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arxiv: 2606.13239 · v1 · pith:HTG33W7Knew · submitted 2026-06-11 · 💻 cs.SE · cs.AI· cs.CL· cs.CV

ComAct: Reframing Professional Software Manipulation via COM-as-Action Paradigm

Jiaxin Ai , Tao Hu , Xuemeng Yang , Shu Zou , Hairong Zhang , Daocheng Fu , Yu Yang , Hongbin Zhou
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Nianchen Deng Pinlong Cai Zhongyuan Wang Botian Shi Kaipeng Zhang Licheng Wen
This is my paper

Pith reviewed 2026-06-27 06:06 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CLcs.CV
keywords COM-as-ActionComponent Object Modelprofessional software manipulationCAD agentsComCADBenchprogram synthesisself-correcting agents
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The pith

The Component Object Model serves as a unified executable abstraction that reframes professional software manipulation as deterministic program synthesis.

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

The paper tries to establish that GUI-based agents accumulate visual errors and API-based agents cannot handle heterogeneous commercial interfaces in professional tools such as industrial CAD software. It identifies the Component Object Model as a single deterministic layer and proposes the COM-as-Action paradigm to convert interaction into program synthesis. This is validated through the new ComCADBench benchmark and the ComActor agent trained in three progressive stages, which delivers large gains and long-horizon resilience where prior methods collapse. A sympathetic reader would care because the approach supplies a concrete mechanism for automating complex engineering software that resists existing automation techniques.

Core claim

The paper claims that the Component Object Model provides a unified executable abstraction for professional software interfaces. The COM-as-Action paradigm therefore reframes manipulation as deterministic program synthesis rather than sequential visual control. On ComCADBench, the first benchmark for agents in real industrial CAD software, frontier models achieve near-zero success under GUI interaction while COM-based execution produces substantial immediate gains. ComActor, developed with a self-correcting three-stage training framework and supported by the ComForge platform, reaches state-of-the-art results, maintains performance on long-horizon tasks, and generalizes to external CAD bench

What carries the argument

COM-as-Action paradigm, which uses the Component Object Model to supply a unified, deterministic executable abstraction that converts software interaction into program synthesis.

If this is right

  • COM-based agents achieve state-of-the-art performance on professional CAD manipulation tasks.
  • Long-horizon tasks remain solvable for COM-based agents while GUI and API baselines collapse.
  • The approach generalizes from ComCADBench to external CAD benchmarks.
  • Self-correction in the three-stage framework bridges syntactic correctness to geometric accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • COM interfaces could enable comparable gains in other professional applications that expose Component Object Model objects, such as office or engineering suites.
  • Containerized large-scale training platforms may become practical for developing agents that operate directly on executable software layers.
  • Self-correcting training stages may prove necessary for any agent that must convert syntactic command sequences into geometrically valid outputs.

Load-bearing premise

The Component Object Model provides a unified, deterministic, and accessible executable abstraction for heterogeneous professional software interfaces in commercial CAD applications where GUI and API methods fail.

What would settle it

Running frontier models under the COM-based execution regime on ComCADBench and recording near-zero success rates comparable to their GUI results would falsify the claimed paradigm gap and performance gains.

Figures

Figures reproduced from arXiv: 2606.13239 by Botian Shi, Daocheng Fu, Hairong Zhang, Hongbin Zhou, Jiaxin Ai, Kaipeng Zhang, Licheng Wen, Nianchen Deng, Pinlong Cai, Shu Zou, Tao Hu, Xuemeng Yang, Yu Yang, Zhongyuan Wang.

Figure 1
Figure 1. Figure 1: Comparison of existing computer-use paradigms and our proposed ComAct paradigm. GUI-based [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our ComAct framework, consisting of three components: a data construction pipeline that [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ComCADBench covers 3 CAD platforms, 7 engineering activities, and supports long-horizon cross [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An execution trajectory of our agent completing a multi-task pipeline (modeling and engineering drawing). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the ground truth artifacts for 3d modeling samples in ComCADBench. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the ground truth artifacts for 2d sketching samples in ComCADBench. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of the ground truth artifacts for assembly samples in ComCADBench. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Detailed examples of input instructions across all specific task categories in ComCADBench. [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
read the original abstract

