FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs
Authors: Zhihan Yin
Jianxin Liang
Yueqian Wang
Yifeng Yao
Huishuai Zhang
Dongyan Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: MLLM
VLM
Hallucination
Benchmark
Chain-of-Thought
Primary area: datasets and benchmarks
TL;DR: This paper proposes a novel benchmark designing for MLLMs' fine-grained hallucination evaluation, revealing the severity of fine-grained hallucinations in advanced MLLMs and experimentally analyzes the limitations of CoT in hallucination tasks.

Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated metrics, or insufficient diversity that fails to adequately assess the hallucination extent in state-of-the-art multimodal models. To address this gap, we propose FREAK, a comprehensive multimodal benchmark designed for fine-grained hallucination assessment in MLLMs. Through high-quality photorealistic images featuring fine-grained counter-commonsense edits, FREAK innovatively evaluates hallucination phenomena in detailed visual perception of MLLMs. Extensive experiments on FREAK show severe hallucination issues in SOTA models regarding detailed visual perception. To enable deeper investigation, we curate a controlled subset to indirectly evaluate the model’s ability to perceive target detailed information. Through systematic evaluation of prevailing Chain-of-Thought
(CoT) prompting techniques within this task, we reveal critical insights regarding hallucination patterns and model reasoning processes.

FREAK: A FINE-GRAINED HALLUCINATION EVALU- ATION BENCHMARK FOR ADVANCED MLLMS
Zhihan Yin', Jianxin Liang', Yueqian Wang', Yifeng Yao',
Huishuai Zhangl Dongyan Zhao 1,2,+
Wangxuan Institute of Computer Technology, Peking University
2 State Key Laboratory of General Artificial Intelligence
{hansqaq} @stu. pku. edu. cn, ( "changhuishuaie dongyanzhaoJ@pku. edu. cn
ABSTRACT
Multimodal Large Language Models (MLLMs) suffer from hallucinations. Ex- isting hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated metrics, or insufficient diversity that fails to adequately assess the hallucination extent in state-of-the-an multimodal models. To address this gap, we propose FFEeA Rained Evaluation Against Knowledge), a comprehensive multimodal benchmark designed for fine-grained hallucination as- sessment in MLLMs. Through high-quality photorealistic images featuring fine- edits, FREAK innovatively evaluates hallucination
grained counter-commonse. phenomena in detailed visual perception of MLLMs. Extensive experiments on FREAK show severe hallucination issues in SOTA models regarding detailed vi- sual perception. To enable deeper investigation, we curate controlled subset indirectly evaluate the model's ability perceive target detailed information. Through systematic evaluation of prevailing Chain-of-Thought (CoT) prompting techniques within this task, we reveal critical insights regarding hallucination pat- terns and model reasoning processes. The data and code are available at Github
INTRODUCTION
Multimodal hallucination typically manifests as generated content that, while logically plausible and commonsensical, includes information absent from the visual input and is factually inconsistent with the provided image evidence (Bai et al. 2025 Liu et al. 2024b). Among various forms, one challenging subtype fine-grained hallucination, where models misperceive or fabricate localized details within image, often defaulting to commonsense knowledge over visual facts (Wu et al. 2025). Despite significant progress in image comprehension and depth reasoning (OpenAl 2025 Zheng et al.) 2025 Shen al. 2025), existing MILMS persistently suffer from multimodal hal- lucination (Bai et al. (025), ), posing a critical gap for stable industrial deployment and everyday use (Magesh et al. (202). To scientifically quantify the extent of hallucination, prior studies have established dedicated evaluation benchmarks, providing a robust foundation for evaluation.
As the capabilities of MLLMS rapidly advance, their performance existing hallucination bench- marks such as POPE (Li et al.) (023b) and AMBER (Wang et al. 2024a) has nearly saturated, thereby diminishing the discriminative power of these benchmarks. This saturation largely stems from the limited difficulty of existing benchmarks and their overly simplistic evaluation protocols, which often rely on binary (true/false) judgments. (Tu et al., 2025 Wu et al., 2025), and thus these benchmarks fail to accurately capture the hallucination levels of current SOTA models. As MLLMs are typically trained on large-scale image-text corpora, they are susceptible to data leakage and mem- orization bias toward specific images. To address this, recent studies have utilized Al-generated commoer-commmnnense (CCS) images, which provide a clear path to test whether a model truly "sees" image relies memorized priors. For example, Guan al. (2024) created Hallu- sionBench, and Liu et al. (2025) proposed PhD. However, these benchmarks are still limited by insufficient sample diversity, suboptimal image quality, and oversimplified task design.
"Corresponding Authors

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Evaluation Against Knowledge), which aims to quantify the fine- grained hallucination severity of MLLMs. FREAK consists of 1,799 questions divided into 6 categories, including Detection, Counting. Attribute, Analysis, Position and OCR tasks, providing a comprehensive suite for MILMs' fine-grained hallucination evaluation. FREAK features its fine-grained CCS questions, high-quality generated images, the diversity of image content, and an objective evaluation methodology.
To address the limitations, introduce FREAK
The construction of FREAK follows a systematic and novel pipeline. First, we instruct LLMs for extensive high-quality CCS description generation. Then, we design a novel "generate-then process that synthesizes to introduce a localized, counte--oommonsense detail. Next, we leverage LLMs to automatically generate a corresponding question for each image. Finally, this automated data creation is comple- mented by a crucial human verification and refinement stage. During this step, our team meticu- lously reviewed each instance and carefully constructed the questions with answers, ensuring free- form questions and multiple-choice questions can directly probe MLLMs" capability for identifying image details and resisting model hallucination.
commonseese--ompllee
image before using a powerful editing model
Extensive experiments on FREAK reveal that even the most advanced models achieve only 45% ac- curacy, with the performance of mainstream models clustered within the 30%-43% range. This per- formance falls significantly short of the human baseline (86.71%) highlighting severe fine-grained perceptual hallucination in current MILMMs
Inspired by advanced reasoning models, we apply CoT prompting across various models but observe consistent performance degradation. Reinforcement not show significant improvement over their base versions. By leveraging FREAK, track model reasoning dynamics and find that during reasoning process, models exhibit an increasing bias toward distractors and losing confidence in correct answers, often ending with choices contra- dicting the initial one. This reveals critical flaws in CoT mechanisms,
(anniing(RL)tuunee reasoning models also do
In summary, our contributions are as follows.
1. We propose automated pipeline for generating fine-grained CCS images by integrat- ing LLMs, image generation models and image editing models to produce highly realistic images with localized CCS details.
2. Based the technical pipeline, we propose FREAK, novel benchmark to evaluate mul- timodal fine-gras mne-grained hallucination. Compared to prior Al-generated hallucination bench- marks, FREAK features objective evaluation methodology, more diverse CCS descrip- tions, and more challenging images with questions, revealing critical issues in MLLMS' detail perception capabilities.
3. Extensive experiments FREAK highlight severe challenges in fine-grained multimodal hallucination for MLLMs. In addition, we discuss the degradation of the CoT prompt, revealing the limitation of CoT reasoning.
RELATED WORKS
Multimodal Large Language Models. Building on rapid advances in LLMs. MLLMs integrating
vision and language have also made substantial progress. Current MLLMS achieve visual-linguistic
alignment primarily through pretraining modular training. Some methods develop end-to-end
models trained holistically on image-text data (Radford et al. 2021
Li et al. 2021
Cho et al.
2021 Wang et al.) (2022). Others preserve frozen LLMs linguistic abilities while tuning lightweight
adapters for cross modal integration (Liu et al.) 2023: Zhu et al. 2023 Li et al.
2023a Chen et al.)
2024d Bai al. 2023). This approach avoids costly full par-mparretter traininn while leveraging L.MMS
generative strengths. For example, BLIP-2 (Li et al. (023a) uses Q-Former to bridge visual and
textual representations. Competitive alignment can also be achieved via simple linear projectors (Liu
al. 2023
Zhu et al. 2023 Liu et al. 2024c).
Multimodal Hallucination. Multimodal hallucination typically manifests as generated content that, while logically plausible and commonsensical, includes information absent from the visual input and is factually inconsistent with the provided image evidence (Bai et al., 2025 Liu et al. 20246).
To mitigate multimodal hallucination, existing approaches fall into two categories: 1) Designing
2

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Table Comparison between FREAK and other Al-generated benchmarks. FREAK shows unique- ness because of the photorealistic images, fine-grained and diverse counter-commonsense(CCC. content, which strongly challenges SOTA models.
Benchmark
ImgNum.
Question
GPT Series Eval.
Typical Sample
Explanation
WHOOPS
Finssein uses smart phone.
Bitton Guetia et al. 2023
VQA
V1.ind Bench
Medieval knight rides motor.
2576
Y/N
89.4 89.4(GPT-
G1 Lee al. 2025
Phi-oes Liu et al. 2025
Max numher dice seven.
750
Y/N
~
HallusionBench
Curves have differ- ent diameters.
346
Y/N
62.28/)
( Gaan et all 2014
VLMBias
eo al. 2025
Only
20.25/o4-minn)
dog has legs.
Free: Furm
42.01(TPT4.1)
The projector
MCQ
facing the screen.
decoding strategies based on heuristic rules to guide models in resisting linguistic priors ct al.
(Leng
2023 Huang et al., 2024a Liu et al. 2024d Wang al., 2025 Chen et al. 2024c Zou al.
2025
2) implementing refined training procedures, such as curating fine fine- grained image-tex text data
or employing RL-based rules for post-training optimization (Chen al. 2025 Wu et al. 2024 Yin
et al.
2024 Liu et al. 2024a).
Multimodal Hallucination Benchmark. Objectively assessing the severity of multimodal hallu- cination remains a challenging issue. Existing mainstream benchmarks, including POPE (Li et al. 2023b), AMBER (Wang et al., 2024a). MHaluBench (Chen et al. 2024a) and others (Qiu et al., 2024 Wang et al.) 2024b Li et al. 2025) exhibit three critical limitations: 1) Unreliable Data Provenance: Benchmarks like POPE derive images from open-source datasets that may overlap with training data of evaluated models. Such data contamination risks inflating performance metrics due model memorization rather than genuine reasoning ability (Chen al., [2024a] Diang et al.) 2024). 2) Narrow Evaluation Scope: Traditional large-scale benchmarks predominantly target ob- ject hallucination (Chen et al. 20246) [ovenii et al., 2024), neglecting diverse hallucination types such OCR, reasoning and object attributes. As a result, SOTA models achieve near-saturation performance, making these benchmarks inadequate for contemporary evaluation. 3) Oversimpli- fied Assessment Paradigm: Prior evaluations rely heavily binary true/false judgment (Guan ct al. 2024) Huang et al. 2024b), introducing significant randomness. Recent efforts leverage AI- generated CCS images to evaluate the robustness of the models. For example, [il Lee et al.) (025); Liu et al. 2025);; Guan et al. (2024); Huang et al. (2024b) create such benchmarks with CCS im- ages. However, these benchmarks still suffer from metric saturation, limited hallucination diversity, and synthetic artifacts compromising visual realism (Bitton- Guetta et al.) (023).. To address these gaps, we propose FREAK, hallucination evaluation framework designed for fine-grained hallu- cination assessment of modern SOTA MILMs. Unlike recent works like MIRAGE (Dong et al.) 2025), LongHalQA (Qiu et al., (024) that evaluate the long-fone formform outputs of MILMs, FREAK fo- cuses on specifically targeting MLLMs with fine-grained CCS visual challenges. Table shows the comparison between FREAK and other Al-generated CCS benchmark.
METHODOLOGY
3.1 AUTOMATIC PIPELINE FOR FINE-GRAINED CCS IMAGES
The visual sources for commonsescm (CCS) images in prior research mainly stem from two methodologies: a) Manual Expert Modification (c.g. HallusionBench (Guan et al.) (024)), which involves human experts altering existing images introduce contradictions commonsense knowl-
3

