Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images
Authors: Chuangchuang Tan
Xiang Ming
Jinglu Wang
Renshuai Tao
Bin Li
Yunchao Wei
Yao Zhao
Yan Lu
Venue: ICLR 2026 Poster (2026)
Keywords: Anomaly Detection，AI-Generated Images
Primary area: datasets and benchmarks

Abstract: The rapid advancement of AI-generated content (AIGC) has enabled the synthesis of visually convincing images; however, many such outputs exhibit subtle \textbf{semantic anomalies}, including unrealistic object configurations, violations of physical laws, or commonsense inconsistencies, which compromise the overall plausibility of the generated scenes. Detecting these semantic-level anomalies is essential for assessing the trustworthiness of AIGC media, especially in AIGC image analysis, explainable deepfake detection and semantic authenticity assessment.In this paper,  we formalize \textbf{semantic anomaly detection and reasoning} for AIGC images and  introduce \textbf{AnomReason}, a large-scale benchmark with structured annotations as quadruples \emph{(Name, Phenomenon, Reasoning, Severity)}. Annotations are produced by  a modular multi-agent pipeline (\textbf{AnomAgent}) with lightweight human-in-the-loop verification, enabling scale while preserving quality. At construction time, AnomAgent processed approximately 4.17\,B GPT-4o tokens, providing scale evidence for the resulting structured annotations. We further  show that models fine-tuned on AnomReason achieve consistent gains over strong vision-language baselines under our proposed semantic matching metric (\textit{SemAP} and \textit{SemF1}). Applications to {explainable deepfake detection} and {semantic reasonableness assessment of image generators} demonstrate practical utility. In summary, AnomReason and AnomAgent serve as a foundation for measuring and improving the semantic plausibility of AI-generated images. The code is available at \url{https://github.com/chuangchuangtan/Semantic-Visual-Anomaly-Detection-and-Reasoning}.

Published as a conference paper at ICLR 2026
SEMANTIC VISUAL ANOMALY DETECTION AND REA- SONING IN AI-GENERATED IMAGES
Chuangchuang Tan 1..3. 12.3, Xiang Ming?, Jinglu Wang', Renshuai Tao Bin Li*, Yunchao Wei Yao 1.2f Yan Luśt
Institute of Information Science, Beijing Jiaotong University
Visual Intellgence +X International Cooperation Joint Laboratory MOE
MMicrosoft Research Asia *Shenzhen University
tanchuangchuangga jtw..eu..u.......... en, yzhao@bjju.edu.ccm yanluemicrosoft.com
ABSTRACT
The rapid advancement of Al-generated content (AIGC) has enabled the syn- thesis of visually convincing images; however, many such outputs exhibit sub- tle semantic anomalies, including unrealistic object configurations, violations of physical laws, or commonsense inconsistencies, which compromise the over- plausibility of the generated scenes. Detecting these semantic-level anoma- lies is essential for assessing the trustworthiness of AIGC media, especially in AIGC image analysis, explainable deepfake detection and semantic authenticity assessment. In this paper, we formalize semantic anomaly detection and rea- soning for AIGC images and introduce AnomReason, a large-scale benchmark with structured annotations quadruples (Name, Phenomenon, Reasoning. Sever- ity). Annotations are produced by a modular multi- pipeline (AnomAgent) with lightweight human-in-the-loog verification, enabling scale while preserv- ing quality. At construction time, AnomAgent processed approximately 4.17 B GPT-4o tokens. providing scale evidence for the resulting structured annota- tions. We further show that models fine-tuned on AnomReason achieve con- sistent gains over strong vision-language baselines under our proposed semantic matching metric (SemAP and SemF/). Applications to explainable deepfake de- tection and semantic reasonableness assessment of image generators demonstrate practical utility. In summary, AnomReason and AnomAgent serve as founda- tion for measuring and improving the semantic plausibility of Al-generated im- ages. We will release code, metrics, data, and task-aligned models to support reproducible research on semantic authenticity and interpretable AIGC foren- sics. The code is available at at com htt.c/////cccaagccnang...aaaa/aa -eciiii-iviaaa oade:e/eeeecttttoooooooooooonnnn -nd-ondd--R-nnn
INTRODUCTION
The rapid advancement of Al-generated content (AIGC) has led to striking progress in photorealis- tie image synthesis, powered by acaee generative models such as Stable Diffusion (Rombach et al. 2022), DALL-E (Ramesh et al., (221). Midjourney (Midjourney, Inc., 2025), and Flux (Labs) 2024.. These models can generate high-quality images and are being widely adopted in design, edu- cation, media, and science. However, despite visual realism, many AIGC-generated images exhibit subtle but significant semantic-level anomaties, such as logical contradictions, physical implau- sibilities, or commonsense violations, that compromise their authenticity. These inconsistencies highlight the need for structured semantic anomaly detection in AIGC images, which not only fa- cilitates deeper analysis of such images but also lays the groundwork for explainable deepfake detection and evaluating generative models' reasoning capabilities regarding commonsense knowl- edge. Furthermore, this approach holds potential for enhancing the semantic coherence of image
Work done during internship at MSRA.
1 CCoresponding Authors: Yao Zhao bjtu www.t.ecu.uc...... and Yan mitiaamlmmmmcroot..cmmmmm

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Published as a conference paper at ICLR 2026
generation models. Our proposed task directly supports these goals by identifying content-aware violations that compromise semantic authenticity.
As illustrated in Fig. Ta), such semantic anomalies include hybrid semantics (c.g., mixing sports
equipment), violations of physics (e.g., gravity- climbers), and anatomical implausibilitics.
These issues not captured by traditional low-level forensic cues (Tan et al., 2023 2024 Durall et a 2020] [Wang et al. 2020 Ojha et al. 2023), yet they critically affect human trust and decision-
making when AIGC is used in factual or sensitive domains.
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Surface-levll Anomalies
Semontic-lavel Anomalles
(a) Content-sware semantic anomalles
(b) Detection example using GPT-5, Qwen2.5vl-78, and AnomAgent
Figure 1: Semantic anomaly detection in AIGC-gemerated images. (a) Illustration of high-level semantic anomalies that are context-dependen and subtle, such as inconsistent physics, anatomy, and reflections caaaellonns-calllleeeess that go beyond surface-level visual artifacts. (b) Comparison of detec- tion performance between general-purpose vision-language models (e.g., GPT-5, Qwen2.5vl-72BB and the proposed AnomAgent. While the former focus on surface-level cues such as lighting and textures, AnomAgent identifies fine-grained semantic inconsistencies and provides structured, ex- plainable outputs with severity ratings.
This work introduces the task of semantic visual anomaly detection and reasoning for AIGC image, which seeks to identify and explain semantic-level anomalies present in synthetic images. These anomalies pertain specifically to violations of commonsense knowledge, physical plausibility, and logical coherence. Formally defined, given an AJGC-generated image as input, the system is required produce a set of structured anomaly descriptions comprising four components: Name, Phenomenon, Reasoning, and a corresponding Severity Score, capturing what is wrong, why it wrong, and how severe is, as illustrated in Fig. [b). Specifically, Name provides a concise summary of the anomaly, while Phenomenon offers detailed description at the semantic level. Reasoning explains the underlying causes of the anomaly. Finally, Severity Score quantifies the anomaly by assigning score that reflects its authenticity. This formulation underscores not only the importance of detecting anomalies but also providing explanations alongside detailed semantic evaluations.
Several studies (Wen et al., 2025) Gao et al. 2025 Zhang et al. 2025 Zhou et al. (2025) have at-
tempted to extract forgery-related evidence using vision-language models ( (VLMs) (Bai et al. 2025) from images to support classification outcomes. However, their performance on anomaly detection frequently relies on surface-level irregularities, such as global lighting conditions or shadow pat- terns, subtle statistical artifacts in texture that are typically imperceptible to human observers (see Fig. [b). Left). In contrast, our task focuses on content-level semantic anomalies, implausible ob- ject interaction, physical violations, or commonsense errors, that are directly visible to humans and therefore more aligned with human judgment (refer to Fig. [b). Right). Furthermore, instead of shallow descriptions, the defined structured anomaly representation makes anomaly analysis inter- pretable and accessible, moving beyond detection to structured reasoning.
To support this task, we build AnomRe the first large-scale benchmark for content-aware se- mantic anomaly detection in AIGC images. AnomReason consists of diverse synthetic scenes an- notated with structured semantic anomalies. Each anomaly entry describes what is wrong, why it is wrong, and how severe the inconsistency is capturing errors in object composition, spatial ar- rangement, interaction logic, or physical constraints. This benchmark is enabled by AnomAgent, a modular multi-agent framework that decomposes anomaly reasoning into object perception, at- tribute analysis, relational reasoning, and anomaly synthesis. To ensure the reliability of annotations
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produced by AnomAgent, incorporate a lightweight human-in-the-lowe verification stage. This hybrid pipeline balances scale and accuracy by filtering and refining automatically generated results. Compared with purely manual annotation fully automated generation, multi-agent plus human verification strategy achieves both interpretability and scalability, allowing AnomReason to provide high-quality structured annotations unprecedented scale. Furthermore, we propose novel anomaly semantic detection metrics based on Average Precision (AP) and Fl-score, referred to as SemAP and SemFl, respectively, facilitate the evaluation of anomaly semantic detection performance.
By shifting the focus from surface-level artifacts to content-level reasoning, our framework opens new research directions in semantic authenticity, explainable forensics, and commonsense reasoning for generative media. Our proposed benchmark and system, AnomReason and AnomAgent, enable several critical applications: (i) training semantic anomaly detector to gain deeper understanding AIGC images. (ii) developing explainable deepfake detectors that not only classify but also provide semantic-level justifications for their predictions, and (iii) conducting AIGC semantic reasonable- ness assessment evaluate the logical coherence of image generation models. These applications strengthen content authenticity auditing and help guide future AIGC model development.
ANONAGENT FRAMEWORK
Detecting semantic anomalies in Al-generated images requires not only visual recognition but also reasoning about commonsense knowledge, physical feasibility, and multi-object interactions. To address this challenge, we propose AnomAgent, modular multi-agent framework designed to emulate human perception and reasoning through structured processes.
As shown in Fig. 2 AnomAgent decomposes the anomaly detection process into three stages: entity parsing, anomaly mining, and structured output generation. Each stage involves specialized agents that collaborate produce interpretable, high-precision semantic anomaly annotations.
Given an input image I, AnomAgent outputs a set of structured anomalies:
A {11, Oc, Fr, ==
where Y1 is the anomaly name, o, the described anomaly phenomenon, the reasoning explains why o is considered anomalous, and 8 E [0, 100) indicates the severity. A score of denotes implausibility, while 100 represents full realism. We believe that asking the VLM justify a severity rating encourages deeper reasoning: the model must not only detect an anomaly but also argue why is mild vs. severe, which have found leads to richer descriptions.
2.1 STAGE 1: VISUAL ENTITY PARSING
Semantic inconsistencies are often object-centric. However, objects in AIGC images can be entan- gled, distorted, hallucinated, making entity extraction unreliable. The Object Recognition Agent Perceiive identifies all semantically distinct entities in the image, with emphasis on human-related objects.
To reduce false negatives, the detection is repeated T times with varying prompts and merged:
T
ott (t) = -bbjectPercaiver(() t t T; o = U ol))
(2)
1=1
Each object o E O is represented by an object name and detailed description.
2.2 STAGE MUL.TI-PERSPECTIVE ANOMALY MINING
Anomalies in AIGC images may arise from incorrect attributes implausible inter-object interac- tions. In this stage, we iterate over each object o, e O and perform two complementary forms of semantic consistency analysis: intra-object tuttrbuue-baseed and inter-object (relation-based)
examines visual attributes of o; such shape, material, and functionality. It identifies internal inconsistencies and produces a set of attribute anomaly candidates:
Attribute-Level Analysis. The Attribute Analysis Agent
Att buteAna (areeeAnnaayyeer))
(3)
3

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Stage I: Visual Entity Parsing
[obj], ohy? -.]
Image
Object Recognition Agent (ObjectPerceiver)
[obj2, ab33
summary
Objects list [0 02, 03, ...
