Plan then Act: Bi-level CAD Command Sequence Generation
Authors: Qiangya Guo
Gang Dai
Zhuoman Liu
Shuangping Huang
Yunqing Hu
Huiyuan Zhang
Tianshui Chen
Venue: ICLR 2026 Poster (2026)
Keywords: CAD Command Sequence Generation; LLMs
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Computer-Aided Design (CAD), renowned for its flexibility and precision, serves as the foundation of digital design. Recently, some efforts adopt Large Language Models (LLMs) for generating parametric CAD command sequences from text instructions. However, our study reveals that LLMs pre-trained on large-scale general data are not proficient at directly outputting task-specific CAD sequences. Instead of relying on direct generation, we introduce a Plan then Act process where user instructions are first parsed into a chain-like operational plan via an LLM, which is then used to generate accurate command sequences. Specifically, we propose PTA, a new bi-level CAD command sequence generation method. The PTA consists of two critical stages: high-level plan generation and low-level command generation. During the high-level stage, an LLM-based Planner completes the planning process, parsing user instructions into a high-level operation plan. Following this, at the low-level generation stage, we introduce an Actioner equipped with a requirement-aware mechanism to extract design requirements (e.g., dimensions, geometric relationships) from user instructions.  This extracted information is used to guide the low-level command sequence generation, improving the alignment of the generated sequences with user requirements. Experimental results demonstrate that our PTA outperforms existing methods in both quantitative and qualitative evaluations.  Code is available at https://github.com/QiferG/Plan-then-Act.

Published as a conference paper at ICLR 2026
PLAN THEN ACT: BI-LEVEL CAD COMMAND SE- QUENCE GENERATION
Qiangya Guo'; Gang Dai Zhuoman Liu Shuangping Huang")
Yunqing Hu Huiyuan Zhang", Tianshui Chen
South China University of Technology *Guangdong University of Technology
'Hong Kong Polytechnic University
"Zhuzhou CRRC Times Electric Co., Ltd.
ecgayêmail, scut, edu. daigangegdut
cdw.aattccdcc........ edu. cchspescut.cdl odu.
ABSTRACT
Computer-Aided Design (CAD), renowned for its flexibility and precision, serves as the foundation of digital design. Recently, some efforts adopt Large Lan- guage Models (LLMs) for generating parametric CAD command sequences from text instructions. However, our study reveals that LLMs pre-trained on large- scale general data are not proficient at directly outputting task-specific CAD sequences. Instead of relying on direct generation, we introduce a Plan then Act process where user instructions are first parsed into a chain-like operational plan via an LLM, which is then used to generate accurate command sequences. Specifically, we propose PTA, new bi-level CAD command sequence genera- tion method. The PTA consists of two critical stages: high-level plan genera- tion and low-level command generation. During the high-level stage, an LLM- based Planner completes the planning process, parsing user instructions into high-level operation plan. Following this, at the low-level generation stage, we introduce Actioner equipped with requirement-aware mechanism extract design requirements (e.g., dimensions, geometric relationships) from user instruc- tions. This extracted information is used to guide the low-level command se- quence generation, improving the alignment of the generated sequences with user requirements. Experimental results demonstrate that our PTA outperforms exist- ing methods in both quantitative and qualitative evaluations. Code is available at stttp::/giitinn http:/uw..ooo/////eefeeeeeeeeeee Qwt.eeeeffPPPPee lan t-eetheeete Act
com
INTRODUCTION
As technology in digital design, Computer-Aidedt Aided Design (CAD) plays indispensable role in industrial design (Cherng et al.] 1998 Brozovsky et al. (024). The goal of CAD to produce CAD models based on user requirements (Miller) 2004 Ralph et al. 2009), requiring designers to leverage professional expertise in determining structural features and shape parameters. While cur- rent CAD software offers extensive design functions, two practical challenges persist: 1) designers must master specialized skills, such as parameter configuration and geometric constraint definition; 2) the design process involves numerous operations, which remain time-consuming and inefficient.
Recently, Text-based CAD generation attracts significant attention (Xu ct al. 2024; Alrashedy et al.,
2024 Wang et al.) 2025b). For instance, Khan al. (2024a) and Li al. (202bb) use text encoders to interpret user instructions and autoregressively generate CAD sequences. These methods (Khan) ct al. 2024a Li al. 2024b)trained from scratch have limited semantic parsing and modeling capabilities and may struggle with complex CAD modeling tasks. Therefore, some methods (Xu jet al. 2024. Li et al. 2025a Wang et al., (2225) attempt to use large language models (LLMs) pre- trained large-scale general data to understand user input and generate CAD command sequences.
"Atthors contributed equally. Cooresponding author

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Published as a conference paper at ICLR 2026
Low-level Command
User Instruction
High-level Plan
Linst
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Figure 1: Illusrationn of user instruction, high-level plan, low-level command sequence, and CAD model result. The same color represents operation steps and their most relevant requirement in- formation in user instruction. The "Contextual relationship" indicates there strong contextual connection between the operation steps.
Although these LLM-basee methods make some progress, our investigation finds that LLMs are not proficient at directly outputting task-specific CAD control sequences (cf. Section 3).
To address the above issues, inspired by human planning strategies (Masicampo & Baumeister, 2011 Rogers et al.) 2013, 2015), we propose a reasonable assumption that decomposing abstract
user instructions into chain-like operation plan can improve the accuracy of command sequence generation. To validate this assumption, we compare the performance of CAD generation using user instructions alone versus combining them with operation plans. The results (ef. Section 3 demonstrate the effectiveness of the chain-like operation plan. Based on the above analysis, we do not request LLMs to generate command sequences directly. Instead, we consider process where user instructions are first parsed into a chain-like operational plan via an LLM, followed by the generation of the accurate command sequence.
In this work, we propose PTA (Plan then Act), a new bi-level CAD command sequence generation method. The PTA consists of two key stages: high-level plan generation and low-level command sequence generation. During the high-level stage, we develop a Qwen3-8B (Yang et al.. (025a) based Planner to translate abstract user instructions into chain-like operation plan. While at the low-level stage, we design an Actioner equipped with a requirement-aware mechanism to generate accurate low-level executable command sequences.
For the design of Actioner, we consider that while the high-level plan is derived from the user in- struction, primarily provides global operational guidance for command sequence generation. To produce an accurate and precise command sequence, it is essential to integrate the high-level plan with detailed operational specifications. As shown in Figure we focus on two aspects: 1) When executing each operation step in high-level plan, it should not equally focus on all information in the user instructions, instead, it should pay more attention on the most relevant requirement in- formation for current operation step; 2) The operation steps are not isolated but have contextual relationships with each other. Based on the above considcrations, we design the requirement-awann cross-attention, employing the operation steps in the high-level plan as
mechanism. First, queries to retrieve the most relevant requirement information. Then, we use self-attention to cap- ture the contextual relationships between the operational steps. The final fused information guides the Transformer decoder to generate an accurate low-level CAD command sequence. This aware- ness and integration of requirement information enhances the alignment of the generated command sequence with user requirements.
We summarize our contributions follows:
We propose PTA, new bi-level CAD modeling method that first decomposes user structions into high-level operation plans via LLM,, then completes the low-level command sequence generation.
We design a requirement-wwan mechanism that automatically extracts critical requirement information from the user instruction, improving the alignment of the generated CAD se- quences with user requirements.
Experimental evaluations demonstrate that our PTA achieves superior performance com- pared to available state-of-the-art methods.
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2 REALTED WORK
2.1 CAD GENERATION
CAD software typically represents CAD models as Boundary Representation (B-Rep) (Labboune
ct al. 202I), which defines the model's boundary using geometric elements (c.g., point, curve, sur- tace) and their topological relationships. Pie-net (Wang et al.. 2020) and ComplexGen (Guo al.
2022 generate B-rep data by predicting point cloud edges, while Solidgen (Jayaraman et al.) 2022) employs pointer network to produce points and faces, constructing a complete B-rep topology. However, B-Rep only stores geometric appearance, ignoring the operation process. DeepCAD (Wu command sequence representation, expressing the CAD model as chain of 2D sketches (e.g., circles, lines, ares) and 3D operations (e.g., extrusion). This sequence-basee rep- resentation enhances the interpretability and editability of CAD generation and supports designers in flexibly modifying operation steps (Khan et al. (024b)) Moreover, some studies (Badagabettu et al.) 2024 Alrashedy et al.] 2024 Wang al.] 2025c] [uu al. 2024) explore CAD code generation, obtaining CAD models by executing generated code.
al. 202TTintroduce
Wu et al.] (2021); Xu et al. (2022) 2023); Li et al. (2025b encode CAD models into high- dimensional vectors or codebooks, and generate command sequences based on high-dimensional representations. These data, such as vectors or codebooks, are abstract and not convenient for user interaction.
(2022) Wang et al. (2025c): Qin et al. (2025); Karadeniz et al. (2025); Mallis
et al. (2024): ( et al. (003) utilizes sketches to drive the generation of CAD command se-
quences. However, sketch data requires professional designers to draw, making it unfriendly for
CAD beginners. Some studies (Li et al. 2024a Dupont et al.] [022]] Lambourne et al. 2022
al. 2023) focus on CAD reverse engineering. You al. (2024); Chen et al. (2025b a) generates
CAD operation sequences from CAD images, while Yang et al. (2025b;; Khan et al. (0224); Ma
al. (2024) focuses on point cloud-driver CAD generation. Yin al. (2025) Zhou et al. (2023);
Xu et al. (2021): Dupont et al.] (2024), on the other hand, considers using B B-rep as conditional data [upo)
to generate the corresponding CAD operation sequences.