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CCS Content Generation
Question Construction,illeernng
Frangt Pwtsecics
Cijeed Nemm
(Trellic Lights
poaci lap
JSON
FREAK
the pose pl Beric
lucal
2. CCS Image Generation
Contoeeraaaeee Hage
J5ON
Image- generation model
Editing model
Docd
Figure Generation pipeline of FREAK, including a three-step generation paradigm. We first prompt LLMs to generate CCS content for later stages, next use image genration model and editing model to generate CCS image. Finally we filter and verify the generated data to form FREAK benchmark. In this example, image shows the positions of the red and green lights swapped, incorrectly with red at the bottom and green the top, which violates both commonsense and the traffic regulations of various countries.
exhibit the generation process of "Traffic Light". The generated
edge; b) Direct Prompt-to-Image generation (e.g. PhD (Liu et al. 2025) and WHOOPS (Bitton- Guetta et al. 2023)) where LLMs generate textual descriptions for image generation models. These approaches exhibit critical limitations: Manual modification suffers from low scalability due to its labor-intensive nature, which hinders large-scale dataset construction. On the other hand, directly prompting LLMs to generate CCS descriptions quickly results in repetitive objects and low-quality descriptions, hindering the large-scale production of diverse CCS descriptions. Beyond the de- scription aspect, image generation models show poor adherence to CCS CCS-specific prompts. They predominantly generate commonsense compliaan images due to the lack of CCS-related training data, making it difficult to produce realistic CCS images. In short, image generation models can only reproduce patterns they have already encountered. To address this issue, we deploy the image generation model and editing model in iterative pipeline: it first generates normal factual images, then applies localized modifications through the editing model for fine- iin-igrinedd CCS details.
3.1.1 STEP 1: GENERATION CCS DESCRIPTION
To obtain diverse CCS images, it is essential to first generate varied CCS descriptions, such as "a fox with square ears" "a sofa facing away from television". For scalable and non-repetitive description creation, we begin by specifying target object ("fox" and "sofa" in the above exam- ples respectively), and then prompt LLMs to generate attribute descriptions. Finally, we derive tuple (O, A, W) for subsequent generation stages, where O denotes target object, A denotes correct description of specific attribute of O, W denotes the CCS description of the same attribute.
3.1.2 STEP 2: GENERATION OF CCS IMAGES
We employ a two-stage image generation framework. First, we construct a prompt using the target object O and its correct attribute description A, then feed it to the image generation model F to produce a normal image: P = F(O,A). Next, we use the image editing model E to modify P conditioned on the CCS description W. yielding CCS images: CCS E(P, W). This framework ensures that the resulting CCS images remain photorcalistic while incorporating localized modifica- tions that deliberately contradict commonsense expectations.
4

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Position
Counting
position
8%
OCR
Wher porticual rabternbhp betwert
finger wobli
and the landle the lettle is the pictuct fayy the same ods opponie andert
glosein le pktarc"
Detection
A.
34%
Opporrz
Analysis 18%
D. ast
D..mm laxe.
FREAK
Analysis
Attribute
right
Plzzc deaniie color Google
platee.
briels.
Counting 23%
Attribute 27%
duating
nufoeltre cdll balakz
Detection
OCR
Distribution catagories
bask pictue! tribll
member at pollion en dhe polian the inage?
oolor
Muitiple- Question
Free-form Question
1,000 (80%)
799 (44%)
A. vuible
coonge yellow
Distribution of question formats
Bood
heae
Overview of FREAK. Items in FREAK be categorized into six subtasks, each com- prising questions that are straightforward for human solvers. The right panel shows representative examples for each subtask. Notably, certain questions assigned more than one subtask.
Figure 2:
3.1.3 STEP 3: QUESTION CONSTRUCTION, DATA FILTERING AND HUMAN STUDY
In FREAK, we adopt both multiple-choice questions and free-form questions as evaluation for- mats. All questions are generated by LLMs based on (O, W, CCS). For free-form questions, the ground truth corresponds to the CCS description W, while the hallucinated answer is derived from the commonsense attribute A. For multiple-choice questions, each image paired with one question and four answer options: A. Correct Option: Semantically aligned with the counter-commonsenss attribute W; B. A-based Distractor: Represents the commonsense attribute A (i.e., the hallucina- tion option): C. Al-generated Distractor: Synthetic option derived from semantic prompts of W and A; D. Fixed Open Option: "The correct answer is not listed". This design measures model robustness against commonsense interference via the A-based distractor and the open option. Note that in a minority of cases where multiple plausible commonsense responses may exist, FREAK employs only the dominant distractor for simplified assessment.
After obtaining the tuples (O, A, W, WCC with questions, we filter the data according to the fol- lowing rules. 1) Image Filtering: The selected images must retain photorealistic characteristics, while the image content must be strictly aligned with the CCS description W. 2) Deduplication: We remove duplicate entries with overlapping semantics in W (e.g.. "a clock with 4 clock hands" "a clock with 5 clock hands"). This process ensures data diversity and avoids semantic redundancy.
To validate dataset rationality and detect potential biases, we conduct blind-test experiment with 100 inexperienced undergraduates. We ensured that: (1) participants were unaware of the experi- ment's purpose, image characteristics, or related content; (2) they were informed that images and questions might be counter-intuitive and were asked to answer faithfully based on the image; (3) each participant randomly answered 17-19 questions to prevent learning effects; and (4) all partici- pants were undergraduate students from various disciplines. These guidelines align with the prompts used for MLLMs to ensure fairness. The experiment establishes baseline for human performance, quantifying the average performance of untrained humans on the annotated dataset and validates the reliability of the annotated dataset.
FREAK: FINE-GRAINED EVALUATION AGAINST KNOWLEDGE
For better understanding of our benchmark, here analyze its subtasks, statistics and evaluation methodology. The overall composition of FREAK and representative examples for each category illustrated in Figure
5

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4.1 STATISTICS AND APPLICATION TASKS
FREAK comprises 1,786 CCS images and 1,799 questions, with 1,000 multiple-choice questions and 799 free-form questions respectively.
To better understand how MILMs' capabiliities change when solving different problems, divide FREAK into six tasks for cognitive evaluation: 1) Detection: Requires models to identify salient structures of target objects. In FREAK's CCS images, these structures may be missing or replaced with foreign ones; 2) Counting: Evaluates models' ability to enumerate the target structures. This task emphasizes hallucination detection rather than numerical proficiency, as more than 90% of the cases contain fewer than six targets; 3) Attribute: Demands descriptions of geometric attributes (e.g., shape, size, color) for specified structures; 4) Analysis: Tests the models' inference capabilities based on visual content. The model is required to autonomously locate relevant visual cues after understanding the question. We exclude math and specialized knowledge because they are irrelevant for assessing fine-grained multimodal hallucinations: 5) OCR: Challenges models to extract target text or identify specified characters. In FREAK, all letters are standard English letters, and we specifically focus on hallucinations in textual content; 6) Position: Requires the model determine the spatial locations or relationships of specific objects or structures within the image. We use these tasks to probe MILMS' abilities through different application-oriented setting. Notably, due their comprehensive nature, some items in FREAK fall into multiple subtasks. This overlap enables a more objective evaluation of MLLMs' performance across subtasks.
4.2 EVALUATION
We evaluate models on both free-form and multiple-choice questions. For free-form questions, we adopt the I.LM-as-judge approach: L.M is given with the ground truth and commonsense answer to determine whether the MLLM's response incorporates the CCS content the image, assigning each response to one of three categories: Correct, Commonsense Error, or Other Error. For multiple-choice questions, correctness is determined directly based on the selected option. The primary performance metric is accuracy (Acc), computed as the proportion of correct responses across all questions. To directly measure the influence of the model's parametric knowledge, we define the Hallucination Rate (H alluRale) as the proportion of cases where the model either outputs commonsense answer in free-form questions or selects the commonsense distractor in multiple-choice questions.
5 EXPERIMENT
We conduct experiments to evaluate the effectiveness of FREAK in measuring fine-grained hallu- cinations of advanced MILMs. We first describe the experimental setups, then present the main results and key findings. Further in-depth analyses are presented in Section
5.1 EXPERIMENT SETUPS
Model Use. During the construction of FREAK, we employ Seedream3.0 0 and SeedEdit3.0 as gen- eration and editing model for its powerful generating and editing capabilities (Gao et al. 2025). We evaluate diverse set of SOTA models. Proprietary models include Gemini-2.5 series, OpenAl 03, 04-mini, GPT-4. 1 and Claude-4.0-Sonen
Open-source models include Qwen2.5-VI series, InternVL.3 series, Kimi-VL-a3K series, GLM-4.5V, Phi-4-multimodal, Skywork-R1V33 MiniCPM- 4V. DeepEyes-7B. This selection covers both general-purpose purpose multimodal models and emerging sessoiing-peccaalizedd architectures, ensuring broad coverage of current capabilities of MLLMs.
Inference Details. For multiple-choice questions, we apply eyclic permutation across option or- ders to mitigate randomness and position bias, thereby obtaining more reliable assessments of mod- els' capabilities. report averaged results over both multiple-choice questions and free-form ques- tions. All models adopt identical prompts during inference.
We recruit 100 undergraduates, each completing only 18 randomly assigned questions in FREAK prevent experiential bias accumulation during testing. Their aggregated
Human Baseline.

