Vision Language Models are Biased
Authors: An Vo
Khai-Nguyen Nguyen
Mohammad Reza Taesiri
Vy Tuong Dang
Anh Totti Nguyen
Daeyoung Kim
Venue: ICLR 2026 Poster (2026)
Keywords: vision language models
multimodal reasoning
benchmark
bias
Primary area: datasets and benchmarks
TL;DR: A benchmark to demonstrate that VLMs primarily rely on prior knowledge while ignoring visual input.

Abstract: Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may notoriously sway their outputs toward wrong or biased answers. In this work, we test how the knowledge of popular subjects hurts the accuracy of vision language models (VLMs) on standard, objective visual tasks of counting and identification. We find that state-of-the-art VLMs are strongly biased (e.g., unable to recognize that a 4th stripe has been added to a 3-stripe Adidas logo), scoring an average of 17.05% accuracy in counting (e.g., counting stripes in an Adidas-like logo) across 7 diverse domains spanning animals, logos, chess, game boards, optical illusions, and patterned grids. Removing image backgrounds nearly doubles accuracy (by 21.09 points), revealing that background visual cues trigger these biased responses. Further analysis of VLMs' reasoning patterns shows that counting accuracy initially rises with thinking tokens, reaching ~40%, before declining with model overthinking. Our work presents an interesting failure mode in VLMs and a human-supervised automated framework for testing VLM biases. Code and data are available at: vlmsarebiased.github.io.

Published as a conference paper at ICLR 2026
VISION LANGUAGE MODELS ARE BIASED
An Vo* KAIST an. wn..o@kaisst.cc..
Khai-Nguyen Nguyen*
Mohammad Reza Taesiri
College of William and Mary knguyenO 70mm007wmmmmmmmmmmmmmmmmmmmmmmmm edu
University of Alberta
mt @emallmma..oo...
Vy Tuong Dang KAIST vydang@kaist.ac. kr
Anh Totti Nguyen'
Dacyoung Kim KAIST kimd kaist ac
Auburn University anh. ng 8 eww.i.m....oooo..om....o........
ABSTRACT
Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them downstream tasks but also may notoriously sway their outputs toward wrong or biased answers. this work, we test how the knowl- edge of popular subjects hurts the accuracy of vision language models (VLMs) on standard, objective visual tasks of counting and identification. We find that state- of-the-art VLMs are strongly biased (e.g., unable recognize that a 4th stripe has been added to 3-stripe Adidas logo), scoring average of 17.05% accuracy in counting (e.g., counting stripes an Adidas-like logo) across diverse do- mains spanning animals, logos, chess, game boards, optical illusions, and patterned grids. Removing image backgrounds nearly doubles accuracy (by 21.09 points), revealing that background visual cues trigger these biased responses. Further analysis of VLMs' reasoning patterns shows that counting accuracy initially rises with thinking tokens, reaching ~40% before declining with model overthinking. Our work presents an interesting failure mode in VLMs and a human- numan-supervised automated framework for testing VLM biases. Code and data are available vimsarebiased github.............
INTRODUCTION
Large language models (LLMs) are trained on the Internet data and learn a vast amount of prior knowledge that (a) helps them downstream tasks but (b) sometimes sways their answers towards incorrect or biased choices (Vo et al., 2025; Sheng et al., 2019; Gallegos et al., 2024). Interestingly, LLMs also memorize visual knowledge from their colossal text-only corpus (Sharma al., 2024), e.g., the US national flag has 50 stars and 13 stripes or chickens have two legs (Fig. 1). Because vision language models (VLMs) are built by pre-training LLMs either exclusively for late fusion with vision encoders) (Liu et al., 2023; Bai et al., 2023) or on mix of text, image. and multimodal data in an early fusion manner (Team, 2024), they may inherit strong biases from the text corpus when answering visual questions (Lee et al., 2023).
text data (i.e.,
Prior evidence (Guan al., 2024b; Lee al., 2025) showing VLMs are biased was exclusively on artificial Y/N questions that often directly contain biased statement, e.g., "Is the mouse smaller than the cat?" (Liu et al., 2024), which is framed to contradict their counterfactual (CF) image where the cat is smaller. Therefore, it is unclear (1) how much the image contributes to VLMs' wrong answers or whether due to adversarial text prompt; and (2) how such biases impact everyday, objective visual tasks that neutral. unbiased prompts. We aim to assess how the knowledge of VLMs about popular facts (e.g., chickens have two legs) negatively impacts the accuracy of VLMs on objective vision tasks involving counting, identification (Q1 & Q3 in Fig. 2) and basic geometry (Fig. 1f). For example, we show CF image of a 3-legged chicken and ask VLMs "How many legs does this animal have?" (Fig. la).
"Equal contribution.
I Equal advising.

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Published as a conference paper at ICLR 2026
Examples of VLM failures across domains of VLMBiaS
How many legs does this animal have? Answer with a number in curly brackets, e.g., (9).
How many points are there star in the logo this car? Answer with a number in curly brackets, c.g.. [9).
How many stripes there in this flag? Answer with number in curly brackets, e.g.. (9).
How many chess pieces there this board? Answer with number curly brackets, e.g.. (9).
How many rows are there on this board? Answer with a number in curly brackets, e.g.. (9).
Are the two horizontal lines parallel? Answer in curly brackets, e-g.. [Yes) or (No).
Hi How many circles are there in cell C3? Answer with a number in curly brackets, e.g. (9).
b.
g. H
2
3
13
X
32
X
Yes
3
2
13
X
32
No
2
13
X
3
2
X
X
13
X
32
X
Yes
3
X
2
X
3
X
13
X
32
X
X
Yes
3
X
Bias
2
x
3
X
13
X
32
X
9
X
Yes
x
3
X
GT
3
4
14
/
31
10
No
2
Gemini-2.3 Pro
Sonnet-3..
3.7
20
GPT-4.1
o3
88
04
-minn
Figure 1: VLMs fail on 6 counting tasks (a-e & g) and one low-level vision task (f).
Leveraging state-of-the-as (SOTA) image editors, VLMs, and image processing libraries, we propose VLMBias, framework for automating the enumeration of biased subjects and questions and the generation of counterfactual images. Humans manually review all generated images and reject those that are deemed low-quality debatable. We test VLMs questions spanning diverse subjects in the decreasing order of popularity: (a) animals 9%, (b) logos @ (c) flags F; (d) chess pieces A; (e) game boards [ (f) optical illusions and (g) patterned grids Hi (see Sec. 3). For all subjects, the tasks are counting and object identification, except for the optical illusion questions, which were originally designed
human vision under illusion (e.g., Are the lines // parallel?).
We test five SOTA VLMs: 3 thinking models of Gemini-22 22. Pro (Google, 2025), @ o3, -immmin (OpenAI, 2025b); and non-thinking models of [ Sonnet-3 (Anthropic, 2025) GPT-4.1 (OpenAI, 2025a). Our key findings are:
37
All five VLMs recognize the VLMBias subjects from the original, unmodified image (Fig. 2a), scoring 100% accuracy on both identification and counting questions (Sec. 4.
2. VLMs consistently fail to count counterfactual elements across all 7 domains (Sec. 4.2): On ? animals, accuracy drops to 1.01% (birds) and 2.50% (mammals) when one leg is added (Sec. A.1). On G logos, VLMs achieve only 0,44% (car brands) and 17.57% (shoe brands) accuracy when signature elements are modified (Sec. A.2). Similar failures occur when counting stars & stripes in CF F flags (Sec. A.3); counting à pieces altered chessboards (Sec. A.4), and counting rows & columns of counterfactual L game boards (Sec. A.5). On optical illusions, VLMs are heavily biased to the well-known answers, performing at random chance (Sec. A.6).
3. Besides being biased toward common prior knowledge, VLMs also biased toward the dominant patterns in an image. In our novel patterned grids, VLMs often incorrectly think the cell in question also follows the pattern in the surrounding cells (Secs. A.7 and 4.2).
4. To confirm VLM failures to count (QI & Q2) are due to their visual bias, we further test VLMs on Y/N identification questions (Fig. 2; Q3) but they also similarly struggle to answer (Sec. 4.3). In another experiment where the subject name (e.g., "Adidas") is added to each CF image (e.g.. 4-striped logo), VLM counting accuracy further drops by -2 to points, confirming the bias learned from the text corpus influences its counting (Sec. A.9).
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Published as a conference paper at ICLR 2026
5. After the background pixels in CF images masked out, VLM accuracy almost doubles (+21.09), suggesting that the background contents invite VLMs to choose the biased answer (Sec. 4.4).
6. As more reasoning tokens are used, the mean accuracy of VLMs rises to an empirical ceiling of 40% (across subset of the questions). Beyond this point, thinking longer actually correlates with a steeper decline in accuracy (Sec. 4.5).
RELATED WORK
Bias in LLMs and VLMs LLMs exhibited biases across various domains, including social (Shin et al., 2024; Hu et al., 2025), cultural (Kadiyala et al., 2025; Li al., 2024; Naous et al., 2024; Abid et al., 2021; Wang et al., 2024), demographic (Zhao et al., 2023; Kumar et al., 2024), political (Bang et al., 2024; Potter et al., 2024), cognitive (Echterhoff et al., 2024; Koo et al., 2024), and biases related to specific names, numbers, or values (Zhang et al., 2024a; Koevering & Kleinberg, 2024), These biases often correlate with the over-represented associations between textual cues and specific classes or attributes (e.g., associating older people with forgetfulness) (Parrish al., 2022) in the pretraining data. Biases are not limited to textual data but extend into the visual domain. VLMs also exhibit gender biases (Hall et al., 2023; Xiao al., 2024; Hirota al., 2022; Fraser & Kiritchenko, 2024), stereotypical portrayals (Ruggeri & Nozza, 2023; Janghorbani & De Melo, 2023; Raj et al., 2024), and social biases (Howard al., 2024; Sathe al., 2024).
