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REVIEW 3 major objections 4 minor 25 references

A 401K-parameter network reaches 89 percent plant-disease accuracy by splitting attention into shape and texture branches.

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STA-Net, a 401K-parameter model with a decoupled shape-texture attention module, reaches 89.00% accuracy and 88.96% F1 on the CCMT plant disease dataset.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection A useful domain-specific attention module for lightweight plant disease classification, but the paper must clarify whether the headline 89.00% is test or validation accuracy and document what data DeepMAD consumed. the 3 major comments →

arxiv 2509.03754 v1 pith:NSMY2OHL submitted 2025-09-03 cs.CV cs.AI

STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification

Zongsen Qiu This is my paper
classification cs.CV cs.AI
keywords plant disease classificationlightweight CNNshape-texture attentiondeformable convolutionGabor filterneural architecture searchfine-grained visual classificationedge deployment
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

This paper sets out to show that a lightweight plant-disease classifier can be made accurate and edge-friendly if attention is built from domain knowledge instead of reused from generic object recognition. The authors build STA-Net in two steps: a training-free neural architecture search (DeepMAD) selects an efficient MBConv backbone under a parameter budget, and a new Shape-Texture Attention Module (STAM) splits spatial attention into a shape branch (deformable convolution) and a texture branch (learnable Gabor filters). On the public CCMT dataset, the final model reaches 89.00% top-1 accuracy and 88.96% macro-F1 with 401K parameters and 51.1M FLOPs, above its NAS backbone alone and above the same backbone with CBAM, at about one-sixth the parameters of the smallest MobileNetV4. The claim matters because it offers a template for embedding task-specific visual priors into attention for fine-grained classification under edge-device constraints.

Core claim

The central claim is that decoupling spatial attention into one branch tuned to shape and one tuned to texture is a better inductive bias for distinguishing visually similar plant diseases than learning a single generic attention map from data. Concretely, the paper reports that inserting STAM into a DeepMAD-searched backbone raises top-1 accuracy from 86.84% to 87.41%, and adding SE channel attention on top yields 89.00% accuracy and 88.96% macro-F1, matching MobileNetV4's 89.23% at a fraction of the parameters and FLOPs. The authors explain the result as a serial-filtering effect: SE first emphasizes informative channels, and STAM then localizes lesion regions by responding to irregular co

What carries the argument

The Shape-Texture Attention Module (STAM) is a spatial attention module with two parallel branches: a shape-aware branch built on DCNv4 deformable convolution, whose sampling offsets adapt to irregular lesion boundaries, and a texture-aware branch built on a bank of eight learnable Gabor filters that respond to oriented frequency patterns. The two single-channel attention maps are concatenated and fused by a small convolution block, then applied residual-wise to the original feature map. The supporting machinery is DeepMAD, a training-free entropy-based neural architecture search that picks the kernel sizes, expansion ratios, strides, and repetition counts of the MBConv blocks under a preset

Load-bearing premise

The load-bearing premise is that DeepMAD's training-free architecture search selected the backbone without using the CCMT labels or test set; the paper does not state what data the search used to rank candidates, so if the search peeked at the evaluation split, the reported 89.00% would partly reflect selection rather than generalization.

What would settle it

Re-run the DeepMAD search twice, once with no data and once with CCMT training labels, then train the resulting STA-Net on the standard train split and test it on the untouched test split; if the chosen backbone or accuracy changes materially, the reported result is not a clean generalization figure. As a second check, replace the STAM branches with identity mappings at matched parameter count; if accuracy does not drop from 89.00%, the decoupled attention module is not the cause of the gain.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • MobileNetV4-comparable accuracy (89.00% vs 89.23%) is reachable with 0.401M parameters and 51.1M FLOPs, about one-sixth the parameters and under one-third the FLOPs of the smallest MobileNetV4.
  • Combining SE channel attention with STAM spatial attention gives higher accuracy than either module alone, indicating the two attention types are complementary.
  • STAM alone beats CBAM on the same backbone (87.41% vs 87.20%), supporting the claim that domain-specific decoupled attention outperforms a generic spatial attention module for fine-grained disease features.
  • Placing STAM at the 28x28 and 14x14 stages, not in shallow or deepest layers, is part of the claimed design: intermediate feature maps still carry enough spatial detail for lesion shape and texture.

