, Arc, Circle, Extrude) each carry explicit numeric parameters, together with the confidence scores $s_i = (s^{\text{cmd}}_i, s^{\text{args}}_i)$ that TCADGen assigns to each command's type and arguments. TCADGen's dual-channel feature aggregator encodes the parameter text and the appearance text separately with DeBERTa, projects both into a shared semantic space, and fuses them through capsule-inspired dynamic routing; an enhanced mask decoder with bidirectional LSTM then predicts the entire sequence in parallel. In the second stage, CADLLM (Llama 3.2 3B fine-tuned with LoRA on 1,000 samples) takes the draft CCS plus its confidence scores and rewrites the low-confidence command types and parameter values. The annotation pipeline is the supporting mechanism: it generates parameter descriptions with an LLM and accepts one only when a reverse verification can rebuild the ground-truth CCS from it at LCS ratio $\geq 0.9$, with a reflection optimization loop for failures.

What would settle it

Run the trained pipeline on descriptions written from scratch by human engineers who see only the finished parts, not the modeling sequences, and compare CCS accuracy and average LCS ratio to the reported 0.864 and 0.983; a large drop would show the result depends on annotation-style wording. A second check is to feed a vague conceptual description and observe whether the model silently fabricates parameters rather than flagging missing information.

Watch

Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that the task of generating a CAD Command Sequence (CCS) from text decomposes cleanly into two stages: TCADGen maps the appearance and parameter descriptions to a draft sequence plus per-command confidence scores, and CADLLM maximizes the probability of the final sequence given the descriptions, the draft, and those confidence scores. Evaluated on a DeepCAD-derived set of 155,503 training and 5,647 test samples, the combined pipeline reaches 0.966 average command accuracy and 0.947 F1 on command prediction, 0.864 full-sequence accuracy with an average LCS ratio of 0.983 against the ground-truth modeling sequences, and the lowest Chamfer distance (3.12), MMD (2.78), and JSD (8.38) among the compared methods. The paper's takeaway is that a small, accurate generative model and a large language model that edits the small model's low-confidence predictions together outperform either approach alone, and that the required text annotations can be produced semi-automatically with verification loops.

Load-bearing premise

The framework assumes the text prompts used for training and testing are representative of what real designers will type, yet every description in the dataset was produced semi-automatically from the ground-truth CAD model itself rather than written by a human user.

Editorial extensions

If this is right

Detailed-design text prompts can be converted into executable CAD command sequences without human intervention, at 0.966 average command accuracy on the evaluated benchmark.
The LLM refinement stage reaches its best reported accuracy with only 1,000 fine-tuning samples, so adapting CADLLM to a new command vocabulary or product family is inexpensive.
Parameter-description annotations for large CAD datasets can be produced fully automatically, with LLM reverse verification replacing human labeling of modeling instructions.
Adding the LLM editor cuts the reported Chamfer distance of generated geometry from 120.99 to 3.12, bringing generated parts much closer to the target shape than Transformer-only drafting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct test the authors did not run: give the trained pipeline part descriptions written from scratch by human engineers who see only rendered parts, never the modeling sequences, and compare CCS accuracy with the reported 0.864; a large drop would show the result depends on annotation-style wording.
The confidence-gated editing recipe is a general pattern: any sequence generator that emits per-token confidence could be paired with a small fine-tuned LLM to repair its weakest outputs, for example in code generation or robotic assembly planning.
The 0.9 LCS acceptance threshold treats any description above the bar as equally usable; a graded score that weighs errors by severity (a wrong extrusion distance hurts more than a swapped line coordinate) would better match industrial use.
A natural boundary case the framework does not address: incomplete or conflicting parameter descriptions, where the model would need to ask a clarifying question or flag uncertainty instead of fabricating values to keep the sequence syntactically valid.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know upfront. First, this is a genuine two-stage system—a small dual-channel transformer (TCADGen) plus an LLM refinement stage (CADLLM) that uses TCADGen's per-token confidence scores—and the combination works better than either alone. That is a legitimate new contribution, not a retread of DeepCAD or Text2CAD. Second, the headline numbers (0.864 CCS accuracy, CD 3.12) are real on the paper's own test set, but that test set is nearly lossless paraphrases of the ground-truth sequences. Keep that grain of salt when reading the abstract.