Unlike those works, we study VLM bias in visual question answering (VQA), specifically, in cases where the visual cues in a CF image strongly bias predictions toward the common answers (Fig. 2).
Counting with VLMS Counting is a challenging task that requires VLMs to understand the prompt, match language to objects in the image, and perform accurate object localization. Counting comprises approximately 10% of questions in many VQA benchmarks (Acharya et al., 2019). Prior work has demonstrated that VLMSs struggle with counting tasks, especially on large-count scenarios (Paiss et al., 2023; Camphell et al., 2024). For instance, Xu et al. (2025) showed VLMs achieve only 20-48% accuracy on object counting in MSCOCO (Lin et al., 2014) and VCR1.0 (Zellers al., 2019). Yin al. (2023) found that VLM performance improves with fewer objects (i.e., less than 10). BlindTest (Rahmanzadehgervvi et al., 2024) reported 58.07% accuracy on their benchmark but noted that VLMs perform counting better when objects are more spatially separated. These results suggest that accurate localization is key to solving counting tasks. Recently, OpenAI (2025c) claimed that 04 -4-mini and @ o3 can solve BlindTest with 90% accuracy when allowed to use tools (e.g., image cropping, zooming). However, these works do NOT examine counting on counterfactual images.
Table 1: Our VLMBias presents natural, objective counting and identification questions while prior benchmarks insert biased statements into the prompt. Detailed comparisons with the closest works are in Sec. C.
Benchmark
Biased prompt
Biased image
CF images
Generation method
Adversarial text injection
Top leaderboard
Primary question types
GPT-4p
PhD-ces iL et 2024)
750
DALL-E
In-prompt
Y/N
GPT-4p
VLind Mend-Bench (T.ee 2025)
x
2,576
DALLEE
n/a
Y/N
DALL-E FLUX
Sonnet-3.5 3.5
ViLP (Luo al., 2025)
v
600
En-prompt
70.0%
Identitication
GPT-4V
HallusionBench (Guan et al,, 2024a)
V
v
181
manual
n/a
31.4%
Y/N
V 1,392 semi automaaee
In Pi-image
a --mini
Counting (Q1, Q2)
VLMBias (ours)
title
20.25%
Y/N (Q3)
In this paper, we show that (1) VL.Ms rarely count familiar objects directly in counterfactual images due to bias, instead defaulting to prior knowledge rather than performing visual analysis, even when counting small quantities (c.g.. 3-legged chickens; Fig. la); and (2) VLMs underutilize their available tools (Sec. A. and pointing capabilities (Sec. A.1 due to overconfidence from their strong biases. (3) Moreover, to disentangle counting ability from bias, we further introduce bias rate, which the proportion of responses that match the expected biased answer. This enables to quantify the extent of a model's 's reliance on memorized priors rather than visual reasoning, helping partially reveal when emrrr arise from bias rather than an inability to count.
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VLMs have sufficient knowledge (bias) about the subject
Adidas
ID. What shoe logo this?
VLM
bias-relevant background
Counting. How many white stripes are there on the logo of the left shoe?
3
stripe +
prevents X from correctlyy answering visual questions
counterfactual Image
3
X
How many white stripes Q2. Count the white stripes on the logo of the left shoe.
logo of the left shoe?
VLM
4 V
modified object
Yes X
Y/N question
VLM
Q3. Is this the Adidas logo?
to confirm the bias
No
VLMBiss benchmark
Figure Given subject (e.g., Adidas logo), we first confirm that all VLMs have sufficient knowledge of the subject via an ID and counting sanity-check questions (a). Then, we test VLMs on the counterfactual image (b) and report its accuracy on the counting (Q1 & Q2) and Y/N identification task (Q3). For all tasks, we test the hypothesis that the visual bias cues in the background (c) may be so strong that they cause VLMs to ignore the anomalous details and default to biased answers.
Visual Hallucination VLMs are known to hallucinate when questioned about the content of generated images (Huang et al., 2024; Tong et al., 2024), optical illusion (Wu et al., 2024), and counter-commonsenee images (Bitton-Guetta et al., 2023; Zhou et al., 2023). Ye-Bin et al. (2024) removed commonly appearing objects from their original scenes to find that VLMs often think the removed object still there via Yes/No diagnostic questions. VLMs also struggle to count where they are provided with real image and number of options that include incorrect and adversarial options (Parcalabescu et al., 2022). In contrast, our textual prompt is natural but our image is CF.
Existing benchmarks have four key limitations (Tab. 1): (1) using biased wordings in the prompt or answer choices to set up VLMs to hallucinate; (2) mostly relying on Yes/No or identification questions instead of objective counting tasks; (3) using diverse VQA-like questions created by LLMs or human annotators that are not systematically sampled to be in specific topics for comparison and deeper analysis; (4) not exploring in-image adversarial text injection, which suggests the bias originated from the text corpus.
We address these limitations by: (I) using neutral prompts with biased CF images; (2) employing objective counting questions that are challenging for VLMs (Rahmanzadehgery) et al., 2024); (3) VLMBias allows us to compare VLM counting accuracy and bias rates across subjects of varying popularity; and (4) systematically testing in t--mmage text injection effects.
THE VLMBIAS BENCHMARK
We modify the signature elements of every well-known subject (e.g., changing the Adidas logo from 3-striped to 4-striped; Fig. 2c) and ask VLMMs to count. We assess how VLMMs would be biased towards the common knowledge and overlook the abnormality injected into the CF image.
Counting is a common, objective task that makes up ~00% of questions in many VQA benchmarks (Acharya et al., 2019), Exact counting suitable to evaluate the visual analysis capabilities of VLMs as it requires (a) localizing relevant objects and (b) keeping track of the running total instead of relying on shortcuts like some VLMs do (c.g.. "User is asking me to count legs. And am seeing a chicken, so there must be two legs "). Counting is specific, real-world-yyp of question that allows us to compare VLM biases across different topics.
Taxonomy To test VLM biases, choose 7 unique, diverse topics of decreasing popularity. i.e., from common animals, logos, flags to optical illusions and novel visual pattern (H:) that we create from scratch that did not exist before.
(I) reatosteellitic images are used in tasks: animals and logos. These images cover common subjects including natural (+5) and man-made ones (.)) They created and modified by SOTA
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text-to-image generators (Gemini-2.0 Flash, and GPT-4o). To mitigate potential bias from using the same model families for image generation and evaluation, we evaluate across different model families and consistently observe the same failure phenomenon (Sec. A.17).
(2) Abstract images are used in 5 tasks: flags, 2 chess pieces, # game boards, optical illusions, and H patterned grids. These images created using code, not text-to-image models. We divide this category into three sub-categories: (a) well-known objects (P. a, IE); (b) optical illusions (+), which are less common than flags; and (c) novel patterned grids (it).
Controls Each test image is re-scaled to three resolutions of D E {384, 768, 1152] by multiplying the original image to the scaling factor max(W,H) to preserve the original aspect ratio. However, our results show that image resolution has a marginal impact to VLM accuracy on our benchmark (Sec. A.1 To minimize the language bias in the prompt, we use two different prompts per test image, written in neutral, descriptive terms (e.g, stylized curves for Nike swooshes). In each task, we ask questions (Fig. 2b). For instance, we ask the below questions for the leg counting task (h)):
D
Q1: How many legs does this animal have? Answer with a number in curly brackets, e.g., (9).
Q2: Count the legs of this animal. Answer with a number curly brackets, e.g., (9).
Q3: Is this animal with 4 legs? Answer in curly brackets, e.g., [Yes) or (No).
Bias Definition We define "bias rate" as the frequency that VLM answers match the pre-defined responses (i.e., "3" in response to Q1 & Q2; Fig. 2) that correspond to common knowledge (i.e., Adidas logo has "3" white stripes in). These biased responses are incorrect w.r.t. the counterfactual image. The mean bias rates per task for all 5 VLMs are in Fig. 4.
3.1 TASK 1: COUNTING ANIMAL LEGS WHEN AN EXTRA LEG IS ADDED P
Pretrained on the Internet data, VLMs must have colossal prior knowledge of the number of animal legs from both textual and image data. Following this hypothesis, we generate images of well-known animals but with one extra leg (e.g.. 3-legged birds or 5-legged dogs) and ask VLMs to count legs.
Images We design a 3-step data generation process. Step 1:
We ask 04 ni to generate a list of 100 well-known animals. Step 2: For each animal, we ask Gemini-24 0. Flash to generate side-view images. Step 3: We instruct Gemini-2 0 Plash to add one extra leg to each image in Step 2. We manually filter these images to retain one high-quality image per category (where the animal shows clearly 3 or 5 legs). The final set consists of 91 different animals: 23 three-legged birds and 68 five-legged mammals. In total, we generate 91 animals x 3 resolutions 273 images. More details in Sec. E.
3.2 TASKS 2-5: COUNTING VISUAL ELEMENTS IN MODIFIED FAMILIAR PATTERNS: T LOGOS FLAGS, a CHESS PIECES, AND I GAME BOARDS
We expand four other domains: Logos of famous car and shoe brands, national flags, chess pieces, and game boards. For example, on logos, our hypothesis is that VLMs contain strong bias between a brand's logo and its signature visual elements (e.g., an Adidas logo must have stripes; Fig. 2). For each domain, we create CF images by making systematic, minimal modifications to familiar visual elements, using the same methodology as Task ( 1e) or Python scripts (2, H
Images For logos (Sec. F), we modify graphical features (points, prongs, circles, stripes, curves) of three car brands and two shoe brands using Gemi n 22.2.00. O Fl Flash and GPT-40, placing them in realistic contexts (vehicles and athletic footwear) for a total of 207 images. For flags (Sec. G), we systematically add or remove one element (stars or stripes) from 20 flags, creating 120 flag images. For chess pieces (Sec. H), we generate 144 chessboard images by removing replacing exactly one piece from the starting board of western chess and xiangqi. For game boards (Sec. I). we add or remove exactly one row or one column from the board across four game types (chess, xiangqi, Sudoku, Go), producing 84 CF images in total.