Natural language, characterized by high flexibility and accessibility, is commonly used to express requirements. Some research (Khan et al., 2024b) [ et al., (024b) aims to use natural language structions to drive the generation of CAD command sequences. Tex12CAD (Khan et al. 2024b) first proposes an end-to-end generation framework that translates text instructions into CAD command sequences. CAD Translator (Li et al.
2024b) proposes a cascading contrastive strategy to align
text with CAD sequence. Many works (Li et al. 2025a Liao et al. 2025 Guan et al. 2025 Wang
ct al. 2025a Gong et eepploe how LLMs empower natural language text instruction to generate
CAD models. /Zhng et al., 2024 Wu et al. 2024, Yuan et al.
2025 Zhang et al. (025) fine-tune
Multimodal Large Language Models (MLLMs) by taking basic CAD models and editing control conditions, enabling modifications or completions of CAD models. Xu et al. (024); Kolodiazh- nyi al. (2025) utilizes MLLMs generate command sequences from multimodal data (i.e. text, images, and point clouds). Different from these methods that request LLMs to generate command sequences directly, we consider a process where user instructions are first parsed into an operation plan, followed by the generation of the command sequence.
3 MOTIVATION
In this section, we conduct detailed analyzes of the above-discussed points: 1) pre-trained LLMs on large-scale general data struggle to directly generate task task-specific low-level CAD control commands; 2) operation plans can improve the accuracy of generating CAD command sequences.
We first explore scenarios where LLMs are used to directly generate low-level CAD command se- quences. We select the state-of-the-an closed-source LLM GPT-4o (Hurst et al. 2024) and the open-source LLMs LLaMA3. 1-8B (Dubey et al.) 2024) and Qwen3-88 (Yang et al. 2025a). For GPT-40, we provide an example of user instructionncommmad sequence and prompt it to gen- erate command sequences based on user instructions. Unfortunately, the Invalid rate (IR) of the CAD sequences generated by GPT-4o reached 70.35%. This demonstrates that the sequences gen- erated by GPT-4o mostly cannot be correctly executed to produce 3D CAD models. We also fine- tune LLaMA3.1-88 and Qwen3-8B to enhance their ability to generate CAD command sequences. However, despite some performance improvements, they still achieve Invalid rates of 33.46% and
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User Instruction
Low-level Stage (Actioner)
Low-level
The CADD model has disc
Fist
Requiremeut-Awe Methanism (RAM)
Transformer Decader
Command
base wilk qyandee
Text Encoder
isdius the ey sylinder half that the base
K.V
23
CAD Model
G Planner (Qwen3- 8B)
High-level Stage
wweight Ishured
Cross tten.
Self
then.
Ppian
Stretch the ciztle to obvain dise
Drow a circle
Text
Selext top surface Strtch
Encoder
second cylinder
High-leve Plan
Figure 2: Overview of the proposed PTA. The PTA comprises two stages: the high-level plan gener- ation and the low-level command sequence generation. During the high-level stage, Planner parses user Instructions into high-level operation plan. While at the low-level stage, Actioner maps the high-level plan into a low-level exccutable command sequence. Within the Actioner, the user instruction and high-level plan separately fed into text encoder to obtain instruction features Finst and plan features Fplan- Subsequently. Finst and Fplan are used as inputs to a Requirement- Aware Mechanism(RAM). The RAM employs Fplan as the query automatically perceive and fuse the most critical design requirements from user instructions. The fused features I' fuuee serve as guidance for the Transformer decoder to direct the generation of low-level command sequence.
20.06%, respectively. Furthermore. we apply direct preference optimization (DPO) Rafailov et al., 2023) to the finetuned Qwen3-8B. However. the invalid rate still reached 18.29%. The high Invalid rates indicate that LLMs struggle to learn the low-level CAD control command chains. For details of the preliminary experiments, please refer to the Appendix A.4
Next, we analyze the assumption that "operation plans can improve the accuracy of CAD sequences generation" by comparing two scenarios: 1) generating command sequences based on only user in- structions, and 2) incorporating operation plans into user instructions. We conduct experiments task-specific model trained from scratch and find that both scenarios achieve low invalid rates(2.666 and 0.63%), but with the operation plan included, the generated command sequences show signifi- cant improvement metrics: Median CD (Chamfer Distance) improved from 200.32 to 104.56, and F1 score increased from 45.16 16 67.42. These results validate assumption. For details regarding the Invalid Rate (IR), Chamfer Distance (CD), score, please refer to Section [.1.3
In practice, to avoid increasing the user's input burden, the operation plan should be automatically derived by the model through parsing user instructions. Although LLMs struggle to directly generate low-level command sequences, aim to leverage their semantic parsing and reasoning abilities to generate operation plans, improving the accuracy of command sequence generation.
4
METHOD
4.1 OVERALL SCHEME
Our core idea is to use LLM to parse user instructions into operation plans, improving the accuracy of CAD sequence generation. We propose PTA, a new bi-level generation method. As shown in Figure? our PTA comprises two stages: high-level plan generation and low-level command sequence generation. First, during the high-level generation stage, we fine-tune Qwen3-8B (Yang jet al., 2025a) to serve as Planner. The Planner's role is to analyze user intent and generate high-level operation plan. This stage is defined as:
P Planner(I),
where P denotes high-level plan and represents instruction.
Subsequently, at the low-level command sequence generation stage, we input the original user in- struction and generated high-level plan into Actioner equipped with requirement-avee mech- anism. This mechanism uses the operation steps in the high-level plan as queries to automatically perceive and fuse requirement information from the user instruction. The fused information serves
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Prompt: you are CAD lask planner Your rule generate CA operation steps as a high-level operation plan based on user design instruction. There is example reference.
example: user instruction : rectangular prism with flat top and bottom. The length the prism approximately three times its width. The beight of the prism is approximately two times its width, The paism solid and has no holes openings.
high-level plan:"
1. Create the coordinate system
2. Draw a rectangle
4, scale the rectangle
5. Transform
2D sketch into
6. Extrude the sketch get a rectangular prism ")
Now, based the given reference, parse instructions high-level operation plan. Output high-level plan,
Figure 3: An example prompt is provided for the Planner.
as contextual guidance, directing the Transformer decoder to generate a low-level executable com- mand sequence. We define the low-level generation stage as:
Ö Actioner(1,P))
where Ô denotes the low-level command sequence.
4.2 HIGH-LEVEL PLAN GENERATION
In the high-level plan generation stage, our goal is parse CAD design tasks into operation plan via an LLM. Although LLMs demonstrate strong semantic understanding and reasoning capabilities, some studies (Yao al. 2025 Liu et al., 2025) point that hallucination in LLMS can lead to erroneous information during reasoning. Based on this consideration, we do not request the LLM fully parse user requirements and directly generate an operational process with detailed operation types and specific parameters. Instead, we aim to analyze user intent and provide relatively simple yet clear high-level process plan.
In practice, we select the open-source LLM Qwen3-8B (Yng et al. 2025a to be base Planner. As shown in Figure we use prompt that includes an example guide Qwen3-8B assume the role of CAD Planner, encouraging it to generate high-level operation plans. To enhance its professional capabilities, we perform full-parameter supervised fine-tuning (SFT) using paired user instruction I and high-level plan P. along with the prompt (the details of the training process and dataset are provided in Section 5. 5.1.2 and Appendix (.2). The process supervised by an objective Lptan:
T
Optan =
Σ
log P(pe pet, Prompl),
where Pt denotes the token at time step of the high-level plan.
4.3 LOW-LEVEL COMMAND SEQUENCE GENERATION
At this stage. our goal is to efficiently utilize operation information in the high-level plan and the requirement information from user instructions to guide the generation of low-level executable com- mand sequences. We design an Actioner equipped with requirement-awaan mechanism. As shown in Figure 2 within the Actioner, we employ a pre-trained BERT (Bidirectional Encoder Represen- tations from Transformers) (Devlin et al.] (019) model
text encoder. The user instruction I and the high-level plan /' are separately fed into the text encoder to obtain instruction features Finst Xd and plan features Fptan R-N,xd, N, is the length of the text encoding, and dp is the dimension of the encoding:
Fsnst BERT(I),
Fpion BERT(P).
(5)
Then, requirement-aware mechanism (RAM) is designed to extract requirement information from Finst and integrate with Fptan- The fused features F' guide Transformer decoder generate CAD command sequences.
4.3.1 REQUIREMENT-AE
MECHANISM (RAM)
To effectively utilize requirement information provided in user instructions, we introduce Requirement-Aware Mechanism (RAM), as illustrated in Figure 2 This mechanism integrates in-
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struction feature Finst and plan feature Fllan into two multi-head attention modules. First, cross- attention module uses the plan features as queries to identify the most critical requirement from the user instructions for operation steps. This process is represented
O Atten (Q1 Fiian: K1 V1 Finst).
(6)
Next, we simply add the output O and plan features Fplan to obtain preliminary fused vector. Further, considering the contextual relationships among different operations in the plan, we employ this preliminary fused vector the query, key, and value into self-attention module to obtain the final, fully integrated vector. This fused vector serves as guidance information to instruct the Transformer decoder in generating the final low-level executable command sequence. The self- attention defined as:
Fuuee= = Attenz (Q2 = K2 = V22 = Fplan + O).