What's good: the annotation pipeline is practical. They generate parameter and appearance descriptions with LLMs/VLLMs, then verify by reversing the description back to the CCS via an LLM, accepting only LCS >= 0.9. That's a usable quality-control trick. The confidence-conditioned refinement is also well-motivated—the LLM is told which tokens are low-confidence and focuses its edits there. The limitations section is candid about the detailed-design scope and about the lack of geometric constraints. They also show a clean data-size curve for CADLLM (0 to 1000 samples), which supports the 'small data is enough' claim.

The soft spots, in rough order of severity. (1) The distribution problem is real and load-bearing: train and test text are synthetic, generated from the same ground-truth CCS, and accepted only if reversible to that CCS. So the task is closer to 'correct a corrupted transcript' than 'understand a user's intent.' The limitation paragraph admits this but the abstract and introduction overclaim. A human-prompt test set, even a small one, would fix this. (2) Table 3 has an internal inconsistency: the text reports CD=4.52 for TCADGen+CADLLM while the table says 3.12. Minor, but it undermines trust in the quantitative claims. (3) No error bars or multiple seeds, so we can't tell how much of the gap over CADFusion is noise. This is common in the subfield, but still a gap. (4) The 'CCS accuracy' metric is never formally defined; I assume it means all-commands-correct, but say so.

Verdict: this deserves a serious referee. It's a solid systems paper for the text-to-CAD crowd. I would like to see it published after a revision that addresses the evaluation distribution and fixes the table. The core idea is worth engaging with. I wouldn't cite it in my own work yet, but I'd keep an eye on the revision.

Referee Report

4 major / 5 minor

Summary. The paper proposes a two-stage framework for generating CAD command sequences (CCS) from textual descriptions. In the first stage, a semi-automated LLM/VLLM pipeline creates parameter and appearance descriptions from ground-truth CAD models, with quality control based on reverse reconstruction and LCSratio thresholds. In the second stage, TCADGen, a dual-channel Transformer with dynamic feature aggregation, predicts a CCS and per-command confidence scores; CADLLM, a LoRA fine-tuned LLM, then refines the predicted sequence using the description, the predicted CCS, and the confidence scores. The authors evaluate on a DeepCAD-derived dataset and report substantial gains over DeepCAD, Text2CAD, CAD Translator, and CADFusion on command accuracy, sequence similarity, and geometric metrics such as Chamfer Distance.

Significance. If the reported results hold under realistic input conditions, the framework would be a useful contribution to text-to-CAD sequence generation: it combines a small, efficient Transformer with an LLM-based correction stage, and it provides a fairly detailed annotation pipeline with explicit quality thresholds. The paper's strengths include a released code repository, extensive ablations (Tables 1, 2, 3, 10), acknowledgment of limitations, and an explicit LCSratio-based verification mechanism rather than a purely subjective quality filter. However, the headline accuracy numbers are measured on machine-generated descriptions that are filtered to be near-losslessly recoverable to the ground truth CCS. This makes the reported accuracies an upper-bound estimate for realistic user prompts, and the paper does not currently quantify the gap. The central claim is defensible within the stated 'detailed design phase' scope, but it is not yet demonstrated for general text-to-CAD use.

major comments (4)

