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REVIEW 3 major objections 4 minor 1 cited by

A Closed-Loop Multi-Agent Framework for Aerodynamics-Aware Automotive Styling Design

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that LLM-driven agents can close the loop between creative car-styling concepts and rapid aerodynamic validation, predicting drag and pressure from renderings without CFD.

desk verdict A plausible but entirely unvalidated pipeline proposal; the closed-loop drag claim depends on an unexamined rendering-to-geometry step and a surrogate with no demonstrated generalization. read the letter →

arxiv 2508.03370 v1 pith:HMR2OBAK submitted 2025-08-05 cs.CE cs.MA

Xinyu Jin , Shengmao Yan , Qingtao Wang , Shisong Deng , Yanzhen Jiang , Shuangyao Zhao This is my paper · ORCID
classification cs.CEcs.MA
keywords automotivestylingdesignmulti-agentLLMframeworkdiffusionmodelssurrogatedragmodel3Dpointcloudreconstructionaerodynamicpredictionconceptualautomationclosed-loopvalidation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The authors are trying to establish that the earliest, most subjective phase of car design—turning a vague brief into a concept shape—can be connected directly to quantitative aerodynamics in a single automated loop. Their framework lets multiple AI agents interpret a fuzzy requirement, produce concept sketches, generate photorealistic renderings with diffusion models, and then convert those renderings to 3D point clouds. A lightweight surrogate model—a fast approximation of full CFD—called the Drag Prediction Agent reads the point cloud and returns near-instantaneous drag coefficient and pressure field predictions, replacing time-consuming CFD. The payoff, if the system works, is that designers see the aerodynamic consequences of a styling idea before committing to simulation, and can iterate rapidly on concepts. The central claim is not a new aerodynamic theory but a new workflow: creative generation and engineering validation integrated as a closed loop rather than separate handoff stages.

What carries the argument

The carrying mechanism is a two-stage agent pipeline joined by a rendering-to-geometry bridge. Conceptual generation agents turn a vague brief into sketches and photorealistic images; then the images are converted to 3D point clouds; then the Drag Prediction Agent, a lightweight surrogate model trained to mimic CFD outputs, consumes the point cloud and predicts drag and pressure. Everything the framework promises depends on this bridge: the point cloud recovered from a photorealistic rendering must preserve the exterior body shape well enough that the surrogate's aerodynamic numbers are meaningful.

What would settle it

Render a set of known car bodies with the same geometry but different paint colors, lighting conditions, and viewpoints; feed each rendering through the point-cloud conversion and surrogate drag predictor. If predicted drag changes substantially across renderings of the same body, the pipeline is reacting to rendering artifacts rather than the shape that determines aerodynamics, and the closed-loop validation claim fails.

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Extended reading notes

Core claim

The paper's discovery is that the closed loop between generative design and rapid engineering validation can be automated end-to-end. In the first stage, LLM-driven agents decompose ambiguous design requirements into concrete styling directions, generate concept sketches, and use diffusion models to produce photorealistic renderings. In the second stage, those renderings are converted to 3D point clouds, and a Drag Prediction Agent—built on a lightweight surrogate model—returns drag coefficient and pressure field predictions without running CFD. The authors present the direct integration of these two stages in one unified system as the primary contribution, giving designers near-instantaneous aerodynamic feedback at the concept stage.

Load-bearing premise

The whole loop stands on the assumption that converting a photorealistic rendering into a 3D point cloud preserves the true exterior geometry of the car well enough that a surrogate model can predict its actual drag; if that conversion loses or distorts shape, the fast drag numbers describe the rendering, not the vehicle.

Editorial extensions

If this is right

  • Designers could test many more styling variants early, because drag and pressure feedback arrives in near-real-time instead of after a CFD run.
  • The same closed loop could be redirected at other vehicle performance targets—downforce, cooling airflow, or cabin noise—by swapping in an appropriate surrogate model.
  • Ambiguous, high-level design briefs could drive concrete concept geometry without a human manually translating them into CAD specifications.
  • Styling decisions that previously locked in a shape before any aerodynamics check could be revised quickly, while the concept is still cheap to change.