(7)
4.3.2 TRANSFORMER DECODER FOR CAD SEQUENCE
Following (Wa et al. 2021 Khan et al. 2024a we represent the CAD command sequence as chain of sketch primitives and extrusion operations. Each token in the command sequence is 2D token, representing either the 2D-coordinates of sketch primitives, extrusion operation param- eters (e.g., euler angles, extrusion distance, Boolean operations, sketch scale). or end markers(e.g. curve/loop/sketch/nnd sequence). We initialize tokens in the CAD command sequence as one-hot vectors with dimension of 256. The input command sequence at timestep / can be represented We simply split C1a-1 to C%, and C E PgNc-1x250 The input
as C1#-1 E N. 1 x2x256
command sequence F
E R
1xd embedding can be obtained via Equation 8
F 1 = Ci. C W7 C - W t-1 + pos,
where WT-1, W. R are learnable weight, pos R. N, xd is the positional embedding.
We utilize a Transformer decoder (Vaswani et al. 2017 Dai et al. 2023 to fuse the guidance vector Ffuse and generate the executable command sequence. The decoder consists of L blocks; each block takes as input the command sequence embedding F output from the previous block, along with the guidance vector Fusee First, F undergoes self-attention operation. And then serves query retrieve relevant information from the fused guidance vector Ffuses generating command sequence embedding Fl F Finally, the embedding output from the last layer passes through an MLP to produce the final executable command sequence. The training process is supervised by the objective Lacti
N log P(ee C,t,L,P)
Lact
Σ
where e denotes token at time step of command sequence.
During inference, the input CAD command sequence is unavailable. Instead, we use the generated tokens { as the input of the step f, combine them with Fjuse to predict the next token. It is necessary to emphasize that during the training process of the Actioner, real high-level plan data required. However, during the inference process, high-level operation plan generated by the finetuned Planner based user instructions rather than provided by the user, thus not imposing additional input pressure on users.
EXPERIMENTS
5.1
EXPERIMENTAL SETTINGS
5.1.1
DATASET
We conduct experiments the publicly available dataset Text2CAD (Khan et al. 2024b).
Text2CAD dataset (Khan et al,, (024b) contains 170k CAD command sequences. Each CAD se- quence corresponds user instructions at four levels (Le.. 0. 1, 3), totaling 660k text instructions.

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User Instruction
GPT-4o LLaMA3.1
DeepCA
Text2CA
PTA (Ours)
GT
LQ 3D model of ring with hollow conter:
LoA dume dimessiona, trangulr shape with hole in berier
LifTee CAD model features a rectannäar priam with by gylnniiicll hote the cenire
Lh:The CAB model includes a flat recargular object together
and combined height about 0.60 units.
CAD model consists of a ttree dimepsocua. his
created by find satting up ccondinate gyeem then aketching betagong beaen with a cr hoje on the Plone
axis in form a splid body. The mealinng port has height approximaley y 0.75 unil and s monetticc shape.
L3The fist part of the CAD model a recancular defined co The hase condits of amgle mazaneutrr sketia made up of four lines while
first lsee starts
mezangulor
prtam then catuded by deeth of ans tnwarts the socund by These four oelindical sbapes are then 0.28
togother with the base form the firal (A model.
Figure 4: Qualitative comparisons between our method and SOTA CAD command sequence meth- ods. Bold in "User Instruction" indicates the user instruction level, while red highlights shapes or details that require more attention. Due to the I.3 Instruction being too long, we make some omis- sions. The red boxes specifically compare Tex12CAD and our PTA. In the second row, our PTA follows the user instructions to create triangle, while Tex12CAD incorrectly generates rectangle. Additional visual results are provided in the Appendix A.7
Following the original Tex12CAD dataset split, we use 150k CAD sequences for training and 8k test and validation sequences.
In addition to the pairs of (user instruction, command sequence), the Tex12CAD dataset (Khan et al., 20245) also provides nli_data The nli ali_data can be regarded natural language translation of the CAD command sequence, containing the most detailed low-level operations and specific operational values. In this paper, we prompt Dwen2.5-32B-Instruuu Qwen et al. (2025) extract or aggregate high-level operation plan from the nli data (we provide the complete high-level construction process and some discussions in the Appendix A.2 and Figure 7).
5.1.2 IMPLEMENTATION DETAILS
PTA includes Planner and Actioner. For Planner, we choose Qwen3-8B (Yang et al. 2025a) as the base model, utilizing full parameter supervised fine-tuning. The maximum input sequence length is 2048. We use Adam W optimizer (Loshchlovv & Hutter, (017) with a learning rate of 10 The Planner is fine-tuned for 1 epoch NVIDIA RTX A800 GPUs (batch size of 6). For Actioner, the BERT (Devlin et al. 2019) encoder has an encoding dimension of 1024, with a maximum input se- quence length of 512. The requirement-awaan mechanism consists of three layers of cross-ates cross-attention and three layers of self-attention, with 8 attention heads and a feature dimension of 1024. The Transformer decoder (Vaswani al. 2017) includes decoder blocks with 8 attention heads. The maximum sequence length for CAD is 272, with an encoding dimension of 256. During the opti- mization process, we freeze the parameters of the BERT encoder, using Adam optimizer (Kingmal & Ba 2014) with a learning rate of 10 and train for 40 epochs on 3 NVIDIA RTX 3090 GPUs with a batch size of 16.
5.1.3 EVALUATIONS
Following Tex12CAD Khan et al. 2024b), our metrics include: 1) GPT-4V evaluation, where GPT- 4V (Achiam et al. 2023) required to select the CAD model that best matches user instructions from the candidates generated by various methods; 2) using Chamfer Distance (CD) to assess the geometric alignment between the ground truth and predicted CAD models: 3) evaluating the Fl score of the primitives and extrusions of the predicted CAD sequence with the ground truth se- quence; 4) calculating the Invalid Ratio (IR) to evaluate the proportion of generated invalid CAD
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CDL
FI1
User Level
Method
GPT-4V1
JSDL
MMDJ COVT
IR.
Mean
Median
Sketch Extrusion
GPT-4o
7.60
338.52 247.11
207.71
40.78
10.37
16.12
63.51
70.35
LLaMA3.1
10.70
262.26
201.16
176.36
35.85
19.32
22.54
58.61
33.46
Abstract (LO)
DeepCAD
13.50
253.06
191.44
137.89
30.10
18.77
30.49
67.07
14.96
Text2CAD
23.90
233.84
187.31
130.13
24.06
24.22
39,50
85,30
1.78
PTA (Ours)
44.30
183.14
113.79
26.35
19.69
40.63
50.61
90.33
0.63
GP
6.80
278.75
232.70
187.62
39.36
9.66
16.76
72.34
73.71
LLaMA3.1
13.20
269.45
201.41
156.35
40.11
17.37
23.63
65.34
43.52
Beginner
DeepCAD
18.60
281.35
232.33
139.00
32.65
15.89
32.77
76.06
16.61
(L1)
Text2CAD
20.50
244.84
206.28
129.40
22.91
21.87
43.74
91.75
1,50
PTA (Ours)
40.90
200.25
142.50
25.52
18.66
37.50
52.07
94.53
0.68
GPT-4o
8.00
260.63
200.36
103.66
32.44
20.79
22.10
73.23
81.47
LLaMA3.1
18.40
214.70
115.31
87.99
30.51
25.66
29.38
72.29
37.99
Intermedia.
DeepCAD
15.30
210.79
111.06
50.34
25.36
27.21
40.76
62.51
15.63
Text2CAD
24.60
150.04
74.16
30.65
17.33
40.65
48.55
93.40
1.77
(L2)
PTA (Ours)
33.70
140.53
67.73
11.91
16.95
43.75
52.55
94.20
1.37
GPT-4o
11.20
83.52
44.83
63.99
13,42
40.83
32.07
73.86
66.91
LLaMA3.1
14.90
75.91
20.24
30.03
8.57
44.36
43.69
67.04
26.57
Expert
DeepCAD
17.60
69.34
8.24
20.45
7.66
53.21
62.67
54.86
12.81
(L.3)
Text2CAD
25.80
26.41
0.37
3.15
5.18
73.34
63.80
93.31
0.93
PTA (Ours)
30.50
20.08
0.31
2.85
4.76
78.12
64.95
94.59
0.71
Table 1: Comparisons with state-of-the-art command sequence methods, open-source LLM, and
closed-source LLM. Mean CD, Median CD, JSD, and MMD are multiplied by 10 COV multi- plied by 102 GPT-4V Evaluation, F1, and IR multiplied by 100%.
sequences. Additionally, following Xu et al. 2022 2023). we also use 5) the Jensen-Shannon Divergence (JSD), which measures the distribution similarity between the generated CAD model and the Ground Truth. 6) Coverage (COV), which quantifies the percentage of real data covered by generated samples; Minimum Matching Distance (MMD), which evaluates the closest match between generated samples and real data.
5.1.4 COMPARED METHODS
We compare PTA with the available SOTA Text-to-CAD method Text2CAD (Khan et al. 2024b), the classic VAE-based CAD sequence generation method DeepCAD (Wu et al. 2021), the open- source LLM LLaMA3. 1-8B (Dubey et al. 2024), and the closed-source LLM GPT-4o (Hurst et al., 2024). To make DeepCAD suitable for text input, we replace its CAD encoder with pre-trained BERT. To ensure a fair comparison, except for GPT-4o, we train or fine-tune all methods on the Text2CAD trainset. And we evaluate all methods on the Text2CAD test set. Although there are other Text-to-CAD methods (Wang et al. 2025b Li et al.) 2025a 2024b Liao et al.) 2025), their official implementation code not open-sourced we can not conduct fair comparison with them.