[objt, abj3
Stage 2: Putti-Perseective Anomaly Mining
Loop Processes
01
Artribute Analysis
Agent
10g,
Relationship Reasoning
Agent
Relationship Anomaly
01 02. 03
AttributtAnalyzer)
Artiiutt
(RelationReasoner)
Anomaly oe
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2
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Relscionship Reesoning Agent
02 Relezionship Anomaly List
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(AttributeAnalyzeri)
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(RelationReasoner)
Abnormal Proposals
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Stage 3:
Anomaly Consolidation and Structuring
Annotations
Name: [Phonomenon
Abnormal
Proposals
(Anomalyintegrator)
Anomaly Integration
Agant
Agent (AnomalyFormatter
Anomaly Formatting
Severity Score: (Rate the from O 100)
Reasoning: (euplenation of why this phenomenon]
Observed Phenomenon: [Desciiption of visible anomay).
Figure 2: Overview of the AnomAgent pipeline for semantic anomaly annotation. Stage I parses visual entities and yields an object list O. Stage 2 performs multi-perspectivg anomaly mining, producing attribute candidates Caur and relational candidates Cee,, which scored and pruned to C+ Stage 3 consolidates candidates (merging near-duplicates to C) and outputs structured anomalies A = (,, 0, r, v)} (Name, Phenomenon, Reasoning, Severity).
Relationship-Level Analysis. The Relationship Reasoning Agent (Relat ionReasoner) eval- uates spatial, semantic, and functional interactions between og and the rest of the scene, guided by its own attribute anomalies c(t) as contextual priors:
clil Reeaationeessooee (o,, O for), Cau
(4)
The agent first enumerates pairwise and groupwise relations, then filters semantically implausi- ble ones using both visual context and object-specific inconsistencies. We define the intermediate
((()) [0]
2-1' Liel = {c
[O]
anomaly lists across all objects as: Latt
The total set of candidate i-1
anomalies is: C - (cltt)
In addition, mitigate hallucinations and reduce abnor- mal omissions, we implement two-step process AttributcAnalyzer and RelationReasoner. First, anomalies are comprehensively identified from multiple perspectives; subsequently, these anomalies are verified and structurally outputted.
rel
2.3 STAGE ANOMALY CONSOLIDATION AND STRUCTURING
Raw anomaly candidates are noisy, redundant, and linguistically inconsistent. Annotations require clean, standardized outputs. We next consolidate and structure the raw candidate set into inter- pretable anomaly annotations.
Integration. The Anomaly Integration Agent (Anomall)nttegratoo)) consolidates overlap- ping or redundant candidates and removes noise: Č AnomallyIntegratorr//
Formatting. The Anomaly Formatting Agent (Anomaly maps each anomaly can- didate Ć into structured four-field annotation: A ([v,, (1 vz) ( In Fig. 3 present

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Published as a conference paper at ICLR 2026
Name: Implausible pipe placement on shoulder
Two large cylindrical pipes resting on the individual's shoulder without visible stabilization or grip. The pipes appear precariously balanced, showing no deformation of the jacket or adjustment posture account their weight.
Phenomenon:
Reasoning: Cylindrical pipes of this size and material would exert significant downward force, causing visible compression
jacket fabric and requiring individual their body adjust their posture for balance. Additionally, the lack of hand contact or securing mechanism makes it physically implausible the pipes to remain stable on shoulder without rolling
Severity Score: 15
Figure 3: Example of structured anomalies. This figure illustrates detected anomaly where two cylindrical pipes are unrealistically balanced on the individual's shoulder. By structuring the anomaly as {Name, Observed Phenomenon, Reasoning, Severity Score), the model not only pro- vides a clear description of the anomaly but also offers an interpretable reasoning process, making it easier understand why this arrangement is physically implausible. The severity score quantifies the degree implausibility, enhancing the model's ability to observe and explain semantic- anomalies. This structure allows for transparent and interpretable anomaly detection, improving the detection model's trustworthiness and explainability.
examples of structured anomalies. This structured output enables applications such as quality as- sessment and deepfake detection.
AnomAgent provides modular, interpretable, and scalable solution to semantic anomaly detection. By decomposing the reasoning process across multiple agents, it aligns with human judgment and supports practical applications such as quality auditing and explainable detection. Additional details about AnomAgent are provided in Appendix B
ANOMREASON BENCHMARK
Despite growing interest in semantic anomaly detection, there is standardized benchmark de- signed to evaluate semantic-level anomalies in AIGC content. We construct AnomReason, a large- scale dataset of photorealistic AIGC images annotated with structured semantic anomalies across attribute, relational, and commonsense dimensions.
3.1 DATA CONSTRUCTION
We collect diverse set of image-text pairs by crawling approximately 600K user prompts and their corresponding outputs from Midjourney (Midjourney. Inc.. 2025). To ensure content realism and diversity, we apply CLIP-based (Radford et al. (021) filtering on embedding alignment, extract- ing 109,058 visually realistic samples. A subset of 9,911 images is further manually verified for semantic richness and authenticity.
To enhance generative diversity, we synthesize additional samples using Stable Diffusion 3.5 (AI 2024) and Flux (Labs) 2024), using the same prompt pool. After automated and manual filtering, we construct a photorcalistic dataset comprising 21,539 images: 9,911 from Midjoumny, 4,645 from SD3.5, and 6,983 from Flux.
3.2 AUTOMATIC ANNOTATION WITH ANOMAGENT
We apply the P-T-4o-based AnomAgent framework (Sec. 2 the full image set, producing 174,872 structured candidate anomalies across 21,539 images. Across the full corpus, the pipeline consumed about 4.17 billion GPT-4o tokens generate and refine candidate anomalies before HITL screening Each anomaly contains textual name, observed phenomenon, commonsense-be based reasoning, and a severity rating [0, 100].
To ensure annotation reliability, we introduce a lightweight human-in-the-loopp (HITL) quality con- trol stage. Each candidate anomaly a is screened by a trained annotator answering single-choice
We report tokens aggregated API usage logs, including both prompt and completion.
5

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Published as a conference paper at ICLR 2026
(a) Total Image Coant per Cotegory
(6) Amnotat ora Befone and After Human Didluetion
Siverity Statnbution: Before Afher Human Exslustion
Figure 4: AnomReason statistics. (a) Total image count per category: Flux contains 6983 images, Sdv3.5 contains 4645, and Midjourney has the most with 9911 images. (b) Annotations before and after human evaluation: Flux has a reduction from 8.20 to 5.88 annotations per image, Sdv3.5 decreases from 8.11 to 5,86, and Midjourney shows slight drop from 8.07 to 5.96 annotations per image. (c) Severity distribution hefore and after human evaluation: It showing a shift towards lower severity values after human evaluation, reflecting the refinement process in the annotation quality.
Ini Firrruurry date of
Difreesnty ditrestten of keaseig
Figure 5: Top30 noun frequency distributions for the Phenomenon and Reasoning fields.
question: 'Is this structured description correct for the given image?' with three options: AC- CEPT/REJJCT/UNSURE.
h(a) E 11, for ACCEPT, REJECT, UNSURE,
Ainal { A: M(a) 1)). (5)
This low-cost protocol removes implausible ballucinations while preserving structured quality. Af- ter HITL filtering, the average valid annotations per image drops from to 5.9, indicating refined semantic focus (Fig. 4
Lexical Analysis of Phenomenon and Reasoning fields. To further characterize the semantic cov- erage of AnomReason, conduct a corpus-level lexical analysis of the Phenomenon and Reasoning fields across all verified anomalies. For each field, we extract the head noun of the main semantic unit and compute term frequencies. Figure 5 reports the top-30 most frequent concepts for both fields. The Phenomenon distribution shows balanced mix of object-centric (c.g., 'hand', 'fingers', 'sur- face'), environment-level ('background', 'shadows'), and interaction- terms ('contact', 'body interaction'), indicating that anomalies span local appearance, object articulation, and scene-level inconsistencies. The Reasoning distribution highlights the dominant semantic principles invoked in explanations, including realism violations ('absence", 'physics', 'plausibility"), object-function mis- matches ('interaction', "affordance', ) and contextual logic ('environment' 'coherence"). Together, these distributions suggest that AnomReason covers wide spectrum of semantic error modes, from fine-grained object abnormalities to higher-level physical and commonsense violations, providing rich supervision for training and evaluating reasoning-capatiee VLM.
Dataset Split. We retain 21,533 images for training/testing after removing six duplicates/corrupted items. We adopt a deterministic 50/50 train/test split (jD) 21, 25333,,, comprising 10,765 images for training and 10,774 for testing. Each split includes image-level metadata, anomaly counts, sever- ity scores, and intermediate agent outputs support replicability and ablation studies. We will release all results encompassing intermediate outputs from AnomAgent nodes alongside structured annotations filtered through HIL processing mechanisms.
3.3 EVALUATION PROTOCOL
We propose a structure- aware evaluation protocol tailored for semantic-level anomaly reasoning. Each annotation tuple (y: o, r, v) contains description and reasoning field which are compared

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Published as a conference paper at ICLR 2026
against ground truth using BERT Zhang* al. 2020 We define three evaluation views: Phe (Phenomenon), Rea (Reasoning), and Full (combined fusion).
We perform one-to-one anomaly matching at the image level based similarity thresholds {0.7, 0.8, 0.9), and compute precision/recall curves to derive:
SemAP,
1
Σ IED
(P,
SemF1
1
Σ (T, IED
D
D
Severity scores can optionally serve confidence scores in ranking tasks. We adopt the 'distilbert- base-uncased" model for computing BertScore. Comprehensive details are provided in Appendix
EXPERIMENTS
We conduct comprehensive experiments evaluate the effectiveness of our benchmark, methodol- ogy, and task formulation in three progressively structured settings. These experiments are designed assess both the capabilities of current VLMs and the benefits of targeted fine-tuning for semantic anomaly analysis. First, we evaluate structured semantic anomaly detection and reasoning on the AnomReason benchmark, measuring whether VLMs can both identify anomalous phenomena and explain their semantic violations /Sec.[4.1 Second, we extend this reasoning capability to a more applied setting: explainable deepfake detection on AnomReuson- Al-generated content classification and grounded explanation (Sec. 4.2). Third, we audit modern text-to-ine -iatte generators by quantifying semantic plausibility and anomaly severity in their outputs using structured reasoning (Sec. 4.3
which requires accurate
Table 1: Comparative performance on the AnomReaxon-Test.
Model
SemAPrs
SemAP Rea
SemAPrul
SemFl Ihe
SemFl
SemFI
Long VA-7B Zhang al.
2024
0.2579
0.2593
0.2494
0.1641
0.1617
0.1578
LLaVA-OV-78 LL
2024
0.3280
0.2987
0,3012
0.2044
0.1837
0.1867
Phi-3.5-Vision Abdin al 2024
0,3466
0,3040
0.3117
0.2960
0.2572
0.2647
MiniCPM-V-2.6 at al. 2024
0.3898
0.3501
0.3537
0,1891
0.1698
0.1715
InternVL.2-8B Wain al.
2024b
0.3697
0.3424
0.3424
0.3789
0,3500
0.3507
IntemnVL2.5-8H (Chen al.
2024b
0,3343
0,3070
0.3087
0,3008
0.2733
0.2764
InternV1.3-8B (Zhu
0.4552
0.3676
0.3927
0.1948
0.1614
0.1703
InternVL3-9B (Zhu
0,3871
0,3371
0.3514
0,3953
0.3456
0.3595
mPLUG-OW13-7B (Ye
2024
0,4026
0.3661
0.3678
0.1247
0.1141
0.1144
Qwen2-VL-7B (Wang et
20644
0,4090
0,3564
0.3678
0.1307
0.1208
0.1207
InternVL2-26B Waan ct al.
