Title: CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning
Venue: NeurIPS 2025
Accepted: yes
Keywords: CAD Reasoning

Multi-Modal Dataset

Constraint-Driven Synthesis
Abstract: Computer-Aided Design (CAD) is pivotal in industrial manufacturing, with orthographic projection reasoning foundational to its entire workflow—encompassing design, manufacturing, and simulation. However, prevailing deep-learning approaches employ standard 3D reconstruction pipelines as an alternative, which often introduce imprecise dimensions and limit the parametric editability required for CAD workflows. Recently, some researchers adopt vision–language models (VLMs), particularly supervised fine-tuning (SFT), to tackle CAD-related challenges. SFT shows promise but often devolves into pattern memorization, resulting in poor out-of-distribution (OOD) performance on complex reasoning tasks. To tackle these limitations, we introduce CReFT-CAD, a two-stage fine-tuning paradigm: first, a curriculum-driven reinforcement learning stage with difficulty-aware rewards to steadily build reasoning abilities; second, supervised post-tuning to refine instruction following and semantic extraction. Complementing this, we release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning, comprising 200,000 synthetic and 3,000 real-world orthographic projections with precise dimensional annotations and six interoperable data modalities. Benchmarking leading VLMs on orthographic projection reasoning, we show that CReFT-CAD significantly improves reasoning accuracy and OOD generalizability in real-world scenarios, providing valuable insights to advance CAD reasoning research. The code and adopted datasets are available at \url{https://github.com/KeNiu042/CReFT-CAD}.

CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning

Abstract

Computer-Aided Design (CAD) is pivotal in industrial manufacturing, with orthographic projection reasoning foundational to its entire workflow-encompassing design, manufacturing, and simulation. However, prevailing deep-learning approaches employ standard 3D reconstruction pipelines as an alternative, which often introduce imprecise dimensions and limit the parametric editability required for CAD workflows. Recently, some researchers adopt vision-language models (VLMs), particularly supervised fine-tuning (SFT), to tackle CAD-related challenges. SFT shows promise but often devolves into pattern memorization, resulting in poor out-of-distribution (OOD) performance on complex reasoning tasks. To tackle these limitations, we introduce CReFT-CAD, a two-stage finetuning paradigm: first, a curriculum-driven reinforcement learning stage with difficulty-aware rewards to steadily build reasoning abilities; second, supervised post-tuning to refine instruction following and semantic extraction. Complementing this, we release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning, comprising 200,000 synthetic and 3,000 real-world orthographic projections with precise dimensional annotations and six interoperable data modalities. Benchmarking leading VLMs on orthographic projection reasoning, we show that CReFT-CAD significantly improves reasoning accuracy and OOD generalizability in real-world scenarios, providing valuable insights to advance CAD reasoning research. The code and adopted datasets are available at https://github.com/KeNiu042/CReFT-CAD .

1 Introduction

Computer-Aided Design (CAD) is now integral to industrial product development, underpinning design, manufacturing, and simulation workflows. In the design phase, engineers use CAD drawings or rasterized orthographic projections for their precision and facile editability. During manufacturing, these drawings are converted into constraint-based parameter tables; for simulation, they yield boundary-representation (B-Rep) data or textual geometry descriptions. A truly user-centric pipeline thus requires accurate semantic parsing of orthographic projections: automated extraction of parameter tables enables direct generation of 3D models meeting stringent manufacturing and simulation standards. By contrast, existing reverse-engineering methods rely on expensive 2D/3D scanning hardware and labor-intensive post-processing, greatly restricting scalability and widespread industrial adoption.

(e) CReFT-CAD for View Matching

(a) Qwen2.5-VL (without tuning) for Full Parameter List Output

Given the front, top, and side views, please output the

numeric values of the following parameters in this

exact order ..., separated by commas.

530, 540, 1, 3 ... , 208, 224, 391

Given the front, top, and side views, please output the

numeric values of the following parameters in this

exact order ..., separated by commas.

590, 560, 2, 2 ... , 208, 224, 391

(b) Deepseek (without tuning) for Full Parameter List Output

Given the top view <   >, and front view <   >, which one <      > is the

right side view?

2*97+2*194+146,  2*379+210, 1, 3 ... , 208, 224, 391

(c) CReFT-CAD for Full Parameter List Output

Given the <image>, is the Pier Column Cross-Bridge

Dimension equal to 210?

No, the correct answer is 194.

Given the front, top, and side views, please output the

numeric values of the following parameters in this

exact order ..., separated by commas.

The right side view is <     >.

(d) CReFT-CAD for Parameter Validation

Given the front, top, and side views<image>, what is the quantity of

pile bases and pier colunm?

The quantity of pile base is 6; the quantity of pier column is 2.

(f) CReFT-CAD for Component Counting

Wrong

Answer!

Wrong

Answer!

O

O

?

（）

?

?

With the advent of deep learning, orthographic projection reasoning has typically been framed as a standard 3D reconstruction pipeline: models process rasterized drawings to output B-Rep structures [1, 2], point clouds [3, 4], or meshes [5]. However, this paradigm suffers from two critical limitations. First, industrial drawings demand near-zero tolerance for missing or erroneous components: pixellevel discretization errors in reconstruction can propagate to 3D models, leading to failures in downstream manufacturing and simulation. Second, practical CAD workflows require parametric editability and precise semantic alignment-key capabilities that common 3D representations lack. While some methods generate CAD command sequences to recover editability, they still overlook the nuanced semantics of orthographic views, thus failing to reliably map 2D drawing features to their intended 3D counterparts.

Recent works leverage the modality alignment and semantic reasoning capabilities of vision-language models (VLMs) [6-12], addressing CAD challenges via supervised fine-tuning (SFT) [13-15]. However, orthographic projection reasoning requires genuine inferential ability, and task-specific SFT often induces pattern memorization and out-of-distribution (OOD) performance degradation rather than fostering deeper reasoning [16]. Inspired by DeepSeek R1-Zero's Group Relative Policy Optimization (GRPO) [17], we propose Curriculum-driven Reinforcement Fine-tuning for CAD (CReFT-CAD).

CReFT-CAD is a versatile framework enabling interactive orthographic projection reasoning across the full lifecycle of industrial design-from design and manufacturing to simulation (see Fig. 1). It consists of a two-stage paradigm integrating reinforcement learning and supervised tuning: the first stage is Curriculum-driven Reinforcement Fine-tuning , where we introduce a difficulty-aware reward mechanism that incrementally increases task complexity, gradually exposing the model to increasingly complex reasoning challenges to promote stable policy optimization; the second stage is Supervised Post-tuning , where we build on the reinforced model and further refine its instructionfollowing and reasoning capabilities via multi-task SFT. Our qualitative and quantitative evaluations show that CReFT-CAD achieves robust orthographic projection reasoning and strong generalization in OOD scenarios. Another key barrier in orthographic projection reasoning is the scarcity of opensource datasets with high-fidelity annotations. To address this, we present TriView2CAD, the first large-scale benchmark tailored to industrial CAD pipelines. Leveraging real-world design archives, we use a constraint-driven synthesis pipeline to generate 200,000 synthetic and 3,000 real-world threeview sets, each annotated with precise, one-to-one dimensional labels linked to their corresponding geometric primitives. This design allows models to extract quantitative measurements from rasterized drawings and enforce exact geometric constraints for 3D reconstructions ready for simulation.