Reading between the lines

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

  • Beyond the paper's claims, the load-bearing scientific question is whether point clouds recovered from rendered images carry enough geometric fidelity for surrogate drag models; this assumption is testable and likely contested.
  • Beyond the paper's claims, if the rendering-to-point-cloud bridge is lossy, the drag predictions could be measuring the renderer's lighting and material choices rather than the true body shape, so varying lighting while holding geometry fixed would expose the failure.
  • Beyond the paper's claims, the same framework logic generalizes to other design domains where photorealistic images are cheap and simulation is expensive, such as architecture, wind turbines, or drone fuselages, provided a suitable surrogate exists.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The manuscript (arXiv:2508.03370) proposes an LLM-driven multi-agent framework for automotive exterior styling that couples conceptual generation with aerodynamic validation. In the conceptual stage, agents interpret fuzzy requirements, produce sketches, and generate photorealistic renderings via diffusion models. In the validation stage, renderings are converted to 3D point clouds, and a lightweight surrogate model predicts drag coefficient and pressure fields, replacing CFD. The authors state that the primary contribution is the seamless integration of creative generation with a rapid engineering validation loop in a unified, automated system.

Significance. If the claimed system performs as described, it would provide a genuinely useful early-stage design tool, enabling designers to explore styling variations while receiving near-instantaneous aerodynamic feedback without full CFD. The potential significance is high: the integration of generative AI with physics-informed validation in an automated loop is an active research direction, and a working system with quantitative validation would be a meaningful contribution to computational engineering design. However, the manuscript as provided contains only the abstract, with no methods, equations, experimental results, or comparisons to CFD. The central claims about reconstruction fidelity, surrogate accuracy, and end-to-end automation are entirely unsupported. There are no machine-checked proofs, no reproducible code, and no external validation data to assess. The significance therefore remains conditional on substantial evidence that is not present in the submitted text.

major comments (3)
  1. [Abstract (full text not provided)] The load-bearing assumption that photorealistic renderings converted to 3D point clouds preserve the true exterior surface geometry is unvalidated. A single rendered image is a 2D projection with lighting, shading, and material effects; monocular reconstruction cannot recover occluded geometry, absolute scale, or watertight surface continuity. If the recovered point cloud differs from the intended 3D shape, the subsequent drag predictions apply to the wrong geometry. The manuscript must provide quantitative comparisons between reconstructed point clouds and ground-truth CAD models across varied styles, viewpoints, and lighting conditions. Without this, the validation loop cannot certify the actual design.
  2. [Abstract] No evidence is given that the lightweight surrogate model predicts drag coefficient and pressure fields accurately on novel shapes. The abstract reports no error metrics, no comparison to CFD, and no test on shapes outside the training distribution. The claim that the surrogate 'replaces time-consuming CFD simulations' requires at minimum a table or plot of predicted versus CFD-computed drag coefficients for held-out concepts, with errors reported per shape family. If the surrogate is only evaluated on its training distribution, the framework would be circular and would not demonstrate generalization to the generative agents' outputs.
  3. [Abstract] The end-to-end automation claim is not demonstrated. The abstract describes a pipeline but gives no example of an ambiguous requirement, no generated sketch or rendering, no reconstructed point cloud, and no validation result. To support the central claim, the manuscript should include case studies where varied natural-language requirements are processed end-to-end and the final validation signal is compared with a reference CFD run. The pipeline's usefulness depends on whether the validation signal is a faithful function of the actual 3D design, and this must be shown explicitly.
minor comments (4)
  1. [Abstract] The phrase 'seamless integration' is a qualitative claim; without a concrete demonstration of handoffs between agents and data formats, it should be replaced by a specific description of the interfaces.
  2. [Abstract] The 'lightweight surrogate model' is not specified. Details on architecture, training data size, input representation, and output resolution for pressure fields are needed for reproducibility.
  3. [Abstract] The term 'ambiguous requirements' is never operationalized. The manuscript should define what constitutes ambiguous input and how the agents convert it into measurable design parameters.
  4. [Abstract] The abstract mentions 'diffusion models' but does not identify which variants or how they are conditioned on the sketches; this omission obscures the workflow's actual novelty.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the abstract describes an integrated pipeline whose surrogate model is a fitted estimator, not an equation-level derivation that reduces to its own inputs.

