Guidance Matters: Rethinking the Evaluation Pitfall for Text-to-Image Generation
Authors: Dian Xie
Shitong Shao
Lichen Bai
zikai zhou
Bojun Cheng
Shuo Yang
WU JUN
Zeke Xie
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model; Classifier-free Guidance; Text-to-Image Generation
Primary area: generative models
TL;DR: Most diffusion guidance methods are not truly superior to simply increasing the CFG scale.

Abstract: Classifier-free guidance (CFG) has helped diffusion models achieve great conditional generation in various fields. Recently, more diffusion guidance methods have emerged with improved generation quality and human preference. However, can these emerging diffusion guidance methods really achieve solid and significant improvements?  In this paper, we rethink recent progress on diffusion guidance. Our work mainly consists of four contributions. First, we reveal a critical evaluation pitfall that common human preference models exhibit a strong bias towards large guidance scales. Simply increasing the CFG scale can easily improve quantitative evaluation scores due to strong semantic alignment, even if image quality is severely damaged (e.g., oversaturation and artifacts). Second, we introduce a novel guidance-aware evaluation (GA-Eval) framework that employs effective guidance scale calibration to enable fair comparison between current guidance methods and CFG by identifying the effects orthogonal and parallel to CFG effects. Third, motivated by the evaluation pitfall, we design Transcendent Diffusion Guidance (TDG) method that can significantly improve human preference scores in the conventional evaluation framework but actually does not work in practice. Fourth, in extensive experiments, we empirically evaluate recent eight diffusion guidance methods within the conventional evaluation framework and the proposed GA-Eval framework. Notably, simply increasing the CFG scales can compete with most studied diffusion guidance methods, while all methods suffer severely from winning rate degradation over standard CFG. Our work would strongly motivate the community to rethink the evaluation paradigm and future directions of this field.

Published as a conference paper at ICLR 2026
GUIDANCE MATTERS: RETHINKING THE EVALUATION PITFALL FOR TEXT-TO-IMAS GENERATION
Dian Xie Shitong Shao' Lichen Bai'
Zikai Zhou'
Bojun Cheng' Shuo Yang² Jun Wu
Zeke Xie't
'The Hong Kong University of Science and Technology (Guangzhou)
"Harbin Institute of Technology (Shenzhen)
*Cogniser Information Technology
ABSTRACT
Classífier-free guidance (CFG) has helped diffusion models achieve great condi- tional generation in various fields. Recently, more diffusion guidance methods have emerged with improved generation quality and human preference. However, these emerging diffusion guidance methods really achieve solid and significant improvements? In this paper, we rethink recent progress on diffusion guidance. Our work mainly consists of four contributions. First, reveal critical evalu- ation pitfall that common human preference models exhibit a strong bias towards large guidance scales. Simply increasing the CFG scale can easily improve quan- titative evaluation scores due to strong semantic alignment, even if image quality is severely damaged (e-g., oversaturation and artifacts). Second, we introduce a novel guidance-awan aware evaluation (GA-Eval) framework that employs effective guidance scale calibration to enable fair comparison between current guidance methods and CFG by identifying the effects orthogonal and parallel to CFG ef- fects. Third, motivated by the evaluation pitfall, we design Transcendent Diffu- sion Guidance (TDG) method that can significantly improve human preference scores the conventional evaluation framework but actually does not work in practice. Fourth, in extensive experiments, diffusion guidance methods within the conventional evaluation framework and the proposed GA-Eval framework. Notably, simply increasing the CFG scales compete with most studied diffusion guidance methods, while all methods suf- fer severely from winning rate degradation over standard CFG. Our work would strongly motivate the community to rethink the evaluation paradigm and future directions of this field. ]
empirically evaluate recent eight
INTRODUCTION
In recent years, diffusion models (Sohl- (Sohl-Dickstein et al. 2015 Ho et al. 2020 Song et al. 202 bj
Lipman et al.
2023] have become cutting-edge techniques for generating high-quality contents,
including image Nichol et al. 2022 Rombach et al.) 2022 Peebles & Xie 2023 Esser et al.) 2024).
video (Ho et al. 2022 Blattmann al. 2023b a Shao al.) 2025b), and 3D assets (Poole et al.
2023 Yi et al. 2024). The flexibility of diffusion process enabled versatile inference and training
process (Shao et al. 2024 2025c Zhou et al. 2025 Huang et al. 2023). and can be applied to tasks in other domains (Li et al. 2025). A keys to empiniaal success lies on the powerful conditional and
controllable generation ability of diffusion models, empowering rich AIGC applications.
Classifier-free guidance (CFG) (Ho & Salimans 2021) is exactly very popular and powerful guid- ance method that enables direct control over the denoising process with text prompts. Recent stud- ies (Hong et a 2023) [Hong] 2024 Ahn et al.) 2024 (Si et al., 2024, Chung et al. [025] Bai et al.) 2025a Shao et al. 2025a Zhou et al., 2025) have proposed several diffusion sampling or guidance methods improve conditional generation quality. Meanwhile, in those studies, the evaluation of text-to-image generation starts to mainly depend on recent human preference models, such as HPS
Correspondence zekexie @ hwust-gg..ccc a..us--gz.edu.cn.

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Published as a conference paper at ICLR 2026
Prompt: "An astronaut riding horse."
HPS v2:
31.4546
v2: 32,2331
HPS v2: 32.5681
HPS v2: 32,5991
Figure 1: The HPS v2 scores of generated images under different CFG scales {5.5, 10, 15, 15, 20), respectively. Model: Stable Diffusion-XI.. HPSv2 exhibit a strong bias to large CFG scales a wide range, even if generation quality starts to degrade due strong guidance.
v2 (Wu et al,, 2023), PickScore (Kirstain et al. 2023). and ImageReward (Xu et al. 2024). which considered be superior conventional evaluation metries. This commonly true in human preference datasets.
However, researcher overlooked that human often prefer images with gorgeous colors (Lin al. 2024), which are correlated to highly saturated images generated with a large guidance scale. This overlooked phenomenon has led to serious evaluation pitfall that human preference models would give better ratings on images generated with relatively large guidance scale, as illustrated in Fig We present several qualitative cases in Fig is easy for CFG to achieve comparable better performance to many methods by simply increasing the guidance scale.
We raise key question: may recent advanced diffusion guidance methods overfit the overlooked trge--gidaace bias in human preference models? In this work, to validate how much the recent advanced methods' performance is biased by the evaluation pitfall, we try to disentangle their effects from large CFG effects by calculating the effective guidance scale and then comparing the winning rates between a method and its effective CFG seale. By evaluating these methods with CFG under the same effective guidance scales, discovered that most methods mainly exploited large guidance scale to achieve high performance, their winning rates dropped considerably compared with effective CFG.
avoid such overlooked evaluation pitfall. We surprisingly
Our contributions can be summarized as follows:
First, we reveal a critical evaluation pitfall that common human preference models, such HPSv2 and IewgeReward, exhibit strong bias towards large guidance scales. As Fig shows, simply increasing the CFG scale can easily improve quantitative evaluation scores due to strong semantic alignment, even if image quality is severely damaged (e.g., oversat- uration and artifacts).
Second, introduce novel guidance-aware evaluation (GA-Eval) framework that em- ploys effective guidance scale calibration to enable fair comparison between current guid- ance methods and CFG by identifying the effects orthogonal and parallel to CFG effects.
Third, motivated by the evaluation pitfall, we easily design Transcendent Diffusion Guid- ance (TDG) method that imitate the creation of weak condition in other methods during the sampling process. This is exactly an example method that can significantly improve human preference scores in the conventional evaluation framework but cannot actually improve generation quality over CFG.
Fourth, in extensive experiments, we empirically evaluated eight diffusion guidance meth- ods within the conventional evaluation framework and the proposed GA-Eval framework. Notably, simply increasing the CFG scales can compete with most studied diffusion guid- ance methods, while all methods suffer severely from winning rate degradation over CFG.
2

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Prompt: "Anine partralt besutiul vusirr wiich, sck-l zul. detuiled
Prompt: hugicio afa boar
Z- 2-Simpling
My 9.51
CRG++
Prompt: "A kangarso orange foodle and songlasees mands on grass to fromr of Sodwy Opera House with Vandonee Mistorr" vipe.
Prompt: Mln hird and a brown hoar.
5.5
e-CIC
PAG
CPG
5.5
CCF
6.88
SRC
Prompt: fontary portrelt person partrqued civettttc destopuon brotaikrr armasphere.
background
Procupt: dog."
CPG 5.5
CFG
12.16
SAG
CFG IF 5,5
CEG o4 8.69
FreeU
Prompt: "A Burger King Mor."
Prempt: bender rocker planet with lot ofcornuses
CBG
CFG Me 8.49
TDG
CFG
14.00
APG
Figure 2: Visual comparison of different methods with their corresponding results under effective guidance scale w The e-CFG method, namely standard CFG with effective CFG scales calibrated by GA-Eval, can easily achieve performance improvement comparable to most recent diffusion guidance or sampling methods.
RELATED WORK
2.1 DIFFUSION MODEL
Diffusion model (Sohl-Dickstein al., 2015) generate high-quality samples by gradually adding noise to the data and learning the inverse process. Denoising diffusion probabilistic models (DDPM) (Ho et al. 2020) applies this process to image generation. In the reverse process, a pure Gaussian noise was gradually transformed to original data:
1 a2
3,
X
X;
(x(,t) + o:z,
(1)
VI
- at
where z ~ (O, I), B, = Otz, and ea is the noise prediction network. For simplicity, we will ignore the argument in (x(x,,)) in the following paragraphs.
To improve sampling efficiency. denoising diffusion implicit models (DDIM) Song et al. 2021a non-Markovian sampling process that reduces the number of inference steps while maintaining generation quality. The reverse process can be written as:
X; OiCo (xx)
Xx-1 Va--
+ Oc-1E0 (xi),
(2)
3

