arxiv: 2604.07026 · v1 · submitted 2026-04-08 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Not all tokens contribute equally to diffusion learning

Guoqing Zhang , Lu Shi , Wanru Xu , Linna Zhang , Sen Wang , Fangfang Wang , Yigang Cen

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:50 UTC · model grok-4.3

classification 💻 cs.CV

keywords diffusion modelstext-to-video generationclassifier-free guidancecross-attentionsemantic alignmenttoken frequency biasattention reweighting

0 comments

The pith

A rectification framework corrects diffusion models' neglect of important tokens in text prompts by suppressing dominant ones and realigning attention.

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

The paper shows that text-to-video diffusion models often overlook semantically important tokens from the conditioning prompt, producing biased or incomplete video outputs. This stems from long-tailed token frequencies in the training data and from spatial misalignment in cross-attention layers. The authors introduce DARE to address both issues through dynamic suppression of frequent low-density tokens during guidance and adaptive reweighting of attention maps to favor high-density tokens. If the approach holds, the same base models can generate videos that better reflect the complete meaning of a prompt. Readers should care because more faithful semantic control would make text-to-video systems more usable for precise creative and descriptive tasks.

Core claim

We observe that conditional diffusion models neglect semantically important tokens during inference due to distributional bias from long-tailed token frequencies and cross-attention misalignment. To address this, we propose Distribution-Aware Rectification and Spatial Ensemble (DARE), consisting of Distribution-Rectified Classifier-Free Guidance (DR-CFG) that dynamically suppresses dominant low semantic-density tokens and Spatial Representation Alignment (SRA) that adaptively reweights cross-attention maps to enforce representation consistency for important tokens.

What carries the argument

The Distribution-Aware Rectification and Spatial Ensemble (DARE) framework, which combines dynamic suppression of dominant tokens in classifier-free guidance with adaptive reweighting of cross-attention maps based on token semantic importance.

If this is right

Consistent gains in generation fidelity and semantic alignment across multiple benchmark datasets.
Better capture of underrepresented semantic cues without overfitting to frequent low-density tokens.
Prevention of attention dilution so that high semantic-density tokens exert stronger spatial guidance.
More balanced conditional distributions learned by the model during the rectified training process.

Where Pith is reading between the lines

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

The same token-balancing logic could apply to text-to-image diffusion models where prompt fidelity is also limited by attention and frequency effects.
Measuring semantic density at inference time might enable automatic prompt editing to further boost results without retraining.
If early data curation incorporated similar density balancing, the learned models might require less correction at generation time.
The approach suggests checking whether non-diffusion generative models exhibit comparable token neglect under classifier-free guidance.

Load-bearing premise

The observed neglect of important tokens stems primarily from long-tailed frequency bias and cross-attention misalignment, and that suppressing dominant tokens plus reweighting attention will improve balance without introducing new artifacts or reducing overall quality.

What would settle it

Compare generations from the same prompts and model with and without DARE on a test set containing both frequent and rare but semantically critical tokens; if the versions with DARE show no increase in inclusion of elements specified by the rare tokens, the central claim fails.

Figures

Figures reproduced from arXiv: 2604.07026 by Fangfang Wang, Guoqing Zhang, Linna Zhang, Lu Shi, Sen Wang, Wanru Xu, Yigang Cen.

**Figure 1.** Figure 1: Visualization and Analysis of Conditional Semantic Information Distribution. (a) shows the similarity between conditional semantic representations. The off-diagonal regions indicate that the semantic similarity between different tokens is low, reflecting a discrete distribution. (b) presents the attention scores and corresponding distribution maps of selected underfitted tail tokens generated by Seedance… view at source ↗

**Figure 2.** Figure 2: Overall Computational Architecture of the Distribution-Aware Rectification and Spatial Ensemble (DARE) Method. By computing token importance weights online and incorporating them into the subsequent DR-CFG and SRA modules, DARE corrects the distributional bias in the model’s fitting to prompt tokens. token based on its cumulative loss (Lci ) and frequency of occurrence (Nci ) recorded during training, as d… view at source ↗