2024b
0.4209
0.3728
0.3865
0,4048
0,3590
0,3722
Qwen2.5- (Bai
2025
0,4674
0,3902
0,4155
0.4240
0,3548
0,3775
Qwen2.5-VL-722 [Bai el al. 2025
0,4926
0,4353
0.4423
0.3912
0,4104
Gemini-2.5-pro (Gongle 2025
0.3755
0.3127
0,3384
0. 1995
0.1674
O. D.1806
GPT-o3 (penAA] 2024
0.4470
0,3762
0.4058
0.4431
0.3724
0.4021
GPT-5 OpenAll 2026
0.3760
0.3212
0.3469
0.4407
0.3762
0.4065
GPT-40 Hurst
0.4908
0.4562
0.4727
0.5304
0.4930
0.5109
AnomReaaonon-7BB
0.5221
0.5130
0,5162
0.5066
0.4977
0.5009
4.1 AIGC SEMANTIC VISUAL ANOMALY DETECTION AND REASONING
The first task evaluates whether VLMs can detect and explain semantic anomalies using struc- tured outputs. The core challenge lies
only localizing errors but also providing plausi- ble reasoning chains grounded in commonsense or physical priors. We adopt the AnomReason test set and use SemAP and SemF1 metrics across phenomenon (Phe), reasoning (Rea), and full (Phe+Reea) views. We fine-tune Qwen2.5-VI-778 via A on the train split, resulting in baseline 7n (AR-7B). (training details App.DD
Results. Table [ presents comprehensive results on semantic anomaly detection and reason- ing. Overall, most off-the-shelf VLMs struggle with the full task (SemAPpar). with values typ- ically below 0.42, indicating limited semantic understanding in the absence of targeted supervi- sion. Among open-source models, Qwen2.5-VL-72B performs best (SemAPrui= 0.4568), fol- lowed by VL3-88 and IntermVL2-266 However, they still lag behind the top- perfoorrnnnng AnomReasonor-77 which achieves a new ofate-of-the-arr SemAP Pram= 0,5162 and the highest rea- soning score SemAP Re== 0.5130, demonstrating the effectiveness of fine-tuning
structured

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Published as a conference paper at ICLR 2026
anomaly supervision. Notably, AnomReasonor-78 also surpasses GPT-4o in all SemAP metries. In terms of SemF1 (alignment quality), GPT-40 retains a slight edge (SemFlyau= 0.5109), but AnomReasonor-75 is highly competitive (SemFlrur= 0.5009), particularly excelling in reasoning (SemFl 0.4977 vs. 0.4930 for GPT-4o). This highlights that our fine-tuned model closes the gap to proprietary systems not only in detection but also in the quality of generated descriptions.
Interestingly, most models show stronger anomaly observation (SemAPPhe) than reasoning (SemAPRea). with some (e.g., VL3-83) showing wide gap (0.4552 vs. 0.3676), suggesting that identifying "something wrong" is easier than articulating "why". In contrast, AnomReasonor- 7B exhibits the most balanced profile, with reasoning performance nearly matching observation. This reflects the benefit of our structured annotation format and supervision signal, which jointly improve both detection and explanation.
Overall, these results demonstrate that current VLMs exhibit limited semantic anomaly competence in AIGC images, particularly in reasoning. Task-aligned supervision on AnomReason consistently improves both observation and reasoning quality, closing much of the gap stronger closed-soure systems. This highlights the necessity of a semantic-level benchmark and validates the design of our structured supervision.
Table 2: Explainable deepfake detection on AnomReason-
Models
Acc
CSemAP mr
CSemA Rus
CSemAP
CSemFl Fln
CSemF Iusa
CSemF
VA-
Zhune et al. 2024
53.45
0,0722
0,0714
0,0688
0.0458
0.D448
0.0436
LLaVA-OV-78
La 2024
66.26
0.1235
0.1124
0.1141
0.0818
0,0738
0,0752
Phi-3.5-Vision
Auai m 2024
41.33
0.0685
0.0602
0.0616
0.0570
0.0497
0.0511
MiniCPM-V-226
[Hu st.
20z4
55.05
0.0233
0.0205
0.0209
0.0109
0.0100
0,0100
IntemVLL-8BB
Wante
44744b
59.46
0.0739
0.0702
0.0697
0.0720
0.0680
0.0678
Intem\VL22.555f7
[Chen il
A1244
63.61
0.1
0. 0,1085
0,1089
0.0988
0.0911
0.0919
VLtemVL.3-8B
ar
0,2559
0,1031
0.1089
IntemVL3-9B
ZZiü
2014
64.04
0.2291
0.1987
0.2081
0.2344
0,2041
0.2132
mPLUG
YY
2024
0.0404
3.0130
0.0117
Qwen2-VL-7B
Waing et at
202448
67.21
0.1710
0,1421
0.1496
0.0524
0,0458
0.0467
IntemVL2-2288 WSing
54.73
0.0194
0.0179
0.0180
0.0160
0.0166
Qwen2,5-VI-775 [150l et
20.55
65.41
0.1295
0,85
0.1
0.1124
0.0943
0. 1.0044
Qwen2.5-VL.722B
[1:3
77.60
0.2626
0,2337
0,2453
0.2427
0,2159
0.2266
Gemini-2. (Google 2025
85.65
0,2631
0,2192
0.2382
0.1488
0.1249
0.1351
GPT-a3 (OpenA] 2114
85.60
0.3189
0,2690
0.2898
0.3187
0,2685
0.2895
GPT:5 (ixnAi
2013)
75.22
0.1790
0.1535
0.1658
0,2114
0,1811
0.1957
GPT-40 (Hirrs:
2024
87.76
0.3750
0,3487
0,3612
0.4054
0.3770
0.3905
AnomReasoba-78
82.61
0,3684
0.3574
0.3613
0.4048
0,3929
0,3972
4.2 EXPLAINABLE DEEPFAKE DETECTION
We extend our semantic anomaly reasoning framework (Sec. 4.1 to an explainable deepfake detec- tion setting. The goal is twofold: (i) determine whether image is Al-generated, and (ii) return structured anomaly explanations aligned with Sec. 3.3 To this end, we construct AnomReason- Deepfake, where real images sampled from LAIONreLAION-HE (LAION e. V. 2023) paired with content-based structured descriptions. The task therefore probes both AIGC detection and semantic explanation within unified benchmark.
Metrics. In addition to binary detection accuracy (Acc), we introduce classification variants of our semantic metrics, denoted as CSemAP, u and CSemF'1, for (Phe, Rea, Full]. Explana- tions are scored only when detection is correct, and assigned zero otherwise. This ties explanation quality valid classification, discouraging post-hoc rationalization for mispredictions and promot- ing joint interpretability.
Results. Table 2 presents results across Ace and classification aware semantics metries. GPT-4o achieves the best overall detection accuracy (87.76%) and strong CSemFlran(0.3e0 (0.3905,, establishing a strong proprietary baseline. Notably, our fine-tuned AnomReasonor-77 attains competitive accu- racy (82.61% (6) and surpasses GPT-44o on CSemAPRea (R..00.3574 vs. 0.3487) and CSemF11ca.0 (0.39992 vs. 0.3770), highlighting its strength in generating causally grounded explanations. Open-source VLMs show large variance. Larger models like Qwwn2.55VL-722 achieve decent detection (77.60%) and moderate explanation ability (CSemFlyaii 0.2266), but remain behind task-aligned models. Some models (e.g., Intern VL3-8B) perform reasonably on CSemAPPun(0.2555) but poorly F1, suggest- ing limited calibration between confidence and semantic consistency. Overall, AnomReasonor--7 offers high-accuracy, interpretable alternative to closed models. These results underscore the util-
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ity of cecteReasson-Deefaaii testbed for joint detection and structured explanation, and support our pipeline's effectiveness for training explainable AIGC detectors.
Compared with gencral-purpose VLMs, AnomReasonor achieves substantially stronger perfor- mance when both classification and explanation are required. While the focus of our work is seman- tic reasoning rather than low-level forgery cues, the classification-wware metries demonstrate that semantic inconsistency signals provide complementary benefits to traditional deepfake detection. This suggests that semantic anomaly reasoning forms useful capability orthogonal to specialized forensic detectors.
Table 3: Semantic Reasonableness of AIGC Image Generators.
Reasonor
AnomAgent
AIGC Model
MAI (U)
AF())
CAP())
MAI ())
AF(()
CAP(L)
Sana 1.5 Xic et al.
2025
4.55
6.29
28.62
6.66
9.09
60.49
SDXL Lightring Lin er 2024
4.40
6.06
26.66
6.47
8.84
$7.21
Sana Sprint 1 6B (C hen el AUMSA
4.32
6.09
26.31
6.42
8.78
$6.35
Qwen-image Wu ct
20258
4.45
6.20
27.59
6.37
8.72
55.61
HiDream----- Ca cl a.
2025
4.44
6.19
27.48
6.32
8.81
55.68
SDv3.5 Large [Esse et al.
2024
4.60
6.44
29.62
6,23
8.54
53.19
Janus Pro (Chen ct
2025b
4.28
28.89
6.49
8.69
56.39
Janus (Chen
2025b
26.94
8.41
FLUX.I [dev] [Wu al.
2025a
4.36
26.51
6.22
8.67
53,92
BRIA-3.2 BBria 2025
4.38
6.14
26.89
6.11
8.60
52.61
SDv3.5 Large Turbo (Esser ct al.
2024
4.40
6.12
26.93
6.10
8.44
51.48
Lumina Image V2 (Qn
2025
4.30
6.05
26.01
6.07
8.40
$1.03
HiDream- I-Dev (Cai al. 2025
4.42
6.15
27.18
6.00
8.44
50.62
OmniGen V2 Wu et al. 2023b
4.21
5.89
24.80
6.11
8.49
51.86
Hunyuanlmage-4
Team 2025b
4.10
5.95
24.40
6.14
8.55
52.51
4.3 AIGC SEMANTIC REASONABLENES: ASSESSMENT
we assess the semantic reasonableness of text-to- image generators. Perceptual metries overlook commonsense, physics, and interaction plausibility, whereas structured outputs DYanee/Pecooeerroo//eessnnnnnsss Score) enable semantics-aware auditing at scale.
Beyond instance-level evaluation (Sec. 4.1
We curate 246 prompts from Midjourney public galleries that correspond to real-photo style content. We first deduplicate/lluster candidate prompts via CLIP embeddings, then filter to photo-realistic style using the Qwen3-30B (Team 2025a). This yields diverse prompts covering multi-object in- teractions, human articulation, and physical dynamics. Each generated image is evaluated indepen- dently by two assessors: (i) AnomReasonor-78 (fine-tuned in Sec. 4.1) and (ii) AnomAgent. Both produce anomalies with an Severity Score $ E [0, 100).
Semantic Metrics. We define three complementary metries to quantify the semantic plausibility of an image (i) MeanAnomaly impluubbilyyl (MAI): MAI(T) E.eă( 100-s(a) (ii) Anoma- 100
lyFrequency (AF): Total number of anomalies identified I. (iii) CumulativeAnomallyena (CAP): CTP(TT MAT(T) AF(T) The MAT captures the aggregated implausibility of all de- tected anomalies. CAP reflects both the severity and frequency of semantic violations. All scores designed such that lower is better, with zero indicating flawless realism.
Results. Table 3 reveals clear differences in semantic plausibility across AIGC models. Hunyuanlmage-2.1 and OmniGen V2 achieve the lowest CAP scores under AnomReasonon in- dicating fewer and less severe semantic anomalies, particularly in commonsense and physical inter- actions. In contrast, models like Sana 1.5 and SDXL Lightning exhibit higher anomaly frequency and implausibility. suggesting challenges in compositional reasoning and realism. Interestingly, we observe distinct failure modes: some models (e.g., Janus Pro 7B) exhibit higher AF but lower MAI, implying frequent yet subtle errors, while others (e.g., SDv3.5 Large) show fewer but more severe implausibilities. These insights go beyond perceptual quality, revealing gaps in physical logic and social commonsense. While both AnomReasonor and AnomAgent show consistent rel- ative rankings, AnomAgent tends to produce higher anomaly counts across the board, reflecting its greater sensitivity and fully automated nature. Despite this, their strong rank-order alignment supports AnomAgent's robustness and suitability for scalable zero-shot auditing. Notably, Anom- Reasonor benefits from supervised training human-curated explanations, whereas AnomAgent

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Published as a conference paper at ICLR 2026
operates without any human intervention mmiereetion-hhghhlgghnnng the promise of fully automatic, semantics- aware evaluation pipelines for future aligmment assessments.
The cross-model assessment reveals large semantic variability among state-of-the-art generative models, indicating that high perceptual quality does not guarantee semantic plausibility. Anom- Reasonor provides a scalable, model-agnostic diagnostic that exposes systematic weaknesses in physical, contextual, and interaction logic, offering foundation for evaluating and improving next- generation AIGC systems.
5 RELATED WORK
ATGC visual anomaly detection and reasoning:
With the proliferation of Al-generated con-
tent (AIGC), recent research (Liu al.] 2024 Huang et al. 2025a Wen al. 2025 Kang et al. 2025| Gao et al. 2025 Huang et al. 2025b Zhang et al. 2025 Zhou et al. 2025 Tan et al. 2025b
Guo et al. 2025 Chen al. 2024a) has shifted beyond low-level forensics explore semantic-level
anomalies in AIGC images. Some methods leverage VLM interpretability to highlight suspicious re- gions (Liu al. 2024] Huang al. 2025a Kang et 2025). while others combine manual inspec-
tion with LLsdbased post-processing (Li et al. 2025 Tan et al., 2025a). Prompt-engineering and
commonsense-injection techniques have also been used to elicit finer-grained descriptions (Zhang
al. 2025 Gao et al. 2025 Wen et al. 2025 Zhou et al. 2025).