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Published as a conference paper at ICLR 2026
where or v1 ay.
2.2 DIFFUSION GUIDANCE AND SAMPLING
Classifier-free guidance (CFG) (Ho & Salimans 2021 interpolates conditional output and uncon- ditional (or negative conditional) output of diffusion model, align the generated image to the corre- sponding condition. Given text prompt c, CFG use same network ea(-) to predict the unconditional noise of eincond ca (x,, 2). and the conditional noise ci <oond co(X,, c). The updated noise in each denoising step was the interpolation between them under the guidance scale w:
& = C,
.ueondd + w (ccond
Ci
uncoodd)
(3)
Self-Attention Guidance (SAG) (Hong et al. 2023), Perturbed-Attention Guidance (PAG) (Ahn
et al. 2024), and Smoothed Energy Guidance (SEG) (Hong] 2024) uses blurred attention-man map or
perturbed attention-map create weak condidion term in CFG. CFG++ (Chung et al. (025) aiming
at tackling the off-manifold challenges inherent in traditional CFG. They reformulate text-guidance
inverse problem with a text-conditioned score matching loss. Z-Sampling and W2SD (Bai) itt al., 2025a by use the guidance gap between denoising and inversion to inject semantic information during sampling process. FreeU (Si al., 2024) amplified specific features in U-Net (Ronneberger it al. 2015) to improve performance for free. Although these methods have satisfying performance on current benchmarks, it can also achieved by enlarging the guidance scale. Kynkäänniemi et al. (2024) demonstrated that using CFG only when the noise intensity is within a certain range yields better results. Karras et al. (2024) used a degraded version of diffusion model to guide a complete one. But it is difficult to find a degraded model of pretrained model. Adaptive Project Guidance (APG) (Sadtt et al. 2025) project the latent parallel to the latent updated by conditional noise, which successfully eliminated settssaturttion under large guidance scale. Nevertheless, it faces discrimi- nation from many metrics, as they prefer images of
2.3 EVALUATION FOR TEXT-TO-IMAGE GENERATION
Traditional metrics for evaluating image quality include Inception Score (IS) (Salimans al. 2016)
and Fréchet Inception Distance (FID) (Heusel et al. 2017), but they cannot assess text-image align- ment. Meanwhile, Jayasumana al. (2024) have pointed out that FID and IS struggles represent the rich content in modern image-to-text generation. The simplest way to evaluate images gener- ated from prompt is to use CL.IP (Radford et al., 202T) to calculate text-image similarity, while cannot reflect human preference. To address this prohlem, reward models are introduced to serve as metrics for human preference (Liu et al. (024). HPS v2
(Wu et al. 2023). ImageReward (Xu iet al. 2024), PickScore (Kirstain al. 2023), MPS (Zhang et al. 2024), and Social Reward (Isa- janyan et al. 2024) are evaluation metrics fine-tuned on
data with reinforcement learning. These metrics are better aligned with human preferences, and have been widely used in benchmarking T2I models. Nevertheless, people naturally prefer colorful images, evaluation met- rics based human preferences tend give higher scores pictures with bright colors. Therefore, these metrics are easily blinded by images generated with a large guidance scale, as they also hold the same property of nigh-saturation. To the best of our knowledge, such evaluation pitfall have never been discussed before.
3 GUIDANCE MATTERS TO EVALUATION
In this section, we first demonstrate that the performance improvements brought by many guidance methods are largly exaggerated and similar to large CFG, and then propose the GA-Eval framework for more fair and reasonable evaluation.
Bias of Large Guidance Scale. We will present prevalent existence of the evaluation bias this subsection. As some literatures (Bai et al. 2025a Sadat et al., 2025) mentioned, diffusion models tend to generate oatrrsaturated images under a large guidance scale, which will seriously affects the aesthetic quality of the image. Nevertheless, a d noise ? to the direction of conditional noise ef in every timestep, thus making the generated image more aligned to the prompt.
guidance scale would amplify the predicted
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HPS
ImageReward
AES
PickScore
CLIPScore
21.7
29.3
28.4
29.2
0.05
3.975
202
5.950
21.5
28.9
0.80
5.925
28.9
21.4
0.75
5.000
5.875
21.3
20.7
20
1.
20
10
Scale
Guisance
Scale
Guidonoc State
Different evaluation metrics under different guidance scales, Model: Stable Diffusion-XLL Dataset: Pick-a-Pic. Except AES and PickScore, other metrics would give higher ratings images generated with a larger guidance scale within w [5.5220]
Figure 3:
However, this phenomenon could bias many evaluation metrics based on CLIP (Radford et al. 202T], as the CLIP model assumes the image is well-aligned the prompt in this case. Moreover, for metrics fine-tuned on human-preed guman-preferenced data, the overlap between the training data and over- saturated results making these metrics easily biased by large guidance scale. We give case studies and quantitative results Fig I Fig 2 and Fig 3 Most of T2I evaluation metrics would prefer images generated with a large guidance scale. Such bias may lead to results that do not reflect the real performance of other guidance methods, since can simply increasing the guidance scale to acquire comparable or better performance on these metrics.
be achieved by increasing the guid- ance scale in CFG, we need a method to verify that whether method's ' performance can be achieved by large guidance scale. Since this phenomenon was closely related guidance scale, we can de- couple the CFG component from other methods to calculate the effective guidance scale w², thus verifying whether they exploit large guidance scale achieve better performance.
Effective Guidance Scale. Because better performance
For naive CFG in Eq 3 conditional noise et conud and the unconditional noise ef The guidance scale w denoted as:
the updated noise in every timestep E, is the linear combination of the unccond
& euncoodd
? -cenoondd
cond 7
-
uncond
Ae
For other guidance methods, their noise updated in every timestep è7 can be decomposed to euncoond and two other components about Ae:
c, c + ef + +
uncond
(5)
where + is the orthogonal component of é; - euncond in the direction of Ae, and is the parallel uncoud in the direction Ae. Since any vector can be decomposed into vectors
component of हŒ Et
orthogonal to each other.
According to the vector projection method, the orthogonal component &f can calculated by:
( E Projoc (εt
uncond)
uncond)
Es
E
(c C
uncond Ac)
(6)
(ह) et
umoond)
Ael12 Ac.
Therefore, the parallel component e is given by:
d! ( uncond Ac)
(ec Ac.
We define the effective guidance scale at timestep t is the ratio of the amplitude of the parallel component e, a to the amplitude the guiding direction Ae.
E uncond + w. A E
(8)
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Published as a conference paper at ICLR 2026
Thus, the effective guidance scale can be acquired by:
||c||]
|
where ||
represents the mo-nm.
However, some guidance methods [Bai 2025a Chung et al. 2025) modified the sampling
process during the update of the latent x,, rather than explicilly modifying the predicted noise &
But these two approaches are interchangeable. For instance, in the case of DDIM from Eq 2
we can
simply reformulate the process as follows:
VatXt-1 V ax-1x Ixe
? Van V Vat-18.
(10)
Therefore, we can acquire è from the original latent x, and the sampled latent X,-1. Note that the specific formulation of Eg I0 depends on the scheduler used by corresponding guidance method. Additionally, since w may vary across different timesteps f, we use a fixed effective guidance seale for each timestep by averaging along the sampling path: w E. Wg
Guidance-aware Evaluation. In this part, we introduce the GA-Eval framework that empirically evaluate diffusion guidance methods with effective guidance scales.
Although such bias could leads to an evaluation pitfall, we can use the bias itself to give a fair com- parison with effective guidance scale. To determine whether method's s performance is exaggerated, we can measure how much the winning rate degrades after applying our settings to original CFG.
Given prompt set C = {c, the images generated from each guidance method, CFG, and CFG with effective guidance scale denoted as ; X X.-CFG ) where they were generated with corresponding prompt c; E C. Denote binary comparison functions follows:
[1 M(X,c,) > M(x.Fo.c)
0
otherwise,
(1I)
1 M(X;,,,) > M(X..CFG .c),,
CFG
0
otherwise,
where M is an evaluation metric, indicates the rating better on the left side. The corresponding winning rate is given by:
N
CPG 17
CHG
1/CCPG
CFG
(12)
N
N
By denoting the degradation of the winning rate, An = 1y CR of CFG we can quantify whether a method just exploits a large guidance scale to achieve high performance.
4 TRANSCENDENT DIFFUSION GUIDANCE
In this section, we show that, motivated by the overlooked evaluation pitfall, we can easily design an effective TDG method that significantly increases the popular human preference metries in the conventional evaluation framework but exposures the pitfall in the proposed GA-Eval framework.
Numerous recent works have demonstrated that use weak conditions or models can improve the generation performance of diffusion models, without any additional training or input. Like under- trained model (Karras et al. 2024), perturbed attention map Ahn et al. (2024); ) Hong et al. (2023).
skipped layers (Hyung al. 2025 Chen et al.) 2025), etc.
Motivated by these recent works, we propose TDG to imitate the creation of weak condition. Specif- ically, TDG randomly replace tokens in text prompt with empty token Ø, thus creating weakened text prompt c* Then use the noise prediction network (+(-) to acquire the weak conditional score Eweak. which combined with Econd and cuncond in CFG to the diffusion sampling process. We illus- trate TDG in Fig 4 and present the pseudo-code in Algorithm I
Due to page limits, we formally
introduce TDG with more details and results in Appendix