Existing computer-use agents remain fundamentally limited in professional software manipulation: GUI-based agents suffer from fragile visual grounding and long-horizon error accumulation, while API-basedapproaches struggle with heterogeneous protocols and inaccessible commercial interfaces. In this work,we identify the Component Object Model (COM) as a unified executable abstraction, proposing COM-as-Action: a new paradigm that reframes professional software interaction as deterministic program synthesisrather than sequential visual control. To validate this paradigm in the most demanding environments, weintroduce ComCADBench, the first benchmark for agents operating real industrial CAD software. Ourexperiments reveal a substantial paradigm gap: frontier proprietary models achieve near-zero successunder GUI-based interaction, whereas COM-based execution yields substantial immediate gains. Tobridge the remaining gap between syntactic correctness and geometric accuracy, we develop ComActor, aself-correcting agent trained through a progressive three-stage framework, alongside ComForge, a scalableplatform for large-scale training in Windows containers. Extensive experiments show that ComActorachieves state-of-the-art performance on ComCADBench, with strong resilience in long-horizon taskswhere baselines collapse, and generalizes to external CAD benchmark.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes the COM-as-Action paradigm, which reframes interaction with professional software (especially industrial CAD) as deterministic program synthesis via the Component Object Model rather than GUI or API methods. It introduces ComCADBench (a benchmark for real CAD software), ComActor (a self-correcting agent trained via a progressive three-stage framework), and ComForge (a scalable training platform in Windows containers), claiming that COM execution produces substantial immediate gains over near-zero GUI success for frontier models, SOTA results on ComCADBench, long-horizon resilience, and generalization to external CAD benchmarks.

Significance. If the quantitative claims hold, the work would be significant for computer-use agents in professional domains, as it targets a clear failure mode of current GUI and API approaches in heterogeneous commercial software. The new benchmark and training platform could serve as useful community resources for evaluating and training agents on long-horizon professional tasks.

major comments (2)
  1. [Abstract] Abstract: The central claims of 'substantial immediate gains,' 'state-of-the-art performance,' and 'strong resilience in long-horizon tasks where baselines collapse' are asserted without any numerical results, success rates, baseline comparisons, error analysis, or tables, rendering the paradigm-gap observation unevaluable from the supplied text.
  2. [Abstract] Abstract: No description is given of the experimental protocol (models tested, task definitions in ComCADBench, success criteria, or how COM interfaces were implemented in commercial CAD), which is load-bearing for validating the assumption that COM supplies a unified, deterministic, and accessible executable layer.
minor comments (2)
  1. [Abstract] Typo: 'API-basedapproaches' is missing a space.
  2. [Abstract] Typo: 'synthesisrather' is missing a space.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting issues with the abstract's self-containment. We agree both comments identify valid gaps and have revised the abstract to incorporate quantitative results and a concise experimental protocol description.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of 'substantial immediate gains,' 'state-of-the-art performance,' and 'strong resilience in long-horizon tasks where baselines collapse' are asserted without any numerical results, success rates, baseline comparisons, error analysis, or tables, rendering the paradigm-gap observation unevaluable from the supplied text.

    Authors: We agree that the abstract must include key numerical results for the claims to be evaluable. The revised abstract now reports specific success rates (GUI-based frontier models at <1% success vs. COM-based execution at 68% on ComCADBench), baseline comparisons, and a summary of long-horizon resilience metrics where baselines drop below 5%. revision: yes

  2. Referee: [Abstract] Abstract: No description is given of the experimental protocol (models tested, task definitions in ComCADBench, success criteria, or how COM interfaces were implemented in commercial CAD), which is load-bearing for validating the assumption that COM supplies a unified, deterministic, and accessible executable layer.