5.2
MAIN RESULTS
5.2.
QUALITATIVE EVALUATION
We present the visual results generated by our PTA and the comparison methods, as shown in Fig- ure We observe that GPT-4o, LLaMA3.1, and DeepCAD can only perceive simple general shapes, exhibiting poor generation results. Text2CAD may capture some details, but it still struggles to ac- curately generate local details and complex shapes ( as shown in rows 2,3,4 of Figure 4T. In contrast, our PTA can faithfully follow the user instructions, achieving results that are closest to Ground Truth in terms of both overall shapes and local details. If the user instructions provide detailed operation steps and parameters (cf. the last row of Figure 4.. both our PTA and Text2CAD can produce results close to the ground truth (GT). However, in practice, this level of user instruction imposes significant input pressure on users and is not user- feiefrrendly for professsssssss.s... Additional visual results are provided in the Appendix A.7
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User Level Method
Mean Median
CD1
Avg-
FI1
IR]
User Level
Guidance
Actioner
Mean Median
CDL
Avg. FIf
IR]
Fasan only
280.07 259.31 28.82 3.05
Planner only 248.93 218.97 41.87 20.06
Abstract
Abstract (LO)
Concat
198.36 159.98 98 55.91 2.32
(LU)
Actioner only
247.21 200.32 45.16 2.66
Add
208.06 163.27 55.17 2.92
PTA (Ours)
183.14 113.79
60.54 0.63
RAM
183.14
113.79 60.54 0.63
Planner only 247.33
206.71 46.66 25.37
Fptan only
299.62
238.04 40.63 3.57
Beginner
Actioner only
266.57 189.59 53.34 4.71
Beginner (LI)
Concut
228.72 171.87 56.99 1.78
(LI)
Add
227.51
180.91
57.11 1.24
PTA (Ours) 200.25 142.50
62.68 0.68
RAM
200.25
142.50
62.68 0.68
Intermedia.
Planner only 199.36 93.38
53.73 18,90
Eoton only
259.79
216.74
47.43 3.66
Actioner only 180.72
79.74
55.48
Intermediate Concat
147.66
60.14 70.16
(L2)
PTA (Ours)
140.53
67.73
62.96 1.37
(L2)
Add
151.02
69.77
59.90 0.93
RAM
140.53
67.73
62.96 1.37
Planner only 45.78
15.33
52.91
15.66
Expert
Fatan only
200.33
179.31
59.82 2.31
(1.3)
Actioner only 37.13
0.45
67.89
1.14
Expert (L3)
Concat
24.38
0.36
71.44 0.93
PTA (Ours)
20.08
0.31
72.36 0.71
Add
24.84
0.35
71.62 1.01
RAM
20.08
0.31
72.36
0.71
Table 2:
Comparison between single-stagg stage gener- ation and our bi-level generation (PTA)
Table 3: Ablation Study of RAM.
5.2.2 QUANTITATIVE EVALUATION
Based on the Tex12CAD dataset (Khan et al. 2024b), experiments are conducted across 4 lev- els User Instruction. As shown in Table 1 our PTA achieves higher GPT-4V evaluation scores across all user levels compared to the baseline methods, demonstrating that the CAD models gen- erated by PTA more faithfully match user instructions. Additionally, PTA achieves the lowest CD, JSD, MMD, and the highest COV, FI score. Specifically, the Abstract and Beginner lev- els, Median CD improved by 39% by (93.133.33- and (029,4000--2) (39.50-50.61) and 8.33 (43.74-52.00 respectively. For the Intermediate and Expert levels, user instructions already provide some operational processes (in practice, these level of user instruction increases the user's s input burden), which may overlap to some extent with the high-level plan. How- ever, our PTA can better utilize planning sequences and user-prove eser-provided requirements, resulting in performance improvements. It is worth noting that PTA achieves extremely low invalid ratio, with an average IR of 0.85% across the four user levels. In other words, the success rate of the CAD sequences generated by PTA reaches impressive 99.15%. We observe that LLMs (GPT-4o, LLaMA3.1 1) are not adept at outputting task-specific low-level CAD command sequences, resulting in high invalidity rates.
and 31% 2006228->422.00, JSD improved while the Sketch FI scores improved by 11.11
5.3 ANALYSIS AND DISCUSSIONS
We conduct ablation studies to analyze our PTA. We first compare bi-level generation with single- stage generation. And then we explore the efficiency of the designed RAM. Details and visual results of ablation experiments, more analysis, are provided in the Appendix A.2 A.4 A.5 A.6
5.3. DISCUSSION ABOUT BI-LEVEL GENERATION
We conduct ablation experiments to validate the superiority of bi-level generation. The experimental results are shown in Table 2 We find that when fine-tuning the Planner model (Qwen3-8B) di- rectly generate low-level command sequences based on user instructions(Plannes Only), it produces results similar to other LLMs (LLaMA3.1 1-BBBBBB GPT-4o), exhibiting a high Invalid Ratio. When we remove the high-level plan input from Actioner, demanding ate command sequences (Actioner Only), it performs significantly worse than the complete PTA in metrics such CD and F1. These results demonstrate the efficacy of bi-level generation.
only user instructions to gener-
5.3.2 DISCUSSION ABOUT RAM
We explore how to efficiently utilize the requirement information from user instructions and the operation information from high-level plans guide low-level command sequence generation. As shown in Table if using only high-level plan features as guidance to generate command sequences (Fptan only), due the absence of requirement information, it struggles generate CAD sequences

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Published as a conference paper at ICLR 2026
CDL
F1f
Plan type
IR|
Mean
Median
Sketch
Extrusion
Nli data
220.61
156.00
39.88
84.85
2.46
High-level plan
183.14
113.79
50.61
90.33
0.63
Table 4: Comparison between dlia plan and high-level operation plan.
User Instruction
Text2CAD
PTA (Ours)
A CAD model composed of and stright liaes, the shage of a water drop.
dimensional, rectanular prsm with combination of struight and ouvvdd resembling stylized lotter SS' or simplified representation building rool.
Figure 5: Failure cases. Red text indicates information that requires special attention.
that satisfy the user requirements. Especially, since the user instructions of L2,3 provide more information, their performance worsens significantly after the user instruction input is removed. Additionally, compared to concatenating (Concat) or adding (Add) plan features and instruction features, the guidance vector obtained through requirement-aware mechanism (RAM) can fully utilize requirement information in the user instructions, achieving the lowest CD and highest F1.
5.3.3 DISCUSSION ABOUT HIGII-LEVEL PLAN
In this section, we provide an analysis of the high-level plans and the nli_data in the dataset. The nli_data serves as natural language translation of the command sequence, containing low-level control actions and specific parameters. In contrast, the high-level plan represents complete oper- ational flow, where each operation step generalizes the underlying control actions without including detailed parameters (cf. Appendix Figure 77.. We analyze whether nli.data can used as plan. We retrain PTA using nli data in place of the high-level plan, and the results shown in Table 4 We find that using nli data yields inferior results compared to the high-level plan. The reason is as follows: when using nli_data plan, the Actioner merely acts translator from natural language to command sequence, while the LLM-based Planner must generate a complete and detailed low- level operational process with accurate parameters from the user instruction. Due the inherent hallucination issues in LLMs, this process is highly challenging, and the generated nli daidata plan often contains erroneous information, resulting in inaccurate command sequences. In contrast, with the high-level plan, the Planner only needs to extract a global operational flow, while the Actioner perceives requirement information from the user instruction to supplement operation details and parameters. This approach enables PTA to achieve more comprehensive and accurate parsing of information, leading to improved performance.
5.3.4 ANALYSIS OF FAILURE CASES
As shown in Figure 5 we observe that when user instructions contain descriptions that are uncom- mon the training set (e.g., "shape of water drop". "representation of building or a roof"), PTA struggles to generate CAD models that fully meet requirements. However, thanks to LLM-based Planner and the introduced requirement-awe basic shapes and requirements (e.g., composed of ares and straight lines, combination of straight and curved lines). A simple solution to address this issue is to perform data augmentation for rare descriptions during training.
mechanism, PTA still perceives some
CONCLUSION
In this paper, introduce PTA, bi-level CAD command sequence generation method. We em- ploy a Planner to generate high-level operation plan based on user instruction, and then develop an Actioner that maps the high-level plan to low-level executable command sequence. Exper- iment validates the efficiency of PTA. In the future, we aim to explore the potential of PTA in multioodalityConddine CAD Generation.
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7 ETHICS STATEMENT
This research aims to the automatic CAD models generation to assist professional design in the CAD domain. The data we used is developed using LLMs based on existing publicly available datasets. This process does not involve human participation, and the developed data is solely for research purposes in CAD generation technology.
REPODUCIBILITY STATEMENT
We have made every effort ensure the reproducibility of our research. Our PTA detailed in Section (Section All implementation details, including model architecture and training hyper parameters are provided in the Experimental Setting (Section B.Ip. The data we used is developed based on existing publicly available datasets, with details provided in the Appendix[A.2 To facilitate direct replication and further research, we will release our source code, the used dataset, and model checkpoints upon publication, contributing to the open-source community.