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Transcendent Diffusion Guidancee
Clossifier-free Guidance
Copy
X
X
cx(-)
co(-)
cx(+)
cx
c+(-)
co( Ø)
c e*)
ca(x,, c)
co(x;, 2)
co (x(x)
&
E
X
X
Figure 4: Comparison of classifier-free guidance Ho & Salimans (2021) and transcendent diffusion guidance (TDG).
EMPIRICAL ANALYSIS AND DISCUSSION
In this section, we empirically study how recent diffusion guidance methods perform in conventional text-to-image evaluation framework and our GA-Eval framework.
5.1 EXPERIMENTAL. SETTINGS
Models:
We choose Stable Diffusion-XI XL (Podell ct al.) 2024), Stable Diffusion 2.1 (Rombach al.
2022]. Stable Diffusion 3.5 (Esser et af. 2024). and DiT-X1/2 (Peebles & Xie 2023) for image generation. We
5.5, T 50 for SD-XL and SD-2.11, 4.5, T 32 for SD-3.5, and
= 4, 50 for DiT-XL/2.
Datasets: We the first 100 prompts from Pick-a-Pic dataset (Kirstain al. 2023), 200 prompts from DrawBench dataset (Saharia et al.] 2022). Human Preferenced Dataset (HPD) [Wu et al. (023) with 3200 prompts, and GenEval dataset (Ghosh al., (024) with 553 prompts. We also bench- marked some methods on COCO-30K (Lin et al. 2014). And ImageNet-50K, (Deng et al. 2009) for the evaluation class-to-image generation.
Evaluation Metrics: We use HPS v2 Wu et al. 2023), Aesthetics-Predicto (AES) (Schuhmann al. 2022). PickScore (Kirstain et 2023), ImageReward (Xu et al. 2024). and CLIPScore (Rad- ford et al., (20T)) and Fréchet Inception Distance (FID) (Heusel et al. 2017), Inception Score
(IS) (Barratt Sharma 2018 Salimans et al. 2016) for COCO-30K or ImageNet-50K.
Baselines: We select following guidance methods for comparison: Z-Sampling (Bii et al. 2025a), CFG++ (Chung al. 2025). Perturbed-Attentioonn Guidance (PAG) (Ahn 2024), Self Attention Guidance (SAG) (Hong al. (2023), Semoothed-Energee Guidance (SEG) (Hong (024), FreeU (Si ct al. 2024), Adaptive Project Guidance (APG) (Sadat et al.) 2025), and Transcendent Diffusion Guidance (TDG) proposed in See The detailed hyper- paraeeerraeeters of each method can be found at
Sec B
5.2 MAIN RESULTS
According to GA-Eval, almost all methods exaggerated their real performance which cannot be reflected in conventional evaluation paradigm. We present the results of eight diffusion guidance methods for SD-XL Table [ We also give an illustration of winning rate and its degradation on HPD dataset in Fig 5 Except APG that cannot compete with standard CFG, most methods

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Wint compore
O644
585
Z-Aampinng
016
565
.00
ypt
Stapping
Figure 5: The winning rate CP compared to effective CFG and their degradation An of different methods on HPD dataset. Left: ypcCRG Right: An.. Among all methods, applying w has minor influence to APG. Meanwhile, AES demonstrated negative An on many methods, which means AES would give worse ratings to images generated with a large guidance scale.
Table 1: The winning rates n/ cPG Cpc-CF and its degradation An of different methods on various datasets. Model: Stable DiL (w = 5.5). We sort these methods based on average C/C labeling those greater than 55% in blue and the others in red. Most of methods have w > w, and suffering from severe winning rate degradation. Only recent Z-Sampling can still maintain high winning rates xfc- after setting CFG scale as w", while some methods even cannot compete with effective CFG.
Method
HP'
ImageReward
AES
PickScore
CLIPScore
Average !)
Pick-a-Pic
(1) TT0 /1y" CR3 /An)
(1 (x700
Sampling
90% / 74%
16%
75% / 63%
12%
65% / 76% f-11%
72%
73%
55%
55%
13.51
CFG++
82% / 68%
14%
70% / 58%
12%
39% / 53% / 1-14%
56%
/ 57%
1%
60% 5.4% 6%
61% / 58%
8.91
75% 59%
16%
45% 34%
11%
666
-2%
62%
56%
6%
444% 99%
52%
8.14
TDG
62% 48% / 14%
61% / 46%
15%
49%
/ 42%
/ 77%
51%
51%
0%
63% 53% 10%
57%
/ 48%
8.27
SEG
60% 56% / 4%
59% / 49%
10%
57%
56%
/ 1%
35%
339%
2%
47% 4.4% 5%
52%
47%
6.10
PAG
61%%
13%
51% 40%
11%
55%
50%
5%
45%
42% 3.
49% (44% 5%5
52%
45%
5.98
FreeU
52% / 42%
1066
56% / 44%
12%
53% / 55%
--2%
27% 25%
2%%
42%
/ 33% 99%
46% / 400%
7.47
20% 0%%
38% 291
9%
5%
0%
46%
38
15.05
DrawBench
(y /f AAa)
Z-Sampling
90% / 68% 22%
76%
59%
17%
64% 4%
72% / 60% 12%
62% 50%
12%
74%
60%
11.94
PAG
61% / 60% / 1%
54%
50%
5%
60% 64%
-4%
49% 44%
4%
42% 46%
44%
53% 53%
6.04
TDG
67%- 50% 17%
64%
56%
8%
48% 50%
-2%
56%
51%
5%
54%
1%
58%
52%
8.24
SAG
73% J 60%
12%
58% / 43% / 15%
64% 66% -2%
53%
52%
2%
46% / 400 66
59%
52%
7.42
CFG+
77% J 60%
18%
64% 555 9.
56% 53%
3.%
52% | 42%
10%
55% 46%%
99%
61% 511%
8.89
SEG
64% (69%) / 44%
50%
/ 550% / 1.%
68% 6666
2%
40% 38%
25k
38% 40%
-2%
52%
/51
6.13
FreeU
48% / 37%
11%
54% / /
64% 62% / 2%
25%
22% 2%
/45 1% 46%
47% / 43%
7.44
APG
22% / 21% / 2%
1%
1%%
44% 1%
48% 48%
39%
13.56
HPD
(y) (y CRR
Sammllin
91% / 72% 20%
74%
60%
59% 67%
88%
65%% 5%
55% / 45% 10%
6.2%
12.41
CRG++
83% / 66%
166
67% / 57%
10%
44% 48%
.4%
51.% 43/% 9%
61% / 52% 99
61% 531%
9.08
54% 42%9
11%
64%
33%
50%
55
433%
5%
7.36
PAG
68% / 65% / 44
51%% 49%
3%
57%
58%
1-1%
44%
43%
1%
42% / 40%
2%
53% 51%
5.96
SBG
70% 66% / 5%
54%
50%
3%
57%
58%
( 1-1%
37%
34%
3%
40% 38/.
22%
52%
/ 49%
6.07
TDG
67% 47% 20%
61%
51%
10%
42%
47% 1-5%
55%
50% 5%
55%
(47% 88%
56%
48%
8.31
FreeU
63% / 50% / 12%
55% / 50%
6%
51%
/ 55% 44%
26%
211% 15%
38% 34% 44%
47%
/ 42%
7.38
50% 52%
-2%
13.29
suffer from significant winning rate degradations over effective CFG in GA-Eval. The HPS v2 degradation in CFG++ SAG, TDG, and Z-Sampling even greater than 15%. Most of baselines, except Z-Sampling, have the averaged of CCG near or worse than 50%. In the experiment of SD-2.1, I, Table Sec 4 our GA-Eval also reveals the prevalently overlooked evaluation pitfall. We also use GenEval to evaluate the fine-grained property and semantic alignment. In Table most methods are worse than effective CFG in terms of the average GenEval scores.
Despite suffering winning rate degradations, some methods still maintained a considerable winning rate after applying w In Table [ the winning rate of HPS v2 of Z- Z-Sampling is approximately
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Table 2: The quantitative results of different methods on GenEval. Model: Stable Diffusion-XL (w = 5.5). We labeled methods surpassed effective CFG with blue, and others with red. Increasing the CFG scale can also significantly increase the GenEval scores.
Method
Single abject (t)
Two object (1)
Countin (?)
Culors (1)
Position (?)
Culor altribution (f)
Overall (?)
CFG
98.91%
75.63%
37.66%
85.11%
10.87%
26.88%
55.84%
100.00%
76.77%
53.75%
87.23%
11.00%
21.00%
58.291%
8.28
e-CFG
100.00%
73.74%
47.50%
86. 16%
9.00%
27.00%
57.23%
Z-Sampling e-CFG
100.00%
75.76%
46.25%
84.04%
17.00%
20.00%
57.18%
16.05
98.75%
77.78%
36.25%
85.11%
16.00%
25.00%
56.48%
CFG++ e e-CFG
100.00%
81.82%
46.25%
85.11%
16.00%
20.00%
58.20%
8.81
100.00%
76.77%
48.75%
87.23%
12.00%
24.00%
58.13%
FreeU c-CFG
98.75%
74.75%
41.25%
76.60%
9.00%
25.00%
54.22%
7.62
100.00%
76.77%
48.75%
88.30%
12.00%
25.00%
58.47%
PAG 93.75%
69,70%
37.50%
82,98%
9.00%
26.00%
53.15%
c-CFG
100.00%
76.77%
42.50%
85.11%
9.00%
28.00%
56.90%
6.13
SAG
97.50%
75.76%
46.25%
86.17%
9.00%
25.00%
56.61%
7,36
CFG
100,00%k
78.79%
43.75%
86. 17%
10.00%
26.00%
57.45%
SEG
97.50%
71.72%
41.25%
81.91%
9,00%
18. 18.00%
53.23%
6.19
e-CFG
98.75%
77.78%
41.25%
84.04%
9.00%
26.00%
56.14%
APG
98.75%
70.71%
40.00%
87.23%
12.00%
27.00%
55.95%
14.12
e-CFG
100,00%
78.79%%
46.25%
89.36%
12.00%
31.00%
59.57%
70%, where the averaged winning rate is greater than 60% across different datasets. CFG++ also has certain but lower superiority.
Meanwhile, many metrics obviously biased by large guidance scale. Given w w, most metrics have An > 0, which means they raised ratings to images from effective CFG with large guidance scale. Among these metrics, only AES have reversed results. GenEval also demonstrated the evalu- ation pitfall brought by large guidance scales.
Besides, we conduct experiments COCO-30K and ImageNet-50K with Z-Sampling and CFG++. Due to page limit, the results are presented in Table g and Table iin See D After applying w", the performance of e-CFG are getting closer to the corresponding method. However, they are still worse than the original CFG in FID and IS.
5.3 DISCUSSION AND ANALYSIS
The results in various metrics in Table with our observations in Fig HPSv2, ImageReward, and PickScore are largely affected by large guidance scales. This might be because they were trained on numan-prefereneee data with high saturated images well aligned to the prompt, which very similar to images generated with large guidance scale. For CLIPScore, its semantic alignment may also contribute to such bias, as large guidance seales naturally improve image-prompt alignment. In GenEval, large guidance scale also contributes the increased seman- tie correctness of object-level properties. Although most metrics are biased, AES provides a fair evaluation, since it only evaluates images. However, this also makes it unable evaluate prompt- following abilities in generated images. The AIGC community eagerly needs a human preference model robust large CFG.
Evaluation Metries.
Since most evaluation metrics are biased, CFG with larger guidance scale of course bring severe winning rate degradations these studied methods. In principle. many meth- ods implicitly amplified their effective guidance scale. Z-Sampling uses a normal guidance scale in denoising, and a smaller guidance scale in inversion. The guidance gap between denoising and in- version indirectly amplified the guidance scale. For methods like PAG, SAG, SEG, and TDG, which created an additional weak condition term to the updated noise in each timestep, the difference of conditional noise and weak conditional noise is parallel to A with significant portion, which result a larger w The formulation of CFG++ also increased the guidance scale compared to original CFG. Notably, APG holds very low winning rate before and after apply wh, with only AES maintained normal. It is because APG alleviate over-saturation in generated images, other biased metrics would give worse evaluation ratings. So these metrics cannot reflect the real performance of APG. APG
Guidance Methods.