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Table Evaluation results of SOTA MLLMs, which are outperformed by human experts with wide margins. The highest model performance in each column is highlighted in green, and the second- highest highlighted in blue. The reasoning process is enabled if the MLLM is capable.
Accuracy (T)
HalluRate (i)
Dete:
Count.
Analy,
Pos.
Overall
Count.
Analy.
Attr.
Pos.
Overall
Human Haseline
86.93
88.65
83.44
83,92
14.24
88.08
86.71
7.19
6.76
10.94
5.22
6.22
Non- Cor-Rearonnn Models
GPT-4.1
50.25
19.45
53.26
48
18.82
39.37
42,01
36.62
46 20
49.23
38.41
40.61
49.58
44.54
43.03
20.12
29.65
48
38.41
44.11
47.75
54.91
38.
$2.65
48.76
IntemVL3333B
40.06
17.84
28.09
46.46
46.81
37.21
37.24
44.99
48.20
57.15
40.36
33.76
46.68
48.79
Qwen2.5-VL-72B
47.23
16.84
28.09
46.58
45.70
38.22
39.39
38.43
51.60
51.77
39.36
35.29
52.76
46.82
2ween..5V1L-322
38.33
16.74
25,80
31.77
34.65
46.17
47.37
40.96
56.76
49.66
mhil---mliinoaal
39.49
18.89
25.52
36,00
37.34
32.77
33.32
38.13
34.63
51.05
37.64
31.25
47.22
42.13
MiniCPM-IV
31.09
Kimi- EL-AAB-Innsuue
39.69
20.98
25.69
11.01
35.65
33.76
35.04
45.25
43.17
$4.59
42.27
38.55
48.51
18.49
Reasoning Models
Gemini-2 2.5-Pro
47.85
23.98
35.67
56.12
56,90
43,41
45.49
37.21
41.27
46.40
29.26
24.33
45.75
40.26
Gemäni-2.. 2ommani-2.5-Fashh
44.04
20.81
32.10
48.02
48.51
35.16
40.02
10.97
44.47
18.13
36.14
30.29
52.81
44.75
Claude Sandee-.0-Sooaeee S.S-nt
29.93
17.48
24.96
33.95
36.20
25.45
29.85
53.22
49.49
57.56
51.30
45.17
62.73
55.64
21.14
38.77
37.36
52.50
of-mini
44.51
21.96
30,43
47.
46.55
35.87
40.79
40.48
42.09
50.01
38.02
36.96
$5.07
44.82
VL-A3B-Thinnnng
43.94
31,82
$4.77
GLM-4.5V
47.85
19.41
26.99
47.89
56.53
37.41
41.19
39.53
47.49
55.05
37.23
27.15
52.77
46.17
Skywork-RIV3
43.67
12.31
28.61
39:57
40.87
36.88
35.50
42.04
52.78
54.70
45.82
37.45
52.70
50.28
42.47
18.30
24.58
48.46
45.61
35,36
37.68
43.23
44.38
54.24
37,99
32.61
56.42
47.15
DeepEyes
25.53
16:21
24.60
34,89
36,80
27.71
28.39
54,11
48.80
$4.90
44.35
37,77
53.73
53,40
results establish the human performance baseline. We report detailed inter-annotator agreement statistics (see Section 3). More statistics results about human-blind test can be found in Appendix C
5.2
MAIN RESULTS
Table shows the detailed performance. Based on these results, the key findings are as follows.
Overall performance gap between humans and MLLMs. On FREAK, SOTA models achieve only about 45% accuracy, compared to 86% for humans, revealing gap of roughly 40 percentage points. This indicates that the tasks in FREAK are relatively straightforward for untrained humans, yet remain a major challenge for current MLLMs, reflecting an inconsistency between model in- telligence and human reasoning. Furthermore, the HalluRates of most models are close exceed their accuracy across different tasks, highlighting severe weaknesses in fine-grained halluci- nation control. Figure shows the evaluation results of the full series of Qwen2.5-VE and Intern VL3 models. Except for performance degradation at specific sizes, model performance on FREAK gener- ally increases with model size, consistent with the Scaling Law. Interestingly, small models such as MiniCPM-VV and MiMo- VL-RL2508 achieve results comparable to large-scale models, suggesting that reducing hallucination may require an emphasis on model architecture and training processes.
Uneven performance across tasks. Breaking results down by task type. models perform worst on Counting tasks, while achieving relatively better results on Attribute and OCR tasks. Although counting appears particularly challenging for MILMs, most counting questions in FREAK involve small numbers, revealing severe failures in quantity perception. Attribute tasks in FREAK primarily comprise shape, color, texture, and other low-level visual tasks. In contrast, Analysis, Position, and Detection questions are predominantly high-level comprehension tasks. Models show better performance on lowelevel problems, whereas hallucination becomes more severe high-level tasks. This trend may be explained by the stronger reliance of high-level reasoning on linguistic priors, which causes models to over-rely on their parametric knowledge rather than visual evidence.
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Table Comparison of accuracy between Nor- mal Images and CCS Images.
Qwen2.5 V-VL-32
0.1%
InternVL3-398
3.5%
1.2%
0.2%
Model
Size
Normal Img-
CCS Img-
Probabiity
14B
91.26
34.69 (156.67)
V1.
38B
93.63
43,97 (149.66)
-10
34.1%
Qwen2.55VL_78
InternVL3-114B
7B
86.04
34.28 (151.76)
6.9
Qwen2.5VL
2.6%
2.3%
32B
90.31
36.25 (154.06)
-1.7%
42
Kulh
Datrete
Faurhh Opon Opetan
Grondd Decarto
Routh Opee Optlnn
Acuuracy(3
40
728 703
88-
Figure 4: After replacing the standard image with CCS image, the variation in the model's probability distribution across options for the same error case. Tase.The increase in ground-truth probability significantly exceeded that of the other two options, indicating targeted probabil- ity enhancement toward correct answers.
86
78
34
32
30g
10
20
30
50
70
Model Size(B)
Figure Accuracy evolution across sizes.
Table Performance of different models under two response modes: (1) direct answer generation; (2) reasoning before final output. For non-reasoning models, employ CoT prompting, while for reasoning models, we activate their reasoning mode. For o3, we use "low" and "high" respectively in reasoning effort parameters of OpenAl API.
Accuracy (1)
HalluRate (1)
Model
Size
Direct
CoT
Direct
CoT
GPT-4.1
42.01
40.66((1145)
45.43
46,30 (+2.65)
VL3777VL3378B
78B
39.32
33.91((5.41)
48.76
52.83 (14.07)
IntemnVL.3-388
38B
37.24
36.40((0.84)
48.79
49.00(10.21)
Qwen2.5-VL-72B
72B
39.39
33.39((6.00)
46.82
50.95 (14.13)
Qwen2.5.VT.35
29.82((4.83)
54.52((4.86)
Phi-4-multimoda
33.32
(5.09()8.23)
42.13
46.83(14.70)
Kimi-V
16B
35.04
30.56((44488
48.49
52.11(f3.62)
Gemini-2.5-Faach
38.10
40.02 (11.92)
47.93
44.75((3.18)
o3
45.15
43.00((2.15)
41.53
43.67()2.14)
MIMo-VL- VL-RL2508
7B
41.86
37.68((4.18)
43.10
47.15((4.05)
4L5
108B
41.62
41.19((0.43)
46.54
46.17((0.37)
Reasoning process shows no clear advantage. Reasoning models do not demonstrate significant advantages except for Gemini-2.5-Pra For instance, among the OpenAl models, o3 improved ac- curacy by only 1% compared the non-reasoning model GPT-4.11 while Kimi-VL.-A31 Thinniinnn outperforms the SFT variant by less than 2%. Notably, the small non-reasoning model MiniCPM-4V surpassed all open-source reasoning models. Table shows the performance differrences of various models when reasoning before answering versus outputting answers directly. Most models including o3 exhibit varying degrees of metric degradation after activating thinking. The above experimen- tal results indicate that reasoning does not yield noticeable improvement for multimodal models; on the contrary, most models exhibit performance degradation. We will provide an analysis why reasoning shows advantage in Section
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InternVLL3-388 (Successful Case)
Qwen2.5-VL-328 (Successful Case]
Kimi-VL-A3B-tthing -hiinnkinggg (Successful Case)
1.0
1.0
1.0
0.8
309
0.8
425
0.8
364
Robabilit
0.6
0.6
203
0.6
0.0
0.4
0.6
0.8
0.0
0.2
0.6
0.8
1.0
0.0
0.2
0.6
0.0
1.0
InternVL3-388 (Error Case)
Qwen2.5-VL-322 (Error Case)
Samn-VL-3B-thhinlnng (Error Case)
1264
1700
1336
2robvebiln
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0.6
0.6
0.8
Thinking process
Thinking process
Thinking process
Figure 5: Clustering results showing the evolution ground-truth probabilities during the reasoning processes. The legend indicates the representative count for each clustered curve. The green curve demonstrates a thought process distinct from conventional MLLMs. Evaluation conducted on multiple-choice questions with cyclic permutation, where each question is repeated six times.
ANALYSIS
6.1 TEST MLLMS WITH NORMAL / CCS IMAGE PAIR
Can MLLMs truly perceive fine-g grained modifications? This is the question of fine-graine multimodal hallucination. To investigate, we construct subset of multiple-choice questions where the original data tuple (O, A, W, CCS) is preserved, but the CCS image is replaced with its corre- sponding normal image This yields two distinct tuples: (O, A, W, CCS) and (O, A, W. T). We evaluate Qwen2.5-VL and InternVL3 with both types of data, with results summarized in Table Results show that the accuracy drops sharply to around 50% for both models when switching from normal images CCS images. We further analyze the probability shifts across the four options in eтror cases. Compared to using normal images, Figure that the average selection probability of the distractors decreases by 11% when switching to CCS images. For the remaining three options, the probability distribution exhibits targeted shifts: the correct option receives substantially larger probability increase than the other two. This may suggest that even error cases, the models can still extract and comprehend critical information about fine-gu taue-grained modifications from CCS images. An example of normal and CCS settings can be found in Fig
6.2 EXPERIMENT ON COT REASONING
Table 9 shows that enabling CoT reasoning leads varying drops in accuracy on FREAK, accom- panied by an increase in HalluRate. To further investigate the performance degradation of CoT, we track the evolution of option probabilities during the reasoning process for Intern Qwen2.5-VV and Mini-VL-A3B A3t-thinking models. Specifically, we record the probability of the correct option at the end of each reasoning step within the CoT process when the model solves multiple-choice ques- tions. We then apply time-series K-means clustering to group reasoning trajectories across questions to intuitively understand model reasoning patterns. The resulting clusters in F reveal two typ- ical failure modes: (1) The model favors incorrect option from the start and reinforces it through- out reasoning, driving the ground-truth probability close to zero, accounting for over 70% of cases; (2) In the remaining cases, the model initially favors the correct answer but abruptly switches to the incorrect one after generating hallucinated content at later stages. Sampling shows that this late- stage hallucination causes correct initial judgments reverse, finally degrading the performance.

