B-repLer: Language-guided Editing of CAD Models

Changjian Li; Niladri Shekhar Dutt; Niloy J. Mitra; Yilin Liu

REVIEW 4 major objections 6 minor 2 cited by

B-repLer: Language-guided Editing of CAD Models

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read B-repLer turns plain-language editing prompts into valid B-rep CAD edits by rewriting the model's latent code, bypassing construction history entirely.

desk verdict First native text-driven B-rep editing in latent space, but synthetic prompts and a tiny baseline leave the human-instruction claim unproven. read the letter →

arxiv 2508.10201 v4 pith:WZI46W3M submitted 2025-08-13 cs.GR

Yilin Liu , Niladri Shekhar Dutt , Changjian Li , Niloy J. Mitra This is my paper

classification cs.GR

keywords CADeditingB-repmultimodalLLMtext-guidedshapelatentspacesyntheticdatasetboundaryrepresentationcomputer-aideddesign

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

B-repLer addresses text-guided editing of CAD solid models, which earlier approaches could only attempt by converting B-reps to images and back, or by relying on sketch-and-extrude construction history. The paper's central claim is that editing can be done natively: a fine-tuned multimodal LLM localizes the region to change and expands a vague user prompt into concrete geometric instructions, and a transformer then rewrites the B-rep's latent codes to produce a valid edited solid. To train this, the authors build a synthetic paired dataset by deleting single faces from ABC shapes with Fusion360 and asking an mLLM to generate both detailed geometric instructions and abstract user prompts from before/after renders. On this dataset, they report 69.8% valid outputs and 53.4% exact-match success, against 0% success for an image-based mLLM+HoLa baseline. A sympathetic reader would take the contribution to be the combination of a latent-space edit operator with an LLM localization stage, which opens text-driven CAD editing without expensive construction-history data.

What carries the argument

The load-bearing object is the HoLa holistic B-rep latent space: each face is encoded into a $\mathbb{R}^{32}$ token that jointly captures geometry and topology, and decoding from this space helps preserve B-rep validity. On top of it, the two-stage design—an mLLM localizer producing a bounding box and a detailed instruction, and a bespoke transformer that autoregressively predicts the edited latent sequence conditioned on text, image, and per-face RoI features—carries the semantic interpretation and the geometry update. The bounding box and RoI features are what align 2D visual cues with 3D B-rep tokens.

What would settle it

Collect editing prompts from actual CAD users (or from recorded Fusion360 design sessions) for the same class of single-face operations, run B-repLer on them, and measure exact-match success. If the rate falls far below the 53.4% reported on mLLM-synthesized prompts, the claim that the model understands genuinely abstract user intent is unsupported. A second check: run the mLLM+HoLa baseline on the full BrepEDIT test set instead of the 11-sample subset used in Table 1; if its exact-match success climbs to a comparable level, the reported advantage is an artifact of evaluation scale.

Watch

Extended reading notes

Core claim

The paper's thesis is that high-level, ambiguous text instructions can be turned into concrete, valid B-rep edits by splitting the task into where and how. Stage one fine-tunes Qwen2.5-VL to take the rendered source model and the user prompt, output a 2D bounding box localizing the edit, and expand the prompt into a low-level 'instruct prompt.' Stage two trains a transformer encoder-decoder to translate the source B-rep latent sequence, conditioned on the instruct prompt, image features, per-face RoI features, and the bounding box, into the latent sequence of the edited B-rep, which the frozen HoLa decoder turns back into a solid. The paper reports that on its BrepEDIT test set this produces

Load-bearing premise

The entire evaluation rests on the assumption that abstract, vague prompts synthesized by an mLLM from single-face before/after image pairs—such as 'strengthen the tip'—faithfully represent how real users phrase editing intentions and what edits those intentions require; the paper itself notes in Section 5.4 that the data covers only single-step operations, with no physical simulation or multi-face design constraints.

Editorial extensions

If this is right

Text-driven editing becomes possible for B-rep datasets that lack construction history, including freeform B-spline geometry, not just prismatic sketch-and-extrude parts.
The two-stage decomposition makes the model's reasoning inspectable: users can see the predicted bounding box and the expanded geometric instruction before committing to the edit.
The synthetic data pipeline—single-face deletions plus an mLLM that writes both detailed and abstract prompts—can be scaled to other operation types without manual annotation.
If the reported numbers hold, image-mediated editing routes (B-rep to image to B-rep) are not competitive for exact edits on this task.
The reported validity and success rates define a concrete baseline for future native B-rep editors to beat.

