Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences

Euihyun Kim; Keun Park; Yeoneung Kim

REVIEW 4 major objections 2 minor 225 references

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences

T0 review · 4 major / 2 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A human-in-the-loop reward model steers pretrained diffusion generators away from floating-material and boundary flaws without retraining them.

desk verdict The submission contains the wrong full text—a wireless survey with a different author list and arXiv footer—so the claimed topology-design method and its results are absent and unverifiable. read the letter →

arxiv 2508.01589 v1 pith:KBM5JUPF submitted 2025-08-03 cs.LG cs.AI

Euihyun Kim , Keun Park , Yeoneung Kim This is my paper

classification cs.LGcs.AI

keywords topologyoptimizationdiffusionmodelshumanpreferencealignmentclassifierguidancerewardmodelmanufacturabilityfloatingmaterialdetectionboundaryviolations

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 proposes that the failure modes of diffusion-based topology optimization—floating components and boundary discontinuities that surrogate predictors miss—can be suppressed at sampling time by a lightweight reward model trained on minimal binary human feedback. It argues that gradients of human-aligned reward classifiers, injected into the reverse diffusion process of a frozen pretrained generator, push generated designs toward physically plausible, manufacturable structures. If correct, the claim matters because it offers a modular path to trustworthy generative design: an expert's visual judgment can be folded into an existing generator without any retraining, and with only a small labeling effort.

What carries the argument

The named mechanism, censored sampling, is classifier guidance in the reverse diffusion sampler: lightweight reward models, trained on binary human evaluations of generated topologies, supply gradient directions that adjust the intermediate density fields during denoising. Floating-material and boundary-violation classifiers play the role of human-aligned reward functions, and their gradients are combined with the pretrained generator's score estimates to steer samples back toward manufacturable designs. This is what carries the argument, because the generator itself is frozen and all preference information enters through the reward models' gradients.

What would settle it

Collect a held-out set of guided and unguided generated topologies, have human experts label floating material and boundary violations without knowing which samples were guided, and compare flaw rates; if the guidance reduces classifier-detected flaws but does not reduce expert-identified flaws, or if it transfers poorly to load cases absent from the classifier's training distribution, the central claim fails.

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Extended reading notes

Core claim

The central claim is that modulating the reverse diffusion trajectory of a pretrained topology-diffusion generator with gradients of reward classifiers trained on binary human evaluations yields designs that are structurally performant, physically plausible, and manufacturable. The classifiers are trained to detect two specific flaw types—floating material and boundary violations—and are integrated into the sampling loop, so their gradients guide each denoising step away from unrealistic outputs. The paper reports substantial reductions in failure modes and improved design realism across diverse test conditions, and emphasizes that the approach is modular and requires no retraining of the diffusion model.

Load-bearing premise

The method assumes that a lightweight classifier trained on a small, unspecified set of binary human judgments reliably recognizes physical and manufacturing flaws across the entire distribution of generated topologies, so that its gradients push the sampler toward genuinely manufacturable designs rather than toward the classifier's blind spots.

Editorial extensions

If this is right

With the paper's method, a topology-diffusion model can be aligned to new manufacturing constraints by collecting a small set of binary human evaluations and training lightweight classifiers, leaving the expensive diffusion model untouched.
Suppressing floating material and boundary violations at sampling time should directly lower the rate of unusable designs, reducing the need for post-hoc repair or rejection.
Because the reward classifiers are separate modules, the same frozen generator can be steered toward different constraint sets by swapping in different human-aligned classifiers.
The reported reductions in failure modes, if they hold across diverse test conditions, suggest that expert visual judgment can be encoded as gradients rather than as hard constraints or surrogate physical predictors.

Reading between the lines

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

An implicit testable extension is whether the same guidance loop suppresses flaw types beyond the two the classifiers were trained on, or whether it merely reshapes outputs to avoid those two patterns at the cost of new, unseen failure modes.
Because classifier gradients can exploit blind spots, a natural stress test is to measure whether guided samples remain on the manifold of physically valid topologies under out-of-distribution loads, rather than only matching the binary labels.
The approach suggests a more general recipe: any human-articulable design flaw that can be labeled as present or absent could become a reward classifier, so the method may transfer to printability, assembly clearance, or cost constraints with the same modular scaffolding.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Kim, Park, and Kim's abstract describes a sensible idea: train lightweight binary classifiers on human judgments of floating-material and boundary-violation defects, then use their gradients to guide the reverse diffusion of a pretrained topology generator. That is standard classifier guidance, as the abstract concedes by citing preference alignment. The application to manufacturability filtering is a reasonable niche contribution if the numbers hold.