3.3 TASK 6: TESTING VISION ON ORIGINAL AND MODIFIED OPTICAL ILLUSIONS 4
Recent VLMs show improved performance on optical illusion tasks, with 04-min achieving 71.49% accuracy Illusion VQA (Shahgir et al., 2024). However, these VLMs might have mem-
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orized the common optical illusions rather than perceiving visual information. To investigate this hypothesis, we create two scenarios: (1) original optical illusions (c.g., the Ebbinghaus illusion where two identical central circles appear to be different sizes because of the surrounding context circles) and (2) slightly modified versions of the original where the final answer should reverse (e.g.. where Ebbinghaus illusion pattern but where central circles actually different in size; Fig. 8).
VLMSs cannot see extra leg in the puma and extra stripe in the Adidas logo
(a) (b) Ql: How many legs does this animal have? Answer with number in carly brackets, e.g.. (9).
[c) Q3: Is this an animal with legs? Answer curly brackets, e.g., (Yos) or (No).
(d) (0)
Q1: How many visible white stripes are there in the logo of the left shoe? Answer with number in curly bracket, c.g. (9)
Q3: Are the logos these shoes Adidas logus? Answer in curly brackets, egg. [Yes) [No).
(a) original Puma (Q1)
(b) CF
(c) CF
(d) original
(e) CF
(I) CF
Puma (Q1)
Puma (Q3)
Adidas (Q1)
Adidas (Q1)
Adidas (Q3)
3
Yes
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Yes
X
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3
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Yes
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Yes
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Yes
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Yes
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Yes
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GT
4
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5
No
3
/
4
/
No
Gemini-24. 2.5 Pro
Sonnet 3.7 3.7
W GPT-4.1
o3
88
04 44-mmnn
Figure 3: VLMs fail to detect subtle changes in counterfactuals (CF) and default to biased answers.
Images We six optical illusions (Makowski al., 2021): Müller-Lyer (Müller-Lyer, 1889; Howe Purves, 2005), Zöllner (Zöllner, 1862; Wallace, 1975), Ebbinghaus (Titchener, 1905; Aglioti et al., 1995), Vertical-Horizonta (Fick, 1851; Hamburger Hansen, 2010), Pogendorff (Poggendorff, 1863; Green & Hoyle, 1963), and Ponzo (Ponzo, 1910; Yildiz et al., 2022). For five of these illusions, we generate 24 images per type (12 original and 12 modified versions with varying illusion strength). For the Heriizal-ooiioontal illusion which uses a fixed T-shape, we create 12 images (6 original and modified). This approach yielded (24 + 12)
396 images in total. More details in Sec.
3.4 COUNTING THE CIRCLES OR LINES IN AN ANOMALY CELL AMONG A PATTERNED GRID Hi
Previous tasks leverage common knowledge, (e.g., chickens have two legs) to set the CF image (Fig. lb). Here, we test how VLMs may be biased towards the pattern inside the image itself, not towards the external knowledge. To do that, we construct a grid where all cells follow certain pattern except for an anomaly cell, and default to the overall pattern of the surrounding cells.
VLMs would recognize that cell's unique content or
G grids (G (6...., 12}) in two styles: dice grids with circles (Fig. 1g. Fig. 33a-b) and tally grids with tally marks (Fig. 33c-d). All grids follow symmetric pattern where shape count increases from at edges to [(G 1)/2] at center, based on distance from nearest edge. We introduce one anomaly per grid by modifying single non-edge cell: (1) in tally grids, adding or removing one tally mark; (2) in dice grids, removing a circle or replacing it with another shape (triangle, square, star). For each grid dimension, we select two different anomaly locations, creating 14 base scenarios (7 dimensions x 2 locations). This yields grid types x modification types x 14 scenarios x 3 resolutions 168 images. More details in Sec. K.
Images We generate

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Published as a conference paper at ICLR 2026
4 RESULTS
4.1 SANITY CHECK: VLMS do RECOGNIZE FAMILIAR VISUAL SUBJECTS
Here, we first verify that the subjects in our VLMBi as are, in fact, known VLMs. If VLMS fail to recognize the subjects in these unaltered images, there is no basis to attribute their failures on CF images their language bias.
Experiments We evaluate five VLMs
Correct
Bla-Akgned
Cdlir Drora
Gemini-2. Pro, [ Sonnet-3. 7, GPT-4.1, t o3, and o4-mini; Tab. 27) on of 66 unmodified images span- ning our 6 out of 7 VLMBi as tasks (animals, logos, flags, chess pieces, game boards). We ex- clude pattern grids from the sanity check since the patterns are created from scratch and do not exist the Internet. For five counting tasks (from ' to iff,, we ask two questions (identifi- cation and counting: Fig. 2a) per image for total of 132 questions. Since the optical illusion is not counting task, instead ask VLMS to identify; (I) the name of the illusion; and (2) the question & correct answers associated with each illusion (see the sanity-check prompts in Sec. L.3).
3433N
40%
75 735
22.445
800
#
Mean
Figure 4: On the counterfactual images of VLMBias as, five VLMSs mostly output answers that match the biased choices that we predefine for each question, 75.70% of the time.
Table 2: All VLMs achieve 100% on identification and counting tasks with unmodified images, show-
ing that they fully recognize the original version. But VLMs struggle with counting on counterfactual images-thh the mean accuracy of state-of-the-art VLMs across our 7 tasks is 17.05%. @ o4-mini achieves the highest accuracy (20.25% %) which however is still low. VLMs with "thinking" capabili- ties
+) also perform poorly like non oo-thinningg models (f,,
Model
Accuracy counting questions (QI &Q2) on counterfactual images
Unmodified
a.
b.
d. a
H
f. GG
Task mean
Task mean
Gemini-2.5 Pro
0.00
1.96
10.42
26.74
2.38
49.81
20.83
16.02
100.00
+
Sonnet-3..
0.00
2.72
13.75
9.03
1.79
54.29
34.52
16.59
100.00
GPT 4.1
9,52
9.07
2.50
8.68
0.00
48.61
18.75
13.88
100.00
o3
0.92
7.60
5.00
42.71
2.38
50.38
20.54
18.50
100.00
104-mi
0.18
9.31
14.58
44.10
4.76
51.26
17.56
20.25
100.00
Mean
2.12
6.13
9.25
26.25
2.26
50.87
22.44
17.05
100.00
Results All five VLMs score 100% accuracy on all the questions (see Tab. 2). That is, for counting tasks, VLMs correctly recognize the subjects and the expected counts (e.g., puma has four legs and the Adidas logo has three stripes; Fig. 3a&d). For all 6 illusion types. VLMs able to identify the name (e.g.. "Ebbinghaus illusion" in Fig. 8), the associated question ("Are the two red circles equal in size?") and its correct answer ("Yes"". The results here set the ground for the claims in subsequent sections that VLMs' low accuracy on counterfactual images (17.05% accuracy; see Tab. 2) stems from their prior knowledge of the subjects (see Sec. A.S).
4.2 VLMS STRUGGLE COUNT THE SIGNATURE ELEMENTS COUNTERFACTUSL IMAGES
Experiments We use the same experiment setup as in Sec. 4.1 but test VLMs on CF images. Specifically, we evaluate five VLMs on the ' animal, @ logos of famous brands, ~ national flags, chess pieces, H game boards. We also test VLMs on counting the shapes or tally marks inside an anomaly cell in Hi patterned grids where the total number of shapes or marks does not follow the patterns in the surrounding cells (Fig. Ig). Furthermore, we test VLMs 6 classic ts optical illusions, i.e., Müller-Lyer, Zöllner, Ebbinghaus, and 31). Each illusion is presented in two versions: (a) its original form and (b) counterfactual, modified version where the groundtruth answer is reversed (Fig. 8). For both versions per illusion, ask VLMs the same Y/N question (see Sec. J).
Horiiooall Pogendorff, and Ponzo (Figs. 30
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Results VLMs generally fail to detect modifications across all seven domains, with performance varying depending on the tasks:
T VLMs exhibit poor performance (2.12% accuracy) when counting legs of counterfactual 3-legged
and 5-legged animals (Tab. 2a, Fig. 18). VLMs show slightly lower performance at counting bird legs compared to mammal legs (1.01% % vs. 2.50%; Tab. 6a), likely because bird legs are thinner and thus more challenging to detect. More results are in Sec. A.1.
@ For logos, accuracy is significantly worse on logos than shoe logos (0.44% 17.57%; Tab. 6b). This might be because a logo on a car often appears much smaller than a logo on a shoe photo (Fig. lb& Fig. 21 vs. Fig. 2b & Fig. 22). More results are in Sec. A.2.
For flags, VLMs perform better on counting stars (11.79% Tab. 6c) than on counting stripes (4.52%; Tab. 6c). Counting stripes may be harder because stripe is often placed right next other stripes in a flag while stars are spatially separate symbols (Fig. 6b vs. d, and Fig. 24). More results are in Sec. A.3.
On counting chess pieces, thinking VLMs (Gemini-2.7 Pro, o3, and 04 (4-minn) significantly outperform non-thinking models (>26% vs. <10%: Tab. 6d), suggesting that explicit reasoning capabilities help detect anomalies (Fig. 26). More results are in Sec. A.4.
il All VLMs perform extremely poorly (2.26% mean accuracy; Tab. 7) on counting rows and columns of counterfactual board-gane game image (Fig. 6c-e), e), as low as 0% accuracy on Sudoku and Go boards (Fig. 28a-b). More results are in Sec. A.5.
On optical illusions, all 5 VLMs achieve performance close to random chance (mean accuracy of 50.87%; Tab. 6e) across original and CF versions. 78.02% of the time, VLMM give responses that align with well-known prior knowledge but are incorrect for our CF images (23.744 74% accuracy). More results are in Sec. A.6.
