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Unlearning text-trigger backdoors in diffusion models hits 100% removal for pixel triggers and 93% for style attacks.

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SKD-CAG erases adversarial text triggers from diffusion models by distilling the model's own clean outputs through cross-attention guidance, claiming 100% and 93% removal for pixel and style backdoors.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The abstract promises an unlearning method for diffusion backdoors; the body is a different paper, so the actual claims are unverifiable. the 3 major comments →

arxiv 2508.18235 v1 pith:UXQ7J4OZ submitted 2025-08-20 cs.CV

Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation

Ashwath Vaithinathan Aravindan , Abha Jha , Matthew Salaway , Atharva Sandeep Bhide , Duygu Nur Yaldiz This is my paper
classification cs.CV
keywords backdoor attackdiffusion modelknowledge distillationcross-attentionmachine unlearningtext triggertext-to-image generationadversarial defense
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

The paper tries to show that a text-to-image diffusion model can be selectively disarmed after it has been backdoored: the bad association between a planted textual trigger and a manipulated output can be erased without degrading normal image generation. The proposed method, SKD-CAG, treats the backdoored model itself, when prompted without the trigger, as a clean teacher and uses that clean output to guide the model's response to poisoned prompts. Because the trigger response is neutralized at the cross-attention level, the defense removes the backdoor while leaving ordinary generation quality and robustness intact. If true, this would give generative models a practical targeted-unlearning defense rather than requiring retraining from scratch.

Core claim

The central claim is that backdoor associations in text-to-image diffusion models are addressable in the cross-attention layers, where text tokens interact with visual features. SKD-CAG performs self-knowledge distillation: the backdoored model produces clean outputs when the trigger is absent, and those clean outputs serve as the distillation target for poisoned prompts, while cross-attention guidance suppresses the trigger-specific attention response. The paper reports removal accuracy of 100% for pixel-based backdoors and 93% for style-based attacks, with image fidelity and robustness preserved. This is framed as evidence that targeted unlearning, rather than full retraining, is a viable

What carries the argument

Self-Knowledge Distillation with Cross-Attention Guidance (SKD-CAG). The backdoored model's trigger-free outputs act as a self-generated teacher signal, and cross-attention maps are used to identify and neutralize the attention-level pathway through which the adversarial text trigger activates the poisoned behavior. This lets the method remove the trigger response selectively while keeping the model's normal capabilities.

Load-bearing premise

The poisoned association is separable and visible enough in cross-attention layers that attention-level guidance can erase it while the model's own trigger-free outputs remain clean enough to serve as the distillation target.

What would settle it

Poison a diffusion model with a backdoor that changes global style but leaves no trigger-free clean outputs (or subtly contaminates them), then apply SKD-CAG and measure whether removal accuracy falls below the reported 93%; alternatively, probe after unlearning with an adaptive trigger that activates the same downstream behavior without producing a strong cross-attention spike.

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

If this is right

  • Pixel-backdoor triggers embedded through training data can be removed from a diffusion model with 100% accuracy under the reported settings.
  • Style-based attacks, which are harder because they alter global appearance, are still removed at 93% accuracy.
  • The unlearning preserves image fidelity and robustness, meaning the defense does not simply degrade the model.
  • Cross-attention guidance gives a concrete locus for targeted unlearning, suggesting that backdoor behavior is visible in attention maps.
  • Targeted unlearning is positioned as a scalable defense alternative to full retraining of poisoned generative models.

Where Pith is reading between the lines

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

  • If backdoor associations are genuinely separable at the cross-attention level, the same guidance could be adapted to other text-conditioned generative architectures and to attacks using concept or style triggers beyond the two tested families.
  • A natural stress test would be attacks that spread the trigger across multiple denoising steps or layers; if those bypass attention-level neutralization, removal accuracy should drop, which would delimit the method's scope.
  • The self-distillation target assumes trigger-free outputs are truly clean; if an adversary contaminates those too, the reported removal numbers could overstate real defense.
  • Attention-map localization could be reused post-unlearning as a diagnostic to verify that the trigger pathway is actually dead rather than merely suppressed on the tested prompts.
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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 / 3 minor