---

The reasoning trajectories of VL-A3B---Ti--aee while broadly similar to traditional MLLMs, exhibit more complex patterns (green curves in Figure(3). We suggest that the RL-trained reasoning models enhance their capabilities to perform genuine iterative analysis regarding specific problems through reasoning outputs. However, approximately 77% of Kimi-VL-AAB-- -3B---hhinn errors stem from initially incorrect choices whose probability remains unchanged during reasoning. This indi- cates that only textual reasoning fails to correct text outputs with vision information, resulting in no significant performance gain on FREAK.
Based on the subset analysis and the visualization of reasoning processes across models, we con- clude that while MLLMs can perceive the modified CCS information FREAK. they still tend to rely on internal knowledge and favor distractors. Particularly during the textual reasoning phase, this bias often manifests as late-stage hallucinated content that reinforces incorrect choices. This rigid pattern sharply reduces the probability of selecting correct options and highlights the negative effects of CoT reasoning. We argue that the key to addressing this issue lies in enhancing the model's visual information perception capabilities and adjusting the balance between visual information and MLLMs' parametric knowledge.
LIMITATION
The primary limitation of FREAK lies in its relatively small scale. Since each item in FREAK is manually verified to ensure quality, further scaling has not yet been achieved. In addition, FREAK relies on external image generation and editing models, which may introduce subtle biases im- perceptible artifacts into the CCS images. The current analysis of CoT under the fine-grained hallu- cination setting also leaves room for deeper investigation.
As future work, we plan to explore more cost-efficient verification pipelines to scale up the dataset size. To address potential biases introduced by editing models, we provide an ablation study in Appendix D.I
CONCLUSION
We propose FREAK, a novel benchmark designed for fine-grained multimodal hallucination eval- uation. FREAK features images that violate commonsense only in details, posing significant chal- lenges to current SOTA models and revealing the gap between humans and MLLMs in understanding image details. We further investigated the models' performance the subset of FREAK and exper- imentally revealed the limitations of CoT in hallucination evaluation. Like any benchmark, FREAK has limitations such relatively small dataset. Nonetheless, FREAK provides new insights for future research and establishes a new standard for hallucination evaluation of MLLMs.
ACKNOWLEDGMENTS
The work is supported by National Science Foundation of China (No. 62576015, 62576016) and Beijing Natural Science Foundation (L253001).
ETHICS STATEMENT
This paper proposes a benchmark for fine-graine greiggr hallucination evaluation MLLMs. All data generated during the research are produced by human-aligned L.MMs, image generation models and editing models to prevent the biases from human intervention. Note that some data may involve aspects of human culture and commonsense, such as modifying structural details of landmark build- ings. To prevent potential discrimination, we have reviewed all data scheduled for public release. The evaluation process of FREAK strives to be transparent and reproducible, adhering high stan- dards of research integrity and ethical conduct. No personally identifiable information was collected or processed.
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REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we provide all necessary details and materials. Specifically, the dataset generation process and the prompts used are described in Appendix B while inference setups and experimental implementations are presented in Appendix [ and evaluation outputs of each MLLM in the supplementary materials.
In addition, we include the source code
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A OVERVIEW OF THE APPENDIX
This appendix is organized follows: Section B discusses about the uniqueness of FREAK and data generation details of FREAK. Section C contains experiment details and provides additional experiment results. Section contains ablation study about the used prompt. SectionFFoo coottanns additional error cases of different tasks. Section[[contain the details about the use of LLMs in this paper.
B FREAK DETAILS
B. UNIQUENESS FREAK
FREAK is characterized by its fine-grained CCS content, which poses significant challenges to ex- isting multimodal models. We compare different benchmarks in Table The uniqueness of FREAK lies mainly in the following aspects: 1) Fine-grained editing in realistic images: As shown in Ta- ble images FREAK appear realistic overall but contain anomalous details. Unlike other bench- marks that often use artistic or illustrative images, images of FREAK are out-of-domain for existing MLLMs, making them particularly challenging. 2) Advanced question design: Currently, main- stream hallucination benchmarks used in the industry, such POPE, primarily employ true/false questions as the core evaluation methodology. which involve a high degree of randomness. FREAK uses multiple-choice questions and free-form questions ensure flexible and objective evaluation method. 3) Diverse CCS content FREAK includes six subtasks, with various objects exhibit- ing different CCS content in different images, enabling comprehensive evaluation of fine-grained hallucinations in MILMs. 4) Revealing the gap between humans and models The questions in FREAK are relatively easy for humans but highly challenging for existing models. highlighting the limitations of current systems while providing an effective benchmark for future academic and industrial research. Unlike other benchmarks that expose model hallucinations through specially designed tasks, FREAK focuses assessing MILMs' comprehension of CCS visual information, revealing severe persistent hallucination phenomena. In future work, we will explore more types of CCS modifications, such temporal CCS phenomena in multi-image sequences.
B.2 DATA GENERATION DETAILS
The images in FREAK are characterized by their photorealism and localized CCS details, posing significant challenges to model capabilities. The first step involves preparing a noun list serve as target entities for subsequent CCS image generation. These nouns must correspond to tangible entities. During the construction of FREAK, we utilized the 1,000 labels from ImageNet-1K and prompted LLM to generate objects containing iconic and detailed (i.e., occupying small areas in images) structures. with complex morphological features. By modifying the detailed characteristics of such objects, fine-grained data that contradict commonsense are constructed. While ImageNet- 1K includes some fine-grained categories (e.g., Golden Retriever, Labrador, German Shepherd), FREAK is designed to be answerable without requiring domain-speciff expertise. It intentionally avoids subtle inter-class distinctions (c.g., that a frilled shark has six gill slits, while a great white shark has five). Therefore, we filtered out overly fine-grained categories, retaining only commonly recognized entities.
For given entity, we first generate CCS content by using prompts to guide LLM in producing details related to that entity that contradict common knowledge. The prompt template can be found in Figure Briefly, instruct the L.M to modify distinctive attributes of the object in way
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that deviates from reality, ensuring the alterations are both adversarial and semantically relevant to the original entity, rather than arbitrary or unrelated. To better control the quality of the generated CCS content, guide the model to perform edits in several aspects:(1) quantity modification, (2) color and shape alteration, (3) deletion or addition of key structures, (4) replacement of critical components, and (5) logical or physical manipulation that violates real-world constraints or everyday experience. For each type of modification, we provide several examples to help the model correctly understand the desired editing approach and avoid generating "artistic" or surrealistic imaginations. We also require the model to provide step-by-step reasoning during output generation and finally output with JSON format and get tuple (0. A, W), encouraging attributes of the object and produce adversarial, high-quality CCS content.
ground its edits in realistic
should be noted that directly instructing models to randomly generate CCS content leads to quickly exhibited repetitive patterns. Furthermore, we observed that the characteristics of CCS content vary significantly across different models.
Subsequently, based on the object O and correct description A of one of its attributes, we instruct the LLM to generate a prompt for image generation. To ensure scene diversity, the LLM is required to autonomously select appropriate contexts and enrich details of both the scene and the object within it. Since the generated images must be realistic and ordinary, we enforce the inclusion of supplemental terms such "photorcalistic" in the output prompt. The prompt is listed in Figure g
Utilizing this image generation prompt, we can synthesize images with normal content. We em- ployed Seedream 3.0 the image generation and SeedEdit 3.0 editing model due to its powerful capabilities in generation and modification. We believe that more advanced image generation and editing models can yield better CCS image generation results. Subsequently, we used the generated normal images as input and, combined with the prepared CCS content, performed detailed editing on the original images. Limited by the current capabilities of image generation models, even when using image editing models to introduce CCS modifications rather than directly generating CCS images, the resulting pictures may still fail to incorporate the required CCS content. Therefore, after obtaining CCS images, manual screening is necessary. The screening criteria primarily focus on three aspects: (1) The CCS content must valid and reasonable. Since the quality of LLM- generated CCS content varies, we require that the CCS aspects in the images must correspond to detailed visual features. (2) The CCS modifications should not be highly repetitive; for example, not all animals should have their leg counts altered. (3) The images must be realistic and the errors clearly identifiable, avoiding ambiguity or misleading appearances. Finally, the screened images, along with the originally generated content from the L.M,, are used instruct the LLM generate distracting options, therehy forming a complete instance.
To ensure alignment with human preferences and mitigate potential biases introduced during the annotation process, we employed a human baseline approach to evaluate the validity of the data. We recruited 100 ordinary university students, each assigned only 18 randomly selected questions to prevent experience accumulation during the task. Without prior contextual hints, these participants achieved an accuracy rate of 86.71% %. which empirically validates that the benchmark data aligns well with human reasoning preferences and effectively avoids potential biases.
Our generation pipeline demonstrates high scalability: given only object names, it synthesizes di- verse CCS images for each target objects. During FREAK's construction, we directly leverage the ImageNet- 1K label set as the object source, and prompt LLMS to generate approximately 1,500 en- tities to compensate for objects absent in ImageNet such as landmarks, famous branded products, and various foods. By substituting other noun collections (e.g., domain-specifie lexicons or larger label list), the pipeline achieves zero-shot generalization to new object categories for CCS image generation. This implies that FREAK's data generation approach can be extended to a wider vari- cty of entity types, ensuring scalability of the data. Moreover, this method allows room for further design of more fine -rained hallucinations. For instance, by creating subtle morphological differ- ences among different species within the same biological category generate more challenging data samples.
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Table 5: Human performance metrics on the multiple-choice task. We randomly shuffled the order of the answer choices and ensured that the number of questions with A, B, and C as the correct answer was balanced. Option D was fixed as: 'Correct answer is not listed.'
Choice
Precision
Recall
Fl-score
Support
Choice A
0.83
0.86
0.84
340
Choice B
0.86
0.86
0.86
335
Choice
0.87
0.83
0.85
335
Accuracy
0.85
0.85
0.85
1010
Macro Avg
0.64
0.64
0.64
1010
Weighted Avg
0.86
0.85
0.85
1010
C EXPERIMENT DETAILS
In this section, we will detail the implementation, especially the implementation details of experi- ments in Section
C.1 MAIN EXPERIMENT DETAILS
For open-source models, ficial API. When making requests to the model, we consistently use the parameters temperature-0 and seed=42 ensure reproducibility. For the main experiment, we use the prompt in Figure I0 For CoT reasoning, we use the prompt in Figure
VLLM--0.00.1 for inference, for closed-source models, of-
C.1.1 HUMAN BLIND TEST DETAILS
We conducted human blind test to validate the reliability of our dataset and to establish a human performance baseline for model comparison. To ensure fair and controlled experimental setup, enforced the following conditions: (1) participants were not informed of the experiment's purpose, the characteristics of the images, any details related to the questions; (2) participants itly told that some images and questions might be counter-intuitive and were instructed to answer strictly based the visual content; (3) each participant answered only 17-19 randomly selected questions to avoid familiarity effects; (4) all participants were undergraduate students from diverse academic majors; (5) each question was answered by one or two participants, ensuring full cover- age of all items. Given the number of participants and by the central limit theorem, the aggregated responses can considered representative of human choices for these tasks.
explic-
These instructions were designed to align with the prompting conditions used for MLLMs and thus avoid any unfairness. We report the human results in the main table, and for transparency and reproducibility, we provide additional metrics for the multiple-choice questions in Table 5
C.1.2 MORE EXPERIMENT RESULTS OF MULTTPPLE-CHICE QUESTIONS
We use Cyclic Permutation for evaluation of multiple-choice question. We repeated each question six times, each time altering the permutation order of the options. Since we have three options besides option D, we generated all possible permutations of these three options, resulting in six rep- ctitions for each original question. Note that for each repetition, the labels preceding the options strictly adhere to the sequence A, B. C. D. with only the content of the options being swapped. For model outputs, we use regular expressions to extract the model's 's selections. For instances where certain models deviate from the specified output format, given that FREAK aims to evaluate the de- gree of hallucination rather than instruction followown--ooooinngggg capability, we employ GPT-4o-min to assist in judging and selecting the correct answer based the unformatted content, thereby avoiding mis-
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Table More evaluation results on FREAK, including Accuracy, Consisteney Accuracy, Weighted-Preeision Weiclled-Recall, and Weighted-F1. It should be noted that due to the use of cyclic permutation, the support for options A, B, and is 2000 each, whereas the support for option D is O. After excluding option D, the weighted metrics are equivalent to the macro-averaged metries.
Model
Size
Accuracy
Consist. Acc.
Precision
Recall
F1
13.98
32.40
45.08
43.98
41.52
Gemini2.5 Bash
41.00
42.84
41.88
Gemini2.5 pro
42.73
33.00
45.24
42.73
43.95
o4-mini
41.58
27.60
42.92
41.58
42.21
GPT 4,1
43.78
31.50
45.51
43.78
44.45
Clanded sonnet
30.60
VL3778LL3-78
78B
43.63
35.90
43.65
43.63
43.63
IntemVLL3-3B
38B
42.60
35.00
42.69
42.60
42.62
IntemV11.3144
148
36.03
25.30
36.
36.03
36.01
InteroVL3-888
41.23
24.80
41.64
41.23
40.86
39.58
26.20
41.86
39.58
40.61
Qwen2.5-V1-72R
72H
36.97
30:40
37.82
36.97
37.37
Qwon2.5-VL-322
328
36.05
28:30
36.25
36.03
13
Qwen2.5-VL-7B
7B
36.08
26.00
36.67
36.08
36.34
Owen2.5-VL-BB
36.39
Phi 4 multimodal
37.56
19:20
39.10
37.56
38,07
Kimi--L-A3B--nstie
16B
39.23
24.70
39.29
39.23
39.20
MiniCPM
46.06
34.50
46.29
46.05
46.13
VL-33B-Thn
41.32
MiMo-VL-RR VL-RL
42.50
29.50
42.79
42.50
42.42
GLM 4.5V
1080
42.78
34.30
42.84
42.78
42.79
Skywork RIV3
38B
36.57
22.20
37.07
36.57
36.82
Deepkyes
28.67
18.60
28.78
28.67
28.72
judgments caused by formatting errors. The prompt used for this auxiliary evaluation is provided in Figure 15
Moreover, the precision, recall and Fl score of multiple-choice questions are measured in Table o for reference. From the results in the table, it can be observed that compared to the Average Accuracy, the Consistency Accuracy metric shows significant decrease across all models, indicating that the models are considerably affected by the permutation of answer options and exhibit notable positional non-robustness. This indirectly reflects the challenging nature of FREAK for the models, as well as their low certainty in answering the questions. The Intern VL3-78B model achieved the highest Consistency Accuracy 35.90, and within the same model series, Consistency Accuracy increases with model size, demonstrating clear positive correlation and reflecting the effectiveness of Scaling Laws. contrast, smaller parameter models experienced a more substantial decline Consistency Accuracy, such MiniCPM-VA and Phi-4 Multimodal, implying that limited parameter size leads to weaker stability and robustness.
For Precision, Recall, and F1 metries, additionally present the Precision, Recall, and F1 scores corresponding to options A, B. and C in Table 7 It is evident that some models exhibit significant differences in Precision and Recall across different options. Despite the use of Cyclic Permutation, model performance still varies under different option orders. This issue persists even in state-of-the- art models like GPT-4.1.
C.1.3 LLM-AS-JUDGE DETAILS
We use LLMs evaluate MILMS' performance on free-form question. Specifically, we employ GPT-5-mini as the judge model for the evaluation of FREAK, as the tasks in FREAK do not involve complex reasoning or computations. We instructed GPT-5-mini to categorize the outputs of VLMs into exactly three classes based on the provided image, question, correct answer, and commonsense answer: 1) Correct: The model's 's output aligns with the image and the corresponding question. 2) Commonsense Error: The model produced commonsense answer, which in the context of FREAK contradicts the correct answer. 3) Other Error: The model generated other types of incorrect content, which often occur during counting tasks. This approach does not require the
17