However, existing benchmarks such as FakeClue and Ivy-Fake focus on authenticity classification or artifact explanation. They provide coarse labels or clues for real/fake discrimination, but lack structured quadruple annotations that capture commonsense, physical and relational inconsistencies. Consequently, models trained them struggle to perform detailed reasoning severity assess- ment. Our work differs by modelling anomalies at the object-atribute-rrlatoo level and offering interpretable explanations, thus enabling downstream tasks like semantic reasonableness auditing- We also adopt a multi-agent annotation framework with human verification, which yields more con- sistent and scalable annotations than directly prompting monolithic LLM.
AIGC Image Semantic Reasonableness Assessment: Assessing the semantic reasonableness of Al-generated images (AIGC-ISRA) involves determining whether the visual content aligns with real-world commonsense, object plausibility, and coherent inter-object relationships. While current
AIGC image quality assessment efforts (Peng et al. 2024 Liu et al. 2023) Lu et al.] 2023 Yang al., 2024.] Yu al., 2024) focuses primarily on image mmage-prompt alignment and perceptual quality, but overlook semantic plausibility. For example, (Peng al. 2024) propose CLIP-based metrics to assess prompt consistency, yet they fail to capture scene-level semantic inconsistencies. (Liu al. 2023) explore chain-of-thought evaluation for NLG tasks, offering improved human align- ment but lacking grounding and localized reasoning for visual content. Our work addresses this gap via a structured and content-aware evaluation framework that builds upon multi-agen aggett annotator (AnomAgen() to detect and explain attribute violations, spatial logic failures, and inter-object contra- dictions. This enables interpretable and fine-gre graiin assessment of semantic plausibility, bridging key limitation of existing AIGC-IQA and VLM-based approaches.
CONCLUSION
We introduce the task of semantic anomaly defection and reasoning in Al-generated image. To en- able this task, we built AnomReason, a benchmark annotated via a multi-agent framework (AnomA- gent) and human verification, providing structured quadruples (Name, Phenomenon, Reasoning, Severity). Unlike existing authenticity datasets, AnomReason targets commonsense, physical and relational violations and includes severity scores, enabling finer-grained reasoning beyond real/fake classification. We propose semantics-aware metrics and showed that off-the-shelf vision-language models struggle on this task; model fine-tuned on AnomReason (AnomReasonor-775 outper- formed open-source baselines and approached proprietary systems. We also demonstrated appli- cations in explainable deepfake detection and generator assessment. Current limitations include the dataset's 's moderate scale and focus images; future work will extend videos and refine annota- tion quality. We will release code, data and models to foster research into semantic plausibility and more trustworthy multimodal systems.
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ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation China
NN.UU4BB00799 No.62506030 Grant U23B2022, No.62336001), Beijing Natural Science Founda- tion (No. 4264127), China Postdoctoral Science Foundation( Grant No. 2025M781453 and Shen-
zhen R&D Program(Gram) J DtT.:////0......00000S00//////1.
SYSPG20241211140333000
ETHICS STATEMENT
This work adheres to ethical principles surrounding data usage, model training, and large-scale evaluation. All datasets used in this study are either synthetic, automatically generated from text prompts (e.g.. via Midjourney), released under open-source licenses. No personally identifiable information (PII, biometric data, or user wverggeneateed content is used. All visual data used in training evaluation is publicly available synthesized, and filtered avoid unsafe offensive content.
Human annotations used in the AnomReason dataset were collected through internal quality- controlled pipelines, focusing solely semantic inconsistencies in Al-generated content. These annotations are descriptive, non-judgmental, and pertain to visual plausibility, physics, and common- sense violations rather than identity or sensitive topics. Annotators were not exposed real-world sensitive content. Additionally, we introduced the AnomAgent, an automated system that detects and explains semantic anomalies in images without human involvement, relying on large vision- language models (c.g., GPT-4o, Qwen2.5-VDL While the AnomReasonor model was fine-tuned on filtered, verified annotations, AnomAgent entirely zero-shot, demonstrating generalization without human bias injection during training.
This work also uses LLMs (e.g., GPT-4o) for data annotation and linguistic refinement. For annota- tion, LLMs are used under structured prompts that enforce semantic rigor and reduce hallucination. For writing, they were used only for grammar polishing, as stated Section and did not con- tribute conceptually.
The proposed methods aim to improve the semantic transparency and quality of AIGC systems. While anomaly detection framework could assist in automated auditing filtering pipelines, we explicitly caution against its use in high-stakes decision makinn or content moderation without human oversight. We strongly discourage deployment for surveillance enforcement purposes, and advocate for responsible use aligned with human-centered values, faimess, and transparency.
REPRODUCIBILITY STATEMENT
In accordance with the ICLR Author Guide, we provide a detailed reproducibility statement for this work.
Dataset Availability. The AnomReason benchmark constructed from 21.539 Al-generated images, sourced from Midjourney, Stable Diffusion 3.5, and Flux. All images are synthetic and filtered for realism using CLIP-based selection and manual checks. We will release the complete dataset, including raw images, structured anomaly annotations, severity scores, and metadata.
Annotation Pipeline. Annotations were generated using the AnomAgent multi-agent system with GPT-4o, refined through human in- in-the-loop (HITL) verification. The pipeline produced 174,872 candidate anomalies, reduced to an average of 5.9 verified anomalies per image. Intermediate out- puts from each agent stage will also be released to support ablation studies.
Model Training. We fine-tuned Qwen2.5-VL-2 using LoRA adapters inserted at every fourth transformer layer. The visual encoder was frozen. Training used NVIDIA A6000 GPUs, batch size of 4, and gradient accumulation of 8, for one epoch over the training split. LoRA rank was with scaling factor a = 16 and dropout 0.5. All hyperparameters and training scripts will be provided.
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Evaluation Protocol. We designed semantic evaluation metries, SemAP and SemF1, based on BERTScore similarity between predicted and ground-truth anomaly fields. We tested thresholds E {0.7, 0.8, 0.9} and reported averages. We will release the full evaluation toolkit, including BERTScore settings, matching rules, and baseline implementations.
Released Artifacts.
We will publicly release:
The AnomReason dataset and splits.
All anomaly annotations with HITL verification.
The AnomReasonor-78 fine-tuned model weights.
Code for training, evaluation.
Limitations to Reproducibility. Some annotation stages depend on external APIs (c.g.. GPT-4o), which may introduce variability due model updates. To mitigate this, we release all intermediate outputs used in the dataset construction. The HITL step, while low-cost, involves subjective human judgment and may not be identically reproducible.
Summary. All code, data, models, and evaluation protocols will be released under open-source license to maximize reproducibility and transparency.
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@1 Name: Foreground and Background Lighting Mismatch
The lighting on the tennis court foreground is bright and even, while the background wall appears dimmer and less illuminatedd. This creates a noticeable contrast that disrupts the coherence.
Phenomenon:
real-workd photography, foreground and background elements should share similar lighting conditions maintain visual consistency. The stark difference illumination suggests a mismatch between the generated elements.
Reasoning:
Severity Score:
Name: Foreground and Background Depth Mismatch
The tennis court foreground appears sharply defined and in focus, while the background wall lacks depth detail. The transition between the two areas is abrupt, creating sense of separation rather than integration.
Phenomenon:
Reasoning: Real-worid scenes typically exhibit gradual depth changes, with objects the foreground appearing sherper and more defined, while those in the background become progressively softer and less detailed. The abrupt transition here dissupts spatial coherence.
Severity Score:
Foreground and Background Color Mismatch
The tennis court foreground has uniform blue color, while the background wall appears green. This color inconsistency particularly noticeable around the edges Iwo areas meet.
Phenomenon:
Real-world scenes typically exhibit consistent color gradients across adjacent surfaces. The abrupt change in color the edge of the court and wall suggests a mismatch between the generated elements.
Reasoning:
Severity Score:
@4 Name: Right and Wrist Movement
Phenomenon: The right is extended outward, but the wrist and fingers unnaturally stiff and lack vislble motion or tension. The wristband is also misaligned, overlapping the wrist and extending awkwardly over the forearm.
real-life tennis, players use their ams and wrists dynamically during swings, with visible tension and motion. The stifnness and misalignment of the right arm and wristband suggest artificial overlay than natural interaction.
Reasoning:
Severity Score: 25
Tennis Racket Grip and Hand Positioning
Phenomenon:
The tennis racket grip appears overly tight, with the hand positioned unnaturally close handle. The wristband on the left arm also misaligned, overlapping the wrist and extending awkwardly over the forearm.
Reasoning:
real-iiee tennis, players grip the racket loosely for light and misaligned wristband suggest artificial overlay rather than natural interaction between the hand and racket.
flexibility.
Severity Score:
@6 Name: Forearm and Wrist Alignment
Phenomenon: Human forearms and wrists naturally curve slightly due muscle tension and bone structure. The rigid posture disrupts anatomical plausibility of the arm's movement.
real-life tennis, players grip the racket loosely for better control and flexibility. tight grip and misaligned wristband suggest an artificial overlay rather than natural interaction between the hand and racket.
Reasoning:
Severity Score:
Figure 6: Structured visual anomalies in tennis scene. AnomReasonor-7B identifies both surface-level inconsistencies (e.g.. lighting and color mismatch) and deeper semantic-level anoma- lies, such as biomechanically implausible wrist articulation and unnatural hand-racket interaction. Each anomaly is described with a structured triplet: Name, Phenomenon, Reasoning.
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Name: Plant growth anomaly
Phenomenon: seedlings being planted appear unusually and slender compared typical young plants. They lack natural curvature or branching, and their leaves are dispropartinnttlly large relative the stems.
(a)
Reasoning: Young plants typically grow in a more compact and robust manner, with leaves developing gradually as they mature. The exaggerated height and leat size suggest artificial exaggeration rather than a realistic representation of plant growth.
Severity Score: 20
Name: Anatomical inconsistency in the horse's body posture
The horse appears to lying down, but its front legs unnaturally positioned, with one leg bent at an angle that defies natural anatomy and weight distribution. Additionaly,, the does align with lypical resting behavior for horse.
Phenomenon:
(b)
horse's & head lilled forward,
Horses naturally lie down by bending their front legs at knees and placing them under their bodies. The observed positioning suggests either mechanical error an attempt to create a more dynamic pose, which is inconsistent with the horse's 's anatomy and behavior.
Reasoning:
Severity Score:
Name: Inconsistent ball trajectory and motion blur
The shuttlccokk appears to be mid-air but lacks realistic motion blur. suggesting it was not recently hit Additionally, the shuttiscock's 's position relative to the players does align with typical badminton gameplay dynamics.
Phenomenon:
(c)
Shuttlecocks typically exhibit significant motion blur when in flight due to their fast speed and aerodynamic properties. Furthermore, the shutllecock's 's posilion suggests was hit by one player but not yet returned, which is inconsistent with the active gameplay depicted.
Reasoning:
Severity Score: 25
Name: Water spray directionality
The water spray created by the jet ski is directed primarily forward and slightly upward, rather than creating more dynamic fan-like pattern. The spray appears less dispersed and lacks the expected turbulence and motion associated with high-speed water.
Phenomenon:
(d)
High-speed jet skis typically create more pronounced and dynamic spray pattem due to the force exerted on the water. The observed spray direction and dispersion are inconsistent with real-world physica, making appear artificial and unrealistic.
Reasoning:
Severity Score: 20
Name: Person orange jacket
The person wearing orange jacket appears partially obscured by table. yet their and hand remain visible and focus. This creates an optical Illusion where the person's body seems to pass through the table.
Phenomenon:
(e)
Objects cannot physically pass through solid surfaces like tables. The visibility of person's arm and defies the laws physics spatial logic, making scene appear unrealistic.
Reasoning:
Severity Score: 30
Name: Unroalistic Dynamics
The character's hair appears unnaturally stiff and straight, with visible motion or interaction with the wind or surrounding environment. The strands do not bend, sway, or align with the character's 's posture or the canyon's airflo..