Summary. The abstract announces SKD-CAG, a defense that unlearns adversarial text triggers from text-to-image diffusion models via self-knowledge distillation and cross-attention guidance, reporting 100% removal accuracy for pixel backdoors and 93% for style-based attacks while preserving robustness and image fidelity. The submitted full text, however, is an unrelated manuscript introducing LanSE, a Language-Grounded Sparse Encoder for analyzing AI-generated content, with its own title, abstract, method, and experiments. No section of the body defines SKD-CAG, its loss function, cross-attention guidance, attack model, experimental setup, or baselines. Consequently, the abstract's central quantitative claims have no evidential support in the manuscript as submitted.

Significance. If substantiated, a method that selectively erases text-trigger backdoors in diffusion models while preserving generation quality would be a meaningful contribution to generative-model safety. The stated idea of using the model's trigger-free outputs as a distillation target and guiding cross-attention layers is plausible and worth investigating. However, the submitted manuscript does not present the method, its equations, or any experiments; the reported 100% and 93% figures cannot be checked or interpreted. The body's content, while potentially interesting for content analysis, is unrelated to the claimed backdoor-unlearning results. As submitted, the paper does not make a verifiable scientific claim about SKD-CAG.

major comments (3)
  1. [Abstract vs. full text] The full text is a different paper on LanSE; no section of the body mentions SKD-CAG, cross-attention guidance, knowledge-distillation unlearning, backdoor attacks, pixel backdoors, style-based attacks, or removal accuracy. The body's only limitation statements (Appendix I) concern LanSE, not the claimed defense. Every quantitative claim in the abstract—100% and 93% removal accuracy, preserved robustness, preserved image fidelity—is therefore unsupported. This is not a local gap; the claimed method and experiments are absent.
  2. [Abstract, mechanism] The core premise, that 'the backdoored model still produces clean outputs in the absence of triggers' and that cross-attention guidance can neutralize backdoor influences, is asserted without a formal statement of the unlearning objective. The only distillation equations in the body, Eqs. (5) and (6), belong to LanSE's modality distillation and have no connection to unlearning. There is no definition of how the trigger is represented, how the distillation target is constructed, or how cross-attention guidance is applied. The soundness of the mechanism cannot be assessed.
  3. [Abstract, evaluation protocol] The abstract reports 'removal accuracy' without defining it, and no datasets, attack configurations, held-out trigger sets, baselines, error bars, or fidelity metrics are given. If the evaluation uses the same triggers as the unlearning objective, the reported figures largely measure the training objective rather than generalization to unseen triggers—the circularity concern raised by the reader. Because the manuscript contains no evaluation section, this concern cannot be resolved by scrutinizing the text; a complete protocol is needed before any soundness judgment is possible.
minor comments (3)
  1. [Title and metadata] The title and abstract describe SKD-CAG, while the body's title, authors, abstract, and running content describe LanSE. This mismatch must be resolved before any further review.
  2. [References and related work] The reference list supports LanSE and content analysis; there is no related-work discussion of text-trigger backdoors in diffusion models or of existing unlearning defenses. The claimed contribution is not situated in its literature.
  3. [Abstract grammar] Minor wording issue: 'generative models lack effective mitigation techniques against' is incomplete; the sentence trails off.

Circularity Check

0 steps flagged

No circular step can be exhibited: the submitted full text is an unrelated LanSE manuscript, so SKD-CAG has no equations or experiments to reduce.

full rationale

The abstract claims SKD-CAG achieves 100% pixel-backdoor and 93% style-attack removal via self-knowledge distillation with cross-attention guidance. However, the full text under review is a different paper, 'Human-like Content Analysis for Generative AI with Language-Grounded Sparse Encoders' (LanSE), with different authors and no mention of SKD-CAG, backdoor unlearning, cross-attention guidance, or any of the claimed experiments. Circularity requires quoting a specific equation, definition, or fitted parameter that makes a 'prediction' equal to its input by construction. No such derivation exists in the submitted text. The closest potential circularity—self-distillation to the backdoored model's own trigger-free outputs could make 'removal accuracy' on the training prompts a measure of the training objective—cannot be established because the paper does not state the training loss, the metric definition, or whether held-out triggers were used. The manuscript also contains an explicit limitation note in Appendix I, but it concerns LanSE's encoder limitations, not the abstract's SKD-CAG claims. The correct verdict is therefore not circularity but unsupported/absent methodology: the central claim has no supporting method, equations, or results in the submission. Under the hard rule that circularity must be exhibited by the paper's own text, the circularity score is 0.

