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REVIEW 3 major objections 7 minor 22 references

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density

T0 review · 3 major / 7 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A physics-guided Diffusion Transformer pretrained on electron-density point clouds learns a shared molecular representation that transfers across six EDBench tasks and beats the same architecture trained from scratch, especially under limit

desk verdict Masked diffusion pretraining on electron-density point clouds gives consistent, believable transfer gains on EDBench, but the 'preserve total electron number' claim rests on a scale-free point average, not a validated quadrature. read the letter →

arxiv 2608.03260 v1 pith:GFXVDPVD submitted 2026-08-04 cs.LG

Liang Shuang , Haocheng Wang , Jiayi Song , Shuquan Ye , Ben Fei This is my paper
classification cs.LG
keywords electrondensityself-supervisedpretrainingdiffusiontransformermolecularrepresentationlearningelectron-numberconsistencyEDBenchtransferprediction
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

ED-DiT asks whether raw three-dimensional electron-density fields can serve as a self-supervised pretraining signal for molecular machine learning. The paper's answer is yes: a Diffusion Transformer trained to denoise masked, corrupted log-density point clouds, regularized by an electron-number consistency loss, learns a shared encoder that transfers to six EDBench tasks. Across property regression, shell classification, molecule–density retrieval, and molecule-conditioned density prediction, the pretrained encoder beats the same architecture trained from scratch, with the largest margins under 1–10% labels. The electron-number consistency loss is the physics-guided mechanism: it forces reconstructions to preserve total electronic mass, and ablations show it improves density prediction and classification. A sympathetic reader should take the claim as: electron density is a usable pretraining modality, not just a prediction target.

What carries the argument

Masked, multi-noise-level diffusion denoising on log-density point tokens, regularized by an electron-number consistency loss. The DiT backbone embeds coordinates and log-density into point tokens, applies self-attention with timestep-adaptive layer normalization, and outputs local point features plus a global representation. The electron-number loss is $L_N = \left(\frac{N_p(\rho^{\mathrm{mix}}) - N_p(\rho)}{N_p(\rho)+\epsilon}\right)^2$ with $N_p(\rho)=\sum_i w_i \rho_i$, $w_i=1/N_{\mathrm{valid}}$; this is the mechanism that ties local reconstruction to the conserved total electron number, making the pretraining physics-guided rather than purely geometric.

What would settle it

Take the raw cube files from EDBench's training split, integrate the electron density on the full voxel grid, and compare with the paper's uniform-weight point-sampled proxy on the same molecules. If the proxy deviates systematically from the voxel integral, the consistency loss is not preserving the true electron number, and the physics-guided interpretation of the pretraining objective would need to be replaced by a proper quadrature-weighted loss. A second check would be to retrain with voxel-weighted quadrature and see whether the downstream gains persist.

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Extended reading notes

Core claim

The central discovery is that masked diffusion denoising on electron-density point clouds yields transferable molecular representations, and that a global conservation loss makes those representations more physically consistent. ED-DiT converts each molecule's electron density into a set of points with coordinates, density values, and uniform sample weights; log-density tokens are corrupted with Gaussian noise at random diffusion timesteps, about half are masked, and the model predicts the injected noise using clean context. The reconstruction is regularized by an electron-number consistency term comparing the point-sampled proxy of the mixed density field to ground truth. Pretrained this wa

Load-bearing premise

The load-bearing premise is that the uniform-weight point-sampled proxy $N_p(\rho)=\frac{1}{N_{\mathrm{valid}}}\sum_i \rho_i$ approximates the true integrated electron number; if EDBench samples density points non-uniformly in space, the electron-number consistency loss does not actually conserve the physical electron count.

Editorial extensions

If this is right

  • Electron-density fields can serve as a task-agnostic pretraining signal for molecular models, complementing pretraining on SMILES, graphs, and conformers.
  • A single pretrained encoder can be adapted with light task-specific heads to regression, classification, retrieval, and field prediction without task-specific pretraining.
  • Label efficiency improves sharply: at 10% labels, OE RMSE drops from 0.0293 to 0.0138 and MM RMSE from 0.899 to 0.381.
  • Enforcing electron-number consistency during pretraining improves downstream density prediction and classification, and reduces the mean electron-number error from $3.29\times10^{-2}$ to $7.57\times10^{-4}$.
  • The pretrained density backbone transfers to molecule-conditioned density prediction, beating both the scratch model and the DeepDFT/HGEGNN baselines.