But the numbers are not in the artifact. The supplied full text is 'Diffusion Models for Future Networks and Communications: A Comprehensive Survey' by a different author team, with footer arXiv:2508.01586v1. No topology optimization, no human feedback collection, no classifier training, no sampling-loop integration, no failure-mode statistics. The claimed 'substantial reductions in failure modes' appear only in the abstract. There is no paper to evaluate.

The stress-test note is right, and I'd go further: the absence is total. The only material we have is the abstract, so soundness is unverifiable rather than weak. The novelty is modest—it is a transfer of known guidance tricks to a new domain. Even if the correct PDF appeared, the central empirical question would be whether the reported improvement is measured independently of the guiding classifiers. If the same classifiers used for guidance also count the failure modes, part of the gain is circular. A second open question is whether a small binary human label set generalizes across the topology distribution, or whether the gradient steering just walks toward the classifier's blind spots. These are questions to ask once a real paper exists.

For now, the submission is not reviewable. A serious editor should desk-reject and ask the authors to upload the correct manuscript. If and when the actual paper appears, it deserves a referee only if it includes independent evaluation of the failure-mode metrics, a description of the human label budget, and baselines against unguided sampling and simpler post-hoc filtering. This version: no.

Referee Report

4 major / 2 minor

Summary. The abstract describes a human-in-the-loop diffusion framework for topology design: a pretrained diffusion generator is steered at sampling time by gradients of lightweight classifiers trained on binary human evaluations of two failure modes (floating material and boundary violations). The abstract claims that this modular approach, requiring no generator retraining, yields 'substantial reductions in failure modes and improved design realism across diverse test conditions.' The full text supplied for review, however, is an entirely different manuscript: a survey titled 'Diffusion Models for Future Networks and Communications: A Comprehensive Survey' with a footer reading arXiv:2508.01586v1. It contains no topology optimization, no human feedback collection, no failure-mode classifiers, and no sampling-loop integration. The central empirical claim of the abstract is therefore unsupported by any method description, protocol, baseline, or quantitative result in the submitted material.

Significance. If the mechanism were realized and validated, the idea of using lightweight human-preference classifiers as sampling-time guidance for a frozen generative topology model would be a useful contribution, bridging preference alignment and engineering design constraints without retraining the generator. However, the submitted manuscript provides no implementation, no experiments, and no code or proofs, so the significance of the actual contribution cannot be assessed. The only verifiable content is an unrelated survey, which does not bear on the claimed result.

major comments (4)

[Full text, p. 1 (footer) vs. Abstract] The full text submitted for review is not the paper described in the abstract. It is the survey 'Diffusion Models for Future Networks and Communications: A Comprehensive Survey' with footer arXiv:2508.01586v1, authored by different researchers. None of the claimed components—topology optimization, binary human evaluations, floating-material or boundary-violation classifiers, or gradient-based guidance of the reverse diffusion trajectory—appears anywhere in the full text. The central claim is therefore absent from the reviewable material, and no technical assessment of the proposed method is possible. This is a load-bearing failure of the submission.
[Abstract, paragraph 4] The claim of 'substantial reductions in failure modes and improved design realism across diverse test conditions' is an empirical statement, but the submitted material contains no measurements, baselines, experimental protocol, error bars, or statistical tests. The claim cannot be checked or reproduced from the manuscript, and it is not supported by any of the survey content that constitutes the supplied full text.
[Abstract, paragraph 3] The method's core elements are unspecified in the submitted material: the binary human evaluation protocol (number of annotators, label counts, inter-annotator agreement), the classifier architecture and training procedure, the guidance-scale selection, and the precise gradient update rule used to modulate the reverse diffusion process are all missing. Without these details, the load-bearing assumptions that lightweight classifiers trained on sparse human labels generalize across the generator's output distribution and that classifier gradients keep the sampler on the learned manifold cannot be evaluated.
[Abstract, paragraphs 3-4] The evaluation design is not described. If the planned failure-mode reductions are measured using the same classifiers that provide the sampling guidance, then any improvement would be partly circular. The manuscript must specify an independent evaluation—for example, held-out human raters, physical simulation, or manufacturability checks—and state which primary metric is used for the reported reductions.