[§3.2.1, Eq. (2), Appendix A.5] The evaluation distribution is not representative of real user text. Both training and test parameter descriptions are generated by an LLM from the ground-truth CCS and are accepted only when a reverse LLM can reconstruct a CCS with LCSratio ≥ 0.9 (Eq. 2); appearance descriptions are likewise generated from multi-view and point-cloud representations of the same ground-truth model. The test set therefore consists of near-lossless paraphrases of the target sequence, not of underspecified or differently-worded human prompts. The abstract and introduction claim automatic generation 'from parameters and appearance descriptions' and 'textual prompts' without this caveat. To make the central claim credible, the authors should either add an evaluation on human-written or human-paraphrased descriptions, or explicitly restrict all headline claims to the machine-generated detailed-design setting and quantify the expected degradation on open-ended prompts.
[RQ3, Table 3] There is an internal inconsistency in the quantitative results. The text in RQ3 states that TCADGen+CADLLM achieves a Chamfer Distance of 4.52 (↓116.47), while Table 3 reports CD = 3.12 (↓117.87) for the same method. The text also attributes CD 142.83 to CAD Translator, but Table 3 lists CAD Translator with a much lower value (2.94 or - 2.94 10.92, depending on column alignment). These discrepancies reduce confidence in the exact numbers and must be corrected.
[RQ3, Table 10] The metric 'CCS Accuracy' is used as a headline result (e.g., 0.864 in Table 10 and 86.4% in the text) but is never defined. It is unclear whether it is the proportion of exactly matching command sequences, a full-sequence token-level accuracy, or an exact-match of both command types and parameters. Since the paper also reports 'Command Prediction ACC' and LCSratio, the relationship between these metrics should be stated precisely, including how continuous parameter values are matched after decoding.
[§4, Tables 1-3, 10] No error bars, confidence intervals, or multiple-seed runs are reported for any of the main quantitative comparisons. Several improvements over baselines are modest (e.g., average command accuracy 0.890 vs 0.847 for the dual-channel ablation), and without variance estimates it is hard to assess whether these differences are significant. At minimum, the authors should report standard deviations across seeds or a significance test for the central comparisons.

minor comments (5)

[Table 1] The table formatting is broken in places, e.g., '0.8220.760' and '0.9620.957' appear to be concatenated numbers; please fix the alignment and separators.
[Table 3] The 'CAD Translator' row has inconsistent column counts ('- 2.94 10.92'), which makes it unclear which value corresponds to CD, MMD, or JSD; the row should be filled in consistently.
[Appendix A.3] The training and inference times are stated without specifying whether they include CADLLM fine-tuning time or only inference; please clarify the hardware and time budget for each component.
[Appendix A.5.3 / Figure 9] The caption says the CADLLM output 'exactly matches the ground truth,' but the figure shows red and green highlighted modifications; please clarify whether this example is representative or a selected success case.
[References] Several references are incomplete (e.g., 'Khan, 2025' is a dataset access citation without a full bibliographic entry, and 'Google, 2024' / 'Meta, 2024' are cited only by access date); please complete the reference list.

Circularity Check

1 steps flagged · score 6.0 of 10

End-to-end text-to-CAD accuracy is measured on descriptions synthesized from the ground-truth CCS and admitted only if the ground-truth CCS is recoverable; the prediction is a round-trip by construction.

self definitional [Section 3.2.1(ii), Eq. 2; Appendix A.5.1; Table 13 'Parameter description generation' prompt]
"The parameter description is primarily generated based on the CCS. ... we validate the accuracy of the generated CCS parameter description by reverse verification. It is deemed correct if the model can reconstruct the ground truth CCS from the generated description. ... Descriptions with an LCSratio above 0.9 are accepted. ... 6. Do not change any parameter values in your description."

Every accepted parameter description is produced from the ground-truth CCS by an LLM prompt that forbids changing any parameter value, and it is accepted only if a reverse LLM reconstructs that same CCS with LCSratio >= 0.9 (Eq. 2). Consequently, at test time the input text is a near-lossless paraphrase of the target output, so the reported end-to-end accuracies (Table 2: 0.864 CCS ACC, 0.983 avg LCS; Table 3 geometry metrics) measure the ability to invert the annotation pipeline rather than to generate a CAD sequence from independent human text. The loop is explicit and anchored to ground truth, so this is a partial, benchmark-level circularity: the relative ranking against DeepCAD/Text2CAD on the same synthetic distribution remains a meaningful comparison.

