ImageDoctor: Diagnosing Text-to-Image Generation via Grounded Image Reasoning
Authors: Yuxiang Guo
Jiang Liu
Ze Wang
Hao Chen
Ximeng Sun
Yang Zhao
Jialian Wu
Xiaodong Yu
Zicheng Liu
Emad Barsoum
Venue: ICLR 2026 Poster (2026)
Keywords: Image reward model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress in reinforcement learning for preference alignment. However, existing approaches typically quantify the quality of a generated image using a single scalar, limiting their ability to provide comprehensive and interpretable feedback on image quality. To address this, we introduce ImageDoctor, a unified multi-aspect T2I model evaluation framework that assesses image quality across four complementary dimensions: plausibility, semantic alignment, aesthetics, and overall quality. ImageDoctor also provides pixel-level flaw indicators in the form of heatmaps, which highlight misaligned or implausible regions, and can be used as a dense reward for T2I model preference alignment. Inspired by the diagnostic process, we improve the detail sensitivity and reasoning capability of ImageDoctor by introducing a ``look-think-predict" paradigm, where the model first localizes potential flaws, then generates reasoning, and finally concludes the evaluation with quantitative scores. Built on top of a vision-language model and trained through a combination of supervised fine-tuning and reinforcement learning, ImageDoctor demonstrates strong alignment with human preference across multiple datasets, establishing its effectiveness as an evaluation metric. Furthermore, when used as a reward model for preference tuning, ImageDoctor significantly improves generation quality—achieving an improvement of 10% over scalar-based reward models.

Published as a conference paper at ICLR 2026
IMAGEDOCTOR: DIAGNOSING TEXT-TO-IMAGE GEN- ERATION VIA GROUNDED IMAGE REASONING
Yuxiang Guo Jiang Liu Ze Wang"
Hao Chen Ximeng Sun?
Yang Zhao' Jialian Wu Xiaodong Yu²
Zicheng Liu
Emad Barsoum 2
'Johns Hopkins University AMD
http:://image--bctorr..ooo wit.oe........tu..........
ABSTRACT
The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress reinforcement learning for preference alignment. However, exist- ing approaches typically quantify the quality of generated image using a sin- gle scalar, limiting their ability to provide comprehensive and interpretable feed- back on image quality. To address this, we introduce ImageDoctor, unified multi-aspect T2I model evaluation framework that assesses image quality across four complementary dimensions: plausibility, semantic alignment, aesthetics, and overall quality. Imageloctta also provides pixel-level flaw indicators in the form of heatmaps, which highlight misaligned or implausible regions, and can be used as a dense reward for T2I model preference alignment. Inspired by the diagnostic process, we improve the detail sensitivity and reasoning capability of ImageDoctor by introducing a "look- book-think-pae thiink--redict paradigm, where the model first localizes potential flaws, then generates reasoning, and finally concludes the evaluation with quantitative scores. Built on top of a vision-langual combination of supervised fine-tuning and reinforcement learning, ImageDoctor demonstrates strong alignment with human preference across multiple datasets, establishing its effectiveness as an evaluation metric. Furthermore, when used as reward model for preference tuning, ImageDoctor significantly improves genera- tion quality-achieving an improvement of 10% over scalar-base based reward models.
model and trained through a
INTRODUCTION
With the rapid evolution of text-to-image (T2I) architectures (Goodfellow et al., 2014 Croitoru et al., 2023 Gu et al. 2023] Xie et al. al.] 2024 Tian et al. 2024 Wang et al. 2025c). the quality of generated images has vanaee significantly. Modern T2I systems can now produce highly realistic outputs that closely follow textual instructions, enabling a wide range of applications in areas such as art, design, and entertainment. These advances, however, give rise critical question: how reliably evaluate the quality of generated images that may from poor instruction adherence, low aes- thetic quality, or counterintuitive artifacts. Furthermore, with the rise of reinforcement learning (Liu let al.; (023) Xue et al.) 2025) and test-time scaling (Guo et al.) 2025 Ma et al.] (2025)), evaluators play an increasingly important role: not only can they serve as reward functions or verifiers to mea- sure quality, but they are also expected to provide actionable feedback to improve generation.
Current human preference models, such as HPS (Wu et al. 2023b), ImageReward (Xu al. 2023)
and PickScore (Kirstain et al. (023). mostly predict a single scalar score of quality. However, com- pressing the evaluation into a single scalar is often insufficient to capture the detailed flaws of the generated images. For instance, two images may receive the same score, yet differ substantially: one may be aesthetically pleasing but poorly aligned with the prompt, while the other may fol- low the prompt faithfully but contain unrealistic artifacts. Relying solely on single score cannot disentangle these factors, limiting both the interpretability and the usefulness the feedback for guiding model improvement. Moreover, existing evaluators can only provide an overall judgment of image quality but lack spatially grounded feedback, i.e., they cannot identify where in the image
"Equal Contribution +Corresponding Author

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Published as a conference paper at ICLR 2026
Prompt:
PickScore: 19.5
ImageReward: 1.87
CLIPScore:097
Flow GRPO PickScare
Denseflow GRPO Inste
Given coption on image generated based this caption, please onalyze the provided image
Imagebocter (Ours)
AI All IMAGE
need focus on The bounding M401467 266333344 [238,133,3055 38993488
Al go brrrrr
Visuolization
"go brirrrr
Caption: [An inage of cut wearing green bow tie stonding the bench.)
Look
that saya berrer'
Semantic Alignment: While image successfully depicts the core subjectt-o "ca"" and hoo
generate green bow clear focal
Aesthetic: composition belanced with
zoint lighting natural, contributing a pleasent visual experience. Plousiilitt: reduce image's 's ovenal
generally appeor realistic, the cot
good Think represent specified "greer" bow color detract its oversll quality.
Overall Impression: Image presents elearly identifisble
follure
fedlro
Misaiggmant
Artifact
4D Scores Heatmaps:
Semantic Aligrment Score: 0,67
Aestietic Soore: 1.0
Plousibiiity Score: 0.75
Overall Score: 0.7
Predict
with monk with challinze
Figure Comparison between ImageDoctor and scalar-based reward functions. Left: Im- ageDoctor follows a "look-think-prediie" paradigm, providing rich feedback with four-dimensional scores and heatmaps that highlight misalignment and artifact locations. Right: Leveraging this fine- grained feedback, DenseFlow- PoRPOOOOOGGOOO (Sec. 4 generates images with more faithful and realistic local details, outperforming Flow-GRPO, which relies on the scalar-based reward PickScore.
the problems occur. In practice, many T2I failures stem from partial prompt adherence: while the majority of the prompt may be satisfied, fine grained details are often missing or incorrect. This absence of localization further reduces the interpretability and actionability of these evaluators, es- pecially when used reward functions. These challenges highlight the need for evaluators that can provide comprehensive feedback, offering both multi-dimensional quality scores and localized diagnostics-much like doctor diagnosing the problems in an image.
In this work, we propose ImageDoctor, unified evaluation framework that produces holistic scor- ing and spatially grounded feedback in the form of artifact and misalignment heatmaps. Steering the reasoning strengths and commonsense knowledge of multi-modal large language models (MLLMs), ImageDoctor is built on fine-tuned MILM backbone to achieve a deep joint understanding of im- ages and prompts. To achieve flaw localization, we introduce a lightweight heatmap decoder that produces the heatmaps highlighting misalignment and artifact locations conditioned on the input prompt, image features, and the response generated by the MILM. Inspired by the process of med- ical diagnosis, we further propose a "look-think "ieinkc-redit" paradigm as shown in Fig, Before final judgment, ImageDoctor performs grounded image reasoning, which consists of two steps. First, pinpoints potential flawed regions that require closer attention in the image ("look") Then, analyzes these regions by integrating the localized visual evidence with contextual understanding, generating structured reasoning that evaluates the image from multiple aspects ("think "). We incen- tivize this grounded image reasoning capability with cold start and reinforcement finetuning.
Reinforcement learning from human feedback (RLHF) (Xue et al. 2025 Liu et al. 2025) has been proven effective in enhancing both image quality and text-image alignment for T2I genera- tion. Nevertheless, current RLHF approaches, such as Flow-GRPO (Liu et al. 2025). rely solely on sparse image-level rewards, which overlook spatially localized feedback and thus fail provide fine-grained guidance during training. To address this limitation, we introduce DenseFlow-GRPO, a new RLHF framework that enhances T2I models with both image-level and pixel-level dense re- ward signals. By leveraging the rich diagnostic feedback from ImageDoctor, DenseFlow-Gii GRRPO delivers more precise and spatially aligned supervision, enabling T21 models to learn not only what constitutes good image globally, but also how to refine local regions in a fine-grained manner.