Reading between the lines

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

  • A testable extension, not explored in the paper: the same masked-denoising-plus-conservation recipe could apply to other conserved three-dimensional fields such as spin density or charge density.
  • Because gains are largest at 1–10% labels, the method points toward data-efficient quantum-property prediction on large unlabeled molecular corpora; the paper only demonstrates this within EDBench's five-dataset setting.
  • Since remaining density errors concentrate in extreme high-density regions, adding higher-multipole or local-moment constraints to the electron-number loss is a natural next experiment to see whether tail errors shrink; the paper leaves this open.
  • The uniform-weight electron-number proxy is only one quadrature choice; replacing it with the true voxel-integrated density might make the physics-guided claim tighter, but the paper does not report that comparison.
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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 / 7 minor

Summary. The paper proposes ED-DiT, a self-supervised pretraining method for molecular electron-density point clouds. A Diffusion Transformer encoder is pretrained to denoise masked, noised log-density values across diffusion timesteps, with an auxiliary electron-number consistency loss. The pretrained encoder is then fine-tuned on six EDBench tasks: orbital-energy prediction, energy correction, multipole-moment prediction, open/closed-shell classification, molecule-electron-density retrieval, and molecule-conditioned electron-density prediction. The central empirical claim is that this pretraining consistently outperforms the same architecture trained from scratch, especially under 1% and 10% labels, and that the electron-number constraint improves physical consistency and density prediction.

Significance. If the claims hold, the paper is a useful demonstration that explicit three-dimensional electron-density fields can serve as a self-supervised pretraining signal for a shared molecular encoder. The controlled scratch baseline, the breadth of six downstream tasks, and the supplementary error analysis (local-density-variation quartiles, long-tail statistics) are genuine strengths. However, the distinctive physics-guided component—the electron-number consistency loss—is currently a mean-density proxy rather than a validated estimator of total electron number, and the empirical conclusions rest on single-seed runs. With validation of the proxy or a reframed claim, plus uncertainty estimates, the paper could make a solid contribution.

major comments (3)
  1. [§3.3, Eqs. (2)–(3), (14)–(15); §8.1] The manuscript's distinctive physics-guided claim is not supported by the loss as written. Eq. (14) defines Np(ρ)=Σ_i (1/N_valid)ρ_i, which is a simple average over valid points, not an approximation of ∫ρ(r)d³r. After the coordinate normalization in Eqs. (2)–(3), the per-molecule radius R is divided out and is not provided to the network or the loss, so the physical volume element d³r is unavailable. Uniform weights are valid quadrature weights only if the valid points are uniform samples of a fixed physical volume; the paper provides no evidence for this, and cube-file-derived point sets are generally not volume-uniform after validity selection. Consequently, L_N enforces equality of mean density on the valid-point subset, not preservation of total electronic mass. The supplementary physical-consistency metric in Fig. 5 is computed from the same proxy and therefore cannot validate the
  2. [§4.4 and Tables 1–5] All headline results and ablations appear to come from a single random seed ('random seed 2026', Section 4.4), and no error bars, standard deviations, or confidence intervals are reported. The claim of 'consistently outperforms' is therefore stronger than the evidence: some comparisons are tiny (e.g., OE MAE 0.005847 vs 0.005842 in Table 5; OE RMSE 0.007951 vs 0.008011 in Table 4), while the label-efficient gains are large but may be sensitive to the particular data subsample. I request at least three independent training runs per condition for main comparisons and ablations, with standard deviations, or a bootstrap over test molecules if repeated training is infeasible.
  3. [§6.2, Table 4] The ablation shows that the electron-number consistency loss improves EDP, EC, and OCS but harms OE and MM (OE RMSE 0.007951→0.008011; MM RMSE 0.15768→0.17216). The manuscript partially acknowledges this, but the title, abstract, and conclusion present electron-number consistency as a core physics-guided contribution. The current framing exceeds what the ablation supports. Please provide a deeper analysis of when L_N helps, present it explicitly as a regularizer with task-dependent effects, or reduce the strength of the claims.
minor comments (7)
  1. [§4.3 and Table 2] HGEGNN is used as an official baseline but is never cited or described; X-3D and PointVector-S are also described only by name. Please add references and training/preprocessing details for all baselines.
  2. [§4.4] The diffusion noise schedule (e.g., linear or cosine β_t) is not specified, so the pretraining procedure is not fully reproducible. Please add the schedule and any clipping details.
  3. [Table 7] The entries '17.20107.1947' and '54.498028.0745' appear to be formatting errors; they should read '17.2010 / 7.1947' and '54.4980 / 28.0745'.
  4. [§8.1 and Figures 5–6] 'Mean moment error' is not defined. Please state how the moment is computed and whether it refers to dipole/quadrupole moments or a generic density moment.
  5. [§4.1] 'ed_retrievel_5w' is a typo for 'ed_retrieval_5w'.
  6. [General] No code or pretrained-model release is mentioned. Please add an availability statement.
  7. [§3.2, Eq. (6)] The valid-point mask v is passed to DiTBlock, but the paper does not describe how masking is applied inside attention. Please clarify.

