Generative Universal Verifier as Multimodal Meta-Reasoner
Authors: Xinchen Zhang
Xiaoying Zhang
Youbin Wu
Yanbin Cao
Renrui Zhang
Ruihang Chu
Ling Yang
Yujiu Yang
Guang Shi
Venue: ICLR 2026 Oral (2026)
Keywords: Multimodal Large Language Models
Primary area: foundation or frontier models, including LLMs

Abstract: We introduce *Generative Universal Verifier*, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, providing the fundamental capability of reflection and refinement on visual outcomes during the reasoning and generation process. This work makes three main contributions: (1) We build **ViVerBench**, a comprehensive benchmark spanning $16$ categories of critical tasks for evaluating visual outcomes in multimodal reasoning. Results show that existing VLMs consistently underperform across these tasks, underscoring a substantial gap from human-level capability in reliable visual verification. (2) We design two automated pipelines to construct large-scale visual verification data and train **OmniVerifier-7B**, the first omni-capable generative verifier trained for universal visual verification and achieves notable gains on ViVerBench(+$8.3$). Through training, we identify three atomic capabilities in visual verification and demonstrate how they generalize and interact synergistically. (3) We propose **OmniVerifier-TTS**, a sequential test-time scaling paradigm that leverages the universal verifier to bridge image generation and editing within unified models, enhancing the upper bound of generative ability through iterative fine-grained optimization. Beyond generation, we extend universal verifier to broader world-modeling interleaved reasoning scenarios. Empirically, OmniVerifier-TTS achieves improvements on T2I-ReasonBench(+$3.7$), and GenEval++(+$4.3$), outperforming existing parallel test-time scaling methods, such as Best-of-N. By endowing multimodal reasoning with reliable visual verification, OmniVerifier advances both reliable reflection during generation and scalable test-time refinement, marking a step toward more trustworthy and controllable next-generation reasoning systems.

Published as a conference paper at ICLR 2026
GENERATIVE UNIVERSAL VERIFIER AS MULTIMODAL META-REASONER
Xinchen Zhang .2 Xiaoying Zhang Youbin Wu Yanbin Cao Renrui Zhang?
Ruihang Chu Ling Yang Yujiu Yanglt Guang Shi? 'Tsinghua University BByteDance Seed Princeton University
zhangxc24@mails ts tsinghua.... edu.en youbinwu@byteedance. com, yang. yuwiuusuiii..... tsinghua. edu. https://omniverifii fier git.ub..gttuuiioo.
ABSTRACT
We introduce Generative Universal Verifier, novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, providing the fundamental capability of reflection and re- finement on visual outcomes during the reasoning and generation process. This work makes three main contributions: (1) We build ViVerBench, comprehen- sive benchmark spanning 16 categories of critical tasks for evaluating visual out- comes multimodal reasoning. Results show that existing VLMs consistently underperform across these tasks, underscoring a substantial gap from human-level capability in reliable visual verification. (2) We design two automated pipelines to construct large-scale visual verification data and train Omni Maniieeifiir--.. the first omni-capable generative verifier trained for universal visual verification and achieves notable gains ViVerBench(+8.33 Through training, identify three atomic capabilities in visual verification and demonstrate how they generalize and interact synergistically. (3) We propose p scaling paradigm that leverages the universal verifier to bridge image generation and editing within unified models, enhancing the upper bound of generative ability through iterative fine-grained optimization. Beyond generation, we extend univer- sal verifier broader world-modeling interleaved reasoning scenarios. Empiri- cally, OmniVerifier achieves improvements on T'2l-ReasonBennhhH+ 7), and GenEval++(+4133) outperforming existing parallel test-time scaling methods, such Best-of-N. By endowing multimodal reasoning with reliable visual verification, Verifier advances both reliable reflection during generation and scalable test- time refinement, marking a step toward more trustworthy and controllable next- generation reasoning systems.
OmniVerifier-TTS. sequential test-time
INTRODUCTION
The field of multimodal large language models (MLLMs) is undergoing a remarkable revolution in its application scenarios, evolving from simple image understanding (Liu et al., 2023a; Li al., 2023; Zhu et al., 2023; Lu et al., 2024; Zhang et al., 2024a) to complex visual reasoning tasks (Guo al., 2025; Comanici al., 2025; OpenAl, 2025; Zheng et al., 2025; Li et al., 2024). Vision- language models (VLMs) (Guo et al., 2025; Team al., 2025a; Bai et al., 2025; Team et al., 2025b; Seed; 2025) and unified multimodal models (UMMs) (Deng et al., 2025; Xie et al., 2025b; Liao et al., 2025; Wu et al., 2025b; Yang et al., 2025a) integrate vision and language, enabling more comprehensive cross-modal interactions and paving the way toward a more intelligent reasoning and generation system.
Toward next-generation multimodal reasoning, we argue that self- and externally-criiqque will be key drivers of model advancement. In interleaved scenarios, effective progress requires verifying not only textual answers but also visual outcomes, which refer to any model-generraed visual ar- tifact, such as images in text-to-image generation intermediate visual states produced during
Corresponning authors.

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Published as a conference paper at ICLR 2026
stepwise tool-call reasoning. Unlike textual outputs, these visual outcomes are ligh-dimensional and ambiguous, making their reliable assessment difficult without explicit verification. We there- fore contend that visual-outcome verification is fundamental to scaling multimodal reasoning and generation, enabling MILMs not only to generate outcomes but also to iteratively understand, verify (Xiong al., 2025; Wang et al., 2025b), and refine (Madaan et al., 2023; Kumar et al., 2024) their reasoning trajectories and intermediate images during both inference and training. Motivated by this perspective, in this paper we investigate three central questions:
Q1: What is the current performance of MLLMs on visual-outcome verification?
To take systematic assessment of MILMS' current capabilities in verifying visual outcomes, we construct ViVerBench, a challenging and comprehensive benchmark that spans 16 subtasks across 6 categories of visual verification. The benchmark is meticulously constructed through manual annotation by 12 domain experts, resulting in 3,594 diverse and challenging verification questions. For each question, models are instructed generate binary true/false judgment accompanied by detailed explanation, thereby enabling fine-grained evaluation of both decision accuracy and reasoning correctness. We conduct experiments on state-of-the-art VLMs and uncover 3 general limitations: (1) weakness in fine-grained and challenging image-prompt alignment, (2) mismatched representation of world knowledge, and (3) underdeveloped critics for visual reasoning tasks. These findings highlight substantial gap between current VLMs and human- visual verification, and indicate that there remains a long way to go before training VLM to become a powerful and truly multimodal universal verifier.
Q2: How to develop strong generative universal verifier?
We investigate the potential of training a generative universal verifier by designing two automated data construction pipelines for visual verification, motivated by the idea of emulating human strate- gies for constructing challenging data. These pipelines foeus the alignment of fine-grainet and challenging features in image-prompt alignment tasks, enabling the scalable creation of high-quality diverse, and challenging training data. Leveraging this dataset, we directly applied reinforcement learning (RL) (Shao et al., 2024; Yu et al., 2025) to training Sive22.5VV--- der rule-based verifier framework, resulting in OmniVeriffer-77B 7B achieved an 8.3-point improvement and beat GPT-4o (Hurst et al., 2024). Furthermore, through ablation training on verification data across different tasks, we identify three fundamental atomic capabilities underlying visual-outcome verification: explicil alignment, relational verificution, and integrative reasoning, and uncover how they mutually generalize and facilitate each other. These findings suggest minimalist recipe for generative universal verifier training: Instead of training for many tasks separately, constructing training data for fundamental atomic skills is sufficient to enable wide-rame ride-ranging task generalization.
(Bai et al., 2025) un- ViVerBench, OmniVerifier
Q3: How can visual verification be leveraged to enhance reasoning or generation?
Generative universal verifier admits broad range of applications. We propose OmniVerifier-TTS. a sequential test-time scaling paradigm designed for enhancing the generation of unified multimodal models (Deng al., 2025; Wu et al., 2025a) with OmniVerifier- Starting from a generated image, it progressively refines images through multiple rounds of verification and editing, which bridging image generation and editing within a unified TTS framework. Validation across 2 advanced unified multimodal models demonstrates quality, including reasoning-baa based image generation (Sun et al., 2025), and complex compositional- based generation (Ye et al., 2025; Zhang et al., 2024c;b). We further demonstrate sequential TTS exhibits better performance compared to parallel TTS. Beyond TTS, we also extend the universal verifier to broader world-modeling scenarios, pushing the reasoning capabilities of MI..Ms.
Veriieerii achieves notable gains in general generative
Our contributions can be summarized as:
Comprehensive and Challenging Benchmark for Visual-outcome Verification: We in- troduce Biner, manually curated benchmark designed to evaluate visual-outcome verification in both multimodal reasoning and generation. Our evaluation reveals three general shortcomings of MLLMs.
Data Construction and Training Strategy for the Generative Universal Verifier: We propose two automated data curation pipelines to scale visual verification training data, and train OmniVerifier-- achieving substantial improvements on ViVerBench and surpassing
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GPT-4o. Our training identifies three atomic capabilities in provides a minimalist recipe for training a generative universal verifier.
verification and
Sequential Test-time Scaling Paradigm of UMM: We propose OmniVerifier- -Trs, a flexible and general sequential test-time scaling paradigm for the generation of UMMs, suparssing other parallel TTS method such as Best-of-N while achieving a substantial re- duction in inference time.
RELATED WORK
Recent breakthrough in multimodal large language models lie in in the continuously evolving rea- soning paradigms (Peng et al., 2025; Shen et al., 2025; Zhang et al., 2025; Tong* et al., 2025) and more unified foundation models (Xie et al., 2025b; Chen et al., 2025a; Yang et al., 2025b; Guo et al., 2025). Starting from simple image understanding, the long- coag-chhiinoff- of-thhught (longCoT) paradigm trained with reinforcement learning substantially strengthened multimodal reasoning, as seen in Seed-1.5VI (Guo et al., 2025), Kimi-VL(Team et al., 2025a). Scaling test-time reasoning over textual outcomes has become the mainstream strategy for improving reasoning ability. How- ever, this text-centric approach treats vision as merely static context, leaving a semantic gap between perception and symbolic thought. Openai-o3 (OpenAI, 2025) brings visual outcomes into LongCoT, pioncering new 'thinking with images' paradigm of interleaved multimodal reasoning. DeepEyes (Zheng et al., 2025) and MINT-CoT Chen et al. (2025) demonstrate powerful tool-assisted and regsion-selected reasoning through end-to-end reinforcement learning. Meanwwile, architectures are converging toward unified designs that integrate text and image inputs/outputs (Chen et al., 2025c; Xie al., 2025a), making interleaved reasoning within single framework a compelling direction. Mogao (Liao et al., 2025) highlights the potential of interleaved generation under uni- fied architectures, while T2I-R1 (Jiang et al., 2025) and Bagel (Deng et al., 2025) explore 'thinking before generating' for interleaved text-image synthesis. Looking ahead, we argue that self-critique will drive the next generation of multimodal reasoning by enabling autonomous learning through verification on both textual and visual outcomes, without reliance on external outcome labels.
VIVERBENCH: ASSESSMENT OF MLLMS ON VISUAL VERIFICATION
3.1 BENCHIMAR OVERVIEW
is designed Bener
comprehensive and challenging benchmark evaluate MILMs' abil- ity to verify visual outcomes during reasoning and generation. It comprises 16 tasks across main categories: Concept Existence, Object Relationship. World Dynamics, Image Annotation, State Value Evaluation, and STEM, with representative examples shown in Fig. 1.
To ensure sufficient difficulty and unambiguous correctness, we construct the benchmark through a systematic pipeline that combines manual annotation, programmatic generation, and augmented open-source data. For evaluation, we further introduce two complementary metrics: rule-based and model-based, that assess not only answer accuracy but also explanation validity. Full task definitions, dataset construction details, and evaluation methodologies are provided in the Appendix A.