full rationale

The paper's model-training chain is not circular in the narrower sense: TCADGen is trained on held-out training data and evaluated on held-out test data, and CADLLM is a supervised correction model trained on TCADGen outputs paired with ground-truth CCS, so no fitted parameter is renamed as a prediction. The self-citation to AutoForma (Liao et al., 2024) in Related Works is descriptive and not load-bearing. The main circularity is at the benchmark-construction level: the test text is generated from the ground-truth CCS and filtered by reverse reconstructability, so the absolute end-to-end accuracy is a round-trip score rather than a measure of prediction from realistic user text. The paper's Limitations paragraph honestly restricts the scope to the detailed design phase, which mitigates but does not remove this benchmark circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central empirical claims depend on several hand-chosen thresholds and domain assumptions about the CCS representation, the LCS metric, LLM verification reliability, and the representativeness of synthetic descriptions. No new physical entities are postulated.

free parameters (5)

LCS acceptance threshold = 0.9
Descriptions with LCSratio >= 0.9 are accepted; this threshold was chosen by hand and controls the annotation quality of the whole dataset (Eq. 2, Section 3.2.1).
Reflection retry limit = 2
The optimization loop stops after two retries; the paper does not show sensitivity to this limit (Section 3.2.1).
CADLLM fine-tuning dataset size = 1000
Selected as the 'best model performance and training cost balance' in RQ3; the entire refinement model depends on this hand-tuned size (Section 4, Fig. 5, Table 10).
Low-confidence focus threshold = 0.98
The CADLLM prompt instructs the model to focus on parameters with confidence below 0.98; this threshold is arbitrary and not ablated (Appendix A.7, LLMCAD Enhanced CCS prompt).
Point cloud sampling count = 8000
Point cloud density chosen for appearance description generation; affects description quality but is not justified (Appendix A.2).

assumptions (5)

domain assumption The sketch-extrusion CCS representation (Line, Arc, Circle, Extrude) is sufficient to express the target CAD models.
Section A.1 defines the command vocabulary; the whole framework cannot represent operations outside this vocabulary, such as fillets, lofts, or free-form surfaces.
domain assumption LCSratio between reconstructed and ground-truth CCS is a valid proxy for annotation and generation quality.
Eq. (2) uses LCSratio as the acceptance metric; it ignores parameter-value magnitude and geometric validity.
domain assumption LLM reverse verification correctly recovers ground-truth CCS from generated text descriptions.
Section 3.2.1 and Appendix A.6 rely on this to label parameter descriptions; if the verifier is biased, the annotation quality numbers are inflated.
domain assumption Synthetic descriptions generated from ground-truth CAD models are representative of user text prompts at inference.
Section 3.2.1 describes the annotation pipeline; all experiments use these synthetic descriptions, so real-user performance is unmeasured.
domain assumption DeepCAD sketch-extrusion models approximately cover industrial detailed design tasks.
Appendix A.2 uses DeepCAD for training and testing; the motorcycle frame example in A.4 still requires manual assembly.

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Cite this review

Pith. "Pith review of Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models." pith.science (2026). https://pith.science/paper/NAW2SFIN

@misc{pith2026250519490,
  author       = {Pith},
  title        = {Pith review of: Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NAW2SFIN}},
  note         = {Machine review of arXiv:2505.19490}
}

read the original abstract

Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/

Figures

Figures reproduced from arXiv: 2505.19490 by the authors.

**Figure 2.** LLM-Based Semi-Automated Annotation for CAD Datasets Process. This pipeline primarily generates [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Transformer-based CAD Generator (TCADGen). [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: The impact of data size on CADLLM performance. As the data size increases from 0 to 1000 samples. translating the human modeling language into standard CAD command sequences? As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Illustration of the CAD Command Sequence (CCS) representation. The process shows two sketch [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: The motorcycle frame design process: Parts generated using our framework are manually assembled to [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Parameter Description Annotation Process Flow. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

**Figure 9.** Figure 9: Final CCS generated by CAD LLM using TCADGen CCS output and confidence, with red and green [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

72 extracted references · 70 canonical work pages

[1]

Exact matches: LCS ratio = 1

work page
[2]

High-quality matches: LCS ratio ≥ 0.98

work page
[3]

Acceptable matches: LCS ratio ≥ 0.9

work page
[4]