Circularity Check

1 steps flagged · score 4.0 of 10

Physical-consistency diagnostic reports the training loss itself; main transfer claims are externally benchmarked and not circular

  1. fitted input called prediction [Section 3.3, Eqs. 14–15; Section 8.1, Figure 5]
    "We further investigate the role of electron-number consistency (N) in physics-guided pretraining by comparing the denoising-only objective with its variant augmented by the electron-number constraint. As shown in Figure 5, electron-number consistency substantially improves physical consistency: the mean electron-number error is reduced from 3.29×10−2 to 7.57×10−4, while the mean moment error decreases from 49.8 to 1.37."

    The only electron-number quantity defined in the method is the point-sampled proxy Np(ρ)=Σ wiρi (Eq. 14), and the electron-number consistency loss L_N (Eq. 15) is the squared relative error between Np on the mixed reconstructed/GT density and Np on the GT density. Section 8.1 then reports a 'mean electron-number error' as evidence of physical consistency. As written, this diagnostic is the same point-sampled proxy that L_N directly minimizes—unless it is computed from the continuous integral ∫ρ d³r, which the paper does not state. Thus the reported improvement is the training objective renamed as an evaluation: adding L_N lowers L_N by construction, so it cannot independently demonstrate preservation of the true total electronic mass.

full rationale

The core transfer claim of the paper is evaluated on EDBench downstream tasks that are external to the pretraining objective, and the comparisons to the same architecture trained from scratch isolate the effect of pretraining; those results are not circular. The only circular element I identified is the physical-consistency diagnostic in Section 8.1/Figure 5, which uses a metric that is effectively the electron-number loss itself, so the claimed improvement in 'physical consistency' is partly a restatement of the fitted objective. Separately, the uniform-weight quadrature in Eq. 14 is not validated against raw cube integrals, but that is a correctness/support gap rather than a circular step. Self-citations such as Fei et al. 2023 for 3D point-cloud SSL are background context and not load-bearing. No uniqueness-theorem or ansatz-smuggling pattern appears. The score reflects one supporting diagnostic that reduces by construction, while the central transfer claims retain independent, externally benchmarked evidence.

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

The central claim rests on the validity of the electron-density point-cloud representation, the electron-number proxy, and the transferability of diffusion-pretrained representations. No new physical entities are introduced.

free parameters (1)
  • electron-number loss weight lambda_N = 0.10
    Chosen from {0.05, 0.10, 0.15} based on downstream validation performance (Table 5), where N010 is best on 3 of 5 tasks.
assumptions (3)
  • domain assumption EDBench electron-density fields from DFT are an accurate target for pretraining
    The paper uses EDBench DFT densities without questioning their accuracy beyond the benchmark construction.
  • ad hoc to paper The point-sampled sum with uniform weights approximates the continuous electron-number integral
    Section 3.3 Eq. 14 defines Np with wi=1/Nvalid, which is only exact for uniform point distributions in space.
  • standard math Masked diffusion denoising is an effective self-supervised objective for representation learning
    Borrowed from Ho et al. (2020) and Point-MAE; the paper provides no new justification for this assumption.