Axiom & Free-Parameter Ledger

1 free parameters · 3 axioms · 0 invented entities

The claimed 100%/93% removal accuracy rests on three load-bearing premises: the trigger-free clean-output assumption, the cross-attention localization assumption, and an unstated evaluation-protocol assumption about held-out triggers. The true hyperparameter count is unknown because the body of the submission is an unrelated paper; the abstract reports no distillation or guidance settings. No new physical or architectural entities are postulated.

free parameters (1)
  • Cross-attention guidance strength and distillation weights (SKD-CAG hyperparameters) = not reported in abstract
    The claimed trade-off between trigger removal accuracy and image fidelity must be controlled by these settings; the abstract reports neither values nor a sensitivity analysis, so the central numbers float on undisclosed choices.
axioms (3)
  • domain assumption The backdoored model's output for trigger-free prompts is clean and can serve as the distillation target for poisoned prompts.
    Stated in the abstract as the mechanism the method exploits. If the backdoor also contaminates the model's general behavior, self-distillation would propagate the backdoor instead of removing it.
  • domain assumption Backdoor influence is localized and addressable in the cross-attention layers of the diffusion model.
    The abstract says the method 'neutralizes backdoor influences at the attention level.' No evidence is provided in the abstract that trigger associations occupy separable cross-attention subspaces; if they are distributed across the network, attention-level guidance cannot do the claimed work.
  • ad hoc to paper Reported removal accuracy is evaluated on triggers not used during unlearning or otherwise reflects generalization.
    The abstract does not state the evaluation protocol. If the same triggers are used for unlearning and evaluation, the 100% figure largely measures the training objective, which is a circularity the submission does not address.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation." pith.science (2026). https://pith.science/paper/UXQ7J4OZ

@misc{pith2026250818235,
  author       = {Pith},
  title        = {Pith review of: Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UXQ7J4OZ}},
  note         = {Machine review of arXiv:2508.18235}
}
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read the original abstract

Text-to-image diffusion models have revolutionized generative AI, but their vulnerability to backdoor attacks poses significant security risks. Adversaries can inject imperceptible textual triggers into training data, causing models to generate manipulated outputs. Although text-based backdoor defenses in classification models are well-explored, generative models lack effective mitigation techniques against. We address this by selectively erasing the model's learned associations between adversarial text triggers and poisoned outputs, while preserving overall generation quality. Our approach, Self-Knowledge Distillation with Cross-Attention Guidance (SKD-CAG), uses knowledge distillation to guide the model in correcting responses to poisoned prompts while maintaining image quality by exploiting the fact that the backdoored model still produces clean outputs in the absence of triggers. Using the cross-attention mechanism, SKD-CAG neutralizes backdoor influences at the attention level, ensuring the targeted removal of adversarial effects. Extensive experiments show that our method outperforms existing approaches, achieving removal accuracy 100\% for pixel backdoors and 93\% for style-based attacks, without sacrificing robustness or image fidelity. Our findings highlight targeted unlearning as a promising defense to secure generative models. Code and model weights can be found at https://github.com/Mystic-Slice/Sealing-The-Backdoor .

discussion (0)

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Forward citations

Cited by 2 Pith papers

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

  1. Scaling Exposes the Trigger: Input-Level Backdoor Detection in Text-to-Image Diffusion Models via Cross-Attention Scaling

    cs.CR 2026-04 unverdicted novelty 7.0

    SET detects input-level backdoors in T2I diffusion models by learning a benign cross-attention response space from clean samples and flagging deviations under multi-scale perturbations.