Our experiments demonstrate that ImageDoctor achieves state-of-the-art alignment with human judgments, substantially improving score prediction accuracy across all dimensions (average PLCC 0.741 vs. 0.586 of the previous best on RichHF-18K) Beyond serving as a metric, ImageDoctor generalizes well to downstream applications: as verifier, it reliably selects higher-quality gener- ations in test-time scaling: as a reward model, it drives consistent gains in reinforcement learning. In particular, integrating ImageDoctor into Flow-GRPO yields superior preference alignment, and
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further utilizing the dense heatmap feedback in DenseFlow-GRPO achieves the strongest improve- ments and delivers images with more faithful local details.
The main contributions of this paper are summarized below: We propose ImageDoctor, a unified T2I evaluation model that produces dense feedback, including multi-aspect scores and heatmaps localizing flaws, enabling interpretable and fine- gre-grained assessment.
We introduce toook-think-predice paradigm that equips ImageDoctor with structured reasoning by integrating visual grounding and textual analysis. ImageDoctor is further refined through reinforcement finetuning with tailored reward functions, enhancing adher- ence to human preferences while ensuring spatially grounded reasoning.
We present DenseFlow-GRPO. novel T2I reinforcement learning method that incorpo- rates ImageDoctor's dense spatial feedback into the reward signal, providing region-aware supervision and leading to more robust improvements in image generation.
Extensive experiments human preference datasets demonstrate the ImageDoctor's supe- rior alignment with human preference, and we further validate its effectiveness by applying it to downstream tasks, serving as a verifier and reward model.
RELATED WORK
2.1 TEXT-TO-IMAGE GENERATION
Text-to-image (T2I) generation is core task in generative modeling. It aims synthesize seman- tically aligned images from natural language prompts, while balance the aesthetic quality and plau- sibility. Early approaches based on Generative Adversarial Networks (GANs) (Goodfellow al. 2014) and Variational
Eitto-nnoeers (VAEs) (Kingma Welling, (013) demonstrated feasibility but were limited by low diversity and coarse details. More recently, diffusion models (Ho et al. 2020 Nichol et al., 2021: Rombach et al., 2022) have emerged as a dominant paradigm, achiev- ing significant gains in image quality and diversity. Flow-hased models (Zhao et al. 2024 Esser et al, 2024 Labs 2024) provide another class of likelihoodl-ban based generative models, relying on stochastic denoising steps, thereby enabling efficient sampling and reducing inference overhead. In addition, autoregressive models (Tian et al., 20244] Xie et al. 2024) are gaining attention for their compositionality and controllability, bridging vision and language more effectively.
2.2 T21 EVALUATION MODELS
With the rapid progress in T21 generation, evaluation models have also advanced, though the task remains highly challenging. CLIPScore (Hessel et al., (021] was one of the earliest automatic met- rics that leverages pretrained CLIP to compute the similarity between the generated image and its prompt. PickScore (Kirstain et al. 2023) and Rewae (Xu et al. 2023) fine-tune CLIP (Rad- ford et al. 2021) and BLIP (Li et al. 2022). respectively, large-scale human preference datasets, significantly improving alignment with subjective human judgments. The Human Preference Score (HPS) series (Wu al. 2023b Ma et al. 2025b) expand the scale of annotations enhance preference alignment. In particular, HPSv3 (Ma et al. 20255) leverages an MILM backbone to capture rich semantic representations from images and captions and incorporates uncertainty-ave aware ranking to learn human preferences from paired comparisons. ICT-HP (Ba et al. 2025) proposes the Image-Cone -aeee-Coottinn----e Text (ICT) and High- Preference (HP) models by finetuning CLIP model to evaluate both text-image alignment and the image aesthetics and detail quality. While both HPSv3 and ICT-HP demonstrate promising results in predicting image-level scalar scores, they do not pro- vide localized evaluation and offer interpretability regarding why an image is judged as good or bad. Recently, UnifiedReward -hink (Wang et al. 2025a) and VisualQuality-Ri explored reinforcement learning for evaluation model training for image quality score prediction. RichHF (Liang et al. 2024) and HELM (Lee et al. 2023) attempt to broaden evaluation by consid- ering multiple dimensions, moving beyond single-score preference modeling.
(Tian et al. 2024)
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WANTED TeAASDNNCer
WANTED ThA TrAggDDacHe
<think> I need to focus on the bounding box area </think>
Alignment
Aesthetic Score:
Plousibility Score: 0.7, Overall Score: 0.8
Misalignment Location: <MIS>
Artifact Location:
</answer>
Misalignmentt
Artifact
MLLM
Heatmap Decoder
MLP
Dot Producc
Upsumpling
Visual
Prompt:
Siven caption
WANTED The ImageDoctor.
image generated based this coption, please analyze provided image in detail.
Bi-directional Cross-Attention Luyer
x2
Caption: (A WANTED the ImageDactor wearing glasses.
Task
Heatmap Token
Image Feature
Figure 2: ImageDoctor architecture. Given prompt-image pair, the MLLM follows "look- think-predict" paradigm for T2I evaluation by localizing potential flaw regions, analyzing them, and generating holistic scores and special task tokens. The task token, with learned heatmap token and image features are fed into the heatmap decoder to produce the misalignment and artifact heatmaps.
IMAGEDOCTOR
ImageDoctor aims to provide rich, interpretable, and accurate diagnoses for T21 generation. To this end, we design novel unified model architecture to generate both image-level scores and pixel-level heatmap evaluations leveraging the strong image and text understanding capabilities multimodal large language models (MLLMs) (Sec. 3.I. To generate interpretable and accurate evaluations, propose a book-think-predicee paradigm, where the model first identifies possible local flaw regions and generates explicit reasoning about image details before providing final evaluations (Sec. 3.2)
3.1 MODEL DESIGN
Overview. The overall pipeline of ImageDoctor is shown in Fig. 2 The input text prompt P and the corresponding image E TR. HxW are passed into the MI.LM backbone. ImageDoctor reasons about image details and image-text semantics to produce both holistic scores and localized diagnostic signals. Specifically, it outputs four scalar scores, i.e., semantic alignment, aesthetics, plausibility, and overall scores sa, Vd (align, aesth, plau, over]. which evaluate image quality from different aspects. In addition, it provides localized feedback by marking image regions that are implausible misaligned with the text through the artifact and misalignment heatmaps Hy I x W. Vd E fart, mis} generated by the heatmap decoder.
Heatmap Decoder. While scalar scores can be directly predicted via the text output of the MLLM backbone, generating pixel-wise heatmaps requires unified model that supports both text and im- age outputs. To enable this, we design a lightweight heatmap decoder. The decoder takes the image features extracted by the visual encoder together with a learned heatmap token and a task token t {<ART>, <MIS>} representing the artifact and the misalignment tokens, respectively. The task tokens are generated by the MILM backbone and fused with the input image, text prompt, and rea- soning chains guide accurate heatmap prediction. Inspired by the SAM mask decoder (Kirillov et al. 2023), we adopt a bi-directional cross-attention design to fuse the token and image embed- dings. The updated image features are passed through a series of convolution upsampling layers to upscale to the original image size, from which the updated heatmap token are used to dynamically predict the heatmap. The detailed architecture of the heatmap decoder can be found in Section
3.2 GROUNDED IMAGE REASONING
As shown in Fig. I ImageDoctor adopts a thok--tiin-preeiict paradigm to generate its evalua- tions for given image and text prompt. Instead of directly generating the final prediction, it first
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localizes potential flaw regions by predicting the flaw region bounding boxes ("look"), then an- alyzes and reasons about these flaws and overall image quality ("think"), and finally produces a conclusive judgment ("predict"), mimicking the image evaluation process of human. To enable the grounded image reasoning capability, we design a two-phase training pipeline. In the cold start phase, conduct supervised fine-tuning to teach the model to predict image scores and heatmaps in the "look-think-predic" reasoning format. In the second phase, ImageDoctor adopts online re- inforcement fine-tuning with Group Relative Policy Optimization (GRPO) (Shao et al. (0244 to further incentivize the reasoning ability to generate rich and reliable image feedback.
3.2.1 COLD START
Since MILMS are designed for general image understanding, in the first cold start stage, we first finetune the MLLM backbone on the image evaluation task by training the model to directly predict the image evaluation scores. In the second cold start stage, we train ImageDoctor on chain-of- thought (CoT) data learn the flook-think-predict reasoning process for both score and heatmap predictions. To prepare the CoT data, we first detect the highlighted regions in the ground-truth arti- fact and misalignment heatmaps to generate flaw region bounding boxes. Then, we employ Gemini 2.5 Flash (Comanici et al., (025) with carefully designed prompts to produce detailed reasoning between the image and corresponding human annotations. Finally, we organize the bounding boxes, reasoning traces, and ground-truth human annotations into the "look-think-pree" prredict- CoT format. The ImageDoctor model is optimized by:
The details can be found in Section B
f= Σ log log zei, I, + Σ [ Hall2,
where is the CoT reasoning text, I and P are the input image and text prompts, Ha and H. are the ground-truth and predicted heatmaps, respectively.
