Pith. sign in

REVIEW 4 major objections 3 minor 38 references

Detailed radial scale height profile of dust grains as probed by dust self-scattering in HL Tau

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

Pith's one-line read This paper uses dust self-scattering polarization to derive HL Tau's dust scale height profile, finding a thick inner disk ($H/R\ge0.15$) and a thin outer disk ($H/R<0.05$), with turbulence strengthening inward.

desk verdict Plausible and potentially important radial H/R profile for HL Tau, but the abstract alone cannot support the quantitative alpha claims and the provided full text is a different paper; referee needed to check the radiative-transfer degeneracies. read the letter →

arxiv 2508.01233 v1 pith:4AYAPBD7 submitted 2025-08-02 astro-ph.EP astro-ph.SR

classification astro-ph.EPastro-ph.SR
keywords dustscaleheightHLTauprotoplanetarydiskself-scatteringpolarizationsettlingturbulencecircumstellar
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 paper claims that polarization from dust self-scattering can map the vertical thickness of a protoplanetary disk's dust layer as a function of radius, and applies this to HL Tau. It resolves the inner disk's polarized emission and finds a near-far side brightness asymmetry, which it interprets as a geometrically thick inner dust disk with aspect ratio $H/R \ge 0.15$. In the first ring near 20 au, the azimuthal pattern of polarization implies a moderately thick ring with $H/R \approx 0.1$. Beyond that, the absence of asymmetry points to a thin, settled dust layer with $H/R < 0.05$. If correct, the disk transitions from a turbulent inner region to a quiescent outer region, with the turbulence parameter $\alpha$ varying from $10^{-5}$ at 100 au to $10^{-2.5}$ at 20 au.

What carries the argument

The load-bearing observable is the polarized intensity arising from dust self-scattering of thermal emission. Two geometric diagnostics carry the argument: (1) a near-far side asymmetry in polarized intensity, which occurs when the scattering layer is geometrically thick because the near side of the disk intercepts and scatters more radiation toward the observer than the far side; and (2) the azimuthal distribution of polarization in a ring, which is enhanced along the minor axis when the ring has a finite vertical thickness. These patterns are modeled with radiative transfer to convert the observed polarization morphology into constraints on the dust scale height $H$ as a function of radius $R$.

What would settle it

If multi-wavelength polarization observations show that the near-far asymmetry amplitude varies with wavelength in a way that tracks optical depth rather than scattering geometry, the thick-inner-disk interpretation would be falsified; alternatively, a radiative transfer model that reproduces the asymmetry with a thin dust layer by adjusting illumination or opacity gradients would settle the question.

Watch

Extended reading notes

Core claim

The central discovery is that the radial scale height of dust grains in HL Tau is not constant: the inner disk is vertically extended with $H/R \ge 0.15$, the 20 au ring has $H/R \approx 0.1$, and the outer disk is thin with $H/R < 0.05$. This profile is inferred from dust self-scattering polarization, specifically a near-far side asymmetry in polarized intensity in the inner disk and an azimuthal contrast along the minor axis in the first ring. The authors argue that these polarization features directly trace the geometric thickness of the scattering dust layer, and that no other disk property produces the same pattern. The resulting picture is a disk whose vertical structure requires turbulence that increases inward, from $\alpha \sim 10^{-5}$ at 100 au to $\alpha \sim 10^{-2.5}$ at 20 au, implying that dust settling is efficient in the outer disk but strongly perturbed in the inner region.

Load-bearing premise

The central premise is that the observed near-far side asymmetry in polarized intensity directly traces the vertical thickness of the dust scattering layer, rather than arising from radial optical depth gradients, nonuniform illumination, or degeneracies in the scattering phase function.

Editorial extensions

If this is right

  • HL Tau's dust layer is not uniformly settled: the inner region is puffed up to $H/R \ge 0.15$, while the outer disk has settled to $H/R < 0.05$.
  • The turbulence parameter $\alpha$ must increase inward, from about $10^{-5}$ at 100 au to $10^{-2.5}$ at 20 au, to sustain the inferred vertical structure.
  • Dust settling and planet formation timescales in HL Tau are strongly radius-dependent, with the outer disk more conducive to quiescent planetesimal formation and the inner disk more mixed.
  • The 20 au ring's moderate thickness ($H/R \sim 0.1$) provides a direct constraint on the local turbulence and the ring's formation mechanism.