**Figure 3.** Figure 3: Visual evaluation of the effects of varying proportions of low-semantic tokens on model performance. Furthermore, to provide an intuitive comparison of how 7 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Visualization of Token-Level Guidance Dynamics Before and After DARE from the Perspective of Attention Scores and Distributions. Seedance1.0_exp W. DARE Prompt: The camera pans to the left, capturing a villa residential area surrounded by lush greenery, with a large parking lot in the distance filled with cars. Seedance1.0_exp W. DARE Prompt: 机场上，全景镜头拍摄一架客机向画面左侧飞，逐渐降落。画面左侧是落日下的机场塔台。随后镜头轻微下摇，画面里出现一个小绿点闪动。 S… view at source ↗

**Figure 5.** Figure 5: Visual quality evaluation of generation results based on a self-constructed evaluation dataset and the VBench benchmark. the airplane landing, resulting in deviations in the generated video. In the VBench evaluation set, the prompts are generally simpler and shorter, so the overall video content does not exhibit significant differences. However, substantial disparities can be observed in the quality of g… view at source ↗

**Figure 6.** Figure 6: The loss curve during training is shown. Introducing Lsra accelerates the model’s fitting of the conditional semantic representation, enhancing the semantic consistency between the video and the conditional text. However, in the later stages of training, this leads to oscillations in the loss, causing model instability and a shift in focus towards the consistency between the conditional semantics and indi… view at source ↗

**Figure 7.** Figure 7: Visual comparison and analysis of the attention scores produced by high-density semantic tokens during the inference stage. Using 101 high-density semantic tokens extracted through statistical analysis, (a) illustrates the changes in the attention scores of different tokens before and after introducing DARE; (b) shows the proportion of tokens that exhibit a significant increase in attention scores after in… view at source ↗

**Figure 8.** Figure 8: Qualitative comparison of generation results produced by Seedance before and after introducing DARE on the self-constructed dataset and the VBench benchmark. results. On the VBench benchmark, although the prompts are relatively simple, the generated results often lack dynamic motion. For example, in the second row, the airplane and train remain largely static when generated by Seedance. After applying DAR… view at source ↗

**Figure 9.** Figure 9: Qualitative comparison of generation results produced by Wan 2.1 before and after introducing DARE on the self-constructed dataset. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗

**Figure 10.** Figure 10: Qualitative comparison of generation results produced by Wan 2.1 before and after introducing DARE on the VBench benchmark. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗

read the original abstract

With the rapid development of conditional diffusion models, significant progress has been made in text-to-video generation. However, we observe that these models often neglect semantically important tokens during inference, leading to biased or incomplete generations under classifier-free guidance. We attribute this issue to two key factors: distributional bias caused by the long-tailed token frequency in training data, and spatial misalignment in cross-attention where semantically important tokens are overshadowed by less informative ones. To address these issues, we propose Distribution-Aware Rectification and Spatial Ensemble (DARE), a unified framework that improves semantic guidance in diffusion models from the perspectives of distributional debiasing and spatial consistency. First, we introduce Distribution-Rectified Classifier-Free Guidance (DR-CFG), which regularizes the training process by dynamically suppressing dominant tokens with low semantic density, encouraging the model to better capture underrepresented semantic cues and learn a more balanced conditional distribution. This design mitigates the risk of the model distribution overfitting to tokens with low semantic density. Second, we propose Spatial Representation Alignment (SRA), which adaptively reweights cross-attention maps according to token importance and enforces representation consistency, enabling semantically important tokens to exert stronger spatial guidance during generation. This mechanism effectively prevents low semantic-density tokens from dominating the attention allocation, thereby avoiding the dilution of the spatial and distributional guidance provided by high semantic-density tokens. Extensive experiments on multiple benchmark datasets demonstrate that DARE consistently improves generation fidelity and semantic alignment, achieving significant gains over existing approaches.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper flags a real token neglect issue in text-to-video diffusion and offers DARE with DR-CFG plus SRA as a practical patch, but the gains read as incremental and the causal claims need tighter evidence.