Reading between the lines

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

The same localize-then-rewrite recipe could transfer to other structured shape representations (meshes, parametric feature graphs, point clouds) wherever a holistic autoencoder exists.
Because the training data contains only single-face deletions, the model's behavior on multi-face design intents—symmetry, mating, structural reinforcement—is unknown; a natural extension is multi-step synthetic edit chains.
Two internal inconsistencies should be resolved before relying on the dataset claims: the abstract names BrepEDIT-240K while the body describes BrepEDIT-10K, and the abstract's phrase '(user) validated' does not correspond to a user study in the method section.
Coupling the latent edit function with a physical simulator would let prompts like 'strengthen' or 'improve aerodynamics' be checked against engineering criteria instead of geometry alone.

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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

Here's my take. The paper's real contribution is two-fold: it's the first to edit B-rep CAD models directly in a learned latent space from text instructions, without needing construction history, and it ships a synthetic dataset (BrepEDIT) that should be useful to the community. The two-stage design—fine-tuned mLLM localizer plus a transformer in the HoLa latent space—is sensible, and the numbers on the held-out synthetic test set (53.4% exact success, 0.914 face F1, 69.8% validity) are non-trivial. The method clearly learns something structural about these edits. The paper also gets credit for being upfront about its limitations in Sec 5.4, which is more than many papers do.

The soft spots are real, though. The baseline is 11 samples; 0% success there is suggestive at best. The ablations are done by dropping features at inference time, not retraining, so they only give an upper bound and don't tell you what each component actually contributes. The abstract says BrepEDIT-240K, the body says 10K shapes / 100k operations / 600k annotations, and the '(user) validated' claim is not backed by any evidence in the text. That kind of mismatch bothers me.

The bigger issue, which the stress-test note identifies correctly, is that all the user prompts are generated by Gemini from the same before/after rendered images and ground-truth bounding box that define the task. The test set is sampled from that same synthetic distribution. So the 53.4% success is a measure of how well the model fits the synthetic prompt distribution, not how well it interprets what a real CAD user would say. Stage 1 is the only place user language enters, and if a real prompt uses different vocabulary or doesn't name a feature, the localization could fail and Stage 2 can't recover. The authors acknowledge single-step and synthetic limitations but don't test distribution shift.

That said, I don't think this is a load-bearing flaw in the sense of making the method a tautology—the model can fail, and the numbers are credible. It just means the central claim about 'vague and ambiguous' human instructions is currently unproven. The fix is straightforward: run a small human study, collect some real user prompts, and test on them; also fix the reporting inconsistencies and rerun the baseline on a larger sample.

Who should read this: anyone working on CAD editing, multimodal LLMs for 3D, or synthetic dataset generation for shape editing. The novelty is real and the dataset is a contribution in its own right. I'd send it to reviewers, but with the expectation of major revision and a request for the release of code and data.

Referee Report

4 major / 6 minor

Summary. B-repLer proposes a two-stage architecture for language-guided editing of B-rep CAD models without requiring construction history. In Stage 1, a fine-tuned Qwen2.5-VL mLLM maps a user prompt plus a rendered view of the input B-rep to a 2D bounding box localizing the edit region and to a more detailed 'instruct prompt.' In Stage 2, a transformer operates in the HoLa B-rep latent space, conditioning on B-rep tokens, DINO/RoI image features, the bounding box, and the text instruction, to autoregressively predict the edited B-rep latent sequence, which is decoded by the pretrained HoLa decoder. The authors introduce BrepEDIT (called BrepEDIT-240K in the abstract and BrepEDIT-10K in the body) by deleting single faces in Fusion360 from filtered ABC models and using Gemini to generate six user/instruct prompt pairs per edit direction. On the synthetic test set, the paper reports 69.8% validity, 53.4% exact success, and face F1 of 0.914, versus a GPT-4.1+HoLa image-based baseline evaluated on 11 samples (0% success). Ablations are performed by removing components at inference time from a model trained with all components.