Reading between the lines

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

  • The same near-far asymmetry diagnostic could be applied to other inclined protoplanetary disks observed in polarized millimeter continuum, providing a general method to map radial dust scale height profiles without resolving the vertical structure directly.
  • If the inner disk is indeed as thick as $H/R \ge 0.15$, the resulting vertical flaring would change the illumination pattern and shadowing across the disk, which may be testable in scattered-light images at near-infrared wavelengths.
  • The inferred steep inward increase of $\alpha$ could point to a turbulent source that operates only at small radii, such as the magnetorotational instability in a partially ionized inner disk, rather than a uniform alpha disk; this distinction could be tested by measuring the polarization asymmetry at multiple wavelengths.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The manuscript (arXiv:2508.01233) claims to derive a radial dust scale height profile for HL Tau from polarized intensity observations, inferring H/R >= 0.15 for the inner disk, H/R ~ 0.1 at 20 au, and H/R < 0.05 in the outer disk, and from this a turbulence profile with alpha increasing from 10^-5 at 100 au to 10^-2.5 at 20 au. The full text supplied for review is not the paper under this identifier; it is arXiv:2508.01237, a SketchAgent diagram-generation paper. Thus the radiative transfer modeling, degeneracy checks, and uncertainty estimates that would support the claims are not available for review.

Significance. Measuring dust scale height profiles and turbulence in protoplanetary disks is central to planet formation, and a robust determination of H/R as a function of radius in HL Tau would be a valuable constraint. The abstract promises such a measurement and a resulting turbulence profile, which, if supported by the modeling, would be of high interest. However, the submission provides no verifiable evidence for these claims, and the current form cannot be assessed for scientific soundness.

major comments (4)
  1. [Full text] The submitted full text is for arXiv:2508.01237 (SketchAgent), a different paper about sketch-to-diagram generation, not for the HL Tau polarization study identified in the abstract. The actual radiative transfer modeling, the definition of H/R, the treatment of the scattering phase function, and the alpha-turbulence derivation are entirely absent. The claims in the abstract cannot be verified without this material, and this missing support is load-bearing.
  2. [Abstract] The statement that the near-far polarized intensity asymmetry 'is attributed to a geometrically thick inner dust disk' is an interpretation, not a demonstrated uniqueness result. The abstract provides no evidence that alternative explanations—such as azimuthal variations in the scattering phase function, radial or vertical optical depth gradients, differential illumination due to the scattering surface height, or grain polarization efficiency gradients—have been ruled out. Without radiative transfer models that vary these parameters and show that only H/R >= 0.15 matches the data, the central inference is unsupported.
  3. [Abstract] The turbulence profile (alpha increasing from 10^-5 at 100 au to 10^-2.5 at 20 au) is presented as being 'required' by the data, but the abstract gives no indication of how this profile is derived. If alpha is tuned to reproduce the inferred H/R values through a turbulence model, the result is a model inversion rather than an independent prediction; the abstract does not state whether any forward prediction or falsifiable test is made.
  4. [Abstract] No uncertainty estimates are given for any of the H/R values. For example, the azimuthal contrast at 20 au is said to indicate H/R ~ 0.1, but no confidence interval or assessment of systematic errors is provided, so the claimed radial trend from thick to thin is not quantitatively grounded.
minor comments (3)
  1. [Abstract] The notation 'H/R >= 0.15' and 'H/R ~ 0.1' is used without defining whether these are lower limits, point estimates, or medians; this should be clarified in the abstract and the main text.
  2. [Abstract] The paper would be strengthened by situating the result relative to previous HL Tau modeling and ALMA polarization observations; no references or comparison are visible in the abstract.
  3. [Full text] The mismatch between the advertised arXiv identifier and the uploaded full text must be corrected; the submitted file does not contain the paper under review.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established from the available abstract; the supplied full text is for a different paper, so the radiative-transfer inversion chain cannot be inspected.

full rationale

The available evidence is the abstract of arXiv:2508.01233; the supplied full text is arXiv:2508.01237 (SketchAgent), a different paper. Within the abstract, the derivation chain is: polarization asymmetry and azimuthal contrast are observed; these are attributed to a geometrically thick inner dust disk and a moderately thick first ring, giving H/R values; a turbulence model then yields alpha increasing inward. Each step is an inference or model inversion, not a prediction that is equivalent to its inputs by construction. In particular, the alpha profile is derived from the H/R constraints, rather than the H/R values being presented as predictions of a separately fitted alpha. The attribution of the asymmetry to geometric thickness is an astrophysical assumption that could be degenerate with scattering phase function or optical-depth effects, but that is a correctness or uniqueness concern, not a demonstrable circularity in the absence of the paper's equations and model details. Because no specific equation, fit, or self-citation is available to exhibit a reduction of a claimed result to its own input, the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The ledger lists the values the analysis must tune (alpha at two radii) and the domain assumptions required to convert polarization geometry into scale heights and turbulence. No new physical entities are introduced.