NewBodyFeatureOperation,

Mean LCS ratio across all test samples The proportions for each criterion are computed as follows: Proportioncriterion = count(LCSratio ≥ criterion) N , (10) where N is the total number of samples, and LCS(i) ratio is the LCSratio for the i-th sample.The av- erage LCSratio is calculated as: Average LCSratio = 1 N NX i=1 LCS(i) ratio, (11) CCS Parameter De...

work page
[5]

Problems in Parametric Description:

Missing Variables: Generated CCS omitse2 (128 in real CCS). Problems in Parametric Description:

work page
[6]

First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction

Add visual references to support the description. First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction. Then, draw a line tox = 207, y= 112, followed by an arc to x = 220, y= 128with a sweep angle of 64 degrees and a counter-clockwise direction. Next, draw a line to x = 220, y= 190, an arc ...

work page
[7]

• <EOS> marks the end of the entire CAD operation sequence

Remember: • There should be only one <SOL> tag per sketch. • <EOS> marks the end of the entire CAD operation sequence. • Do not include any text or explanations outside of the specified command formats

work page
[8]

You will be given a CAD command description in the following format: • {CAD_COMMAND_DESCRIPTION} Output the resulting CAD operation sequence exactly as specified, with no addi- tional commentary. Continued on next page Table 13 – continued from previous page Process Name Prompt Reflection on parameter description issues You are a CAD parametric descriptio...

work page

Show all 72 references

[10]

Arc End Points: Generated CCS ends atx = 223, y= 130; real CCS atx = 220, y= 128
[11]

Line End Points: Generated CCS ends atx = 207, y= 114; real CCS atx = 207, y= 112
[12]

Extra Commands: Real CCS includes a line to x = 220, y= 190; generated CCS specifiesx = 223, y= 204
[13]

Arc Variation: Generated CCS ends an arc atx = 207, y= 222; real CCS atx = 207, y= 204
[15]

Ambiguous Arcs: Incomplete parameters cause endpoint inconsistencies
[16]

Unclear Pathing: Lacks instructions for connecting arcs and lines or closing loops
[17]

Rotation Inconsistencies: Arc rotation directions are unclear
[18]

Missing Line Dimensions: No explicit dimensions for connecting lines
[19]

Improvement Suggestions:

Undefined Parameters:e2 is missing from the description. Improvement Suggestions:
[20]

Define arcs explicitly with endpoints, radii, and centers
[21]

Clarify pathing, including connection rules and loop closures
[22]

Standardize rotation directions
[23]

Specify line dimensions clearly
[24]

Define all variables, includinge2
[26]

Arc Direction: Generated CCS usesf = −1 (clockwise), real CCS usesf = 1(counter-clockwise)
[27]

Arc Endpoint Discrepancy: Generated CCS ends at x = 220, y= 128, real CCS atx = 223, y= 128
[28]

Line Mismatch: Generated CCS connects tox = 220, y= 190, real CCS tox = 223, y= 204
[29]

Arc Endpoint Discrepancy: Later arcs end atx = 207, y= 220, instead ofx = 207, y= 204
[30]

Fewer Commands: Generated CCS omits lines and arcs
[31]

Problems in CAD Parametric Description:

Extrusion Parameters: Generated CCS usese1 = 0and omitse2; real CCS usese1 = 148and e2 = 128. Problems in CAD Parametric Description:
[32]

Unclear Arc Directions:f not explicitly defined
[33]

Ambiguous Arc Coordinates: Lack of precise endpoint relationships
[34]

Endpoint Proximity Issues: Close arc endpoints cause confusion
[35]

Suggestions for Improvement:

Incomplete Extrusion Definitions:e1 and e2 inadequately described. Suggestions for Improvement:
[36]

Define arc directions explicitly
[37]

Derive arc endpoints explicitly from previous commands
[38]

Implement validation checks for consistency
[39]

Provide clear extrusion parameter definitions
[40]

First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction (f = 1)

Ensure sequential coherence in parametric commands. First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction (f = 1). Then, draw a line tox = 207, y= 112. Next, draw an arc to x = 223, y= 128with a sweep angle of 64...
[41]

What the 3D model looks like
[42]