2 Related Work

2.1 Orthographic projection reasoning for CAD

Understanding and reasoning with orthographic projection is a fundamental and longstanding challenge in CAD-related tasks [18-20]. With the development of 3D reconstruction methods, existing methods for orthographic projection reasoning have largely centered on standard 3D reconstruction pipelines. SPARE3D [21] introduces a dataset for evaluating the spatial reasoning capabilities of AI systems via 2D line drawings of 3D objects. Contrastive-SPARE3D [22] proposes a selfsupervised binary classification network that helps learn 3D object line drawing representations that are detail-sensitive and view-invariant. PlankAssembly [23] presents a transformer-based sequence generation model that learns flexible input-output mappings. IsoTGAN [24] introduces a novel Gaussian-enhanced Euclidean attention mechanism and a geometric constraint loss function to further enhance local image features. GaussianCAD [25] employs a custom sparse-view 3D reconstruction method, removing reliance on vector CAD sketches and real-world 3D data.

2.2 Applications of LVLMs in CAD-Related tasks

Recent efforts applying vision-language models (VLMs) to CAD tasks fall into three main categories: 1) Direct Adaptation of Pretrained VLMs. CAD-Recode [26] leverages a VLM to translate raw point cloud inputs into executable Python code.Both LEAM [27] and LLM4CAD [28] generate CAD models by jointly leveraging text descriptions and image inputs. CAD-Assistant [29] introduces a novel approach using VLMs as planners, where the model interacts with Python APIs to accomplish diverse CAD tasks. 2) Supervised Fine-Tuning (SFT). CAD2Program [13] generates 3D parametric models from 2D CAD drawings. CAD-MLLM [30] enables parametric CAD modeling from text, images, and point clouds, and introduces the Omni-CAD dataset. 3) Reinforcement Learning-based Tunings. RLCAD [31] introduces a reinforcement learning environment for generating complex CAD models. CADCrafter [32] presents an image-to-CAD generation framework, fine-tuned via Direct Preference Optimization (DPO) to directly translate input images into executable CAD representations.

3 TriView2CAD: Benchmarking orthographic projection reasoning

As previously noted, a critical barrier in CAD research is the absence of an open-source dataset with high-fidelity annotations for orthographic projection reasoning. Existing benchmarks focus on 3D reconstruction from rasterized 2D views, yet diverge from real-world engineering drawing interpretation in three key aspects:

Lack of precise dimensional annotations. Existing orthographic projection benchmarks provide only rasterized drawings without precise dimension annotations. This causes models to prioritize relative scale over exact measurements, and pixel-level errors in rasterized images can easily propagate into the reconstructed 3D model, resulting in inaccuracies. Additionally, the limited editability of reconstructed 3D models prevents corrective adjustments during design and simulation.

Lack of explicit logical reasoning tasks. Existing datasets do not evaluate a model's inferential capabilities within or across orthographic views. Consequently, models trained on these benchmarks learn pixel-level reconstruction skills but fail to handle real-world challenges such as inferring omitted annotations or leveraging structural symmetries.

Lack of essential CAD modalities. Most datasets contain only one or two modalities-typically raster images and, at best, a single 3D format (meshes or point clouds). This limited coverage omits critical data representations (e.g., parameter tables, vector CAD files, executable commands, STEP/B-Rep) required for end-to-end CAD design, manufacturing, and simulation pipelines.

3.1 Dataset construction

In this work, we focus on prefabricated bridge piers owing to their inherently modular structure-composed of repeated, standardized components-that enables a compact yet expressive parameter space for dataset synthesis. Moreover, prefabricated piers are ubiquitous in modern infrastructure and backed by extensive CAD drawing and manufacturing documentation archives.

Drawing

Constraints

Text

Inter-/Intra-View

Constraints

Parameter Space

ezdxf

Step 2: 2D Drawing and Raster

Image Generation

Step 3: 3D Model Generation

Step 1:

Constraint-Guided

Parameter Sampling

Rendering

Layers

separating

Entities

separating

Execute

Front

Side

Top

Orthographic

Projection

Expert Knowledge

3-D Model

TRR

64120

DXF

TriView2CAD comprises 200,000 synthetic samples and 3,000 real-world samples. The synthetic data are split 80/20 into training and test sets; due to the scarcity of real-world engineering drawings, all 3,000 real-world samples are reserved exclusively for testing. The synthetic test set evaluates in-domain performance. As shown in Fig. 3, real-world images typically feature more complex structures-primarily due to redundant annotation lines (causing occlusions and overlaps with other components) and the inclusion of non-numeric annotations. These samples thus assess out-ofdistribution (OOD) generalization in authentic CAD scenarios. To align with end-to-end industrial workflows, TriView2CAD provides six interconnected data modalities per sample: structured parameter tables (JSON), vector CAD drawings (DXF), raster images (PNG), executable modeling scripts, and two standard 3D formats (STEP and B-Rep). This comprehensive modality suite supports tasks spanning early design to downstream manufacturing and simulation. As shown in Fig. 5, we adopt a constraint-driven synthesis pipeline, with detailed steps outlined below:

Step 1: Constraint-Guided Parameter Sampling We first analyze real-world CAD drawings to define a 15dimensional parameter space, governed by two constraint classes. Intra-View Constraints enforce topological closure and physical validity within each projection: every component must form gap- and overlap-free contours, and paired dimensions adhere to domain-specific engineering constraints (e.g., 'Cross-Bridge Pier Spacing' < 'Cap Beam Cross-Bridge Dimension'). Inter-View Constraints ensure cross-projection consistency by requiring measurements annotated in one view to exactly match their counterparts (height, width, depth) in other views. By embedding these constraints into the sampling pipeline, we ensure all synthetic designs are geometrically coherent and compliant with real-world engineering standards.

Step 2: 2D Drawing and Raster Image Generation We use the ezdxf library to convert each sampled parameter vector into a vectorized 2D CAD drawing (DXF format), mapping numeric dimensions to geometric primitives (e.g., lines, circles, arcs) to form coherent orthographic projections. We utilize ezdxf's layer separation functionality to partition primitives into semantic layers, enhancing the editability of the resulting CAD drawings. Each DXF file is then imported into FreeCAD [33], where high-resolution screenshots are captured to generate three orthographic views (front, top, side).

Top

Side

3D Model

Front

A

212.5

200

212.5

2520025

10

375

625

[50

150

50]

10dm

250

250

125_125

212.

2511

200

25

250

Step 3: 3D Model Generation We utilize the FreeCAD Python API to reconstruct a 3D model from its corresponding parameter vector. Consistent with our 2D generation pipeline, we decompose the

Training Tasks

Dichotomous choice task

Multiple choice task

Parameterization task based on CoT

Input Prompt

Response

Yes/NO

A/B/C/D

2*82+2*188+148, 2*398+160,

..., 222

Given the front, top, and side views, please refer to the required field names below, and

determine whether the given parameter values ...match the image.