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did not exploit large guidance scale, although it explicitly uses large CFG scales w 15 for SD-XL and w = 10 for SD-21.
For methods maintained effectiveness compared to effeective CFG, like Z-Sampling and CFG++ This indicates that the performance improvement of these methods not solely achieved by in- creasing the guidance scale. They indeed have some effective components orthogonal to CFG. Meanwhile, their FID and IS also indicate that their distribution are shifted away from ground-truth compared CFG, since the later was trained on ground-truth images with fixed guidance scale.
Diffusion Models. Not limited to results of SD-XL in Table T we have conducted experiments on other models as well. The results of SD-2.1 and SD-3.5 presented in Table 4 and Table in Sec D In SD-2.1, I, for CFG++ and Z-Sampling, their winning rate degradations are even larger than SD-XL (above 20%). For simple models like SD-2.1, is quite casier rectify the sampling trajectory to improve the generation quality, although they often collapse to a larger guidance scale. For SD-3.5, its generation capability is already very powerful, which limits the improvements due to guidance methods.
CONCLUSION
In this work, we report a critical but overlooked evaluation pitfall hidden in recent advances in diffusion sampling and its evaluation for text-to-image generation. These pitfalls severely dam- age development of this line of research. As human preference models exhibit strong bias to large guidance scales, are the first to reveal that most recent diffusion guidance methods even cannot outperform standard CFG with large guidance scales. To more properly evaluate diffusion guidance methods, we propose novel GA-Eval framework that disentangles the effects orthogonal and paral- lel to the CFG effect. Motivated by the findings, we also propose TDG to have improved evaluation scores in the conventional evaluation framework. Through comprehensive experiments in the GA- Eval framework, we expose that most claimed improvements, including TDG, essentially mirror the effects of simply increasing the CFG scale, with all methods showing inflated performance metrics under proper evaluation. We believe that this work serves as crucial wake-up call for the commu- nity to rethink the evaluation paradigm for AIGC tasks and more properly recognize true innovation in diffusion models.
ACKNOWLEDGMENTS
This work was supported by the Science and Technology Bureau of Nansha District Under Key Field Science and Technology Plan Program No. 20247D002 and Dream Set Off - Kunpeng and Ascend Seed Program.
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A LLM USAGE
In this paper, no Large Language Model (LLM) was employed as a research or writing tool. All research conception, data analysis, writing, and editing were independently accomplished by the author.
B EXPERIMENTAL. SETTINGS
All our experiments were conducted on NVIDIA GeForce RTX 4090 GPUs with 24GB VRAM.
guidance scale 5.5 and inference step T 50. For Z-amplinng, inversion guidance scale Winv = 0, optimization steps À = T. For PAG, we use PAG scale = 3, and apply PAG layer "mid" of the backhone of SI-XXL. For CFG++
In SD-XL,
guidance scale w = 0.4. For TDG, we use = 1.8 and B = 2.6. For APG, we use guidance scale w = 15. For FreeU, we use 61 = 1.3, b2 1.4, 81 0.9, and s2 = 0.2. For SAG, we use SAG scale 0.75. For SEG, we use SEG scale "seg 3, standard deviation of Gauusian blur o = 100, and apply on layer "mid" of the backbone of SD-XL.
In SD-2.1, we use guidance scale = 5.5 and inference step T = 50. For Z-Sampling, we use inversion guidance scale Whav = optimization steps T. For PAG, we use PAG scale 3, and apply PAG on layer "mid" of the backbone of SD-XL. For CFG++, we use guidance scale 0.4. For FreeU, we use b 1.4, b2 1.6, 81 = 0.9, and 82 0.2. For SAG, we use SAG scale 0.75.
In SD-3.5, we use b-t NormalFloat quantization to run on RTX-4090, with guidance scale w 4.5 and inference step T = 32. For Z-Sampling, we use inversion guidance scale Winv = O, optimization steps = T. For PAG, we use PAG scale = 3, and apply PAG on layer "13" the backbone of SD-3.5. For TDG, use 9 1.6 and / 2.4.
In DiT-XL/2, we use guidance scale w = and timestep T 50. For Z-Sampling, we use inversion guidance scale Winv = 0, optimization steps T.. For CFG++, use guidance scale w 0.5.
C TRANSCEDENT DIFFUSION GUIDANCE
In this section, we will present limitations of previous guidance techniques. After that, we will introduce the proposed method transcendent diffusion guidance (TDG), and illustrate how can we improve the sampling process of diffusion models by enlarging search space.
C.I MOTIVATION
Previous improvements to CFG have been limited to the guidance scale w or use self-defined func- tions to substitute Cuncond Coond- However, these methods are limited to the operation in Eq interpolation between the two outputs of the diffusion model.
To solve this problem, we need expand the search space of the sampling process. Obviously, due to the search space of CFG restricted on a line, we only need to have a point outside of this line to obtain the whole hyperplane as the search space. Besides, the point should be acquired through the text prompt e without introducing any additional inputs.
Driven by the analysis above, we propose transcendent diffusion guidance to improve the sampling process of diffusion models, which illustrated in Fig. 4 Specifically, TDG corrupts the text prompt create a weakened text prompt c*. Then use the noise prediction network c0(-) to acquire the
weak conditional score Éweak- Combining with fcond and funcond in CFG, we can expand the search space to the entire hyperplane. During the sampling process, larger search space will accelerate the convergence of latent towards the optimal solution, thereby improving the generation quality of the model.
C.2 WEAK CONDITION
In this subsection, will provide detailed explanation how obtain weak condition.
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Algorithm The Sampling of Transcendent Diffusion Guidance
Input:
text condition c, guidance scale factor g. balance scale factor 3, random replace ratio À c" random_replace((,,
XT I)
for t in T,T -,,,,,.. ....... do
Lnnondd (x( 2)
gond = 60 (x,, c)
weak c0 (2,, c*)
& (c + Gr ((toak)
+
w.p.B (crond eunoond) +
wig (ccond Ø+1
-c c.raak)
9-1
wook
x;-1 N (N (x: vI-arE)) Ei), Ex)
v1-aa
end for return Xo
As mentioned in the main text, many works demonstrated that introduce an additional weak model or weak condition could improve the generation performance. Meanwhile, they should avoid in- troducing any additional input and be computationally efficient. Therefore, we obtain the weak condition outside of CFG by modifying the text prompt. Specifically, consider a text prompt [c1, C2, Cn). where is the i-th token of c. We randomly choose some tokens with in-
dex E I, and replace them with empty token 2::
= 10. iEi cs I
(13)
By combining these new tokens together, we obtained the weakened prompt c* = [ct c. In practice, |Z] 5. which means we randomly replace half tokens in with On the one hand, the modified prompt only requires the original prompt to be obtained without introducing additional input. On the other hand, to obtain c², only needs be calculated at the beginning, which is computationally efficient compared to those methods need to manipulating attention maps at every denoising step.
Therefore, the weak conditional score is given by:
weak = < c*).
(14)
C.3 AL.GORITHM
After obtaining the weak condition. we can construct the hyperplane through an extra guidance term. Compared to CFG in 3. TDG reformulated the diffusion score in each denoising step with following add-on:
E
(
uncond
+ cl
wook) +
w. 9...3
3+1 (c cond
uncond
I (oond
uncond
(15)
cond mooak
3
weak
where g and | are guidance scale factor and balance scale factor, respectively. For noise prediction network ca(-), the linear combination of conditional score e goond the weak conditional score e weak and the unconditional score emmoond will create three non-collinear points, thus creating a h While the original CFG only limits its search space the combination of gonnd and et e.hnoond which straight line. Therefore, the model can have a hyperplane as the search space, which transcend the restrictions of the original search space of CFG. We give brief illustration Fig g And the hyperparameter grid search result was illustrated in Fig 9
The pseudo-code is provided Alg Fig 4 also briefly illustrates the differences between TDG and CFG. Qualitative comparison results are presented in Fig 6 The hyperparameters of TDG are determined through grid search, and the criteria is HPSv2 winning rate compared to original CFG. give analysis the number of inference step T' to demonstrate the superiority of TDG. In Fig Z performance on CFG with T = 20, and TDG always maintain better performance than CFG when
The results be found in Fig [ Meanwhile,
TDG with T 16 achieved comparable
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SrableDiffusion-355
StableDiffusion-XL
womon, pointed by von Gryh. storry right.
Point of a becutiful
keyboard mode water the water reflects light
Photo of megn Lege space stotion inside aid a bedroom.
A dog sits front of and watches the Telexision
Two men and one womon
stonding in kitchen.
created and rg duck beaks googly
banare ducks,
Coino
Tad
CFO
Figure 6: The qualitative results of TDG.
HPS
TDG CFG
28
27
25
24
10
20
30
40
50
Timestep
Figure 7: Comparison of HPS v2 Score of TDG and CFG at different timesteps. Model: SD-3.5.
T> 16. We also present ablation results of TDG in Fig 10 There is no significant performance degradation when mask ratio smaller than 0.6.
D SUPPLEMENTARY EMPIRICAL RESULTS
We give more of HPS v2 scores increasing with in Fig 11 the phenomenon be verified across different prompts. and has further verified in Fig 3 For simplicity, we give the quantitative performance of SD-XL, SD-2.1, and SD-3.5 are given in Table 5 Table and Table 7 respectively. Specifically, the winning rates and the degradation of different methods on SD-3.5 IS given in Ta- 3le The results are consistent with our findings in the maintext. More cases of visual comparison between other methods and their corresponding effective CFG are presented below.
In addition. we recent reward models and benchmark, like HPSv3 (Ma et al., 2025 Uni- fiedReward (Wang et al.) 2025). and OnelG-Bench (Chang et al.) 2025). to valid their robustness on the evaluation pitfall. And the results are presented Table evaluation pitfall, with most methods suffer winning rate degradation. Mcanwhile, due to OnelG- Bench require to use theirs prompts to evaluate, we only conduct experiments on SD-XL with dif- ferent guidance scales. The Alignment score, Reasoning score, and Text guidance scale. But Diversity score decreases on the contrary, this also meet the discovery in Karras al. 2024).
and 12 HPSv3 still hold this
increasing with
To further demonstrate that human-urrrrrnnn prefernceeee metrics favor saturated images, we give 2 toy exper- iments images with different saturation: 1) Real images with different saturation; 2) Generated
17