3.2 EVALUATION OF ADVANCED MLLMS
We evaluate range of stat-of-the-art VLMs on ViVerBench, including closed-source models: Grmini-2.55Pro (Comanici et al., 2025), GPT-5, GPT-4o (Hurst et al., 2024), OpenAl-o1///4mini (Jacch et al., 2024), Seed 1.5-VL (Guo et al., 2025), and two open-source models: Qwen2.5-VI 72B (Bai et al., 2025) and Intern A28B (Wang et al., 2025a). We evaluate these models us- ing rule-based verification, which directly determines the correctness of true/false labels without assessing the accompanying explanations using another judge model.
As shown in Table 1, Gemini-2.5. 2.5-Pro achieves state-of-the-arr score of 0.745 ViVerBench; however, the score remains far from that of a reliable general visual verifier. In addition, we identify three reasons for the substantial gap between these advanced models and human evaluation:
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Concept Existence
World Dynamics
Object
Static Physics
Prenupt majestic castle perched high cltff-..s
Promptt Analyze the image, whether it satisfics physical nealism, euch as consistency with real-world rules like Answee False
laage seiling ship graeefully glides across the water, also beartig a red flag.
Answer False
window, neflection the should include thas beam. However, display it connectly.
Explanation: There
Explanation: this
generated image, nu flag
salling shap, w...
image
Attribute
Dynamic Physics
Prompt: In the picture is theee-story cotiage The second floor c... there is white flower pot the far right side of the bed.....
Promat You ace given inages omm Your took -ne. determine whether the sequence as whole is physically plausible. Answer
Answer Talse
Explanation: The prumpt indicates there flower Hooeer
Explanation: should fall onto gryy step
pol Howeves,
traching it, abgects
Abstract Patterns
Prompt The entire compositon is containee within
State Value Evaluation
lange, thin-lined circle... one of the vertices of the
innermod quadeilateral is "*olld black diattond Answer False
Maze
Isplanation: -nu the black diamond on
triangle,
reached the maze extt by followwing valid path.
solving' scenatio. oas detecmine whether the playes has
Prompt:
You are given image related to "mare-
Object Relationship
Explanation: player's path posses through
Spatial
FrozenL.ake
Promet In 3x3 goid image, -.. the top-right cell contains male with brown hair with mouth open, and to the below of top-right cell a male with orange hair crying with mouth opea with eyes closed, .... Answer Tallsr
Prompti on whethererr the player's more from the finst image to the second image beings the game state closer the gull state.
Answer
Explanetion: player's
not
Explanation:
prompt, middle--ightt orange hair crylngg with
step
goal
open eyes closed,
Robotics
Promge an..aaallye this intermediate state,
Non- Non-Spatial
delemnine if tellows correct sequeace. ans
Promut: street artist is fully focused on drowing a poctrait, bolding charcoal pencil one hand and geatly brushing anway dust with the other.
Answer: False
Explanation orange gray
purple placed
orange
Answer Talle
gray black
Explanation The artis''s is other hand was not brushing away the duse.
GUI
Prompt image shows mobile interface with the commandi suctich atrplane mode.....
Image Annotation
Answer
Esplanation: The highlights the test "aieplane clicking the higgle button un the right.
Bounding Box
by
Prompt Draw box around bottle
False
STEM
Explianaiio:: What bux 1 refers is the buttle furthest the current location.
Charts
Pointing
Prampt: are given image and code: import ..... axplat(( circle, .... colorz linewlth::))
Point the disection indicaled
Answer
soad matches the
Explanation: code shows the color circle 'blue', which inconsistent,
Answer Falle
the
Explanation The selected direction opposiite
the urientation of most cars.
LaTeX
Counting
Prampt: whether was generated by LaTeX ...h dott N.L....
Prompt: many blank grids are
picture?
Answen the end the second
Answer False
Explanation:
Ieplanation:
supemeript the imagee this
blank and not marked.
Figure I: ViVerBench Overview. ViVerBench has a 1:1 ratio of true to false answers; here, we show only the false ones to better highlight data difficulty.
Weakness in Fine-Grained and Challenging Image-Prompt Alignment State-of-the-art VLMS exhibit a performance gap of about 0.2 compared to human performance on the three Concept Exis- tence tasks. This shortfall stems from the difficulty models face in achieving precise, point-to-point alignment between complex compositional prompts and images, especially in cases of overlapping attributes across objects, small-scale elements, or occluded and blurred attributes. Human cogni- tion employs deliberate and iterative verification strategies (i.e., 'double checking") to resolve such ambiguities, yielding far superior accuracy visual verification.
Mismatched Representation of World Knowledge Despite advanced VLMs equipped with ex- tensive world knowledge, including general physical laws within their language components, our experiments reveal significant paradox: this knowledge is not effectively activated in visual verifi- cation tasks. The notable performance gap between VLMs and humans both Static and Dynamic Physics tasks reveals fundamental Knodaitge-Modality Gap.
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Table 1: Rule-based evaluation of advanced VLMs on ViVerBench.
Concept Ixistence
Ubjject
Dynomics
Image
Annetation
Value Evaliaatin
Ohj
Altr
Abs.F
Spa
N-Spst
5-19y
D.Pry
BBoxx
Poirr
Oror
Marc
F1aak:
Robool
G1H
Cran
Quen 2.5 72B
0.596
0.642
0.678
0.550
0.813
0,600
0.507
0.839
0.344
0.615
0.317
0.507
0.513
0.706
0.625
0.923
0.661
Intern V1.1.
0.684
01791
0.617
0.181
01420
01847
0:119
0.518
8.66
0.01
GPT
0.500
0.611
0.538
0.503
0.645
OponAl od-miul
0.745
0.746
0.781
0.763
0.754
0.558
0156
0.727
Seed 1.S.L.
0.737
0.763
0.611
0.770
0.851
0.588
0.535
0.963
0.570
0.10
0.527
0.318
0.671
0.855
0.720
0.907
0.231
UpenAles 63
0.723
0.725
0.301
9.713
0.753
0.722
0.082
01302
0.885
0.633
0.117
0.771
0.627
0.875
0.73
11587
0.735
GPPSS
0.006
11737
03440
0.724
0.766
0.775
01.65
1831
0:385
0.694
0.500
0.365
0.588
0.356
0.70
1176
0744
8.70
01750
0.856
0.875
0.761
0.745
0.304
0.563
0.745
0.500
01940
09322
0.985
1,000
0M32
Q.-VV. 2.6-VL.70
0.551
0.504
0.694
0.830
0.471
0.67%
0,61
0-467
0.927
0:406
0.671
0.625
0.596
(1744)
0.570
VeniiVerlller 7B (Durs)
0.725
0.713
0.514
0,742
0,679
0.517
0.615
0.802
0.670
0.556
0.563
0.482
0.728
0.652
0.548
01912
0.633
Underdeveloped Critics for Visual Reasoning Tasks The most significant performance gap be- tween VLMs and human performance arises in tasks requiring reflective reasoning. In tasks like Maze, FrozenLake, and Robotics, humans achieve near-perfect performance, demonstrating robust reflective abilities. contrast, most VLMs perform near chance level; for example, the best- performing model, Gemini 2.5 Pro, achieves only score of 0.580 on the Maze task. These re- sults expose the inability of current models to reliably leverage learned rules for verification under complex visual reasoning scenarios.
Finding 1. Limitations of Advanced MLLMs in Visual Verification
Weakness in fine- fine-grained and challenging image-prompt alignment
Mismatched representation of world knowledge
Underdeveloped critics for visual reasoning tasks
These gaps highlight the distance from ftmann-level visual verification.
4 OMNIVERIFIER, BUILDING AND FINDINGS OF UNIVERSAL VERIFIER
The limited performance of current advanced MILMs on visual verification motivates us inves- tigate methods that can progressively strengthen their capabilities. In Section 4.1, we introduce methods for scaling challenging visual verification data. Section 4.2 presents comprehensive abla- tion studies across diverse tasks to probe the core atomic capability of visual verifier, and Section 4.3 describes the performance of Omni Verifier-BB.
4.1 AUTOMATED CONSTRUCTION OF VISUAL VERIFIER DATA
We aim to scale both the quantity and quality of data through automated construction rather than heavy manual effort. The core challenge is avoiding overly nitpicking judgments. Current gen- erative models still fall short of fully aligning with complex prompts, leaving room for subjective criticism. Specifically, they often fail to maintain precise object attributes and consistent spatial relationships in complex scenes. Prompts also often involve subjective or abstract notions (e.g., at- mosphere, style, fine-grained details), which lack uniquely correct answers and thus make images seem 'not fully aligned.' This also explains why advanced MLLMS struggle with fine-graine and challenging progppt alignment, therefore, the key lies in the construction of suah-tuuaity, challenging, and rigorously accurate visual verification data.
We begin with complex images and construct true and false examples in a reversed manner. For synthetic images, prompts are drawn from ShareGPT-44-Imaan enriched by GPT-5, with focus on introducing multiple objects, diverse attributes, and both spatial and non-spatial relationships. These complex prompts are then used with Seedream 3.0 (Gao et al., 2025) to generate complex images. For natural images, 20k samples are taken from LVIS (Gupta et al.. 2019), and GPT-5 is applied to filter out simpler ones, retaining only complex cases. This process yields a repository complex images, which serves as the foundation for subsequent visual verifier data construction. To achieve optimal generalization and data augmentation effects, design two automated pipelines shown in Fig. 2 for paired true and false data construction:
(Chen et al., 2025b) and further
Methodl: Image-Fixed, Prompt-Modified We construct true and false data by modifying prompts. For each complex image, we first GPT-5 to generate strictly constrained prompt that
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Complex Images
Method1: Image-fixed Prompt-Modified
Filter
True Prompt
Falst Prompt & Explanation
TXT
Imagges
Prompts
GPT-S Recaption
Complex Prompts
Serdeeam
Complex Images Gallery
Images
CIT-S Rocaption
GETS Modify
Method2: Prompt-fixed Image-Inpainting
Image Bbus
Prompt & Explanation
Methodt:
True Image Prompt False Data: image Falls Prungt Explanation
Complex Images
O SAMET
Select
Method2:
True Images
Maska Ehouss
Eagainting
Emages
True Data: True image Prompt
Ralse Data: False Image Pronpt & Explanation
Figure 2: Automated pipeline for visual verifier data construction.
describes only clearly identifiable elements (objects, attributes, spatial relationships, and scenes), while avoiding speculation, subjective judgments, or unnecessary embellishment. This yields highly faithful prompts that aligg closely with the complex image, which we treat as true data. We then fur- ther modify these prompts with GPT-5 by altering details, for example, adding or removing objects, changing attributes, or modifying spatial relations and provide corresponding explanations.
Method2: Prompt-Fixed, Imaaeningainting We construct true and false data by inpainting im- ages. We first apply SAM 2.1 (Ravi et al., 2024) to segment objects in the complex image, obtaining the corresponding masks and bounding boxes. The mask area serves measure of data difficulty, based on which we dynamically select masks and use FLUX.I-des (Labs, 2024) for in- painting to generate false images. Meanwhile, GPT-5 is employed generate strictly constrained prompts, where the bounding box of selected object is highlighted to ensure explicit descriptions of its attributes and spatial position. This design prevents incomplete prompts in high-difficulty cases. Finally, we obtain both the prompts and their accompanying explanations.
Following the construction of visual verifier data via Method and Method apply Seed1.5-VI. (Guo et al., 2025) to clean and retain only samples with Best-of-10 accuracy of at least 0.6.