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Cite this review

Pith. "Pith review of ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density." pith.science (2026). https://pith.science/paper/GFXVDPVD

@misc{pith2026260803260,
  author       = {Pith},
  title        = {Pith review of: ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GFXVDPVD}},
  note         = {Machine review of arXiv:2608.03260}
}
read the original abstract

Pretraining has shown strong potential for learning transferable representations, yet it remains underexplored for electron-density-based molecular learning. Electron density provides a continuous three-dimensional description of molecular electronic structure, capturing both local spatial patterns and global physical quantities. This raises a key question: can electron-density fields be used for self-supervised pretraining to learn a shared representation that transfers across diverse electronic-structure-related tasks? We propose ED-DiT, a physics-guided Diffusion Transformer for self-supervised pretraining on electron-density point clouds. ED-DiT learns reusable representations by reconstructing corrupted and partially masked log-density fields across diffusion noise levels. An electron-number consistency constraint is further introduced to preserve the total electronic mass. The pretrained encoder can be adapted to property prediction, open-/closed-shell classification, molecule-electron-density retrieval, and molecule-conditioned electron-density prediction. Experiments on six EDBench tasks show that ED-DiT consistently outperforms the same architecture trained from scratch, especially under limited supervision. For molecule-conditioned electron-density prediction, it reduces RMSE from 2.2474 to 1.3753 and surpasses the available baseline. With only 10% labels, it improves orbital energy prediction RMSE from 0.0293 to 0.0138. These results demonstrate the effectiveness of physics-guided electron-density pretraining for learning transferable molecular representations.

Figures

Figures reproduced from arXiv: 2608.03260 by the authors.

Figure 1
Figure 1. Overview of ED-DiT. (a) ED-DiT performs self-supervised physics-guided pretraining on 3D electron-density [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Qualitative comparison between DFT electron-density surfaces and ED-DiT predictions. For each molecule, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Signed residual maps for molecule-conditioned electron-density prediction. The residual is computed as the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: t-SNE visualization of fine-tuned ED-DiT embeddings on the EDP validation set. Each point represents [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Physical-consistency diagnostics for denoising-only pretraining and its variant with electron-number [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Physical-consistency diagnostics under different electron-number loss weights [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 8
Figure 8. Figure 8: Qualitative electron-density prediction exam [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison between DFT electron [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 9
Figure 9. Figure 9: Quantitative validation of the relationship between local electron-density variation and prediction error. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

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Reference graph

Works this paper leans on

22 extracted references · 6 canonical work pages

  1. [7]

    Chemberta: Large-scale self-supervised pretraining for molecular property prediction.arXiv preprint arXiv:2010.09885,

    Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. Chemberta: Large-scale self-supervised pretraining for molecular property prediction.arXiv preprint arXiv:2010.09885,

    arXiv 2010

  2. [8]

    Self-supervised graph transformer on large-scale molecular data.arXiv preprint arXiv:2007.02835,

    Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. Self-supervised graph transformer on large-scale molecular data.arXiv preprint arXiv:2007.02835,

    arXiv 2007

  3. [9]

    Molecular contrastive learning of representations via graph neural networks.arXiv preprint arXiv:2102.10056,

    Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. Molecular contrastive learning of representations via graph neural networks.arXiv preprint arXiv:2102.10056,

    arXiv

  4. [10]

    Pre-training molecular graph representation with 3d geometry.arXiv preprint arXiv:2110.07728,

    15 ED-DiTA PREPRINT Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. Pre-training molecular graph representation with 3d geometry.arXiv preprint arXiv:2110.07728,

    arXiv

  5. [11]

    3d infomax improves gnns for molecular property prediction.arXiv preprint arXiv:2110.04126,

    Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò. 3d infomax improves gnns for molecular property prediction.arXiv preprint arXiv:2110.04126,

    arXiv

  6. [12]

    Unified 2D and 3D Pre-Training of Molecular Representations

    Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. Unified 2d and 3d pre-training of molecular representations.arXiv preprint arXiv:2207.08806,

    work page Pith review arXiv

  7. [13]

    Multi-modal molecule structure-text model for text-based retrieval and editing.arXiv preprint arXiv:2212.10789,

    Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar. Multi-modal molecule structure-text model for text-based retrieval and editing.arXiv preprint arXiv:2212.10789,

    arXiv

  8. [14]

    3d-molt5: leveraging discrete structural information for molecule-text modeling

    Qizhi Pei, Rui Yan, Kaiyuan Gao, Jinhua Zhu, and Lijun Wu. 3d-molt5: leveraging discrete structural information for molecule-text modeling. InInternational Conference on Learning Representations, volume 2025, pages 24395–24420,

    work page 2025

Show all 22 references
  1. [16]