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Searhh spaceo CRG
CRC
F,
TDG Afooe
Soith spreef TTG
X--1
M
Cancoud
Evrek
M
X
Figure 8: TDG vs. CFG in manifold space. Compared to CFG, TDG enlarges the search space to hyperplane.
0.3200
(.0 35000 0.5000 3.5100 C.000
0.70
-5,350 5200 0,500
SA3E
5400 S000 1ecco 5600 SEEO E5000
6560 AM00 N00 6,7000
-5-4200 sxop 0,7200 0.600 7.7000 esc3 wrte o 2400 2,000 0.0002 07000 1,0000 8931 1.5200
Guldance
5-5000 0 eo0t 0,6300 wsst 5500 U. Ivco 5000 SAEE 500400 7.00 6.670
00.5
4-400 8207 6,500 0.6030 3.100 0.6220 AECO 0.500 8870 0402 7000 9002 0.200
scale
Figure 9: TDG hyperparameter grid search heatmap, with HPSv2 winning rate compared to CFG as the criteria. Model: SD-XL.
images with different guidance scale; We also use Spearman' test valid the correlations between the image saturation and metric score, the alpha value was set to 0.05.
1) For real images, we randomly selected 20 Ground-Truth images from MS-COCO dataset, and changed their saturation with different levels. The scatter map are presented in Fig [2] Most prompts have metric score positively correlated with image saturation. HPSv2, Significant positive correlations: 14, Positive correlation not significant: 1, No significant positive correlation: 5. In ImageReward, Significant positive correlations: 14,
Positive correlation not significant: 2, No significant positive correlation: 4.
2) For generated images, we randomly select 20 prompts from Pick-a-Pic, DrawBench and HPD. Then generated then with different guidance scales. The scatter map are presented in
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28.4
28.2
28.0
TDG
CFG
27.6
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Mask Ratio
Figure 10: Different mask ratio of prompt tokens TDG. Model: SD-XL..
Table 3: The winning rate and its degradation of different methods on various datasets. Stable Diffusion-3.5 (w 4.5).
Model:
Method
HPS v2
ImageRoward
AES
PickScore
CLIPScore
Average yj
w
Pick-a-Pie
(1) (R /xp-cR/ (An)
(1) (RP
PAG
53%/ 0%
44% / /33% 1%
53% 57% 4.4%
36% / 43%
/-7%
44%
/.1%% 3%
46% 47%
TDG
01%%
13%.
50%
551%
/-1%
45% 551%
/-6%
38% /47%
/-9%
53%
/47% 6%
49% 49%
6.62
Z-Sampling
366% 44%
41%
54%
1-13%
46% 499%
/33%
31%/ 37%
/-6%
53%
58%
/-5%
42% 47%
9.57
DrawBench
(g) (1"""/)""""000
PAG
31%% /2%
44% 40% / 3%
61% 64% /33%
42% / 40% /2%
39%
/42%
7-3%
48% / 47%
5.41
TDG
48%
499
46%
7. Z-Sampling
50% 441%% 9%
50% / 45% 4%
51% 477% 4% 34% / 44%
999%
46% 48%
11%
46% / 45%
9.34
And the problem still remains: In HPSv2, Significant positive correlations: 15, Pos- itive correlation not significant: No significant positive correlation: 3. ImageReward, Significant positive correlations: 12, Positive correlation not significant: 1, No significant positive correlation: 7.
Fig 13
We analyzed the w/ distribution over timestep Fig (a), every method have their own wi distribution on f, but the Wł averaged is still greater than the baseline = 5.5. In Fig [4 the different scheduler have minor effect on the wf.
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Prompt: "A beautiful blonde with freckles eating desserts with dog dining table. The background of Eiffel Tower neon fireworks lighting up night sky."
HPS v2: 30.3956 Prompt: "A cat. chubby, very fine wispy and extremely long swirly wavy fur, under water, Kuniyoshi Utagawa, Hishida Shuns."
HPS v2: 29.7943
HPS v2: 30.5297
HPS v2: 30.7722
HPS v2: 28.6557
HPS v2: 29.2244 Prompt: "A brown colored giraffe." "
HPS v2: 29.6573
HPS v2: 29.7427
HPS v2: 29.6685
HPS v2: 29.9727
HPS v2: 30.2884
HPS v2: 30.3286
Figure 11: The HPS v2 scores of generated images under different CFG scales w (5.5, 10, 15, 20). respectively. Model: Stable Diffusion-XI LL. HPSv2 exhibit strong bias to large CFG scales in wide range, even if generation quality starts to degrade due to too strong guidance.
Table 4: The winning rates and the degradation of different methods various datasets. Model: Stable Diffusion-2.1 (w = 5.5).
Method
HPS v2
ImageReward
AES
PickScore
CLIPScore
Average
w
Pick-a-Pic
(
/1f CRG AA)
7 Sampling
90% 660% / 30%
80% / 62% / 18%
70% 63% / 7%
84% / 73% / 11%
71%
46%
25%
79% / 61%
11.80
CFG+
82% / 62%% 20%
73% 58%
15%
58%2 55%
3%
65% 49%
16%
57% 55% 22%
67%) / 56%
9.09
FreeU!
64% 61% 3%
73% /67%
66%
63% /77%
6%
52% 45% 77%.
50% (47% 33%
60% 55%
6.57
PAG
48% /45% 3%
49%
/41% 8%
54% 55%
(-1%
49% / 45%
/ 4%
46% / 44% 72%
49% 466%
5.62
APG
/31% 1-20%
443% 1-22%
49%
53%
34%%
45% 58%
30% 444%
SAG
42% / 28% 144%
52% / 37%
15%
47% 46/% / 1%
30% / 23% 7%
56% / 39%
17%
45% / 35%
6.53
DrawBench
FP CR0 /An)
CFF
FreeU
76% 68% /8%
68% 6.4% 5.%
67% 65%% / 2%
57% 54% / 3/%
50% / 488% / 2.5
64% / 60%
6.65
Sampling
8%
66%
73% 59%
CFG++
80% / 62% / 17%
70% / 57%
/ 12%
67%
66% 2%
58% / 48%
11%
60%
/ 566%
/ 44%
67% / 58%
9.08
SAG
42% / 30%
11%
54% 42%
11%
62%
66%
-4%
38%
38%
1%
55% 488%
7%
50% 45%
PAG
46% /42% 4%
42%
42%
1%
50%
45% 44
46%
48%
11%
42% /43%
2%%
45%
444%
5.63
APG
16% / 29% (-13%
30% 42%
1-12%
38% 48% 1-10%
28% / 40%
f-12%
38%
7.4%%
.6%
30% 411%
10.48
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Table Quantitative results of different methods. Model: Stable Diffusion- XL 5.5).
Dataset
Method
HPS v2 (t)
ImageReward (1)
AES (f)
PickScore (t)
CLIPScore (t)
CFG
28.51
0.845
6.112
22.50
31.01
APG
28.70
0.937
5.995
22.53
32.28
c-CFG
29.30
1.032
5.992
22.61
32.23
CFG++
29.21
1.025
6.091
22.60
31.46
c-CFG
29.05
0.990
6.103
22.64
31.39
FreeUT
28.88
0.929
6.156
22.11
30.01
c-CFG
28.90
0.955
6.115
22.63
31.23
Pick-a-Pic
PAG
28.92
0.863
6,174
22.47
30.54
e-CPG
28.68
0.884
6.126
22.56
31.09
SAG
28.87
0.884
6.087
22.55
30.97
e-CFG
28.70
6.022
22.55
31.26
SEG
28.96
0.875
6.178
22.35
30.47
e-CPG
28.70
0.893
6.123
22.56
31.12
TDG
28.91
0.972
6,092
22.61
31.19
e-CFG
28.96
0.955
6.104
22.61
31.33
7-Sampling
29.31
1.037
6.118
22.67
31.30
e-CPG
28.80
0.909
6.003
22.40
CFG
28.44
0.514
5.569
22.22
29.26
APG
28.67
0.672
5.576
22.31
33.27
e-CFG
29.23
0.770
5.609
22.36
33.37
CFG++
29.14
0.742
5.622
22.34
29.71
e-CFG
29.05
0.718
5.600
22.40
29.73
FreeU
28.59
0.567
5.648
21.86
28.92
e-CFG
28.84
0.657
5.603
22.34
29.45
DrawBench
PAG
28.75
0.530
5.682
22.21
29.06
c-CFG
28.66
0.584
5.581
22.30
29.35
SAG
28.75
0.599
5.586
22.30
32.54
c-CFG
28.53
0.629
5.521
22.28
32.90
SEG
28.86
0.533
5.700
22.14
28.63
c-CFG
28.66
0.592
5.588
22.30
29.31
TDG
28.95
0.723
5,566
22.39
29.69
e-CPG
28.95
0.643
22.36
29.49
Z-Sampling
29.21
0.724
5.656
22.37
29.
c-CPG
28.64
0.556
5.538
22.13
29.55
CFG
28.51
0.845
6.112
22.50
31.01
APG
28.70
0.937
5,995
22.53
32.28
e-CFG
29.30
1.032
5.992
22.61
32.23
CFG++
29.21
1.025
6.091
22.60
31.46
e-CIG
29.05
0.990
6.3
22.64
31.39
FreeU
28.88
0.929
6.156
22.11
30.01
e-CPG
28.90
0.955
6.115
22.63
31.23
HPD
PAG
28.92
0.863
6.174
22.47
30.54
e-CIG
28.68
0.884
6.126
22.56
31.09
SAG
28.87
0.884
6.087
22.55
30.97
e-CFG
28.70
0.931
6.022
22.55
31.26
SEG
28.96
0.875
6.178
22.35
30.47
e-CFG
28.70
0.893
6.123
22.56
31.12
TDG
28.91
0.972
6.092
22.61
31.19
c-CFG
28.96
0.955
6.104
22.61
31.33
7-Sampling
29.31
1.037
6.118
22.67
31.30
c-CFG
28.80
0.909
6.003
22.40
31.56
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Table 6: Quantitative results of different methods. Model: Stable Diffusion-2.11 (w 5.5).
Dataset
Method
HPS v2
ImageRewand (t)
AES (f)
PickScore (1)
CLIPScore
CFG
26.25
-0.353
5.658
20.14
27.08
APG
26.03
-0.441
5.675
20.05
31.97
e-CFG
26.00
-0.420
5.537
20.17
30.29
CFG++
27.13
0.045
5.751
20.43
27.72
c-CPG
26.99
-0.030
5.731
20.45
27.49
FreeU
26.93
-0.004
5.759
20.34
27.08
Pick-a-Pic
e-CFG
26.58
-0.251
5.706
20.30
27.35
PAG
26.46
-0.274
5.679
20.23
27.24
e-CFG
26.51
-0.258
5.672
20.24
27.46
SAG
26.18
-0.330
5,653
19.96
31.66
c-CFG
26.53
-0.240
5.704
20.25
32.29
Z-Sampling
27.09
0.009
5.762
20.53
27.88
c-CFG
26.66
-0.124
5.684
20.20
28.01
CFG
27.09
-0.218
5.373
21.13
27.83
APG
26.78
-0.304
5.366
21.02
31.08
e-CFG
27.06
-0.181
5,300
21.11
30.64
CFG++
27.96
0.116
5.474
21.35
28.68
e-CFG
27.80
0.065
5.430
21.33
28.47
FreeU
27.88
0.155
5,512
21.36
28.23
DrawBench
e-CFG
27.55
-0.079
5,405
21.29
28.04
PAG
27.28
-0.150
5.378
21.18
27.85
e-CFG
27.26
-0.155
5,387
21.19
27.99
SAG
26.92
-0.213
5,446
21.00
31.27
e-CFG
27.17
-0.188
5.361
21.15
31.30
Z-Sampling e-CFG
27.83
0.082
5.467
21.42
28.62
26.96
-0.288
5.377
21.04
27.98
Table Quantitative results of different methods. Model: Stable Diffusion-3.5 (w 4.5).
Dataset
Method
HPS v2
ImageReward (t)
AES (t)
PickScore (t)
CLIIPSore (1)
CFG
28.43
1.004
5.921
21.94
28.72
PAG
28.49
0.918
5.994
21.77
28.19
e-CFG
28.54
1.053
5.925
21.96
28.92
Pick-a-Pic
TDG
28.56
0.951
5.890
21.82
28.57
c-CFG
28.56
1.016
5.921
21.87
28.83
Z-Sampling
28.30
0.980
5,853
21.63
28.
c-CFG
28.53
0.990
5,881
21.71
28.38
CFG
29.23
0.962
5.429
22.72
30.39
PAG
29.25
0.894
5.531
22.55
29.70
cCCFG
29.42
1.031
5.429
22.77
30.62
DrawBench
TDG
29.42
0.996
5.435
22.66
30.50
e-CFG
29.47
1.005
5.446
22.71
30.52
Z-Sampling c-CFG
29.32
0.974
5.434
22.61
30.17
29.50
1.040
5,420
22.68
30.27
Table 8: Quantitative results of different methods on COCO-30K. Model: Stable Diffusion-XI
(w 5.5).
Method
FID ({)
HPS v2 (f)
AES (f)
w
CPG
13.56
28.73
5.558
5.5
Z-Sampling
17.32
29.56
5.666
10.81
e-CFG
14.21
29.22
5.550
CFG++
17.27
29,46
5.643
8.99
e-CFG
16.07
29.28
5.609
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Table 9: Quantitative results of different methods on ImageNet-50k, Model: DIT-XL/2 (w 4).
Method
FID (L)
IS (f)
AES (1)
w
CFG
21.22
474.9
4.911
4
Z Z-Sampling
25.43
471.7
4.853
9.17
e-CFG
24.04
465.1
4.834
CFG++
24.33
480.1
4.950
8.01
e-CFG
25.55
480.0
4.791
Table The winning rate and its degradation of different methods on Pick-a-Pic datasets. Model: Stable Diffusion-XL. (w = 5.5).
Method
HPS v3
OneIG
Pick-a-Pic
(1) CTG
/n) CTG /An))
PAG
71% / 68% 3%
44% 444% / 0%
CFG++
55% / 54% 1%
36% / 26% 10%
7 Sampling
79% / 67% / 12%
54% / 55% 11%
APG
44% 39% 5%
24% / 26% -2%
FreeU
39% 35% 4%
26% 21% 5%
TDG
59%
49% / 10%
41% / 32% 99%
SAG
66%
56% / 10%
45% / 30% 15%
SEG
62% / 56% 6%
23% / 25%
--2%
Table 11: Quantitative results of different methods on Pick-a-Pic dataset. Model: Stable Diffusion- XL (w = 5.5).
Dataset
Model
HPS v3 UnifiedReward (to CFG)
UnifiedReward (to e-CFG)
CFG
6.837
PAG
7.781
0.16
0.16
c-CFG
7.341
CFG++
7.648
0.1
-0.02
e-CFG
7.612
7-Sampling
7.776
0.38
0.4
e-CFG
7.203
Pick-a-Pic
APG
7.263
-0.24
-0.19
e-CFG
7.615
FreeU
7.038
-0.25
-0.32
e-CFG
7.660
TDG
7.493
0.14
-0.03
e-CFG
7.461
SAG
7.432
0.25
-0.03
e-CFG
7.231
/
SEG
7.534
-0.18
-0.14
e-CFG
7.359
/
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Table 12: Quantitative results of SD-XL OnelG-Bench with different guidance scales.
Guidance Scale
Alignment (1)
Diversity (1)
Reasoning (1)
Style (1)
Text (1)
5.5
.5638
.4399
.1932
.3325
.0128
6,0
.5738
.4357
.1910
.3328
.0178
6.5
.5771
.4294
.1970
.3374
.0170
7.0
.5820
.4242
.2000
.3420
.0164
7.5
.5837
.4225
.2076
.3363
.0181
8.0
.5936
.4169
.2048
.3366
.0166
8.5
.5917
.145
2086
.3358
.0182
9.0
.5944
.1099
.2133
.3411
.0267
9.5
.5934
.4085
.2154
.3387
.0194
10.0
.5936
.4043
.2226
.3377
.0259
35.
Imagetewan
-11
-15
(a) Scatter map of image saturation HPSv2
(b) Scatter map image saturation with ImageReward
Figure 12: Reward evaluation images with different saturation, each color represents images with different saturation. 20 images were randomly picked MS-COCO dataset with changed saturation. Model: SD-XL.
21-
44-
27-
03
0.1
02
83
64
(a) Scatter map of image saturation with HPSv2 (b) Scatter map image saturation with ImageReward
Figure 13: Reward evaluation on images with different saturation, each color represents images generated by a prompt. 20 prompts were randomly selected in Pick-a-Pic, DrawBench, and HPD, and corresponding images were generated with different guidance scale. Model: SD-XL.
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20.
106
34
0.
Efhcciv Gurbbnc Sta8
Peel
Eftectee Gurdanos Scaa
8.0
12.5
ICC
10.0
0.
Timellp
Tirsedpp
(a) w over timestep i.
(b) w| over timestep ł with different scheduler.
Figure 14: Effective guidance scale value over timestep. Model: SD-XL. Dataset: Pick-a-Pic. Note that except Z-Sampling, CFG++, and APG, which modified the noise scheduling process, other methods use EulerDDisreteSSheedaler by default.
25