read the letter

Hi, the main point is that these authors noticed diffusion models for text-to-video often drop key prompt tokens, which they link to long-tailed frequency bias in the data and cross-attention getting pulled toward less useful tokens. Their DARE framework tries to fix it with DR-CFG, which suppresses dominant low-semantic tokens during training to push the model toward underrepresented cues, and SRA, which reweights attention maps to keep important tokens from being diluted spatially. That combination is what they present as new, and it sits on top of standard classifier-free guidance and attention tricks rather than inventing a fresh paradigm. They do a reasonable job spelling out the two failure modes and showing how the fixes target each one without obvious contradictions in the setup. The reported experiments on benchmarks claim better fidelity and alignment, which is the kind of outcome people care about in this area. The soft spots are that the abstract stays light on numbers, baselines, and ablations, so it is hard to gauge how much each piece actually moves the needle versus just better hyperparameter choices. The story about frequency bias and misalignment driving the neglect is plausible, but the paper would be stronger with more direct checks that the dynamic suppression does not create new artifacts or reduce diversity. No load-bearing circularity shows up in the method description. This is the sort of work that matters to people building or tuning text-conditioned video generators who want prompt adherence fixes they can implement. A reader already working on diffusion attention or guidance mechanisms would get the most out of it and could test the ideas quickly. It is solid enough to deserve a serious referee rather than a desk reject, mainly because it targets a visible practical gap with a clear proposal and some empirical backing. I would recommend sending it for review and asking the authors to add tighter controls and analysis on the mechanism.

Referee Report

1 major / 1 minor

Summary. The manuscript observes that conditional diffusion models for text-to-video generation frequently neglect semantically important tokens, resulting in biased or incomplete outputs under classifier-free guidance. It attributes this to long-tailed token frequency bias in the training data and spatial misalignment in cross-attention maps. To mitigate these issues, the authors propose the Distribution-Aware Rectification and Spatial Ensemble (DARE) framework, comprising Distribution-Rectified Classifier-Free Guidance (DR-CFG) that dynamically suppresses dominant low-semantic-density tokens to encourage balanced conditional distributions, and Spatial Representation Alignment (SRA) that adaptively reweights cross-attention according to token importance while enforcing representation consistency. The paper claims that DARE yields consistent improvements in generation fidelity and semantic alignment across multiple benchmark datasets, outperforming prior approaches.

Significance. If the empirical claims hold under rigorous validation, the work could meaningfully advance practical control in text-conditioned diffusion models by directly targeting token-level biases that degrade semantic fidelity. The dual focus on distributional debiasing during training and spatial reweighting at inference offers a coherent extension of classifier-free guidance techniques, with potential applicability to related conditional generation tasks.

major comments (1)

Abstract: the central empirical claim that DARE 'consistently improves generation fidelity and semantic alignment, achieving significant gains over existing approaches' is asserted without any quantitative metrics, specific baselines, dataset names, or statistical controls, which is load-bearing for assessing whether DR-CFG and SRA deliver the promised benefits.

minor comments (1)

Abstract: the notions of 'low semantic density' and 'high semantic-density' tokens are invoked repeatedly but receive no formal definition or operationalization, which could be clarified to support reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below and agree that revisions to the abstract are warranted to strengthen the presentation of our empirical claims.

read point-by-point responses

Referee: Abstract: the central empirical claim that DARE 'consistently improves generation fidelity and semantic alignment, achieving significant gains over existing approaches' is asserted without any quantitative metrics, specific baselines, dataset names, or statistical controls, which is load-bearing for assessing whether DR-CFG and SRA deliver the promised benefits.

Authors: We agree that the abstract would be strengthened by including concrete quantitative details. The full manuscript reports extensive experiments with specific metrics (e.g., FID, CLIP similarity, and user study scores), baselines (standard classifier-free guidance and prior token-aware methods), and datasets in the experimental section. To address the concern, we will revise the abstract to explicitly reference key quantitative gains, name the primary benchmarks and baselines, and note the consistency of improvements. This change will be incorporated in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation or method definition

full rationale

The paper introduces DARE as a new framework with DR-CFG for distributional debiasing via dynamic token suppression and SRA for spatial reweighting of attention maps. These are presented as novel techniques motivated by observed token neglect from frequency bias and attention misalignment, with claims supported by empirical results on benchmarks. No equations, derivations, or self-referential reductions appear in the abstract or summary. The methods do not reduce by construction to prior fitted quantities, self-citations, or renamed known results; they are described as independent interventions. The central claims rest on external validation rather than internal definitional loops, making the derivation chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract introduces no explicit free parameters, axioms, or invented entities beyond the named method components; all claims rest on empirical observation of token bias.