Phenomenon:
(f)
Human hair responds dynamically to environmental factors like wind, gravity, and movement. absence these effects makes appear artificial and out of place within the scene.
Reasoning:
Severity Score: 20
Figure 7: Generalization across diverse semantic anomaly types. AnomReasonor-77B detects inconsistencies in plant growth patterns (a), horse anatomy (b), shuttlecock dynamics (c), water spray physies (d), occlusion logic (e), and hair movement (f). These outputs demonstrate the model's capability to reason over visual semantics beyond surface-level cues.
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Summarize whether this image Generated by Artificial Intelligence, please only return yes no.
No, this image is real photograph.
Output your reasoning. If Al-generated, semantic anomalies: Name, Phenomenon, Reasoning, Severity.
N#W Deseription of the Image:
The image depicts an individual walking street an urban environment, person dressed stylish layered outfit suitable cooler weather, Key elements the attire include:
Outerwear: The individual is wearing long, dark gray that reaches mid-calf length. The coat appears be made of a woolen material, suitable for cold weather.
2. Scarf: colorful scarf with striped pattern in red, white, and draped around the neck. The scarf adds vibrant contrast to the otherwise neutral tones of the outfit.
3. Inner Layer: Heneath the cont, there is a black top visible neckline, suggesting a layered look.
Bottoms: individual wearing leggings tights, which partially
visible under the
5. Footwear: Black ankle boots with slight heel worn, completing the outfit.
#thll Background Elements:
The setting is urban street with mix of architectural styles.
The buildings in the background have traditional design, featuring brick facades and large windows with metal grilles. One building has red brick section, while another has white facude with black grilles.
The street surface asphalt, there are visible road markings, including a yellow indicating no-parking zone.
The overall lighting suggests it might daytime, with natural light illuminating
#AW Analysis and Reasoning:
This image captures fushion-oriented individual in urban setting. The combination of the layered clothing, vibrant scarf, and stylish footwear suggests an emplasis on fashion and personal style. The urban backdrop provides context, indicating the photo was taken in city environment. The individual's pose and attire suggest they may be posing for fashion-related purpose, such strect-style photo shoot casual outing.
#MW Final Summary:
The image shows person walking on
urban uren, dressed stylish outfit consisting of long gray coat, colorful striped searf, black leggings, and black ankle boots. The background features traditional brick buildings with metal grilles. indicating city setting. The overall composition suggests focus on fashion and urbun aesthetics.
Figure 8: Real-image evaluation example. Under the two-round protocol, the model predicts the image is real (Round 1), and in Round produces only neutral scene description without anomaly annotations. This demonstrates correct suppression of anomaly reasoning on commercial-style photograph.
A THE USE OF LARGE LANGUAGE MODELS (LLMS)
In accordance with ICLR 2026 Author Guide, we disclose the use of large language models (LLMs) at two stages of this work:
(1) Language Refinement: OpenAl's GPT-4o is used solely for grammar and fluency enhancement during manuscript preparation. It do not contribute the conceptual development, experimental
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MEGA OOK
Summarize whether this image Generated Artificial Intelligence, please only return yes
No, this image is real photograph.
Output your reasoning. If Al-generated, semantic anomalies: Name, Phenomenon, Reasoning, Severity.
Description of the Image:
The image appears to advertisement for hair products, specifically wigs hair extensions. features three distinct views wig with grudient color scheme and wavy texture.
Left Side (Top View):
The view shows wig placed mannequin head. The wig has lace front. which visible at the top of the image.
TTee hair color transitions from dark brown the vibmant red at the tips, creating gradient effect.
hair is styled in loose waves, giving voluminous textured appeurance.
TTee background includes a tripod stand, suggesting that the image was professionally taken.
2. Right Side (Top View):
This view shows another wig placed on a mannequin head, similar to the left side with slightly different angle.
The hair color and style consistent with the left side, featuring gradient from brown to red and loose waves.
The lace front also visible, indicating that this likely lace-front wig designed for natural hair integration.
Bottom Right (Side View):
This view provides side perspoctive of the same wig seen in the previous images.
The huir is styled in loose and the gradient color transition clearly visible.
The texture and volume of the hair are emphasized, showcasing its fullness and movement.
4. Text Overlay:
At the top corner image, there is text that reads EMEGALOOK! in bold, colorful letters. Helow smaller text states Y"YOUR HAIR YOUR GL OLOOY.""
The text suggests that the image is promoting brand or product related to hair styling extensions.
# Reasoning:
The presence of mannequin heads and professional lighting indicates that this promotional image intended for advertising purposes.
The consistent styling and color gradient across all three views suggest that these are examples of the same product being showeased from different angles.
The text overlay reinforces the commercial intent of the image, highlighting the brand name and slogan.
#6j Conclusion:
This image professionul advertisement for a hair product, likely wig or hair extension, featuring gradient scheme wave styling. The overlay identifies the brand ("MEGALOOK," emphasizing the product's focus on cahancing hair appearance.
Figure 9: Real-image evaluation example. The model again answers real in Round and outputs coherent, human-like deseription of the photographed scene in Round No anomaly hallucinations occur, reflecting strong generalization to natural street photography.
design, data analysis, or substantive content generation. All technical ideas and results are the original work of the authors.
(2) Data Annotation Assistance: LLMs, including GPT-4o and Qwen, are employed as annotation agents to assist in generating structured anomaly descriptions for Al-generated images, following
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constrained prompt-response schema designed by the authors. These models used to gen- erate candidate amnotations or supervise anomaly detection model training (c.g.. AnomReasonor, AnomAgent). All prompt designs, schema definitions, quality control procedures, and usage deci- sions were made by the authors, who maintained full control and responsibility over data curation.
The of LLMs is strictly bounded to assistive roles; they not involved in authorship, nor in the scientific reasoning or conclusions of this work.
B DETECTION RESULTS OF ANOMREASONOR
B.I QUALITATIVE ANALYSIS
We present qualitative results from AnomReasonor-755 trained on our proposed AnomReason dataset, to analyze its ability to detect and explain semantic-level visual anomalies in Al-generated images. Unlike surface-level cues zues-such as pixel artifacts, texture mismatches, or inconsistent Sehiing-semantic-levee anomalies reflect inconsistencies with real-world physical laws, common- sense reasoning, spatial logic, and anatomical plausibility.
Figures highlight how structured reasoning outputs-comee composed of Name, Phenomenon, Reasoning. and Severity Score-enable fine-grained interpretation of visual errors that would be challenging to localize using traditional classification- or pixel-level approaches.
Figure(ppreeents a synthesized tennis court scene containing a variety of both surface- and semantic- level anomalies. The model detects lighting, depth, and color mismatches between foreground and background, indicating surface inconsistencies. More critically, identifies human-ccnnin centric semantic anomalies including biomechanically implausible wrist articulation, unnatural racket grip tension, and anatomically misaligned arm--orearm joints. These violations of physical and anatomical real- ism are explicitly explained, demonstrating the model's understanding of dynamic human interaction and contextual coherence.
Figure demonstrates the model's ability generalize across diverse contexts and anomaly types. Examples include:
(a) exaggerated plant growth violating botanical development norms,
(b) horse in an anatomically impossible lying posture,
(c) shuttlecock trajectory inconsistent with typical gameplay physies,
(d) jet ski spray patterns that defy real-world water dynamics,
(e) human limbs passing through occluding surfaces,
(f) static hair ignoring environmental effects such as gravity or wind.
These structured explanations go beyond low-level discrepancies, offering human-aligned insight into the visual implausibility of each scene.
These results showcase the benefit of semantic-level anomaly detection for building interpretable and trustworthy AIGC assessment systems. By explicitly modeling and articulating inconsistencies in visual semantics- rather than relying on post-hoc explanations or pixel cues Anns--Anoeaaaooorr. provides rich, localized, and buman-aligned insights into failure modes in Al-generated images.
B.2 RESULTS ON REAL IMAGES
To assess whether the model overfits to AIGC-specific artifacts or can generalize to natural images, we additionally evaluate AnomReasonor on a set of weal-world photographs (examples in Fig. X and 9... We adopt the two-round protocol described in App. D.2 (1) the model first answers a binary question (real vs. Al-generated); (2) conditioned on the prediction, it either produces structured anomaly annotations (for AI images) or provides neutral content description (for real images). Across diverse real images, AnomReasonor consistently responds with "real" and generates detailed scene descriptions without hallucinating anomalies. This behavior demonstrates that the model does not indiscriminately project anomaly patterns learned from AIGC images onto real photos, and that its reasoning module remains stable even when anomaly supervision is absent. Qualitative
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Theediatts
AS
Thuedeolts
CSee
Figure 10: Threshold sensitivity analysis. CSemAPFul (left) and CSemFl (right) under т {0.1.... 0.9}. Metrics are stable up to 0.7 and degrade only under excessively strict similarity thresholds (r 2 0.8). The selected thresholds (0.7, 0.8, 0.9) thus span stable, moderately strict, and failure regimes.
results further confirm that structured anomaly predictions emerge only when the model is confident the image Al-generated, aligning detection with downstream explanation and highlighting the robustness of the two-step design.
B.3 THRESHOLD SENSITIVITY ANALYSIS.
We evaluate the robustness of our semantic matching thresholds by sweeping т E and reporting CSemAP Pull and CSemFI (Fig. [Op. Both metries remain stable for 0.1 S S 0.7, indicating low sensitivity to moderate changes in the similarity cutoff. Performance drops only when r 2 0.8, where matching becomes overly strict and penalizes natural linguistic variation. Our chosen thresholds (0.7, 0.8, 0.9) therefore capture three regimes- stable (0.7), stricter (0.8), and feirufe (0.9) providngg a balanced measure of semantic alignment while maintaining robust- ness to minor paraphrasing differences.
B.4 SCALING TO STRONGER BASE MODEL
To validate that our training protocol and the AnomReason benchmark generalize beyond a single architecture size, we fine-tune significantly stronger base vision-language model, VLen3-VL-SB. using the same LoRA setup and training procedure.
Table reports results on AnomReason- Deeeffake We compare against: (i) GPT-4o, the strongest closed-source system in study; and (ii) AnomReasonor-7B (qwen2.5-v1-7BB, our best- performing 7B model from the main paper.
Table 4: Performance of a stronger fine-tuned model (Qwen3-VL-8B) on AnomReason- -eeepake The model surpasses both GPT-4o and our previous 7B AnomReasonor across all semantic metrics.
CSemA
CSem Bea
CSemAPPul
Ipac
CSemFlga gea
CSemF yun
GPT-4n
87.76
0.3750
0.3487
0.3612
0.4054
0.3770
0.3905
AnomReusonor-P7 (Setting 2)
82.61
0.3684
0.3574
0.3613
0.4048
0.3929
0.3972
AnomRcasonor (Qwen3--B)
0.3890
0.3802
0.3832
0.4129
0,4034
0.4966
Scaling the base architecture from 7B 8B yields consistent gains across all semantic evaluation metrics. Notably, AnomReasonon Qwen3-8B) outperforms GPT-4o on both CSemAP and CSemFl, demonstrating that the benefits our training framework are tied specific model family or size. These results further confirm that AnomReason enables strong semantic reasoning capabilities when applied to modern VLMs.
C EVALUATION PROTOCOL DETAILS
This appendix provides a comprehensive description of our semantic evaluation protocol, used to score structured visual anomaly predictions descrihed in Sec. 3.3
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C.I SEMANTIC SIMILARITY: BERTSCCORE CONFIGURATION
We instantiate all field-wise similarities with BERTScore (FI variant). Unless noted, we freeze the backbone and preprocessing across all experiments ensure comparability. Given hypothesis h and reference r, we use the BERTSScore Fl:
2 P(h,r) R(h,r)
BERTScoro(h.r) P(h,r) + R(h,r)
E [0,
and define
simPhe BERTScoree o),
simRea BERTTScore (f, r);;
simpall = a simphe + a simmmm
The balanced mean for Full provides a smooth joint signal while discouraging over-optimizing single field. We 0.5 unless otherwise stated.
C.2 MATCHING AND CONTINGENCY COUNTS
For view {Phe, Rea, Full and threshold 0..,,0 define the indicator
Tg T, T) = HK (sim, (ā. 2 r).
(8)
Ranking. Sort predictions Ā(I) fa k by confidence á in descending order. Assignment.