#E For patterned grids, VLMs achieve poor performance 22.44% accuracy. 43,45% of count predictions match biased answers from surrounding cells (Fig. 4Ht). When VLMs make incorrect counting predictions, over half (56.02%) follow the global grid pattern rather than identifying the target anomaly (Fig. 33). More results are in Sec. A.7
Overall, our findings across seven domains suggest that VLMs rely heavily on prior knowledge to answer questions rather than visual information. This conclusion is reinforced by the stability of results: repeating each experiment 5 times yields nearly identical outcomes, with mean accuracy varying by less than one percentage point (Sec. AII This is further supported by our linear-probing results that show that on leg counting, the vision encoders of VLMs already sufficiently encode visual information, achieving 95.26% accuracy ( (Sec. A.8). However, the visual information stream may be impaired by the bias the language model.
We also observe similarly poor and biased behaviors in the most recently released models of @ GPT-5 (OpenAI, 2025a) and Grok (xAI, 2025) (Sec. A.13). Furthermore, VLMs are severely biased laaasedaskking them to double check their answers, to rely exclusively on image details to make decisions only marginally improves accuracy (Sec. A.) Interestingly, providing in-context few-shot demonstrations of counterfactuals (e.g., of pumas having 5 legs) does not help (Sec. 12) and even leads to some thinking models replying with doubts about the validity of the demonstrations.
4.3 Y/N QUESTIONS CONFIRM VLMS ARE NOT ABLE TO DISTINGUISII THE COUNTERFACTUAL FROM ORIGINAL IMAGES
Prior sections have shown that VLMs struggle to count the key elements in well-known subjects at poor accuracy of 17.05% (Tab. 2). And 755% of the time, their answers match the biased choices. Here, aim to confirm that VLMs
biased that they are unable to tell the difference between the original version and the counterfactual by a direct Yes/No identification question of Q3: "Is this an animal with 4 legs?" when the counterfactual (e.g., 5-legged puma Fig. 3c) is shown.
Experiments We ask VLMs the Q3 question given our sets of original and CF images. The correct answer is "Yes" for original cases and "No" for all CF cases (Fig. 3c).
Results All VLMs achieve 100% accuracy on the original images, but collapse to a mean of 25.1 11% on the counterfactual versions (Tab. 3). That is, VLMs often answer "Yes", overlooking the fact that
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the well-known subject has been modified (Fig. 3c&f). In sum, the results in this section provide supporting evidence that VLMs are too biased to recognize that the subject has changed in counterfactual images, leading to poor counting accuracy Sec. 4.2.
Table Mean accuracy (%) of VLMs on ques-
Table 4: Counting performance improves noticeably
tion Q3 (e.g... 'Is this animal with 4 legs?")
(+21.09 in accuracy and -40.58 in bias rate) after the background is removed from the image. The background contributes significantly to VLM biased behaviors.
over all 7 subjects when the image is original
(4 legs) or counterfactual (5 legs). VLMs often
answer Yes" even on counterfactuals.
Model
Original
Counterfactual (A)
Accurscy
1
Semini-
100.00
20.63 (-79.37)
Before
Alter (A)
Beforv
Afler (A)
M BosneL-33 -3.7
100.00
23.08 (-76.92)
Genini-2 -2 Pxo
16.02
40.73 (x24.71 71)
76.79
(93666633.80) (:36.00)
+ GPT-A,"
100.00
26.10 (-73.90)
Sonnet-3.
16.59
42:54 (+35.95)
76.63
39.74 (:36 89)
63
100.00
26.15 (-73.85)
GPM-4.4
(+25.777
G cd-mini
100.00
29.61 (-70.39)
(416.75)
(-40-17)
o4-mmini
20.25
32.54 (+12.29)
73.06
28.47 (-45.19)
Mean
100.00
25.11 (-74.89)
Meun
17.05
(8.14())11.9)
75.70
35.12
4.4 BACKGROUND CONTRIBUTES SIGNIFICANTLY TO VLM COUNTING FAILURES
What in the CF images made VLMs count so poorly? We hypothesize that the background strongly invites VLMs to default to the biased answer they recognize the familiar subject. We test whether removing the background might help VLMs count more accurately.
Task
Animals
Logos
Chess Pieces
Reardgumes
lllssoo
Grids
Approach
Lang BAM cropping
LangSAK
LLM generated SVG code
Script
Script
Script
Script
Before
After
Table 5: Examples of how backgrounds are removed in each task.
Experiments For each task, we first remove the background from the images (see Tab. 5) and then ask all 5 VLMs the same counting questions (Q1 & Q2). For photo-realistic subjects (i.e., 7,, @) we segment the target object from its background using La LangSAM (Medeiros, 2025). For abstract patterns, we L.e--rgeeeteed SVG code (F) and Python scripts (2, [. #)) to remove the background or make them substantially different from the original (e.g., EU flag in Tab. 5c).
Results Averaged over 5 VLMs, the counting performance increases substantially when the background is removed, i.e., +21.0 1.009 in accuracy and -40.58 in bias rate; Tab. 4). These large gains show that the background sets the VLM up be biased. Furthermore, shows that if VLMs able to crop the image accurately, their counting performance would significantly improve.
4.5 THINKING LONGER REDUCES BIAS IN VLMs, BUT OVERTHINKING HARMS ACCURACY
Thinking VLMS (i.e., H Grok 4, o3, x c4-mini) are trained to use extended reasoning tokens to improve accuracy on harder tasks. However, yet our previous results showed that they achieve only marginal improvements over non-thine won-thinkinn VLMs (Tab. 2). Here, we investigate whether the relationship between reasoning length and accuracy on counting and how thinking with tools (e.g.. cropping, zooming; Sec. A.15) could help.
Experiments We use data from Secs. A.13, A.15 and 4.2 to examine the relationship between reasoning tokens and the accuracy of thinking VLMs. For tool-using VLMs (i.e. 2 4 o4 -0minn with tools; see Sec. A.1 our analysis shifts to reasoning time versus accuracy, as this metric better represents the model's effort during Python code execution.

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1003
HD Grok
0.d ci-mini w/ tools
MMem 64.2%%
rán Plear: TLOt5)
Dearre (leer
02
44443
Ressrding leugth (rilen))
Rosenning jangh (ob606)
Reotcring
Figure 5:
For thinking VLMs (0,0,8) 89 ) accuracy improves with reasoning tokens up point (red
points), after which overthinking harms performance. In contrast, for thinking VLMs with tools (o4-mini w/ tools), extended reasoning time leads continuous accuracy improvement, while all VLMs show consistent reduction in bias rate. Notably, o3 doesn't 't use reasoning 36.1% of the time (#reasoning tokens- = 0; see first bubble of o3), likely due to overconfidence its prior knowledge.
Results Consistent with (Ghosal al., 2025; Gema et al., 2025), we find that thinking longer helps VLMs (0,0 0 !) improve accuracy up to point (red circles in Fig. 5), after which it hurts accuracy.
However, for thinking VLMs with tools (here, o4-mini with tools), using tools for a longer time (in seconds) monotonically improves accuracy overtime (Fig. 5; 14-minn w/ tools). However, separate challenge for tool-use VLMs such as 04-mint ni that it is overconfident and uses tools only for 29.66 99.66% of the VLMBias questions (more results in Sec. A.15).
Thinking VLMS without tools demonstrate reduction in bias rate as reasoning tokens or time increase (Fig. 5). Perhaps overthinking makes VLMs consider multiple alternatives, selecting the common bias option less frequently. Besides, is notable that o3 avoids reasoning entirely (#reasoning tokens O; first bubble in Fig. 50), which is likely due to its overconfidence in prior knowledge.
DISCUSSION AND CONCLUSION
Limitations VLMs with image generation capabilities (GPT-4o, Gemin -2.0 Fl Flash) also carry their own biases, making it non-trivial to control generated images as expected. For example, when prompted to generate an Audi car but with 5-circled logo, Semini-22 0 Flash often generates the car with the original 4-circled Audi logo instead.
VLMBias reveals that SOTA VLMs exhibit strong visual bias, achieving only 17.05% mean accuracy on counterfactual images while defaulting to prior knowledge 75.70% of the time. This behavior consistent across all model types: thinking models (+,
perform marginally better than non-thinking ones (19, x Interestingly, o4-mini with tools only increase the counting accuracy slightly by +1.9 (23, 18% + 25.08%) because the model is overconfident and often answers right away, using tools & code only 29.66% of the time (Sec. A.15). In contrast, time-limited humans can score a 445% to ~96% accuracy on our benchmark (Sec. A.19), substantially better than VLMs including those trained to count (e.g., Moondream-22B).
Experiments on Pixtral and @wen2. 5-VL show interesting traces of the inverse scaling phc- nomenon (McKenzie al., 2023): Larger VLMs tend to perform worse and exhibit 1.266 higher bias rates on VLMBia as than smaller VLMs (Sec. A.14).
VLMs explicitly trained to count (such as Mo 1oomm222 and Moondream-223 can score mean accuracy of 36.02%, substantially better than 17.05% of SOTA VLMs and their bias rates are 2. x lower as well (Secs. 1. and 16).
ETHICS STATEMENT
We strictly adhere to the ICLR Code of Ethics and identify no significant ethical concerns in this work. We ran a small anonymous online survey (consent obtained, no PII collected, minimal risk), which falls under the scope of benign behavioral interventions eligible for IRB exemption. All other experiments
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synteetic/rograammaiicaall generated images and publicly available models. Synthetic logos and flags are included solely for non-commercial research purposes, with no endorsement implied, and subject to removal upon request.
THE USE OF LARGE LANGUAGE MODELS
We used large language models in constructing our dataset in four ways: (i) to generate candidate lists of subjects (c.g., animals), (ii) to generate part the images in our dataset, (iii) evaluate their performance on the tasks, and (iv) to observe their failures, which informed the design and ideation of the benchmark. In addition, we used LLM-based tools for minor text editing (e.g., grammar) and for coding assistance. The authors take full responsibility for all content, and no LLM qualifies for authorship.