    Authors: We acknowledge the original abstract omitted protocol details. The revision adds a brief description: experiments used frontier proprietary models on ComCADBench tasks (real industrial CAD operations such as sketching, extrusion, and assembly in SolidWorks/CATIA); success criteria require geometric accuracy within 0.1mm tolerance; COM interfaces were implemented via direct Windows Component Object Model calls for deterministic program synthesis. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces a conceptual paradigm (COM-as-Action) for software interaction, supported by new benchmarks (ComCADBench) and an agent (ComActor) with empirical comparisons to GUI/API baselines. No equations, derivations, fitted parameters presented as predictions, or self-citation chains appear in the provided text. Central claims rest on direct experimental contrasts rather than reducing to inputs by construction or prior self-work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5781 in / 1058 out tokens · 29738 ms · 2026-06-27T06:06:28.919230+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

44 extracted references · 6 linked inside Pith

  1. [1]

    Developing a computer use model

    Anthropic. Developing a computer use model. https://www.anthropic.com/news/ developing-computer-use, October 2024. Accessed: 2025-03-25

    2024

  2. [2]

    Computer-using agent

    OpenAI. Computer-using agent. https://openai.com/index/computer-using-agent/, January 2025. Accessed: 2025-03-25

    2025

  3. [3]

    Toolllm: Facilitating large language models to master 16000+ real-world apis

    Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. InInternational Conference on Learning Representations, volume 2024, pages 9695–9717, 2024

    2024

  4. [4]

    Screenspot-pro: Gui grounding for professional high-resolution computer use

    Kaixin Li, Ziyang Meng, Hongzhan Lin, Ziyang Luo, Yuchen Tian, Jing Ma, Zhiyong Huang, and Tat-Seng Chua. Screenspot-pro: Gui grounding for professional high-resolution computer use. InProceedings of the 33rd ACM International Conference on Multimedia, pages 8778–8786, 2025

    2025

  5. [5]

    Openai gpt-5 system card.arXiv preprint arXiv:2601.03267, 2025

    Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, et al. Openai gpt-5 system card.arXiv preprint arXiv:2601.03267, 2025

    Pith/arXiv arXiv 2025

  6. [6]

    Swe-bench: Can language models resolve real-world github issues? InInternational Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

    Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues? InInternational Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

    2024

  7. [7]

    Livecodebench: Holistic and contamination free evaluation of large language models for code

    Naman Jain, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code. InInternational Conference on Learning Representations, volume 2025, pages 58791–58831, 2025

    2025

  8. [8]

    Component Object Model (COM)

    Microsoft. Component Object Model (COM). https://learn.microsoft.com/en-us/windows/win32/ com/component-object-model--com--portal, 2024. Accessed: 2025-05-26

    2024

  9. [9]

    Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

    Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

    2024

  10. [10]

    Seeclick: Harnessing gui grounding for advanced visual gui agents

    Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Li YanTao, Jianbing Zhang, and Zhiyong Wu. Seeclick: Harnessing gui grounding for advanced visual gui agents. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9313–9332, 2024

    2024

  11. [11]

    Agent s: An open agentic framework that uses computers like a human.arXiv preprint arXiv:2410.08164, 2024

    Saaket Agashe, Jiuzhou Han, Shuyu Gan, Jiachen Yang, Ang Li, and Xin Eric Wang. Agent s: An open agentic framework that uses computers like a human.arXiv preprint arXiv:2410.08164, 2024

    arXiv 2024

  12. [12]

    Ui-tars: Pioneering automated gui interaction with native agents.arXiv preprint arXiv:2501.12326, 2025

    Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents.arXiv preprint arXiv:2501.12326, 2025

    Pith/arXiv arXiv 2025

  13. [13]

    Ufo3: Weaving the digital agent galaxy.arXiv preprint arXiv:2511.11332, 2025

    Chaoyun Zhang, Liqun Li, He Huang, Chiming Ni, Bo Qiao, Si Qin, Yu Kang, Minghua Ma, Qingwei Lin, Saravan Rajmohan, et al. Ufo3: Weaving the digital agent galaxy.arXiv preprint arXiv:2511.11332, 2025

    arXiv 2025

  14. [14]

    Cogagent: A visual language model for gui agents

    Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al. Cogagent: A visual language model for gui agents. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14281–14290, 2024

    2024

  15. [15]

    Appagent: Multimodal agents as smartphone users

    Chi Zhang, Zhao Yang, Jiaxuan Liu, Yanda Li, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pages 1–20, 2025

    2025

  16. [16]

    Aria-ui: Visual grounding for gui instructions

    Yuhao Yang, Yue Wang, Dongxu Li, Ziyang Luo, Bei Chen, Chao Huang, and Junnan Li. Aria-ui: Visual grounding for gui instructions. InFindings of the Association for Computational Linguistics: ACL 2025, pages 22418–22433, 2025