  2. Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

    cs.LG 2026-07 conditional novelty 5.0

    A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.

Reference graph

Works this paper leans on

111 extracted references · 24 canonical work pages · cited by 2 Pith papers · 6 internal anchors

  1. [1]

    Morriss-Kay, G. M. The evolution of human artistic creativity.������� �� ����������, 158–176 (2010)

    2010

  2. [2]

    A.��������� ���������� ��� ������� ������� �� ��� ������� ����� (John Wiley & Sons, 2013)

    Raaflaub, K. A.��������� ���������� ��� ������� ������� �� ��� ������� ����� (John Wiley & Sons, 2013)

    2013

  3. [3]

    Gripshover, S. M. Writing systems as a reflection of ancient societies (2020)

    2020

  4. [4]

    URL https://doi.org/10.1038/s41562-023-01775-7

    Living in a brave new ai era.������ ����� ����������, 1799 (2023). URL https://doi.org/10.1038/s41562-023-01775-7. Published 20 November 2023. 20

    work page doi:10.1038/s41562-023-01775-7 2023

  5. [5]

    & Sinha, P.���������� ��� ��� �������� ��� ���� ���� ������� �������� ������HBR Insights Series (Har- vard Business Review Press, 2024)

    Review, H., Mollick, E., De Cremer, D., Neeley, T. & Sinha, P.���������� ��� ��� �������� ��� ���� ���� ������� �������� ������HBR Insights Series (Har- vard Business Review Press, 2024). URL https://books.google.com.sg/books? id=MIvGEAAAQBAJ

    2024

  6. [6]

    & Machery, E

    Porter, B. & Machery, E. Ai-generated poetry is indistinguishable from human- written poetry and is rated more favorably.��������� ���������, 26133 (2024)

    2024

  7. [7]

    Nightingale, S. J. & Farid, H. Ai-synthesized faces are indistinguishable from real faces and more trustworthy.����������� �� ��� �������� ������� �� �������� ���, e2120481119 (2022)

    2022

  8. [8]

    Will ai write scientific papers in the future?�� ����������, 3–15 (2022)

    Gil, Y. Will ai write scientific papers in the future?�� ����������, 3–15 (2022)

    2022

  9. [9]

    Nobel turing challenge: creating the engine for scientific discovery

    Kitano, H. Nobel turing challenge: creating the engine for scientific discovery. ��� ������� ������� ��� �������������, 29 (2021)

    2021

  10. [10]

    URL https://arxiv.org/abs/2409.13686

    Geng, M.�� ���The impact of large language models in academia: from writing to speaking (2024). URL https://arxiv.org/abs/2409.13686. 2409.13686

    Pith/arXiv arXiv 2024

  11. [11]

    URL https://arxiv.org/abs/2510.10472

    Zou, Q.�� ���Fml-bench: Benchmarking machine learning agents for scientific research (2026). URL https://arxiv.org/abs/2510.10472. 2510.10472

    arXiv 2026

  12. [12]

    Mahmood, F.�� ���Deep adversarial training for multi-organ nuclei segmen- tation in histopathology images.���� ������������ �� ������� ���������, 3257–3267 (2019)

    2019

  13. [13]

    Generating Synthetic X-ray Images of a Person from the Surface Geometry

    Teixeira, B.�� ���Generating synthetic x-ray images of a person from the surface geometry (2018). URL https://arxiv.org/abs/1805.00553. 1805.00553

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  14. [14]

    & Ommer, B

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models (2022). URL https://arxiv.org/ abs/2112.10752. 2112.10752

    Pith/arXiv arXiv 2022

  15. [15]

    & Kundargi, S

    Kottapalli, V. & Kundargi, S. Transforming real images to studio ghibli style: A comparative study of deep learning approaches

  16. [16]

    A.�� ���Medical large language models are vulnerable to data- poisoning attacks.������ ����������, 618–626 (2025)

    Alber, D. A.�� ���Medical large language models are vulnerable to data- poisoning attacks.������ ����������, 618–626 (2025)

    2025

  17. [17]