Scan k 1. Kr; for each ((K) choose the unmatched ground-truth a* E A(T)) maximizing (im,( a) subject to T. 1. If such a" exists, mark true positive (TP) and lock both; else, mark a false positive (FP). Unmatched ground truths are false negatives (FN). Tie-breaking. multiple achieve the same similarity, prefer the with the highest simpull (then smallest index). This one-to-one greedy rule prevents multi-matching and implicitly penalizes duplicates.
C.3 PER-IMAGE A PER-TIRREHIOLD AGGREGATION, DATASET AGGREGATION
For image at threshold T, let cumulative counts rank & be TP, r) and FP (k, r). Define
(k;
Pi(k (k, FP, Rr( [A(1)
(9)
AP at
APo(T,T) = Σ Pi(k, ((k,t)(RA) (Rik - R1(k=-177))
(10)
in new at
Per-image SemAP (average over thresholds).
1 SemAP, IT
Σ AP.(L,)), T= (0.7,0.8,0.9), IT| =
(11)
FET
Dataset SemAP (macro over images).
SemAP, = DD
SemAP, (!)..
(12)
IED
Per-image Fl at
(T) Ry(r)
TP,
TP,
Fl,(1,T) (((7)
Pf(T) = TP, FP, RT(T) =
(13)
[A(T)
Dataset SemFl. We report the simple average across images and thresholds:
SemF1,
F1.I,I
(14)
[D] IT|
FET
with per-threshold breakdowns provided below.
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In Sec. introduce classification-awaar variants of semantie metrics, denoted as CSemAP and CSemFl, for v E {Phe, Rea, Full]. Let y(I) E Areal,A]] be the ground- truth source and i(I) the predicted source. For view {Phe, Rea, Full} and threshold set T = {0.7, 0.8, 0.9), define
CSemAP,
1
Σ LED TET
Σ
(D(1)) v(1) AP.(L.T),
(15)
[D]
T
CSemF1,,
Σ Σ
(i(1)) F(t) (I,
(16)
IED FET
Explanations are scored only when detection is correct, and assigned zero otherwise. This ties explanation quality valid classification, discouraging post-hoc rationalization for mispredictions and promoting joint interpretability.
D TRAINING DETAILS
D.1 DETAILS FOR SEc
We fine-tune Qwen2.5- VL using LoRA modules inserted into the multimodal attention blocks, while keeping the visual encoder frozen. Specifically, we adopt the official Qwen2.5-VIL-78 back- bone (7B parameters) with its native image projector, freezing the vision tower (ViT). LoRA is applied to the attention projections (q-proj. k-proj, v-proj) at every 4th transformer layer, with a rank 8, scaling factor a = 16, and dropout rate of 0.5. Fine-tuning is conducted for one epoch using batch size of 4 and gradient accumulation of 8 on 4 A6000 GPUs. Input resolution is dynamically adjusted with a minimum of 50,176 pixels and a maximum of 614,656 pixels.
Supervision data and construction We use the official train split of the AnomReason dataset for training, strictly retaining only verified anomalies to ensure label quality. For each image /, we construct one multiple training instances depending on sequence length constraints. When an image contains m verified anomalies { 0j. Ty. my))y= Jy-1. we pack them into a single instance in randomized order unless token limits require splitting.
Human: Analyze the provided AI-generated image to detect all semantic anomalies. Provide detailes led list of anomalies using the following structure:
- 01. Name: [Phenomenon Name] - Phenomenon:
[Description of the visible anomaly] Reasoning:
(Step- Sstep-by-stee bt-ptep explanation of why this phenomenon is unrealistic or illogical] Severity Score: [Rate
the issue from 0 (completely unnatural) to 100 (fully realistic)]
The model is expected to produce responses such as:
Woman's s shirt fabric rigidity Phenomenon: The shirt worn by the woman appears overly smooth and rigid, showing no folds, creases, deformation where it interacts with the baby the reclining posture.
81. Name:
Reasoning: Soft fabrics typically conform to the body and surrounding objects, creating visible folds and compression due to gravity and physical contact. The lack of such effects reduces the plausibility of the material behavior.
Severity Score: 20.
@2. Name:
Pillow compression inconsistency
Phenomenon: The pillow supporting the woman's head
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and shoulder appears unnaturally amooth and rigid, showing no visible deformation or compression despite the weight applied.
Reasoning: Pillows are designed to compress under pressure, creating indentations and folds where weight is applied. The lack of deformation suggests unrealistic material behavior, detracting from the plausibility of the scene. Severity Score: 25.
This format ensures consistent alignment between training supervision and evaluation, facilitating learning of structured anomaly descriptions and reasoning patterns. The use of step-wise justifica- tions and severity scoring further supports interpretable and rank-aware detection.
D.2 DETAILS FOR SEC
We fine-tune Qwen2.5-VL-7B using low-rank adapters (LoRA), following the same hyperpa- rameters described Appendix [.II with the exception that we set the dropout rate for LoRA (lora dropout) to 0.9 for this setting.
Turn 1: Source classification. The first turn asks the model to determine the image source in binary form:
Q: Summarize whether this image is Generated by Artificial Intelligence, please only return yes or no.
A: <Yes, this image is generated by AI No, this image is real photograph.>
Turn 2: Semantic anomaly explanation. If the image is Al-generated, the second turn prompts the model to enumerate all semantic-level inconsistencies using a structured output format:
Q: If AI-generated, 1ist semantic anomalies: Name, Phenomenon, Reasoning, Severity.
A: [
[Name: <yl>, Observed: <ol>, Reasoning: <rl>, Severity: <vl>),
If the image is labeled as real, the second answer defaults to a content-based explanation of what observed in the image without marking any anomaly. This dual-turn QA formulation enables the model to explicitly separate generation source classification from semantic inconsistency identifica- tion, promoting robust deepfake understanding and explainability.
ADDITIONAL. DETAILS ON ANOMALY DETECTION AGENTS
In this section, provide detailed insights into the design and implementation of the anomaly detection agents used in our framework. Each agent plays a crucial role in analyzing different aspects of the input image, allowing for in-depth semantic analysis of Al-generated images. The design of these agents is driven by the need to model high-level perceptual reasoning and commonsense logic, which are essential for detecting complex semantic anomalies that would otherwise go unnoticed by traditional anomaly detection methods.
E.
OBJECT PERCEIVER AGENT
The ObjectPerceiver agent is tasked with identifying and parsing all semantically distinct entities within an input image. This is the first step in the anomaly detection pipeline, it helps isolate the
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relevant objects in the image for further analysis. By detecting objects in the image, the agent serves as the foundation for subsequent analysis.
The motivation behind the Object Perceiver is provide structured identifi- cation objects in the image, which can then be used for deeper anomaly detection in subsequent steps. The design intentionally focuses high-level object semantics rather than low-level fea- tures like lighting or texture inconsistencies, which might not be as impactful for detecting semantic anomalies.
Design Motivation:
Task: Analyze all objects and individuals in the image. For each object or individual, provide detailed, accurate, and comprehensive description, while identifying any inconsistencies, anomalies, or illogical aspects. Ensure no object or body part omitted.
Follow the steps below and provide your analysis in the structured format specified:
Identify and describe all objects and individuals in the image.
For each object or individual, provide a detailed, accurate, and comprehensive description.
Highlight any inconsistencies, anomalies, or
illogical aspects in:
Shape and Structure: Are there distortions,
missing parts, or unnatural forms?
Material and Texture: Are there abrupt texture
changes or mimathhes??
Lighting and Shadows: Are the lighting and shadows consistent with the environment?
Physical Properties: Are there any violations of real-world physics or logic (e.g., floating objects)?
Common Sense Verification: Are there any
semantic inconsistencies (e.g., a door handle a chair)?
Human Anatomy (if applicable) Identify unnatural features such as missing limbs, extra fingers, or disproportionate body parts.
Output Format:
Each object/body part should be described individually in the following structured format:
#Namel:
Detailed Description.
#Name:: Detailed Description.
Example Output:
with three arms, of which is unnaturally attached to his back. He wears a blue jacket.
Person:
Chair: A wooden chair that appears to be floating without support, casting no shadow.
Dog: A golden retriever with two tails, one of which is blurry and semi-transparent.
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Highlight all implausible, unnatural, or inconsistent details while ensuring full coverage of the image content. Only output the 1ist in the specified format.
Effectiveness:
This prompt ensures that the Object Perceiver focuses capturing the main objects and entities in the image while disregarding irrelevant details. By emphasizing the semantics cach object, the agent avoids common pitfalls of pixel-based anomaly detection and lays the groundwork for higher-level analysis.
E.2 ATTRIBUTE ANALYZER AGENT
The Attribute Analyzer is responsible for detecting anomalies in the visual attributes of objects, such as shape, texture, material, and other intrinsic properties. This agent examines internal incon- sistencies within the objects themselves and identifies any attributes that deviate from the expected real-world norms,
The rationale behind the Attribute Analyzer is that many semantic anomalies manifest as inconsistencies in object attributes. For example, an object might appear out of place because of an unusual shape or texture that violates expectations based on the scene context. By focusing on attributes, this agent can detect low-level anomalies that might not be caught by higher- level reasoning alone.
Design Motivation:
AttributeAnllyzer Step1 Prompt:
Task: Analyze {Current object} in the image.
Focus on analyzing and identifying any anomalies in the following aspects:
Shape and Structure
Are there unnatural forms or distortions?
Are proportions consistent with the object's design?
2. Functionality
Does the object behave logically in real-world scenarios?
Are there physical impossibilities (e.g., unsupported structures)?
Human Body Structure Verification (if applicable)
Are limbs, fingers, and facial features correctly placed and proportional?
Are there unnatural fusions, duplications, or disconnections?
Deliverable:
Highlight all implausible, unnatural, inconsistent details.
Ensure thorough analysis that covers all aspects of the image content.
Provide concise, evidence based explanations for all findings.
AttributeAnalyzer Step2 Prompt:
Object: [Current object]
Description Input: (Attributeenallzee Stepl Reaponse]
Analyze the detailed description of [Current object] and identify all unreasonable, contradi contradictory.
Task:
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physically impossible details specific to this ob object.
Provide structured list of issues using the following format:
Abnormal Phenomenon Name: The name of the observed anomaly.
Observed Issue: The unnatural feature found.
Explanation: Why this characteristic is unrealistic.
Example Output:
1. Abnormal Phenomenon Name: Streetlight No Power Observed Issue: The streetlight is glowing but has no power source or wiring.
Explanation: A streetlight requires electrical connection to function, and no wires or batteries are visible.
Instructions:
Analyze only (Current object].
Output only issues directly related (Current object), using the specified format.
Effectiveness: This two-stage approach encourages detailed yet structured interpretation of each object's s attributes. The separation of observation and reasoning mimics human perceptual processes and aligns well with structured evaluation protocols introduced Sec. 3.3
E.3 RELATION REASONER AGENT
The Relation analyzes spatial, semantic, and functional relationships between objects in the image. It checks if objects are interacting in a plausible way or if their relationships defy physical logical laws. This agent is critical for detecting anomalies that arise from the interaction between objects.
Design Motivation: The design of the Relation Reasoner is motivated by the fact that many mantic anomalies stem from the way objects relate to one another. For example, objects that should interact, such hand holding a cup. may appear be floating or not touching each other at all. By modeling relationships, the agent detects high-level semantic inconsistencies that are often missed by attribute analysis alone.
RelssionReasoner Step1 Prompt:
Task: Analyze the spatial and logical relationships between [Current object} and the following objects: (fall objects from ObjectPerceiver))
You should evaluate:
One-to-one relationshipg (e.g., (Current object)
with each object)
One-to-mane relationships (e.g., [Current object] in relation multiple objects collectively)
Context Descriptions:
{Object Descriptions from AttributeAnalyzer)
Focus Areas:
1. Perspective Errors:
Are objects placed in impossible or illogical locations relative to (Current object]?
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2. Physical Interactions: Are there unnatural interactions (e.g., floating without support, overlapping unnaturally)?
Logical Contradictions: Are there contradictions with real-world behavior common-senue logic?
Instxuctions:
Focus analysis on {Current object] as the primary subject.
For one-to-one relationships, evaluate individual pairings.
For one-to-many relationships, consider collective spatial, logical, and contextual coherence.
Output Format (structured report for each issue)
Relationship: Describe the relationship being analyzed.
Observed Issue: Detail the anomaly or inconsistency.
Explanation: Explain why the issue is illogical or unrealistic.
Deliverables:
Analyze all one-to-one and one-to-many relationships involving {Current object].
Ensure detailed reasoning and structured output for each detected issue.