Significance. If the reported results are taken at face value, this is a useful first step toward native text-based editing of B-rep models, bypassing construction history and supporting freeform B-spline geometry. The two-stage decomposition (where to modify, then how to modify) is reasonable, and the use of a pretrained B-rep latent space is a sensible means of preserving output validity. The dataset, if released as described, would be a substantial community resource, and the paper is honest in listing several limitations (single-step operations, no physical simulation). However, the central claim about interpreting high-level, vague human user instructions is not established by the experiments, because all prompts are generated by the same Gemini pipeline used to build the training and test distributions. The validity rate is also materially lower than the word 'consistent' used in the abstract, and the only baseline comparison is statistically very weak.

major comments (4)

[Sec. 3.2 and Sec. 5.3] The paper's central claim is that B-repLer interprets high-level, ambiguous user instructions, but every user prompt in BrepEDIT is generated by Gemini from the before/after rendered images and the ground-truth bounding box (Sec. 3.2, step 3). The test set is sampled from this same synthetic distribution, so the 53.4% success, 69.8% validity, and the Stage-1 IoU/cosine numbers in Table 1 and Sec. 5.3 measure fit to that distribution, not transfer to real user language. There is no evidence that these Gemini-generated prompts match how CAD users phrase edits, and the abstract's '(user) validated' claim is not substantiated anywhere in the body. This is load-bearing because Stage 1 is the only point where user language enters the pipeline; if a human prompt uses different vocabulary, omits location cues, or expresses intent without naming a feature, the predicted bounding box and instruct
[Sec. 5.2 and Table 2] The ablation study removes components at inference time from a model that was trained with all components. This is explicitly acknowledged ('due to compute constraints, we performed the ablations only at the inference time') and even called an upper bound, but the subsequent sentence—'each ablated variant shows a measurable drop in performance'—is used to validate the design choices. This is not a valid ablation of the components' contribution during learning: removing a feature the encoder was trained to use can simply reflect train/test mismatch, not the importance of that feature. For example, removing the RoI features from a model whose encoder learned to rely on them is expected to degrade results even if the features are ultimately redundant. The claim that the ablation 'validates our design choices' should be removed or replaced with results from retrained variants, which is the s
[Abstract and Table 1] The abstract states that B-repLer produces 'consistently producing valid, high-quality CAD outputs', and Figure 2's caption says 'Outputs are valid B-reps.' Table 1 reports a validity ratio of 69.8%, and the full model in Table 2 reaches 70.4%. This means roughly one output in three is not valid under the paper's own OpenCASCADE-based check. The word 'consistent' is not supported by these numbers. The paper should qualify the claim (e.g., 'in the majority of cases') or, preferably, report the validity of only the successful edits and analyze the failure modes. As it stands, the central abstract claim is contradicted by the paper's own evaluation.
[Table 1 and Sec. 5.1] The mLLM+HoLa baseline is evaluated on only 11 samples, where it achieves 0% success. With n=11, a 0/11 success rate has an upper 95% confidence bound of roughly 24%, so the result is not statistically meaningful evidence that the baseline cannot succeed. The text's assertion that 'the success rate being so low, we believe this evaluation is sufficient' is not a substitute for error bars or a larger sample. At minimum, the paper should report the exact set size in the main text, provide confidence intervals, or present this comparison as anecdotal. This does not undermine the main method, but it weakens the quantitative comparison claim.

minor comments (6)

[Abstract vs. Sec. 3] The dataset is called BrepEDIT-240K in the abstract and BrepEDIT-10K in the Introduction and Section 3. The statistics (10k shapes, 100k editing operations, 600k annotations) also conflict with the '240K' name. Please reconcile these numbers and names.
[Sec. 4.2 / 4.3] It is not stated explicitly whether Stage 2 is trained with ground-truth bounding boxes and ground-truth instruct prompts, or with Stage 1's predictions. This detail matters because at inference-only predicted boxes are used; if training uses ground truth, there is a train/test mismatch that should be discussed and quantified if possible.
[Sec. 5.3] The 'normalized distance' between bounding boxes is not defined. Please specify what is normalized (e.g., by image dimensions or object scale) and report the standard deviation or variance across the test set.
[Fig. 7 caption] The caption says both failure cases 'led to a model that was identical to the original pre-edit B-rep.' This is confusing: an 'unsuccessful edit' should mean the output matches the input, but the first case (inaccurate interpretation) seems to describe a wrong edit. Please clarify what is meant.
[Sec. 4.1] The assumption that 'users roughly pick a viewpoint exposing their expected editing' is not evaluated. Since the rendered view is an input to both stages, a sensitivity study on viewpoint variation would strengthen the paper. At minimum, acknowledge the assumption more prominently.
[General] Several references are to arXiv-only or under-review works; this is acceptable, but the two 'Code was not publicly available at the time of submission' notes for FlexCAD and CAD-Editor should be moved to a footnote or software-availability section for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No prediction reduces to its input by construction; the evaluation's synthetic-prompt loop is a validity limitation, not a circularity.