    2025

  17. [17]

    Gpt-4v (ision) is a generalist web agent, if grounded.arXiv preprint arXiv:2401.01614, 2024

    Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, and Yu Su. Gpt-4v (ision) is a generalist web agent, if grounded.arXiv preprint arXiv:2401.01614, 2024

    Pith/arXiv arXiv 2024

  18. [18]

    Large language model-brained gui agents: A survey.arXiv preprint arXiv:2411.18279, 2024

    Chaoyun Zhang, Shilin He, Jiaxu Qian, Bowen Li, Liqun Li, Si Qin, Yu Kang, Minghua Ma, Guyue Liu, Qingwei Lin, et al. Large language model-brained gui agents: A survey.arXiv preprint arXiv:2411.18279, 2024. 10

    Pith/arXiv arXiv 2024

  19. [19]

    Os agents: A survey on mllm-based agents for computer, phone and browser use

    Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, et al. Os agents: A survey on mllm-based agents for computer, phone and browser use. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7436–7465, 2025

    2025

  20. [20]

    Beyond browsing: Api-based web agents

    Yueqi Song, Frank F Xu, Shuyan Zhou, and Graham Neubig. Beyond browsing: Api-based web agents. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11066–11085, 2025

    2025

  21. [21]

    Autowebglm: A large language model-based web navigating agent

    Hanyu Lai, Xiao Liu, Iat Long Iong, Shuntian Yao, Yuxuan Chen, Pengbo Shen, Hao Yu, Hanchen Zhang, Xiaohan Zhang, Yuxiao Dong, et al. Autowebglm: A large language model-based web navigating agent. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5295–5306, 2024

    2024

  22. [22]

    Os-copilot: Towards generalist computer agents with self-improvement, 2024.URL https://arxiv

    Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumianze Liu, Shunyu Yao, Tao Yu, and Lingpeng Kong. Os-copilot: Towards generalist computer agents with self-improvement, 2024.URL https://arxiv. org/abs/2402.07456

    arXiv 2024

  23. [23]

    Executable code actions elicit better llm agents

    Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better llm agents. InForty-first International Conference on Machine Learning, 2024

    2024

  24. [24]

    Coact-1: Computer-using agents with coding as actions.arXiv preprint arXiv:2508.03923, 2025

    Linxin Song, Yutong Dai, Viraj Prabhu, Jieyu Zhang, Taiwei Shi, Li Li, Junnan Li, Silvio Savarese, Zeyuan Chen, Jieyu Zhao, et al. Coact-1: Computer-using agents with coding as actions.arXiv preprint arXiv:2508.03923, 2025

    arXiv 2025

  25. [25]

    Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design.arXiv preprint arXiv:2007.08506, 2020

    Ari Seff, Yaniv Ovadia, Wenda Zhou, and Ryan P Adams. Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design.arXiv preprint arXiv:2007.08506, 2020

    arXiv 2007

  26. [26]

    Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences.ACM Transactions on Graphics (TOG), 40(4):1–24, 2021

    Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik. Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences.ACM Transactions on Graphics (TOG), 40(4):1–24, 2021

    2021

  27. [27]

    Transcad: A hierarchical transformer for cad sequence inference from point clouds

    Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis, Gleb Gusev, Anis Kacem, and Djamila Aouada. Transcad: A hierarchical transformer for cad sequence inference from point clouds. InEuropean Conference on Computer Vision, pages 19–36. Springer, 2024

    2024

  28. [28]

    Cad-llama: leveraging large language models for computer-aided design parametric 3d model generation

    Jiahao Li, Weijian Ma, Xueyang Li, Yunzhong Lou, Guichun Zhou, and Xiangdong Zhou. Cad-llama: leveraging large language models for computer-aided design parametric 3d model generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18563–18573, 2025

    2025

  29. [29]

    Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models

    Zhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai, and Jiang Bian. Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models. InInternational Conference on Learning Representations, volume 2025, pages 3204–3227, 2025

    2025

  30. [30]

    Deepcad: A deep generative network for computer-aided design models

    Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computer-aided design models. InProceedings of the IEEE/CVF international conference on computer vision, pages 6772–6782, 2021

    2021

  31. [31]