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Prompt: "A man is cooking, MineCraft Style."
CFG (w = 5.5)
e-CFG (w 11.46)
Z-Sampling
Prompt: "A tabby cat sleeping on wooden island in an old looking kitchen."
CFG
(w 5.5)
e-CFG (w = 11.44)
Z-Sampling
Prompt: "The sun beyond the mountain glows, the yellow river seawards flows.
CFG (w = 5.5)
e-CPG (w = 11.69)
Z-Sampling
Figure 15: Qualitative Results of Z-Sampling.
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Published as a conference paper at ICLR 2026
Prompt: "New York Skyline with 'Hello World' written with fireworks the sky."
Hello World
Worle
CFG (w = 5.5)
e-CFG (w 9.33)
CFG++
Prompt: "A magnifying glass over page of a 1950s batman comic."
CFG (w 5.5)
eFFG (w 10.54)
CFG+
Prompt: "Cat wearing cowboy hat rides on corgi during sunset the Wild West."
CFG (w = 5.5)
c-CFG (w" 10.15)
CFG+
Figure 16: Qualitative Results of CFG++.
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Published as a conference paper at ICLR 2026
Prompt: "Room with book and white carpet."
(w 5.5)
e-CFG 11.02)
SAG
Prompt: "A featring a dog by artist Koyamori."
CFG (w = 5.5)
e-CFG (wf 6.37)
SAG
Prompt: "A half-robot, half-humanoid male andrvid, actor Liam Hemsworth, in a statue-like pose with shiny skin and blank stare display." "
CFG (w 5.5)
c-CFG (w = 9.37)
SAG
Figure 17: Qualitative Results of SAG.
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Published as a conference paper at ICLR 2026
Prompt: "An elephant under the sea.
CFG (w 5.5)
e-CFG (w 6.41)
SEG
Prompt: " man and his dog riding on bike."
CFG (w 5.5)
e-CFG (W 6.79)
SEG
Prompt: "A spanish water dog breed as Arthur Morgan from red dead redemption.
CFG (w 5.5)
c-CFG (w 8.32)
SEG
Figure 18: Qualitative Results of SEG.
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Published as a conference paper at ICLR 2026
Prompt: "The president being abducted by aliens."
CFG (w = 5.5)
e-CFG (w 7.41)
PAG
Prompt: "A cramped bathroom with a sink in the corner"
CFG (w 5.5)
e-CFG (w 6.53)
PAG
Prompt: lynx dressed a flight suit."
CFG (w = 5.5)
c-CFG (w = 7.53)
PAG
Figure 19: Qualitative Results of PAG.
30