ACKNOWLEDGEMENTS
This work was supported by National Natural Science Foundation of China (No.6257613) 62176093), National Key Research and Development Program of China (N0.2023YFC3502900)
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Yu Yuan, Shizhao Sun, Qi Liu, and Jiang Bian. Cad-editor: A locate-then-infi framework with automated training data synthesis for text-based cad editing. arXiv preprint arXiv:2502.03997, 2025.
Zhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai, and Jiang Bian. Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models. arXiv preprint arXiv:2411.05823, 2024.
Zhanwei Zhang, Kaiyuan Liu, Junjie Liu, Wenxiao Wang, Binbin Lin, Liang Xie, Chen Shen, and Deng Cai. Geocad: Local ceotelry-controllable cad generation. arXiv preprint arXiv:2506. 00li::2506.13337.. 2025.
Shengdi Zhou, Tianyi Tang, and Bin Zhou. Cadparser: A learning approach of sequence modeling for b-rep cad. In IJCAI, pp. 1804-1812, 2023.
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A APPENDIX
A.1 CAD COMMAND SEQUENCE
Following Wu et al. 22021: Wang al. (2025b); Khan et al. (2024a|b) b). the CAD model is repre- sented as a sequence of modeling operations exccuted by the user to construct a 3D shape. The sequence stores key modeling commands and parameters (cf. Figure 6 in the order of CAD con- struction. This representation uses a sketch-and-extrude format, where each 2D sketch is repre- sented closed loop composed of multiple curves. Following Khan et al. (2024a b), we represent sketches using a sequence of 2D coordinates, adding endpoint markers at curves, loops, faces, and the end of sketches. For extrusion commands, each command marked by parameters.
Commands
Parameters
<SOL>
0
x, z line end- poi-point
(Line)
x, arc end-point ar sweep angle counter- clotterre flag
A (Arc)
center
(Circle)
radius
8, y.y sketch plane orientation
Pa. Puu sketch origin
E (Extrude)
4 scale of associated sketch profile
e 02 extrude distances toward both sides
& Boolean type,
extrude type
<EOS>
Figure 6: CAD commands and their main parameters. <nOS>iindcates the end of the whole sequence.
<SOL> the start of loop;
A.2 HIGH-LEVEL PLAN
A.2.1 CONSTRUCTION OF HIGH-LEVEL PLAN
In our bi-level generation, need to construct data triplets of (user instruction, high-level plan, command sequence). Our experiments are conducted on the Text2CAD dataset (Khan et al. 2024b). In the Text2CAD dataset, in addition to user instructions and command sequences, it provides detailed text-based action sequence and operation parameters for each command sequence, named daia. As shown in Figure 7 we provide prompt and high-level plan example Qwen2.5- 32B-instruct (Qwen et al. 22255), guiding it to extract or aggregate high-level operation plan from the nli ali_data. We construct high-level plan data for the TextCCAD datasets and use it real high-level plan data to train our PTA.
A.2.2 DISCUSSION ABOUT THE QUALITY OF HIGH-LEVEL PLAN
In this section, we evaluate the quality of the constructed high-level plans. First, inspired by Chen et al. (2025c); Gong et al. (2025), we conduct a user study. In addition, we perform comparative experiments with OpenAl o3. For the user study, we randomly select 1,000 high-level plan samples, which are evenly divided into five groups. We then invite 30 volunteers with at least a bachelor's is de- gree, and every volunteer is assigned to one data group. For each sample, participants are presented with the user instruction and nli data, and then asked judge whether the corresponding high-level plan is satisfactory (binary classification: any unreasonable aspect is counted as unsatisfactory). In total, collect 6,000 responses, with satisfaction rate of 98.75%.
For the comparative experiments, we employ an advanced closed-source LLM OpenAI o3 (OpenAl 2025) to generate high-level plans and resulting 100,000 triplets of (user instruction, high-level plan,
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Nli data
Prompt
#HA Perf***Inbee "Intemmdditte Part Rectangular Prism**
You are CAD operation extractor, and your role is imitate the examples
#*Ae* 1t Step 1: Create the coordinate system**
Create new coordinate system with the Euler angles (0.0, 0.0.
90.0) translation vector (-0.0174, 0.0174, 0.061).
extract or aggregate operation process from detailed operation descriptions. Output only the high-level process.
high-level
#*ee * Step Draw
sketch**
Drew sketch on the X-Y plane of the created coordinate system.
oxeew + Step 3: Create faces and loops
Create a new face (Face)) 1) the sketch.
facel), 1, create loop [loopl_ 1) follows:
line (linel) from point (0.0, 0.0) to (0.7849,
Example(......
0.0).
Cruate line (linet) 2) from point (0.7849, 0.0) to point (0. (0.7849.
0.2616).
Create line (linel) 3) from point (0.7849, 0,26116 6) to point (0.0.
0.2616).
Create Line (linel) from point (0.0, 0,26166 to point (0.0,
Qwen2.5-32B -2nts---u
0.0).
#*iea* Stup 4: Suale the 2D sketch+*
Scale the 2D skotcc by a factor of 0,7849.
awroww Step Transform the 2D sketch into 31**
Transform the scaled 2D systerm parameters.
3D using coordinate
1. Create the coordinate system
#*Ro# * Stsp 6: Extrude the 2D sketch**
2. Create 2D sketch
Excrrude the 2D skelch along the normal by depth of 0.01 74 and
3. Draw rectangle
in the opposite direction by depth of 0.0.
4. Scale the 2D sketch
#eee + Part D
Below the details internediate part:
5. Transform the 2D sketch into 3D
Shape: reclongular prism with flat and bottum. The length of the prism approximatelly three times its width, The beight of the prism is approximataly two times its width. The prism is solid and has no holes or ogunings.
6. Extrude the 2D sketch to obtain prism
High-level Plan
Length: 0. 7848837200302226
Width: 0.0 0.01744 0011144188846551626880
6262
Height: 0.7848857209302336
Figure 7: Construction process of our high-level operation plan.
command sequence). Using this dataset, we retrain PTA. Similarly, also retrain PTA using the 100,000 triplets constructed with Qwen2.5-32B- Instructt The experimental results are provided in the Table The results show that they achieve comparable performance. Considering that plan generation involves constructing a complete high-level workflow from detailed operational steps, is capable of accomplishing it.
which is not difficult task, and D0wen2.5-32Binnstte
In summary, we believe that the high-level plans generated by Qwen2.5-32B-Insstee Innstuttt are of high quality and can be used to train our method.
A.2.3 IMPACT OF FINE- ON THE PLANNER
We present the performance changes of the Planner before and after fine-tuning. As shown in Ta- 6l prompt base Planner model (Qwen3-8B) without fine-tuning to generate high-level plans based on user instructions (Base Planner) and use these plans to guide the generation command se- quences. The results demonstrate that after fine-tuning, the performance has significantly improved. The results demonstrate that, compared to using only prompts (Base Planner), the fine-tuned Plan- (Prof. Planner) achieves better performance, validating the effectiveness of fine-tuning.
A.2.4 DISCUSSION ON THE BACKBONE LLM FOR THE PLANNER
We also compare the results of using 1een2.557B-IInseee and Qwen3-8B as the base LLM for the Planner, where both are tuned using high-level plan. The experimental results shown in Table 5 The results indicate that Qwen3-8B performed slightly better than Qwen2.5-78 5-B 7---Isttt, though the difference was not significant.
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CD
Avg. F1f
User Level Base LLM
IR.
Mean
Median
Abstract
Qwen2.5-7B In-tructttt
191.21
115.97
58.92
1.13 0.63
(LO) Qwen3-8B
183.14
113.79
60.54
Table 5: Comparison of the Planner Foundations.
CDL
Avg. FI1
User Level Plan
IR]
Mean
Median
Abstract
Base Planner 190.02
124.51
52.00
1.62
(LO) Prof. Planner 183.14
113.79
60.54
0.63
Beginner Base Planner 223.43
158.47
50.32
1.41
(LI) Prof. Planner 200.25
142.50
62.68
0.68
Intermediate Base Planner 181.68
79.61
50.80
2.73
(L2) Prof. Planner 140.53
67.73
62.96
1.37
Expert Base Planner 44.81
0.45
68.07
1.88
(L3) Prof. Planner 20.08
0.31
72.36
0.71
Table 6: Comparison of the Planner Before and After Fine-Tuning
A.3 COMPARED METHODS
We provide implementation details of compared methods, including Text2CAD Khan et al. 2024b), DeepCAD (Wu et al. 2021), ), the open-source LLM LLaMA3. (Dubey et al. 2024). and the closed-source large model GPT-4o (Hurst et al. 2024). While there other Text-to-CAD methods such as CAD-GPT (Wang et al. 2025b), CAD-MLLM (Xu et al.) 2024), TCAD-Gen (Liao et al. 2025). their official code are currently not available, and don't It compare them with PTA.
Text2CAD. Text2CAD is the first method to generate CAD sequences based on textual input. We official code for reproduction and conduct fair comparison with PTA.
DeepCAD. DeepCAD first represents CAD models as a sequence of sketch primitives and 3D ex- trusion operations. It employs transformer encoder encode CAD models into high-dimensional vectors and decoder to reconstruct CAD models. To adapt DeepCAD for textual input, we replace the original CAD encoder with pre-trained BERT (Devlin al.) 2019), while retaining its origi- nal decoder to generate CAD command sequences. Other implementation details remain consistent with the official code.