pith-pipeline@v0.9.0 · 5573 in / 927 out tokens · 33831 ms · 2026-05-10T18:50:20.211231+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel echoes

?

echoes
ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

DR-CFG... dynamically suppressing dominant tokens with low semantic density... mitigating the risk of the model distribution overfitting to tokens with low semantic density... Pw reflects the model’s convergence state regarding the low-importance tokens c′; a smaller Pw indicates a higher degree of fitting to c′. By adjusting the weight of L_fm via Pw, we amplify the contribution of high-importance tokens
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection echoes

?

echoes
ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

SRA... adaptively reweights cross-attention maps according to token importance... preventing low semantic-density tokens from dominating the attention allocation, thereby avoiding the dilution of the spatial and distributional guidance provided by high semantic-density tokens

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

21 extracted references · 18 canonical work pages · 14 internal anchors

[1]

Qwen Technical Report

Bai, J., Bai, S., Chu, Y ., Cui, Z., Dang, K., Deng, X., Fan, Y ., Ge, W., Han, Y ., Huang, F., Hui, B., Ji, L., Li, M., Lin, J., Lin, R., Liu, D., Liu, G., Lu, C., Lu, K., Ma, J., Men, R., Ren, X., Ren, X., Tan, C., Tan, S., Tu, J., Wang, P., Wang, S., Wang, W., Wu, S., Xu, B., Xu, J., Yang, A., Yang, H., Yang, J., Yang, S., Yao, Y ., Yu, B., Yuan, H., Y...

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y ., English, Z., V oleti, V ., Letts, A., et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127,

work page internal anchor Pith review arXiv
[3]

Normalized attention guidance: Universal neg- ative guidance for diffusion model.arXiv preprint arXiv:2505.21179,

Chen, D.-Y ., Bandyopadhyay, H., Zou, K., and Song, Y .-Z. Normalized attention guidance: Universal neg- ative guidance for diffusion model.arXiv preprint arXiv:2505.21179,

work page arXiv
[4]

Seedance 1.0: Exploring the Boundaries of Video Generation Models

Gao, Y ., Guo, H., Hoang, T., Huang, W., Jiang, L., Kong, F., Li, H., Li, J., Li, L., Li, X., et al. Seedance 1.0: Exploring the boundaries of video generation models.arXiv preprint arXiv:2506.09113,

work page internal anchor Pith review arXiv
[5]

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Guo, Y ., Yang, C., Rao, A., Liang, Z., Wang, Y ., Qiao, Y ., Agrawala, M., Lin, D., and Dai, B. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning.arXiv preprint arXiv:2307.04725,

work page internal anchor Pith review arXiv
[6]

Latent Video Diffusion Models for High-Fidelity Long Video Generation

He, Y ., Yang, T., Zhang, Y ., Shan, Y ., and Chen, Q. Latent video diffusion models for high-fidelity video genera- tion with arbitrary lengths. arxiv 2022.arXiv preprint arXiv:2211.13221. Ho, J. and Salimans, T. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598,

work page internal anchor Pith review arXiv 2022
[7]

CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Hong, W., Ding, M., Zheng, W., Liu, X., and Tang, J. Cogvideo: Large-scale pretraining for text-to-video gener- ation via transformers.arXiv preprint arXiv:2205.15868,

work page internal anchor Pith review arXiv
[8]

HunyuanVideo: A Systematic Framework For Large Video Generative Models

Kong, W., Tian, Q., Zhang, Z., Min, R., Dai, Z., Zhou, J., Xiong, J., Li, X., Wu, B., Zhang, J., et al. Hunyuan- video: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603,

work page internal anchor Pith review Pith/arXiv arXiv
[9]