REPRODUCIRILITY STATEMENT
We follow the standard bascline settings used in established evaluation benchmarks or from each model's ' default test protocol. Full implementation details appear in Secs. D to K. The code and data public at mansaarebisee..ithubb..
ACKNOWLEDGMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (CXTT))RS-2025005177
and Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IIP)) grant funded by the Korea government (MSIT)(ITP-2025-RSS 11201489).
We also thank Khang Gia Le (MBZUAD) Linh Nguyen (Bucknell University), Pooyan Rahmanzade- hgervi, and Logan Bolton (Auburn University) for feedback, support, and discussion of carlier results. AV was supported by the Hyundai Motor Chung Mong-Koo Global Scholarship. AN was supported by the NSF Grant No. 1850117 & 2145767, and donations from NaphCare Foundation & Adobe Research.
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APPENDIX FOR: VISION LANGUAGE MODELS ARE BIASED
CONTENTS
Introduction
2 Related work
3
The VLMBias Benchmark
3.1 Task I: Counting animal legs when extra leg is added Tx
3.2
Tasks 2-5:
Counting visual elements in modified familiar patterns: logos, flags, 2 chess pieces, and [म game boards
3.3
Task Testing vision on original and modified optical illusions
3.4
Counting the circles or lines in anomaly cell among patterned grid :
4
Results
4.1
Sanity check: VLMS do recognize familiar visual subjects
4.2
VLMs struggle to count the signature elements in counterfactual images
4.3
Y/N questions confirm VLMs not able to distinguish the counterfactual from original images
4.4
Background contributes significantly to VLM counting failures
4.5
Thinking longer reduces bias in VLMs, but overthinking harms accuracy
Discussion and Conclusion
10
Appendix
20
A Additional findings
23
A.1
VLMs fail to recognize that extra leg is added common animals ?x
23
A.2 VLMs struggle to detect logo modifications, often relying on context rather than visual detail 1
23
A.3
VLMs fail to count visual elements in modified flags
23
A.4
Thinking models better detect chess piece changes in modified chess starting positions 2
24
A.5 VLMs cannot count rows and columns in simple game boards i
24
A.6 VLMs are biased towards the known illusions and fail recognize the changes in the counterfactual, modified versions
24
A.7 VLMS are biased towards the global pattern in grid Hi
25
A.8 Linear probing: The vision encoders of VLMs actually extract sufficient leg count information from animal images 9x
25
A.9 VLMs are even more biased when the subject name is inserted into the image
27
A.10 Helpful prompts do not ameliorate the bias issues in VLMs
27
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A.11 Re-running experiments multiple times yields consistent results...
28
A.12 Providing in-context examples of animals with abnormal legs fails for 04 04-mini as it sometimes distrusts the provided labels
29
A.13 Thinking VLMs show limited improved accuracy
29
A.14 Larger open-source VLMs are more biased
30
A.15 04-min uses tools to analyze images only ~30% of the time and mostly outputs directly biased answers
31
A.16 Small VLMs trained explicitly on counting significantly outperform proprictary SOTA VLMs
32
A.17 Same failures across model families rule out image generation bias
33
A.18 Image resolution has minimal impact on VLM performance across VLMB as tasks
34
A.19 Humans @ can count animal legs almost perfectly after 2 seconds of analyzing the image
34
A.20 Locate-then-count prompting does not significantly improve counting accuracy
35
A.21 Adding subject name to text prompts further decreases VLM accuracy
36
A.22 VLMs fail to detect modifications even with side-by-side comparison
37
A.23 Even when attending to correct regions, VLMs still fail to generate correct answers
37
Human study details
39
Detailed comparison with existing VLM bias benchmarks
C.I Source of bias
41
C,2 Benchmark scale
C,3 Image generation method
41
D Models and access details
42
E Task 1: Counting legs with added limb K
43
E.1
Task design
43
E.2 Implementation and image generation
43
E.3 Qualitative results
44
E.4
List of animals
44
F
Task 2: Counting elements in modified brand logos T
46
E.
Task design
46
F2
Implementation and prompts
................................
47
F3
Qualitative results
48
Task 3: Counting stripes/stars modified national flags
51
G.1
Task design
51
G.2
Implementation and image generation
G.3
Qualitative results
52
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H
Task 4: Counting chess pieces modified starting position !
54
H.1
Task design
54
H.2
Implementation and prompts
54
H.3
Qualitative results
55
I
Task 5: Counting rows and columns of game boards ir
57
L1
Task design
................................
57
1.2
Implementation and prompts
................................
57
1.3
Qualitative results
59
Task 6:
Visual testing with both original and modified optical illusion
60
J.1
Task design
60
J.2
Implementation and prompts
60
J.3
Qualitative results
62
K
Task 7: Counting circles or lines an anomaly cell within patterned grid ::
64
K.1
Task design
64
K.2
Implementation and prompts
65
K.3
Qualitative results
66
Details of prompts
67
L.1
Examples of Q1, Q2 and Q3
67
L,2 Prompts used for image generation and image editing
70
L.3 Prompts for sanity check
72
M Additional qualitative results
73
M.1
Qualitative results the use of helpful prompts prompts.........
73
M.2
Qualitative results on the use of seate-thee-coum prompts ................................
75
M.3
Qualitative results pointing VLMs
77
M.4
Qualitative results on few-shot prompting
M.5 Qualitative results on o4-mi 4-minni chat interface with tools
85
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A ADDITIONAL FINDINGS
A.1 VLMS FAIL TO RECOGNIZE THAT AN EXTRA LEG IS ADDED COMMON ANIMALS '
Experiments We use the same experiment setup in Sec. 4.1 but test VLMs on CF images. Specifically, we evaluate five VLMs on the v animal images where an extra leg is added to (a) a bird (three legs instead of two) and mammal (five legs instead of four). We ask each VLM with default settings to count legs (QI and Q2; Fig. 2b).
Table VLMs perform poorly across 6 (out of 7) VLMBias tasks, spanning photo-realistic images (hh animals and @ logos) and abstract images ( flag, 2 chess pieces, optical illusions, and ti patterned grids).
9% Animal
h. C Logo
Flag
Model
Birds
Mammals
Mean
Shoes
Cars
Mean
Stars
Stripes
Mcan
Gemini-2.4 Pro
0.00
0.00
0.00
5.80
0.00
1.96
11.54
8,33
10.42
Sonnes-3.7
0.00
0.00
0.00
8.15
0.00
2.72
20.51
1.19
13.75
GPT-4.
5.07
11.03
9.52
25,36
1.11
9.07
3,21
1.19
2.50
o3
0.00
1.23
0.92
21.01
1.11
7.60
5.13
4.76
5.00
0.00
0.25
0.18
27.54
0.00
9.31
18.59
7.14
14.58
Mean
1.01
2.50
2.12
17.57
0.44
6.13
11.79
4.52
9.25
Chess/Xiangqi Pieces
e.
Optical Illusions
# Patterned Grid
Model
Chess
Xiangqi
Mean
Original
Modifidd
Mean
Remove
Rep/Add
Mean
Gemini-2.5 Pro
17.36
36.11
26.74
73.16
26.52
13.10
28.57
20.83
+ Sonnez=3.7
7.64
10.42
9.03
42.68
65.91
54.29
35.71
33.33
34.52
2 GPT-4.1
11.81
5.56
8.68
92.17
5.05
48.61
10.12
27.38
18.75
o3
56,94
28.47
42.71
91.67
9.09
$0.38
14,88
26.19
20.54
04-minn
55.56
32.64
44.10
90.40
12.12
51.26
12.50
22.62
17.56
Mcan
29.86
22.64
26.25
78.02
23.74
50.87
17.26
27.62
22.44
Results On average, VLMMs perform poorly (2.12% accuracy) at counting legs of 3-legged and
5-legged counterfactual animals (Tab. 27x, Fig. 18). Furthermore, 94.14% of the wrong answers match the original, well-known leg counts (Fig. 4, Fig. la, and Tab. 20), demonstrating that VLMs rely mostly memorized prior knowledge answer rather than inspecting the legs in the image (see Fig. 3c, and Sec. A.8).
VLMs slightly worse at counting the legs of birds than counting the legs of mammals (1.01% vs. 2.50%: Tab, 69m). Bird legs (Fig. la) are typically thinner, which may make it harder to detect than mammals' legs (Fig. 3b). On birds, except for X GPT-4. all VLMS score 0% accuracy (Tab. 63*).
A.2 VLMS STRUGGLE TO DETECT LOGO MODIHICATIONS, OFTEN RELYING ON CONTEXT RATHER THAN VISUAL DETAIL T
Experiments We replicate the experiment settings from Sec. A.1 on our e logo task, evaluating five VLMs on modified shoe and car logo images.
Results VLM performance on car logos (0.44%; Tab. 60)) is significantly worse than on shoe logos (17.57%; Tab. (0), as the emblem is small relative to the vehicle (see Fig. lb). In contrast, shoe logos occupy more image area (see Fig. 3e) and involve only a few simple curves or stripes (i.e., one extra curve for Nike, one added stripe for Adidas). These results highlight two key limitations: VLMs fail to attend to small, context-embedded visual changes and instead rely on memorization, without visually verifying the @ logo itself (e.g.. by zooming in (Taesiri et al., 2023)).
A.3 VLMS FAIL TO COUNT VISUAL ELEMENTS IN MODIFIED FLAGS
Experiments We follow the procedure from Sec. A.I on our F flag tasks. Five VLMs are prompted to count either the number of stars or the number of stripes in original and modified versions of
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national flags. Modifications consist of adding removing single star stripe, and each model uses its default settings.