Given the front, top, and side views, please refer to the required field names below, and

choose all options that match the image. ...Options: A. 860, 833, ..., 356 B. 860,

<mask>, .., 356  C... D ...

Given the front, top, and side views, please output only the numeric values of the

following parameters in this exact order..., separated by commas.

model into discrete entities and generate scripted commands to precisely position each primitive in compliance with the intra- and inter-view constraints defined in Step 1. Executing these scripts automatically produces industry-standard STEP and B-Rep files, closing the loop from parameter sampling to editable, simulation-ready CAD geometries.

3.2 Evaluation of TriView2CAD

We benchmark seven leading vision-language models on TriView2CAD to assess their high-precision orthographic projection reasoning capabilities. The evaluation results are presented in Sec. 3.2. Aligning with real-world CAD workflows, we design three complementary evaluation tasks to rigorously test a model's ability to extract precise dimension annotations from rasterized orthographic projections and infer the underlying geometric relationships: (i) Dimension recognition and pairing Models identify each annotated dimension in a single orthographic view and map it to its associated geometric feature. (ii) Primitive counting Models count instances of specified CAD elements (e.g., number of pier columns), assessing their ability to parse structural composition. (iii) Composite Parameter Computation Models compute engineering-critical derived quantities-for example, Cross-Bridge Pier Spacing is calculated as the sum of Pier Column Cross-Bridge Dimension and Pile Spacing. Collectively, we define a 15-dimensional parameter space consisting of 6 recognition parameters, 3 counting parameters, and 6 composite calculation parameters. In evaluation, each parameter is treated as an independent prediction target. Overall accuracy is computed as the total number of correctly predicted parameters divided by the total number of parameters across all test samples. This evaluation suite reveals the strengths and weaknesses of each model in addressing the tightly coupled semantic and quantitative requirements of orthographic projection CAD reasoning.

4 Methodology

Inspired by the success of reinforcement learning-based tuning methods [34, 35], we present Curriculum-driven Reinforcement Fine-tuning for CAD (CReFT-CAD), an orthographic-projection reasoning framework. CReFT-CAD integrates a ViT-based visual encoder with Qwen2.5 [36]. First, the framework adopts a curriculum of three progressively challenging reasoning tasks paired with a difficulty-aware reward mechanism, empowering the model to transcend rote pattern matching and develop robust reasoning capabilities while enhancing out-of-distribution (OOD) generalization. Second, Supervised Post-tuning refines the model's instruction-following abilities to accommodate interactive, real-world CAD queries.

4.1 Curriculum-driven reinforcement fine-tuning

Orthographic projection reasoning is a sophisticated reasoning task that involves not only dimension recognition and pairing but also primitive counting and more complex composite parameter computation. Building upon GRPO, we propose a Curriculum-driven Reinforcement Fine-tuning (CReFT) strategy, which incrementally exposes the model to increasingly complex training tasks to enhance its reasoning and generalization capabilities. Our task design aligns with the core steps expert engineers follow when verifying CAD drawings, incorporating dichotomous choice tasks, multiple-choice tasks, and chain-of-thought (CoT)-based parameterization tasks. For each task, we design a tailored, efficient reward function to ensure the model can steadily optimize its reasoning capabilities.

Data engine. We utilize 160,000 image-text instruction pairs from the synthetic training set of TriView2CAD, where each prompt includes an orthographic projection and all 15 parameter key-value pairs (see Fig. 4).To ensure a balanced training signal across the three tasks, 50% of the training responses are fully correct, while the remaining 50% contain errors of varying degrees. Specifically, for dichotomous choice tasks, each negative response includes n erroneous parameter values, where n follows a uniform distribution N ∼ Uniform ( a, b ) , where a = 1 and b = 15 .

For multiple-choice tasks, p parameter values are masked out in each image-text instruction pair. The masking mechanism is designed to ensure the diversity of distractor options, preventing the model from merely deriving answers through parameter matching-thereby avoiding circumvention of the complex reasoning required for the task. Instead, it compels the model to actively engage in decision-making, evaluating multiple potential solutions and selecting the correct one based on a deeper geometric understanding. For each instruction pair, unmasked values are correct; for incorrect parameter value lists, all unmasked values are correct except for those in q randomly selected erroneous entries. Both the number of masked values p and the number of erroneous entries q follow normal distributions, introducing smooth, gradual variations in task difficulty that enable the model to learn in a more structured, progressive fashion.

For parameterization tasks, we construct image-text instruction pairs grounded in Chain-of-Thought (CoT) reasoning. Specifically, for Composite Parameter Computation tasks, expert knowledge is embedded into step-by-step reasoning prompts to guide the model toward correct outputs. The step-by-step reasoning workflow is as follows: 1) identify the parameters required to compute the composite parameter; 2) specify the calculation formula for the composite parameter; 3) compute the predicted result using the formula and relevant factor parameters.

Training strategy. Given that our task involves highly specialized inputs and outputs (as opposed to general-purpose problems), directly applying a pretrained VLM to perform reasoning and generate the required outputs is ineffective. Consequently, the reinforcement learning (RL) reward mechanism cannot function properly-with rewards consistently remaining zero. To mitigate this, we pre-sample a subset of image-text instruction pairs to "warm up" the model before initiating each training task, enabling proper initialization of the RL reward mechanism.

4.1.1 Task 1: Dichotomous choice task

For this task, the model's output is restricted to dichotomous responses: "yes" or "no." A "yes" response is returned if and only if all parameters in the 15-dimensional parameter space are fully accurate, whereas any error-even in a single parameter-triggers a "no" response. This stringent criterion incentivizes the model to generate fully accurate parameterizations. Consistent with this design, the reward function is defined as follows:

4.1.2 Task 2: Multiple choice task

Training strategy. Task 2 introduces multiple-choice tasks to further increase decision-making complexity. These tasks require the model to select from multiple parameter value lists, which significantly enhances the cognitive load compared to binary decisions. Building on the first task, we pre-sample a subset of image-text instruction pairs to conduct instruction-following training on the LLM. This training enables the model to generate structured outputs in the form of multiplechoice questions, facilitating the activation of the reinforcement learning (RL) reward mechanism. Additionally, we design a stringent reward function that encourages the model to fully utilize imagetext instruction pairs, ensuring it extracts and leverages all relevant information from both visual and textual modalities. The reward function incentivizes the model to select the most accurate option from the provided choices. Any incorrect selection yields a reward of 0, compelling the model to prioritize precision and reliability in its decision-making.

Let S correct be the set of correct parameter value lists, S incorrect be the set of incorrect parameter value lists, and S Selected be the set of selected parameter value lists by the model. The reward function R p 2

is defined as:

̸

̸

̸

Where S Selected = S correct means the model selects only the correct parameter value lists; S Selected ⊇ S correct but S Selected = S correct means the model selects all correct lists but fails to select all of them; S Selected ∩ S correct = ∅ and S Selected ∩ S incorrect = ∅ means the model selects both correct and incorrect parameter value lists; S Selected = S incorrect means the model selects incorrect parameter value lists.