3.2.2 REINFORCEMENT FINETUNING
After cold start, we further perform reinforcement finetuning (RFT) with GRPO (Shao et al. 2024)
to enhance the reasoning ability of ImageDoctor. Given pair of input (I, P). ImageDoctor as the policy model Ty. generates a group of N candidate responses {o (o'...., ~N ). For each response o', compute a reward score RA using combination of reward functions. The rewards normal- ized within the group to compute the group-normalized advantage. RFT allows the model to explore diverse reasoning paths, directing it toward reasoning trajectories with high reward signals and en- hancing its generalization capability. The detailed GRPO formulation can be found in Section
We design suite of verifiable rewards encourage the model focus on the correct flaw regions, produce accurate evaluation scores, and generate precise heatmaps, including a grounding reward (Rc). a score reward (Rs)) and heatmap reward (R
Grounding Reward (Ra) aims to evaluate whether the model can accurately locate the flaw regions in an image. The model should ideally generate compact set of bounding boxes, both in number and area, that effectively cover the potential flaw regions. The grounding reward R& has three complementary components: 1) Completeness. The union of all bounding boxes should adequately cover the entire highlighted area in the artifact and misalignment heatmaps. We compute the ratio be- tween the area covered by the union of all bounding boxes and the total intensity of the heatmaps. 2) Compactness. Each bounding box should only cover flaw regions with minimal normal regions. We compute the average heatmap intensity within each predicted bounding box and then take the mean across all boxes, yielding higher rewards for bounding boxes with less normal regions. 3) Unique- ness. The model should not predict redundant bounding boxes, and thus the overlap between any pair of boxes should be minimized. We measure the Intersection over Union (IoU) between each pair of bounding boxes and apply a penalty for large overlaps. Implementation details are in Section D
Score Reward (R.)) evaluates how well the predicted scores āœ align with ground-truth human scores 8a. We use & distance and design R,s = Σg - ||sæ Salli, which encourages the model to produce score predictions that are close human judgments.
Heatmap Reward (R measures the similarity between predicted heatmaps H. and human anno- tated heatmaps Ha- We use l2 distance and define Ru Es 1 | H. 3 This formulation
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assigns higher rewards when the predicted maps closely match the annotations, thereby encouraging the model to produce precise and sharp flaw localization heatmaps.
The total reward is the combination of the three rewards: R RR + Rs + RH.
4 DENSEFLOW-GRPO: IMAGEDOCTOR AS DENSE REWARD
Reinforcement learning from human feedback (RLHF) (uee et al.
2025 Liu et al. 2025 has
demonstrated great success in improving image quality and image-text alignment for 121 genera- tion. However, existing RLHF methods such as Flow-GRPO (Liu et al. 2025) adopt an image-level formulation without fine-grained supervision. Specifically, given a prompt the flow model pe samples group of G individual images {atj)m and the corresponding reverse-time trajectories {(rh) -1"""" =m))_= Flow-GRPO optimizes the flow model by maximizing the following:
6
CROO (6)
Σ
T
Σ
min (ri(0) clip (1i(0), -e, 1-,, 1+0) 4:) -3D (po (opll
i=1
(2)
where the likelihood ratio ro) and normalized advantage A} of the i-th image are computed
Ps p
xijy c) x
A; R(ag c) mean( [R(z) e) std std([ e)
r()))))
(3)
Puu x
In Eq. both r() and reward R are computed on the image level. The reward signal is applied uniformly across all pixels in the image, treating every region equally, regardless its quality. Finer- grained supervision is more desirable, as it allows low-quality regions be penalized more, while encouraging high-quality areas. To fill this gap, we propose SonneFloo-GRPO which enables both image-level and pixel-level fine-gra tne-grained dense reward signals for flow model RL training, leveraging the rich image feedback generated by ImageDoctor. We first reformulate the likelihood ratio at each trajectory step allow pixel-wise advantage customization:
(a xi C)k.
s h, w) sg [r(())]
(4)
sg ( x), c)H,w]
where pa( æ/ a) is the pixel-wise likelihood, h, w denote the pixel location, and sg[-] the stop-gradient operation that only takes the numerical value, corresponding to detack in PyTorch. We can then apply the dense pixel-wise advantage that combines image-level reward R and pixel-level reward Rp::
(1, w: = Ro(ard C, h, w)
e, w)
(5)
std( h, w)
where (n e, h, w) R(w c) (1 Rp(w), h, w)) is the dense reward function.
The DenseFlow-GRe objective is defined as:
JDenso GTHW
Σ t,thh.w
min (s (, w)At(h, w), clip clip( (( w), 1-2, 1+8) Ai(h, wi))
where we omit the KI. regularization term for brevity. Note that in Eq. (4). (tb, h, w)) is numerically equal to r())) but allows the pixel-wise advantage to backpropagate through w....).... This is similar to the GSPO-token (Zheng et al. 2025) formulation, and we find more stable than directly computing the pixel-wise likelihood ratio.
the local image regions
EXPERIMENTS
5.1 EXPERIMENTAL. SETUP
Datasets. We train and evaluate ImageDoctor on the RichHF-18K (Liang et al.] 2024) dataset. RichHF-18K is subset of Pick-a-Pic (Kirstain et al. 2023). consisting of 16K training samples,

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Table 1: Performance comparison of score prediction on RichHF-18K.
Method
Plausibility
Aesthetics
Semantic Aligument
Overall
Average
PLCC1
SRCC
PLCC 1 SRCC f
PLCC
SRCC
PLCC
SRCC
PLCCT
SRCC
ResNet-50 He et al. 2016
0.495
0.487
0.370
0.363
0.108
0.119
0.337
0.308
0.328
0.319
CLIP (Radford
0.390
0.378
0.357
0.360
0.398
0.390
0.353
0.352
0.374
0.370
PickScore Kirsam c air 2023
0.010
0.028
0.131
0.140
0.346
0.340
0.202
0.226
0.172
0.183
RichHF [Linng
0.693
0.681
0,589
0.474
0.580
0.586
0.582
ImageDoctior (Ours)
0.727
0.711
0.681
0.662
0.808
0.799
0.745
0.725
0.741
0.724
Table 2: Performance comparison of heatmap prediction on RichHF-18K.
Artifact
Misalignment
Method
All data
GT
GT
|
All data
GT = 0
GT 0
MSE 4
MSE L
OC
KLD L
SIM
MSE
MSE
CC
KLD
SIM
ResNet-50 al. 2016)]
0.00996
0.00093
0.506
1.669
0.338
CLIP Gradhent (Smonyan al. 2013
0.00817
0.00551
0.015
3.844
0.041
RichHF (Liang erae 2024
0.00920
0.00095
0.556
1.652
0.409
0.00304
0.00006
0.212
2,933
0.106
ImageDDctorr (Ours)
0,00891
0,00076
0.571
1.477
0.412
0.00299
0,00003
0.225
2,863
0.108
IK validation samples, and IK test samples. For each text-image pair, it provides two heatmaps and four fine-grained scores annotated by a total of 27 annotators. of ImageDocctor, we also test it the GenAl-Benn -ench (Li et al. 2024) and 7/FA (Hu et al. 2023).
To further assess the generalizability
Evaluation Metries. We follow the official evaluation protocols of the datasets. For score predic- tion. employ Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Correlation Coefficient (SRCC), which measure how well the predicted scores correlate with human annota- tions. For heatmap prediction, we report the Mean Squared Error (MSE) between predictions and ground truth. Additionally, we adopt standard heatmap metrics (Liang et al.] 2024) including KI. Divergence (KLD), Similarity (SIM), and Correlation Coefficient (CC), providing comprehensive assessment of spatial prediction quality.
Implementation Details All experiments are conducted four AMD MI250 GPUs. We adopt Owen2.5-VL-33. (Bai et al., (025) as the MLLM backbone. Training is performed for 5 epochs in cold start stage and epochs in stage 2. We train for 400 steps for RFT. Learning rates are set to 1 x 10-5 for cold start and 1 x 10-6 for RFT. Training images are resized to 512 x 512.
5.2 MAIN RESULTS
Results on RichHF-18K. Table I shows the score prediction results across four dimensions on RichHF-18K. We compare with ResNet-50 (He et al. 2016). CLIP (Radford et al., (02T]) models fine-tuned on the RichHF-18K dataset, as well as the the-the-shelf PickScore (Kirstain et 2023)
model. In addition, we compare with the RichHF model (Liang et al.) (024). which is trained on RichHF-18k and able to generate both score and heatmap predictions. ImageDoctor achieves the best performance across all dimensions, substantially improving semantic alignment (PLCC: 0.808 vs. 0.474) and raising the average PLCC from 0,586 to 0.741 compared to the previous best method RichHF, demonstrating much stronger correlation with human judgment. These gains also extend to heatmap prediction results in Table where ImageDoctor achieves the best performance, highlighting its ability to precisely localize flaws in generated images.
Results on GenAL-Bench and TIFA, Table 3 presents the results on the GenAI-Bench and TIFA datasets. To assess the generalizability of ImageDoctor,
evaluate the model trained solely on RichHF-18K without any fine-tuning on these two benchmarks. Despite differences in image sources and the inherent subjectivity of annotators, ImageDoctor consistently outperforms previous human preference models, achieving higher correlations with human annotations.