LLaMA3.1-80 We perform full-parameter fine-tuning for LLaMA3. 1-8B (Dubey et al. 2024) us- ing the User Eestruction-Comman sequence pairs. is worth noting that Transformer-be based CAD generation methods (Wu et al. 2021 Khan et al. 2024a) typically tokenize the command sequence, whereas LLM b-sadd generation methods (Wang al.J [0255]] Xu et al. 2024) output the command sequence in text JSON. Therefore, for LLaMA3.1. I, we use the text JSON for the command sequence.
GPT-4o. We provide prompt along with a user instruction-command sequence example to guide GPT-4o in generating the command sequence based on the given user instruction. Consistent with LLaMA3.I-8B, the command sequence is formatted in text JSON.
A.4 MORE COMPARISONS WITH LLMs
In this section, we provide implementation details of the experiments LLM-bbsed command quence generation. For the closed-source LLMs GPT-4o, OpenAl o3 (OpenAl 2025). OpenAl 04- mini (OpenAl 2025). GPT-5 (Singh et al. 2025). Gemini-2.5-pro,
provide prompt with an ample (cf. Figure 9). asking it to generate the corresponding Command sequence based on the given user instruction. For LLaMA33. 1-8B (Dubey al. 2024) and Qwen3-8B (Yang et al. (025a),
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User Instractionn
High-level Operation Plan
Geserated Model
GT
Create the coortlinste system
Create sketch
The CAD mode features a flat, circular disc with central hole.
Drow a circle
gitta
5. Draw the secoad circle
Extrude circle obtain
coordinste system
2. Create a sbetch
Design a metal plate with three evenly spaced halts The rectangular
Draw rectangle
suroundinn the holes is proportontte sine the cinollar csfouts.
4. Strotch the metangle get mebo
5. Draw three circles
Extrude circles abkain holes
object created by delining a coontinate system with spocific Fulor angles and translation
concats hesagonal primm
system
vectur, then shothing the hosagonal stape un the X.Y planc of the coortinate system.
2. Create a 2D sketch
30 sketch scaled and transfommed extraded
hesagon
vaing same coordinnae system.
Scale the sketth
allong the normal direction to Zorn the heraponal base. The resulting part las beight of spproximately 0,2848 units.
Extrude into hesagunal prism
Figure 8: Illustration of user instructions and high-level plans. The third one is an intermediate-level sample, and red text indicates operation processes provided in its user instructions.
Prompt: You are a CAD command sequence generator, and you generate CA command sequences based on user instructions. command sequence involves lines, ares, circles, each marked by the endpoints, and extrusions. There is example:
example: [.....}
Now, transform user instruction command sequence using the provided example as reference. Output only the command sequence data. Your output not require extra line breaks, quotation marks, or any prefixes.
Figure 9: An example prompt provided for the LLMs.
the same prompt and perform full-parameter supervised fine-tuning (3 epochs, AdamW optimizer with learning rate 10-1). Additionally, we apply direct preference optimization (DPO) (Rafailov et al. 2023) to the finetuned Qwen3-8B; specifically, render multiple CAD sequences generated
by the finetuned Qwen3-8B into images, and Qwen3-VL-8b-Insiine CAD images according the user instructions construct 10000 preference pairs.
(Bai et al. (2025) scores the
The experimental results are shown in Table 7 All closed-source LLMs resulted in extremely high invalid rates (IR), For the fine-tuned LLaMA3.1 1-BB and Qwen3-8B, their IRs show some reduction but remain relatively high. Furthermore, when finetuned Qwen3-8B is further optimized using DPO (Qwen3*). the IR only exhibited a slight decrease. These results demonstrate that directly generating command sequences with LLMs remains challenging.
A.5 IMPLEMENTATIONS OF ABLATION STUDIES
A.5.1 ABLATION STUDY OF BI-LEVEL GENERATION
We conduct ablation experiments on PTA to validate the effectiveness of bi-level generation. The complete PTA includes the Planner and the Actioner. The Planner parses user instructions to gen- erate high-level operation plans, while the Actioner generates low-level executable command se- quences. In the ablation experiments, Planner or the Actioner to directly generate command sequences based on user instructions. We provide detailed implementations. Visual results provided in Figure T0
discard the bi-level generation and request either the
Planner only. We use the Planner base model (Qwen3-8B) and perform full-parameter fine- tuning (SFT) using User InstructionCoommand Sequence data, requesting it to generate low-level ex- ecutable sequences based on user instructions. Following LLM-base method (Wang et al. 2025b), instead of tokenizing the command sequences, we retain the text JSON outputs.
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CD
FI
User Level Method
JSDJ
MMDJ COVT
IR_
Mcan
Median
Sketch
Extrusion
GPT-4o
338.52 247.11 11 207.71
40.78
10.37
16. 12
63.51
70.35
OpenAl o3
312.99 163.39 250.35
33.93
22.22
26.17
61.65
68.09
OpenAl 04-m mini
284.12
218.36 292.87
27.60
15.00
25.67
55.78
77.25
Abstract (LO)
GPT-5
269.73
197.63 295.39
25.86
27.35
37.06
64.84
67.99
Gemini-2.55pm
237.78
186.79 286.59
24.68
26.31
35.75
66.31
85.77
LLaMA3.1
262.26
201.16 176.36
35.85
19.32
22.54
58.61
33.46
Qwen3
248.93
218.97
179.87
28.94
21.33
31.92
71.73
20.06
Qwen3*
226.94
193.07
167.86
26.29
22.47
33.86
76.20
18.29
PTA (Ours)
183.14
113.79
26.35
19.69
40.63
50.61
90.33
0.63
GPT-4o
278.75
232.70
187.62
39.36
9.66
16.76
72.34
73.71
OpenAl
246.92
170.08
144.95
49.07
10.25
16.10
60.26
78.27
OpenAl 04-mini
287.29
207.93
192.71
33.95
10.71
16.56
61.12
86.51
Beginner
GPT-5
226.82
185.92
142.52
36.37
14.70
19.53
62.05
70.22
(L1)
Gemini-2.5-pro
252.79
171.92
154.59
34.19
13.11
13.96
57.99
70.92
LLaMA3.1
269.45
201.41
156.35
40.11
17.37
23.63
65.34
43.52
Qwen3
247.33
206.71
195.09
40.09
22.47
38.48
71.20
25.37
Qwen3*
206.62
185.68
157.66
38.78
27.29
41.68
76.64
24.05
PTA (Ours)
200.25
142.50
25.52
18.66
37.50
52.07
94.53
0.68
GPT-4o
260.63
200.36
103.66
32.44
20.79
22.10
73.23
81.47
OpenAl o3
208.85
159.86
107.69
37.02
23.33
29.41
73.69
68.33
OpenAl 04-mini
203.38
184.75
114.08
40.67
25.44
29.80
72.91
65.29
Intermedia.
GPT-5
183.22
166.09
102.65
35.24
20.00
37.67
69.86
65.89
(L2)
Gemini-2.5-pm
195.99
170.28
112.92
29.33
19.51
31.77
71.35
62.81
LLaMA3.1
214.70
115.31
87.99
30.51
25.66
29.38
72.29
37.99
Qwen3
199.36
93.38
74.03
26.29
31.15
45.38
78.79
18.90
Qwen3*
187.88
92.09
70.54
26.23
35.66
50.11
80.12
PTA (Ours)
140.53
67.73
11.91
16.95
43.75
52.55
94,20
1.37
GPT-4o
83.52
63.99
13.42
40.83
32.07
73.86
66.91
OpenAl o3
86.34
38.86
65.93
12.49
46.67
44.37
62.84
65.32
OpenAl o4-mini
72.09
54.40
78.91
12.27
45.00
44.55
72.42
77.82
Expert (L3)
GPT-5
60.47
40.81
79.26
9.75
58.63
54.30
62.51
75.04
Gemini-2.5-pro
68.85
35.83
75.29
10.85
55.33
54.77
69.52
82.93
1LaMA3.1
75.91
20.24
30.03
8.57
44.36
43.69
67.04
26.57
Qwen3
45.78
15.33
28.67
8.09
45.45
46.39
72.47
15.66
Qwen3*
37.65
14.65
27.04
6.23
$2.98
50.11
77.23
14.05
PTA (Ours)
20.08
0.31
2.85
4.76
78.12
64.95
94.59
0.71
Table 7: Comparisons with LLMs.
Actioner only. After discarding the high-level plan generation stage, the high-level operation plan is unavailable. Therefore, only input user instructions into Actioner. The instruction features used to guide the Transformer decoder in generating the command sequence.
A.5.2 ABLATION STUDY OF RAM
To analyze the requirement-awarr mechanism (RAM), we use guidance obtained through different strategies to direct the Transformer decoder in generating command sequence. The implementa- tion details of the ablated versions are provided below. We present visual results in Figure []]
Fptan only. In this ablated version, we explore whether relying only on the high-level plan can accurately generate low-level executable command sequences. We discard the original user instruc- tion input for the Actioner and use only the high-level plan features to guide Transformer decoder. Experimental results (cf. Table 3 of main paper) demonstrate that without the requirement infor- mation provided in user instructions, it becomes challenging to accurately generate the command sequences.
Concat. We retain both the user instruction and high-level plan inputs. After obtaining the in- struction features Finst and plan features Pptans concatenate them along the length and the
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User Instruction
Planner only
Actioner Only
PTA (Ours)
GT
CAD model festures metunguiar with
cylindrii hole
with slgitly legered up and the offer with (lal lop
Crete a two part cylnnincal object one with tapered and botton and smaller one teg. Stot by seting covecianle
drowing circle the lopered Repsat this process celander rytiddrr top
pane, than extruding
height firse part amoud 0.83 unils, and the second part has a baight of apomatmablly 0.12 uniss.