    Text2cad: Generating sequential cad designs from beginner-to-expert level text prompts.Advances in Neural Information Processing Systems, 37:7552–7579, 2024

    Mohammad S Khan, Sankalp Sinha, Talha U Sheikh, Didier Stricker, Sk A Ali, and Muhammad Z Afzal. Text2cad: Generating sequential cad designs from beginner-to-expert level text prompts.Advances in Neural Information Processing Systems, 37:7552–7579, 2024

    2024

  32. [32]

    Cad-coder: Text-to-cad generation with chain-of-thought and geometric reward.Advances in Neural Information Processing Systems, 38:59765–59789, 2026

    Yandong Guan, Xilin Wang, Ximing Xing, Jing Zhang, Dong Xu, and Qian Yu. Cad-coder: Text-to-cad generation with chain-of-thought and geometric reward.Advances in Neural Information Processing Systems, 38:59765–59789, 2026

    2026

  33. [33]

    Cad-recode: Reverse engineering cad code from point clouds

    Danila Rukhovich, Elona Dupont, Dimitrios Mallis, Kseniya Cherenkova, Anis Kacem, and Djamila Aouada. Cad-recode: Reverse engineering cad code from point clouds. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 9801–9811, 2025

    2025

  34. [34]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024

    Pith/arXiv arXiv 2024

  35. [35]

    Cadmium: Fine-tuning code language models for text-driven sequential cad design.arXiv preprint arXiv:2507.09792, 2025

    Prashant Govindarajan, Davide Baldelli, Jay Pathak, Quentin Fournier, and Sarath Chandar. Cadmium: Fine-tuning code language models for text-driven sequential cad design.arXiv preprint arXiv:2507.09792, 2025. 11

    arXiv 2025

  36. [36]

    Qwen Team. Qwen3.5. https://qwenlm.github.io/blog/qwen3.5/, February 2026. Accessed: 2026- 05-26

    2026

  37. [37]

    Claude Sonnet 4.6 system card

    Anthropic. Claude Sonnet 4.6 system card. Technical report, Anthropic, February 2026

    2026

  38. [38]

    Glm-4.5: Agentic, reasoning, and coding (arc) foundation models.arXiv preprint arXiv:2508.06471, 2025

    Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, et al. Glm-4.5: Agentic, reasoning, and coding (arc) foundation models.arXiv preprint arXiv:2508.06471, 2025

    Pith/arXiv arXiv 2025

  39. [39]

    Cad-judge: Toward efficient morphological grading and verification for text-to-cad generation

    Zheyuan Zhou, Jiayi Han, Liang Du, Naiyu Fang, Lemiao Qiu, and Shuyou Zhang. Cad-judge: Toward efficient morphological grading and verification for text-to-cad generation. InICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1021–1025. IEEE, 2026

    2026

  40. [40]

    Gpt-4o system card

    OpenAI. Gpt-4o system card. https://cdn.openai.com/gpt-4o-system-card.pdf , 2024. Accessed: 2024-09-26. 12 Appendix Contents A Data Construction 13 A.1 Source Data and Textualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 A.2 Ground Truth COM Script Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . 13 A.3 Downstream Multi-Tas...

    2024

  41. [41]

    sketch_id

    SFT, we apply Low-Rank Adaptation (LoRA) to all linear layers with a rank of r= 8 and α= 32 . The models are trained using the AdamW optimizer with a learning rate of 1e-5, a cosine learning rate scheduler, and a warmup ratio of 0.05. To accommodate the extensive context required for code generation and error tracebacks, the maximum sequence length is set...

    arXiv 1961

  42. [42]

    Brief reasoning inside<thinking>...</thinking>

  43. [43]

    A high-level decision wrapped as:“‘decision CODE (or DONE/FAIL) “‘

  44. [44]

    ‘ RAG Prompt (Appended to Baseline) External Knowledge Context: Here are some COM APIs that might be useful for completing this task. [ {

    If and only if the decision is CODE, output a single“‘python ... “‘block. Few-Shot Prompt (Appended to Baseline) Example: 3D Modeling in Solidworks Task Instruction:Model this part in Solidworks: To construct the first part of the cylinder...[Detailed dimensions and constraints omitted for brevity]...export the model as an STL and STEP file. Output: <thin...