Heatmap Visualization. In Fig. we present qualitative examples of heatmap predictions gener- ated by ImageDoctor. For the misalignment heatmaps (Fig. 3 (a)), our model accurately localizes objects that fail to correspond to the prompt while producing fewer false positives. For the ar- tifact heatmaps (Fig. (b)). ImageDoctor effectively highlights all the regions containing artifacts, demonstrating precise spatial grounding of visual flaws. More examples are provided in Section E.4
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Table Quantitative comparison on the GenAI-Bench and TIFA datasets.
Table 4: Ablation study of the proposed modules.
Method
GenAl Bench
PLCC
PLCC
TIFA
SRCC
RichlIF PLOC
SRCC
Settings
PLCC 1
Average
SRCC
CC1
KLD
CC1
Misalignment
KLD
CLiPSoore
PickScore
Imagr Reward
0.164
0.354
0.350
0.413
0.33
0.308
0.61
0.300
0.329
0.346
0.302
0.340
0.057
0.274
Cold Start Stage
Hearmup
Heatmop wil task token
0.653
0.660
0.655
0.650
0.656
0.508
0.532
1.597
1.728
0.123
0.166
3,231
3,031
HPS
0.139
0.380
0.25%
Coll Start Stage
0.220
1.333
0.2
2982
VQASeare
0.409
wio "look"
0.714
0.705
0.534
1.599
0.160
3.131
EvalMuse
0.712
0.549
weo "shink"
0.708
0.698
0.542
1.592
0.190
3,038
HPSV3
0.139
0.435
0.484
0.205
0.184
Pinetuning
grounding reward
ImageDoctor
0,514
0.740
0.764
0.808
0.799
ST
RichHFF
ImageDoctor
6T
RichHF
Image
Nisson
forest.
girl busy yogurt countertop.
Digital mesterpiece, Alice Wenderland stonding lange mushrooms cops spots. Artifact
Misalignment
Figure Visualization of misalignment and artifact heatmaps.
5.3
ABLATION STUDY
We conduct ablation experiments on the RichHF-18K dataset to analyze the contribution of each proposed module in ImageDoctor. The results are shown in Table 4
Task token for heatmap decoder. We introduce the special task tokens in the MLLM backbone to guide the heatmap prediction in the heatmap decoder. To demonstrate its effectiveness, we finetune the ImageDoctor model after cold start stage on both score and heatmap prediction tasks with and without the task tokens. As shown in Table (rows and 3), removing the task tokens results in notable decrease of heatmap prediction performance, e.g., CC drops by 0.024 and 0.042 for artifact and misalignment, respectively.
Effect of "look" and "think". We propose a flook-think-prediicin " paradígm for T2I evaluation, where the model first localizes potential flawed regions by predicting bounding boxes ("look")), and then analyzes and reasons about these flaws ("think") before making predictions. We conduct ablation studies assessing their contribution in Table 4 (rows 5 and 6). Removing either component leads to a performance drop. particular, "think" plays more critical role in score accuracy, with PLCC decreasing from 0.720 0.708 without "think", while "look" provides stronger benefits for heatmap prediction, where misalignment CC falls from 0.224 to 0.160 without "look". These results highlight the complementary roles of "look" and "think". ": the former enhances spatial localization of flaws, while the latter strengthens semantic reasoning for accurate evaluation.
Effect of grounding reward. We introduce a grounding reward in reinforcement finetuning to en- courage the model to accurately localize flawed regions. As shown in Table (row 8), removing the grounding reward leads to decline in score prediction performance. Moreover, it also causes no- table drop in artifact heatmap quality. These results hightlight the importance of grounding reward.
APPLICATION IN DOWNSTREAM TASKS
To further demonstrate the effectiveness of ImageDoctor's rich feedback, explore its applica- tion in downstream tasks. specifically as a verifier in test-time scaling and a reward function for reinforcement learning of T2I model.
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6.1 IMAGEDOCTOR AS VERIFIER FOR TEST-TIME SCALING
Recent works have explored test-time scaling (Ma et al. 2025a Guo et al. 2025) for improving diffusion model performance by generating multiple samples during inference and searching for the best candidate. This approach requires verifier distinguish subtle differences among generated images and reliably select the best candidate. We test ImageDoctor as an image verifier, where we sample 16 images for given prompt and select the best candidates leveraging the four-dimensional scores. The images are generated at a resolution of 1024 x 1024 using the Flux-dev (Labs) (024) model. Visualization results in Fig. show that Image reliably selects images that better align with the prompt, often preferring those with more realistic and coherent details.
6.2 IMAGEDOCTOR AS A REWARD FUNCTION
Setup. We use Stable Diffusion 3.5- medium as the base model and demon- strate the results of using ImageDoctor reward function in Flow-GRPO as well as the proposed HanseFlow-GRPO (Sec. 4
Condidates
PickScone
ImageReward
Iocto
We train the models for 1,300 iterations the Pick-a-Pic prompts (Kirstain al.
elephant behind free. Trunk side and the back legs the other.
2023). We evaluate the base and finetuned model performance
DrawBench (Sa-
haria et al. (022) using IewaeReward (Xu
al. 2023), CLIPScore (Hessel al.
2021 and UnifiedReward (Wang et al.
green apple and black bockpack
2025b) as the metrics.
Figure 4: Qualitative comparison on selected images by different verifiers in test-time scaling. ImageDoctor picks the images that faithfully reflect the text prompt (top) and preserve realistic object scale (boltom).
Results with Flow-GRPO. Flow-GRPO adopts score-only reward functions for training T2I model. We use ImageDoctor predicted scores as the reward function, and compare the performance of using PickScore (Kirstain al. 2023) and RichHF (Liang et al. 2024). As shown in Table [ using ImageDoctor as the reward function consistently offers the highest gain across all evaluation metrics thanks to its strong ability in predicting accurate image evaluation scores.
Base
Flow-GRPO
DenseFlow-GRPO
Table 5: Performance on human preference scores when ImageDoctor serves reward function.
Reward
LewaeReward
CLIPScore
UnifiedReward
Base
0.818
0.951
2.903
cat the right of ternis rocket.
Flow-GRPO
PickScore
1.002
0.941
2.940
RichHF
0.879
0.944
2.921
ImageDoctor
1,029
0,956
2.960
DenseFlow-GiPO
right hydront.
ImageDoctor
1.100
0.969
3.000
Figure 5: Flow-GRPO vs. DenseFlow- GRPO. The artifacts are boxed.
Results with DenseFlow-GRPd ImageDoctor is capable of generating spatial heatmaps and scalar scores, making well-suited for pixel-level feedback. To leverage this property, we introduce DenseFlow-GRPO, which incorporates heatmap-guides dense rewards for more fine-grained optimization. As shown in [ by combining scalar scores with heatmaps, DenseFlow-GRPO achieves the best overall results and outperforms the Flow-GRPO variant with ImageDoctor score prediction as the reward function. These findings demonstrate that ImageDoctor's 's dense, multi- aspect feedback provides fine-grained supervision and leads consistently stronger alignment with human preference. We provide a visual comparison of Flow-GRPO and DenseFlow-GrR -RRP in Fig. Flow-GRPP adopts an image-level formulation that is often insufficient for removing localized arti-

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facts, the reward signal provides sparse global score for the entire image, while DenseGRPO's heatmap-base based dense reward design can target and refine local details to eliminate such flaws.
CONCLUSION
In this work, we introduce ImageDoctor, a unified evaluation framework for text-to-image genera- tion that produces both multi-aspect scores and spatially grounded heatmaps. To enhance the eval- uation accuracy and interpretability, we propose a "look-think thinn--redicct paradigm, which localizes propose DenseFlow-GRPe
flaws, analyzes them, and delivers a final judgment. Furthermore, that utilizes the dense rewards generated by ImageDoctor for finetuning T2I model. Extensive exper- iments demonstrate its versatility- serving as metric, verifier, and reward ImageDoctor provides robust, interpretable, and human-aligned feedback for generated images.
that
REFERENCES
Ying Ba, Tianyu Zhang. Yalong Bai, Wenyi Mo, Tao Liang, Bing Su, and Ji-Rong Wen. Enhancing reward models for high-quality image generation: Beyond text-image alignment. In ICCV, 2025.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502. 13923, 2025.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Oni Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capa- bilities. arXiv preprint arXiv:2507.06661, 2025.
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor lonescu, and Mubarak Shah. Diffusion models in vision: survey. PAMI, 2023.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In /CML, 2024.
lan J Goodfellow, Jean Pouget-Abadie Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurlPS, 2014.
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Joshua M Susskind, and Navdeep Jaitly. Matryoshka diffusion models. ICLR, 2023.
Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, and Pheng- Ann Heng. Can we generate images with cot? let's verify and reinforce image generation step by step. arXiv preprint arXiv:2501.13926, 2025.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, 2016.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. In EMNLP, 2021.
Jonathan Ho. Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurlPS, 2020.
Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable eext-to-imaee faithfulness evaluation with question an- swering. In CVPR, 2023.
Diederik Kingma and Max Welling. 1rXiv:1312.6114 2013.
Auto-encoding variational bayes.
arXiv preprint
10

---

Published as a conference paper at ICLR 2026
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan Yen Lo, et al. Segment anything. In CVPR, 2023.
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick- a-Pic: An open dataset of user preferences for text-to-imaae generation. In NeurIPS, 2023.
Black Forest Labs. Flux. http:::/giithab.ccm/bblaaaa forestt-oooottt-eeee lanstttlabbff 2024.
Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, et al. Holistic evaluation of text- to-image models. In NewrlPS, 2023.
Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. Evaluating and improving compositional text-to-visual generation. In CVPR, 2024.
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre- training for unified vision- vision-language understanding and generation. In ICML, 2022.
Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, et al. Rich human feedback for text-to-image genera- tion. In CVPR, 2024.
Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl. arXiv preprint arXiv:2505.05470, 2025.
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, and Saining Xie. Scaling inference time compute for diffusion models. In CVPR, 2025a.
Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. arXiv preprint arXiv:2508 03789, 2025b.
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint 12..::::122.11722 2021.
Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Kunpeng Ning, Chaoran Feng, Bin Zhu, and Li Yuan. Wise: A world knowledge-informed semantic eval- uation for text-to-image generation. arXiv preprint arXiv:2503.07265 2025.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In /CML, 2021.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In CVPR, 2022.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. In NewrIPS, 2022.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- cal reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Vi- sualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 2013.
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng. and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. In NewrlPS, 2024.
11

---

Published as a conference paper at ICLR 2026
Yibin Wang, Zhimin Li, Yuhang Zang. Chunyu Wang, Qinglin Lu, Cheng Jin, and Jiaqi Wang- Unified multimodal chain-of- of-thh reward model through reinforcement fine-tuning. arXiv preprint wrXhv:2505.03318 2025a.
Yibin Wang, Yuhang Zang, Hao Li, Cheng Jin, and Jiaqi Wang. Unified reward model for multi- modal understanding and generation. arXiv preprini arXiv:2503.05250 2025b.
Ze Wang. Hao Chen, Benran Hu, Jiang Liu, Ximeng Sun, Jialian Wu, Yusheng Su, Xiaodong Yu, Emad Barsoum, and Zicheng Liu. Instella-t2i: Pushing the limits of 1d discrete latent space image generation. arXiv preprint arXiv:2506. 21022, 2025c.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to- image synthesis. arXiv preprint arXiv:2306.00 09341, 2023a.
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score: Better aligning text-to-image models with human preference. In CVPR, 2023b.
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin,
Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12355 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In NeurlPS, 2023.
Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505..m 07818, 2025.
Wenliang Zhao, Minglei Shi, Xumin Yu, Jie Zhou, and Jiwen Lu. Flowturbo: Towards real-time flow-based image generation with velocity refiner. In NeurlPS, 2024.
Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, et al. Group sequence policy optimization. arXiv preprint arXiv:2507.18071, 2025.
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APPENDIX
A DETAILS OF HEATMAP DECODER
To equip the MILM with the ability to generate accurate heatmaps, design lightweight heatmap decoder. The decoder takes image features extracted by the visual encoder, along with a learned heatmap token and task token that specifies the type of heatmap to be produced. First, the special tokens (task and heatmap) serve as queries to attend over image embedding, after which a multi-layer perceptron (MLP) updates all special tokens. Next, the updated special tokens are used keys and values to and the image embeddings act as queries to refine the image features. The updated image embeddings are then passed through series of convolution and deconvolution layers to upscale to the original image size. Finally, before applying the sigmoid activation, we introduce an additional token-to moken-to-image attention: the updated image embeddings attend once more the special token embeddings. The attended heatmap token features are projected through another MLPs, and their outputs are combined with the upsampled image embeddings via a spatial point-wise product. This design strengthens the role of the task tokens in guiding the final heatmap prediction, ensuring that both semantic reasoning and localized visual evidence contribute to the spatial diagnosis. = Back to main paper Section 3.1
WANTED The Imaggelbector.
WANTED The ImageeDcctor.
Misalignment
Artifact
Heatmap Decoder
MLP
Heatmap Token
Dot Product
tok, to img. Attention
Conv, Upsampling
Self Attention
tok. to img. Atteation
img. to tok. Attention
MLP
Task Token
Heatmap Token
Image Feature
Figure 6: The architecture of heatmap decoder.
B GROUNDING IMAGE REASONING GENERATION
For reasoning path generation in Stage of the cold start phase, we employ Gemini-flan with carefully designed prompts to produce detailed reasoning that bridges the image and its human annotations. Since advanced VLMs possess strong visual grounding capabilities, we enrich the input beyond the original image-prompt pair by also providing human-annotated heatmaps indicating the locations of artifacts and misalignments. To preserve the fidelity of the original image, we highlight these flawed regions using circled outlines derived from the heatmaps. rather than directly overlaying them, which enables the VLM to more accurately localize unsatisfactory regions. By combining these localized observations with human- providdd scores, Gemini 2.5 Flash generates reasoning that is more precise and relevant to the evaluated image. This high-quality reasoning chain is then used to fine-tune Qwen2.5-VL VL,, equipping it with structured and grounded evaluation capabilities.
+ Back to main paper Section 3,2.2
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Prompt for Gemini 2.5 Flash 2.5 for Reasoning Data Generation
assistant tasked with generating reasoning process for human evaluation of a generated image. I am providing three images in a specific order: 1. The First Image provided is the 'Original Image': This is the image generated by a text-to-image model based on the input prompt "PROMPT". This Original Image is the SOLE subject of your evaluation.
are a multi-modal
2. The Second Image provided is the 'Artifact Heatmap Image': This image is a visual aid. It is the Original
Image with overlay that ONLY serves to visually pinpoint the artifact locations. Its ONLY purpose is to help you locate the specified coordinates on the Original Image and then describe the visual characteristics of the artifact locations *on the Original Image*.
3. The Third Image provided is the 'Misalignment Heatmap Image': : This image is a visual aid. It is the Original
Image with overlay that ONLY serves to visually pinpoint the misalignment locations. Its ONLY purpose is to help you locate the specified coordinates the Original Image and then describe the visual characteristics of the misalignment locations *on the Original Image*
Below, you will find the human evaluation data of the Original Image for several dimensions, including scores, keyword alignment status.. Your goal is to analyze the Original Image and articulate plausible step-by-step reasoning that would lead to the given scores, speaking from the perspective of the evaluator.
Human Evaluation Results:
Semantic Alignment: How well the image content corresponds to the original caption.
Score: MISALICNMENT SCORE
Aesthetics: Assessment of composition, color usage, and overall artistic quality.
Score: AESTHETIC SCORE
Plausibility: Realism and visual Fidelity of the Original Image, including distortions or unnatural details.
Score: ARTIFACT SCORE
Overall Impression: General subjective assessment the image's quality.
Score: OVERALL SCORE
Your Task: For each of the four evaluation dimensions (Semantic Aligrment, Aesthetics, Plausibility, and Overall Impression), please provide paragraph explaining your reasoning for the score, as if you were the original human evaluator assessing the Original Image.
Refer to specific visual elements of the Original Image that support your reasoning.
For the "Plausibility" or "Semantic Alignment" dimension: Refer to the Second Image (Artifact Heatmap Image) or Third Image (Misalignment Heatmap Image) to visually locate these coordinates on the Original Image, specifically connect your reasoning to these coordinates with the help of provided Artifact or Misalignment Heatmap Image. Then, describe visual nature of the artifact misalignment locations it appears *on the Original Image*. For other dimensions, if relevant, explain any potential
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reasons for Consider all provided human evaluation results, including any labels or listed misaligned keywords, in your reasoning. Output Format for Each Dimension: Conclude each paragraph the following strict format: "Therefore, give it a score of x.xx." Important Instructions:
less than perfect by examining the image.
with a sentence
Do not mention the artifact misalignment heatmap images. Use the coordinates to focus your visual inspection, but describe only what is visible in the Original Image.
Be concise and direct in each evaluation.
Do not include specific coordinates in your reasoning, just refer to the visual characteristics at those locations.
Please now provide the reasoning for each dimension, focusing your analysis and descriptions on the First Image (the 'Original Image' of the "PROMPT") using the Second Image (the AArtffact Heatmap Image') and Third Image (the Misalligmmett Heatmap Image') strictly as visual guide locate arti fact and inmeet locations on the Original Image. Do not mention the Artifact or Masggigmment Heatmap Images in your reasoning, only use them to locate coordinates visually on the Original Image. As if human evaluation results and heatmap are available, you only have the Original Image to evaluate.
Be precise, concise, and strictly refer to the Original Image in all visual descriptions. For each dimension, use sentences: one for the reasoning and for the score conclusion.