4.2 GENERALIZATION OF ATOMIC CAPABILITIES IN THE VISUAL VERIFIER
With these high-quality scalable data, we can more effectively investigate the essence of visual verifiers. Although ViVerBench covers a wide range of tasks, it remains unclear whether these tasks are intrinsically connected. To investigate these potential connections and to explore how to train a more comprehensive visual verifier, focus four fundamentally distinct tasks. Specifically, construct object and attribute datasets following the approach in Section 4.1, and additionally include in-domain spatial and maze datasets. We select these tasks because each evaluates complementary aspect of visual verification: object and attribute tasks probe the foundational ability of explicit image-text alignment, the spatial task captures more complex relational reasoning beyond basic alignment, and the maze task serves as a reasoning challenge, assessing visual verification in the reasoning dimension. Verifier training conducted independently on each dataset, resulting in four distinct experimental settings.
We apply DAPO (Yu et al., 2025) to perform RL training directly on Siwen2.5VVL-3. (Bai al., 2025), using system prompt that encourages the model to reason before answering. The training objective combines rule-based reward, which evaluates the correctness of true/false predictions, with format reward at 9:1 ratio. All four models are trained for 100 steps on 64 NVIDIA A100- 80G GPUs, and the results are shown Fig 3.
Training only on object verification data yields significant improvements across most tasks. For tasks require explicit prompt-image alignment, such as Auribute, Charts, and LaTeX, traing on ob- ject verification data provides notable gains. It also generalizes well to tasks that involve verifying relationships between objects in the prompt, including Spatial, Static Physics, Bounding Box, Point- ing, and GUI. However, for visual reasoning tasks like Maze and Robotics, performance remains instability and does not yield noticeable gains. Notably, the generalization trend of attribute verifi- cation data shows similar trend to object verification data.
Training only on spatial verification data also shows a strong generalization to explicit alignment tasks such Object, Auribute, Charts, and LaTeX. Moreover, it delivers even larger improvements

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CorciptExstennc Existence: Object
Concegts Existence: Attribute
Concept Existence: Abstract Patterns
Object Rddioonhip: Spakial Relationshi
Spatal dita
0.8
Juat Dara
Accuney
0:6.0
0.00
0.025
0.35
0.030
0.6
0.40
0.40
0ejjth-Selattonshhpp Non Spatial Reterionship
World Dynamics: Static Physies
World Dynamics: Dynamic Physics
Image Annotation: Bounding Box
0.20
0.821
0.75
0.30
0.550
0.000
0.70
0.52
0.54
0.525
0.500
0.350
0.95
0.723
0.320
0.90
Mone
45
Image Annotation: Pointting
Image Anmotation: Counting
State Value Evaluation: Mare
State Value Evaluntion: Frazent
0.70
0.
0.50
Senballura
0.55
Spatallira
Sentallers
0.500
Exuuyy
0.40
0.52
0.334
04
176
State
Exaluation: GUI
STEM: Charts
0.70
0.55
230
3.5
0:40
0.52
3.00
ti Dari
0.58
The ringigggn staps
The inng slaps
Thaiiig stops
Trainig stops
Figure 3: Training progress of four visual verification tasks on ViVerBench.
on relational tasks, including Non-Spatial, Bounding Box, and Counting. Nevertheless, for complex visual reasoning tasks like Maze and Robotics, the benefits remain limited.
By contrast, training only on maze verification data exhibits minimal generalization. We attribute this to the sparse and discrete nature of maze images, where paths are rendered as blank space and walls as simple black lines. Such simplistic, synthetic patterns contrast sharply with the rich textures and semantics of natural images, creating significant distribution gap. As result, maze data offers limited transferable signal, and we observe no meaningful gains on broader tasks.
Three Progressively Related Atomic Capabilities in Visual Verification
Finding 2.
Maze
EaoenLake Hobolles
Integrative Reasoning
Explicit Aligument: text and image tain directly matchable elements.
Relational Verification: requires using tex to verify relationships or perform light reasoning, beyond simple visual matching.
Atomic Capability
Spatial Sounding Bax Trinting
Explicit Alignment
Altmibute
Integrative Reasoning: involves holistic interuction between prompt and image for complex higher-order reasoning.
Counting
Relational Verification
Charts
LaTcX
Aberacl taattens
(ttatic Phystes) (Dynanic Physis))
(Printing)
(Conncing)
These capabilities form a layered structure, from perceptual to semantic-relatoonal and finally to task-level reasoning.
Figure 4: Atomic capabilities in visual verifier.
We categorize the tasks associated with the three atomic capabilities of visual verification, as il- lustrated in Fig 4. Tasks shown in parentheses indicate cases where the underlying atomic capa- bility may shift with prompt complexity, rather than being fixed to single type. Our experiments also demonstrate that reinforcement learning facilitates the generalization across atomic capabili- ties (Yuan et al., 2025): we observe strong mutual improvment both within and between Explicit
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Arawe Febe
Answer: Tales
Omniverifier
Omniverifier
Omniverifier
Answer
bdit Prongits Chinge ciy beckpraad
Edit Promppt: Chanse the "Buling S01P" on "Wojjing 3890.
Sequential Scaling
buildings like Bid'ss
Generation
Edit
Unified Multimodal Model
Peaepp Theciit bosting hommer
Bah Fooepts Chonge hakgotend bojjing, with dasoo
Bän Prumpth: Change By "Bojjag "Beijing 1800..
buildiigs Bed's
Figure 5: Corniierifier-Tie sequential test-time scaling pipeline of unified multimodal models.
Alignment and Relational Verification. This yields important training insight for building uni- versal verifier: it is unnecessary to construct task-specific datasets. Instead, a single dataset that captures the underlying visual patterns of these two atomic capabilities sufficient to enable broad cross-task generalization.
In contrast, Integrative Reasoning poses fundamentally different challenge. Tasks in this category often span highly diverse domains, where visual inputs, reasoning patterns, and solution strategies differ substantially. These pronounced domain gaps make it difficult establish shared representa- tions, and training one task task yields minimal transfer to others. Accordingly, we recommend building task-specific datasets tailored to each domain to effectively improve integrative reasoning.
Finding 3. Generalization of Three Visual Verification Atomic Capabilities
Reinforcement learning promotes strong generalization within and between Explicit Alignment and Relational Verification, suggesting that single dataset capturing their shared visual patterns suffices for broad transfer. In contrast, Integrative Reasoning spans heterogeneous domains with little cross- task trunsfer. thus requiring task-specific datasets.
4.3 OMNI-CAPABLE GENERATIVE UNIVERSAL VERIFIER
The findings on atomic capabilities and their generalization motivate training a comprehensive gen- erative universal verifier. Following Methods and in Section 4.1, we construct 28k high-quality visual verification datasets, filtered with Seed1 VI and covering both Explicit Aligmment and Re- lational Verification. Using the training procedure in Section 4.2 with Qwen2.5-VIL-7B as the back- bone, we obtain OmniVerifier-.. Its performance on ViVerBench is reported in Table 1.
With high-quality atomic-level data, reinforcement learning training has endowed the base model with exceptionally potent visual verification abilities. OmniVeriffer-71B demonstrates significant 8.3% overall performance enhancement, surpassing GPT-4o and achieving capabilities comparable to W.en2.5-VL-72B. Notably, Omni Verifier-BB exhibits pronounced improvements in tasks related to Explicit Alignment and Relational Verification, such as Object, Attribute, Spatial and Bound- ing Box. Our findings suggest that targeted reinforcement learning atomic capabilities offers promising direction for building stronger and more generalizable visul verifiers.
5 OMNIVERIFIER-TTE TTSS: MULTIMODAL SEQUENTIAL TEST-TIME SCALING
In this section, we aim to explore how to apply the universal verifier to multimodal generation and reasoning. Specifically, we present OmniVeriffer-TTy which integrates image generation and editing within unified multimodal models through sequential test-time scaling, enabling a flexible paradigm of interleaved generation. Section 5.1 describes the detailed structure of the framework; Section 5.2 presents experiments and visualizations of Omni Verifier-TTS; and Section 5.3 compares parallel and sequential test-time scaling UMMs.
ARCHITECTURE
Achieving precise image generation from complex compositional reasoning based prompts is often difficult a single attempt. However, in many cases, only a small region or small parts of the image is misaligned with input prompt, making it unnecessary regenerate the entire image or
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Edit Promptt Change the city in the harkgroaadd feejjnn- indiuling some bestidingg
Edit Prompt
Edit Prompt
8
besting the Summer Olymniccs in 2000
Change "2013" sign "2as5".
the "200" the sign "2305".
Edit Prempt3 3 Change color indicate the aridic reaction of universal indicator with by hydrechlanic arid.
Edit Prompt Change liquid indicate the acidic reaction after adding univensal indicaloe.
Bdit Prempt 3 Replace reads UNIVERSAL INDICATORE Inslead of 'NNDICATOR SUILIOO
Univensal indicator solation added hndeechlecic solution
Edit Prosspt 1 More the greas wetter halloon to that in resting the floor in
Jepanose scesery, teni sioce lanter,
Edit Prompt-
helium balloon green.
Change flowers the ligh
water ballion living
cherry blasora the lelt feturitic otpeaape refketeel onder water werfice
ginc.
Okange oudiing drawdbertes for to hand On arawdherles, los jatblbeed "FLOUK" and odling pin. ballig the clack wall puarts clickk
Edit Prompt
Edit Prompt Remove one of the thece strawberrics on the table, leastng only two.
Edit Prompt
Change the word "FLORR" the jar to "LOOUR".
Make buth the hour and minute hands clack point
Figure 6: Qualitative visualization of Omni Verifier-TTE -TS.
introduce large changes. A more optimal solution is to perform targeted, regional edits to correct these minor errors and obtain a highly- result.
Motivated by this, we propose a self-refinement pipeline that leverages visual verification to iden- tify inconsistencies. As illustrated in Fig. 5, we employ Omniverrifier as 'misalignment-findee due to its strong capability in explicit alignment and relational verification. The process begins with UMM generating an image from given prompt. OmniVerifier then analyzes this image and outputs binary judgment (true/false) along with an explanation, following the same procedure as in its RL training. If the judgment is false, indicating misalignment between the prompt and the image, OmniVerifier additionally outputs edit prompt, rephrased form of the explanation that offers instructive guidance on how the image should be modified. The UMM subsequently performs fine-grained edits the initial image based this edit prompt to obtain refined result. This it- erative refinement loop continues Veriffer returns a true judgment or the maximum number of refinement steps is reached.
5.2 EXPERIMENT
We conduct experiments using two powerful open- and closed-source models, Qwen-Image (Wu et al., 2025a) and GPT-Image-l, with Verifierie serving as the judge model. The maximum number of refinement steps is set to 10. All experiments are conducted 80G GPU. We evaluate on a reasoning- baasee benchmarks, 221-ReassoBench (Sun et al., 2025), as well as one complex compositional-batt -basd benchmark, GenEval++ (Ye et al., 2025). The results are presented in Table 2.
single NVIDIA-A100
Table 2: Evaluation of Omni Verifeer-iirTT on reasoning and compositional generation benchmarks.
Remson-baod based Gemeration
Compesitional based Generation
Model/ Metric
T11 TEl-BreemBrnch
GenRtalee
Toutal
Eaiky
Sclestifi
Overal
ColenCousi
ColerPts
ProCocunt
PouSiue
Muls-Cwur
Ovenii
5D.3-Mrdiam PLUUK I-der 2004)
2024)
60.0
50.9
0.550
0.125
0.350
0.175
0.190
0:125
0.206
19.1
56.9
46.7
0.150
0,375
0.225
0.14
Jamu-Pru (on(Chm (Chan di sl., 20155g
35.8
$7.3
46.9
56.5
0.490
0.500
8.138
05900
0.075
0.350
0.125
0.346
Bagel (Deng et 2025)
44.6
44.0
52.4
57.7
-9.7
0.325
0,000
0.250
0.525
0.250
0.475
0.375
0.371
Qven-Image
2015g)
55.