Figure 10: Visual ablation results of bi-level generation. Red text indicates information that requires special attention.
User Instruction
Fpiux only
Concat
Add
RAM
GT
and ledurr wille wazsal oedangala
with flat and botteer.
CAD medal conkats osliediiaa disccis the ey tride tmm top owttors while ooe culaaid apend top curved surfact Both crtiadical olgect cruatdd wiinig coeniine
dodkiing ID cintes to boo plan, extuuing then zlog da posiiee disedare.
(The moddd cxeano ptian intereting simal aftet. The buse is geseraal acting up cosntiin graver aberhing oporm with smallert squzo halt, and estrucing zo dootra aloce the remal iirection form body.. The mealings pen beight aparetinatlly 0:75
the affect
Figure 11: Visual ablation results of our introduced RAM. Red text indicates information that re- quires special attention.
concatenated vector to guide the generation of low-level command sequences. Experimental results show that (cf. Tf main paper), compared the " "Fllen only", incorporating constraint infor- mation in the user instructions significantly improves performance. However, it still falls short of the results achieved by our RAM.
Add. In this version, we still retain both the user instruction and high-level plan inputs. We add the instruction features Finst and plan features Fplar. together as the guidance information for the Transformer decoder, which demonstrates results similar to "Concat".
A.6 DISCUSSION ABOUT INCORPORATING REASONING STEPS
A.6.1 IMPACT OF INTERMEDIATE REASONING STEPS
To investigate the impact of reasoning steps, we conduct experiments compare the difference between plans with and without the incorporation of reasoning steps. Specifically, we use Qwen2.5- 32B-Instruct and OpenAI o3 to separately construct plans with reasoning steps (called reasoning plans, cf. I2]. each resulting in 100,000 triplets of (user instruction, reasoning plan, command sequence). These datasets then used to train PTA, respectively. Similarly, we train PTA using the
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User Level
CD
FI
Plan Type
Source
JSDJ
MMDJ COV1
IRR
Mcan
Median
Sketch Extrusion
Qwen2.5-32B
207.01 01.01
125.03
43.60
23.83
36.03
36.33
86.35
2.61
Reasoning
OpenAl o3 208.25
127.65
44.74
22.79
33.59
35.77
87.24
2.18
Abstract
(LO)
Qwen2.5-32B
206.09
126.06
43.55
22.39
37.15
36,92
87.42
2.53
High-level
OpenAl o3
207.75
126.45
43.13
23.90
35.15
35.28
88.21
2.80
Qwen2.5-32B 235.89
181.89
33.03
20.74
26.90
41.33
89.66
3.65
Reasoning
OpenAT o3
228.62
170.15
35.98
26.99
28.12
41.77
90.81
2.75
Beginner
(LI)
Qwen2.5-32B
231.60
175.66
31.76
23.46
25.00
41.14
92.93
3.47
High-level
OpenAl o3
237.94
173.79
32.74
20.17
25.15
39.24
90.15
3.52
Qwen2.5-32B
160.14
78.40
16.26
18.47
32.28
50.47
90.11
1.66
Reasoning
OpenAl o3
158.59
77.88
16.73
16.99
31.40
53.23
92.42
1.04
Intermedia
(L2)
Qwen2.5-32B
154.02
74.06
14.99
18.86
37.81
51.03
92.31
1.17
High-level
OpenAl o3
155.11
76.22
12.27
19.68
34.53
50.12
92.60
1.10
Qwen2.5-32B
38.68
0.45
4.12
6.57
64.84
58.44
92.14
3.05
Reasoning
OpenAl o3
34.66
0.59
4.62
5.79
66.40
57.65
92.57
2.08
Expert (L3)
Qwen2.5-32B
33.81
0.48
3.77
5.26
69.68
62,27
93.67
1.26
High-level
OpenAl o3
33.23
0.51
3.89
5.10
72.21
61.55
91.99
2.46
Table 8: Diseussion about incorporating reasoning steps.
datasets of 100,000 high-level plans without reasoning steps. The results are shown in Table The performance achieved by the reasoning plan is comparable that of the high-level plan, other words, integrating reasoning steps into the plan has minimal impact on performance.
The difference between the reasoning plan and the high-level plan is that the reasoning plan includes reasoning steps, explicitly expressing the operational intent and the logic between operations. As for our PTA, there a requirement-awee awarr mechanism (RAM) present during the command sequence generation. In the RAM, cross-attention integrates requirement information from the user instruc- tion obtain operational intent, while self-attention captures contextual relationships to understand operation logic. To summarize, even if our high-level plan does not include reasoning steps, RAM itself can capture the operational intent and the logic between operations. Therefore, whether inte- grating reasoning steps into the plan has minimal impact the results.
A.6.2 ADDITIONAL DISCUSSION ABOUT RAM
CDL
FIf
User Level Method
JSDJ
MMDJ COVT
IR]
Mean
Median
Sketch Extrusion
Abstract
w/o RAM 231.17 148.
14
56.40
38.06
11.25
26.90
71.76
2.23
(LO)
w/ RAM
208.25
127.65
44.74
22.79
33.59
35.77
87.24
2.18
Beginner (L1)
w/o RAM
254.14
188.11 11
44.78
32.54
13.75
39.36
83.82
3.53
w/ RAM 228.62
170.15
35.98
26.99
28.12
41.77
90.81
2.75
Intermediate
w/o RAM 180.03
106.36
40.31
27.46
16.12
40.49
85.73
1.73
(L2)
w/ RAM 158.59
77.88
16.73
16.99
31.40
53.23
92.42
1.04
Expert (L3)
w/o RAM 63.06
23.83
33.56
18.11
37.50
46.42
86.61
2.04
w/ RAM 34.66
0.59
4.62
5.79
66.40
57.65
92.57
2.08
Table Additional discussion about RAM.
We also conduct experiments to investigate another question: when the plan includes reasoning steps, is it still necessary
RAM to extract requirement information from the user instruction? Specifically, using the reasoning plan dataset conducted by OpenAl o3, we perform experiments on two scenarios: (1) training the original PTA with the reasoning plan dataset; (2) training PTA but
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Published as a conference paper at ICLR 2026
Reasoning Plan
High-level Plan
1. fabricate rectangular with coasial cylinditcal vold, first
establish dedicated coordinate system using the specifted Buler angles and
1. Create the coordinate sy stet
translation, sosuring consistent orggn oitentation for both features.
2. Drw the reclangular for the rectangular prism
2. Next, we skotch closed reciongle to define the prism's foolprini, and add centrally located circle within the
represent the through-bole Both profiles are uniformly scaled to achieve the required final size.
Draw the circle for the cylindrical hole Scale the 2D sketches
4. We then position the scaled sketchrs 3D space according the courtinate
system, aligning the skotch planc's normal for proper extrasion. The rectagge is extruded symeeticaaly form the solid prism, while the circle extruded once create a cylinder matching the prism's is thickness.
Extrude the rectangular and the circle to
generate the 3D prism and cylinder
subtract the cylinder from the prism
5. Finally, subtract the rylinder from the prism, yielding clean, central
by liidiciaa hole though: the reciangularsole solid.
1. To generate a desimed tapered cy cylindiiccl solid, we first create a new coorclinate system with zero Eulet rotation and zero transatton serve common spatial reference all subsequent operations
Create the coordinate system
Open a 2D sketch on the X-Y plane
With that reference frame established, in orter to define the cylinder's cross- section, next open. 2-D skstch on X plane this coordinate system. 3. Within that sketch, in order to obtoin closed, watertight profile suitable for soldd creation, we draw siagle circular face. This circle fully describes
Draw the circular face for the cylindrical solid
Extrude the sketch obtain tapered
base outline of our cylinder.
cylindrical solid
Having completed body while simultaneusil introducting the required slight taper, we extrude sketch along positive During extrusion prooess, gentle laper should be created.
profile,
transfrm
dimensional
Figure 12: Illustration of the Reasoning plan and the High-level Plan.
discarding the RAM, using only the reasoning plan to guide command sequence generation. The results are provided in Table 9 The results show that the original PTA achieves better performance.
We analyse that user instructions contain comprehensive design concepts and requirement informa- tion (e.g., dimensions, geometric relationships). To ensure user satisfaction with the generated CAD models, it is necessary to the RAM to extract requirements and operational details from the original information provided by the user, even if a plan with a reasoning process is provided.
A.7 MORE VISUAL RESULTS
Figure 1 presents qualitative comparisons between our PTA and previous state-of- of-the--rtt methods. Compared to baseline methods, our PTA accurately perceives the design requirements provided in the user instructions and achieves precise alignment with the user requirements.
A.8 USAGE OF LLMS
During the preparation of this work, used OpenAl's ChatGPT (GPT-4 Turbo) in order translate and improve the author's language. After using this tool, the authors reviewed and edited the content needed and took full responsibility for the content of the publication.
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Published as a conference paper at ICLR 2026
User Instruction
GPT-4o
LLaMA3.1
DeepCAD
Tex12CAD
PTA (Ours)
GT
CAD model cirullrr object with a centee..
Lindriica object with slightly hoeero top and bottom, resemliing representation pipe
The CAD model fealures a reclargular mea plate wilh lour citcular culouts.