Begin Evaluation:
C RFT FORMULATION
Given pair of input (I, P), ImageDoctor as the policy model me. generates group of N candidate responses {o ..... 0%, J. For each response o', we compute a reward score R' using a combination of reward functions. The rewards are normalized within the group to compute the group-normalized advantage:
R mean (MRcan([R]) (R))) A'
std( (Ri (R 1
We update the policy model me by maximizing the GRPO objective function:
N min (wtA', clip(w", - e, +e)+)) (DKI |l mer)
(O)
(8)
Tu T.P] denotes the likelihood ratio between me and the old policy model тaad- The clipping threshold regulates the extent which the policy model may update in each step to stabilize training. & controls the KL divergence regularization term that constrains to remain close to the reference model Reft which is the model at the start reinforcement learning. Back to main paper Section 3.2.2
where w¹ molo' I.P)
D GROUNDING REWARD DETAILS
To fulfill the three criteria mentioned in the main paper, design three sub-rewards. First, compute the average intensity within each bounding box and then take the mean across all boxes. This encourages the model to identify compact regions that align with the highlighted areas, yielding
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higher rewards when bounding boxes accurately capture potential flaws. Second, measure the Intersection over Union (loU) between each pair bounding boxes and apply a penalty for large overlaps, which discourages redundant box predictions and promotes compactness in number. Fi- nally, we compute the ratio between the area covered by the union all bounding boxes and the total intensity of the heatmap, ensuring that the highlighted regions fully covered. When the heatmap is blank and no bounding boxes are predicted, we assign reward of 1. Conversely, if heatmap contains highlighted regions but bounding boxes are predicted, if bounding boxes predicted on a blank heatmap, we assign a reward of O. = Back to main paper Section 3.2.2
E EXPERIMENTAL DETAILS
DATASETS
RichHF-18K3 is subset of the Pick-a-Pic, consisting of 16K training samples, 1K validation sam- ples, and IK test samples. For each text-image pair, two heatmaps and four fine-grained scores are annotated by total of 27 annotators.
GenAI-Bench: It contains 1,600 prompts designed to cover essential visuo-linguistic compositional reasoning skills, with prompts sourced from professional graphic designers experienced in T2I sys- tems. More than 15,000 human ratings are collected across ten different T21 models, ensuring both diversity and difficulty.
TIFA: The test set includes 800 generated images based on 160 text inputs from TIFA v1.0. These images produced by five generative models and annotated by two independent annotators, pro- viding additional benchmarks for evaluating generalization.
E.2 EXTENDED QUANTITATIVE RESULTS
In Table 6 we provide additional quantitative results on the RichHF-18E 18K dataset. Compared with the main paper, we include the self-evaluated baseline RichHF and ImageDoctor with the fast infer- ence mode, with additional heatmap metric Normalized Scanpath Saliency (NSS) and AUC-Judd. The fast inference mode is motivated by scenarios where only quantitative scores needed for effi ciency, such as when serving as reward function. To enable this, we append the input prompt with fixed reasoning template-<thinkee already completed, allowing ImageDoctor to directly output scores and heatmaps without explicit reasoning chains. As shown Table [6] this variant incurs only a minor performance drop com- pared to the full model, while maintaining high accuracy with significantly improved speed. For this reason, we adopt this faster variant in Flow-GRPO and DenseFlow-GRe -GRPO, where efficiency in advantage computation is critical.
</think> <answer>-as if the reasoning chain were
E.3 QUALITATIVE RESULTS
Fig. 7 1presents ImageDoctor's responses given an image-promen pair. We observe that ImageDoctor first localizes potential flaw regions, where its reasoning and heatmap predictions closely align. For example, it correctly identifies misaligned keywords such drinking lemonade and making a lot of phone calls. In addition, it detects artifacts appearing in the image, including unnatural glass shapes, distorted hands, unrealistic liquid in the glass, and the phone. Finally, the heatmaps accurately depict the misaligned and implausible areas, highlighting ImageDoctor's strong localization and reasoning capabilities and alignment with human preferences. More qualitative results shown in Figs. T0
to 12
E.4 ADDITIONAL HEATMAP VISUALIZATION
As shown in Fig. we provide additional heatmaps for qualitative comparison. + Back to main paper Section
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Table Score prediction and heatmap prediction results on RichHF-18K. indicates lower is better and 1 indicates higher is better. GT = O refers to empty ground truth heatmap. GT > 0 refers to heatmaps with artifact of misalignment. There are total 69 and 144 out of 955 test samples are empty for artifact and misalignment heatmaps. RichHF; results reported in the paper. RichHF'; self-evaluated results from the official checkpoint.
(a) Performance comparison of score prediction.
Pluusibility
Aesthetics
Semantic Alignment
Overall
Average
PLOC f SRCC
PLCC1
SRCC
PLCC
SRCCT
PLCC
SRCC
PLOC
SRCC
ResNet-50 Ht
6
0.495
0.487
0.370
0.363
0.108
0.119
0.337
0.308
0.328
0.319
CLIP Radiond el al. A124
0,390
0.378
0.357
0,360
0.398
0.390
0.353
0.352
0.374
0.370
PickScore Kirsta al 2023
0.010
0.028
0.131
0.140
0.346
0.340
0.202
0.226
0.172
0.183
RichHF Liang et al.
2024
0,693
0.681
0.600
0,589
0.474
0.496
0,580
0.562
0,586
0.582
RichHF' (Liang
2074
0.704
0.694
0.636
0.618
0.563
0.602
0.648
0.634
0.638
0.637
ImageDoctor-fast
0.722
0.712
0.675
0.656
0.793
0.792
0.728
0,700
0.729
0.715
ImageDoctor
0.727
0.681
0.662
0.808
0.799
0.745
0.741
(b) Performance comparison of artifact heatmap prediction.
All data
GT =0 0
GT'S > 0
MSB
MSE +
CC
KLD +
SIM
NSS f
AUC-Judd f
R0 (He et al.
2016
0.00996
0.00093
0.506
1.669
0.338
2.924
0.909
RichHF
Liang et al. 2024
0,00920
0,00095
0.556
1.652
0.409
3,085
0.913
Richlil
Lrang et al. 2024
0,00920
0,00080
0,545
1.568
0.375
1.808
0.893
ImageDDotorr-fst
0.00879
0.00091
0.569
1.483
0.405
1.877
0.903
ImageDoctor
0.00891
0.00076
0.571
1.477
0,412
1.884
0,903
(c) Performance comparison of misalignment heatmap prediction.
All data
GT
GT
MSE
MSE 4
CC
KLD |
SIM
NSS
AUC-Judd
CLIP Gradient (Simonyan et al. 2013
0.00817
0.00551
0.015
3.844
0.041
0.143
0.643
RichHF (Liang al 2024 RichHF (aang
0.00304
0.00006
0.212
2.933
0.106
1.411
0.841
2024
0.00300
0.00020
0.219
2,900
0.099
1.224
0.794
ImageDoctos fast ImageDoctor
0.00310
0.04001
0.219
2,890
0.121
1,230
0.796
0.00299
0.00003
0.225
2.863
0.108
1.257
0.801
<think> I need to focus on the bounding box area. Proposed regians
Prompt:
(xyaa): L[249, 37.3771888 375,186]: 21116,220 2201,421; 3,2285 234,334,283 2433
necruiter with red sweater with button down shirt undemeatth, drinking lemonade and making a lot of phone calls.
4[377 425, 426]
Semantic Alignment: The image successfully depicts person with red hair wearing sweater, but it fails to show the actively drinkinn lemorade' engaging 'making lot phone calls". Therefore, I give it a score of 0.58.
Aesthetics: The overall visual oppeal is diminished by the somewhat flat lighting and the genenic, uninteresting background elements. Therefore, I give it score of 0.58.
Plausibility: The image exhibits several visual inconsistencies, including the distorted and unnatural appearunce of the hands holding the phone and gloss and phone itself looks a generic, non Funchionall device, Additionally, the liquid in the glass appears unnaturally bright and locks realistic tronsporency. Therefore, I give it score of 0.42. Impression: While image correctly portroys care subject of a person with red hair a sweater, its significont foilure to depict the specifies led octions of drinking and making phone calls, combined with noticeable visual artifacts and somewhat generic aesthetic, results in low overall quality impression. Therefore, I give it score of 0.42. </hink Sementic Aligrment scone: 0,58; Aesthetic Plousibility score: 0.42: Overall Impression score: 0.42 </answer>
0,58;
Misalignment
Artifact
Figure 7: Example of ImageDoctor Predictions.
E.5 ADDITIONAL DENSE- GRPO RESULTS
In Fig. we provide additional comparison between Flow-GRPO refined with PickScore and DenseFlow-GRPe refined by ImageDoctor. Back main paper Section
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Published as a conference paper at ICLR 2026
GT
RichHF
ImageDoctor
GT
RichHF
ImageDoctor
purleitspree
lighting Ciemelii igeiting
A 20 in cyberpurkkkooooo ouffit
Create rydt magical is with bland hai standing in fheld wildhiowtrs
velle jumping off o diff
Photo of cat eating burger like person
In the fereground, stanck rocky with his back the viewer: He is wnopped in dark green overcoat. and grips waaking stick
dag with mouth Misalignment
liberation
Artifact
Figure 8: Additional heatmap visualization.