full rationale

The paper's pipeline is a standard supervised learning setup: the BrepEDIT dataset is constructed by applying single-face deletions in Fusion360 and using Gemini to generate paired user/instruct prompts from rendered before/after images with an overlaid bounding box. The model is then trained on a train split and evaluated on a held-out test split from the same distribution. Although the test distribution is self-referent (Gemini defines both the 'user prompt' and the ground-truth instruct prompt from the same edit), the model's output is not an algebraic transform of its input: the transformer must autoregressively predict HoLa latent tokens, can fail, and is scored against a ground-truth B-rep by OpenCASCADE validity and Chamfer-based F1. No equation in the paper equates a prediction to a fitted parameter, and no load-bearing claim is justified solely by a self-citation. The HoLa latent space [Liu et al. 2025] is prior published work by overlapping authors and is used as an external, fixed encoder/decoder; its adoption is a design choice, not an unverified premise that forces the results. The abstract's '(user) validated' phrase is unsupported in the body, and Section 5.4 candidly lists limitations (single-step operations, no physical simulation), but these are correctness/generalization concerns rather than circularity. Accordingly, the score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The central claim rests on three external pillars: the HoLa latent space, the Fusion360 face-deletion data generation, and Gemini text annotation. None of these are independently re-validated in this paper, and the resulting synthetic benchmark is used for both training and evaluation. The ledger shows that the method itself is a standard seq2seq system whose main scientific content is the new dataset and task framing.

free parameters (4)

Chamfer matching threshold for success = 0.1
Hand-chosen threshold in Section 5 used to decide whether a predicted primitive matches ground truth and whether a model is 'identical'. Directly controls the reported success rate.
Number of rendered views = 32 isometric views
Viewpoint set used for rendering before/after images and computing the bounding box; affects all image-based features and the mLLM localization input.
Transformer hidden dimension and latent dimension = 768 and 32
Architecture dimensions chosen for the second-stage transformer; the latent dimension is inherited from HoLa. These are design choices rather than fitted scientific constants.
mLLM adapter configuration = rank 256, alpha 512, dropout 0.2
Hyperparameters for DoRA fine-tuning of Qwen2.5 VL; chosen by hand and not justified by any principle beyond empirical training convenience.

assumptions (5)

domain assumption The HoLa latent space is a valid, sufficiently expressive representation of B-rep geometry and topology, and edits in this latent space decode to valid B-reps.
Section 4.2 adopts HoLa as the B-rep latent space without re-validating its coverage or validity guarantees on the edit task.
domain assumption Single-face deletion in Fusion360, and the inverse operation, produce the paired before/after edits needed to represent arbitrary user-intended modifications.
Section 3.2 constructs the whole dataset from face deletions that remain valid; the inverse direction is treated as an Add operation. This restricts the edit vocabulary to a single mechanism.
domain assumption Gemini-generated instructions are faithful to the geometric change and are representative of real user language for CAD editing.
Section 3.2 uses Gemini to write both detailed instruct prompts and abstract user prompts from image pairs. No user study or external annotation validates this proxy.
domain assumption At inference time, users provide a viewpoint that matches the rendering and bounding box conventions used in training.
Section 4.1 assumes users pick a viewpoint exposing the expected edit region, and this viewpoint is assumed to be compatible with the 32-view training setup.
ad hoc to paper Filtering ABC shapes to the first 10 data chunks and to models with at most 50 faces produces a representative distribution for text-guided B-rep editing.
Section 3.1 imposes these filters to restrict complexity; the paper provides no evidence that the filtered subset represents the broader ABC or real-world engineering distribution.

invented entities (1)

BrepEDIT dataset (labeled B-rep editing pairs)
purpose: Provides paired text instructions, bounding boxes, and before/after B-rep models for training and evaluation of text-guided B-rep editing.
The dataset is not released at submission, the abstract calls it BrepEDIT-240K while the body describes BrepEDIT-10K with 100k operations, and no external validation or third-party use is reported.