0.75
OmmiVerthie Twseeeeee--TSQeeennnn Imngee)
58.7
0.18
GPI-Image-1
75.8
75.7
71.6
26.8
0.600
01890
0/750
0.575
0,590
0.725
0.755
B.69
76.0
86.6
76.4
72.
77.8
0.675
0,500
0.250
0.575
0.590
0.750
0.800
0.93
OmmiVerife--TTTee GTiagi--)--
161
67.4
71.
757
76.1
0.725
0.825
0.750
0.000
0.575
0.790
0.33
0721
Omni Verifier---TT brings multifaceted improvements to T2I generation. In reason-based genera- tion, it achieves substantial gains on T2l-ReasonBench, boosting Qwen-Image by 3.7 points and GPT-Image-1 by 2.5 points through sequential TTS refinement. In complex composition-baser gen- eration, OmniVerifier-TT
demonstrates clear advantages in aspects such as Color and Pos/Count. These benefits stem from Vemii-erifer-TTSS architecture, which continuously refines generated images within scall-scale adjustments and leverages a generative verifier to combine generation and editing capabilities. Additionally, using Qwen2.5-VLL-7B the verifier, Qwen VL-TVL lags

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Published as a conference paper at ICLR 2026
behind Omni Ver'fie--TTS both benchmarks, underscoring Verifier' superior and accurate visual verification ability in the general T2I domain.
unlocks potential for interleaved image-text generation and reasoning, As shown in Fig. 6, for complex T2I tasks, Omni Verifier progressively generates semantically consistent and high-quality images through iterative self-refinement. It can precisely identify and correct unrealis- tic or flawed elements images, particularly those related to physics or authenticity. This reasoning paradigm enables automated scaling of interleaved image-text data generation. We firmly believe that Omni Verifier-TTS not only enhances image generation but also provides a robust data infras- tructure and performance guarantee for the forthcoming era of interleaved image-text systems.
5.3 COMPARISION BETWEEN PARALLEL AND SEQUENTIAL TEST-TIME SCALING
To further validate the advantages of sequen- tial VmniVerrfier-1TS, we also explore al-
Image 1
Prompt
UMM
Image 2
Verifier
- Image Best
ternative parallel test-time scaling strategies, such as Best-of-N shown in Fig 7. Specif- ically, Omni Venifferifer-77 is employed to com- pare N images generated from the same prompt through progressive pairwise selection, where the final winner is chosen the best im- age. We compare the sequential and parallel OmniVerrifier-T Veriffer--TTS on Qwen- -men- and GPT- Image-1 with = 10, and the results are re- ported in Table 3.
Image N
(4) Parallel-TTS
Intermediate Image
Prompt
UMM
Verifier Image Best
(b) Sequential-TTS
Edit
Prompt
Figure 7: Comparision between Parallel and Se- quential Test-Time Scaling.
Table 3: Evaluation of Parallel and Sequential Test- Time Scaling in UMMs.
Compreitional Goneration
Model/ Metric
T21 Rensonlench
GenEvalit
Idtom
Tranual
Einiyy
Sciembic
Uverll
Uetoe
Count
Colortoent
CotactPos
PewtCout
Pow/Sine
Mots-Com
Overli
Owes- Image tl., 2025g3
OumiVeeellce- WTS.Veillee-TSSSqaaaaaat
38.5
GPT-Imaag-1
75.4
84.6
75.7
71.6
76.8
0.500
0.800
0.700
0.575
0.590
0.725
0.775
0,889
OunnVVerifee-T (TS/aalllll
770
85.8
77.4
72.4
78.1
0.200
0,255
0150
0.575
0350
0.225
0374
0,100
OmmrVVedde--TTS TTSiiieeniTSSSuuuiaaaa
78.1
97.4
77.8
73.7
70.3
0.725
0.825
0.750
0.600
0.575
0.750
0.825
0.721
Sequential TTS offers higher performance ceiling than Parallel TTS. As shown in Table 3, it con- sistently outperforms Parallel TTS across all three benchmarks. Moreover, it requires fewer genera- tion steps per prompt: while Parallel TTS generates 10 images per prompt, Sequential TTS achieves superior results in approximately 47% of the time. This efficiency and advanced performance arises from its ability to fully exploit the generative critiques provided by OnniVerifier, enabling multi- round, fine-gra ane-grined optimization through UMM.
Finding 4. Advantages of Sequential OmniVerifier-TTS
Equipped with generative verifier, sequential TTS has higher performance ceiling than Parallel TTS in unified multimodal models.
CONCLUSION
This paper focuses on Generative Universal Verifer and makes three key contributions. First, we introduce ViVerBench, which to the best of our knowledge, is the first comprehensive benchmark for evaluating MLLMs' verification visual outcomes. Second, we develop two automated data construction pipelines and explore effective training strategies for universal verifiers, culminating in the powerful OmniVerifier-7B. Third, we investigate practical applications of universal verifiers, proposing DmniVeriffer--TS. sequential test-time scaling method that enhances image genera- tion. and further exploring broader world modeling reasoning scenarios. future work, we aim to scale up the universal verifier and examine its potential for improving multimodal post-training.
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ACKNOWLEDGEMENT
This work was partly supported by the National Natural Science Foundation of China (Grant No. 62576191) and the Shenzhen Science and Technology Program (ZDCC20055901103335 103533300000
ETHICS STATEMENT
This paper advances the field of multimodal large language models, while emphasizing the im- portance of responsible use avoid potential negative societal impacts, such as the creation of misleading or harmful content.
REPRODUCIBILITY STATEMENT
Our code is included in the supplementary material, and the implementation details are described in Section 4.2.
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Lifan Yuan, Weize Chen, Yuchen Zhang, Ganqu Cui, Hanbin Wang, Ziming You, Ning Ding, Zhiyuan Liu, Maosong Sun, and Hao Peng. From f() and g(æ) to f(g(z)): LLMs learn new skills in RL by composing old ones, 2025. Notion blog post, available online.
Renrui Zhang, Jiaming Han, Chris Liu, Aojun Zhou, Pan Lu, Yu Qiao, Hongsheng Li, and Peng Gao. Llama-adapter adapter: Efficient fine-tuning of large language models with irriiiiiia attention. In ICLR 2024, 2024a.
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Xinchen Zhang, Ling Yang. Yaqi Cai, Zhaochen Yu, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang. Yu- jiu Yang, and Bin Cui. Realcompo: Dynamic equilibrium between realism and compositionality improves text-to-image diffusion models. CoRR, 2024b.
Xinchen Zhang, Ling Yang, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, and Bin Cui. Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation. arXiv preprint arXiv:2410.07171 2024c.
Yizhen Zhang, Yang Ding, Shuoshuo Zhang, Xinchen Zhang, Haoling Li, Zhong-zhi zhi Li, Peijie Wang, Jie Wu, Lei Ji, Yelong Shen, et al. Perl: Permulation-enhanced reinforcement learning for interleaved vision-language reasoning. arXiv preprint arXiv:2506. /4907, 2025.
Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing" thinking with images" via reinforcement learning. arXiv preprint arXiv:2505. 14362, 2025.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: En- hancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304. 10592, 2023.
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A DETAILS OF VIVERBENCH
A.1 TASK DEFINITION
Concept Existence evaluates whether all elements described in the prompt accurately rep- resented the image. particularly in complex text-image alignment tasks such as compositional text-to-image generation. This evaluation consists of the following components:
Object: Assesses whether all objects mentioned in the prompt present in the image. Chal- lenges arise in complex scenarios, such as detecting small objects, distinguishing between ob- jects sharining overlapping attributes or easily confusable objects, and identifying occluded objects.
Illustaaiive Example of Object Data
This image was generated from the prompt: "This stunning image captures serene autumn scene featuring a traditional East Asian-style pavilion, possibly a temple, majestically perched on a rocky outcrop beside tranquil pool. The spire of another temple is faintly visible behind it. Waterfalls cascade into the water, enhancing the peaceful atmosphere. The surrounding ablaze with vibrant orange and yellow foliage, indicating the beauty of autumn. stone path strewn with fallen leaves leads towards the pavilion.
Question:
Please carefully analyze the image and determine whether all the objects and their quantitics mentioned in the prompt are correctly represented in the image. If all the objects and quantities correctly presented, please answer 'true'; otherwise, answer 'false'. Provide your evaluation strictly in the following ISON format: "aanswer": true/false, "explanation": "If answer is false, briefly summarize the main error."
Answer: False
Explanation: In this generated image, the spire of another pavilion cannot be behind the central pavilion, that object missing, and the answer false.
Evaluates whether all attributes such as color, quantity, and expression specified in the prompt are correctly depicted. The difficulty increases when multiple attributes must be satisfied simultaneously, and when ensuring accurate binding of attributes to the correct objects in complex scenes.
Attribute:
Illustrative Example of Attribute Data
DIVERSIFY THESE NUTS
Question: This image generated from the prompt: "This image features four diverse indi- viduals standing in against plain, light background. Above them, bold. dark blue text reads "DIVERSIFY THESE NUTS." From left right, first man wears a dark blue jacket
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over lighter shirt, teal pants, and baschall cap. The woman in the center has dark, wavy hair and is dressed in yellow jacket and red pants. To her right, a man with beard smiles in yellow hoodic and brown pants. The last man on the right sports a red hoodic and teal pants. All Tour appear relaxed and friendly, with subtle variations in their styles."
Please carefully analyze the image and determine whether all the attributes specified in the prompt (such color, texture, shape, material, lighting, expression, and motion) correctly represented in the image. If all the attributes are correctly presented, please answer "true"; other- wise, answer 'false".
Provide your evaluation strictly in the following JSON format: ""answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explamation: In the generated image, the woman's 's jacket color is red, not yellow; therefore, the color attribute of this jacket is incorrect, and the answer is false.
Abstract Patterns: Focuses on high-level verification tasks involving visual logic puzzles with abstract, challenging patterns and plausible distractors. It tests the model's 's ability to critic about compositional relationships among multiple objects and attributes from varied perspec- tives within abstract scenarios.
Illustrative Example of Abstract Patterns Data
Question: This image was generated by from this prompt: Here is detailed description of the figure: This is a digital graphic featuring a highly symmetrical, geometric design set against plain white background. **Overall Shape and Composition: The main figure is regular hexagon, oriented with flat top and bottom. This hexagon is perfectly subdivided into six identical equilateral triangles. The vertices all six triangles meet at the precise center hexagon, creating a star-like junction. The base of each triangle corresponds to one of the six outer sides of the hexagon. **Color and Pa The design employs simple two-color palette: A warm, vibrant coral salmon-pink. * A cool, rich shade of blue, similar to peri- winkle or royal blue. These two colors are applied the triangles Moving clockwise (or counter-clockwisc) around the center, the colors cycle between pink and blue. This results in four pink triangles and two blue triangles. No two triangles of the same color are adjacent. ViVisual Effect and Style:** **Symmetry: The figure possesses 3-fold rotational symmetry (C3), meaning it looks identical if rotated by 120 240 degrees. The al- ternating color scheme gives it dynamic, pinwheel-like appearance. # **Style;** The style minimalist and flat. There are no gradients, shadows, or textures just solid blocks of color with clean, sharp edges. +* Analogy:** The pattern is reminiscent of kaleidoscope, a spinning top, the top-down view of segmented umbrella, faceted gem. The contrast between the warm pink and cool blue makes the design visually engaging and balanced.
strict alternating pattern.
Please carefully analyze the image and determine whether generated image strictly matches the prompt, including aspects such as position, color, quantity, and shape. Answer "true" if it does, and 'false' if it doesn't.
Provide your evaluation strictly in the following JSON format: "answer":: true/false, "explanation": "If answer is false, briefly summarize the main error""
Answer: False
Explanation: The description states that the design results in four pink triangles and two blue triangles. However, the image, there three pink triangles and three blue triangles. This contradiction indicates that the description does not match the image.