DenseFlow-GRes Ioctor
Flow Now-ERPO w/ PickScore
DenseFlow-GRPO ImageDoctor
Fittning Hernagh, e ohft0 sholcing extrance
see flocig
ROCK
RRck W Roll
DALL
Hater Peeepicettiieg tongae wearing plasees hodding sign th! Aock
Old whet dul dksi
set city reber 1eat, hit teche pgr Hin conmaat Iestrmsdate, ta Enbrent
beroust thin vonEs scince fict fatumantc centume. 201
praphic ahirt dbeat lody
AELDING ICE. CREA
M
Eye
Watting los creare why! obbr cour
cheratic plato Tran flying ovr the dips, grasn nchure bocutE, Fald
Figure 9: Additional DenseFlow-GRPO visualization.
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Published as a conference paper at ICLR 2026
F LIMITATIONS
While ImageDoctor demonstrates strong capability in providing interpretable multi-aspect scoring with spatially grounded feedback, it is important to acknowledge certain limitations that may affect its generalizability and applicability.
Challenge of large-scale fine-grained annotation. Collecting detailed annotations-inee includingg multi-aspect scores and heatmaps- time-consuming and labor-intensive, which limits the vol- ume of available data. The dataset we trained on, i.e. RichHF-18K, is subset of Pick-a-Pic, which mostly generated by some old image generation models, egg. Stable Diffusion XL. This constraint in both scale and recency can restrict the full potential of ImageDoctor. Nevertheless, in this work we show that even with limited and somewhat outdated annotations, ImageDoctor can effectively trained and still provide valuable dense feedback verifier, reward function, and metric.
Human preference is subjective. Quantifying image quality is inherently challenging because dif- ferent people may perceive the same image in very different ways, making it difficult to establish universally agreed-upon standard. This subjectivity often results inconsistent annotations, which can affect dataset quality. It also affects heatmap annotations: for instance, annotators may disagree on the exact regions that constitute misalignment, leading to noisy supervision. Consequently, Im- ageDoctor achieves lower performance on misalignment heatmaps compared artifact heatmaps.
Tradeoff' between Interpretability and Efficiency. While ImageDoctor offers substantially im- proved interpretability through its reasoning chains and pixel-level diagnostics, this naturally intro- duces additional inference cost compared to scalar-only evaluators. This reflects an inherent trade- off between interpretability and efficiency: scalar metries are compact but cannot explain why an image is deficient, whereas ImageDoctor's 's structured reasoning enables clear attribution of issues such as aesthetic flaws, semantic misalignment, or implausible regions. To maintain practicality for real-world deployment, we also provide fast inference mode, in which the model is trained with reasoning chains but prompted to skip them at inference by immediately emitting an end-of of-thinking token. This variant preserves comparable accuracy while significantly reducing computational over- head. Together, these two modes offer a flexible spectrum between interpretability and efficiency, allowing users to select the configuration that best fits their application needs.
Prompt: "Common tool that protects from the rain."
Model: Kolors
Output
Misalignment Heatmap
Artifact Heatmap
Ithink need focus the bounding
Proposed regions: 1,1249,57 298,266) 11246,357.2954400
Semontic Alignment: The image successfully depicts common tool, umbrello, which clearly serves to protect from the rain, fulfilling the core elements of the prompt. Therefore, I give it score of 1.0.
features strong composition the umbrella clear focal paint, complemented by effective light and dark atmospheric scene, The rain effects well-rendered, adding depth visual interest Therefore, give it score of 0.83.
Aesthetics: The
tores that create a dromatic
wet street.
Plausibility: While the overall
visually comvincing, unbrella's 's handle appears somewhat detached unnaturally fleating above the
ground, slightly detrects from complete infegration the tool with its environment, Thenefore, I give scone 0.92. Overall Impression: The image presents compelling and aesthetically pleasing depiction of an umbrella in rainy setting, effectively conveying the intended concept. Despite
visual appeal, the subtie imperfections the unbrella's position achieving a perfect score. Therefore. I give it acore 0.92 </think> Conserr-Semonte Aligrment score: 1.0, Aesthetic score: 0.83; Plousibility score: 0.92; Overall Impression score: 0,92+/onswer>
from
score:0.83.
Figure 10: Example of ImageDoctor Predictions with Prompt from WISE NNu al.
2025
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Published as a conference paper at ICLR 2026
Prompt: "Traditional food of the Mid-Autumn Festival"
Model: 503.5-medium
Output
Misaligrment Heatmop
Artifact Heatmap
Proposed regiong: 1/347.19512.160] 2,014,19, 268,22); 3.2260,193,41 (161,499,411 20,114,2229 convey any specific cultural leading to significant misalignment with the prompt' implied mearing. Therefore, I give score Aesthetics: The image exhibits aesthetic qualities, festuring well well-bolanced compasition with vibront colors and oppoaling lighting that highlights textures
I need focus the bounding
Sementic Alignment: image
atmesphere releted
Festival"
The arrongement the garnishes appear slightly less defined than the main components. Therefore, I give score 0.75.
food
inviting oppetizing presentation, though
snoller
Plousibility: While the food generally appears realistic, there are minor visual inconsistencies that detract from its complete plausibiiity. Specifically,
smaller garnishes, particularly natural imperfections expected real food. Therefore, I give score 0.83.
the
artificial overly smooth texture, locking
Overall Impression: Despite its high aesthetic appeal and general plausibility, the image folls short in conveying the specific cultural context of the "Mid Autumn Festival"
key aspect the prompt: This imoge does fully fulfill the creative intent behind the prompt. Therefore, give it score 0.18. 4/think
discomect significantly impocts the overall Impression,
canswer-Semantie Aligrment score: O:: Acsthetic zcore: 0.75; Plausibiiity score: 0.83: Overall Impression score: 0.18+/answer>
Figure 11: Example of ImageDoctor Predictions with Prompt from WISE (Niu et al. 2025
Prompt:
"The item worn protect head riding motorcycle."
Model: Flux-schnell
I Output
Misalignment Heatmap
Artifact Heatmop
bounding Semontic Alignment: The image clearly depicts helmet worn by person riding motorcycle, accurately representing key elements the prompt. The helmet is designed for protection, the prompt specifically requested "the item womn to protect the head." and the helmet's design. strop, effectively fulfills aspect without any visual misolignments Therefore, give Aesthefics: The composition strong, with the subject centrally framed well-lit against a softly blurred background, creating visually appealing scene, However, the reflectiond on the helmet 5 visor appear somewhat distorted or smudged, slighlly detracting from the overall polish of 0.75. image generally presents realistic scene, Spezifically, the text and branding on the helmet's front panel appear somewhat blurry and indistinct, locking crisp detail, and the reflections the visor show minor distortions rather than clear, netural light patterns, Tharefore, I it motorcycle riding, making largely effective. However, the minor imperfections the helmet's 's details, such as the slightly distorted text and reflections, along with the somewhat artificial appearance the reflections the visor, prevent from achieving higher level of photogrophic realism and excellence. Therefore, give
Proposed regions: 1118,,86 150]: 22990,162,339 211] (331..13 17,,380,222)
including its
1.O.
realism of the image. Therefore, give Plausibility: While
inconsistencies reduce
plausibility.
0.67.
Impression: image successfully conveys intended concept
ranswer> Semantic Alignment score: 10; Aesthetic score: 0.75; Plausibility score: 0.6; Overall Ixpression score: 0.75</nnwwer>
Figure 12: Example of ImageDoctor Predictions with Prompt from WISE Niu al. 2025
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Published as a conference paper at ICLR 2026
G PROMPT FOR IMAGEDOCTOR
The prompt to ask ImageDoctor to multi-aspect scores and spatial feedback with reasoning as following:
Prompt for ImageDoctor for T21 Evaluation
<image>
Given caption and an image generated based this caption, please analyze the provided image in detail. Evaluate it on various dimensions including Semantic Alignment (How well the image content corresponds to the caption), Aesthetics (composition, color usage, and overall artistic quality), Plausibility (realism and attention to detail), and Overall Impression (General subjective assessment, the image's s quality). For each evaluation dimension, provide score between 0-1 and provide a concise rationale for the score. Use of reasoning steps, and enclose all potential important areas and detailed reasoning within <think> and </think> rags. The important areas are zepresented in following format:" need to focus the bounding box area. Proposed regions (xyxy)
process to detail your
which is an enumerated list in the exact x2,
format:l. y1, y2);2. x2,,yl,x2,y22 Here, x1,yl is the top-left corner, and x2,y2 is the bottom-right corner. Then, within the <answer> and </answer> tags, summarize your assessment in the following format: "Semantic Aligrment score:
Aesthetic score:
Plausibility score:
Overall Impression score:
Misalignment Locations:
Artifact Locations:
No additional text is allowed in the answer section.
Your actual evaluation should be based the quality of the provided image.