full rationale

The abstract contains no derivation chain of the kind that would support a circularity finding. The Drag Prediction Agent is a lightweight surrogate model that predicts drag coefficient and pressure fields from point clouds, replacing CFD; a surrogate trained on simulation data is not circular merely because it is fitted, and the abstract does not state that it is evaluated only on its training inputs. The point-cloud reconstruction from renderings is a correctness risk, not a circularity, because it concerns whether the surrogate receives the true exterior geometry rather than whether a prediction is equivalent to an input by construction. No equations, no self-citations, and no fitted parameters renamed as predictions appear in the provided text. Therefore, the default honest finding of no significant circularity applies, with score 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the surrogate model's fitted parameters, the assumption that 2D renderings can be converted into aerodynamic-faithful 3D point clouds, the assumption that CFD is the right ground truth, and the assumption that LLM agents can interpret fuzzy briefs. None of these are validated in the abstract.

free parameters (1)
  • Drag surrogate model parameters
    The drag prediction agent relies on a lightweight surrogate model whose parameters are fit to data. The values, training data, and validation splits are not disclosed in the abstract.
assumptions (3)
  • domain assumption CFD simulation is the appropriate ground truth for drag coefficient and pressure fields.
    The surrogate is proposed as a replacement for CFD, but no comparison to CFD results is reported in the abstract.
  • domain assumption A photorealistic rendering contains enough geometric information to reconstruct a point cloud suitable for aerodynamic evaluation.
    The rendering-to-3D conversion is central to the closed loop, but the abstract provides no evidence that the recovered geometry is accurate enough for drag prediction.
  • domain assumption LLM agents can correctly interpret ambiguous design requirements without human correction.
    The first stage depends on this capability, yet no results are given to show that the agents produce usable design concepts.

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Cite this review

Pith. "Pith review of A Closed-Loop Multi-Agent Framework for Aerodynamics-Aware Automotive Styling Design." pith.science (2026). https://pith.science/paper/HMR2OBAK

@misc{pith2026250803370,
  author       = {Pith},
  title        = {Pith review of: A Closed-Loop Multi-Agent Framework for Aerodynamics-Aware Automotive Styling Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HMR2OBAK}},
  note         = {Machine review of arXiv:2508.03370}
}
read the original abstract

The core challenge in automotive exterior design is balancing subjective aesthetics with objective aerodynamic performance while dramatically accelerating the development cycle. To address this, we propose a novel, LLM-driven multi-agent framework that automates the end-to-end workflow from ambiguous requirements to 3D concept model performance validation. The workflow is structured in two stages: conceptual generation and performance validation. In the first stage, agents collaborate to interpret fuzzy design requirements, generate concept sketches, and produce photorealistic renderings using diffusion models. In the second stage, the renderings are converted to 3D point clouds, where a Drag Prediction Agent, built upon a lightweight surrogate model, provides near-instantaneous predictions of the drag coefficient and pressure fields, replacing time-consuming CFD simulations. The primary contribution of this work is the seamless integration of creative generation with a rapid engineering validation loop within a unified, automated system, which provides a new paradigm for efficiently balancing creative exploration with engineering constraints in the earliest stages of design.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A knowledge-to-DFFD design-space pipeline plus MoE neural-operator surrogate with Mahalanobis uncertainty gates yields CFD-validated 4–10% vehicle drag reductions and ~1.16% MAPE on heterogeneous aero data.