̸

̸

4.1.3 Task 3: Parameterization tasks based on chain-of-thought

Training strategy. For this task's training, we start with the model fine-tuned in Task 2 and sample synthetic data to construct standard orthographic projection reasoning QA pairs. These pairs are used for small-batch fine-tuning. After fine-tuning, we assess the difficulty of predicting the 15-dimensional parameters by evaluating the model's performance on a held-out test set. Each parameter's prediction accuracy is categorized into three difficulty levels: Easy Attributes : Attributes with an accuracy greater than 0.8. Medium Attributes : Attributes with accuracy between 0.2 and 0.8. Difficult Attributes : Attributes with accuracy less than 0.2. This difficulty classification is subsequently used to design the reward function. The reward for each correctly predicted attribute is determined by its difficulty level as follows:

This reward function ensures that the model is motivated to gradually improve across all difficulty levels, with greater emphasis on mastering the more challenging tasks, thus enhancing the model's overall reasoning capabilities in orthographic projection tasks.

4.2 Supervised post-tuning

Following the CReFT stage-during which the model has developed robust reasoning and generalization capabilities-the focus of Supervised Post-tuning shifts to refining the model's instructionfollowing capabilities. We revisit the industrial design application context and, adopting a user-centric approach, reformulate tasks in the paradigm of Visual Question Answering (VQA). Leveraging multimodal language models, we design specific tasks that guide the model to output comprehensive parameter lists and make engineering-critical judgments based on orthographic projections. In this stage, the model is trained to handle the following tasks: 1) Full Parameter List Output Task: Generate a complete and accurate set of parameter key-value pairs- the standard output format for the simulation phase. 2) Parameter Validation Task: Compare extracted parameter key-value pairs with the ground-truth parameter set in the orthographic projections to verify the accuracy of the extracted parameters. 3) View Matching Task: Given two distinct orthographic views, determine if the views correspond to the same 3D object or design based on parameter constraints and rasterized images. 4) Component Counting Task: Identify and count specified components (e.g., bridge piers, columns) in the drawings based on given parameter constraints. This task quantifies design elements to support material estimation and component verification.

Through this post-tuning phase, we strengthen the model's capacity to interpret and respond to complex design queries, thereby enhancing its interactive reasoning capabilities and increasing its applicability to real-world industrial design workflows.

5 Experiments

Implementation Details. All experiments were conducted on NVIDIA A100 GPUs. Most experiments used GOT-OCR2.0 as the base model, trained on a single server with 8 A100 GPUs and

̸

a batch size of 64. We adopted the AdamW optimizer with a cosine annealing scheduler. The hyperparameters were configured as follows:(1) Learning rate: 1e-6 for RL (GRPO) training and 2e-5 for baseline SFT experiments;(2) Maximum input image size: 2,483,776 pixels;(3) Total GRPO training steps: 1500.

Datasets and Metrics. We evaluated both leading vision-language models (VLMs) and our proposed method on TriView2CAD, following the evaluation strategy outlined in Sec. 3.2. The synthetic test set was used to assess in-domain accuracy. Due to the scarcity of real-world data, all 3,000 real-world samples were reserved exclusively for testing-enabling evaluation of out-of-distribution (OOD) generalization in authentic CAD scenarios.

We design four distinct prompt configurations to probe their orthographic projection reasoning capabilities: 1) Test image-Only: Models receive only the target rasterized drawings; 2) Test image + Reference Image: In addition to the target drawings, models are given a reference image-rendered on the same geometry layer-with all dimension names to supply semantic alignment; 3) Test image + Answered Pair: Inputs consist of the target drawings plus a correctly paired raster image and its parameter vector, providing an exemplar mapping between visual features and quantitative parameters; 4) Test image + Attribute Explanation: Models are supplied with the target drawings alongside a detailed textual interpretation authored by professional engineers.

Given that orthographic projection reasoning involves complex multi-step inference and current pretrained VLMs have limited exposure to CAD drawings, we incorporated explicit reasoning guidance into each prompt format to encourage coherent reasoning trajectories. For each of the four prompt configurations, we outlined step-by-step instructions for task completion-structured to guide the model through the reasoning process systematically. The complete set of instructions for each format is provided in the supplementary materials.

5.1 Performance on in-domain test set

The experimental results (Tab. 3) demonstrate that our method significantly outperforms existing vision-language models (VLMs) on orthographic projection reasoning tasks across all prompt formats. Specifically, in the "Without Reasoning Guidance" setting, the "+Answered Pair" prompt achieves the highest accuracy of 83.24%. In the "With Reasoning Guidance" setting, our model reaches 84.03% accuracy with the "+Attribute Explanation" prompt. Notably, our method also delivers strong performance under the "Test Image Only" condition, achieving 80.86% accuracy-highlighting the effectiveness of our proposed approach.

It is worth noting that the performance improvement from "without reasoning guidance" to "with reasoning guidance" is not as significant. This is primarily because, through our CReFT approach, the model learns effective reasoning strategies via the difficulty-aware reward mechanism, thereby reducing the need for additional guidance to achieve further significant gains. Based on our benchmarking of the seven leading VLMs, we present several important findings, as follows: 1) This reasoning task remains a tough challenge for pretrained VLMs. Overall, orthographic projection reasoning entails

not only reading textual dimensions and visual features, but also matching annotations to geometry primitives, counting structural elements, and computing composite parameters. 2) Different prompt formats influence the performance. In both the Without Reasoning Guidance and With Reasoning Guidance settings, different prompt formats lead to varying degrees of performance improvement. 3)Introducing reasoning guidance yields consistent gains across all seven VLMs. Introducing appropriate reasoning guidance significantly enhances the model's ability to perform reasoning tasks effectively. Further analysis regarding the three findings are presented in the supplementary materials.

5.2 Performance on real-world test set

Weevaluate out-of-distribution (OOD) generalization using all 3,000 real-world samples, which reflect authentic CAD scenarios. The baseline model (Qwen2.5-VL) achieves 13.47% accuracy-lower than its 24.54% performance on synthetic data. This performance degradation is primarily attributed to the more complex structures and occlusion relationships in real-world data (Sec. 3.1), which elevates task difficulty. We also compare with a model trained via standard supervised fine-tuning (SFT), which achieves 36.15% accuracy. This improvement stems from substantial gains in Primitive Counting tasks. However, the model still struggles with Composite Parameter Computation-underscoring the limitations of conventional SFT. Our CReFT-based method achieves 46.67% accuracy, demonstrating its ability to effectively mitigate OOD challenges and deliver substantial performance gains on complex real-world data.

5.3 Abalation study

With CoT VS without CoT. The results from Tab. 2 reveal a significant improvement in performance when incorporating chain-of-thought (CoT) reasoning. Specifically, the accuracy for individual Task 3 increases from 46.16% to 74.15% after adding CoT , demonstrating a clear performance boost. When combining multiple training tasks, such as Task 1 + Task 3 and Task 2 + Task 3, the improvements are even more pronounced, with accuracies reaching 77.90%

and 67.75%, respectively. The most notable enhancement is observed in the combination of Task 1 + Task 2 + Task 3, where the accuracy increases from 46.24% to 81.35%. The primary source of these improvements lies in how CoT helps break down complex Composite Parameter Computation tasks into more manageable, incremental steps. The CoT mechanism, however, guides the model through each individual component of the computation, allowing for a step-by-step approach that improves the model's reasoning capability.