Results VLMs achieve higher mean accuracy star modifications (11.79%; Tab. 6th) than stripe modifications (4.52%; Tab. 6*)) This pattern indicates that models are somewhat more attuned to discrete symbol changes (missing or extra stars; Fig. 6d) than to subtle structural alterations (extra or missing stripes; see Fig. 6b), yet overall sensitivity to flag modifications is extremely limited (9.25%; Tab. 6.*)
A.4
THINKING MODELS BETTER DETECT CHESS PIECE CHANGES IN MODIFIED CHESS STARTING POSITIONS
Experiments We evaluate five VLMMs on chess-piece counting task using standard starting positions for both Western chess and xiangqi. For each board type, we generate images in which exactly one piece is either removed or replaced by another piece of the same color. All models use their default settings and are prompted to report the total number of pieces number of a certain piece (e.g., Knights) on the board.
Results VLMs perform significantly better on Western chess (see Fig. 12) than xiangqi (see Fig. 6a) in terms of mean accuracy (29.86 vs. 22.64%; Tab. 62). Thinking models (+ Gemini-2.1 Pro, o3, and + o4-mini) all exceed 26% accuracy, whereas non-thinking models @ GPT-4. 1 and Sonnet-3 3.. 7) remain below 10% (Tab. 62). This suggests that on well-structured abstract images, models with explicit reasoning capabilities better able detect anomalies.
A.5 VLMS CANNOT COUNT ROWS AND COLUMNS IN SIMPLE GAME BOARDS +
Experiments Following our previous tasks, we evaluate five VLMs on counting tasks four M grid-based game boards: chess (8x8), Go (19x19), Sudoku (9x9), and xiangqi (10x9). For chess (see Fig. 6e) and Sudoku (see Fig. 6c), models are asked to report the number of rows and columns. For Go and xiangqi (see Fig. 3f), they report the counts of horizontal and vertical lines.
Table 7: All VLMs' performance extremely low (2.26%) across iff game boards, confirming that current VLMs largely unable perform simple counting operations in structured visual settings
Model
Chess
Go
Sudoku
Xiangqi
Mean
+ Gemini-2.4
Pro
2.08
0.00
0.00
6.25
2.38
C Sonnet-3.7
0.00
0.00
0.00
6.25
1.79
GPT-4.1
0.00
0.00
0.00
0.00
0.00
o3
0.00
8.33
2.38
o4-mini
16.67
0.00
0.00
0.00
4.76
Mean
3.75
0.00
0.00
4.17
2.26
Results All VLMs perform extremely poorly H. (2.26% mean accuracy; Tab. 7). The models
failed to answer any counting questions correctly on Sudoku (see Fig. 6c) and Go (0%; Tab. 7). These findings confirm that current VLMs are unable execute basic visual counting tasks in structured settings and instead default to overconfident but incorrect guesses.
A.6 VLMS ARE BIASED TOWARDS THE KNOWN ILLUSIONS AND FAIL TO RECOGNIZE THE CHANGES THE COUNTERFACTUAN MODIFIED VERSIONS 64
Experiment We test five VLMs 6 classic optical illusions, i.c., Müller-Lyer, Zöllner, Ebbinghaus, Pertical-Horizonie Pogendorff, and Ponzo (Figs. 30 and 31). Each illusion is presented in two versions: (a) its original form and (b) counterfactual, modified version where the groundtruth answer reversed (Fig. 8). For both versions per illusion, we ask VLMs the same Y/N question (see Sec. J).
original and CF versions, all 5 VLMs perform around the random chance (mean accuracy of 50.87%; Tab. 644). 78.02% of the time, VLMs provide answers that are well-known (corresponding to the prior knowledge) but false given CF images (23.74% accuracy).
Results On average,
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4 out of 5 VLMs perform well the original versions of the illusions but poorly the CF versions, exhibiting strong bias to the well-known answers. Notably, 19 Sonnet-3. performs slightly above the random chance (54.29% accuracy). However, it behaves differently from 4 other VLMs, performing much better on the CF versions than on the original illusions (65.91% % vs. 42.68% accuracy; Tab. 6- In sum, our results support the findings that VLMs have poor, low-level vision capability (Rabmanzadehgervi et al., 2024) and that they are overconfident.
perforrs only
A.7 VLMS ARE BIASED TOWARDS THE GLOBAL PATTERN IN A GRID H
Experiments We test VLMs on counting the shapes or tally marks inside an anomaly cell where the total number of shapes or marks do not follow the patterns in the surrounding cells (Fig. 1g).
Counterfactual images: Chess picces, flags, and game boards
many siangyi pieces there this board? Answer with number curly brackets, e.g., (9).
(b): How many stripes there in this flag? Answer with number in curly brackets, e.g. 19].
(c): many rows
puzzle? Answer with curly brackets, e.g. (9).
curly brackets, e.g. (9).
(d): many stars are
flag? Answer with
(e): How many rows are there on this board? Answer with number in curly brackets, e.g. [9].
Xiangqi
(b) US Flag
(c) Sudoku
(d) EU Flag
Chess board
31
13
X
12
X
32
13
X
9
12
X
8
X
@
28
X
13
X
9
12
X
8
32
13
X
9
12
X
8
32
13
X
12
8
Bias
32
13
X
9
12
X
8
X
GT
31
14
Gemini-2.9
Pro
[
Sonnet-3.7
GPT-4.1
o3
8
04
04-mini
Figure 6: VLMs perform poorly at counting elements counterfactual images across domains, often defaulting the biased answers.
and
Results Overall, VLMS perform poorly at 22.44% accuracy. 33.45% of all count predictions, both correct and incorrect, match the biased answers (Fig. 4H) that correspond the surrounding cells. In other words, when VLMs make wrong counting predictions, more than half (i.e., 56.02%) of the time, their answers match the global pattern of most cells in the grid rather than the target anomaly cell in question (Fig. 33). Our results confirm striking influence of the background pattern on VLMs' assessment on a small local region. Here, our patterns in the grids are created from scratch and, therefore, do not represent pattern memorized from the Internet.
A.8 LINEAR PROBING: THE VISION ENCODERS OF VLMS ACTUALLY EXTRACT SUFFICIENT LEG COUNT INFORMATION FROM ANIMAL IMAGES
Sec. 4.1 demonstrates that VLMs exhibit visual bias, defaulting memorized answers 75. 70% of the time across all models. Here, we investigate whether this failure stems from vision encoders' inability detect fine-grained modifications or from language models overriding visual evidence with prior knowledge. This experiment is crucial for understanding the source of VLM biases.
Experiments We conduct linear probing experiments using features from the vision encoder (SigLIP 400M (Zhai et al., 2023)) and the language model (Qwen2 0.5B (Yang et al., 2024)) of LLaVA-OneVisi (Li al., 2025) the 7% animal leg counting task. Following Rah-
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Table 8: Vision encoder features contain sufficient information to distinguish 4-leg from 5-leg animals (95.26% accuracy before projection), but the complete VLM fails dramatically (49.71%), defaulting biased answers % of the time. On abstract images, both linear probing (99.42%) and VLM (65.52%) perform substantially better.
Animals (5-leg vs 4-leg)
Rectangles (5 vs 4) Abstract
Full image
Background removal
Accuracy (%) 1
Linear probing (before projection) Linear probing (after projection) Linear probing (last LLM layer)
95.26
95,98
99.42
91.24
93.39
98.41
89.08
95,40
100.00
OneVision ALaVA--OeViision
(full VLM)
49.71 50.00
41.95
65.52
Random baseline
50.00
50.00
Bias rate (%)
L.a. MlaVAA-OnVission (full VI.M)
99.43
78.30
19-02
100
Accuracy (56)
v1.24
Crestangles))
36.11
Defree
After Projection
Taet TJM Layre
Pull V.M
Projectoon
(a) Linear probe across processing stages
(b) 4-rectangle abstract image (c) 5-rectangle abstract image
Figure 7: Accuracy degradation across VI.M processing stages reveals where bias emerges (a). Vision encoder features maintain high accuracy for both animals (95.26%) and abstract rectangles (99.42%) before projection. As information flows through the LLM, animal counting accuracy collapses to 49.71 % while abstract counting degrades less severely to 65 52%, demonstrating that prior knowledge in language models selectively overrides visual evidence. (b Examples of abstract images.
manzadehgervi et al. (2024), we extract features from three processing stages: (1) before projection (vision encoder output, everage-pooler to 1 1152 dimensions), (2) after projection, and (3) the last LLM layer (both average-pooled to x 896 dimensions). We train a logistic regression classifier on these frozen features distinguish 4-legged from 5-legged
To do this, we create a dataset of 6,594 mammal images (5.598/300/696 train/val/test split) using the same Gemini- 220 Flash-based generation procedure (Sec. E). We restrict this mammals only, as they have more diverse species appearances, allowing us to scale up our datasets. We evaluate under two conditions: (1) full image: full images with backgrounds and (2) background removal: cropped images showing only the lower half containing legs (similar to Sec. 4.4). To isolate the effect of memorized knowledge, we also generate an abstract dataset of rectangles (4 vs. rectangles; Figs. 7b and 7c) with the same size data split.
Results The SigLIP vision encoder successfully distinguishes 4-legged from 5-legged animals and 4-rectangles from 5-rectangles (95.26%; Tab. 8). In contrast, 2 L.LVA-Onev s on-S, which the 5 gITP encoder paired with Qwen2-0.55 L.MM performing at random chance (49.71%; ;6; Tab. 8). Most striking. it outputs "4 legs" for 99,43% of all images (i.c., bias rate) of all 5-legged animal images. Removing backgrounds by cropping legs maintains high linear classifier accuracy (95.26% + 95.9 (5.98%) while reducing the VLM's bias rate (99,43% + 78.30%), though performance of L.L.VA -ereA--neviis. o remains poor (41. 195% Tab. 8).