free parameters (2)
  • Turbulence parameter alpha at 20 au = 10^-2.5
    Chosen to reproduce the inferred moderately thick ring H/R ~ 0.1 from the model.
  • Turbulence parameter alpha at 100 au = 10^-5
    Chosen to reproduce the inferred thin outer disk H/R < 0.05.
assumptions (4)
  • domain assumption Dust self-scattering is the dominant polarization mechanism in the observed HL Tau emission.
    The entire interpretation of polarized intensity as tracing the scattering surface and its geometry rests on this. Invoked in the abstract's framing of 'dust self-scattering'.
  • domain assumption The near-far side polarized intensity asymmetry is a geometric effect of a thick scattering surface, not an optical depth or illumination artifact.
    The abstract attributes the asymmetry to a geometrically thick inner disk, which is the key step connecting data to H/R.
  • domain assumption The vertical dust distribution is governed by turbulent diffusion characterized by a single alpha parameter, with the standard relation between H/R and alpha.
    The abstract converts the scale height profile into a variable turbulence model with specific alpha values, assuming this standard settling-vs-turbulence relation.
  • domain assumption The disk inclination and orientation are known sufficiently well to distinguish near versus far side.
    Without reliable near/far assignment, the asymmetry would be uninterpretable. This is implicit in the abstract's description.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Detailed radial scale height profile of dust grains as probed by dust self-scattering in HL Tau." pith.science (2026). https://pith.science/paper/4AYAPBD7

@misc{pith2026250801233,
  author       = {Pith},
  title        = {Pith review of: Detailed radial scale height profile of dust grains as probed by dust self-scattering in HL Tau},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4AYAPBD7}},
  note         = {Machine review of arXiv:2508.01233}
}
abstract

The vertical settling of dust grains in a circumstellar disk, characterized by their scale height, is a pivotal process in the formation of planets. This study offers in-depth analysis and modeling of the radial scale height profile of dust grains in the HL Tau system, leveraging high-resolution polarization observations. We resolve the inner disk's polarization, revealing a significant near-far side asymmetry, with the near side being markedly brighter than the far side in polarized intensity. This asymmetry is attributed to a geometrically thick inner dust disk, suggesting a large aspect ratio of $H/R \ge 0.15$. The first ring at 20 au exhibits an azimuthal contrast, with polarization enhanced along the minor axis, indicating a moderately thick dust ring with $H/R \approx 0.1$. The absence of the near-far side asymmetry at larger scales implies a thin dust layer, with $H/R < 0.05$. Taken together, these findings depict a disk with a turbulent inner region and a settled outer disk, requiring a variable turbulence model with $\alpha$ increasing from $10^{-5}$ at 100 au to $10^{-2.5}$ at 20 au. This research sheds light on dust settling and turbulence levels within protoplanetary disks, providing valuable insights into the mechanisms of planet formation.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 19 canonical work pages

  1. [1]

    Phi-3 technical report: A highly capable language model locally on your phone

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

  2. [5]

    Multidiffusion: Fusing diffusion paths for controlled image generation

    [Bar-Tal et al., 2023] Omer Bar-Tal, Lior Yariv, Yaron Lip- man, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. ICLR,

  3. [7]

    Controllable generation with text-to- image diffusion models: A survey

    [Cao et al., 2024] Pu Cao, Feng Zhou, Qing Song, and Lu Yang. Controllable generation with text-to- image diffusion models: A survey. arXiv preprint arXiv:2403.04279,

  4. [8]

    Codet: Code generation with generated tests

    [Chen et al., 2022] Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. Codet: Code generation with generated tests. arXiv preprint arXiv:2207.10397,

  5. [10]

    Automated generation of er diagram from a given text in natural lan- guage

    [Ghosh et al., 2018] Sutirtha Ghosh, Prasenjit Mukherjee, Baisakhi Chakraborty, and Rezaul Bashar. Automated generation of er diagram from a given text in natural lan- guage. In iCMLDE, pages 91–96. IEEE,

  6. [12]