    Hager, P.�� ���Evaluation and mitigation of the limitations of large language models in clinical decision-making.������ ����������, 2613–2622 (2024)

    2024

  18. [18]

    Measuring trustworthiness is crucial for medical ai tools.������ ����� ����������, 1812–1813 (2023)

    Kundu, S. Measuring trustworthiness is crucial for medical ai tools.������ ����� ����������, 1812–1813 (2023). 21

    2023

  19. [19]

    Lindsay, G. W. Llms are not ready for editorial work.������ ����� ��������� �, 1814–1815 (2023)

    2023

  20. [20]

    URL https://arxiv.org/abs/2505.18555

    Feng, Y.�� ���Unraveling misinformation propagation in llm reasoning (2025). URL https://arxiv.org/abs/2505.18555. 2505.18555

    work page arXiv 2025

  21. [21]

    Doshi, A. R. & Hauser, O. P. Generative ai enhances individual creativity but reduces the collective diversity of novel content.������� ����������, eadn5290 (2024)

    2024

  22. [22]

    We need a culturally aware approach to ai.������ ����� ��������� �, 1816–1817 (2023)

    Suzuki, S. We need a culturally aware approach to ai.������ ����� ��������� �, 1816–1817 (2023)

    2023

  23. [23]

    Generative ai has a language problem.������ ����� ��������� �, 1802–1803 (2023)

    Choudhury, M. Generative ai has a language problem.������ ����� ��������� �, 1802–1803 (2023)

    2023

  24. [24]

    Shumailov, I.�� ���Ai models collapse when trained on recursively generated data.���������, 755–759 (2024)

    2024

  25. [25]

    & Griffiths, T

    Smith, K., Kirby, S., Guo, S. & Griffiths, T. L. Ai model collapse might be prevented by studying human language transmission.���������, 525 (2024)

    2024

  26. [26]

    Governance of generative ai.������ ��� ���������, 1–22 (2025)

    Taeihagh, A. Governance of generative ai.������ ��� ���������, 1–22 (2025). URL https://doi.org/10.1093/polsoc/puaf001

    doi:10.1093/polsoc/puaf001 2025

  27. [27]

    Human-ai agency in the age of generative ai.����������� ��� ��������������, 100560 (2025)

    Krakowski, S. Human-ai agency in the age of generative ai.����������� ��� ��������������, 100560 (2025). URL https://www.sciencedirect.com/science/ article/pii/S1471772725000065

    2025

  28. [28]

    Tang, D. Y. M. The integrated forward-forward algorithm: Integrating forward- forward and shallow backpropagation with local losses (2023). URL https:// arxiv.org/abs/2305.12960. 2305.12960

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  29. [29]

    SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning

    Dai, G.�� ���San: Hypothesizing long-term synaptic development and neural engram mechanism in scalable model’s parameter-efficient fine-tuning (2025). URL https://arxiv.org/abs/2409.06706. 2409.06706

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  30. [30]

    URL https://arxiv.org/abs/2602.10422

    Anumasa, S.�� ���Navigating heterogeneous protein landscapes through geometry-aware smoothing (2026). URL https://arxiv.org/abs/2602.10422. 2602.10422

    work page arXiv 2026

  31. [31]

    Chen, C., Namboodiri, V. P. & Padget, J. Understanding the vulnerability of clip to image compression (2023). URL https://arxiv.org/abs/2311.14029. 2311.14029

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  32. [32]

    URL https://arxiv.org/abs/2401.09603

    Jayasumana, S.�� ���Rethinking fid: Towards a better evaluation metric for image generation (2024). URL https://arxiv.org/abs/2401.09603. 2401.09603. 22

    Pith/arXiv arXiv 2024

  33. [33]

    & Sharma, R

    Barratt, S. & Sharma, R. A note on the inception score (2018). URL https: //arxiv.org/abs/1801.01973. 1801.01973

    Pith/arXiv arXiv 2018

  34. [34]

    URL https://arxiv.org/abs/2410.02746

    Chen, H.-Y.�� ���Contrastive localized language-image pre-training (2025). URL https://arxiv.org/abs/2410.02746. 2410.02746