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On abstract rectangles with no counterfactuals, linear probing achieves almost perfect accuracy before projection (99.42%) and S LLava-Onevisicnn performs substantially better compared to itself on animals (66.52% vs. 49.71%). Across processing stages, linear probing accuracy degrades slightly on animals (95.26% % - 91.24% + 89.08%; see Fig. 7a) but remains near perfect on abstract images (99.42% + 98.41% - 100.00%; see Fig. 7a). This suggests that the language model increasingly biases representations toward memorized answers. These results confirm that vision encoders successfully detect visual modifications, but language models override this evidence with memorized knowledge.
A.9 VLMS ARE EVEN MORE BIASED WHEN THE SUBJECT NAME IS INSERTED INTO THE IMAGE
Prior sections have shown that VLMs perform poorly on the objective task of counting when the background contains visual cues that strongly correlate with well-known subjects. As VLM outputs may be influenced by adversarial or distracting text in the image (Goh et al., 2021), here, we test how in-image textual cues about the subjects (e.g.. "Fbbinghaus illusion") influence VLMs the same counting questions.
Q: Are the two red circles equal in size? Answer in curly brackets, e.g. (Yes) or (No).
Original illusion
GT: Yes
Modified illusion
GT: No
+in-image text
Ebbinghaus illusion
Ebbinghaus illusion
Figure 8: Original vs. modified versions without (top) and with (bottom) the in-image text ("Ebbing- haus illusion").
Experiments We insert the subject name (e.g.. "Adidas" or "Ebbinghaus illusion"; Fig. 8) into the top of all original and CF images, extending the image vertically but keeping the original content unchanged. We repeat previous experiments asking VLMs the two counting questions (Q1 & Q2).
Results All VLMs perform worse when an in-image text is added (-4.49; Tab. 9). Interestingly, the decrease is more pronounced for thinking models (Tab. 9), such X o4-minn (-6.56), O o3
(-6.41), than for non-thinking ones such as ? Sonnet-33 3. (-2.81) and CPT-4. (-2.67). This result is consistent with recent findings that thinking models tend hallucinate more (OpenAI, 2025b; Zhang et al., 2024b), here more biased toward the text in the image despite contradictory visuals.
A.10 HELPFUL PROMPTS DO NOT AMELIORATE THE BIAS ISSUES VLMS
Previous results show that VLMs rely heavily on prior knowledge to answer objective counting questions. Here, test how incorporating helpful instructions in the prompts may help VLMs become less biased.
Experiments We apply two prompting strategies across all V as tasks:
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Table Adding adversarial, in-image textual cues that state the subject name (e.g., "Adidas") cause VLMs to decrease their accuracy (-4.49) on counterfactual images (b). In contrast, instructing VLMs rely exclusively on the image details answer questions (Debiased) to double-check its answers (Double-Check) only slightly improves accuracy, by +1.87 and +2.70, respectively (c).
Model
a. Baseline
b. Adversarial
c. Helpful textual prompt
w/ In-image text
w/ Debiased Prompt
w/ Double-Check
Gemi ni-2.i Pro
16.02
12.04 (-3.98)
19.72 (+3.70)
20.22 (+4.20)
SSonnet3.7
16.59
13.78 (-2.81)
19.29 (42.70)
20.86 (+4.27)
GPT-4.1
13.88
11.21 (-2.67)
14.38 (+0.50)
16.00 (+2.12)
o3
18.50
12.09 (-6.41)
18.94 (+0.44)
21.02 (+2.52)
o4-mini
20.25
13.69 (-6.56)
22.25 (+2.00)
20.61 (+0.36)
Mean
17.05
12.56 (-4.49)
18.92 (+1.87)
19.75 (+2.70)
We prepend the original question (Q1 and Q2) with "Do not assume from prior knowledge and answer only based on what is visible the image." to encourage models to rely exclusively image contents.
(1) Debiased Prompt:
(2) Double-Check: After VLMs answer the original question, we add a follow-up prompt "Please double-check your answer and give your final answer in curly brackets, following the format above."
These prompts are designed to encourage VLMs to examine the image more carcfully. All experiments use the same images and default model settings in the baseline setup.
Results Both helpful prompting strategies improve VLM accuracy but only slightly over the baseline, +1 +1.87 for Debiased and +2.70 70 for Double-Ched (Tab. 9c). That is, explicitly instructing models to rely on image contents or verify their answer helps to some extent but does not address the core issue of bias (Sec. M.1).
A.11 RE-RUNNING EXPERIMENTS MULTIPLE TIMES YIELDS CONSISTENT RESULTS
To ensure the robustness of findings and provide richer evaluation metrics, investigate whether VLM performance varies significantly across multiple runs and examine other aspects beyond accuracy and bias rate.
Experiments We conduct 5-run experiments on our top-performing thinking and non-thinking VLMs (i.e., o4-mini and Sonnet-2 7) across all VLMBias tasks, For each run, we measure:
1. Mean accuracy across 5 runs: average percentage of correct answers when the model is evaluated 5 times on the same dataset
2. @ rate: the frequency that at least of 5 outputs is correct
3. Bias rate: probability of biased answers across runs
4. Agreement-based consistency: probability of the most frequent answer
5. Model self-reported confidence scores: we ask VLMs in a second turn to provide confidence scores for their first-turn answers.
Table 10: VLMs demonstrate consistently poor performance (17.89% mean accuracy, 26.55% pass @ rate) yet exhibit severe overconfidence (91. 11.25% self-reported confidence score), with high agreement- based consistency (92.79%) indicating they reliably produce the same incorrect answers across runs.
Metric
04-mini
[ Sonnet-
Mean
Mean accuracy
19.54 + 0.68
16.23 + 0.36
17.89
P 5 rate
30.16
22.93
26.55
Bias rate J.
73.66
77.27
75.47
Agreement-basee consistency
90.82
94.75
92.79
Model self-reported confidence score
84.73
97.77
91.25
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Results Mean accuracy scores remain stable across 5 runs (17.89%) Pass@5 rates provide only modest improvement (26.55%), indicating that even with multiple attempts, VLMs cannot effectively solve counterfactual problems in VLMBias, Most striking is the high agreement- based consistency (92.79% model mean). showing VLMs consistently produce identical answers across runs. Despite poor performance, VLMM exhibit severe overconfidence with self-reported confidence scores model mean). The bias rate remains consistently high 77,47% model mean), confirming that VLMs persistently default to memorized patterns regardless of multiple attempts for correction.
A.12 PROVIDING IN-CONTEXT EXAMPLES OF ANIMALS WITH ABNORMAL LEGS FAILS FOR 04 0 AS IT SOMETIMES DISTRUSTS THE PROVIDED LABELS
Few-shot prompting typically improves VLM performance by providing in-context learning examples that help models adapt to specific tasks. Here, we investigate whether visual demonstrations of counterfactual animals 3xs can help VLMs overcome their systematic biases and improve counting accuracy.
Table 11:
o4-mini (thinking model) exhibit strong resistance to few-shot examples and distrusts visual evidence (+1.66-48.12) while @ GPT-4. (non-thinking model) responds effectively to few-shot prompting (+15.75-+51.2)).
Accuracy (%)
Bias rate (%)
Configuration
04-min1
x GPT-4.1
c4-mini
M GPT-4.1
Zero-shot
0.18
9.52
97.25
79.67
Few-shot
1.84 (+1.66)
25.27 (+15,75)
80.51 (-16.74)
70.70 (-8.97)
Few-shot + strong labels
2.57 (+2.39)
23.81 (+14.29)
77.94 (-19.31)
72.53 (-7.14)
Few-shot + strong labels + hint
8.30 (+8.12)
60.81 (+51.29)
13.04 (-84.21)
30.40 (-49.27)
Experiments
We design three few-shot prompting strategies to test on the *x animal counting task:
1. Few-shot: Provide one normal (4-legged) and one counterfactual (5-legged) example, each clearly labeled as "This is an x-legged animal." This establishes the basic task format and demonstrates that animals can have non-standard leg counts.
2. Few-shot + strong labels: Use the same visual examples but reinforce with stronger verification language: "This is an x-legged animal, which has been verified." This approach tests whether stronger language
override model biases.
3. Few-shot strong labels + hint: Build upon the previous strategy by adding an explicit warning to the test question: "HINT: This an animal with an unusual number of legs." This directly alerts the model to expect counterfactual cases.
To ensure robust evaluation, we randomize the order of few-shot examples across questions and vary the animal species used in demonstrations (e.g., dogs, cats, lions). We evaluate these strategies on two models with different capabilities: 04 -mini (i.e., thinking) and GPT-4. 4.1 (i.e., non-thinking).
Results 2 1o4mmin n demonstrates strong resistance few-shot examples, showing only minimal improvement (+1.66; Tab. 11) over zero-shot performance. Qualitative amalysis reveals active distrust of provided labels (Fig. 42), persisting even with strong verification language (Figs. 43 and 44). This distrust causes the model to rely on knowledge priors rather than visual and few-shot evidence. Even with explicit hints, o4-mini reluctantly acknowledges counterfactual cases but continues miscounting (Fig. 45). While this significantly reduces bias-aligned errors (-84.21; Tab. II), accuracy improvement remains modest (+8. 12; Tab. 11) compared to zero-shot.
In contrast, GPT-4.1 responds effectively to few-shot prompting (+14.29-+51.29) Tab. 11). This finding aligns with recent observations that thinking models exhibit increased hallucination rates (OpenAI, 2025b), here manifesting as rejection of accurate visual information.
A.13 THINKING VLMS SHOW LIMITED IMPROVED ACCURACY
Recently, newer thinking VLMs have been released, which need to be evaluated on our benchmark to provide complete view of current models' capabilities.
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Table 12: Full results across proprictary SOTA VLMs (Sec. 4.2), open-source VLMs (Sec. A. 14),
pointing VLMs (Sec. A.1 and tool-using VLMs (Sec. 1.1 Latest thinking VLMs show mixed results on canonical answer blas: GPT-5 achieves modest improvement (30.72%) while Ø Grok-4
underperforms older VLMs (17.71% vs. 04 mini's 20.25%)
Model
Accuracy (%) counting questions (Q) &Q2) counterfactual images
Bias rate (%)
g.
mean
mean
Proprictory SOTA VL.Ms (Sec.