minor comments (2)

[Metadata/footer] The full text footer carries arXiv:2508.01586v1, which does not match the claimed manuscript identifier arXiv:2508.01589; this discrepancy should be resolved by the authors or the editorial office.
[Abstract, paragraph 2] The terms 'physically plausible' and 'manufacturable' are used as success criteria but are not operationally defined; a resubmission should define them in terms of measurable geometric or physical constraints.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be identified because the supplied full text is a different paper; the claimed method and its derivation chain are absent.

full rationale

The abstract describes a human-in-the-loop diffusion framework for topology design, with classifiers trained on binary human evaluations of floating-material and boundary-violation defects and then used as reward gradients during sampling. However, the full text supplied is 'Diffusion Models for Future Networks and Communications: A Comprehensive Survey' by a different author team, and the first-page footer reads arXiv:2508.01586v1 rather than arXiv:2508.01589. The body contains no topology optimization, no human feedback collection, no classifier training for the two named failure modes, no sampling-loop integration, and no reported failure-mode reductions. Circularity analysis requires exhibiting a specific reduction in which a claimed output is equivalent, by construction or by self-citation, to an input of the paper. With the method, equations, and experimental protocol absent, no such reduction can be exhibited. The reader's hypothetical concern that failure-mode reduction might be measured by the same classifiers that provide guidance cannot be substantiated from the provided text, because no evaluation protocol appears. Accordingly, no circular step is identified, and the score is 0; the more pressing issue is that the central claim is unverifiable in the document provided, which is an evidentiary gap rather than a circularity defect.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The abstract leaves the method as a recipe: guidance scale, label budget, and classifier thresholds are unstated design choices; the steering mechanism assumes the validity of classifier-gradient guidance for frozen diffusion generators; and the evaluation assumes the failure detectors generalize to unseen designs. No new physical entities are introduced; the reward classifiers are learned models, not postulates.

free parameters (3)

guidance scale (reward gradient weight)
Classifier-guided diffusion needs a scalar weighting of how strongly the reward gradient perturbs the reverse trajectory; its value and sensitivity are not reported in the abstract, though the claimed failure reduction depends on it.
human feedback budget
The method is framed as needing 'minimal human feedback', but the number of binary labels used to train the failure detectors is not given; classifier accuracy and thus guidance quality depend on it.
failure-detector decision threshold
Detecting floating material or boundary violations from a binary classifier requires a probability threshold that sets the operating point for both guidance and any reported failure rates; it is not specified.

assumptions (4)

standard math Gradient modulation of the reverse diffusion trajectory yields samples from an approximately reward-guided distribution (classifier guidance theory).
The steering mechanism is borrowed from the classifier-guidance literature, referenced only indirectly in the abstract via 'preference alignment techniques'; no derivation is given.
domain assumption Binary human evaluations of structural flaws are consistent, learnable by a lightweight classifier, and transferable to unseen topologies.
The reward signal rests on 'floating material' and 'boundary violation' labels generalizing; rater disagreement, label noise, or coverage gaps would degrade guidance quality. This enters at Abstract paragraph 3.
domain assumption A pretrained topology-optimization diffusion generator exists, can be frozen, and can be steered by classifier gradients without retraining.
The framework is 'modular and requires no retraining of the diffusion model' (Abstract paragraph 4), presupposing a suitable frozen generator and a density-field latent space smooth enough for gradient steering.
domain assumption Evaluation of failure-mode reduction happens on designs not used to train the guiding classifiers.
The abstract does not describe train/test separation for the human labels; if the reported reductions are measured on or near the label distribution, the improvement is inflated (Abstract paragraph 4).