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Table 7: The Precision, Recall, and Fl scores evaluation results various models across the three categories of options A, B, and C. Some models exhibit noticeable variations in performance across different options, indicating the presence of certain option order biases under our task design.
Precision
Recall
Model
Op. A
Op. B
Op. C
Op. A
Op. B
Op. C
Op. A
Op. B
Op. C
03
44,47
45.28
45.49
43.20
44.35
44.40
43.82
44.81
44,94
o4-mini
42.58
43.35
42.82
41.45
41.70
41.60
42.01
42.51
42.20
GPT 4.1
44.69
46.15
45.68
50.95
40.45
39.95
47.62
43.11
42.62
Gemini2.5 pro
44.46
45.85
45.42
42.50
42.55
43.15
43.46
44.14
44.26
Gemini2.5 flash
42.96
43.19
42.36
43,50
39.00
40.50
43.23
40.99
41,41
Claude4 sonnet
29.98
30.66
31.15
27.10
31,20
32.60
28.47
30.93
31.86
Intern VL3-788
44.34
43.88
42.74
43.30
43.00
44.60
43.81
43.43
43.65
Intern VLG-388
42.96
42.69
42.43
40.00
44.10
43.70
41.43
43.38
43.05
Intern VL3-14B
35.89
36.27
36.13
32.25
39.30
36.55
33.97
37.72
36.34
InternVL3-8BB
39.47
43.38
42.08
49.55
30.45
43.70
43.94
35.78
42.87
ILternVL33-B
42.20
40.98
42.41
38.40
43.50
36.85
40.21
42.20
39.43
Qwen2,5-VL-72B
37.95
38.04
37.47
35,10
37.15
38.65
36.47
37.59
38.05
Qwen2.5-VI-32B
36.31
36.24
36.80
34,95
36.35
36.55
35.58
36.24
Qwen2.5-VT.-7
36.74
36.36
36.91
35.25
38.40
34.60
35.98
37.35
35.72
Qwen2.5-VI.3BB
36.48
36.56
36.90
34.05
39.05
35.50
35.22
37.77
36.19
Phi 4V
37.87
38.27
41.16
39.90
41.45
31.30
38.86
39.80
35.56
MiniCPM
46.52
46.72
45.62
43.50
45,90
48.75
44.96
46.31
47.14
Kimi VL A3B(instruct)
39.23
39.11
39.54
43.00
38.80
35.90
41.03
38.96
37.63
Kimi VL A3B(thinking)
40.76
41.51
41.68
49.20
37.05
34.45
44.59
39.15
37.72
GLM 4.5V
43.27
42.26
42.98
41.65
44.90
41.80
42.45
43.54
42.38
Skywork RIV3
37.08
36.56
37.58
36.45
36.25
37.00
36.76
36.40
37.29
VL-RL250
41.72
42.93
43.73
49.35
42.05
36.10
45.21
42,49
39.55
DeepEyes
28.40
29.02
28.92
28.00
29.15
28.85
28.20
29.08
28.89
LLM to output complex scores, aiming to maintain the objectivity of the LLM evaluation through simplified method.
To further investigate the consistency between LLM-as-a-judge and human assessments, we ran- domly sample 100-110 10 questions for tested models and compared the human evaluation results with the LLM evaluation results. Table g shows the calculated consistency between humans and the LLM across different models. From the table, the consistency rate between LLMs and humans ex- ceeds 90% across different models, including both open-source and closed-source models, general MLLMs, and reasoning models, demonstrating a relatively strong alignment. Additionally. the stan- dard deviation of consistency rates is relatively small, with confidence intervals distributed at the high end, further indicating the reliability of LLM-as-judge in the FREAK evaluation. This demon- strates the effectiveness and rationality of using LLM-as-judge for evaluating free-form questions in the FREAK framework.
should be noted that GPT-5-mini's understanding of the images in FREAK is also not entirely accurate. Although we provided the images to the LLMs during the evaluation process, we instructed the models derive only coarse-grained understanding from the images. The LLMs were strictly required to make judgments based solely on the provided text.
C.2 SUBSET EXPERIMENT DETAILS
In Section 6 we collect a subset that contains both normal images and CCS images. The subset has contains pieces of data. We conduct controlled experiment on the subset. Figure shows example the subset.
We first query the model using normal images, then repeat the same questions with CCS Images to compare changes the model's responses. As shown in Section 6 for cases where the model errs after switching CCS Images, we analyze the probability shifts among the four options, which
which
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Table The statistical results of sampling for consistency between the LLM and human evaluators. Consistency Rate refers to the proportion of instances where the LLM's evaluation results align with those of human assessors. The P/N Consistency Rate consolidates Commonsense Error and Other Error into single category, considering only two types of judgments before calculating the consistency proportion. We use GPT-5-mini(2025-08000 as the judge model this study.
Model
Consistency Rate
P/N Consistency Rate
Sample Size
Gemini2.5 pro
87.74
87.74
106
Gemini2.5 flash
91.00
95.00
100
86.54
94.23
104
GPT
94.12 12
95.10
102
Intern VL3-78B
88.24
92.16
102
Intern VL3-388
92.00
96.00
100
Qwen2.5-VL-72B
95.05
95.05
101
Qwen2.5-VL-32B
90.00
94.00
100
Phi 4-multimodal
89.11
98.02
101
MiniCPM-V4
95.00
97.00
100
MiMo LLMo-VL-RL2508
93.00
96,00
100
GLM 4.5V
92.11
99.12
114
Kimi-VL-A3B -ai---L---B-Thiiiin
90.10
95.05
101
Mean
91.08
94.96
Std. dev.
2.66
2.70
95% CI
(89.40, 92.75)
(93.26, 96.65)
reveals directional pattern: even when the model still selects distractor, the probability of the correct option increases, and does so more significantly than other options. This suggests that the model can perceive CCS clues CCS Images, yet structural or inherent limitations still lead it to choose incorrect options, reflecting severe hallucination. Fig. iillussrates the proportional changes of different sample types before and after image substitution: TP: Correct before (with Normal Image) and after substitution; TN: Correct before, but incorrect after; FP: Incorrect before, but correct after; FN: Incorrect both before and after. Combined with the results Table 3, only 3 to 2 of the cases receive the correct responses from the model after switching the CCS images, while to of the cases remain incorrect.
C.3 PROBE EXPERIMENT DETAILS
analyze the probability of ground truth in multiple-choice questions during the reasoning process. To track changes in model preferences during the reasoning process, we first modified the input prompt by removing the few-shot examples (as shown in Figure 12p to avoid constraining or interfering with the model's thinking patterns. By probing the output probabilities of each option during the reasoning process, we can directly analyze the causes of performance degradation in CoT reasoning. This is a key advantage of multiple-choice or closed-ended questions.
In Section 6
We begin by using the prompt guide the model to output both the entire reasoning process and the final answer. The reasoning process is then split at the sentence level and reassembled using a "prefix-sum" style algorithm, forming cumulative sequence of reasoning steps. the end of each intermediate step, append the phrase "So will choose <answer>" to simulate the model's concluded thought. This approach mimics the model's own output and allows us to capture its evolving preference at various intermediate stages. Crucially, it ensures that the subsequent output strictly corresponds to the model's 's choice rather than other content.
In practice, this method is model-agnostic: the original input prompt (without simulated model output) is first wrapped its chat template, and the simulated output is appended directly to the prompt wrapped in the chat template. Note that simply making the simulated output the 'assistant'
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Example of Subsct
Normal Image
CCS Image
Question: Is the blade in the picture approximately standing upright on the table or lying flat against it?
A. Standing upright (near vertical). B. Lying flat (parallel to table).
C. Angled (partially raised).
D. The correct answer is not listed.
Figure 6: Prompt used for LLM-assisted option selection. For models that generated reasoning but failed to output the final choice in the required format, we used advanced LLMS to make the selection based their reasoning traces.
Que7255VV-77i
InteraVEL3-00:
Qwnn VVL-318
26.6%
1p
$ 1.7%
23.1%
TP
fp
11.2%
1,0%
Ip
7 7.55..%%
38.6%
(n
55.0%
(n 61.5%
64.6%
62.9%
Figure 7: Proportion of different type of instances subset. TP: Correct before (with normal image) and after substitution; TN: Correct before, but incorrect after; FP: Incorrect before, but correct after; FN: Incorrect both before and after.
role prompt is ineffective, which will be regarded entire sentence in fact, and model will not continue to generate output based on the simulated content.
This approach is equally applicable reasoning models. By modifying the appended content to include termination token for reasoning, we can simulate the model's terminatrnn of the rea- soning process. For example, in the Lini--L--BB-Thnnnn /thinkt> are used to demarcate the thinking process.
model, the tokens < thinkp and <]
Figure 25 shows the full result of the experiment, the analysis is similar the part in Section 6
20