---

Published as a conference paper at ICLR 2026
Prompt: "A real life photography of super mario, 8k Ultra HD.
CFG (w 5.5)
e-CFG (w 7.71)
FreeU
Prompt: One cat and two dogs sitting on the grass."
CFG (w 5.5)
e-CFG (w 8.29)
FreeU
Prompt: "A man standing a field with two goats."
CFG (w = 5.5)
c-CFG (w" = 8.51)
FreeU
Figure 20: Qualitative Results of FreeU.
31

---

Published as a conference paper at ICLR 2026
Prompt: "A 3D painting of a serious female sorceress in stormy weather, with an anaglyphy
effect.
CFG (w = 5.5)
eFG (w 7.46)
TDG
Prompt: "A carly haired boy rides a skateboard down road."
CFG (w = 5.5)
c-CFG (w 8.83)
TDG
Prompt: man woman riding on the back of a motorcycle.
CFG (w = 5.5)
e-CFG (w = 8.26)
TDG
Figure 21: Qualitative Results of TDG.
32

---

Published as a conference paper at ICLR 2026
Prompt: "A portrait of a beautifid anime girl with pink hair wearing a white I-shirt and looking directly at the viewer."
CFG (w = 15)
e-CG (w 11.77)
APG (w 15)
Prompt: "Lego Arnold Schwarzenegger:
CFG (w= 15)
e-C. (w 11.18)
APG (w 15)
Prompt: "An image of a fantastical city floating the clouds."
CFG (w 15)
c-CFG (w" 16.91)
APG (w 15)
Figure 22: Qualitative Results of APG.
33