Three training tasks. Notably, in the CoT setting, combining Task 1 and Task 3 provides a performance boost, increasing accuracy from 74.15% to 77.90%. However, when Task 2 is added, the model's performance decreases. This decline can be attributed to the design of Task 2, which includes a substantial number of masked elements within the instruction data. By exposing the model to higher uncertainty, Task 2 forces the model to contend with incomplete or ambiguous information, which ultimately hinders its ability to perform effectively on the task. On the other hand, when all three tasks (Task 1 + Task 2 + Task 3) are used together, the model's reasoning ability is fully activated, resulting in a significant performance improvement. In contrast, in the without CoT setting, the model's performance is constrained by its inability to effectively handle multi-step reasoning tasks, where even the best-performing task combination achieves a maximum accuracy of only 46.24%.

6 Conclusion and Limitation

In this work, we address challenges in orthographic projection reasoning for Computer-Aided Design (CAD) by proposing CReFT-CAD, an innovative two-stage fine-tuning paradigm. We demonstrated that the integration of curriculum-driven reinforcement learning (RL) with difficultyaware rewards effectively enhances the model's reasoning ability. To further advance the field, we introduce TriView2CAD, the first large-scale, open-source benchmark specifically designed for orthographic projection reasoning. With its comprehensive dataset of 200,000 synthetic and 3,000

real-world samples, each annotated with precise dimensions and accompanied by six interoperable data modalities. In conclusion, this work lays the groundwork for more robust, scalable solutions to CAD-related challenges, providing both a novel training paradigm and a rich dataset that can foster future research and innovation in the field.

Limitation. The accuracy on complex real-world scenarios has not yet reached a very high level. As such, the model's generalization ability remains a key area for further development. In future work, we plan to explore techniques to enhance the model's robustness and adaptability.

ACKNOWLEDGMENT This work is supported in part by NSFC Project (No. 62176061) and Joint Laboratory of Intelligent Construction Engineering Technology for Operating Railway Lines.

References

Karl DD Willis, Pradeep Kumar Jayaraman, Joseph G Lambourne, Hang Chu, and Yewen Pu. Engineering sketch generation for computer-aided design. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2105-2114, 2021.

Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl DD Willis, and Daniel Ritchie. Inferring cad modeling sequences using zone graphs. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6062-6070, 2021.

Weijian Ma, Shuaiqi Chen, Yunzhong Lou, Xueyang Li, and Xiangdong Zhou. Draw step by step: Reconstructing cad construction sequences from point clouds via multimodal diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 27154-27163, 2024.

Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 6772-6782, 2021.

Long Zhang, Jianwei Guo, Jun Xiao, Xiaopeng Zhang, and Dong-Ming Yan. Blending surface segmentation and editing for 3d models. IEEE Transactions on Visualization and Computer Graphics , 28(8):2879-2894, 2020.

Haiyang Yu, Jinghui Lu, Yanjie Wang, Yang Li, Han Wang, Can Huang, and Bin Li. Eve: Towards end-to-end video subtitle extraction with vision-language models. arXiv preprint arXiv:2503.04058 , 2025.

Ke Niu, Haiyang Yu, Xuelin Qian, Teng Fu, Bin Li, and Xiangyang Xue. Synthesizing efficient data with diffusion models for person re-identification pre-training. Machine Learning , 114(3):1-25, 2025.

Teng Fu, Xiaocong Wang, Haiyang Yu, Ke Niu, Bin Li, and Xiangyang Xue. Denoising-mot: Towards multiple object tracking with severe occlusions. In Proceedings of the 31st ACM International Conference on Multimedia , pages 2734-2743, 2023.

Teng Fu, Haiyang Yu, Ke Niu, Bin Li, and Xiangyang Xue. Foundation model driven appearance extraction for robust multiple object tracking. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, pages 3031-3039, 2025.

Ke Niu, Haiyang Yu, Mengyang Zhao, Teng Fu, Siyang Yi, Wei Lu, Bin Li, Xuelin Qian, and Xiangyang Xue. Chatreid: Open-ended interactive person retrieval via hierarchical progressive tuning for vision language models. arXiv preprint arXiv:2502.19958 , 2025.

Haiyang Yu, Siyang Yi, Ke Niu, Minghan Zhuo, and Bin Li. Umit: Unifying medical imaging tasks via vision-language models. arXiv preprint arXiv:2503.15892 , 2025.

Weijie Yin, Yongjie Ye, Fangxun Shu, Yue Liao, Zijian Kang, Hongyuan Dong, Haiyang Yu, Dingkang Yang, Jiacong Wang, Han Wang, et al. Sail-vl2 technical report. arXiv preprint arXiv:2509.14033 , 2025.

Xilin Wang, Jia Zheng, Yuanchao Hu, Hao Zhu, Qian Yu, and Zihan Zhou. From 2d cad drawings to 3d parametric models: A vision-language approach. arXiv preprint arXiv:2412.11892 , 2024.

Haocheng Yuan, Jing Xu, Hao Pan, Adrien Bousseau, Niloy J Mitra, and Changjian Li. Cadtalk: An algorithm and benchmark for semantic commenting of cad programs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3753-3762, 2024.

Ke Niu, Yuwen Chen, Haiyang Yu, Zhuofan Chen, Xianghui Que, Bin Li, and Xiangyang Xue. Phtcad: Efficient cad parametric primitive analysis with progressive hierarchical tuning. arXiv preprint arXiv:2503.18147 , 2025.

Martin Arjovsky. Out of distribution generalization in machine learning . PhD thesis, New York University, 2020.

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 , 2025.

Masanori Idesawa. A system to generate a solid figure from three view. Bulletin of JSME , 16(92):216-225, 1973.

Weidong Wang and Georges G Grinstein. A survey of 3d solid reconstruction from 2d projection line drawings. In Computer Graphics Forum , volume 12, pages 137-158. Wiley Online Library, 1993.

Byeong-Seok Shin and Yeong Gil Shin. Fast 3d solid model reconstruction from orthographic views. Computer-Aided Design , 30(1):63-76, 1998.

Wenyu Han, Siyuan Xiang, Chenhui Liu, Ruoyu Wang, and Chen Feng. Spare3d: A dataset for spatial reasoning on three-view line drawings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 14690-14699, 2020.

Siyuan Xiang, Anbang Yang, Yanfei Xue, Yaoqing Yang, and Chen Feng. Contrastive spatial reasoning on multi-view line drawings. CoRR , 2021.

Wentao Hu, Jia Zheng, Zixin Zhang, Xiaojun Yuan, Jian Yin, and Zihan Zhou. Plankassembly: Robust 3d reconstruction from three orthographic views with learnt shape programs. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 18495-18505, 2023.

Thao Nguyen Phuong, Vinh Nguyen Duy, and Hidetomo Sakaino. Isotgan: Spatial and geometrical constraints at gan and transformer for 3d contour generation. In Proceedings of the Asian Conference on Computer Vision , pages 435-452, 2024.