    Pith/arXiv arXiv 2025

  35. [35]

    Hessel, J., Holtzman, A., Forbes, M., Bras, R. L. & Choi, Y. Clipscore: A reference-free evaluation metric for image captioning (2022). URL https://arxiv. org/abs/2104.08718. 2104.08718

    Pith/arXiv arXiv 2022

  36. [36]

    & Chen, X

    Yao, Z., Wang, R. & Chen, X. Hifi-score: Fine-grained image description evalua- tion with hierarchical parsing graphs. European Conference on Computer Vision (ECCV), pp. 441–458. Springer (2024)

    2024

  37. [37]

    G.�� ���Typescore: A text fidelity metric for text-to-image generative models (2024)

    Sampaio, G. G.�� ���Typescore: A text fidelity metric for text-to-image generative models (2024). URL https://arxiv.org/abs/2411.02437. 2411.02437

    Pith/arXiv arXiv 2024

  38. [38]

    X., Nie, J.-Y

    Li, J., Cheng, X., Zhao, W. X., Nie, J.-Y. & Wen, J.-R. Halueval: A large- scale hallucination evaluation benchmark for large language models (2023). URL https://arxiv.org/abs/2305.11747. 2305.11747

    Pith/arXiv arXiv 2023

  39. [39]

    URL https://arxiv

    Fang, G.�� ���Humanrefiner: Benchmarking abnormal human generation and refining with coarse-to-fine pose-reversible guidance (2024). URL https://arxiv. org/abs/2407.06937. 2407.06937

    Pith/arXiv arXiv 2024

  40. [40]

    Recognition-by-components: a theory of human image under- standing.������������� ��������, 115 (1987)

    Biederman, I. Recognition-by-components: a theory of human image under- standing.������������� ��������, 115 (1987)

    1987

  41. [41]

    Art and the brain.������� �� ������������� ��������, 76–96 (1999)

    Zeki, S. Art and the brain.������� �� ������������� ��������, 76–96 (1999)

    1999

  42. [42]

    H.����� ��������(Cambridge university press, 1996)

    Clark, H. H.����� ��������(Cambridge university press, 1996)

    1996

  43. [43]

    J., Wade, K

    Nightingale, S. J., Wade, K. A. & Watson, D. G. Can people identify original and manipulated photos of real-world scenes?��������� ��������� ���������� ��� �������������, 1–21 (2017)

    2017

  44. [44]

    Creating, using, misusing, and detecting deep fakes.������� �� ������ ����� ��� �������(2022)

    Farid, H. Creating, using, misusing, and detecting deep fakes.������� �� ������ ����� ��� �������(2022)

    2022

  45. [45]

    Rhodes, G., Proffitt, F., Grady, J. M. & Sumich, A. Facial symmetry and the perception of beauty.����������� �������� � �������, 659–669 (1998)

    1998

  46. [46]

    E., Casati, R

    Santos, P. E., Casati, R. & and, P. C. Perception, cognition and reasoning about shadows.������� ��������� � �������������, 78–85 (2018). URL https: //doi.org/10.1080/13875868.2017.1377204

    work page arXiv 2018

  47. [47]

    Landy, M. S. Texture analysis and perception.��� ��� ������ ������������� ���, 639–652 (2013). 23

    2013

  48. [48]

    URL https: //transformer-circuits.pub/2024/scaling-monosemanticity/index.html

    Templeton, A.�� ���Scaling monosemanticity: Extracting interpretable fea- tures from claude 3 sonnet.����������� �������� ������(2024). URL https: //transformer-circuits.pub/2024/scaling-monosemanticity/index.html

    2024

  49. [49]

    URL https://arxiv.org/abs/2408.05147

    Lieberum, T.�� ���Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2 (2024). URL https://arxiv.org/abs/2408.05147. 2408.05147

    Pith/arXiv arXiv 2024

  50. [50]

    & Sharkey, L

    Cunningham, H., Ewart, A., Riggs, L., Huben, R. & Sharkey, L. Sparse autoen- coders find highly interpretable features in language models (2023). URL https://arxiv.org/abs/2309.08600. 2309.08600