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Table 9: Ablation study on editing models. We use different image editing models and evaluate three MLLMs with CCS images generated by these editing models. Comprehensive results indicates that using of SeedEdit 3.0 doesn't introduce structure bias.
SeedEdit 3,0
Nano Banana
Seedream 4,0
Model
Accuracy
Cousttency
Accuracy
Consitency
Accuracy
Cousteecy
Gemini- 2.5 flash
46.23
44.78
83.18
47.54
79.99
45.65
$3.01
Qwen2.5-V1.333
38.26
38.12
82.32
39.71
86.96
D ABLATION STUDY
D.1 EDITING MODEL ABLATION STUDY
To assess whether the benchmark quality is overly dependent on SeedEdit 3.0, we conducted an ablation study using three different image-editing models: SeedEdit 3.0, Nano Banana, and See- dream 4.0. We randomly sampled 114 MCQ items from FREAK, applied the same editing prompt to all three models, and evaluated the resulting images using three representative MLLMs (Gemini- 2.5-flash, 04-mini, and Qwen2.5-VL-32BB. As shown in Table 9
the answer consistency between SeedEdit 3.0 and the other editors ranges from 80% to 87%, while the accuracy differences for each MLLM remain small. These findings suggest that although different editors may vary slightly in CCS editing, the downstream difficulty of images and questions remains largely stable. Therefore, benchmark performance is not dominated distorted by the specific choice of SeedEdit 3.0, and potential information leakage or corruption unique to this editor can be ruled out.
Importantly, during dataset construction, all edited images underwent manual quality verification, ensuring that only visually natural and semantically coherent images were included in the bench- mark. This further mitigates concerns regarding artifacts introduced by any single editing model.
For the consistency rate, We analyse that even under the same textual modification prompt, dif- ferent editing models, and sometimes even the same model across trials, may produce images that are semantically aligned but visually implemented in different ways. Since text prompts typically contain less information than the visual space they condition, the generated images can vary in com- positional details despite fulfilling the same modification instruction. These subtle variations alter the difficulty the corresponding QA, which results shifts in the MLLMs' performance across editing models.
D.2 PROMPT ABLATION STUDY
During the evaluation process, we employ two types of prompts: one requires the model to di- rectly output the selected answer to the question, and another requires the model reason about the question before generating the answer. All models consistently used the same prompt content. For certain models with specific prompt formatting requirements, we only adjusted the format while keeping the content essentially unchanged.
To eliminate the potential impact of our self-designed CoT prompt, we additionally employ the CoT prompt from Figure 13 which has been validated by previous work enhance model's performance. The evaluation results of each model on FREAK's multiple-choice question seta are presented in Table
The data in the table show that the model's accuracy decreased to varying degrees under the two CoT prompts. Combined with the results from Table conventional MILMss were more adversely affected by CoT prompting. We have analyzed the reasons for this performance degradation with CoT prompts in Section
21

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Table 10: Result of CoT prompt ablation experiment.
Acc(D); Accuracy using direct prompt
(Fig. 10 Acc(vl): Accuracy using CoT prompt v1 (Fig. 11: Acc(v2): Accuracy using CoT prompt
v2
g
Model
Size
Acc(D)
Acc(vl)
Acc(v2)
Intem VL3-38B
38B
42.60
40.28
36.74
IntemVL3-144
14B
36.03
35.13
28.42
Qwen2.5-VI.-92R
32B
36.03
29.95
29.50
Qwen2.5-VL-7B
36.08
32.98
33.32
Phi4 multimodal
6B
37.55
29.13
25.08
E ERROR CASES
We present additional case studies showcasing erroneous responses from Intern VL3-38B (Fig- ures T6 to 21). and the reasoning model Kimi-VL-AABB-Thnninng (Figures 22 to 24 We provide the original questions, corresponding images, options, correct answers, and the models' incorrect outputs. These cases include the models' reasoning processes, revealing that errors in describing target objects in the images led to incorrect choices. For VL-A3B-Thinkee reasoning pattern emerged: the model engaged in repeated deliberation, exhibited hesitation dur- ing reasoning, and even negated its own intermediate conclusions. We posit that this pattern arises because the model perceives CCS details in the image, which conflict with its internal knowledge acquired during training, thereby inducing self-doubt.
a self-reflective
The self-reflect mode observed in the Kimi model provides further evidence of its ability to perceive CCS visual details: when such visual information conflicts with its parametric knowledge, the model exhibits self-reflective reasoning. However, after deliberating for certain length, it still generates hallucinated content and ultimately selects incorrect answers. This suggests that mitigating halluci- nations requires enhancing the model'ss understanding and trust in the visual information it captures, preventing internal knowledge from overriding genuine visual cues. We argue that the key lies in improving the alignment between the visual encoder and the LLM. Simply enhancing reasoning pacity without strengthening visual understanding, as seen in Kimi- VL-A3B- -BThhnninng. allows the model detect inconsistencies but not arrive at correct answers.
F THE USE OF LLMS
We employ LLMs in our data generation pipeline for FREAK produce high-quality CCS content, for which we have provided the relevant prompts. Beyond this specific application, LLMs are used only for reference in the writing individual words and sentences in the paper. We further assure that the methodological ideas are independently conceived by the authors, and all experiments are conducted independently without the use of LLMMs for research assistance.
22

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Prompt: CCS Content Generation
The content I require is as follows: An object in original image is realistic and normal, but I have modified way that preserves its overall structure while introducing detail levelincconsistencies with reality, making it implusiible. Below, will first intruduce several common modification methods.
1. Quantiyy Modification: Add or remove a key structural element of the object so that the result contradicts reality. Note: Modifications must be based distinctive features specific object, applied generically (e.g.. just arbitrarily changing the number of logs on an animal).
Example: ① For a starfish: Add an arm, creating a six-armed starfish. (Explanation: Real-world starfish have five arms; this modification is subtle regarding image and object.) (2 western dining fork: Change the fork from four tines five. (Explanation: Standard western forks have four tines, five is implausible and a detail-level change.) 3 For clock: Change the clock's bands to four pointers. (Explanation: Clocks with four pointers do not exist in reality.) 4 For a snowflake: Change the snowflake from branches to eight. (Explanation: Due water molecule structure, real snowtlakes always have branches; this modification focuses on branch count detail.) () For guitar: Change the guitar from six strings to five. (Explanation:
Standard guitars have six strings; changing the string count is detail modification.)
Color Modification: Alter the color of small pattof object, creating a detail that contradicts the real world. Note: The
color change must facus on very small area; large-ares changes (e. turning all zebra's stripes black) are not allowed, as this makes it different ordinary object (a horse) and violates the requirement that the modified object remains implausible, Note: The color change must result in an object that defies common sense.
Example: For rainhow: Change the innermost color red. (Explanation: Real rainbows strictly follow the order red- from the outside in; the innermost cannot be red, and this change affects only layer.) For traffic light: Change the bottom bulb to blue. (Explanation: Traffic lights are red, yellow, green from top to bottom; changing the bottom green to blue contradicts reality.) (For simplicity, We omit other categories)
vellow-grran--buc-ingg
The above only lists six types, but other modifications violating common sense exist (e.g.. a playing card printed with several suits, a compass with nine directional markers). You need to think carefully and eleverly based on the object's characteristics to devise suitable modifications.
Here, the object you need to consider is: [object). You should analyze its characteristics, including color, shape, key structures, and their quantitics, Indicate which one or severalmodification strategics are possible. After careful and meticulous ihought, list the feasible modification methods for this object. Your response must follow this format:
[object]: [Describe the object sentence, including the various fextures mentioned ahove]. Analysis: [In- analysis of which aspects can be modified).
Format: -enset--onddtionn The required state of the object in the image, determined by the modification rule. E.g., to delete piane's black keys, write: Overhead view of a piano, black keys clearly visible. |</coddition>> visible.//ccodiiiionn
<rule> (Modification rule: delete. add, or modify specific feature of the object in the image] (ange]</ruless /</rll desciiiieiiiiiionnn A rensomable explanation for this modification] dddeseripioon--iamee -naeeeaaeeeeeeeeeeeeee (Filename to (wwee//nmme=//aswwe
Requirements:
Modifications cannot be too difficult or far- fetched. They must allow normal person to notice the issue in the modified image. Provide only 2-4 of the best, most clever modifications (refer to the examples). the current object has no worthwhile subtle modifications, skip it directly!
2.
Your modifications carnot be too specialized or academic!! For specific object (creature, tool), you should choose modify its characteristies member of broader category. For example, for a tiger shark, modify its characteristies as a shark, or even as a fish(e. number of ventral fins) ! Do netmodify features specific to very niche category. as this will make it impossible for humans judge! The criterion is: Can an average person (without professional laxonomic training) recognize this anomaly? In the previous example, oridinary people cannot distinguish tiger sharks, so you cannot modify features specific to tiger sharks (e.g.. tiger sharks five gill slits).
..... simplicity, omit other rules)...
Now, please analyze the obbjett]objeet according to the guidance above and the examples. Suggest what modifications are possible. Respond strictly the required format. Do ndd any extra
Figure& 8: Prompt used for CCS content generation
23

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Prompt: CCS Content Generation
Create prompt for the (object) object. I will use this prompt to generate image via Stable Diffusion.
Note: Do not deliberately emphasize the characteristies of the (object). The generated scene must meet the requirements in parentheses while ensuring photorealistic effect. Additionally: Do not use hopth-of-field-rcane the prompt. Also, require that the (object] is certain distance from the cameraand occupies only small part of the image(you must explicitly state this the prompt)). Determine this distance appropriately based on object. Furthermore, you need to specify a suitable scene this object, where the object only one part of the scene.
Note: Generate only the prompt itself, without any additional explanation. If this is logo, generate the prompt for the corresponding productrobject!
Example: Hyper-realistic acoustic guitar (six strings clearly visible), positioned on the left side of the image, tilted on wooden desk sunlit study, warm ambient lighting, placed at distance from camera, with small speaker next the guitar.
Figure 9: Prompt used for image generation.
Prompt:directly require choice (main experiment)
system:
"You helpful Heee image multiple choice question about the image should
reply the question according image faithfully. Please note that the question maybe confusing image
content might be uncommon, you should answer the question ONLY with the correct choice letter.
Here is example:
[MMAG]]
Question: Teapot the picture handle? Ifso, located?
Choices:
A. Not visible (Cn'tssee
B. Yes, the side.
C. Yes, arched over the
D. The correct answer listed.
Your answer: A
Now please question following above format STRICTLY."
user:
<image>
"Question: [sample['qustion]]]
Choices:
A. (Option1)
B. [Option2]
C. (Option3
[ppioon)
Your answer:"
Figure 10: Prompt used evaluation.
24