Where Pith is reading between the lines

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

  • Editorial inference: the same decoupled shape/texture design could transfer to other fine-grained domains where contour and surface pattern carry the label signal, such as skin-lesion or material-defect classification, but the paper only demonstrates it on CCMT.
  • Editorial inference: the paper's ablations are module-level, not branch-level, so the relative contribution of the shape branch versus the texture branch is not yet established.
  • Editorial inference: since DeepMAD is training-free, the whole pipeline could be rerun cheaply for new crop-disease datasets; the paper does not report how stable the 89.00% figure is across search randomness.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes STA-Net, a lightweight CNN for plant disease classification. The backbone is generated by a training-free NAS method (DeepMAD) using MobileNetV3-style MBConv blocks, and the main contribution is STAM, a spatial attention module with two branches: a shape branch using DCNv4 and a texture branch using learnable Gabor filters. On the CCMT dataset the authors report 89.00% top-1 accuracy, 88.96% F1, 0.401M parameters and 51.1M FLOPs, comparing favorably with MobileNetV3 and MobileNetV4. Ablations (Baseline, +SE, +CBAM, +STAM, +SE+STAM) are used to argue that STAM improves performance and that STAM and SE are synergistic. The paper also discusses why STAM is placed at intermediate spatial resolutions.

Significance. If the reported numbers are held-out test results and the DeepMAD search did not use evaluation-split information, the result is a useful empirical demonstration that a domain-specific decoupled spatial attention module can improve a lightweight NAS-generated backbone for fine-grained plant disease classification at very low computational cost. The efficiency/accuracy trade-off is the strongest point: 0.401M parameters and 51.1M FLOPs with accuracy within 0.23 points of MobileNetV4 is a meaningful result for edge deployment. The paper is not circular in the formal sense: it makes a purely empirical claim and does not derive the attention module from an equation. However, the contribution is incremental and the headline claims are weakened by missing statistical evidence and by ambiguity about which data split produced the reported accuracy.

major comments (3)
  1. [Section 6 / Abstract / Table 4] The reported headline metric is ambiguous. Section 6 states "the final model achieves a validation accuracy of 88.96%", while the Abstract and Table 4 report 89.00% accuracy and 88.96% F1. Section 4.1 describes a 6:2:2 train/validation/test split, but no test-set numbers are ever reported. The value 88.96 appears in Table 4 as the F1 score, not as an accuracy. This is load-bearing because the central claim is that STA-Net reaches 89.00% on the CCMT dataset; if that number is validation accuracy, it is not an unbiased estimate of held-out performance. Please report test accuracy and test F1 separately and reconcile the values.
  2. [Section 3.2] The paper does not state what data, if any, DeepMAD consumes during the architecture search. The text says the method is "training-free" and runs "on a CPU", but many training-free NAS proxies require input images to compute statistics. If those images (or labels) came from the validation or test split, then the backbone itself was selected using information from the evaluation distribution, invalidating the comparison with MobileNetV3/MobileNetV4. Please specify exactly what inputs DeepMAD uses and confirm that no validation or test data were used when ranking candidate architectures.
  3. [Table 4 / Section 4.4] All results in Table 4 are from a single run, with no error bars, multiple seeds, or statistical significance tests. The accuracy differences are small (e.g., 89.00 vs 88.26 vs 89.23; +SE+STAM vs +SE is 89.00 vs 88.03), and the abstract/conclusion use words like "significantly" and "markedly superior." Without variance estimates or significance testing, these qualitative claims are unsupported. Please report mean and standard deviation over at least three independent runs for the main configurations, or explicitly caveat the comparisons as single-run observations.
minor comments (4)
  1. [Section 3.3.2 and Figure 7] The text says the Gabor convolutional layer is composed of eight filters, but Figure 7 is described as illustrating the case k=5. Earlier, k is used for the number of output classes in Table 1. Please clarify whether k denotes the filter count, the class count, or a different hyperparameter, and make the figure consistent with the text.
  2. [Section 4.4] The phrase "FNAS-Attention backbone" appears in the description of the Baseline model, but FNAS is not previously defined. It appears to refer to the DeepMAD-searched backbone; please define it or replace it with the name used in Section 3.2.
  3. [Section 4.2] ThreeAugment is mentioned as an augmentation technique but no citation or description is given. Please provide a reference or a brief explanation.
  4. [Throughout] There are several typographical issues, e.g., "T exture" in the Section 3.3.2 heading, "Shape-A ware" in Section 3.3.1, and "Baseline + ST AM" in Table 4. A careful proofread would improve readability.