Reference graph

Works this paper leans on

37 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Alkin, B.; Bleeker, M.; Kurle, R.; Kronlachner, T.; Sonnleitner, R.; Dorfer, M.; and Brandstetter, J. 2025. AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers

    work page 2025

  4. [4]

    Arechiga, N.; Permenter, F.; Song, B.; and Yuan, C. 2023. Drag-guided diffusion models for vehicle image generation

    work page 2023

  5. [5]

    R.; and Timoshenko, A

    Burnap, A.; Hauser, J. R.; and Timoshenko, A. 2023. Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science, 42(6): 1029--1056

    work page 2023

  6. [6]

    Chen, G.; Yang, H.; Yu, B.; and Ren, H. 2025. Intelligent OPC Engineer Assistant for Semiconductor Manufacturing. Proceedings of the AAAI Conference on Artificial Intelligence, 39(22): 23144--23151

    work page 2025

  7. [7]

    Chen, W.; Su, Y.; Zuo, J.; Yang, C.; Yuan, C.; Chan, C.; Yu, H.; Lu, Y.; Hung, Y.; Qian, C.; Qin, Y.; Cong, X.; Xie, R.; Liu, Z.; Sun, M.; and Zhou, J. 2024. AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . ...

    work page 2024

  8. [8]

    Elrefaie, M.; Dai, A.; and Ahmed, F. 2025. DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction. Journal of Mechanical Design, 147(4)

    work page 2025

Show all 37 references
  1. [9]

    Elrefaie, M.; Qian, J.; Wu, R.; Chen, Q.; Dai, A.; and Ahmed, F. 2025. AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design

    2025

  2. [10]

    J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y

    Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014. Generative Adversarial Networks. arXiv:1406.2661

    2014 arXiv

  3. [11]

    C.; Ozsipahi, M.; and Gunpinar, S

    Gunpinar, E.; Coskun, U. C.; Ozsipahi, M.; and Gunpinar, S. 2019. A Generative Design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics. Computer-Aided Design, 111: 65--79

    2019

  4. [12]

    He, J.; Luo, X.; and Wang, Y. 2025. DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset

    2025

  5. [13]

    Ho, J.; Jain, A.; and Abbeel, P. 2020. Denoising Diffusion Probabilistic Models. In Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.; and Lin, H., eds., Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, N...

    2020

  6. [14]

    J.; Mrosek, M.; Othmer, C.; and Köstler, H

    Jacob, S. J.; Mrosek, M.; Othmer, C.; and Köstler, H. 2022. Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes. SAE International Journal of Passenger Vehicle Systems, 15(2)

    2022

  7. [15]

    Karras, T.; Laine, S.; and Aila, T. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 4401--4410. Computer Vision Foundation / IEEE

    2019

  8. [16]

    P.; and Welling, M

    Kingma, D. P.; and Welling, M. 2014. Auto-Encoding Variational Bayes. In Bengio, Y.; and LeCun, Y., eds., 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings

    2014

  9. [17]

    Labs, B. F.; Batifol, S.; Blattmann, A.; Boesel, F.; Consul, S.; Diagne, C.; Dockhorn, T.; English, J.; English, Z.; Esser, P.; Kulal, S.; Lacey, K.; Levi, Y.; Li, C.; Lorenz, D.; Müller, J.; Podell, D.; Rombach, R.; Saini, H.; Sauer, A.; and Smith, L. 2025. FLUX.1 Kontext: Fl...

    2025

  10. [18]

    Liu, J.; Li, H.; Chai, C.; Chen, K.; and Wang, D. 2025. A LLM-informed multi-agent AI system for drone-based visual inspection for infrastructure. Advanced Engineering Informatics, 68: 103643

    2025

  11. [19]

    V.; Tokmakov, P.; Zakharov, S.; and Vondrick, C

    Liu, R.; Wu, R.; Hoorick, B. V.; Tokmakov, P.; Zakharov, S.; and Vondrick, C. 2023. Zero-1-to-3: Zero-shot One Image to 3D Object. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023 , 9264--9275. IEEE

    2023

  12. [20]

    Lu, J.; Pang, Z.; Xiao, M.; Zhu, Y.; Xia, R.; and Zhang, J. 2024. Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