Zheng Zhou, Zhe Li, Bo Yu, Lina Hu, Liang Dong, Zijian Yang, Xiaoli Liu, Ning Xu, Ziwei Wang, Yonghao Dang, et al. Gaussiancad: Robust self-supervised cad reconstruction from three orthographic views using 3d gaussian splatting. arXiv preprint arXiv:2503.05161 , 2025.

Danila Rukhovich, Elona Dupont, Dimitrios Mallis, Kseniya Cherenkova, Anis Kacem, and Djamila Aouada. Cad-recode: Reverse engineering cad code from point clouds. arXiv preprint arXiv:2412.14042 , 2024.

Tao Wu, Kexue Fu, Qiang Hua, Xinxin Liu, Muhammad Ali Imran, and Bo Liu. Leam: A prompt-only large language model-enabled antenna modeling method. arXiv preprint arXiv:2504.18271 , 2025.

Xingang Li, Yuewan Sun, and Zhenghui Sha. Llm4cad: Multimodal large language models for threedimensional computer-aided design generation. Journal of Computing and Information Science in Engineering , 25(2), 2025.

Dimitrios Mallis, Ahmet Serdar Karadeniz, Sebastian Cavada, Danila Rukhovich, Niki Foteinopoulou, Kseniya Cherenkova, Anis Kacem, and Djamila Aouada. Cad-assistant: Tool-augmented vllms as generic cad task solvers? arXiv preprint arXiv:2412.13810 , 2024.

Jingwei Xu, Chenyu Wang, Zibo Zhao, Wen Liu, Yi Ma, and Shenghua Gao. Cad-mllm: Unifying multimodality-conditioned cad generation with mllm. arXiv preprint arXiv:2411.04954 , 2024.

Xiaolong Yin, Xingyu Lu, Jiahang Shen, Jingzhe Ni, Hailong Li, Ruofeng Tong, Min Tang, and Peng Du. Rlcad: Reinforcement learning training gym for revolution involved cad command sequence generation. arXiv preprint arXiv:2503.18549 , 2025.

Cheng Chen, Jiacheng Wei, Tianrun Chen, Chi Zhang, Xiaofeng Yang, Shangzhan Zhang, Bingchen Yang, Chuan-Sheng Foo, Guosheng Lin, Qixing Huang, et al. Cadcrafter: Generating computer-aided design models from unconstrained images. arXiv preprint arXiv:2504.04753 , 2025.

Juergen Riegel, Werner Mayer, and Yorik van Havre. Freecad. Freecadspec2002. pdf , 2016.

Huilin Deng, Ding Zou, Rui Ma, Hongchen Luo, Yang Cao, and Yu Kang. Boosting the generalization and reasoning of vision language models with curriculum reinforcement learning. arXiv preprint arXiv:2503.07065 , 2025.

Xinhao Li, Ziang Yan, Desen Meng, Lu Dong, Xiangyu Zeng, Yinan He, Yali Wang, Yu Qiao, Yi Wang, and Limin Wang. Videochat-r1: Enhancing spatio-temporal perception via reinforcement fine-tuning. arXiv preprint arXiv:2504.06958 , 2025.

An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 , 2024.

Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, et al. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219 , 2024.

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024. URL https://arxiv.org/ abs/2408.03326 .

Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan. Deepseek-vl: Towards real-world vision-language understanding, 2024.

Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 24185-24198, 2024.

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, et al. Qwen2.5-omni technical report. arXiv preprint arXiv:2503.20215 , 2025.

Qwen Team. Qwen2.5-vl, January 2025. URL https://qwenlm.github.io/blog/qwen2.5-vl/ .

Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261 , 2025.

Anthropic. Claude. https://claude.ai , 2025. https://claude.ai .

Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276 , 2024.

Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191 , 2024.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: We have detailed the contributions accurately in the abstract and introduction. Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We have listed some limitations of our work.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: This work does not include theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We have described the details of implementing.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: We will release all resources upon acceptance.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We have detailed the experimental setting and implementation details.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: The error bars are not used in our experiments.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We have reported the required computer resources.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The research in this paper conforms with the NeurIPS Code of Ethics in every respect.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: There is no societal impact.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: No unsafe data is used in this manuscript.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: All used assets have been cited.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: All details of the new assets in this paper have been detailed.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

A The complete reasoning guidance for each input format

For the Test Image-Only format:

Identify all annotated numbers in the orthographic projection, including dimensions, spacing, and height.

Interpret their meanings based on position, orientation, and surrounding context.

Directly assign values to some parameters, count graphical elements for quantity parameters, and compute spacing parameters based on position.

For the +Reference Image format:

Use the reference template to understand the correspondence between primitive and parameters, and identify annotated numbers in the target image.

Assign parameters based on position and template primitive, counting graphical primitive for quantities.

Calculate parameters based on positional relationships or template definitions, and output the full set of parameter values.

For the +Answered Image format:

Learn the correspondence between structures and parameters from the example image.

Identify and interpret annotated numbers in the target image, considering their position and similarity to the example.

Assign values to parameters, count graphical primitive for quantities, calculate parameters, and apply necessary constraints to produce final results.

For the +Attribute Explanation format:

Clarify parameter definitions and their geometric meanings, including component types and their properties.

Identify annotated numbers in the orthographic projection and infer their corresponding parameters.

Assign values to parameters, compute quantities, derive spacing values from positional relationships, and output the complete parameter set with necessary constraints.

B Further analysis regarding the three findings

1) Remains a Tough Challenge for pretrained VLMs. Tab. 3 reports performance across four prompt formats without and with reasoning guidance. Overall, orthographic projection reasoning entails not only reading textual dimensions and visual features, but also matching annotations to geometry primitives, counting structural elements, and computing composite parameters. Consequently,

none of the off-the-shelf models fully masters this composite task. Qwen2.5-VL [46] achieves the highest accuracy-38.88% under reasoning guidance with the Test Image + Attribute Explanation prompt. This improvement stems primarily from its superior ability to parse geometry layers and read dimension labels. Crucially, its performance on reasoning-intensive parameters remains low. These findings underscore that orthographic projection CAD cannot be solved by prompting alone and requires dedicated fine-tuning strategy.

2) Prompt Format-Dependent Performance Gains. Appending an Attribute Explanation to the Test Image consistently boosts accuracy compared to the image-only baseline, with an average increase of 3 to 4 percentage points across models. This demonstrates that strong text encoders can leverage detailed, engineer-authored descriptions to guide complex geometric and numerical inferences. Similarly, providing a Reference Image or an Answered Pair results in comparable improvements, with a 4 to 5 percentage point increase in accuracy. These exemplars offer explicit visual-textual templates, simplifying the task of matching primitives to their corresponding semantic labels and dimensions. In contrast, using raw images forces models to address both perception and reasoning simultaneously, leading to the lowest performance. Collectively, these findings indicate that multimodal exemplars can enhance existing VLMs' ability to reason over orthographic projection. However, this further underscores that additional fine-tuning strategies are crucial to fully bridging the remaining performance gap.