The CAD model Teatures three- dimensional toctangdar object' with a cyllindiical
flzt, cimular plate with four evenly spaced holes around its perimeter:
The CAD model displeys a hollow cylindincal shape, tube, with hiight 0.75 mits..
pipe
CAD model contains two gommetrical
langer than the other stacked eech other. The larger one has a reflective surface, making it appear shiny smooth.
The CAD mode,
Part and Part Port evlindnci object with a sightl topend tpp and bottom, and Part 2 culudiica object with Die top and bottom, tapered combines Port The
form sngle gylnndooal abject with beight of suproximalely 1.032 units.
Figure 13: Comparisons with the state-of-the methods for CAD generation. Red text indicates
information that requires special attention.
24


Reviews:
[{"review_id": "065zshpdNI", "reviewer": "Reviewer_3FuS", "summary": "This paper proposes a method for generating CAD command sequences from natural language text. The authors first discuss how pre-trained large language models (LLMs) on general large-scale data struggle to directly generate task-specific, low-level CAD control commands. They then note that operation plans can be helpful in improving the accuracy of generating CAD command sequences. Based on these observations, they design a two-stage approach: first, using an LLM to generate a plan from the user text, and then employing a cross-attention network to combine these two text encodings to output the CAD command sequence.", "questions": "Please see weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The motivation for a two-stage approach—first generating a plan and then generating the command sequences—is interesting.\n1. The discussion of directly generating command sequences and the high failure rates associated with this approach provides reasonable grounds for the further use of a two-stage process.\n1. The experimental results appear convincing, showing that the method is useful.", "weaknesses": "1. Direct generation of command sequences with LLMs had higher failure rates, both with prompting and finetuning, although finetuning helped, as mentioned. Did the authors further try other post-training approaches, such as reinforcement learning?\n1. It is not clear why cross-attention between the text and plan is useful. The plan should already be sufficient to generate reasonable command sequences, since it is derived from the user text and is more structured.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TfxYoxw7cm", "reviewer": "Reviewer_oPg7", "summary": "This paper presents a two-tier approach for natural-language based CAD modeling: an LLM-based Planner is first asked to create a high-level plan from user instructions, then a separate Actioner formulates a low-level command sequence executing the information given by the combined and strategically attenuated Plan + original user instruction. The combined features are driven by the Requirement-Aware mechanism introduced in this paper, which (1) identifies contextual relationships among the steps of the Plan, and (2) identifies complementary information between the high-level Plan and the original user-specified requirements. The authors provide extensive baseline comparisons and ablations, showing excellent relative performance of the proposed PTA approach.", "questions": "1. In lines 153-157, why are the Invalid Rates suddenly so much better? I'm particularly curious for Case 1, which seems identical to the experiment in the previous paragraph (where much higher invalid rates were reported)? Could you clarify what is different?\n\n\n## Minor points \nThe paper contains many typos and grammatical errors (some listed below), which are occasionally severe enough to obscure the meaning. Please take another careful proofread. \n\n- l.49-51 -- should that quote have a citation, or are the quotes just to indicate your assumption (in which case they could be removed)?\n- l. 79, 81, 239, throughout -- missing articles (<the> operation steps in <the> high level plan... guides <a> Transformer decoder) or verbs (our goal <is> to efficiently utilize)\n- l. 156-157 -- metrics like CD and F1 should be spelled out, given some additional context, or deferred to later when they're properly introduced.\n- l. 148 -- how were LLaMA and Qwen finetuned for this experiment? Could you include details about the process/prompts/data/parameters to contextualize the experiment, either in the main text or in the appendix (with a forward reference)?\n- l. 244-245 -- clarify the dimensions, e.g. N_t, N_p, d_p. They don't appear in Figure 2 or the text, as far as I saw.\n- l.311 - Is there a reason that CAD-GPT would be a uniquely suitable dataset in your case? If not, it seems odd to mention it purely to say that it's unavailable; I would consider justifying or removing that line.\n- l. 366, 367 -- the description of JSD is unclear to me. The description of COV has grammatical issues that preclude understanding.\n- l. 466 -- efficiency --> efficacy\n- l. 473 -- absence of <required> information\n- l. 474 -- specially --> (specifically? especially?); onformation --> information", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The effective application of this approach to CAD is exciting, particularly with the commanding advantage that this method displays over other state of the art solutions, with respect to both quantitative and qualitative metrics. The overarching principles (multi-step reasoning/inference/generation, multi-technology approaches, reduction of user burden) are also valuable independent of the domain, and they are likely to generalize well beyond CAD. The paper and the method within are well motivated, and presented clearly. The ablation studies effectively demonstrate the contribution of each piece of the approach.", "weaknesses": "1. The main paper is currently lacking a suitable description for the dataset used to finetune the Planner. For example, how different are the nli_data files and the target high level plan that you seek from Qwen? How do you verify the quality/correctness of the Qwen output before using that as the basis for your Planner training? I realize some of this is already in appendix A2, but since it is an important piece of your approach, an additional few sentences in the main text would be very helpful.\n\n2. I am somewhat unsatisfied/unconvinced by the need for the convoluted RAM and joint F_plan + F_inst representation. To be clear, the ablation studies and empirical evidence in the left side of Figure 2 have convinced me that the RAM and joint representation are necessary _given the current structure of the plan_. However, I'm curious what precludes the generation of a high-level plan that is complete and self-contained, such that the instructions are redundant? This is also tied to my previous question about the dataset for Planner training, as an improved Plan quality could dramatically simplify the process without sacrificing performance. Can the authors comment on whether a more complete Plan might be feasible, what the challenges are, and/or why the current approach is preferable/necessary?\n\n3. There is minimal discussion about the limitations and assumptions of this system; the paper would benefit from such a discussion.\n\n4. The current structure of Section 4 is somewhat disjointed and hard to read, thanks to the multiple levels of overviews.  For example, at l.191, the description is sufficiently low level that I found myself wondering about/looking for the details of L_plan, only to find that it would be deferred to l. 232. Similarly, at l.227, I wondered about the specific dataset/training procedure used to train the Qwen model (which are only detailed through Section 5.1, and Appendix). It may be worth another writing pass to smooth out this experience and connect the ideas more directly. Specifically, I would make the overviews slightly higher level, and add sufficient detail to the actual explanation as necessary.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "j3lrDnOj1z", "reviewer": "Reviewer_zpuM", "summary": "This work provides a novel framework, Plan then Act (PTA), for text-to-CAD synthesis, where given a user instruction on the 3D CAD model to construct, the pipeline consists of a “planner” that first converts the instructions to a detailed step-by-step guide and an “actioner” which takes in both the original instruction and the plan to output the low-level CAD construction commands. The planner is implemented as a LLM that  is finetuned on high-level plans extracted from Text2CAD [1].  The actioner consists of BERT text encoders followed by a cross-attention based mechanism (named “Requirement-Aware Mechanism”) to fuse the representations of the user instruction and the high-level plans, from which the construction commands are decoded. Experiments demonstrate that on the Text2CAD test set, the proposed method achieves enhanced performance compared to LLMs that directly output CAD commands across 7 metrics.", "questions": "1. What is the performance of more recent closed source LLMs on Text2CAD?\n2. I am unsure about the necessity of the RAM compared to decoding from the high-level plans. Additional explanation and examples in the text could be beneficial.\n3. What is the performance of training PTA end-to-end, without directly supervising the high-level plans?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The approach is novel and the motivation of a two-stage approach rather than directly generating the low-level construction commands is clear.\n2. Experiment results show a significant improvement across different levels of detail in user prompts compared to open-source LLMs trained on the same dataset.\n3. Ablation experiments are thorough in showing the contributions of each stage in the pipeline.", "weaknesses": "1. While the work compares to multiple open-source LLM baselines, there is only one closed-source model evaluated. It would be good to include reasoning models (e.g. o3 or o4), and stronger closed-source LLMs like Gemini-2.5-Pro and GPT-5.\n2. It is somewhat unclear why the construction commands need to be decoded from a fused representation of the plans and the user instruction instead of just the high-level plans. This makes me wonder is there some information loss when generating the plans?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tMSfliSf4T", "reviewer": "Reviewer_VX6V", "summary": "This paper proposes a bi-level framework for text-to-CAD command sequence generation, which follows a “Plan then Act” paradigm: an LLM-based Planner first converts user instructions into a structured operation plan, and an Actioner then generates low-level CAD commands guided by extracted design requirements such as dimensions and geometric constraints. Experiments show that PTA achieves superior accuracy and better alignment with user intent compared to existing methods.", "questions": "Would it be possible to concatenate the user instruction with the high-level plan and use a single text encoder for the subsequent generation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper is clear, well-structured, and easy to follow.\n2. The proposed method is intuitive and presented concisely.\n3. Both the quantitative and qualitative experiments are comprehensive, convincingly demonstrating the effectiveness of the proposed approach.", "weaknesses": "1. The method lacks novelty. In fact, the Nli_data dataset already contains more detailed plans, allowing for direct end-to-end generation of Nli_data-format outputs without the need to redesign the planning process.\n2. The generation of the high-level plan relies on distilling Qwen2.5-32B-Instruct, whose performance has yet to be thoroughly compared with closed-source models.\n3. While Qwen3-8B is a reasoning-focused LLM, the distillation process from Qwen2.5-32B-Instruct does not incorporate intermediate reasoning steps, which may limit its reasoning capabilities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)