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Cite this review

Pith. "Pith review of Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences." pith.science (2026). https://pith.science/paper/KBM5JUPF

@misc{pith2026250801589,
  author       = {Pith},
  title        = {Pith review of: Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KBM5JUPF}},
  note         = {Machine review of arXiv:2508.01589}
}

read the original abstract

Recent advances in denoising diffusion models have enabled rapid generation of optimized structures for topology optimization. However, these models often rely on surrogate predictors to enforce physical constraints, which may fail to capture subtle yet critical design flaws such as floating components or boundary discontinuities that are obvious to human experts. In this work, we propose a novel human-in-the-loop diffusion framework that steers the generative process using a lightweight reward model trained on minimal human feedback. Inspired by preference alignment techniques in generative modeling, our method learns to suppress unrealistic outputs by modulating the reverse diffusion trajectory using gradients of human-aligned rewards. Specifically, we collect binary human evaluations of generated topologies and train classifiers to detect floating material and boundary violations. These reward models are then integrated into the sampling loop of a pre-trained diffusion generator, guiding it to produce designs that are not only structurally performant but also physically plausible and manufacturable. Our approach is modular and requires no retraining of the diffusion model. Preliminary results show substantial reductions in failure modes and improved design realism across diverse test conditions. This work bridges the gap between automated design generation and expert judgment, offering a scalable solution to trustworthy generative design.

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2024
[232]

Toward democratized generative ai in next-generation mobile edge networks,

R. Zhang et al., “Toward democratized generative ai in next-generation mobile edge networks,” IEEE Network, pp. 1–1, 2025

2025
[233]

Embodied ai-enhanced vehicular networks: An integrated vision language models and reinforcement learning method,

R. Zhang et al. , “Embodied ai-enhanced vehicular networks: An integrated vision language models and reinforcement learning method,” IEEE Transactions on Mobile Computing , pp. 1–16, 2025. Nguyen Cong Luong received the B.S. and M.S. degrees from the School of Electrical and E...

2025
[1990]

He is a Distinguished Pro- fessor with the College of Information and Com- munication Engineering, Sungkyunkwan University, Suwon, South Korea

He was a Tenured Professor with the School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada. He is a Distinguished Pro- fessor with the College of Information and Com- munication Engineering, Sungkyunkwan University, Suwon, South Korea. He is a Fellow of th...

2014

Pith tools

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

[1] [10]

A survey of diffusion models in natural language processing,

H. Zou, Z. M. Kim, and D. Kang, “A survey of diffusion models in natural language processing,” 2023. [Online]. Available: https://arxiv.org/abs/2305.14671

arXiv 2023

[2] [11]

A survey of multimodal controllable diffusion models,

R. Jiang, G.-C. Zheng, T. Li, T.-R. Yang, J.-D. Wang, and X. Li, “A survey of multimodal controllable diffusion models,” Journal of Computer Science and Technology , vol. 39, no. 3, pp. 509–541, 2024

2024

[3] [12]

Stability ai,

“Stability ai,” https://stability.ai/

[4] [13]

A survey on generative diffusion models,

H. Cao, C. Tan, Z. Gao, Y . Xu, G. Chen, P.-A. Heng, and S. Z. Li, “A survey on generative diffusion models,” IEEE transactions on knowledge and data engineering , vol. 36, no. 7, pp. 2814–2830, 2024

2024

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High-resolution image synthesis with latent diffusion models,

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 684–10 695

2022

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Generating high dimensional user-specific wireless channels using diffusion models,

T. Lee, J. Park, H. Kim, and J. G. Andrews, “Generating high dimensional user-specific wireless channels using diffusion models,” arXiv preprint arXiv:2409.03924 , 2024

arXiv 2024

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Gen- erative diffusion models for radio wireless channel modelling and sampling,

U. Sengupta, C. Jao, A. Bernacchia, S. Vakili, and D.-s. Shiu, “Gen- erative diffusion models for radio wireless channel modelling and sampling,” in GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023, pp. 4779–4784

2023

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A Massive MIMO Sampling Detection Strategy Based on Denoising Diffusion Model

L. He, Z. Wang, and Y . Huang, “A massive mimo sampling de- tection strategy based on denoising diffusion model,” arXiv preprint arXiv:2404.13281, 2024

work page Pith review arXiv 2024