    Cogvlm2: Vi- sual language models for image and video understanding

    [Hong et al., 2024] Wenyi Hong, Weihan Wang, Ming Ding, Wenmeng Yu, Qingsong Lv, Yan Wang, Yean Cheng, Shiyu Huang, Junhui Ji, Zhao Xue, et al. Cogvlm2: Vi- sual language models for image and video understanding. arXiv preprint arXiv:2408.16500,

  7. [13]

    Controllable image synthesis methods, applications and challenges: a compre- hensive survey

    [Huang et al., 2024] Shanshan Huang, Qingsong Li, Jun Liao, Shu Wang, Li Liu, and Lian Li. Controllable image synthesis methods, applications and challenges: a compre- hensive survey. Artificial Intelligence Review, 57(12):336,

  8. [14]

    [Hui et al., 2024] Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186,

Show all 38 references
  1. [15]

    Self-planning code generation with large language models

    [Jiang et al., 2024] Xue Jiang, Yihong Dong, Lecheng Wang, Zheng Fang, Qiwei Shang, Ge Li, Zhi Jin, and Wenpin Jiao. Self-planning code generation with large language models. ACM Transactions on Software Engi- neering and Methodology, 33(7):1–30,

  2. [16]

    Coderl: Mastering code generation through pretrained models and deep reinforcement learning

    [Le et al., 2022] Hung Le, Yue Wang, Akhilesh Deepak Got- mare, Silvio Savarese, and Steven Chu Hong Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. NeurIPS, 35:21314–21328,

  3. [17]

    Controllable text-to- image generation

    [Li et al., 2019] Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, and Philip Torr. Controllable text-to- image generation. NeurIPS, 32,

  4. [19]

    Starcoder 2 and the stack v2: The next generation

    [Lozhkov et al., 2024] Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al. Starcoder 2 and the stack v2: The next generation. arXiv preprint arXiv:2402.19173,

  5. [20]

    Wizardcoder: Em- powering code large language models with evol-instruct

    [Luo et al., 2023] Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. Wizardcoder: Em- powering code large language models with evol-instruct. arXiv preprint arXiv:2306.08568,

  6. [21]

    Cotext: Multi-task learning with code-text transformer

    [Phan et al., 2021] Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, and Yanfang Ye. Cotext: Multi-task learning with code-text transformer. arXiv preprint arXiv:2105.08645,

  7. [22]

    Code llama: Open foundation models for code

    [Roziere et al., 2023] Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, et al. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950,

  8. [23]

    Natural language to code translation with execution.arXiv preprint arXiv:2204.11454,

    [Shi et al., 2022] Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang. Natural language to code translation with execution.arXiv preprint arXiv:2204.11454,

  9. [24]

    A sur- vey of automatic code generation from natural language

    [Shin and Nam, 2021] Jiho Shin and Jaechang Nam. A sur- vey of automatic code generation from natural language. Journal of Information Processing Systems , 17(3):537– 555,

  10. [25]

    Moma: Multimodal llm adapter for fast personalized image gener- ation

    [Song et al., 2025] Kunpeng Song, Yizhe Zhu, Bingchen Liu, Qing Yan, Ahmed Elgammal, and Xiao Yang. Moma: Multimodal llm adapter for fast personalized image gener- ation. In ECCV, pages 117–132. Springer,

  11. [26]

    Peer review as a multi-turn and long-context dialogue with role-based interactions

    [Tan et al., 2024] Cheng Tan, Dongxin Lyu, Siyuan Li, Zhangyang Gao, Jingxuan Wei, Siqi Ma, Zicheng Liu, and Stan Z Li. Peer review as a multi-turn and long-context dialogue with role-based interactions. arXiv preprint arXiv:2406.05688,

  12. [27]

    Gemini 1.5: Unlocking multimodal understand- ing across millions of tokens of context

    [Team et al., 2024] Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al. Gemini 1.5: Unlocking multimodal understand- ing across millions of tokens of context. arXiv preprint arXiv:2403.05530,

  13. [28]

    Qwen2: A scalable and ver- satile language model

    [Team, 2024] Qwen Team. Qwen2: A scalable and ver- satile language model. https://qwenlm.github.io/zh/blog/ qwen2/,

  14. [29]

    Verigen: A large lan- guage model for verilog code generation

    [Thakur et al., 2024] Shailja Thakur, Baleegh Ahmad, Ham- mond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri, and Siddharth Garg. Verigen: A large lan- guage model for verilog code generation. ACM Trans. on Design Automation of Electronic Systems, 29(3):1–31,