Flow Matching for Generative Modeling

Lipman, Y ., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M. Flow matching for generative modeling.arXiv preprint arXiv:2210.02747,

work page internal anchor Pith review Pith/arXiv arXiv
[10]

Sadat, S., Kansy, M., Hilliges, O., and Weber, R. M. No training, no problem: Rethinking classifier-free guidance for diffusion models.arXiv preprint arXiv:2407.02687,

work page arXiv
[11]

Make-A-Video: Text-to-Video Generation without Text-Video Data

Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., et al. Make-a- video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792,

work page internal anchor Pith review arXiv
[12]

Score-Based Generative Modeling through Stochastic Differential Equations

Song, Y ., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Er- mon, S., and Poole, B. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

work page internal anchor Pith review Pith/arXiv arXiv 2011
[13]

Wan: Open and Advanced Large-Scale Video Generative Models

Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.-W., Chen, D., Yu, F., Zhao, H., Yang, J., et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314,

work page internal anchor Pith review Pith/arXiv arXiv
[14]

Diffusion-npo: Negative preference optimiza- tion for better preference aligned generation of diffusion mod- els.arXiv preprint arXiv:2505.11245, 2025

Wang, F.-Y ., Shui, Y ., Piao, J., Sun, K., and Li, H. Diffusion- npo: Negative preference optimization for better pref- erence aligned generation of diffusion models.arXiv preprint arXiv:2505.11245, 2025a. Wang, J., Yuan, H., Chen, D., Zhang, Y ., Wang, X., and Zhang, S. Modelscope text-to-video technical report. arXiv preprint arXiv:2308.06571,

work page arXiv
[15]

SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Wang, Y ., Chen, X., Ma, X., Zhou, S., Huang, Z., Wang, Y ., Yang, C., He, Y ., Yu, J., Yang, P., et al. Lavie: High- quality video generation with cascaded latent diffusion models.International Journal of Computer Vision, 133 (5):3059–3078, 2025b. Xie, E., Chen, J., Chen, J., Cai, H., Tang, H., Lin, Y ., Zhang, Z., Li, M., Zhu, L., Lu, Y ., et al. Sana: ...

work page internal anchor Pith review arXiv
[16]

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Yang, Z., Teng, J., Zheng, W., Ding, M., Huang, S., Xu, J., Yang, Y ., Hong, W., Zhang, X., Feng, G., et al. Cogvideox: Text-to-video diffusion models with an ex- pert transformer.arXiv preprint arXiv:2408.06072,

work page internal anchor Pith review Pith/arXiv arXiv
[17]

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Yu, S., Kwak, S., Jang, H., Jeong, J., Huang, J., Shin, J., and Xie, S. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:2410.06940,

work page internal anchor Pith review arXiv
[18]

arXiv:2211.11018 , year=

Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y ., and Feng, J. Magicvideo: Efficient video generation with latent diffu- sion models.arXiv preprint arXiv:2211.11018,

work page arXiv
[19]

Compared with traditional manually annotated datasets, it offers much larger scale and richer semantic diversity, covering a wide range of scenarios

is a large-scale web video–text alignment dataset containing over 10 million video–caption pairs. Compared with traditional manually annotated datasets, it offers much larger scale and richer semantic diversity, covering a wide range of scenarios. This makes it well suited for pretraining and fine-tuning models for video generation and cross-modal alignme...

2024
[20]

and EvalCrafter (Liu et al., 2024a) frameworks using self-designed evaluation prompts. B.2. Evaluation Metrics To comprehensively evaluate the video generation capability of our model, we conduct a thorough assessment of the gen- erated results using the EvalCrafter (Liu et al., 2024a) and VBench (Huang et al., 2024

2024
[21]

As shown in Table 3, after introducing DARE, the performance improves across multi- ple evaluation aspects compared with Seedance (Gao et al., 2025)

and Eval- Crafter (Liu et al., 2024a). As shown in Table 3, after introducing DARE, the performance improves across multi- ple evaluation aspects compared with Seedance (Gao et al., 2025). However, the improvement is relatively modest. In contrast, our method achieves significant performance gains on Wan (Wan et al., 2025). This is mainly because existing...

2025