    Sinitskiy and Vijay S

    Anton V . Sinitskiy and Vijay S. Pande. Deep neural network computes electron densities and energies of a large set of organic molecules faster than density functional theory.arXiv preprint arXiv:1809.02723,

    arXiv

  2. [18]

    Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids.arXiv preprint arXiv:2112.00652,

    Peter Bjørn Jørgensen and Arghya Bhowmik. Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids.arXiv preprint arXiv:2112.00652,

    arXiv

  3. [19]

    Self-supervised learning for pre-training 3d point clouds: A survey.arXiv preprint arXiv:2305.04691,

    Ben Fei, Weidong Yang, Liwen Liu, Tianyue Luo, Rui Zhang, Yixuan Li, and Ying He. Self-supervised learning for pre-training 3d point clouds: A survey.arXiv preprint arXiv:2305.04691,

    arXiv

  4. [21]

    Point-bert: Pre-training 3d point cloud transformers with masked point modeling.arXiv preprint arXiv:2111.14819,

    Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling.arXiv preprint arXiv:2111.14819,

    arXiv

  5. [22]

    Yatian Pang, Wenxiao Wang, Francis E. H. Tay, Wei Liu, Yonghong Tian, and Li Yuan. Masked autoencoders for point cloud self-supervised learning.arXiv preprint arXiv:2203.06604,

    arXiv

  6. [1964]

    Edbench: Large-scale electron density data for molecular modeling.arXiv preprint arXiv:2505.09262,

    Hongxin Xiang, Ke Li, Mingquan Liu, Zhixiang Cheng, Bin Yao, Wenjie Du, Jun Xia, Li Zeng, Xin Jin, and Xiangxiang Zeng. Edbench: Large-scale electron density data for molecular modeling.arXiv preprint arXiv:2505.09262,

  7. [2017]

    Directional message passing for molecular graphs.arXiv preprint arXiv:2003.03123,

    Johannes Gasteiger, Janek Groß, and Stephan Günnemann. Directional message passing for molecular graphs.arXiv preprint arXiv:2003.03123,

    2003 arXiv

  8. [2018]

    Schütt, Pieter-Jan Kindermans, Huziel E

    Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.arXiv preprint arXiv:1706.08566,

    arXiv

  9. [2020]

    Schütt, Oliver T

    Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger. Equivariant message passing for the prediction of tensorial properties and molecular spectra.arXiv preprint arXiv:2102.03150,

    arXiv

  10. [2021]

    Gemnet: Universal directional graph neural networks for molecules.arXiv preprint arXiv:2106.08903,

    Johannes Gasteiger, Florian Becker, and Stephan Günnemann. Gemnet: Universal directional graph neural networks for molecules.arXiv preprint arXiv:2106.08903,

    arXiv

  11. [2022]

    Unicorn: A unified contrastive learning approach for multi-view molecular representation learning.arXiv preprint arXiv:2405.10343,

    Shikun Feng, Yuyan Ni, Minghao Li, Yanwen Huang, Zhi-Ming Ma, Wei-Ying Ma, and Yanyan Lan. Unicorn: A unified contrastive learning approach for multi-view molecular representation learning.arXiv preprint arXiv:2405.10343,

    arXiv

  12. [2023]

    Qi, Leonidas J

    Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas J. Guibas, and Or Litany. Pointcontrast: Unsupervised pre-training for 3d point cloud understanding.arXiv preprint arXiv:2007.10985,

    2007 arXiv

  13. [2025]

    Scireasoner: Laying the scientific reasoning ground across disciplines.arXiv preprint arXiv:2509.21320,

    Yizhou Wang, Chen Tang, Han Deng, Jiabei Xiao, Jiaqi Liu, Jianyu Wu, Jun Yao, Pengze Li, Encheng Su, Lintao Wang, et al. Scireasoner: Laying the scientific reasoning ground across disciplines.arXiv preprint arXiv:2509.21320,

  14. [2026]

    Deepdft: Neural message passing network for accurate charge density prediction.arXiv preprint arXiv:2011.03346,

    Peter Bjørn Jørgensen and Arghya Bhowmik. Deepdft: Neural message passing network for accurate charge density prediction.arXiv preprint arXiv:2011.03346,

    2011 arXiv

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Reviewed August 5, 2026 · model on record in the stance chip above.