  15. [30]

    Structcoder: Structure-aware trans- former for code generation

    [Tipirneni et al., 2024] Sindhu Tipirneni, Ming Zhu, and Chandan K Reddy. Structcoder: Structure-aware trans- former for code generation. ACM Trans. on Knowledge Discovery from Data, 18(3):1–20,

  16. [31]

    Qwen2-vl: Enhanc- ing vision-language model’s perception of the world at any resolution

    [Wang et al., 2024] Peng Wang, Shuai Bai, Sinan Tan, Shi- jie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhanc- ing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191,

  17. [32]

    Mm-diff: High-fidelity image per- sonalization via multi-modal condition integration

    [Wei et al., 2024b] Zhichao Wei, Qingkun Su, Long Qin, and Weizhi Wang. Mm-diff: High-fidelity image per- sonalization via multi-modal condition integration. arXiv preprint arXiv:2403.15059,

  18. [33]

    From words to structured visuals: A benchmark and framework for text-to-diagram generation and editing

    [Wei et al., 2025] Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu, Siyuan Li, Zhangyang Gao, Linzhuang Sun, Bihui Yu, and Ruifeng Guo. From words to structured visuals: A benchmark and framework for text-to-diagram generation and editing. CVPR,

  19. [34]

    Baichuan 2: Open large- scale language models

    [Yang et al., 2023] Aiyuan Yang, Bin Xiao, Bingning Wang, Borong Zhang, Ce Bian, Chao Yin, Chenxu Lv, Da Pan, Dian Wang, Dong Yan, et al. Baichuan 2: Open large- scale language models. arXiv preprint arXiv:2309.10305,

  20. [35]

    Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition

    [Zhang et al., 2023a] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Haodong Duan, Songyang Zhang, Shuangrui Ding, et al. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition. arXiv preprint...

  21. [36]

    Controllable text-to-image generation with gpt-4

    [Zhang et al., 2023b] Tianjun Zhang, Yi Zhang, Vibhav Vineet, Neel Joshi, and Xin Wang. Controllable text-to-image generation with gpt-4. arXiv preprint arXiv:2305.18583,

  22. [37]

    Analysis of knn density estimation

    [Zhao and Lai, 2022] Puning Zhao and Lifeng Lai. Analysis of knn density estimation. IEEE Transactions on Informa- tion Theory, 68(12):7971–7995,

  23. [38]

    A huber loss minimization approach to byzantine robust federated learning

    [Zhao et al., 2024] Puning Zhao, Fei Yu, and Zhiguo Wan. A huber loss minimization approach to byzantine robust federated learning. In AAAI, 2024

  24. [2018]

    Deepseek-coder: When the large language model meets programming–the rise of code intelligence

    [Guo et al., 2024] Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al. Deepseek-coder: When the large language model meets programming–the rise of code intelligence. arXiv preprint arXiv:2401.14196,

  25. [2019]

    Llava-v1.6- 34b: Large multimodal language vision model

    [Liu and Others, 2025] Haotian Liu and Others. Llava-v1.6- 34b: Large multimodal language vision model. https:// huggingface.co/liuhaotian/llava-v1.6-34b,

  26. [2021]

    Palp: prompt aligned person- alization of text-to-image models

    [Arar et al., 2024] Moab Arar, Andrey V oynov, Amir Hertz, Omri Avrahami, Shlomi Fruchter, Yael Pritch, Daniel Cohen-Or, and Ariel Shamir. Palp: prompt aligned person- alization of text-to-image models. In SIGGRAPH, pages 1–11,

  27. [2022]

    Diffusion self- guidance for controllable image generation

    [Epstein et al., 2023] Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self- guidance for controllable image generation. NeurIPS, 36:16222–16239,

  28. [2023]

    Internlm2 technical report

    [Cai et al., 2024] Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, et al. Internlm2 technical report. arXiv preprint arXiv:2403.17297,

  29. [2024]

    Gpt-4 technical report

    [Achiam et al., 2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Alt- man, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774,

  30. [2025]

    Class diagram generation from text requirements: An application of natural language processing

    [Almazroi et al., 2021] Abdulwahab Ali Almazroi, Laith Abualigah, Mohammed A Alqarni, Essam H Houssein, Ahmad Qasim Mohammad AlHamad, and Mohamed Abd Elaziz. Class diagram generation from text requirements: An application of natural language processing. Deep Learning Approache...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.