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Object Relationship evaluates the model's ability verify the spatial relationship and interaction between objects in complex sceniors. This evaluation consists of the following components:
Spatial: Evaluates the model's capability to detect spatial misalignments between objects in images and the corresponding prompts, especially under varying viewpoints and in complex scenes that combine positional relationships with multiple object attributes.
Illustrative Example of Spatial Data
This image was generated from the prompt: In 3x3 grid image, the bottom-led cell contains a female with orange hair with eyes closed, and the top-right of the bottom-left cell is a female with purple hair with mouth open, the top-right cell contains a male with hrown hair with mouth open, and to the helow of the top-right cell is male with orange hair crying with mouth open with eyes closed, and the bottom-left of the hiddle-right cell is female with yellow hair with eyes closed.
Question:
Please carefully analyze the image and determine whether the spatial relationships between ob- jects mentioned in the prompt are correctly represented in the image. If all the spatial relation- ships correctly presented, please answer 'truc'; otherwise, answer 'false".
Provide your evaluation strictly in the following JSON format: "answer": true/false, "explanation": "If answer is false, briefly summarize the main error."
Answer: False
Explanation: The spatial relationship is incorrect. According the prompt, the middle-right cell should contain a male with orange hair crying with mouth open with eyes closed, instead contains a female with pastel pink and blonde hair with eyes closed. Therefore, the image does not match the prompt.
Non-Spatial:
Focuses on interactions between objects that cannot be easily characterized by spatial positioning alone (Huang et al., 2023), with particular attention to challenging cases involving complex interactions between single object and several others.
Illustraivve Example Non-Spatial Data
This image was generated from the prompt: "A sculptor is carving intricate details into a clay bust, shaping with one hand while supporting the sculpture's 's chin with the other." Please carefully analyze all elements in the prompt that involve interactions hetween two objects or actions involving people, and check whether all these relationship-relan related words are correctly reflected in the generated image. If all such relationships accurately depicted, please 'true': otherwise, answer false".
Question:
Provide your evaluation strictly in the following JSON format:
"answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: The sculptor's hand not holding the sculpture's chin.
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World Dynamics evaluates whether single image sequence of images conforms to real- world physical laws, examining the model's world knowledge and its ability to judge physical plau- sibility in visual domain. This evaluation consists of the following components:
Static Physics: Assesses whether a single generated image strictly obeys physical laws such as gravity, buoyancy, and lighting effects (e.g., shadows and reflections). To increase difficulty, the constructed data contain subtle yet clearly identifiable violations.
Illustrative Example of Statie Physies Data
This image was generated by model. Please carefully analyze the image and deter- mine whether it satisfies physical realism, such as consistency with real- world rules like lighting, gravity, melting points, and other physical laws. Answer 'true' it does, 'false' if it doesn't.
Question:
Provide your evaluation strictly in the following JSON format:
("answer": true/false, "explanation": : "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: pencil is placed a water cup filled with water. The part the pencil under the water should be offset to the right.
Dynamic Physics: Evaluates whether a sequence of images follows consistent physical laws
over time, assessing the model's ability judge temporal dynamics and physical plausibility in dynamic scenarios.
Illustrative Example of Dynamic Physies Data
Question: You are given 8 consecutive images representing physical event or motion. Your task is to analyze the temporal progression and determine whether the sequence as whole is phys- ically plausible. Assume the event is closed system, unfolding naturally without any unseen external force human intervention. Consider wide range physical principles, including: Object Permanence: Objects should not appear or disappear without a physical cause. Continu- ity of Motion:
The movement of objects should be smooth and logical. Consistent Positions & Interactions: Objects should interact with each other and their environment in consistent man- change state must align with
Plausible Dynamics: Any acceleration, deformation, real-world physics (e.g., gravity, momentum). Causal Relationships: The state scene in one frame should be a direct and logical cause the state in the next.
Answer 'tree' if the sequence as whole appears physically realistic, otherwise answer "false.. Provide your evaluation strictly in the following JSON format: "answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: The ball in the left should not appear in the right.
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Image Annotation evaluates whether image annotation outputs accurately satisfy the require- ments specified the prompt. This evaluation consists of the following components:
Bounding Box:
Assesses model's ability to judge whether a provided bounding box correctly corresponds to the specific object indicated in the prompt, with particular emphasis on objects that have ambiguous attributes or complex spatial configurations.
Illustrative Example of Bounding Box Data
Question: You are give image with a blue bounding box indicating a selected region solve the question: Draw a box around the white, circular pitl. Evaluate whether the blue bounding box shown on the image accurately point out the correct answer. Note: All positional descriptions given from the photographer's perspective.
Answer 'true' the blue bounding box accurately point the correct answer, otherwise 'false'.
Provide your evaluation strictly in the following JSON format:
""answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: The selected area translucent capsule filled with white particles, not white, circular pill.
Similar to Bounding Box, this evaluates a model's 's ability to judge the correctness of provided points for specific objects in the image.
Pointing:
Illustrative Example
Pointing Data
Question: You are give an image marking with blue point to pointing task, which solves the question: Point the person speaking someone in the Evaluate whether the blue points shown on the image accurately point out the correct answer. Note: All positional descriptions are given from the photographer's perspective.
Answer 'tree if the blue point accurately point out the correct answer, otherwise answer 'false". Provide your evaluation strictly in the following JSON format:
("answer": true/false, "explanation": "If the answer is false, briefly summarize the main error"
Answer: False
Explanation: Point refers to person who is sitting in car and talking to someone outside.
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Counting: Measures whether all objects specified in the prompt correctly represented by points in the image. Each point must correspond to the correct object, and no specified objects should be missed, making this task more challenging than standard pointing tasks.
Illustrative Example Counting Data
Question: You are given an image to counting task, which solves the question: How many red grids are there in the picture by marking all the red grids using blue points Evaluate whether the blue points shown the image accurately identify all targeted objects: red grids. Your assessment should based on the following criteria: Whether each blue point shown the image accurately identify the targeted objects: red grids. All targeted objects must be correctly identified (no targeted object is missed or left unhighlighted by blue points). Each targeted object should correspond to exactly one blue point. Answer 'true' if it correct, otherwise answer 'false'.
Provide your evaluation strictly in the following JSON format: ""answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: In the picture, the third grid in the first row, the fourth grid in the second row, and the third grid in third row above the love heart are marked on both sides.
State Value Evaluation assesses whether task has been successfully completed by analyzing the state a game, robotics environment, or GUI as captured in an image. This evaluation includes:
Maze:
Evaluate model's 's ability to determinw whether the provided path maze constitutes valid solution, without errors such as passing through walls.
Illustrative Example Maze Data
You are given image related 'maze- mazee-sllving scenario. Maze rules:
Question:
marks the goal. Black lines represent walls that cannot be crossed. The blue polyline represents the path previously taken by the player. The player's object is to navigate the maze step-by-step from the start to the goal. Please carefully analyze the image determine whether the player has reached the exit by following valid path, Answer 'true' it has, and "false' it has not.
A dot marks the starting position, and
Provide your evaluation strictly in the following JSON format: "answer": true/false, "explanation": "If the answer is false, briefly summarize the main error."
Answer: False
Explanation: The player's path passes through walls, so not reasonable or valid path.
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FrozenLake: Assesses both step-level and outcome-level correctness, determining whether the sequence of actions is reasonable and whether the tärget game state has been reached.
Illustrative Example Frozenl Lake Data
Question: You are given two images related to the 'FrozenLakke" scenario.
Lrk rules:
Player: The elf character.
Exit: The treasure chest.
Obstacles: The blue patches of water are holes and cannot be entered.
Path: The white, snowy tiles are safe to walk on.
Movement: The player move one step at time: up, down, left, right The two images show:
The first image captures the player's 's current position a given moment.
The second image shows the player's position after making one move. In other words, these two images represent single transition - before and after one move.
Please determine whether the player's move from the first image the second image brings the game state closer to goal state. Answer 'true' the move brings game state closer to goal state, otherwise answer 'false'.
Provide your evaluation strictly in the following JSON format: ""anwwer": true/false, "explanation": "If the answer is false, briefly summarize the main erron""
Answer: False
Explanation: The player's action is not reasonable step that leads toward the goal state.
Focuses on scenarios where a robot stacks blocks, requiring complex reasoning, and evaluates whether the model can accurately judge if the current block arrangement represents valid sequence.
Robotics:
Illustrative Example Robotics Data
Question: This is robotic arm stacking scenario where some blocks must be placed in strict sequential order. You given images: the first shows the target configuration, and the second shows an intermediate state. Please analyze intermediate state to determine follows the correct stacking sequence. Specifically, does this arrangement represent a valid and logical step towards reaching the target, without violating any placement order rules? If the sequence is correct, please answer 'true": otherwise, answer "false".
Provide your evaluation strictly in the following JSON format: "aanswer": true/false, "explanation": "If answer is false, briefly summarize the main error."
Answer: False
Explanation: The orange block and the gray block below the purple block, the orange block and the gray block should be placed first. Therefore, current state incorrect.
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GUI: Covers mobile, PC, and web interfaces, evaluating whether the model correctly de- termine the bounding boxes an image correspond the buttons specified in the prompt.
Illustrative Example of GUI Data
Google
Question: This image shows mobile interface with the command: add new tab. A blue box has been overlaid indicate the interface region corresponding the command. Please determine whether the highlighted area accurately represents the target command. (i.e., whether it includes the command content or whether clicking this area executes the command), Answer 'true' if the highlighted region correctly covers the actionable element of the command; otherwise, answer 'false..
Provide your evaluation strictly in the following JSON format: ("answer": true/false, "explanation": "If answer false, briefly summarize the main error""
Answer: False
Explanation: The box highlights the text "New Tab", but new tab can only be activated by clicking the plus icon on the upper left.
STEM evaluates coding-related judgement tasks in STEM scenarios, assessing whether numerical values, variables, and other information in the image are consistent with the code. This evaluation includes:
Chart: Evaluate whether, in coding tasks involving statistical visualizations (e.g., bar charts histograms), the model accurately determine if the rendered image fully matches the underlying code, including subtle details such as numerical values, colors, and legends.
Illustrative Example of Chart Data
Question: You are given an image and code:
import numpy np import matplotlib. pyplot as plt from mpl_tocckits.mplot33.. import Axes3D import seabern as sns set (style="dark")
x np. (..1inspace-11, 1, 10)
1p. (-1, 1, 10)
mp.array(10, 0.5, 1.01)
x, y, (p.neshgrid Y. z)
u -y v x w 0.1 (x y) fig plt...ff figure)
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ax fig. aog.add_suuae subpiot (111, projection= 3d')
ax. ax.qui (x, (x, Y, 2, u, V, w, length=0.1, normall normalize-Truee Tree, color- olive', 'purple', 'teal'l)
ax.plot_surface (x[;, ;, 01, yl:, 01, 2 01, color-'
yellow', alpha-0.3)
ex.plot_surfacee
:, 1), ylt, i, 11, 21;, ", 11, color='
violet', alpha=0.3)
pe.pllotsurfcceeie :, 2], yl:, :, 2), 21:, 2], color=' eyan
', alpha=0.3)
theta = np. Linspace (0, 2 np.pl, 100)
x_circle np. cos (theta)
y_circle (pthtaa
?_circle np.ones_ (thetaa))) (theta) 0.25
ax.plot (x circle, y_sircie, z_circle, color='blue Linewidth=2)
ax. set xlabel ('X")
setylabl (
ax. SM.set_zlabes )
plt.show()
Please carefully analyze whether the image was generated using this code. You need to closely examine whether the image and the code match exactly, including the legend, variable names, line color, line style, plotting range. data values, and other details. Answer "true" if it does, and 'false' if doesn't.