What it is composed of
[43]

this multi-view image shows

What it can do or its purpose Follow these guidelines when generating your description: • Focus on the most important and distinctive features of the object • Use clear and specific language to describe shapes, structures, and spatial relation- ships • Avoid using phrases like...
[44]

Focus on the general shape and proportions

Function For each aspect, follow these specific guidelines: • Overall Shape: Describe the basic geometric form and approximate dimensions. Focus on the general shape and proportions. • Components: List the main structural elements and their spatial relationships. Identify dist...
[45]

Core Analysis Approach: • Extract basic geometric forms (shapes, volumes, structures) • Look for ANY potential geometric compatibility • Focus on finding similarities rather than differences • Consider different ways of describing the same geometric concept • Default to findin...
[46]

Your task is to describe a provided CAD command sequence based on specific requirements

Ignore Non-Essential Elements: • Color and material properties • Surface textures and finishes • Intended purpose or function • Aesthetic qualities • Subjective interpretations Continued on next page Table 13 – continued from previous page Process Name Prompt Parameter descrip...
[47]

Only output command descriptions—no additional content or explanations
[48]

Include each command, its parameters, and reflect the order of execution in your description
[49]

Describe how the Extrude command and its parameters transform the 2D curve into a 3D model
[50]

NewBodyFeatureOperation

For the Extrude command, fully output the parameters b and u without changing them. Parameter b can be “NewBodyFeatureOperation”, “JoinFeatureOpera- tion”, “CutFeatureOperation”, or “IntersectFeatureOperation”. Parameter u can be “OneSideFeatureExtentType”, “SymmetricFeatureEx...
[51]

Do not change any parameter values in your description
[52]

Describe the sequence in natural language, including every parameter without omission

Form a single, cohesive paragraph of CAD modeling guidance. Describe the sequence in natural language, including every parameter without omission. Here is the CAD command sequence you need to describe: • {CAD_COMMAND_SEQUENCE} Your output should be a flowing, descriptive parag...
[53]

These may include creating lines, arcs, circles, and performing extrude operations

Read the description carefully and identify the CAD operations mentioned. These may include creating lines, arcs, circles, and performing extrude operations
[54]

Convert each identified operation into the corresponding CAD command format as follows: • To start a new sketch: – <SOL> • For a line: – <Line>: x=<xValue>, y=<yValue> • For an arc: – <Arc>: x=<xValue>, y=<yValue>,α=<alphaValue>, f=<fValue> • For a circle: – <Circle>: x=<xValu...
[55]

Start each new sketch with <SOL>

Arrange the converted operations in the sequence they appear in the descrip- tion. Start each new sketch with <SOL>
[56]

After converting all operations, end the CAD operation sequence with: • <EOS>
[57]

Ensure that you only output the CAD operation sequence without any additional explanations or comments
[58]

If any information is missing or unclear in the description, use reasonable default values or omit the parameter
[61]

Generated CCS based on the CAD description: • {GENERATED_CCS}
[62]

Differences between generated and real CCS: • Missing commands in the generated CCS: [List any specific commands present in the real CCS but missing in the generated one.] • Extra commands in the generated CCS: [List any commands that appear in the generated CCS but are not in...
[63]

Problems in the CAD parametric description: • Missing information: [Discuss any information that is not clearly stated in the CAD parametric description, which may have led to missing or t commands.] • Incorrect specifications: [Identify any specifications that are inaccurate ...
[64]

• Ensure that all necessary information for each command is provided to avoid missing commands or incomplete sequences

Improvement suggestions: • Clarify any ambiguous descriptions by providing more specific and detailed parameters. • Ensure that all necessary information for each command is provided to avoid missing commands or incomplete sequences. • Double-check the order of commands in the...
[65]

Review the provided CAD parametric description: • {CAD_DESCRIPTION}
[66]

Examine the real CCS sequence: • {REAL_CCS}
[67]

Consider the provided opinion: • {OPINION}
[68]

Familiarize yourself with these key terms and commands in the CCS: • 〈SOL〉: Start of a 2D closed curve • L (Line) : x, y- Coordinates of the line’s end-point • A (Arc) : x, y- Coordinates of the arc’s end-point; α - Sweep angle; f - Counter-clockwise flag • R (Circle) : x, y- ...
[69]