    Pith/arXiv arXiv 2023

  51. [51]

    & Nanda, N

    Bussmann, B., Nabeshima, N., Karvonen, A. & Nanda, N. Learning multi-level features with matryoshka sparse autoencoders (2025). URL https://arxiv.org/ abs/2503.17547. 2503.17547

    Pith/arXiv arXiv 2025

  52. [52]

    URL https: //arxiv.org/abs/2406.04093

    Gao, L.�� ���Scaling and evaluating sparse autoencoders (2024). URL https: //arxiv.org/abs/2406.04093. 2406.04093

    Pith/arXiv arXiv 2024

  53. [53]

    & Nanda, N

    Dunefsky, J., Chlenski, P. & Nanda, N. Transcoders find interpretable llm feature circuits (2024). URL https://arxiv.org/abs/2406.11944. 2406.11944

    Pith/arXiv arXiv 2024

  54. [54]

    & Belrose, N

    Paulo, G., Shabalin, S. & Belrose, N. Transcoders beat sparse autoencoders for interpretability (2025). URL https://arxiv.org/abs/2501.18823. 2501.18823

    Pith/arXiv arXiv 2025

  55. [55]

    ����� �������� ����������������(2024)

    Lindsey, J.�� ���Sparse crosscoders for cross-layer features and model diffing. ����� �������� ����������������(2024). Accessed June 2025

    2024

  56. [56]

    URL https://arxiv

    Huang, K.�� ���T2i-compbench++: An enhanced and comprehensive bench- mark for compositional text-to-image generation (2025). URL https://arxiv. org/abs/2307.06350. 2307.06350

    Pith/arXiv arXiv 2025

  57. [57]

    & Aila, T

    Kynk¨ a¨ anniemi, T., Karras, T., Laine, S., Lehtinen, J. & Aila, T. Improved precision and recall metric for assessing generative models (2019). URL https: //arxiv.org/abs/1904.06991. 1904.06991

    Pith/arXiv arXiv 2019

  58. [58]

    & Perdikaris, P

    Yang, Y. & Perdikaris, P. Physics-informed deep generative models (2018). URL https://arxiv.org/abs/1812.03511. 1812.03511

    Pith/arXiv arXiv 2018

  59. [59]

    URL https://arxiv.org/ abs/2404.01413

    Gerstgrasser, M.�� ���Is model collapse inevitable? breaking the curse of recur- sion by accumulating real and synthetic data (2024). URL https://arxiv.org/ abs/2404.01413. 2404.01413

    Pith/arXiv arXiv 2024

  60. [60]

    & Shim, H

    Lim, Y., Choi, H. & Shim, H. Evaluating image hallucination in text-to-image generation with question-answering (2025). URL https://arxiv.org/abs/2409. 12784. 2409.12784

    Pith/arXiv arXiv 2025

  61. [61]

    Text-image Alignment for Diffusion-based Perception

    Kondapaneni, N., Marks, M., Knott, M., Guimaraes, R. & Perona, P. Text- image alignment for diffusion-based perception (2024). URL https://arxiv.org/ 24 abs/2310.00031. 2310.00031

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  62. [62]

    Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

    Inoue, N., Furuta, R., Yamasaki, T. & Aizawa, K. Cross-domain weakly- supervised object detection through progressive domain adaptation (2018). URL https://arxiv.org/abs/1803.11365. 1803.11365

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  63. [63]

    & Chen, M

    Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text- conditional image generation with clip latents (2022). URL https://arxiv.org/ abs/2204.06125. 2204.06125

    Pith/arXiv arXiv 2022

  64. [64]

    Presented at CVPR 2024 (2024)

    Narasimhaswamy, S.�� ���Handiffuser: Text-to-image generation with realistic hand appearances. Presented at CVPR 2024 (2024). Available at https://doi. org/10.1109/CVPR52733.2024.00239

    arXiv 2024

  65. [65]

    & Joo, H

    Kwon, P. & Joo, H. Graspdiffusion: Synthesizing realistic whole-body hand- object interaction (2024). URL https://arxiv.org/abs/2410.13911. 2410.13911

    work page arXiv 2024

  66. [66]