RelationReasoner Step2 Prompt:
Relation Input: [RelationReasoner Step1 Response]
Focus Object: The primary subject analysis is [Current object]. All evaluations should center on [Current object] and its relationships with the following objects: (fall objects from fb
Based on the prior relationship analysis, analyze and summarize the relationships between
Task:
[Current object] and the listed objects. Emphasize detection of logical contradictions, physical impossibilities, and semantic anomalies.
Key Aspects to Evaluate:
Logical Coherence:
Are the relationships internally consistent?
Example: An object cannot be both inside and outside another simultaneously.
2. Physical Realism: Do the relationships conform to real-world physical laws?
Example: Objects should not float without visible support.
3. Semantic Plausibility: Are the interactions meaningful and contextually appropriate?
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Example: dog \wearing" cloud is not semantically plausible.
4. Causal Consistency: Do object states logically follow from their relationships?
Example: book balanced steep slope should be expected to fall.
Output Format: For each detected anomaly, provide structured report as follows:
Objects Involved:
List the relevant objects (including [Current object)).
Observed Issue: Describe the logical, physical, or semantic anomaly.
Reasoning: Justify why this relationship is unnatural, implausible, or illogical.
Instructions:
Focus exclusively on {Current object} and its relationships.
Evaluate both individual (one-to-one) and group (one-to-many) zelationships.
Effectiveness: By evaluating the spatial and functional relations between objects, the Relation Rea- soner helps detect anomalies that would violate everyday common sense or physical laws. For example, it would catch cup floating in mid-air without any support person walking through solid wall. This agent significantly enhances the ability to detect high-level semantic anomalies that go beyond object-level properties.
E.4 ANOMALY INTEGRATION AND FORMATTING
After the anomalies are detected by the Attribute Analyzer and Relation Reasoner, they are passed the Anomaly Integration and Anomaly Formatting agents. The Integration agent consoli- dates similar or redundant anomalies and eliminates noise, while the Formatting agent structures the anomalies into a final output format.
Design Motivation: The design of the Anomaly Integration and Formatting agents is driven by the need ensure that the detected anomalies are presented clear and interpretable manner. The Integration agent helps combine similar anomalies into one, while the Formatting agent ensures the final output structured and easy to use for downstream applications like quality assessment or dataset debugging.
Anomaly Integrator Step1 Prompt:
Description for [Current object}:
(AttributeAnalyzeer
Response)
Relationships of (Current object} with other objects (fall objects from objectPerceiver)) RRnessoer Response)
Task: Review and analyze the detailed Description and Relationships of (Current object). Summariza
all unreasonable, contradictory, or physically impossible details related to [Current object], while consolidating similar or repeated anomalies into comprehensive report.
Focus Areas:
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1. Contradictory Details:
Identify conflicting statements or relationships (e.g., "floating" ve. "resting the ground")
Unnatural Behaviors: Highlight features or actions implausible in real-world settings.
Spatial Inconsistencies: Detect impossible
locations or orientations for [Current object] or others.
4. Illogical Physical Properties: Point out violations of physics reality (e.g., water flowing upward)
Instructions:
Consolidate similar anomalies from both Description and Relationships.
Center all findings around {Current object] and its interactions with other objects.
Output Format (structured list):
1 Observed Phenomenon: Brief summary of the inconsistency
Indicate if the issue comes from the Description, Relationships, or Both. Details:
Sources:
Provide specific observations related to the anomaly.
Explanation: Justify why the phenomenon contradictory, unnatural, implausible.
Deliverables:
Focus exclusively on {Current object].
Consolidate and summarize issues across Description and Relationships.
Output only the structured list in the specified format.
AnomalyIntegrator Step2 Prompt:
Anomalies:
(AnomalyIntegratoor Stepl Response)
Task:
Summarize and categorize all detected
unnatural, illogical, inconsistent. phenomena in the image.
For each issue, provide:
1. Object Name: Clearly identify the object (s) involved.
2. Phenomenon:
Describe the unnatural or illogical aspect of the object (s).
Explain why this phenomenon unrealistic, referencing real-world physics, anatomy, perspective, or common sense.
Explanation:
Output Format:
Provide structured list using the
format below:
Example Output:
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1. Object Name Tree
Phenomenon: The tree trunk bends at an impossible 90-degree angle.
Explanation: Real trees cannot grow in this shape due to gravitational constraints.
object Name: Dog
Phenomenon: The dog has three tails, one of which is semi-trrasparent
Explanation: This is anatomically impossible for dogs.
Instructions:
Only output the list in the specified format.
Ensure each anomaly is clearly tied to a specific object.
Exclude unrelated content or commentary.
AnomalyFormatter Prompt:
The following are a list of pre-selected anomalies: [List of AnomalyIntegrator Step2 Response)
Task: From the list above, identify and summarize
the visually prominent and semantically significant anomalies observed in the image.
You must analyze, consolidate, and explain each anomaly in way that is logical, detailed, and persuasive, as if communicating to both experts and con-experts.
Instructions:
1. Merge Similar or Redundant Anomalies
Group phenomena sharing common cause, concept, or visual effect.
Avoid repetition by merging entries describing the same core issue.
2. Resolve Contradictions Thoughtfully
If descriptions conflict, reconcile them using physical laws, biological plausibility, and visual logic.
Summarize both viewpoints if both are partially valid.
3. Filter Out Non-Visible or Insignificant Issues Omit anomalies that are not visually apparent (e.g., minor texture noise).
Focus on what is clearly and prominently visible.
4. Justify with Real-World Logic
Support each anomaly with logical, physical, anatomical,
functional reasoning.
Not Parrot the Input
Rephrase and reinterpret anomalies based on visual evidence and contextual understanding.
6. Ensure Coverage
All input anomalies must be included, either directly or through consolidation.
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Output Format: Write numbered list. Por each entry, use the following structure:
@1. N*Nam***: [Descriptive title of the anomaly]
Abserree Phenomenon
Describe what is visibly wrong in visual terms.
Include positions, shapes, textures, contextual eddities.
Ensure clarity without needing to see the image. **Reasoninge::
Explain why this is implausible.
Support with physical laws, anatomy, real-world logic, or social context.
+*Severity Score**: [0(100; 0 fully unrealistio, 100 - fully realistic]
Example Output:
1. Name
Abnormal number of hands
Observed Phenomenon: The individual on the left has two left hands, one emerging from the elbow and overlapping with the sleeve. Both hands share identical crientation and lack anatomical continuity.
Reasoning: Human anatomy allows one left and one right hand. Two left hands in such arrangement violate biological symmetry and visual plausibility.
Severity Score: 5/100 (highly unrealistic)
Name Suspended chair without support
Observed Phenomenon: A wooden chair is floating approximatell
em above the ground without visible support or shadows.
Reasoning: Gravity requires contact or suspension; absence of legs, shadows, wires defies physical realism.
Severity Score: 10/100 (extremely unnatural)
Final Notes:
Output only the structured list in the format above.
Think critically. Be precise, complete, and persuasive.
Provide human-understanddbbe summary of core visual anomalies in the image.
These final stages ensure that the output of the anomaly detection process both precise and interpretable. The integration step reduces redundancy and focuses on the most signif- icant anomalies, while the formatting step makes it easier present and use the results for further analysis.
Effectiveness:
Overall Design and Significance: The design of the anomaly detection agents is driven by the need for both local and global anomaly detection in Al-generated images. By breaking down the process into three main agents-Objer Perceiver, Attribute Analyzer, and Relation Reasoner- each agent can focus specific aspect of the image, from identifying objects and their attributes to analyzing how they interact in a scene. This modular approach allows for greater flexibility and interpretability in the detection process. Furthermore, the integration and formatting stages ensure that the final output structured and usable for real-world applications.
The multi-agen agent design enables the framework capable handling complex image generation models like those used deepfake detection and other high-level image assessments.
nuanced and robust detection of semantic anomalies, making
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F COMPARATIVE ANALYSIS: HUMAN VS. ANOMAGENT ANNOTATIONS
To evaluate the effectiveness and consistency of our proposed anomaly detection pipeline, we con- ducted comparative study between AnomAgent and human annotators. We used benchmark set of 1,000 Al-generated images with rich semantic content and potential visual inconsistencies.
F.1 ANNOTATION TASK AND SETUP
Each image was annotated independently by:
Human annotators, we select several annotators with advanced academic backgrounds in computer vision, specifically individuals holding master's doctoral degrees..
AnomAgent, using the pipeline described in Appendix t
Annotators were instructed to identify any implausible, inconsistent, or unnatural visual features, along with brief description of the issue. AnomAgent, in contrast, automatically generated struc- tured reports with descriptions, reasoning, and severity.
F.2 QUALITATIVE COMPARISON
Figure 11 shows side-by side comparisons of annotations from AnomAgent and human annotators. Each example highlights different strengths:
Fine-grained detection:
AnomAgent captured subtle anomalies (e.g., missing shadows, unnatural finger joints) often missed by humans.
Consistency: Across images with similar anomalies (e.g., floating objects), AnomAgent produced structurally similar descriptions, whereas human annotations varied in terminol- ogy and detail.
Reasoning support:
Human annotators often provided short descriptions without justifi- cation, while AnomAgent included structured, interpretable explanations.
For dataset comprising 1000 images, human annotators identified 4884 anomalies, whereas AnomAgent detected 8290 anomalies. AnomAgent not only detects more anomalies, but does so with higher consistency and full interpretability with structured anomaly description.
G COMPARISON OF BERTSCORE BACKBONES FOR SEMANTIC EVALUATION
Our semantic evaluation framework (Section 3.3) relies on BERTScore compare structured textual anomaly explanations. However, BERTScore's output depends heavily on the underlying language model used to compute embeddings. To assess the stability of our evaluation metries, we bench- mark several pre-trained language models as BERTScore backbones. This analysis tests whether the relative performance trends of vision-language models (VLMs) hold across different similarity functions.
In addition the default di st lbert uate two widely used and diverse backbones:
model used in the main paper, we eval-
roberta-large-me
poogle/mt5-1aan
We use the exact same evaluation pipeline as in Section 3.3 computing SemAPand metrics between human-verified ground truth anomalies and predictions from each model.
Table and and Table 6 present semantic detection and reasoning performance for all evaluated models across both backbones.
The absolute values SemAPand SemFl Evary across backbones. For example, mnt-laare con- sistently yields lower scores due its multilingual and less fine-grained English representations.
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1. Chicken feet are unrealistic: The anatomy of the chicken's feet appears to be incorrect or not convincing, which could suggest that it is an artificial creation lacking an accurate depiction avian anatomy.
2. Hand abnormalities: The hands fingers in the image might have distortions, irregular shapes, or inconsistent properties that do not align with natural human anatomy, indicating Al-generation imperfections.
(a) Human Annotation
Name: Presence a chicken a medical consultation setting
Phenomenon: chicken positioned on medical furniture and individuals who appear engaged discussion. The chicken treated though it is of the consultation.
clinical environment, surrounded by
Chickens are animals are not typically present sterile medical environments. Their inclusion this setting contradicts hygiene standards and the expected purpose the space, which designed for human healthcare. The lack of veterinary tools further emphasizes the implausibility of the scene.
Reasoning:
Severity Score: 20
Name: Lack of reaction from individuals to the chicken
The elderly person the medical professional not display any visible chicken, despite unusual placement and context. Their body language suggests they engaged normal conversation, ignoring chicken entirely.
Phenomenon:
reaction presence
Reasoning:
In real-world scenarios, the presence of chicken in clinical environment would likely provoke curiosity, surprise, or interaction from the individuals present. The lack of acknowledgment creates semantic disconnect and undermines the plausibility of the scene.
Severity Score: 25
Name: Use of tablecloth in clinical setting
The covered cloth, which is unusual medical environment. Clinical tables typically designed with easy-to-clean surfaces maintain hygiene.
Phenomenon:
The tablecloth contradicts standard practices medical settings, especially in the presence of animal, which could introduce contaminants. This detail undermines the functional coherence the
Reasoning:
Severity Score:
(b) AnomAgent Annotation
Figure 11: Comparison of human annotations and Anom/Agnt outputs. AnomAgent identifies more anomalies with detailed explanations.
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largely unchanged, confirming the robustness Published as a conference paper at ICLR 2026
In contrast, roberta-large-min produces more moderate values, balancing semantic sitivity with surface-level alignment. However, the relative ranking of models e.g. GPT-4o, AnomReasonoor7BB and Qwen2.5 of our evaluation findings.