Circularity Check

0 steps flagged

No circularity: the reported accuracy/F1 are empirical outcomes, not derived from fitted inputs or self-citations.

full rationale

The paper makes no derivation that reduces to its own inputs. The central claim—89.00% top-1 accuracy and 88.96% F1 on CCMT with 0.401M params/51.1M FLOPs—is an empirical measurement of a concrete architecture. The architecture is described by Table 1 and Section 3.3; STAM's forward equations (xdesc = f1x1(x), Mshape = fshape(...), ...) define a module, and the accuracy numbers are produced by training under the fixed protocol of Section 4.2, not by mathematical consequence of those equations. The ablations in Table 4 are direct comparisons: Baseline 86.84%, +SE+STAM 89.00%, with MobileNetV3/V4 as external benchmarks; none of these numbers is a fitted parameter renamed as a prediction. DeepMAD is cited to external work [CGZ+23] and is used as a backbone generator; even if the paper does not state what data (if any) DeepMAD consumed, that is a possible experimental-design/reporting gap, not a circular argument, because the final evaluation is separately performed on the CCMT split. The 'validation accuracy of 88.96%' phrase in Section 6 versus the abstract's '89.00% accuracy' is a reporting inconsistency, not evidence that an output was baked into the inputs. No load-bearing self-citation appears; all cited methods (MobileNet, DCNv4, Gabor, DeepMAD) are external prior work. The paper is self-contained as an empirical study and shows no equation-level circularity.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

The central claim rests on the validity of the DeepMAD proxy, the assumption that shape and texture are the key discriminative cues, and the correctness of the CCMT labels. Architectural choices (compression ratio, Gabor filter count, STAM placement) are free parameters not fitted to test data in a mathematical sense but selected by the authors, introducing selection risk. No new physical entities are introduced.

free parameters (4)
  • STAM compression ratio r = not reported
    Controls the channel compression in the descriptor; text says smaller r improves performance at higher cost, but the actual value used is not given.
  • Number of Gabor filters = 8 per text, k=5 in Figure 7 (inconsistent)
    Text says eight filters, Figure 7 says k=5; the actual configuration is ambiguous.
  • STAM placement = end of stage 2 and stage 3 (28x28 and 14x14)
    Chosen by design reasoning in Section 5.2, not by a formal search; could be tuned on validation.
  • Backbone hyperparameters from DeepMAD = listed in Table 1
    Selected by the DeepMAD search under a parameter budget below 300K; the search itself is an optimization procedure over architecture choices.
axioms (6)
  • domain assumption DeepMAD entropy proxy predicts final model performance without training
    Section 3.2 relies on DeepMAD to select a good backbone without training; if the proxy is unreliable, the backbone quality is unsubstantiated.
  • domain assumption DCNv4 improves shape-aware feature extraction over standard convolutions
    Section 3.3.1 assumes deformable convolutions better capture irregular lesion shapes; no comparative evidence on this dataset is given for this component alone.
  • domain assumption Learnable Gabor filters can adapt to disease textures and improve classification
    Section 3.3.2 assumes Gabor filters provide useful texture features; the ablation only tests the whole STAM, not the Gabor branch in isolation.
  • domain assumption CCMT dataset is accurately annotated and representative
    Section 4.1 relies on the dataset quality; any label noise or distribution bias affects all reported results.
  • domain assumption The 6:2:2 split is random and leaks no information into training
    Section 4.1 assumes the split is properly randomized; if not, test accuracy could be optimistically biased.
  • ad hoc to paper Shape and texture are the dominant discriminative cues for plant disease classification
    Sections 1 and 3.3 posit that shape and texture are the key features; this is a design premise not established by the paper's experiments beyond the aggregate STAM gain.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification." pith.science (2026). https://pith.science/paper/NSMY2OHL