Reviews:
[{"review_id": "KGUfLmZK0C", "reviewer": "Reviewer_JH4a", "summary": "This paper critically reexamines recent diffusion guidance methods beyond Classifier-Free Guidance (CFG) and exposes an evaluation bias: human preference models such as HPSv2 and ImageReward tend to favor images with higher CFG scales, which often leads to overly saturated or artifact-prone generations. To address this, the authors propose GA-Eval, a guidance-aware evaluation framework that calibrates guidance scale effects to ensure fair comparisons across methods. They further introduce Transcendent Diffusion Guidance (TDG), a method that appears to outperform CFG under biased evaluations but in reality does not improve generation quality.\nThe experiments show that, when accounting for this bias, simply increasing CFG scale can match or outperform most recent guidance methods, urging the community to rethink evaluation protocols for diffusion guidance.", "questions": "1. Could the authors clarify how the components that are orthogonal and parallel to $\\Delta \\epsilon$ are computed in Equation 5-7?     Specifically, what projection formulation or numerical approach is used to separate these components in practice?\n\n2.  When generating images from CFG with the effective guidance scale, do the authors use the original pre-trained diffusion models for all methods and apply $w^e$ computed individually for each guidance variant?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "The paper makes an insightful observation that commonly used human preference models, such as HPSv2 and ImageReward, exhibit a strong bias toward images generated with larger CFG scales. This bias frequently leads to higher preference scores for oversaturated or artifact-prone images, revealing a critical weakness in current evaluation protocols for diffusion guidance methods.", "weaknesses": "1. Writing clarity: The overall writing could be improved to enhance readability and make the key ideas easier to follow. Some sections, particularly the methodological descriptions, are difficult to interpret without additional context or intuitive explanations.\n\n2. Equations (5)–(7): The derivations and computation steps for Equations (5) through (7) are not clearly explained. It would be helpful if the authors explicitly detailed how these equations are obtained, what intermediate steps are omitted, and what assumptions or approximations are involved in their formulation.\n\n3. Motivation of Transcendent Diffusion Guidance (TDG): The introduction of the proposed TDG method lacks sufficient motivation and a clear conceptual link to the findings presented earlier. It remains unclear why this method should be effective or how it concretely relates to the identified evaluation bias. Providing a stronger theoretical or intuitive justification for TDG would make the contribution more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zFkV6s4jCm", "reviewer": "Reviewer_2V4Q", "summary": "This paper critically analyzes recent progress in diffusion guidance for text-to-image generation, focusing on four key contributions. It identifies an evaluation pitfall where human preference models favor larger guidance scales, affecting image quality. The authors propose a guidance-aware evaluation (GA-Eval) framework for fair comparisons between methods and classifier-free guidance (CFG). They introduce the Transcendent Diffusion Guidance (TDG) method, which boosts preference scores but is ineffective in practice. Experiments show that increasing CFG scales can compete with most methods, though all suffer from decreased winning rates compared to standard CFG. The work urges reconsideration of the evaluation paradigm and future directions in this field.", "questions": "None", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper emphasizes a significant evaluation flaw in which standard human preference models show a strong bias toward larger guidance scales. This is crucial for the field as it encourages a reevaluation of the contributions of each classifier-free guidance (CFG) method.\n2. The proposed guidance-aware evaluation (GA-Eval) framework is both reasonable and robust. It will assist researchers in accurately assessing diffusion guidance methods.\n3. The introduced Transcendent Diffusion Guidance (TDG) method replicates the creation of weak conditions found in other methods during the sampling process. It highlights the effectiveness of GA-Eval and enhances the overall persuasiveness of the paper.", "weaknesses": "1. While GA-Eval is mathematically sound, it requires user studies to demonstrate its alignment with human assessments.\n2. This paper identifies an evaluation flaw and proposes a guidance-aware evaluation framework. Another approach to addressing this issue is to enhance the reward model and testing benchmarks. Recent reward models, like HPSv3, may rely on vision-language models, and recent benchmarks, such as OneIG, also utilize VLMs to evaluate generated images. These new evaluation tools could potentially diminish the significance of this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SlcLq0jF3e", "reviewer": "Reviewer_5pnH", "summary": "This paper critically examines the evaluation practices in recent text-to-image (T2I) diffusion guidance methods. The authors identify a pervasive evaluation pitfall: human-preference-based metrics exhibit a strong bias toward images generated with large classifier-free guidance (CFG) scales, even when such images suffer from visual artifacts like oversaturation or loss of fidelity. This bias leads to inflated performance claims for many newly proposed guidance techniques.\n\nTo address this, the authors make four key contributions: (1) revealing the pitfall; (2) proposing GA-Eval; (3) Designing TDG: A misleading Transcendent Diffusion GuidanceThe work calls for a paradigm shift in T2I evaluation, urging the community to disentangle true algorithmic innovation from artifacts of biased metrics.", "questions": "No question", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is highly original in both problem framing and methodology. While prior works have proposed new guidance strategies, this is the first to systematically diagnose and quantify a systematic bias in human-preference metrics tied to CFG scale. The technical execution is rigorous. The derivation of the effective guidance scale is mathematically sound and generalizable across sampling algorithms (including those that modify latents rather than noise directly). Experiments are extensive: multiple models, datasets, and metrics are tested, and results are consistently interpreted through the lens of GA-Eval. The inclusion of GenEval for fine-grained semantic alignment and COCO/ImageNet for distributional metrics (FID/IS) further strengthens validity.", "weaknesses": "1. Authors could provide more details on how HPS v2, ImageReward, etc., are biased toward high-saturation/high-alignment images\n2. While GA-Eval is a diagnostic tool, the paper offers limited direction on how to design guidance methods that truly improve generation beyond CFG scaling.\n3. Some recent text-to-image works might need examination in this paper [1,2]. It is intriguing to investigate whether these recent generative reward model are still biased.\n\n[1] Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning\n[2] T2I-Eval-R1: Reinforcement Learning-Driven Reasoning for Interpretable Text-to-Image Evaluation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QY9Qgkv9P6", "reviewer": "Reviewer_5uJX", "summary": "This paper identifies and analyzes a critical yet overlooked evaluation pitfall in diffusion model research-specifically, the tendency of human preference models to favor high CFG scales due to their implicit bias toward color saturation and semantic alignment.\nTo address this, the authors propose a Guidance-Aware Evaluation (GA-Eval) framework, which introduces an“effective guidance scale”to disentangle the actual improvement from the simple effect of scale amplification. They further design a synthetic baseline called Transcendent Diffusion Guidance (TDG), demonstrating that it can“fool”conventional evaluation metrics without real quality improvement.\nThrough experiments across multiple diffusion backbones (SD-XL, SD-2.1, SD-3.5, DiT-XL/2) and datasets (Pick-a-Pic, HPD, DrawBench, GenEval, COCO-30K), they argue that many recent guidance methods achieve their gains mainly by exploiting large-scale biases rather than genuine improvements.", "questions": "1.How is the linearity assumption in the derivation of ωₑ justified, given the nonlinear nature of DDIM/ODE schedulers?\n2.Have you analyzed ωₑ stability across timesteps or different sampling schedulers?\n3.How is GA-Eval’s “fairness” verified without human evaluation? Any correlation with human judgments?\n4.Could GA-Eval itself introduce bias (e.g., penalizing certain visual styles)? What is the theoretical motivation for the TDG noise-mixing rule and its scaling factor?\n5.How sensitive is TDG to masking ratio or weakened prompt settings? Can you provide statistical evidence (e.g., correlation or regression) showing that reward models favor high-saturation images?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "①Timely and meaningful contribution. The paper addresses an important meta-evaluation problem in the rapidly expanding field of text-to-image diffusion models. The argument that large guidance scales bias reward-based evaluation metrics is well-motivated and empirically observable.\n②Novel evaluation framework (GA-Eval). Introducing the effective guidance scale and decomposing noise updates into orthogonal and parallel components provides an interesting analytical perspective for isolating true guidance effects.\n③Strong experimental coverage. The authors compare eight diffusion guidance methods on four major backbones and five evaluation metrics, offering a relatively broad experimental scope.", "weaknesses": "(A) Theoretical ambiguity in ωₑ definition\nThe derivation of effective guidance scale (Eq.4-7) assumes linearity between the unconditional and conditional noise terms. However, in practical schedulers (e.g., DDIM, ODE-based), the latent evolution is nonlinear.\n\n(B) Limited validation of GA-Eval fairness\nThe paper claims GA-Eval enables“fair comparison,”but provides no user study or human evaluation to verify that its results better correlate with actual human perception. Without human validation, GA-Eval could itself introduce a new bias.\n\n(C) TDG lacks principled formulation\nTDG randomly masks text tokens to produce a“weakened prompt,”then combines multiple noise terms (Eq. 13).\nThis method appears heuristic and lacks a theoretical foundation for why such hyperplane mixing would improve quality.\nThe scaling factor∥ϵ_cond−ϵ_uncond∥/∥ϵ_cond−ϵ_weak∥seems ad hoc and could destabilize training or sampling.\n\n(D) Incomplete quantitative analysis of metric bias\nThe claim that reward models favor high-saturation images is intuitively correct but not statistically demonstrated. No quantitative correlation between color saturation and metric scores is provided. Figure 3 illustrates trends, but lacks regression or significance testing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)