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Cite this review

Pith. "Pith review of B-repLer: Language-guided Editing of CAD Models." pith.science (2026). https://pith.science/paper/WZI46W3M

@misc{pith2026250810201,
  author       = {Pith},
  title        = {Pith review of: B-repLer: Language-guided Editing of CAD Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WZI46W3M}},
  note         = {Machine review of arXiv:2508.10201}
}

read the original abstract

Computer-Aided Design (CAD) models, given their compactness and precision, remain the industry standard for designing and fabricating engineering objects. However, language-guided CAD editing is still in its infancy, largely due to missing semantic connection between user commands and underlying shape geometry, a problem exacerbated by the shortage of paired text-and-edit CAD datasets. While recent Multimodal Large Language Models (mLLMs) have attempted to bridge this gap, their reliance on CAD construction history -- often an expensive and hard to obtain input -- severely limits their expressiveness and restricts their usage. We present B-repLer, a novel framework that directly connects natural language with editing CAD models by operating in a learned latent space. Importantly, our approach bypasses the need for construction history, enabling semantic edits on a wide range of geometries, from simple prismatic parts to complex freeform shapes defined by B-Spline surfaces. To facilitate this research, we introduce BrepEDIT-240K, the first large-scale dataset for this task. We demonstrate how this paired dataset can be automatically generated, (user) validated, and scaled by leveraging existing CAD tools, in conjunction with mLLMs, to create the required paired data without relying on any external annotations. Our results demonstrate that B-repLer can accurately perform complex edits on complex CAD shapes, even when the input edit specifications are high-level and ambiguous to interpret, consistently producing valid, high-quality CAD outputs enabling a class of text-guided edits not previously possible.

Figures

Figures reproduced from arXiv: 2508.10201 by the authors.

**Figure 1.** Text-driven B-rep editing. We present B-repLer to natively edit B-rep models. Given a B-rep model along with user’s high-level prompt, our method interprets user instructions and operates in the B-rep latent space directly, achieving non-trivial and fine-grained B-rep modification. Outputs are valid B-reps. Multimodal large language models (mLLMs), trained in a mixed modal setting as a universal model, have been sho… view at source ↗

**Figure 2.** B-rep Editing. Text-based B-rep editing is challenging. For example, (a) an existing baseline method fails to interpret the abstract editing intention, limiting its controllability. (b) We propose a novel native B-rep editing approach, where we link the mLLM to B-rep latent, thus enabling semantic-aware and fine-grained control over various primitives. able to articulate what changes that would require. (ii) Even w… view at source ↗

**Figure 3.** Dataset construction. We construct an annotated dataset in three main steps: (a) Given a B-rep model, we randomly select an operating face, and we then employ Fusion360 [Willis et al. 2021] to delete it. (b) For each successful deletion, we select the best viewpoint to render images before and after the operation, and (c) combine them with additional “Left” and “Right” texts to form an input image sent to an mLLM (i… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Method Overview. B-repLer has two stages. At stage one (left), given the input B-rep model and the input user prompt, we fine-tune an mLLM to produce a bounding box locating the modification and parse high-level instructions to detailed instructions. The low-level inst…

**Figure 5.** Figure 5: Results gallery. Utilizing B-repLer, we have edited a diverse set of B-rep CAD models of varying complexity based on high-level user prompts. Each example in this figure illustrates the user-provided prompt, the original B-Rep model, the predicted instruct prompt and t…

**Figure 6.** Figure 6: Qualitative comparison. We compare our method against a synthetic baseline method - mLLM+HoLa, on a few B-rep editing examples. Compared to ours, this (baseline) method has a rather low success rate, often producing models that lose the original model identity or do no…

**Figure 7.** Figure 7: Failure cases. The mLLM did not adequately understand the detailed requirements outlined in the user prompt, resulting in an unsuccessful edit. Additionally, the mLLM was unable to accurately identify the specific area that needed to be changed. Both of these issues le…

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Forward citations

Cited by 2 Pith papers

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A new dataset and deep learning pipeline generate upper and lower molds, parting surfaces, and auxiliary components for complex injection-molded parts.

Reference graph

Works this paper leans on

33 extracted references · 31 canonical work pages · cited by 2 Pith papers

[1]

* Objectively note the geometric features present / absent or changed between State A and State B

** Identify and Concisely Describe Geometric Differences for Both Directions ( Output This First ) :** * First , meticulously compare the geometry within and around the bounding box on the Left ( State A ) and Right ( State B ) images . * Objectively note the geometric features present / absent or changed between State A and State B . * ** Output this ide...

work page
[2]

hole ,"

** Generate Two Sets of Detailed Instructions ( Output This Step Next ) :** * Second , based * only * on the ** geometric difference ( s ) ** identified and the visual details , generate ** TWO distinct sets ** of 6 descriptions each . * ** Output Format :** * Present the output under two clear headings :`## Instructions for Change : Left -> Right`and`## ...