    2024

  13. [21]

    Morita, H.; Shintani, K.; Yuan, C.; and Permenter, F. 2024. VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling

    2024

  14. [22]

    R.; Su, H.; Mo, K.; and Guibas, L

    Qi, C. R.; Su, H.; Mo, K.; and Guibas, L. J. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 77--85. IEEE Computer Society

    2017

  15. [23]

    Qian, C.; Liu, W.; Liu, H.; Chen, N.; Dang, Y.; Li, J.; Yang, C.; Chen, W.; Su, Y.; Cong, X.; Xu, J.; Li, D.; Liu, Z.; and Sun, M. 2023. ChatDev: Communicative Agents for Software Development

    2023

  16. [24]

    Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021. Zero-Shot Text-to-Image Generation. In Meila, M.; and Zhang, T., eds., Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual ...

    2021

  17. [25]

    Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2021. High-Resolution Image Synthesis with Latent Diffusion Models

    2021

  18. [26]

    Rosset, N.; Cordonnier, G.; Duvigneau, R.; and Bousseau, A. 2023. Interactive design of 2D car profiles with aerodynamic feedback. Computer Graphics Forum, 42(2): 427--437

    2023

  19. [27]

    SAHA, S.; RIOS, T.; MINKU, L.; STEIN, B.; WOLLSTADT, P.; YAO, X.; BACK, T.; SENDHOFF, B.; and MENZEL, S. 2021. Exploiting Generative Models for Performance Predictions of 3D Car Designs. In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. Unit...

    2021

  20. [28]

    Schick, T.; Dwivedi - Yu, J.; Dess \` , R.; Raileanu, R.; Lomeli, M.; Hambro, E.; Zettlemoyer, L.; Cancedda, N.; and Scialom, T. 2023. Toolformer: Language Models Can Teach Themselves to Use Tools. In Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M.; and Levine, S., e...

    2023

  21. [29]

    Shinn, N.; Cassano, F.; Gopinath, A.; Narasimhan, K.; and Yao, S. 2023. Reflexion: language agents with verbal reinforcement learning. In Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M.; and Levine, S., eds., Advances in Neural Information Processing Systems 36: Annu...

    2023

  22. [30]

    Song, B.; Yuan, C.; Permenter, F.; Arechiga, N.; and Ahmed, F. 2023. Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings

    2023

  23. [31]

    Wang, J.; Chen, M.; Karaev, N.; Vedaldi, A.; Rupprecht, C.; and Novotny, D. 2025. VGGT: Visual Geometry Grounded Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2025

  24. [32]

    Wu, H.; Luo, H.; Wang, H.; Wang, J.; and Long, M. 2024. Transolver: A Fast Transformer Solver for PDEs on General Geometries. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 . OpenReview.net

    2024

  25. [33]

    H.; White, R

    Wu, Q.; Bansal, G.; Zhang, J.; Wu, Y.; Li, B.; Zhu, E.; Jiang, L.; Zhang, X.; Zhang, S.; Liu, J.; Awadallah, A. H.; White, R. W.; Burger, D.; and Wang, C. 2023. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

    2023

  26. [34]

    R.; and Cao, Y

    Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K. R.; and Cao, Y. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net

    2023

  27. [35]

    Ye, Y.; Cong, X.; Tian, S.; Cao, J.; Wang, H.; Qin, Y.; Lu, Y.; Yu, H.; Wang, H.; Lin, Y.; Liu, Z.; and Sun, M. 2023. ProAgent: From Robotic Process Automation to Agentic Process Automation

    2023

  28. [36]

    Zhang, L.; Fu, X.; Li, Y.; and Chen, J. 2025. Large language model-based agent Schema and library for automated building energy analysis and modeling. Automation in Construction, 176: 106244

    2025

  29. [37]

    Zhang, L.; Rao, A.; and Agrawala, M. 2023. Adding Conditional Control to Text-to-Image Diffusion Models. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023 , 3813--3824. IEEE

    2023

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