NewBodyFeatureOperation

Pay special attention to the Extrude command parameters: • Parameter b: "NewBodyFeatureOperation", "JoinFeatureOperation", "Cut- FeatureOperation", or "IntersectFeatureOperation" • Parameter u: "OneSideFeatureExtentType", "SymmetricFeatureExtent- Type", or "TwoSidesFeatureExtentType"
[70]

Analyze the differences between the CAD description and the real CCS
[71]

Based on your analysis, modify the CAD parametric description to address any issues found
[72]

Your task is to generate a correct CAD Command Sequence (CCS) based on a CAD description, existing CCS, and confidence levels

Output CAD parametric description ONLY!!! Continued on next page Table 13 – continued from previous page Process Name Prompt LLMCAD Enhanced CCS You are a CAD sequence generation parameter. Your task is to generate a correct CAD Command Sequence (CCS) based on a CAD descriptio...
[128]

The profile is extruded using the E command with orientation angles θ=45, φ=0, γ=0

and radius 32 to form the cross- sectional profile. The profile is extruded using the E command with orientation angles θ=45, φ=0, γ=0... create an angled rod with two straight sections connected at a fixed angle. Starting with SOL command, create a circle R with center coordi...
[2021]

Biology, 10(11):1170

Chairside cad/cam materials: current trends of clinical uses. Biology, 10(11):1170. Meta. 2024. Llama 3.2 11b vision instruct. Accessed: February 5, 2025. Mensah Kwabena Patrick, Adebayo Felix Adekoya, Ayidzoe Abra Mighty, and Baagyire Y Edward

2024
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Journal of King Saud University-computer and information sciences , 34(1):1295–1310

Capsule networks–a survey. Journal of King Saud University-computer and information sciences , 34(1):1295–1310. Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, and Thomas Back. 2024. Reasoning with large language models, a survey. arXiv preprint arXiv:2...

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In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (V olume 3: System Demonstra- tions), Bangkok, Thailand

Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (V olume 3: System Demonstra- tions), Bangkok, Thailand. Association for Computa- tional Linguistics. A Appendix A....

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arXiv preprint arXiv:2501.19054

Text-to-cad generation through infusing visual feedback in large language models. arXiv preprint arXiv:2501.19054. Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik. 2021. Fusion 360 gallery: A dataset and ...

2021 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Exact matches: LCS ratio = 1

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[2] [2]

High-quality matches: LCS ratio ≥ 0.98

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[3] [3]

Acceptable matches: LCS ratio ≥ 0.9

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[4] [4]

NewBodyFeatureOperation,

Mean LCS ratio across all test samples The proportions for each criterion are computed as follows: Proportioncriterion = count(LCSratio ≥ criterion) N , (10) where N is the total number of samples, and LCS(i) ratio is the LCSratio for the i-th sample.The av- erage LCSratio is calculated as: Average LCSratio = 1 N NX i=1 LCS(i) ratio, (11) CCS Parameter De...

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[5] [5]

Problems in Parametric Description:

Missing Variables: Generated CCS omitse2 (128 in real CCS). Problems in Parametric Description:

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[6] [6]

First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction

Add visual references to support the description. First, draw an arc with an end-point atx = 144, y= 112, a sweep angle of 64 degrees, and a counter-clockwise direction. Then, draw a line tox = 207, y= 112, followed by an arc to x = 220, y= 128with a sweep angle of 64 degrees and a counter-clockwise direction. Next, draw a line to x = 220, y= 190, an arc ...

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[7] [7]

• <EOS> marks the end of the entire CAD operation sequence

Remember: • There should be only one <SOL> tag per sketch. • <EOS> marks the end of the entire CAD operation sequence. • Do not include any text or explanations outside of the specified command formats

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[8] [8]

You will be given a CAD command description in the following format: • {CAD_COMMAND_DESCRIPTION} Output the resulting CAD operation sequence exactly as specified, with no addi- tional commentary. Continued on next page Table 13 – continued from previous page Process Name Prompt Reflection on parameter description issues You are a CAD parametric descriptio...

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