    & Liu, D

    Lagzian, A., Anumasa, S. & Liu, D. Multi-novelty: Improve the diversity and novelty of contents generated by large language models via inference- time multi-views brainstorming (2025). URL https://arxiv.org/abs/2502.12700. 2502.12700

    Pith/arXiv arXiv 2025

  67. [67]

    Baer, J.���������� ��� ��������� ��������� � ������������ ��������(Psychology Press, 2014)

    2014

  68. [68]

    & Wu, C.-S

    Chakrabarty, T., Laban, P., Agarwal, D., Muresan, S. & Wu, C.-S. Art or artifice? large language models and the false promise of creativity (2024). URL https://arxiv.org/abs/2309.14556. 2309.14556

    Pith/arXiv arXiv 2024

  69. [69]

    URL https://arxiv.org/abs/2108.01073

    Meng, C.�� ���Sdedit: Guided image synthesis and editing with stochastic differential equations (2022). URL https://arxiv.org/abs/2108.01073. 2108. 01073

    Pith/arXiv arXiv 2022

  70. [70]

    Dall�e 3

    Hristoforu, E. Dall�e 3. https://huggingface.co/ehristoforu/dalle-3-xl-v2 (2023). Accessed: 2025-03-14

    work page 2023

  71. [71]

    Labs, B. F. Flux.1-dev. https://huggingface.co/black-forest-labs/FLUX.1-dev (2024). Accessed: 2025-03-14

    work page 2024

  72. [72]

    Kolors-diffusers

    Kwai-Kolors. Kolors-diffusers. https://huggingface.co/Kwai-Kolors/ Kolors-diffusers (2024). Accessed: 2025-03-14

    work page 2024

  73. [73]

    Stable cascade: Efficient text-to-image generation in highly com- pressed latent spaces

    AI, S. Stable cascade: Efficient text-to-image generation in highly com- pressed latent spaces. https://huggingface.co/stabilityai/stable-cascade (2025). Technical Report

    work page 2025

  74. [74]

    T., Argus, M., Fischer, V

    Schrodi, S., Hoffmann, D. T., Argus, M., Fischer, V. & Brox, T. Two effects, one trigger: On the modality gap, object bias, and information imbalance in 25 contrastive vision-language models (2025). URL https://arxiv.org/abs/2404. 07983. 2404.07983

    Pith/arXiv arXiv 2025

  75. [75]

    URL http://dx.doi.org/10.1016/j.inffus.2023.101861

    Koco´ n, J.�� ���Chatgpt: Jack of all trades, master of none.����������� ������ ��, 101861 (2023). URL http://dx.doi.org/10.1016/j.inffus.2023.101861

    work page arXiv 2023

  76. [76]

    URL https://arxiv.org/abs/2303.12712

    Bubeck, S.�� ���Sparks of artificial general intelligence: Early experiments with gpt-4 (2023). URL https://arxiv.org/abs/2303.12712. 2303.12712

    Pith/arXiv arXiv 2023

  77. [77]

    URL https://arxiv.org/abs/ 2302.04023

    Bang, Y.�� ���A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity (2023). URL https://arxiv.org/abs/ 2302.04023. 2302.04023

    Pith/arXiv arXiv 2023

  78. [78]

    & Dong, B

    Tang, Y. & Dong, B. Demonstration notebook: Finding the most suited in- context learning example from interactions (2024). URL https://arxiv.org/abs/ 2406.10878. 2406.10878

    Pith/arXiv arXiv 2024

  79. [79]

    & Kempe, J

    Dohmatob, E., Feng, Y., Subramonian, A. & Kempe, J. Strong model collapse (2024). URL https://arxiv.org/abs/2410.04840. 2410.04840

    Pith/arXiv arXiv 2024

  80. [80]

    & Etcheverry, L

    Acion, L., Rajngewerc, M., Randall, G. & Etcheverry, L. Generative ai poses ethical challenges for open science.������ ����� ����������, 1800–1801 (2023)

    work page 2023

Showing first 80 references.

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