@misc{pith2026250903754,
  author       = {Pith},
  title        = {Pith review of: STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NSMY2OHL}},
  note         = {Machine review of arXiv:2509.03754}
}
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read the original abstract

Responding to rising global food security needs, precision agriculture and deep learning-based plant disease diagnosis have become crucial. Yet, deploying high-precision models on edge devices is challenging. Most lightweight networks use attention mechanisms designed for generic object recognition, which poorly capture subtle pathological features like irregular lesion shapes and complex textures. To overcome this, we propose a twofold solution: first, using a training-free neural architecture search method (DeepMAD) to create an efficient network backbone for edge devices; second, introducing the Shape-Texture Attention Module (STAM). STAM splits attention into two branches -- one using deformable convolutions (DCNv4) for shape awareness and the other using a Gabor filter bank for texture awareness. On the public CCMT plant disease dataset, our STA-Net model (with 401K parameters and 51.1M FLOPs) reached 89.00% accuracy and an F1 score of 88.96%. Ablation studies confirm STAM significantly improves performance over baseline and standard attention models. Integrating domain knowledge via decoupled attention thus presents a promising path for edge-deployed precision agriculture AI. The source code is available at https://github.com/RzMY/STA-Net.

Figures

Figures reproduced from arXiv: 2509.03754 by Zongsen Qiu.

Figure 1
Figure 1. Figure 1: Overall Architecture of STA-Net 3.2 Lightweight network architecture We employ DeepMAD for designing the backbone network due to its training-free nature and robust mathematical underpinnings. This approach circumvents the high computational costs typically associated with traditional neural architecture search (NAS) by utilizing an entropy proxy indicator to evaluate network structures. It efficiently ide… view at source ↗
Figure 2
Figure 2. Figure 2: MBConv building blocks under both stride=1 and stride=2 conditions [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: STAM satisfy specific task requirements, the significant variability in texture features among different diseases necessitates a more adaptive approach. Accordingly, this study introduces a learnable Gabor filter set for the adaptive extraction of distinctive textures [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Difference between deformable and standard convolutions [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: ShapeBranch [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: TextureBranch [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: GaborFilter [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

25 extracted references · 17 canonical work pages · 1 internal anchor

  1. [1]

    Ccmt-9: A public dataset for crop classification and disease detection in cashew, cassava, maize, and tomato

    James A Asenso, Emmanuel Akomeah, Richard K Arthur, and Vivian Adu-Gyamfi. Ccmt-9: A public dataset for crop classification and disease detection in cashew, cassava, maize, and tomato. Data in Brief , 48:109230, 2023

    work page 2023

  2. [2]

    High-performance large-scale image recognition without normalization

    Andrew Brock, Soham De, Samuel L Smith, and Karen Simonyan. High-performance large-scale image recognition without normalization. In International conference on machine learning , pages 1059--1071. PMLR, 2021

    work page 2021

  3. [3]

    Neural architecture search on a budget: Taming the complexity of one-shot nas

    Wuyang Chen, Xinyu Dai, Hanxiao Liu, Dongdong Chen, and Lu Yuan. Neural architecture search on a budget: Taming the complexity of one-shot nas. In International Conference on Learning Representations , 2021

    work page 2021

  4. [4]