Provide your evaluation strictly in the following JSON format: "answer": trueffalse, "cxplanation": "If the answer is false, hriefly summarize the main error."
Answer: False
Explanation: The code shows z-coordinate of the blue circle is set 0.25, whereas image shows the blue circle is located a z-coordinate 0.75, which is inconsistent, so the code does not match the image.
LaTeX: Assess whether, given LaTeX code and its corresponding image, the model can pre- cisely verify complete correspondence, capturing fine-grained elements such values, vari- ables, and operators.
Illustrative Example LaTTXX Data
())
2x
67 (7
(Tup TuA
d 1xp)
A = 0,
0%p 0%
Question: Please carefully analyze this image and determine whether was generated by com- piling the given LaTeX code:
\begin (align=) H
(Ncal
f \kappa) (\srrt (- (
gamma;
\fract delta I' 1 (0) 11 Aeelta \gamma mm \nu
\frac( delta I" (0) (1: Adelta \gamma |lmmbda \rho
left \gamma \m \roo \gamma (uu \lambda
[frac(1)
gaama /m
Igamma_ \lambda \rho) \right)
|faac \sqrt (-) (aagmmaa gamma) kappa) Lambda D,
Answer 'true' if it does, and 'false' if it doesn't.
Provide your evaluation strictly in the following JSON format:
"aanswr":: true/false, "explanation": "If answer false, briefly summarize the main error""
Answer: False
Explanation: In the LaTeX code, the denominator of the fraction inside the parentheses is d-2, whereas in the image, the corresponding denominator d-1, which presents contradiction. code does not match image.
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A.2 BENCHMARK CURATION PIPELINE
ViVerBench is constructed through a systematic pipeline following two important criteria: (1) en- suring sufficient difficulty and (2) guaranteeing answer correctness with no room for dispute. The data construction process consists of four stages:
Initial Dataset Construction We employ three strategies to construct data for the 16 tasks:
Manual Annotation For tasks in {Object, Attribute, Non-spatial, Static Physics, Counting): the lack of high-quality verification datasets and the difficulty of constructing error-free, un- ambiguous data necessitate expert involvement. We invited 12 domain experts to curate chal- lenging datasets. Starting from perfectly matched image image-prompt pairs, we applied fine- ine-grained editing, inpainting, and prompt modification to create false examples, each accompanied by detailed error explanations. Considerable effort was devoted to ensure both diversity and diffi- culty throughout data construction and selection.
Programmatic Data Generation For tasks in {Spatial, Maze, Frozenlake, Robotics], de- veloped tailored scripts to systematically generate true/false examples, along with their corre- sponding explanations.
Augmented Open-source Data For tasks in {Abstract Patterns, Dynamic Physics, Bounding Box, Pointing, GUI, Charts, LaTeX], we collected high-quality samples from open-source datasets (Feng et al., 2025; Bordes et al., 2025; Cheng et al., 2025; Wu et al., 2024; Hao et al., 2025) and further created challenging true and false examples with detailed explanations, specifically designed for visual verification tasks.
Expert Review and Difficulty Enhancement The initially constructed data were reviewed with thorough review by five experts, who verified whether the true examples were strietly correct and free of ambiguities and whether the explanations of the false example were reasonable and correct. They also assessed task difficulty, providing higher-difficllty annotations for tasks such as Object, Abstract Patterns, and Bounding Box.
Human Evaluation To further validate the dataset, we recruited ten experts for human evaluation. Since many true/false examples are paired from the same prompt or image, the data were evenly split into two groups to prevent prior bias, ensuring that paired samples were placed in different groups. Each group was independently evaluated by five experts.
Dataset Refinement and Final Selection Based on human evaluation results, we identified ques- tions that were frequently answered incorrectly and subjected them to an additional correctness check by five new experts. Incorrect items were removed, and questions with potential ambiguities were refined. This process yielded ViVerBench, comprehensive and challenging visual verifica- tion benchmark with 3,594 data samples.
A.3 EVALUATION METHODS
We evaluate model performance using two complementary metrics: rule-based evaluation and madel-based evaluation. Let N be the total number of one task in ViVerBench. For each sam- ple i, we denote the ground-truth answer ys E [true, false} and the model-predicted answer as Ús E {true, false]. When y/1 = false, the benchmark provides a ground-truth explanation e;, and when = false, the model is required to generate we use a judge model (such as GPT-4.1), denoted as F(e,, éc,, which returns true if e, and è, are considered consistent. We further denote by 1(.) the indicator function that outputs if the condition holds and otherwise.
explanation é; To assess explanation quality,
Rule-based Evaluation Rule-based evaluation measures only the correctness of the predicted an- swer. The accuracy is computed as:
AcCrule-tased
Σ 1 = y:).
(I)
This metric ignores explanations and focuses solely answer correctness.
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Model-based Evaluation Model-based evaluation extends the rule-based setting by additionally requiring explanation consistency when both the ground truth and the prediction are false. The accuracy is defined as:
ACCmodel hased
Σ E:
1( v.)
Σ = v.) (.)) ((((en)))) false
(2)
N
When Y/ is true, prediction is correct only if DJ matches yr. When y: is false, correctness requires not only d = Yi but also judge model verifies consistency between é, and ex. This stricter metric prevents spurious correctness from random guessing by enforcing explanation validity in false cases.
B EVALUATION ON GENERAL BENCHMARKS
GenRM Evaluation For the evaluation of Vision Laiiuage Generative Reward Models, we use VL-RewardBench (Li et al., 2025), focusing on the text-outcome verification capability of GenRM. As shown in Table 4, we observe that OmniVeriffer-7BB achieves substantial and comprehensive im- provements over the baseline, particularly in reducing hallucination. This shows that visual outcome verification can effectively generalize to text-outcome verification, which we attribute to the shared atomic capabilities underlying cross-modee verification.
Table Evaluation Results on VL.ReardBench.
Model
General
Hallucination
Reasoning
Overall Accuracy
Macro Avg Accuracy
Qwen 2. 7B
37.16
45.79
54.40
46.72
45.79
Verifier 7B
41.53
70.09 (+24..))
57.86 (+3.46)
62.80 (+16.08)
56.49 (+10.7)
Evaluation on Mainstream Benchmarks To examine more general and more complex tasks, we additionally evaluate the models on eight mainstream perception and image-reasoning benchmarks (Chen al., 2024; Tong al., 2024; Wang et al., 2024; Hao et al., 2025; Xu et al., 2025; Roberts ct al., 2025; Liu et al., 2023b).
Table 5:
Evaluation Results on Mainstream Perception and Image-reasoning Benchmarks.
Model
MMStar
MMVP
RealWoridQA
MathVision
EMMA
Visul.ogie
ZeroBench
OCRBench
Qwen 7B
72.9
13.7
OmniVerifier 7B
63.9(42.2) (7.7(+4.8)
(-.1(-0.7
25.2(43.1)
29.4(+466)
2-.4
14.4(H07)
(+210)
As shown in Table Veniferi demonstrates clear improvements most benchmarks. This suggests that a critic model trained with RI. on pointwise samples can generalize into a strong policy model. leading broad gains across diverse downstream generation tasks. This finding aligns with observations from LLaVA-Critic-RI (Wang et al., 2025b), and we further validate it in the visual- outcome setting. We argue that this observation, namely critics enhance generation, highlights highly promising direction for future large-scale model development: be trained into strong verifier or critic model, and the two can mutually reinforce one another. enabling multimodal system capable of self-evaluation and ultimately self-improvemennt This also highlights the importance universal verifier.
strong policy model can
C ANALYSIS OF TISE-TIME SCALING EFFICIENCY
We report the average number of iterative refinement rounds when using Qwen-Image and GPT- Image-l T2I-ReasonBench and GenEval++ benchmarks:
From the Table 6, we can observe the following:
Sequential TTS outperforms Parallel TTS in both speed and performance. On GenEval++, Sequential TTS requires fewer than two refinement rounds average to achieve higher-qualit results. In contrast, Parallel TTS generates 10 images per prompt without demonstrating sig- nificant performance gains.
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Table Performance and Refinement Efficiency Across Backbones, Verifiers, and TTS Strategies.
121-ReasonBench
GenEval++
Backhone
Verifier
Test- Scaling
Overall
Mean Round
Overall
Mean Round
Qwen-Image
55.5
0.675
GPT-Image-I
76.8
0.689
Qwen-Image
OmmiVeriifir=771 OmniVeriffer:7B
Parallel Parallel
58.1
10
0.693
10
GPT--mage-I
78.1
10
0,700
10
Owen-Image
Owen 2.5-VL. 7B
Sequential
57.4
4.67
0.682
1.95
Owen-Image
0
Sequential
59.2
3.86
0.718
1.86
GPT--mage-I
Owen VL 7B
Sequential
77.8
2.01
0.693
1.33
GPT-Image-I
OmmiVerifier-77
Sequential
79.3
1.59
0.721
1.27
OmniVerifier substantially reduces hallucinations and improves judgment accuracy. Across all backbones, employing OmniVerifier consistently requires fewer refinement rounds than using Qwen 2.5-VL. RL training equips Verifier with stronger visual-outcone verifi- cation capabilities, which mitigates hallucinations and produces accurate explanations and edit prompts. Consequently, higher-quality results are attained with fewer refinement rounds.
Stronger backbones reduce verification rounds, shortening inference time. GPT-Image- demonstrates clear advantages over Qwen-Image in reasoning and world knowledge. On T21-ReasonBenchh, this translates nearly threefold faster inference. Stronger backbones also follow instructions more accurately, enabling precise edits and refinements. We therefore antic- ipate that, more powerful unified models emerge, OmniVeriifir- Verifier-TTS will consistently deliver faster inference and higher-quality results.
D RAISING THE PERFORMANCE CEILING WITH STRONG VERIFIERS
We have added the results of using Gemini 2.5 Pro as the visual verifier on 121-RehaonBench and GenEval++, as shown in Table 7:
Table 7: Results of Sequential TTS with Gemini 2.5 Pro on T21-ReasonBench and GenEval++.
Backbone
Verifier
Test-Time Scaling T21-ReasonBench Overall
GenEval++ Overall
Qwen-Image Qwen-Image
55.5
0.675
Omniverifter-P1 Gemini 2.5 Pro
Sequential
59.2
0.718
Qwen-Image
Sequential
62.7
0.736
GPT-Image-1
76.8
0.689
GPT-Image-1
Omniverifier-78 Gemini 2.5 Pro
Sequential
79.3
0.721
GPT-Image-1
Sequential
81.6
0.746
Due to Gemini-2.5-PPP strong world knowledge and visual-judgment abilities, achieves signifi- cantly better results on both benchmarks. We also find that incorporating TTS with strong verifier pushes the baseline into a substantially higher performance tier, markedly extending its achievable upper bound. Looking ahead, plan to further improve the model by scaling up both its size and the amount of high-quality training data.
E ADDITIONAL VISUALIZATION RESULTS FOR OMNIVERIFIER-TTS
In this section, we provide a detailed analysis of the strengths and weaknesses of Sequential TTS versus Parallel TTS.
Sequential TTS significantly raises the generation performance ceiling of unified model. Through multiple rounds of self-refinement, Omniverifier continuously leverages its world knowledge, rea- soning ability, and critic capability to guide visual generation through fine-grained editing. This allows it to handle highly complex compositional prompts, including domains unseen in the training data, as shown in Fig. 8. In contrast, for such highly challenging prompts, Parallel TTS struggles to generate accurate outputs regardless of the number of samples. This is because does not per- form targeted reflection and correction for the global prompt condition; its errrorn-eerror approach without self-reflection makes it difficult achieve breakthroughs in complex scenarios.
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table, there models of cytinder, sphers, cane, cans, and secizngsiar the tallest and the shorlmt.