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Prompt:
think before answer (CoT experiment)
system:
"You are helpful agent.Here' is image with multiple choice question about the image content.
You should reply the question according the image faithfully. Please note that the question maybe confusing the image content might uncommon, You should thinking briefly first and you **MUST give your final choose with <ansser><</nswwe>>**
You should follow the format below STRICTLY
format: Think first, give your discussion about the question and the image BRIEFLY. Then summarize: The final answer is <answer> (VB/://))//////)//////////
Here example:
#
[IMAGE]
Does the Teapot in the picture have a handle? If so, where is located?
Choices:
A. Not visible / Can't see.
B. Yes, the side.
C. Yes, arched the top.
D. The correct answer is not listed.
Your answer:
From the image the handle the side elearly, the answer <answer- неннненн
Now please answer the question following the above format STRICTLY.
user:
<image>
crusstion(smmple[[qqqssss
Choices:
A. (Option1)
B. [Option2]
[Opiion3]
D. (Option4)
Your answer:"
Figure 11: Prompt used for CoT evaluation. Results are listed in Table 9
25

---

Prompt: Probe Experiment
system:
"You helpful agent. Here is image with multiple choice question. The image content might be uncommon or the question might be confusing, so you should analyze image systematically provide step-by-step reasoning. Moreover, take time to examine details carefully. Finally, you **USTT** give your final choose with <answer></answer>.
Remember that you should think step by step. Take time to examine details carefully, But when come the final answer, please provide your choose with the characten(A/B//CID <answer> </answer>
Most IMPORTANTLY: finally provide your choice in <answer=</answer! For example:
<answor-B</answor <answor-O</asswor Daassr
<image>
"Question: (samplc[questioo]]]
Choices:
A. (Option1:
B. (Option
C. (Option3)
D. [Option4)
Your answer:"
Figure 12: Prompt used for Figure 5 This prompt delete the example to avoid fixed thinking pattern.
26

---

Prompt:
CoT prompt v2 (Ablation study)
system:
"You are helpful agent. Here is image with multiple choice question about the image content. You should reply the question according the image faithfully. Please that the question maybe confusing the image content might be uncommon, you should think first and finally answer the question with correct choice letter. example:
[IMAGR]
Question:Does the Teapot in the picture have a handle? If so, where is located?
Choices:
A. Not visible / Can't sec.
B. Yes, the side.
C. Yes, arched over the top.
D. The
is listed.
Your answer:
Let's think step by step. First, I'll look at the overall structure - I can see it's white/ceramic teapot. Next, I need to locate the handle. Looking at the left side of the teapot, don't seo handle there. Checking the right side... can see what appears to be curved handle attached to the side. Based on this systematic examination, the handle is located on the side of the teapot. Sanswer-B B<aaswee//
"Qusstion: Dusstion:(samplel [sutti[[qucsstoon]] quccstio']]
Choices:
[Option]
B. Option2
Option3
D. [Option4]
Your answer:"
assistant:
LLet's think step by step. \nn"
Another CoT prompt that used in previous work (Kojima al. 2023). We this prompt to eliminate potential performance degradation caused by the prompt in Figure tself.
Figure 13:
27

---

Prompt: Prohe Experiment
system:
"You helpful agent.Here Here is multiple-choice question and its correct answer. Additionally, provide you with fillinn-hhe-blan response for this question. Now, based on the response, please
select the option that matches the response best (In other words, the response is the same with the chosen options in meanings). Requirements are as follows:
Here are two examples: #*ldAA*a
Question: What the shape of the spare on the Jeep the picture?
Choices:
A. Square, rectangle.
B.Cirele.
C.Not visible.
The correct answer not listed.
Given responsc: The spare not visible, its cover square, the final answer is ~ansser-C</answer> Your answer: C. #itlii
Question: What the shape of the kick drum (bass drum) in the drum kit shown in the picture? Choices:
A. Squaro(Cube).
B. Perfect kound(Cylinder).
C. A circle, but the bottom straight. So the shape is perfect round!
D. The correct answer is not listed.
Given response: Circular. Therefore the answer B, Perfect Round(Cylinder)
Your answer: B.
Now please answer the question following the instruction, you don't need output the explanation.
user:
QQusstion /sampleeqquestonn]]
Choices:
A. [Opioon]
B. (ption2
C. Optionn
OOpiion4)
Your answer:"
Figure 14: Prompt used for LLM-assisted option selection. For models that generated reasoning but failed to output the final choice in the required format, we used advanced LLMs to make the selection based on their reasoning traces.
28

---

Prompt: LLM--s-judge prompt
You are helpful agent. Here an image and a question with the ground truth. will provide you with response for this question. Now, you should determine whether the model's response aligns with the ground truth based on the question and the answer. Requirements are as follows:
1. We provide answers, as well as the image itself, may not align with your common sense or prior knowledge. You must determine whether the model's response is correct solely based on the answers we provide (both the ground truth and the typical incorrect answer) and the model's own response. You must not your own observation the image or your personal common sense preferences to judge the correctness of the model's answer.
ground truth answer and typical incorrect answer for each question. These
2. The ground truth answer or the typical incorrect answer may contain content marked with the '#' symbol. This means that long as the model's response covers the content enclosed by the symbols, it should be considered as belonging to that category. If there are multiple segments marked with #' either the ground truth or the typical incorrect answer, the model's response will be classified under that category long as it covers any of the content marked by these symbols.
3. You should give you judge with the following format: First, provide your judgment within the tags <judge>correct pondgg/=corrct//wooo/yyyyoe cooon//ypical/judgeee
where 'correcf' indicates that the modcl's response accurate, 'wrong' indicates an incorrect response, and 'typical' signifies that the model provided typical error. Then, explain the reasoning behind your judgment within the <cxplanation> Your explanation here. </explanation> tags.
4. Remember to analysis the ground truth and the typical error. If the model's response matches the ttpiccal error, you should judge with bhiia---tpical//jjugge>
5. Please note that you need to comprchensively evaluate the conrectness based on the question format, the model's response, the correct answer, and the incorrect answers. It is not necessarily required for the model's response to include all information from the correct answer. For example, if the correct answer contains additional information that is not required by the question, the model's response should not be considered incorrect for omitting it. On the other hand, even if the model reaches the same conclusion as the correct answer, if the model's analysis contradicts the correct answer, the response should still be considered incorrect. 6. Your judge must align with human preferences.
NOTE: You must follow the format with <judge><</udge>> and sexplnattion<<<xxplaaaiioos
Figure 15: Prompt used for LLM's judgment.
Error Case suevica ma
Quesition
Image
How is the neck the violin in the picture?
A. The nook is not visible.
Approx. 130-135mm.
Less than
The correct answer is not listed.
Model Output
From the image, the neck violin appears be visible and can estimated in length. The nock of standard violin typically messures around 130 135mm. Given the propoctions and typical size of violin, the nook
within this nage.
The finel anower is Sanmwar Dolammaar.
Figure 16: An Detection example of error Intern VL3-38B
29

---

Error Case suwebi oae
Quesition
Image
Does the cachus the picture have spines? If what type spines they?
There is visible spines.
Yes, the cactus
rigid spines, X
C. Yes, the catus has microscopic glochids.
D. The correct answer is not listed.
Model Output
From the image, the coctus clearly has spines. These spines appear rndial rigid, extending fine, spines. microscopic glochids,
body
correct answer Balaee-B-lanwwer..
The final anower is BanssereBolnnwar..
Figure 17: An Detection example of error Intern VL3-38B
Error Case suevica ma
Quesition
Image
What device used make the monitor appear to float in air picture?
The monitor is floating. No other devices are attached monitor.
Articulating Arm Bracket. X
C. Clear Stand.
The
is listed.
Model Output
the imago, is cleir monitor atrached moll-moomee bracket with extending This of beacket commonly known articulning
positioned in way that it appears float in the air. The bracket visible on the lef) side monioor
support the
The Bainwa-B
Figure 18: An Analysis example of error Intern VL3-38B
30

---

Error Case suweb oae
Quesition
Image
What color is the portside navigation light the ship in picture?
White / yellow.
Green.
X
C. Red.
D. The correct answer is not listed.
Model Output
From the image, the navigarion light the poronde the ship clearly visible and This consistent with suaddrd maritime savigation porssie light However, this the green light is on the porside.
light
The final anower is Banssee-Bolnnwar.
Figure 19: An Attribute example of erro on Intern VL3-38B
Error Case suevica ma Quesition
Image
What letters written the emblem in the picture?
A. BMM.
BMW.
X
The correct answer is not listed.
Model Output
the image, the emblem clearly shows lettors "BMW The emblem centrally located on car's hood, and letters distinctly visible withir the ciecular
The Bicaaaaaar......
Figure 20: An OCR example of error Intern VL3-38B
31

---

Error Case
Quesition
Image
Can the hanging soup ladle in the picture removed?
No, permanently fixed.
Yes, designed detachable.
X
C. Unclear (no visible attachment mechanism).
The correct answer is not listed.
Model Output
From the imago, the ladle hanging from book, which devignal deteciable book
indican
that it can be casily removed.
The answer is Cansegr-D--nnseee
Figure 21: An Analysis example of error case on Intern VL3-38B
Error Case
Quesition
Image
gloves picture matched pair (one left one right??
No, two left gloves.
Yes, a complete left-right pair. X
C. No, two right gloves.
The correct answer is not listed.
Model Output
<thinkb So, let's look at the image. The gloves one has thumb on the glowe),
other law thumb left" Whit, Whit, a manched poir one left and one right. Lets check. The laft glowe (right hand) and right glove (left hendy? Wait, no. Wast,, right
viike
image, glove
image) and the other for the left hand (tumbb the right side of the image).
actuaily, matched pair one left and one right. Let's check.. The two gloves: one is right. glove tthmb the left when viewed), the other left glove (thumb right viewedl). they matchedd pair. the a -hiiink--R
00033 AT
Figure 22: An Analysis example of error case on Kimi-VL-A3B Thinnkinn. The model merges reff-reflective reasoning pattern. This pattern appears when the model realize the image's content conflicts with it's parametric knowledge.
32

---

Error Case Ra-D dialing
Quesition
Image
Is the handrail continuous between the upstairs and downstairs in the picture?
No, the left upstairs are Segmented with brenks,
B. Fully continuous,
X
Altematt
The correct answer is not listed.
Model Output
CthinkD look image. handrail right the stains, Whit, the quosion is about continity The handrull gous from lower level
seems connect. Watt, let's check, The a single contnnuus piece?? Wait, image, cominuous weoden structure along the stairs So the answer should be 4hhiink-BB
Figure 23: An Attrirubte example of error case on self-reflective reasoning pattern. This pattern appears when the model realize the image's 's content conflicts with it's parametric knowledge.
The model merges
Error Case
Question
Image
In the image, is one of the canopy panels of that sun umbrella noticeably different from the other panels?
Model output
<iiiint--S No, look
Walt, onz the punch- wark, actually, check the aructure. Wai,, the unibec auneelll's pancle are any differen? Wal, no wall. Waik, dee unbrelle's canopy all mas not Wask
spasaton check apain,
mmge shows boach umbrrlls an walt, amaally
netiioaily
theme difitrent puncle? Walt, actually ao Walt, the sornec answer no. Wal. wast, wast, think inade a mistie Walt, nos lnt's agiic. The unttella partlic art they the Whit, wail, corrett image
West, chock. Wart, umbeclla's
answer
precise anelyse
mumppl
the znswer <5 Whiik No, all appear be simier in co and design: there is no noticeable difference smong them
puncls
umbeella the image
The model merges self-reflective reasoning pattern. This pattern appears when the model realize the image's content conflicts with it's parametric knowledge.
Figure 24: An Attrirubte example of error case on Kimi-VL-A33B
33