    A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

    Wuyang Chen, Zhen Gao, Zhen Zhang, Dongdong Chen, Hanxiao Liu, and Lu Yuan. Deepmad: A training-free and general-purpose predictor for neural architecture search. arXiv preprint arXiv:2308.01621 , 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  5. [5]

    Randaugment: Practical automated data augmentation with a reduced search space

    Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages 702--703, 2020

    work page 2020

  6. [6]

    Searching for mobilenetv3

    Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V Le, and Hartwig Adam. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision , pages 1314--1324, 2019

    work page 2019

  7. [7]

    Squeeze-and-excitation networks

    Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 7132--7141, 2018

    work page 2018

  8. [8]

    Mobilenets: Efficient convolutional neural networks for mobile vision applications

    Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 , 2017

    Pith/arXiv arXiv 2017

  9. [9]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 770--778, 2016

    work page 2016

  10. [10]

    Unsupervised texture segmentation using gabor filters

    Anil K Jain and Farshid Farrokhnia. Unsupervised texture segmentation using gabor filters. Pattern recognition , 24(12):1167--1186, 1991

    work page 1991

  11. [11]

    Sgdr: Stochastic gradient descent with warm restarts

    Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 , 2016

    Pith/arXiv arXiv 2016

  12. [12]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 , 2017

    Pith/arXiv arXiv 2017

  13. [13]

    Darts: Differentiable architecture search

    Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 , 2018

    Pith/arXiv arXiv 2018

  14. [14]

    Mobilenetv4 - universal models for the next generation of mobile devices

    Han Pham, Zihang Dai, Amir Gholami, Connor R Banbury, Bo Chen, Yunhan He, Mostofa Patwary, Shshard Saxena, Andrew Sun, Vincent Tung, et al. Mobilenetv4 - universal models for the next generation of mobile devices. arXiv preprint arXiv:2404.10518 , 2024

    Pith/arXiv arXiv 2024

  15. [15]

    Large-scale evolution of image classifiers

    Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alex Kurakin. Large-scale evolution of image classifiers. In International conference on machine learning , pages 2902--2911. PMLR, 2017

    work page 2017

  16. [16]

    Mobilenetv2: Inverted residuals and linear bottlenecks

    Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4510--4520, 2018

    work page 2018

  17. [17]

    Very deep convolutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014

    Pith/arXiv arXiv 2014

  18. [18]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc V Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning , pages 6105--6114. PMLR, 2019

    work page 2019

  19. [19]

    Cbam: Convolutional block attention module

    Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) , pages 3--19, 2018

    work page 2018

  20. [20]

    Eca-net: Efficient channel attention for deep convolutional neural networks

    Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu. Eca-net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 11534--11542, 2020

    work page 2020

  21. [21]

    Dcnv4: Improved deformable convolution networks with flash attention

    Jiarui Wang, Lewei Zhu, Jifeng Dai, and Limin Wang. Dcnv4: Improved deformable convolution networks with flash attention. arXiv preprint arXiv:2405.07843 , 2024

    Pith/arXiv arXiv 2024

  22. [22]

    Neural architecture search with reinforcement learning

    Barret Zoph and Quoc V Le. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 , 2016

    Pith/arXiv arXiv 2016

  23. [23]

    Diversified visual attention networks for fine-grained object classification

    Biao Zhao, Xiao Wu, Jiashi Feng, Qi Peng, and Shuicheng Yan. Diversified visual attention networks for fine-grained object classification. IEEE Transactions on Multimedia , 19(6):1245--1256, 2017

    work page 2017

  24. [24]

    Random erasing data augmentation

    Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. In Proceedings of the AAAI conference on artificial intelligence , volume 34, pages 13001--13008, 2020

    work page 2020

  25. [25]

    Shufflenet: An extremely efficient convolutional neural network for mobile devices

    Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 6848--6856, 2018

    work page 2018

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.