Edit Prompt]
Edit Prompt benwoe the case the rateggale pour the seall plaste silin cyitnder.
L &Rppaa the "sate" is prosept
ataanne ophure beateeto'' srall ophor sharter" and
criatte sphoree legs whherced coaill spheee)
iiceee lef demuu siloet enteecr alu biad enselh. chit wish hedopad plane inp
Edit Prompt
Edit Prompt
Move phone from the black wroughtizen round table the green wonden chatio, placing next to the backpack.
Remove the phume from the lable, leaving only the brown latte on the side the table closst to the chate.
chitertakl, lukk,
both emojine top-tiht winking the left eye, bottom--att winking the right eye, and betturn-nghh right with both eyes closed.
Change winking eye dog top-right dog's should
Edit Prompt1
Prompt Change righteyy faee a wink, mesning the dog's left chould appoor winking apposr small dal.
winkiing while
ege
remains
wheel divided into three sections labeled "A,"", "8, and "C," with the pointer landing in the section for "C""
Edit Prompt
Edit Prompt
hiiding hupld busl fraese
spriiht
Dokite racradal dividigg las konemlell wineet tile and susge the lnveriaft gry rapee wheel centure three secionn aadi Libeld with leiten
Move labeled
uwilth lnging lor teperss karn kack
B
red haired
males
Edit Prompt
Raples EAlls Prodect
"bunny cass hund both hands while wearing
with
brnny eazs (both hands raised above her head mimis nabbii cars).
I.Replace the red-baired double-V
liiing with sapervrdd acegloning
back dross white hained petncess wears dress and left hand.
2 2.Replacz the white baired gitt's pointing gosture with lefl- "sumber geture (the hand theil forming the shape removing
gesture
graphic).
Figure 8: More results of Omni OmniiVerifee-TTTS
However, Sequential TTS is constrained by the backbone's 's editing capability and is subject to error accumulation, which affects the final image quality. In Fig. 9, we present some failure cases of Sequential TTS using GPT-Image-1. While the final image aligns with the prompt, GPT--mmge-1 exhibits limited robustness to its own generated image distribution, causing a gradual yellowing of images over successive edits. Although OmniVerite generally provides correct edit prompts, a weak backbone editing capability prevents accurate application of these changes. Consequently, errors accumulate across multiple rounds of self-refinement, ultimately degrading the final image quality. This highlights that mitigating error accumulation a critical challenge for future unified models tasked with multi-round generation.
Phonegh Beylass the auddle
cenend del
AMM
makile do
giane en the
and addirrly drep
Tall Pronept Fowm Ail cemera
Edit Paompee
Edi:Promp1?
Kap thes
Seoring entra dingle
and aod the
aiddiegleen tn singl degg
Selt Pronget andtee Ligerrp makigge
Fdit Prompet
Edit Pronept Be noadee side ana
ceneir our weree
ploco strucc lave rove
plore the Lale
Edit Frangi Dodor Aye dedeee
Edi Tramgt
Krytertte yfh
tor pootion
Likk An wederdiiii
Figure 9: Failure cases in image quality caused by the backbone's limited editing capability.
F LONGCOT PATTERN ANALYSIS OF OMNIVERIFIE
We train OmniVerifier with end-to-end reinforcement learning, without requiring any cold start. The training objectives include a format reward to regulate reasoning behavior and rule-based reward to assess answer correctness. After training, observe that the model autonomously learns high-quality and well-structured chain-of-thought (CoT) pattern. This humanlike CoT reasoning is precisely what visual verifiers need in complex scenarios, illustrated below:
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Illustrative Examples of OmniVVerifier LongCo'T' Patterss
This image was generated from the prompt: "Above the picture are the hlue sky and white clouds, and there trees both sides and in the distance. There residential houses both sides of the alley. On the left side, from building. There a red lantern hanging under the eaves of the small cottage side of the picture. There are flower pots placed beside the small cottage closest to the camera on the right side. There is an empty chair on the left side of the middle allcy, and red fruits are placed on the chair the right side. A utility pole stands on the left of the scene."
Question:
far, there low bungalows and two-story red-brick
middle of the
Please carefully analyze the image and determine whether all the objects and their quantitics mentioned prompt are correctly represented in the image. presented, please answer 'true"; otherwise, answer 'false'. Provide your evaluation strictly in the following ISON format:
the objects and quantities are correctly
{ ""answer": true/talse. "explanation": "If the answer is false, briefly summarize the main error.) }
OmniVerifter-77::
<think>
bet'a break
prompt compare to Image:
1. ++Dlue sky and white clouds.+: The image shows blue sky with white clouds, which.
1a correctly represenced.
2. *xIrees on both sides and in the distancewx: There Lrees on beth sides of the
siley and in the distance, which is correctly represented.
3. **Residential houses both sides the alley==: There residential houses both sides of the alley, which is correctly reprasented. bungalows
story red-brick building on the left side*.: left side of the image shows low bungslows and a tue-story red brickrick building, which is correctly represented.
5. #Redd lantern hanging under the
There is red lantera visible in the image, so this is net correctly sepresented.
6. ++Foowr pota beside the smal: cottage closast the canera the right siders:
There are flower pots visible besice the amall cottage on the right aide, which is correstly represented.
7. +*Eppy chair on the left side of the middle alley+:: There is an empty chair on the left side of the middle alley, which is correctly zepresented.
B. *xRed fruits the cheir the zight sidev.: There red fruits the chair on the right side, which is correctly representad.
wittility pole the scene, which is correctly represented.
left of the
Thera is utility pole the left of
ezror the prempt is ebsence the image. sowaver, the rest of the objects and quanzities menticned are correctly zepresented in the image. </think
lentern, which present in
{ "answez": false, "explanation": "The main esror is the absence of red laatern hanging under the eaves of the small cottage in the middle of the left side of the picture.")
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Illustrative Examples of OmnivVerifier LongCo'T' Patterss
S
This image generated from the prompt: "A cartoon girl with cat's tail, short curly white hair, and sharp nails is smiling uniform and a white long sleeved haori jacket. She is squatting down, looking at a little cat and gently stroking it. There are also many kittens surrounding her. The background features East Asian style shrine buildings and cherry blossom trees."
Question:
her lips closed.
wearing Demon Slayer Corps
Please carefully analyze the image and determine whether all attributes specified the prompt (such as color, texture, shape, material, lighting, expression, and motion) are correctly represented in the image. If all the attributes are correctly presented, please answer 'truc"; otherwise, answer 'false'. Provide your evaluation strictly in the following JSON format: ("answer": true/false, "explanation": "If the answer is false, briefly summarize the main error.") }
OmniVerifter-7B:
<think>
prompt compare them the imaga: 1. **Cartoon girl with cat's tailex: The character in the Image has cat'a tail.
break
attributes mentioned
2. **Short curly whize hairvA: The character has short, curly white hair. explicitly mentioned sharp, but
3. **Sharp railsaw: The character's nails
they appear to be normal.
*+5miling with her 1ípa cloaedx::
amiling: appears
frowning.
**feemon Slayer Corpa uniformew: character is wearing a form that resemble
the one from the Demon Slayer anine, out it's not explicitly labeled as such.
**White long-slesved heori jacketsr: The charecter is wearing a white long sleevee haori jacket.
6.
7.
**Squatring down.e: The character is squatting down. li:tle
character looking 9. .*Sently stroking itee: The character is gently streking s cat.
.-Looking
10. +>Many kittens surrounding heree: There are mulziple eats sucrounding the
character.
12. **east asian-style shrine buildings.e: The background features East Asien-style
shrine buildings.
12. +*Cherry blosson treestv: The backgrourd features cherry blossom trees, amiling, contradicts prompt. Hooever, attributes are correctly represented in the image. </think
chacactes
"answer*; false, "exp anatioon on"" "The sniling, which contradtcts the prompors descrtption.")
1a character 1a
We find that by decomposing complex problem into series of specific and simpler subproblems, this structured and organized reasoning style can greatly improve the model's verification accuracy. Moreover, as discussed in Section we observe that training with rule-based rewards without direct supervision on textual outputs does not significantly affect text modeling. The model is still capable of generating coherent LongCoT reasoning, explanations, and edit prompts for TTS self-refinement.
Finding 5. Minimal Supervision Suffices for Generalization
Optimizing only for binary true/false outcomes via rule-based reinforcement learning enhances veri- fication while preserving the model's 's explanatory language capacity. Thus, task-specific supervision over explanations is not required to maintain language modeling quality.
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G BROADER WORLD-MODELING INTERLEAVED REASONING
We further employ task-specific data to train verifiers for maze navigation and robotics, in order to explore whether Omriverifier Verifier can provide meaningful critiques in broader world modeling reason- ing scenarios. As shown in Fig. 10, we use Qwen2.5-VL-722. (Bai et al., 2025) as the policy model. In the maze scenario, where each action corresponds moving up, down, left, right, the policy model often makes mistakes such as walking through walls. With the strong visual-outcome reflec- tion capability of OmniVeerifeer these errors can be promptly corrected. Similarly, in the robotics scenario, where the placement of blocks follows a logical order, OmniVerifie provides powerful error correction for the policy model, substantially improving accuracy in such reasoning tasks.
Initial
Duwa
Down
Up
Hight
Down
Down
Down
Right
Down
Left
OnaiVecifer paln hes dieedy through up
OnaiVesiber posh throngh
backtnac
backineck
Soma pink
pink
Pick up boown lasert boswn Pidk brown detdonn brown
Veatiee onder Incomeet block and black the butiun, these two abouldd be placed first
OeniVeelfeen The incoreett the blak the buttion of the baowa black, and the ahould be plazed fint
Figure 10: Extend Verifier world world-modeling interleaved reasoning tasks: Maze and Robotics.
H PROMPTS USED IN THE TWO AUTOMATED PIPELINES FOR VISUAL VERIFIER DATA CONSTRUCTION
Methodl: Prompt Image Recaption
You aze a powerful Image Captioner. Given an image, you must generate caption is vesccurste faithful.s the visual content.
You muat strictly follow these quicelines:
1. Accuracy Pirst
Describe only the elements that can be clearly iden=ified in the image (objects, stc.).
attributes, spatial relationahips,
No guesses, subjective assumpriens, or irrelevant details are elloved. not fabricate elements that are present
image.
2. Rescription Rlaments
Focus on obless attributes (coler, Lexture, shape, material). Include visible
elements (beckground, envirenment, lighting). Cover actions, pases, expressions, and spatial relacionships. Accurately capture any numbers,
quartities that discernible.
3. Language style
Use an cbjective
tone. Avoid subjective evaluations (e.g., (beautiful, " \oute").
Keep the captios clear and precise, without unnecessary embellishment.
The length of the caption should be determined by the complexity of the image, but must not excend 50 worda.
Output Format Raquirament: output only one caption, without any explanation or additionsl
Method1: Prompt for Caption Modification
You are a powerful image coption acitor. Your
++gubile yet importaat modificalionss- key aitribate details in an image caption (e.g., color, texture, shaps, material, pose, facial expression, numbers, latters, quantities, eta.). requirement
modified detail must be sasignificent encugh that inage generated from the originel caption would not satisfy the modified caption.e. **Rulog:*
1. You are allowed to nake vinnyy one moditicationns modifications should vediverserr attribute (such as ccloz). Any attribute-relate description can be the target of
linited single type
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your edits, such as shape, number, coloz, letter, quantity, expression, pose, and 8o
3. Vague ambiqueua modifications le.g., changing Whiite" to leff-white, or 'some"
to \a few*) are VAnct valid because thei: bounderies are hard to define. You output the joen format, with the following keys: The modified prorpt (after your editl. specific key **Exmmlle 1:49
you changed.
twinput:**
à movie scene: black and white phoLograph showing old sitting iaside He is wearing a mask and looking into the camers through the front rearview mirrer. A hird-shaped ornament hangs the left side of the rearview mirror. There also GPS navigation devica below the center console, with the sereen displaying the number <oeo",
include some cazy stmosphere. The background is blurred, with faint view of the outside. .vOstput:::
controla, creating simple
Modified prompt: "A movie scene: a black-and-white photogreph shewing an old man sitting inside
He is front rearview mirror. à birc-ahaped ornament hargs on the left side of the rearvlew mirrot. There is screen displeying the number '710'. other parts of the car include some buttons and cazy atmorphere. Tha background blurred, with
mask and looking inso the camera through the navigation
below
censole,
the
controls, creating simple faint view of the outaide.",
Changed detail: "The screen number was changed from. '700' to '710'."
xvHoow your turnsRx *<Input:**
..Output:4N
Method2: Prompt for Image Recaption
+.Image Captioning Instructiens
evRoleva You are high-presision image captioner.