Your task is provided as follows: Text Caption: [PROMPT]
H LLM USAGE STATEMENT
We employed Gemini-2.5 Flash for preparing reasoning path generation and ChatGPTS refine sentence structure and enhance the readability of the manuscript. In addition, Nano Banana was used assist in generating illustrative figures for clearer presentation. The LLMs were not involved in research ideation experimental design. LLM assistance language editing did not influence the substance of the work.
21


Reviews:
[{"review_id": "w94QtyzdrS", "reviewer": "Reviewer_LnNs", "summary": "This paper presents ImageDoctor, a unified and interpretable evaluation framework for text-to-image generation. ImageDoctor provides multi-dimensional feedback and introduces pixel-level diagnostic heatmaps for grounded and fine-grained evaluation. The model adopts a \"look-think-predict\" paradigm. Experimental results show that ImageDoctor achieves state-of-the-art correlation with human judgments and improves text-to-image generation quality.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The use of heatmap-guided dense rewards for T2I reinforcement learning is  innovative.\n\nExtensive experiments across multiple datasets demonstrate consistent improvement in human alignment metrics.\n\nThe paper is well-written and easy to follow.", "weaknesses": "ImageDoctor’s reasoning quality may depend heavily on the backbone MLLM (Qwen2.5-VL). The paper lacks analysis on robustness across different model sizes or architectures.\n\n\nThe “look-think-predict” reasoning chain increases inference time compared to scalar evaluators. A discussion of trade-offs between interpretability and efficiency would strengthen the work.\n\n\nHow sensitive is ImageDoctor’s performance to the choice of MLLM backbone? Would smaller or different architectures (e.g., LLaVA, BLIP2) yield similar trends?", "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": "ldm65H1lzA", "reviewer": "Reviewer_yq1W", "summary": "This paper proposes ImageDoctor, a unified framework for Text-to-Image (T2I) evaluation that simultaneously outputs multi-aspect scores and spatially grounded heatmaps, offering richer and more interpretable feedback than traditional single-scalar assessments. The paper also introduces DenseFlow-GRPO, a method for T2I model fine-tuning, with experimental results demonstrating the value of pixel-level feedback in improving evaluation accuracy and eliminating local artifacts.", "questions": "Answer the questions in the weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  ImageDoctor mitigates the lack of fine-grained diagnostic signals in existing T2I evaluation metrics. Its multi-aspect scores and heatmaps greatly enhance the practicality and interpretability of the feedback.\n2.  The proposed DenseFlow-GRPO successfully leverages the dense rewards from ImageDoctor, achieving T2I model optimization superior to that of traditional sparse rewards (Flow-GRPO), and is particularly adept at handling local artifacts.\n3.  The experimental results demonstrate a clear advantage performance.", "weaknesses": "1.  Does the reward model exhibit \"reward hacking\" during optimization with DenseFlow-GRPO? How does the paper address this issue, and do the reported experimental results come from trials where reward hacking was successfully mitigated?\n2.  Were the same seeds used in the visualization examples for DenseFlow-GRPO and Flow-GRPO to ensure a fair comparison? In the visualization example, a tennis racket still contains artifacts; is this limitation due to the reward model not being strong enough, or is it an inherent issue with the base diffusion model? If it is a problem with the base model, can the effectiveness of the proposed reward model and DenseFlow-GRPO in addressing artifacts be validated using a more powerful base model?\n3.  Compared to existing lightweight scalar reward models, what is the specific overhead of ImageDoctor, in terms of inference speed and resource consumption? Please provide a quantitative comparison.\n4.  Can you add a discussion about more recent reward models? For example, works like 'Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment' and 'HPSv3: Towards Wide-Spectrum Human Preference Score'.", "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": "t3QGxQNWMn", "reviewer": "Reviewer_z1tc", "summary": "This paper proposes a novel VLM-based evaluation framework for text-to-image generation, named ImageDoctor. ImageDoctor not only provides multi-dimensional scoring capabilities, such as aesthetics and text-image alignment, but also offers pixel-level localization of flawed regions, enabling it to actively identify areas of misalignment and visual implausibility. Notably, the latter capability introduces a fresh perspective for reward modeling in text-to-image generation. Combined with the authors' proposed DenseFlow-GRPO method, which leverages pixel-level supervision signals for reinforcement learning, the framework effectively enhances the performance of image generation models.", "questions": "1. Given that ImageDoctor's localization capability is trained on the RF-18K dataset, to what extent can it generalize to detect a diverse range of physically implausible or commonsense-violating anomalies?\n\n2. The authors have successfully engineered a VLM capable of comprehensive image \"diagnosis\", including flaw localization. Have the authors considered applying this diagnostic capability directly within a unified understanding and generative model? We posit that integrating such a critical feedback mechanism internally could potentially offer more direct and significant improvements to the model's own generative capabilities, perhaps even surpassing the gains achieved by the current approach of using an external reward model for reinforcement learning.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Comprehensive Evaluation Framework: It introduces ImageDoctor, a novel VLM-based framework that provides not only multi-dimensional scoring (e.g., on aesthetics and text-image alignment) but also pixel-level localization of flawed areas, offering a more thorough analysis than mere scoring systems.\n\n- Innovative RL Paradigm for Image Generation: The pixel-level supervision signal from ImageDoctor paves the way for a novel reward model, which, combined with the proposed DenseFlow-GRPO reinforcement learning method, effectively enhances the performance of image generation models.", "weaknesses": "- The model's ability to localize flaws is trained on the RF-18K dataset. While effective, this dataset's relatively small scale may constrain the full potential of the Vision-Language Model (VLM), potentially limiting its generalizability and precision in identifying a wider variety of visual anomalies.\n\n- The evaluation is based on GenAI-Bench and TIFA. To more rigorously demonstrate ImageDoctor's advanced capabilities, particularly in pinpointing violations of physical laws and common sense, future work could benefit from testing on newer, more challenging benchmarks such as WISE[1] and ABP[2].\n\n[1] WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation\n\n[2] Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation", "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": "yxpL47YNqW", "reviewer": "Reviewer_bUcB", "summary": "In this paper,the authors introduces ImageDoctor, a unified,multi-aspect evaluation framework for Text-to Image(T2I) models. Unlike previous methods that provide a single scalar, ImageDoctor assesses image quality across four dimensions: plausibility, semantic alignment, aesthetics, and overall quality.ImageDoctor also provides pixel-level flaw indicators in the form of heatmaps, which highlight misaligned or implausible regions, and can be used as a dense reward for T2I model preference alignment. The model is built on a multi-modal large language models(MLLMs) and adopts a “look-think-predict” paradigm. Training involves a two-phase process: cold start and reinforcement finetuning with Group Relative Policy Optimization(GRPO) using tailored rewards. Furthermore, the paper proposes DenseFlow-GRPO, which utilizes ImageDoctor’s dense, pixel-level heatmaps as a dense reward signal. Experiments demonstrates that ImageDoctor achieves strong alignment with human preference across multiple datasets. Furthermore,when used as a reward model for preference tuning, ImageDoctor achieves an improvement of 10% over scalar-based reward models.", "questions": "1.When there are multiple overlapping flaws in the image, the bounding boxes generated in the \"look\" stage may have \"overlapping coverage\". Will this cause the \"think\" stage to be unable to distinguish between different types of defects?\n2.Could the authors provide a detailed explanation through which refining the bounding boxes via reward in the RFT phase leads to more accurate artifact heatmaps and higher score prediction accuracy? Does better \"look\" translate to better \"think\"?\n3.The MLLM output involves reasoning chains and scores. How critical is the rich textual reasoning generated by the MLLM to the final quality of the heatmaps, beyond just the task token?\n4.The paper chooses Qwen2.5-VL as the backbone of MLLM and does not mention the comparison with other mainstream multi-modal models such as Gemini 2.5 Flash and GPT-4o. What is the core reason for choosing Qwen2.5-VL? If replaced with Gemini 2.5 Flash, will ImageDoctor have performance differences in score consistency (PLCC/SRCC) and heatmap quality (MSE/CC)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.ImageDoctor breaks through the limitation of a single score by evaluating images from four meaningful dimensions(plausibility, semantic alignment, aesthetics, and overall quality).\n2.The ablation study demonstrates that the “look-think-predict” paradigm significantly improves the model’s overall performance.\n3.ImageDoctor is further introduced DenseFlow-GRPO, which leads to superior generation quality, especially in refining local details.\n4.As a reward model, ImageDoctor improves the quality of T2I model generation by 10% compared to scalar based reward models.\n5.ImageDoctor's heatmaps and inference chain can directly locate defect areas, filling the gap of lack of interpretability and spatial localization in T2I evaluation.", "weaknesses": "1.ImageDoctor is trained mainly on RichHF-18K,which is limited in size and flaw type. These may affect its generalization ability to state-of-the-art T2I model generated images.\n2.ImageDoctor involves a MLLM, a heatmap decoder, a two-phase training pipeline.Complex processes are not replicated and promoted on a large scale.", "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)