work page
[3]

Fill the specified hole on the main planar face

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[4]

Eliminate the hole to improve surface integrity for sealing

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[5]

Remove the 5 mm diameter through - hole located 10 mm from the top edge on the front face

work page
[6]

Delete the cylindrical cutout positioned near the filleted corner

work page
[9]

Delete the feature to reduce stress concentration near the corner fillet

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[10]

## Instructions for Change : Right -> Left

Remove unnecessary hole to simplify manufacturing . ## Instructions for Change : Right -> Left

work page

Show all 33 references

[11]

Add a 5 mm diameter through - hole located 10 mm from the top edge on the front face
[12]

Create a cylindrical cutout positioned near the filleted corner
[13]

Drill a 5 mm through - hole on the main planar face at the specified coordinates
[14]

Add alignment hole for assembly fixture
[15]

Introduce pilot hole for subsequent tapping operation
[16]

Create hole to provide access for wiring . *( Scenario : Left image has 5 mm fillet , Right image has 10 mm fillet ) * ## Geometric Change Summary : ### Left -> Right Change : Increase the upper edge fillet radius from 5 mm to 10 mm . ### Right -> Left Change : Decrease the up...
[17]

Modify the fillet radius on the upper edge from 5 mm to 10 mm
[18]

Change the radius of the specified edge blend to 10 mm
[19]

Update the corner fillet to a 10 mm radius
[20]

Increase fillet radius to reduce stress concentration
[21]

Adjust edge blend for improved ma nu fa ct ura bi li ty ( mold flow )
[22]

## Instructions for Change : Right -> Left

Modify fillet to meet revised design specification R10 . ## Instructions for Change : Right -> Left
[23]

Modify the fillet radius on the upper edge from 10 mm to 5 mm
[24]

Change the radius of the specified edge blend to 5 mm
[25]

Update the corner fillet to a 5 mm radius
[26]

Decrease fillet radius to provide sharper edge for component mating
[27]

Adjust edge blend to revert to previous design specification R5
[28]

# ** ADDITIONAL NOTES **

Modify fillet to increase available surface area adjacent to the edge . # ** ADDITIONAL NOTES **
[29]

** BE PRECISE AND UNAMBIGUOUS ** with all instructions
[30]

** ENSURE DIVERSITY IN THE INSTRUCTIONS ** within each set
[31]

Think creatively for functional / intent instructions (4 , 5 , and 6)
[32]

** ACCURATELY REFLECT THE GEOMETRIC CHANGE ** required for each direction (L - > R and R - > L )
[33]

modify

** When noting the Geometric Change Summary , be very precise . Do not simply say " modify " but rather note the exact change in geometry . Listing 2. Example prompt to the LLM
[2024]

Guiding Instruction-based Image Editing via Multimodal Large Language Models. InProc. Int. Conf. on Learning Representations. Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. 2023a. Guiding instruction-based image editing via multimodal large lang...

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CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning- Enhanced Multimodal LLMs. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 7880–7888. Zhengyi Wang, Jonathan Lorraine, Yikai Wang, Hang Su, Jun Zhu, Sanja Fidler, and Xiaohui Zeng....

2024 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

* Objectively note the geometric features present / absent or changed between State A and State B

** Identify and Concisely Describe Geometric Differences for Both Directions ( Output This First ) :** * First , meticulously compare the geometry within and around the bounding box on the Left ( State A ) and Right ( State B ) images . * Objectively note the geometric features present / absent or changed between State A and State B . * ** Output this ide...

work page

[2] [2]

hole ,"

** Generate Two Sets of Detailed Instructions ( Output This Step Next ) :** * Second , based * only * on the ** geometric difference ( s ) ** identified and the visual details , generate ** TWO distinct sets ** of 6 descriptions each . * ** Output Format :** * Present the output under two clear headings :`## Instructions for Change : Left -> Right`and`## ...

work page

[3] [3]

Fill the specified hole on the main planar face

work page

[4] [4]

Eliminate the hole to improve surface integrity for sealing

work page

[5] [5]

Remove the 5 mm diameter through - hole located 10 mm from the top edge on the front face

work page

[6] [6]

Delete the cylindrical cutout positioned near the filleted corner

work page

[7] [9]

Delete the feature to reduce stress concentration near the corner fillet

work page

[8] [10]

## Instructions for Change : Right -> Left

Remove unnecessary hole to simplify manufacturing . ## Instructions for Change : Right -> Left

work page