3)Introducing reasoning guidance yields consistent gains across all seven VLMs. Introducing reasoning guidance demonstrates that step-wise reasoning guidance helps models better decompose the multi-step orthographic projection tasks. In the absence of reasoning guidance, visual exemplar prompts (+Reference Image and +Answered Pair) deliver the largest relative improvements. However, when reasoning guidance is added, the Attribute Explanation prompt shows the greatest uplift. It stems from reasoning guidance is textual form, which synergizes most effectively with textual inputs. Hence, models with stronger text encoders (e.g., DeepSeek-V1) exhibit disproportionately larger boosts. These results underscore the necessity of integrating structured reasoning guidance to advance orthographic projection CAD reasoning.

C More performance on in-domain test set

Tab. 4 presents the performance in-domain test set. The first row (Qwen2.5-VL) represents a trainingfree baseline, which exhibits consistently poor performance across all settings, indicating the inherent difficulty of this reasoning task without any fine-tuning or adaptation. The second row (Qwen2.5-VL with SFT) demonstrates a significant performance improvement, particularly when provided with reference images, answered pairs, and attribute explanations. However, despite these gains, the model still suffers from notable drops in more complex reasoning tasks, especially those involving composite parameter computation. In contrast, our method consistently outperforms both baselines across all settings, achieving the highest accuracy in all prompt configurations, with particularly strong results under reasoning guidance. These results validate the effectiveness of our approach in handling complex geometric reasoning and highlight its robustness across both simple and compositional inference scenarios.

D Failure cases and analysis

As illustrated in Fig. 5, we present four distinct failure cases, each highlighting different types of errors that can occur during the 3D model reconstruction process due to incorrect 2D parameterization.

520-Bs)/

[520Bs]/2

185

185

185

RRR

X

RRR

TRR

230

185

180

30

H7c

520

520

160

200

160

0120

007

091

（0120

e120

110

300

110

520

Fig. 5(a) demonstrates the error induced by incorrect dimensional parameters. Specifically, due to errors in the parameterization of certain dimensions, the constructed 3D model exhibits significant scaling issues, leading to a distorted and non-accurate representation. This failure emphasizes the critical importance of precise dimensional input when translating from 2D projections to 3D models, as even minor errors in size parameters can drastically affect the final output. Fig. 5(b) illustrates a failure caused by incorrect counting, which results in an incorrect number of primitives being identified and subsequently integrated into the 3D model. The discrepancy in the number of primitives

reflects the direct impact that improper counting and recognition of geometric elements can have on the success of the 3D reconstruction. In Fig. 5(c), we observe a stacking issue caused by errors in the calculation of composite parameters. The failure manifests as improper alignment of components in the final model, where parts of the 3D structure fail to align correctly with each other. Lastly, Fig. 5(d) shows a failure resulting from exceeding physical boundaries due to incorrect composite parameter calculations. In this case, the incorrect parameters lead to elements of the 3D model extending beyond the physical constraints or limits of the original design. Together, these four failure cases provide strong evidence that errors in the parameterization of 2D models directly lead to inaccuracies in 3D model reconstruction. Each case illustrates the cascading effect of parameter errors from the 2D representation to the final 3D model, further underscoring the necessity for robust and precise parameterization methods in 2D-to-3D model conversion processes.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a solution for using foundational models to help CAD in generating orthographic projection of the models, which is a crucial in the design pipeline, instead of directly going for 3D model generation. To this end, a new dataset is proposed and a fine-tuning strategy based on curriculum learning is presented. Experiments are done and compared with several foundational models. The results show the proposed new data with the fine-tuning strategy can achieve good results on the specific tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is written clearly, easy to follow and understand.\n2. The experiments can show certain promising aspects of the proposed methodology and the analysis on the comparative performances under different settings is helpful\n\nWeaknesses:\n\n1. There is an unclear difference between the proposed method and other similar methods in CAD, especially when it comes to the technical contribution.\n\n2. The realism and diversityi of the data itself seem to limited. \n\n3. Limited real-world applicability\n\nPlease see my comments below\n\nQUESTIONS:\nFollowing my comments below, I have a few questions below:\n\n1. It seems that the primary contribution of the paper is the data. However, the majority of the data seems to be synthetic which is normally cleaner, easier to handle than real-world data. Since the root of the research is a real-world application. This might hurt the contribution of the paper. Also, from the provide visualization of the data, they seem to be restricted to a certain type of objects. Considering CAD is used almost in every industry, I wonder how diverse the data is. I am not an expert in CAD, so I will leave this to the domain expert to judge the value of the new data.\n\n2. The proposed fine-tuning strategy is interesting. As an AI and machine learning conference, one natually asks the question how general the proposed method is when applied to other data and tasks. Currently, the curriculum design seems to be centered around the specific data and task. It would be good to discuss if there is fundamental difference between this specific curriculum design and other simiar research. I am asking for more experiments but only wonder if this new CAD task requires a categorically different curriculum design.\n\n3. The OOD test on real-world data is highly appreciated. However the analysis is rather short. It is important for papers like this one to analyze what difficulties when facing real-world data. In what sense they are out of distribution? Is the worse performance mainly caused by the lower quality of real-world data, or the diversity? As a result, the paper did not seem to achieve the goal it claims to achieve in the beginning.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-04"}

comments: SUMMARY:
This paper performs orthographic projection reasoning in CAD workflows by introducing CReFT-CAD, a two-stage fine-tuning framework for vision–language models.The main idea is a Curriculum-driven Reinforcement Fine-Tuning (CReFT), a three-task curriculum (binary choice, multiple choice, chain-of-thought parameterization). They then perform supervised post-tuning of the output. The paper also introduces a TriView2CAD Benchmark, which is a new dataset of 200 K synthetic + 3 K real-world three-view samples.

STRENGTHS AND WEAKNESSES:
Strengths
+ The integration of RL followed by fine tuning seems to be reasonable.
+ TriView2CAD’ dataset can fill a critical gap in CAD reasoning datasets

Weaknesses
- Despite improvements, real-world accuracy remains under 50 %
- It is unclear what the different models make up the TriView2CAD dataset. Only a limited set of examples is shown in the paper making it unclear if the method will work on more complex CAD models with complex extruded features instead of just cylinders and blocks.

QUESTIONS:
How sensitive are results to the chosen reward weights?
Have you analyzed which specific real-world drawing features (e.g., occlusions, annotation styles) cause most OOD failures? A breakdown by task type would be helpful.
What are the complex models in TriView2CAD dataset? Does it include more complex features than cylinders and blocks?