Metrics related reasoning quality (SemAPRea. SemFI show greater variation across backbones than observation-only metrics. This aligns with the expectation that free-form reasoning texts intro- duce lexical and structural variability, making similarity estimation more dependent model semantics.
Across both backbones, AnomReasonor-7h achieves top-tier performance, closely tracking passing proprietary models on reasonin--related submetrics. This reinforces its strong generalization structured explanation tasks and validates the effect of fine-tuning on our anomaly supervision.
While the choice of BERTScore model influences raw scores, the relative performance trends among models remain stable. Thus, our benchmark conclusions not overly sensitive to the specific similarity function used providing evidence of metric robustness and cross-backbone reliability.
Table 5: Performance comparison using roberta-laree mn as BERTScore backbone.
Model
SemAl're Phe
SemAl RinAPPRee Rea
SemAP'rul Full
SemF Phe
SemFl Rea
Sem Full
VA-7VA-BB VA-7B
0.2243
0,2002
0,2176
0.1372
0.1185
0.1325
LLaVA-OV-7B
0,2487
0.2107
0.2358
0.1514
0.1269
0.1435
Phi-3.5-Vision
0.2427
0.2129
0.2324
0.2074
0.1798
0.1984
MiniCPM-V-2.66
0.2949
0.2621
0,2844
0.1417
0.1244
0.1366
IntemVL2-8B
0,2620
0.2297
0,2512
0,2674
0.2346
0.2563
VL2.5888
0.2455
0.2079
0,2320
0.2179
0.1843
0,2062
VL3emB
0.3320
0.2811
0.3127
0.1405
0.1190
0.1332
Intemn
0,2697
0.2313
0.2507
0.2738
0.2352
0.2542
mPLUG-OW13-7B
0.3102
0.2768
0.2984
0.0965
0.0857
0.0928
Qwen2-VL--BB
0.3156
0.2749
0,3006
0.1032
0.0914
0.0989
InternV1.2-26B
0.2876
0.2532
0.2727
0,2760
0.2434
0.2621
Qwen2.5-VL-27
0.3190
0.2791
0.3017
0,2893
0,2531
0.2739
Qwen2.5-VL-72B
0.3379
0,2906
0.3125
0,3033
0,2607
0,2809
Gemini-2.5-pro
0.2485
0.2290
0.2360
0.1330
0.1221
0.1264
GPT-o3
0,2860
0,2578
0,2860
0.2834
0,2555
0,2834
GPT-5
0,2322
0.2156
0.2207
0,2718
0.2526
0,2582
GPT-4o
0.3434
0.2955
0.3083
0.3707
0.3188
0.3325
AnomRecssonor-7B
0.3342
0.3197
0.3151
0.3243
0.3103
0.3059
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Table 6: Performance comparison using google/ntt--arrgee as BERTScore backbone.
Model
SemAP Phe
SemA Rea
SemAP GemAPrull Full
Sem Fl Phe
SemFi Rea
Sem Full
Lon-VA-BB VA-7B
0.0535
0.0635
0.0393
0.0315
0.0331
0.0214
LLaVA-OV-7B
0.0993
0.1044
0.0834
0.0613
0.0599
0.0500
Phi-3.5-Vision
0.1326
0.0853
0.0943
0.1092
0.0666
0,0743
MiniCPM-V-2.6
0.1289
0.0926
0.0884
0.0598
0.0426
0.0407
InternVL2-8B
0.1224
0.1243
0.1076
0.1252
0.1243
0.1089
Intern VL2.58B
0.1029
0.1024
0.0869
0.0911
0.0844
0.0743
Intern VL3-8B
0.1642
0.1764
0.1488
0.0661
0.0706
0.0596
InternVL3-99
0.1391
0.1121
0.1125
0.1428
0.1125
0.1143
mPLUG-OW13-7B
0.1173
0.1267
0.0964
0.0350
0.0379
0.0287
Qwen2-VL-7B
0.1306
0.1393
0.1136
0.0403
0.0427
0.0348
I12e22V
0.1774
0.1417
0.1488
0.1701
0.1341
0.1420
Qwen2.5-VL-77B
0.2034
0.1697
0.1775
0.1839
0.1531
0.1605
Qwen2.5-VL-72B
0.2078
0.1798
0.1850
0.1864
0.1611
0.1662
Gemini-2.5-pro
0.1905
0.1402
0.1632
0.0989
0.0718
0.0839
GPT-o3
0.1811
0.0991
0.1330
0.1793
0.0973
0.1311
GPT-5
0.1724
0.0999
0.1346
0.2031
0.1177
0.1587
GPT-4o
0.2708
0.2171
0.2387
0.2933
0.2352
0.2586
AnomReasoono-7BB
0.2736
0.2612
0.2653
0.2655
0.2532
0.2574
36


Reviews:
[{"review_id": "pPjooY2t0z", "reviewer": "Reviewer_D51R", "summary": "This paper presents a novel task and dataset for semantic visual anomaly detection and reasoning, called AnomReason. The dataset was constructed using an iterative hybrid annotation framework, AnomAgent, which combines VLM generations with human verification.\nThe authors conduct extensive benchmarking of both open-weight and proprietary models on their dataset. Furthermore, they fine-tune a VLM on the AnomReason training set, demonstrating notable performance improvements as a result.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a novel dataset that addresses an important gap in semantic visual anomaly detection and reasoning.\n\n- The dataset’s design is rigorously supported by large-scale human annotation efforts, ensuring the validity and reliability of the resource.\n\n- The authors provide extensive benchmarking of various vision-language models, offering valuable insights into model performance on this new task.", "weaknesses": "**[W1]** The paper does not provide statistics on the diversity of visual phenomena present in the benchmark. This omission makes it difficult to assess how the dataset splits are affected, particularly regarding potential entity overlap. Without this information, there is a risk of data leakage across splits in terms of topic, domain, or subject, which could impact the validity of fine-tuning and evaluation.\n\n**[W2]** A significant limitation in the experimental results (Sections 4.1 and 4.2) is the lack of counterfactual examples, i.e. AI-generated images that do not contain semantic anomalies, or real-world images. Including such examples would allow for a more thorough assessment of the fine-tuned model’s ability to distinguish between anomalous and non-anomalous cases, and between AIGC and non-AIGC.\n\n**[W3]** The authors rely on BERTScore to compare extracted anomalies to the ground truth. Given the known limitations of BERTScore (especially its insensitivity to subtle semantic differences) it may be more appropriate to use an LLM-based judge for evaluation, as suggested in recent literature. Also, it is important to validate the proposed metrics with human evaluation. Conducting a sampled error analysis would help identify failure modes and provide additional support for the validity of the chosen metrics.", "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": "w6wvDXUr1l", "reviewer": "Reviewer_9Vob", "summary": "This paper proposes AnomReason, a large-scale benchmark for semantic anomaly detection and reasoning in AI-generated images. It introduces AnomAgent, a modular multi-agent framework that detects and explains semantic inconsistencies through structured outputs (Name, Phenomenon, Reasoning, Severity). The dataset includes 21K photorealistic AIGC images with 170K verified anomalies. New BERTScore-based metrics (SemAP, SemF1) evaluate phenomenon- and reasoning-level accuracy. Experiments show that existing VLMs struggle with commonsense reasoning, while the fine-tuned AnomReasonor-7B achieves state-of-the-art performance.", "questions": "please see the weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed AnomReason dataset and AnomAgent framework are well-designed and provide valuable resources for both the forgery detection and image generation communities.\n2. The paper conducts extensive reasoning experiments across a wide range of VLMs and generative models, providing a comprehensive analysis of their capabilities in semantic-level reasoning, detection and generation.", "weaknesses": "1. The paper claims to employ an agent-based framework for benchmark construction; however, the system is essentially composed of prompt-driven GPT-4o modules, which cannot be considered true autonomous agents. The human-in-the-loop stage merely filters GPT-4o outputs rather than providing substantive corrections, thus retaining the inherent biases of GPT-4o. This design choice results in the strong performance of GPT-4o in Table 1—surpassing even newer models such as GPT-5 and GPT-o3—leading to a unfair comparison.\n2. The evaluation metrics (SemAP and SemF1) rely on text similarity thresholds for anomaly matching, which can significantly influence performance outcomes across the Phe, Rea, and Full evaluation views. However, the paper does not provide a clear rationale or sensitivity analysis for the chosen threshold values, making it difficult to assess the stability and robustness of the reported results.\n3. The paper conducts extensive experiments on various VLMs and generative models, but it fails to distill clear and generalizable conclusions from the results. Moreover, in the deepfake detection task, the evaluation lacks comparison with specialized forgery detection models.\n4. In the deepfake detection task presented in Table 2, the fine-tuned AnomReasonor-7B achieves only 82% accuracy, performing even worse than GPT-4o’s zero-shot reasoning despite the absence of any cross-domain generalization challenge. This raises concerns about the validity and robustness of the proposed dataset and the effectiveness of its supervision strategy.\n5. The paper defines a new task, semantic anomaly detection and reasoning, claiming that existing MLLM-based detectors operate only at the surface level while the proposed method captures content-level semantics. However, in the context of forgery detection, all MLLM-based approaches inherently perform high-level semantic analysis, as low-level forensic clues are beyond their perceptual capacity. While structuring semantic anomalies is beneficial, this additional task definition appears unnecessary.", "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": "4ku6sX4LOX", "reviewer": "Reviewer_NTPW", "summary": "This paper proposes **AnomAgent**, a multi-agent system capable of automatically annotating perceptible anomalies in images, effectively replacing the need for manual labeling. Furthermore, the authors construct a benchmark containing over 20,000 samples generated through this fully automated annotation pipeline, featuring a structured output format. In the experiments, the authors validate the effectiveness of their approach from three complementary perspectives.", "questions": "Please refer to the *Weakness* section for detailed explanations.  \nIf the authors can adequately address my concerns, I would be very willing to raise my score.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The fully automated annotation pipeline effectively addresses the inconsistency issues that commonly arise among human annotators, greatly facilitating the process of dataset construction.  \n2. The constructed benchmark is highly valuable for research on interpretable image forgery analysis.  \n3. The paper introduces the first metric designed to evaluate **semantic anomalies**, filling an important gap in semantic-level assessment for image authenticity evaluation.", "weaknesses": "1. It is unclear what role the **Severity Score** plays in the overall framework — please clarify its purpose and how it contributes to the evaluation or annotation process.  \n2. Please provide more detailed information about the **manual verification** procedure. Specifically, explain how high-quality and image-relevant annotations are selected and validated, and justify the rationality and reliability of this filtering process.", "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": "tpId9q7ro2", "reviewer": "Reviewer_EDEz", "summary": "This paper proposes AnomReason, a large-scale benchmark annotated with structured quadruples to capture commonsense, physical, and relational inconsistencies in generated images. The dataset is built using AnomAgent, a modular multi-agent framework that decomposes anomaly detection into entity parsing, anomaly mining, and structured output generation, with lightweight human-in-the-loop verification. The author also provides AnomReasonor-7B, a model fine-tuned on AnomReason, which achieves comparable performance with GPT-4o.", "questions": "1. Could you further analyze the results to determine what kinds of anomalies current T2I models are most likely to generate?\n2. Given that AnomReasonor-7B shows the strongest anomaly detection ability, could you analyze why its performance is inferior to GPT-4o on AnomReason-Deepfake? Is it because not all AI-generated images in this benchmark exhibit visible anomalies?\n3. How will the selection of hyperparameters in SFT influence AnomReasonor-7B’s performance?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is clearly written and well-structured.\n2. The introduction of AnomReason, AnomReason\u0002 Deepfake, and AnomAgent provides both a comprehensive benchmark and a scalable annotation framework for semantic-level anomaly detection.", "weaknesses": "1. The evaluations in Sections 4.1 and 4.2 are limited to the proposed AnomReason and AnomReason\u0002 Deepfake benchmark. The paper should include results on additional third-party anomaly detection datasets, such as FakeReasoning, Spot the Fake, etc.\n2. The evaluations in Sections 4.1 and 4.2 primarily evaluate general-purpose VLMs rather than models specifically designed or fine-tuned for anomaly or forgery detection, such as FakeReasoning, Spot the Fake, Aigi-holmes, etc. \n3. The proposed pipeline mainly relies on prompting and LoRA fine-tuning. There is limited method innovation in model architecture or training strategy, which somewhat reduces the technical novelty of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)