Your goal la to generate an accurate, concise, and fact-based caption based on the given image **Ceneration Rules..
hounding indicating target object: object name.
1. **objeot Inclusionny
The caption esmust mention the object inside the bounding box사,, describing its appearance and location accurately.
The caption should also include other important objects in the
2. *sAttribute Isva
Deseribe observable sttributes objects, such color, texcure, shape, material, 1' ghhhing, facial expressions, and aotiors.
3. **Spatial felationshipas.
Clesrly describe spatial relations between objects (e.g., top/bettem, left/right, front/back, zear/far).
*xInteracti
Describe actions or interactions between objects (e.g., Asitting on, Vholding,"
Leaning against").
5. **Factual Rоcuracy*v
Only describe content that can be confirmed from the image. add guesses, subjective imagination, irrelevan: details. If an attribute or detail cannot be confirmed, do not mantion it. ishist
sirila" Objects*s
II there aze ocher objects of the same calegory in the Image, clearly distinguish and deseribe hounding box onject's spectfia location features.
7. *xExplantionss
In additien to the caption, you need to provide as explenstion indicating where the the image outside the bbox
generated image incorrect assuming that all correctly generazed, apeci fy the arror withir the bbox (1.0., the object inside the generated correctly). 8. ++Length Limit..
caption must noz
english words.
<<Loog Enabled Self Correction Processvi
*-Step I Initial Draft*.
Cenerate caption following al: rules.
StSt Sel
Verify following:
1. Caption explicitly mentions the bounding bex object
coject's attributes and location azcerately deseribed.
included when visible. 4. vescription is based only on abservable facts (ns guesses or imaginary details). Explonation reasonable.
Spatiel end interaction relatienships
6. Caption lengch is < 60 worda.
Step Correction Loop..
If vranyse check fails:
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Revise the cap_icn to meet all rules.
Re-run self-chech
Repeat until all checks
ArFinal Output Pormat<A
Always output enly the vafinal, corrested** caption and explanstion in this format:
captian:
explanation:
AxExmplees
->Input** Bounding box object: Leddy best
grass. girl dress, holds Leddy bear. The girl the right holds red balloon, with white house visible in the distance.",
*vOutput** { ception:
girls
wearing
explanation: "The generated image is missing
taddy bear." *ngput** Rounding box object:
+*Output**
I LIMITATIONS AND FUTURE WORKS
Although Verifier and Mernfierifier-1 demonstrate promising performance, identify two key limitations that suggest directions for future work:
One limitation is that certain tasks may generalize less effectively. For tasks such large domain gap, and optimizing for them requires task-specific data. We posit that truly universal verifier should perform robustly across diverse tasks. Future work will explore strategies in train- ing and data construction to enhance Veriffersss generalization, moving closer to a genuinely universal verifier.
involve
Verifeeriie- is influenced by the backbone. Currently, due to issues with training data and strategies, unified multimoda model sensitive the distribution of the images it generates or edits. Some models exhibit unusual behaviors under multi-step self-refinement; for instance, GPT- Image-1 tends produce increasingly yellowish images after iterative edits. Importantly, these artifacts affect only style and do not compromise verification performance. We view this as subtle limitation of the backbone rather than Omni Verifier-TTi TTS itself, and we encourage further efforts to enhance style consistency under multi-step self-refinement.
J USE OF LARGE LANGUAGE MODELS
We used a large language model solely to assist in polishing English writing and improving clarity. research ideas, experiments, results, and interpretations are entirely our own.
33


Reviews:
[{"review_id": "22uLZci2xN", "reviewer": "Reviewer_YEzL", "summary": "The paper targets a missing skill in multimodal LMs: checking whether generated/used images actually match the prompt/reasoning. It builds ViVerBench to show current VLMs lag far behind humans, trains a 7B OmniVerifier via two automatic visual-verification data pipelines, and plugs the verifier into a sequential test-time scaling setup so the model can detect mistakes and issue edit prompts.", "questions": "- In OmniVerifier-TTS, the iterative editing process depends on the “edit prompts.” How are hallucinated or semantically incorrect edit suggestions filtered or corrected during inference?\n\n- For the two automated pipelines you use to construct large-scale visual verification data, how sensitive is the final verifier’s performance to noise or inconsistencies introduced by the upstream models?  and did you consider any robustness or denoising steps beyond the current filtering?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Well-motivated problem: visual-outcome verification is clearly under-served and the benchmark exposes real gaps.\n\n- Data pipelines are scalable and produce clean true/false supervision for several atomic verification skills.\n\n- Nice systems angle: using the verifier to improve generation (not just evaluate) via sequential TTS is practical and shows gains.", "weaknesses": "- Overstated Universal Claim: The \"Universal Verifier\" title seems to overstate the model's capabilities, especially given the limitations candidly discussed by the authors. The admission that it \"generalize less effectively\" to tasks like mazes due to a large domain gap suggests the verifier is still highly dependent on task-specific data distributions.\n- Evaluation is mostly on the authors’ benchmark / close tasks; less evidence for robustness on messy, real-world images.", "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": "P8OJJZ1FUp", "reviewer": "Reviewer_Ctie", "summary": "This paper introduces the \"Generative Universal Verifier,\" a concept aimed at endowing next-generation multimodal models with the crucial ability to reflect upon and refine their visual outputs. The work presents three core contributions:\n\n1）ViVerBench: A new, comprehensive benchmark spanning 16 task categories designed to evaluate the verification of visual outcomes. Experiments on this benchmark reveal that existing SOTA VLMs significantly underperform human-level capability.\n\n2）OmniVerifier-7B: The authors design two automated data construction pipelines to train a 7B generative verifier, which achieves a substantial +8.3 gain on ViVerBench. Through this process, they identify and analyze three \"atomic capabilities\" of visual verification: Explicit Alignment, Relational Verification, and Integrative Reasoning.\n\n3. OmniVerifier-TTS: A novel sequential test-time scaling (TTS) paradigm is proposed. This method uses the verifier to bridge image generation and editing within unified models, enabling iterative, fine-grained optimization. This sequential approach is shown to outperform parallel TTS methods like Best-of-N on benchmarks such as T21-ReasonBench and GenEval++.", "questions": "1）Addressing Performance Drops: Why does the OmniVerifier-7B model, after being trained with RL on alignment and relational data, show such a performance drop on the 'Chart' task and stagnate on 'Static Physics' (Table 1)? These tasks (especially 'Chart') are prime examples of the atomic capabilities the model was supposedly trained to improve.\n\n2）Rationale for GPT-5 vs. Gemini 2.5 Pro: What was the rationale for using GPT-5 for the visual verification data construction pipelines? Given that your own evaluation (Table 1) identified Gemini 2.5 Pro as the superior model for these verification tasks, were there specific capabilities (e.g., superior prompt generation, better compliance with modification instructions) where GPT-5 was found to be more suitable for the data creation role?\n\n3）Path for Integrative Reasoning: Given the poor generalization of \"Integrative Reasoning\" tasks, do you see a path toward improving their generalization (e.g., through different training strategies or data augmentation), or do you believe a task-specific \"mixture-of-verifiers\" model is the more practical future direction?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1）Problem Importance: The paper addresses a highly important and timely problem. As MLLMs move towards complex, interleaved reasoning and generation, the ability to self-critique and refine *visual* outputs, not just text, is a fundamental requirement for building more reliable and controllable systems.\n\n2）High-Quality Benchmark: ViVerBench is a major contribution in itself. It is comprehensive, challenging, and meticulously constructed. The evaluations on it provide clear and actionable insights, identifying specific weaknesses in current SOTA models (e.g., mismatched world knowledge, underdeveloped critics for visual reasoning).\n\n3）Deep Capability Analysis: The investigation in Section 4.2 that leads to the identification of three \"atomic capabilities\" (Explicit Alignment, Relational Verification, Integrative Reasoning) is a standout feature. The discovery that the first two capabilities generalize well, while the third is task-specific, is a deep insight that provides clear guidance for training more efficient and effective universal verifiers.\n\n4）Novel Sequential TTS: The OmniVerifier-TTS framework is a significant methodological advance. It smartly reframes test-time refinement from a parallel *selection* problem (Best-of-N) to a sequential *optimization* problem. By using the verifier to generate concrete \"edit prompts,\" it enables iterative, fine-grained correction, leading to a higher performance ceiling than parallel methods.", "weaknesses": "1）Unclear Data Construction Diagram: The diagram illustrating the data construction pipeline in Figure 2 is confusing, particularly for \"Method 2: Prompt-fixed Image-Inpainting\". As per the flowchart, the line from GPT-5 seems to originate only from the SAM-processed 'True Image'. The 'False Image', generated by FLUX inpainting, lacks a clear connection to the final \"Prompt & Explanation\" output. This makes the data-pairing process for false examples ambiguous and difficult to follow.\n\n2）Limits of \"Universality\": The paper honestly acknowledges that the \"Integrative Reasoning\" capability (e.g., Maze, Robotics) shows minimal cross-task generalization and requires task-specific data. This challenges the \"Universal\" claim to some extent. The paper would be strengthened by further discussing the implications of this finding for building a *truly* universal verifier.", "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": "8rAuvm6ZgN", "reviewer": "Reviewer_Q1VF", "summary": "This paper introduces Generative Universal Verifier, a new approach to help AI models better verify and improve visual outputs during reasoning. The authors first create ViVerBench, a comprehensive benchmark showing current models struggle with visual verification tasks. They then develop automated methods to build training data and train OmniVerifier-7B, which significantly improves verification performance. Finally, they apply this verifier through OmniVerifier-TTS, a sequential refinement system that enhances image generation quality through iterative verification and editing.", "questions": "1. What are the specific failure cases where sequential TTS performs worse than parallel approaches?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The structure of this paper is clear, which investigates three central questions. \n2. The ViVerBench benchmark is well-designed with 16 diverse task categories and careful human validation, providing a solid foundation for evaluating visual verification capabilities.\n3. The identification of three atomic capabilities (Explicit Alignment, Relational Verification, Integrative Reasoning) offers important insights into how visual verification works and generalizes.\n4. The sequential TTS approach shows clear advantages over parallel methods like Best-of-N, achieving better results with fewer generation steps.", "weaknesses": "1. Computational costs of running multiple verification and editing steps aren't adequately addressed, which could limit practical application. The authors should discuss the extra inference cost when adopting OmniVerifier-TTS.\n2. As shown in Table 1, the core component, OmniVerifier-7B, achieves an overall score of only 0.653 on ViVerBench. Relying on such a judge can introduce a fundamental risk into the entire iterative refinement process.\n3. While the authorsdemonstrate in Table 2 that their specifically trained OmniVerifier-7B judge outperforms QwenVL, they do not explore whether a judge with superior foundational abilities would yield further gains. For instance, as shown in Table 1, Gemini-2.5-Pro achieves the highest score on the ViVerBench benchmark. Would a more powerful judge like Gemini-2.5-Pro lead to significantly better TTS results？", "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 (Oral)