LIMITATIONS:
Authors acknowledge limited real-world accuracy and plan to improve generalization.
The dataset complexity seems very limited from the examples shown in the paper. I am willing to change this if the authors explain how diverse their dataset is.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces CReFT-CAD, a two-stage fine-tuning paradigm for orthographic projection reasoning in the context of Computer-Aided Design (CAD). The method integrates curriculum-driven reinforcement learning with difficulty-aware rewards, followed by a phase of supervised post-tuning to bolster instruction following and semantic extraction. To support evaluation, the authors present TriView2CAD, a large-scale benchmark containing 200,000 synthetic and 3,000 real-world samples, each linked to six interoperable data modalities with precise dimension annotations. Experiments benchmark leading vision-language models (VLMs) and demonstrate that CReFT-CAD yields improved accuracy and out-of-distribution generalization on orthographic projection reasoning tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1: The introduction of TriView2CAD is a meaningful addition to the field. The benchmark is described in detail and includes synthetic and real-world samples across six data modalities, supporting a wide spectrum of CAD tasks. This addresses long-standing challenges in the lack of open, precisely annotated datasets for orthographic projection reasoning.\n\n2: The proposed CReFT-CAD leverages a curriculum-driven reinforcement learning approach with customized, difficulty-aware reward functions. This approach is designed to systematically build up reasoning ability, followed by supervised post-tuning targeted at enhancing instruction. The methodology is well described and is tightly aligned to the practical needs of the CAD workflow (design, manufacturing, simulation).\n\n3: Extensive experiments are conducted, benchmarking seven VLM baselines plus the proposed method. The results show CReFT-CAD's clear superiority over existing VLM baselines, especially on complex tasks and out-of-distribution (OOD) real-world scenarios.\n\nWeaknesses:\n\n1: **Analysis of Prompt Engineering.** Several prompt configurations are presented, both with and without reasoning guidance. While it is shown that reasoning guidance helps, it is not always clear if the observed gains are attributable to the underlying model improvements or just to the engineered prompt. In addition, the evaluation assumes a fixed set of prompt templates, which could constrain generalizability.\n\n2: **Insufficient Analysis of Dataset Diversity.** The paper does not provide a thorough analysis of the characteristics of the proposed dataset, TriView2CAD. Based on the visualizations provided, many CAD models seem structurally similar, often consisting of repetitive patterns (e.g., cylinder-cuboid-cylinder arrangements). Such structural uniformity might limit the diversity of the benchmark, potentially inflating performance metrics or restricting the robustness evaluation of the models. Therefore, it is crucial to systematically assess and explicitly discuss the dataset's geometric and structural diversity.\n\n3: **Evaluation of Industrial-Level VLMs.** Although the paper benchmarks various existing vision-language models (VLMs), it neglects to include current industrial-grade or commercially deployed VLMs in its evaluations. Given that the practical motivation is closely tied to industrial CAD applications, testing state-of-the-art industrial VLM would be highly valuable. Such analysis could provide clearer insights into how the proposed method compares against real-world industry standards and further demonstrate the practical significance of the proposed framework.\n\nQUESTIONS:\n1: Could authors provide further analysis of the curated datasets (e.g., the diversity of shapes/objects, etc)?\n\n2: How would the performance of industry-level VLMs (such as GPT-4o, Gemini 2.5 flash) be on the proposed benchmark?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to address a long-standing challenge in the field of CAD: enabling AI models to perform accurate reasoning on orthogonal projection drawings. The authors point out that existing methods, whether traditional 3D reconstruction or simple SFT, suffer from issues such as insufficient accuracy, lack of editability, and poor generalization ability.\n\nTo tackle these problems, the paper proposes two major core contributions:\n\nCReFT-CAD Method: An innovative two-stage fine-tuning paradigm. The first stage employs curriculum-driven RL, cultivating the model's deep reasoning capabilities through tasks ranging from easy to difficult and a difficulty-aware reward mechanism. The second stage enhances the model's instruction following and interactive capabilities through supervised post-fine-tuning.\n\nTriView2CAD Dataset: The first large-scale, open-source benchmark specifically designed for this task. It comprises 200,000 synthetic samples and 3,000 real-world samples, featuring precise dimension annotations and six interoperable data modalities, providing valuable resources and evaluation standards for research.\n\nExperimental results demonstrate that CReFT-CAD significantly outperforms several leading VLMs on the TriView2CAD benchmark and exhibits stronger generalization ability on real-world OOD samples.\n\nSTRENGTHS AND WEAKNESSES:\n**Strenghths:**\n* The \"orthogonal projection reasoning\" problem addressed by this paper is one of immense value in the industry, yet remains insufficiently resolved within the deep learning community. The authors provide a compelling motivation for their proposed approach.\n\n* The CReFT-CAD method's design, which mimics the human cognitive learning process by progressing from easier to more challenging tasks, effectively contributes to the stable establishment of the model's reasoning capabilities. \n\n* On benchmark tests, CReFT-CAD's accuracy significantly surpasses all baseline models, unequivocally demonstrating the effectiveness of their methodology.\n\n**Weaknesses**\n* All experiments in the paper are concentrated on a specific CAD category: prefabricated bridge piers. While this serves as an excellent starting point, its parameter space and geometric complexity remain relatively limited.\n\n* The paper claims that its method enhances \"reasoning ability,\" which is demonstrated through its task design and performance metrics. However, the analysis regarding the precise nature of the reasoning capabilities the model has acquired is not yet sufficiently in-depth.\n\nQUESTIONS:\n* What major challenges do you foresee in applying the CReFT-CAD framework to other CAD domains? Would differences in parameter space and increased geometric complexity necessitate significant modifications to the curriculum or reward functions?\n\n* Could you provide specific examples of incorrect answers given by the model on real-world datasets? Analyzing these failure cases could help us better understand the limitations of the current method and directions for future improvements.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the problem of reasoning 3D CAD shapes with canonical orthographic projection images from multiple views. First, a large scale dataset is prepared with annotated drawings rendered in orthographic views and paired 3D shape data using parametric variations of constrained pier spacing configurations. Then, four tasks of varying difficulties are designed to probe if vision language models can truly reason by establishing correspondences between the orthographic views and the 3D shape. Using reinforcement learning and instruction tuning, the GOT-OCR2.0 model is aligned on 4 tasks of increasing complexity using curriculum learning and demonstrated to reason significantly better than off the shelf. Vision language models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- the paper tackles an important and under-explored problem of reasoning 3D CAD shapes from orthographic drawings. Drawings are indeed a key component in CAD design and manufacturing workflows, and not many previous works have explored techniques to improve reasoning of vision language models on drawings. \n- the large scale dataset with paired data containing drawings, their dimensions, and STEP files are useful for the community to build upon, and is a good contribution.\n- the proposed method clearly works well as demonstrated by strong quantitative results.\n\nWeaknesses:\n\nI don’t see any major weaknesses in the paper with regards to problem scoping or technical contributions. But a few points:\n- The exposition could be improved. It is not clear why reinforcement learning is required for this problem. The data preparation for training and specifics of how the model output connects with the loss function must be made more clear.\n- both the synthetic and real world data for testing are parametrically varied piers. This narrows the scope quite drastically and generalization outside of this distribution was not demonstrated.\n- the practical impact of better reasoning orthographic view reasoning achieved in this work is not clearly demonstrated by showcasing improvements in specific downstream applications.\n\nQUESTIONS:\n- Can the authors motivate better on why reinforcement  learning was required and supervised finetuning with standard next token prediction loss wasn’t sufficient? This design decision should be explained and it would be valuable for readers.\n- Can the method be tested on examples that are not piers? This is crucial to understand how well the training scheme helps the model to truly understand orthographic view images of CAD shapes.\n- Can the authors touch upon real world CAD problems that would benefit from the improvements in reasoning achieved in this work? This would solidify the impact of this work and help readers understand the motivation of this paper.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-06"}