---

Meeilli
2 2
2 52 35
25 21
Figure 25: The overall experimental results of the probe experiment, where all curves clustered from the original samples. The red curve represents the probability evolution in successful cases, the blue curve corresponds error cases, and the green curve captures specific thinking pattern observed in the reasoning model.
34


Reviews:
[{"review_id": "I7DgFNtP21", "reviewer": "Reviewer_BnmH", "summary": "This paper introduces FREAK, a benchmark designed to evaluate fine-grained hallucinations in multimodal large language models (MLLMs). Unlike prior benchmarks focused on object-level errors, FREAK targets subtle, counter-commonsense inconsistencies within photorealistic images across six categories (Detection, Counting, Attribute, Analysis, Position, OCR). Built through an automated generate-then-edit pipeline, FREAK includes human-verified annotations for reliability. Experiments on 17 state-of-the-art models show that even top models achieve only around 45% accuracy, far below the human score of 86.7%, indicating that fine-grained hallucination remains a major challenge. The benchmark also reveals that reasoning (e.g., CoT) can amplify hallucinations rather than mitigate them.", "questions": "* Why restrict to photorealistic images? Please justify the choice and discuss how conclusions might change for non-photographic domains (e.g., paintings/animations).\n\n* Your analysis suggests CoT often degrades performance/hallucination rates (e.g., Table 4). Would the same trend hold with stronger reasoning strategies (self-refinement, verification-first CoT, or error-detection loops), especially when early visual misperception could be corrected mid-reasoning? [1-3]\n\n**Referneces**:\n\n[1] Gao, Jun, et al. \"Interleaved-modal chain-of-thought.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[2] Madaan, Aman, et al. \"Self-refine: Iterative refinement with self-feedback.\" Advances in Neural Information Processing Systems 36 (2023): 46534-46594.\n\n[3] Zhou, Qiji, et al. \"Image-of-thought prompting for visual reasoning refinement in multimodal large language models.\" arXiv preprint arXiv:2405.13872 (2024).", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The paper introduces FREAK, a large-scale, AI-generated CCS benchmark that evaluates models across six tasks (Detection, Counting, Attribute, Analysis, Position, OCR), offering a comprehensive assessment of fine-grained hallucination.\n\n* Experiments show that most SOTA models perform far below human baseline on FREAK (≈45% vs. 86.7%), indicating that the benchmark meaningfully exposes robustness gaps under CCS conditions.\n\n* The paper is readable and well-structured, making the motivation, pipeline, and evaluation easy to follow (e.g., the generate-then-edit pipeline and curated Q/A process).", "weaknesses": "* Novelty concerns: CCS-style evaluation has appeared in prior benchmarks (e.g., PhD), and the six task types are common in MLLM evaluation, which may narrow the incremental contribution despite FREAK’s fine-grained focus.\n\n* Although FREAK emphasizes photorealistic images with localized CCS edits, real-world usage spans paintings, animations, and other styles; it’s unclear how well the benchmark reflects such domains or whether deduplication alone guarantees sufficient diversity.\n\n* The human baseline would benefit from reporting inter-annotator agreement or additional statistics to aid reproducibility. (The paper notes a 100-participant blind test but gives limited agreement detail.)\n\n* Since an LLM-as-judge paradigm is used for free-form scoring, a discussion/analysis of the judge model’s bias and reliability would strengthen the evaluation methodology.\n\n* Presentation quality: Numerous minor typos/formatting issues (spacing, quotation, punctuation) detract from polish and should be corrected in revision.", "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": "DV6s7rzI3Y", "reviewer": "Reviewer_EXmZ", "summary": "This paper introduces a new fine-grained hallucination benchmark for Multimodal Large Language Models (MLLMs), addressing the limitations of previous benchmarks, which often suffer from low quality, oversimplified tasks, saturated metrics, and limited diversity. The proposed benchmark is constructed from AI-generated, fine-grained counter-commonsense (CCS) images. To build it, the authors first define CCS concepts and then generate and refine images using a generative model. Experimental results show that current MLLMs still perform significantly worse than human experts on this benchmark. Furthermore, the study reveals that reasoning-based approaches such as Chain-of-Thought (CoT) can actually degrade performance in this setting.", "questions": "* How reliable is the image editing model used for generating CCS images? Are there any ablation studies or analyses that verify its impact on the benchmark quality?  \n    \n* What is the clear rationale for introducing a fine-grained hallucination benchmark in MLLMs? The paper should better explain why such a benchmark is necessary and how it addresses real-world hallucination issues.  \n    \n* How reliable is the LLM-as-judge approach used for evaluating free-form questions? Has its consistency with human evaluation been validated?\n\n#####", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper highlights the limitations of previous MLLM hallucination benchmarks, such as low quality, oversimplified tasks, and insufficient diversity. The authors clearly compare their proposed benchmark, FREAK, with existing CCS benchmarks and demonstrate its superiority.  \n    \n* The finding that Chain-of-Thought (CoT) reasoning negatively impacts performance on fine-grained hallucination tasks is insightful and well explained.  \n    \n* The experiments are extensive, and the analyses are thorough and well-structured.", "weaknesses": "Major Weaknesses\n\n* **Dependence on the Image Editing Model:** The quality of the generated CCS images likely depends heavily on the performance of the image editing model. Since this model is not trained specifically on CCS images, it may struggle to edit them properly, potentially leading to corrupted or unrealistic results. It is unclear how the authors ensured that the edited images appear natural. An ablation study or analysis regarding the influence of the image editing model on the benchmark quality would be valuable.\n\n* **Insufficient Justification for the Fine-Grained Hallucination Benchmark:** The motivation for introducing a fine-grained hallucination benchmark is not well justified. The CCS images seem highly artificial, and it is questionable whether users would encounter such counter-commonsense images in real MLLM usage scenarios. The paper should provide stronger reasoning for why this type of benchmark is necessary and how it reflects practical hallucination issues.  \n    \n* **Reliability of the LLM-as-Judge Evaluation:** The paper adopts an LLM-as-judge approach for free-form question evaluation, but its reliability is uncertain. It would strengthen the work to validate this approach by comparing its results with human judgments and analyzing the performance gap between LLM-based and human evaluations.\n\nMinor Weaknesses\n\n* **Ambiguity in Wording (Line 40–41):** The phrase “This saturation arises from inherent limitations in both difficulty and evaluation methods” is unclear. The authors should clarify what is meant by inherent limitations in this context.  \n    \n* **Clarity in Table 2:** It would be helpful to separate reasoning-based models from non-reasoning models in Table 2\\. Without this distinction, the statement “reasoning shows no clear advantage” does not align well with the presented results.\n\n* **Typos and Formatting Issues**  \n  * Line 53: diversity the of → the diversity of  \n  * Line 141: judgments (spelling issue)  \n  * Line 147: MIRAGE Dong et al. (2025) (missing space)  \n  * Table 4: The arrow direction in the “Accuracy – CoT” column for GPT-4.1 appears incorrect.", "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": "VeozxywgFG", "reviewer": "Reviewer_bnQv", "summary": "This paper proposed FREAK, a challenging hallucination benchmark for vision-language models. It follows a 3-step pipeline at construction: 1) generate counter-commonsense (CCS) textual description using LLMs 2) generate CCS images using an image editing models and 3) generate MCQ/open-ended questions using LLMs followed by postprocessing and a human inspection process. Results show that even humans only achieve 86% accuracy, followed by gemini-2.5-pro at 43%, indicating the challenging nature of this benchmark. They also find that CoT/reasoning does not bring performance boost.", "questions": "1. Tab1 can be better formatted. Also, the fourth column should be “GPT Series Eval”\n\n2. In line 243-244, when multiple groundtruths may exist, why is the dominant distractor adopted to construct the dataset?\n\n3. One of the goals of blind-test human experiment is to detect potential biases, whose results are not shown. Did you find any bias in the annotated datasets?\n\n4. It’s better to provide some analysis on why reasoning does not boost performance.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The benchmark dataset is developed with strong motivation and should be beneficial for diagnosing models.\n\n2. The blind-test human experiments for  validation makes the dataset more solid.\n\n3. The in-depth analysis in sec6 provides insights on why the model hallucinates, potentially inspiring better model development.", "weaknesses": "1. A major drawback of this work is the taxonomy is not exhaustive or mutually exclusive, nor is it hierarchical. For instance, in fig2, the example question for the “Analysis” dimension also seems to fit into the “Position” dimension. Besides, while spatial relation is included, questions to probe general relations between objects seem missing (e.g. the “riding” relation as in “a person riding a horse”).\n\n2. In sec4.2, why are errors divided into commonsense errors and others? Meanwhile, using LLMs to classify error types is not validated.\n\n3. In sec4.2, “Hallucination Rate (HalluRate) is the proportion of cases where the model either outputs a commonsense answer in free-form questions or selects the commonsense distractor in multiple-choice questions.” How to judge whether a model outputs a commonsense answer? Is every open-ended question paired with a commonsense answer for judgement?\n\n4. In sec6.1, can you elaborate on the input/output/groundtruth of the normal and CCS settings?", "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": "20rome7UUs", "reviewer": "Reviewer_Xkgu", "summary": "This paper introduces FREAK, a benchmark for evaluating fine-grained hallucinations in MLLMs using photorealistic images with subtle counter-commonsense (CCS) edits created via a novel \"generate-then-edit\" pipeline. FREAK tests detailed perception across 6 categories. Experiments show SOTA MLLMs struggle significantly (~45% accuracy vs. 86.71% human), revealing severe limitations, and Chain-of-Thought prompting often degrades performance.", "questions": "1. The image editing process could introduce subtle visual artifacts. How do the authors ensure that models are identifying the semantic, counter-commonsense errors, rather than simply learning to detect these low-level editing artifacts?\n2. The benchmark is explicitly designed for 'fine-grained perception.' While valuable, this focus seems to neglect hallucinations that arise from multi-step reasoning. Could the authors discuss this trade-off and the benchmark's limitations in assessing these more complex failure modes?\n3. For a semi-automated pipeline, the resulting dataset of ~1.8k images is smaller than expected. Could the authors quantify the main bottleneck in their workflow (e.g., image generation failure rate, human verification time) that limits the benchmark's scalability?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper writes well.\n2. The focus on fine-grained hallucinations, where models find it difficult to perceive subtle details contradicting common sense, is highly relevant.\n3. Instead of relying solely on direct text-to-image generation or manual editing, the two-stage process (generate normal -> edit CCS detail) allows for the creation of high-quality, photorealistic images with precisely controlled, localized CCS elements.\n4. The gap between SOTA models and humans clearly indicates that fine-grained perception remains a major challenge.\n5. The investigation into why CoT prompting degrades performance is valuable.", "weaknesses": "1. While the images are generated/edited, the initial CCS descriptions and the subsequent questions/distractors are generated by LLMs. This introduces the possibility of biases inherent in the LLMs used during benchmark creation.\n2. The quality and nature of the CCS images depend heavily on the capabilities of Seedream3.0 and SeedEdit3.0.\n3. It focuses primarily on perception, less on deeper reasoning.\n4. The pipeline is model-assisted, but the dataset size is still very small. It would be better if it could be scaled up.", "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)