4.2)
Gemini-2.5 Pro
0.00
10.42
26.74
2.38
49.81
20.83
76.79
Sonnet-3.7
0.00
2.72
13.75
9.03
1.79
54.29
34.52
16.59
76.63
GPC-4.
9.52
9.07
2.50
8.68
0,00
48.61
18.75
13.88
76.62
<3
0.92
7.60
5,00
42.71
2.38
50.38
20.54
18.50
74.81
04-mini
0.18
9.31
14.58
44.10
4.76
51.26
17.56
20.25
73.66
P Grak-4
2.56
9.58
34.72
8.93
8.93
54.32
GPT-5
4.76
14.95
25.83
84.72
18.15
48.48
18.15
30.72
57.36
Mean
2.56
11.67
35.81
5,48
50.60
19.90
19.10
70.03
Open-source
VLMss (Sec. A.
8 Pixtral-123
0.00
1,47
18.52
1.02
10.13
50.94
2.99
12.15
58.96
M Pixtrral-Large-2222
0.00
8.09
7.66
1.39
7.83
51.77
18.45
13.60
72.31
Owen2 Qwen2.5-VI.77 5-VI-73
0.18
13.48
23.75
0.70
9.58
55.19
13.43
16,62
52.56
0.00
7.84
11.25
1.74
2.98
53.03
20.24
13.87
67.94
Mcan
0.05
15.29
1,21
7.63
52.73
13.78
14.06
62.94
Pointing
VLMs (Sec. A. A6]
Moondream-23
74.36
16.91
55.00
35.07
1.79
49.75
0.00
33.27
46.78
Mo DB-D
45.79
19.57
59.58
24.31
640.71
54,29
4.46
38,39
32.80
& Mo
48.90
9.18
36.25
36.81
53.57
56.06
13.99
36.39
23,92
Mean
56.35
15.22
50.28
32.06
38.69
53.37
6.15
36.02
34.50
Tool-asing VLMs (Sec. A.1
04-mini (chat w/ tools)
3.30
15.63
21.57
51.04
14.06
$2.08
17.86
25.08
68.92
Experiments We replicate the previous experiments Q1 and Q2 our tasks of VLMBi on the latest notable VLMs: @ GPT-5 (OpenAI, 2025a), Grok 1ook-440 (xAI, 2025).
Results & Grok-4 does not surpass x 04 1--mmnn n and o3 (1.71% % 20.25% and 18.50% Tab. 12). Meanwhile, @ GPT-5 outperforms
o4-mini and o3 (30.72% vs. 20.25% and 18.50% Tab. 12), particularly excelling on the chess pieces (84.72%) However, @ GPT-5 still falls far short of expectations, and these latest results do not change our conclusions that VLMs remain biased toward canonical answers on our VLMBias.
A.14 LARGER SOUR-SOURCE VLMS ARE MORE BIASED
The prevailing assumption in the field is that larger models with more parameters should perform better visual reasoning tasks due increased representational capacity. However, it remains unclear whether this scaling benefit holds for tasks requiring models to override strong prior knowledge, as larger models may suffer from inverse scaling (McKenzie al., 2023) having memorized more biased associations from training data.
Experiments We evaluate four open-source VLMs of varying sizes on all VLMBias tasks: M Pixtral-129 (Agrawal et al., 2024), M raxtral--
(MistralAl, 2024), Owen2. 5-VL-7B, and A Owen2. 5-VL-72B (Bai et al., 2025) (Tab. 28). We use the same experimental setup as previous sections, asking counting questions (Q1 and Q2) on counterfactual images across all 7 domains and measuring both accuracy and bias rates.
Results Larger models do not consistently outperform smaller variants and often exhibit increased bias. The mean accuracy across all open-source VLMs is remarkably low (14.06%; Tab. 13), with the smallest model & Qwen2. 5 -V) achieving the highest accuracy (16.62%), which is com- parable to SOTA closed-source models (17.05% mean accuracy). More concerningly, larger VLMs demonstrate substantially higher bias rates (72.31% for M ExtraalLLargee411 for &. Pixtral aall122 12B; 67. 944 for & Owen2. 5-VL- VLL-2BBBBBBB vs. 52.56% for & Owen2. 5-VL-7B; Tab. 13). This pattern suggests that increased model size may actually reinforce memorized biased
58.96%
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associations rather than improve visual reasoning capabilitiis. Moreover, since open-source VLMs are much smaller than closed-soure -ource ones, they contain less knowledge and consequently show lower bias rates compared to closed-soune source models (62.94% 75.70% These findings support the hypothesis that more knowledge leads to more bias in counterfactual scenarios.
Table 13: Larger open-source VLMs do not outperform smaller variants and exhibit higher bias rates. The smallest VLM ( Qwen2. Swven..-VII-77 5-VL-7B with 7B parameters) achieves the high- est accuracy (16.62%) while larger VLMs show substantially increased bias rates (72.31 % for dm P Pixtraa- -araallaaage----- 2411-2211 vs. 58.96% for & Pixtral-1255 supporting the hypothesis that more knowledge leads to more bias.
Model
Accuracy counting questions &Q2) counterfictual images
Bias rate
a.79
h.@
c.
d.2
e
f.
g
Task mean
Task mean
# Pixtral-128
0.00
1.47
18.52
1.02
10.13
50.94
2.99
12.15
58.96
Ab Pixxtral--arge-2444
0.00
8.09
7.66
1.39
7.83
51.77
18.45
13.60
72.31
$ 0wen2.5-VL-73
0.18
13.48
23.75
0.70
9.58
55.19
13.43
16.62
52.56
Swen2.5-VL--2
0.00
7.84
11.25
1.74
2.98
53.03
20.24
13.87
67.94
Mean
0.05
7.72
15.29
1.21
7.63
52.73
13.78
14.06
62.94
A.15 04-MINI USES TOOLS ANALYZE IMAGES ONLY ~30% OF TIME AND MOSTLY OUTPUTS DIRECTLY BIASED ANSWERS
Previous experiments evaluate VLMs through API access without tool capabilities. By leveraging tools such as zooming and localization, VLMSs can potentially improve their counting accuracy by examining visual details more carefully. However, it remains unclear whether VLMs recognize when visual reasoning is needed for familiar subjects familiar subjects with strong bias cues.
We compare @ o4-mini in two configurations: (1) standard API access without tools, and (2) ChatGPT interface (OpenAI, 2025) with full Python tool access (e.g., zoom, crop images). We evaluate both versions counting questions (Q1 and Q2) in VLMBias tasks. For the ChatGPT interface, we access via Puppeteer and measure tool usage frequency through the tool tag in the JSON provided by OpenAl's Data Export, and record thinking time to assess computational effort. Due to the rate limit of the ChatGPT interface, we evaluate these two configurations on the 11 1122px resolution subset.
Experiments
Table 14: 2 14-minn mi with tool access shows only modest improvements (+1.9 accuracy, -2.11 bias rate) despite having access to python tools (c.g., zooming, cropping). The limited gains suggest that Python tools cannot overcome deep-seated biases as effectively specialized built-in counting mechanisms (Sec. A.1
Model
Accuracy counting questions (Q) &Q2) counterfictual images
Bias rate
a.
b.0
c.
d.a
e
f.64
g.ll
Task mean
Overall
c4-mimm of (API w/o tools)
0.0
13.24
18.75
53.12
5.36
50.38
21.43
23.18
71.03
144mini (chat w/ tools)
3.30
15.63
21.57
51.04
14.06
52.08
17.86
25.08
68.92
A (tools API)
+3.30
+2.39
+2.82
-2.08
+10.49
+1.7
-3.57
+1.9
-2.11
Results Tool access provides modest improvement, increasing accuracy from 20.25% 25.08%
(+4.83; Tab. 14). Similarly, the bias rate decreases slightly from 72.41 1% to 68 68.92% (-3.49; Tab. 14), indicating marginal improvement avoiding memorized answers. However, despite having access to zooming and localization tools, x 04-mini employs them in only 29.66% of queries on average (Tab. 16). That is, the model defaults to direct visual assessment 70.34% the time, suggesting overconfidence in memorized knowledge prevents recognition of when visual reasoning is needed. Importantly, when tools are activated, performance improves noticeably: accuracy increases by +26.51 and bias rate decreases by -33.81 on average compared to baseline (Tab. 15). This demonstrates that tools are highly effective when used, but the low activation rate (29.66%) severely limits their overall impact model performance.
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Table 15: Performance when 14-mini acti- vates tool-using capabilities 299666 of queries; Tab. 16). Tool use substantially improves accu- racy (+26.51) and reduces bias rate (-33,81) for task mean. Yet, the low tool usage rate driven by overconfidence in memorized knowledge limits overall performance.
Table 16: @ o4-mini uses available tools in
only 29.66% of queries average. The low usage rate indicates that overconfidence in memorized knowledge prevents recognition of when visual reasoning is needed.
Task
Avg. time
Tool use (%)
Task
Accuracy
Bias (%)
Animals
Animals
8.33
79.17
Logas
5.46
10.87
Logos
68.75
18.75
Flags
14,00
38.75
c. Flags
60.0
36,0
Chess Pieces
16.59
37,50
Chess Pieces
75.0
25.0
Game Boards
10.69
26.79
fff Game Boards
53.33
13.35
Optical Iluusions
2.27
6.82
1 Optical Illusions
76.47
23.53
Patterned
16.55
iti Pattemed Grid
19.23
50.0
Task mean
10.78
29.66
Task mean
51.59 (+26.51)
35.11 (-33.81)
A.16 SMALL VLMS TRAINED EXPLICITLY ON COUNTING SIGNIFICANTLY OUTPERFORM PROPRIETARY SOTA VLMS
Fine-tuning VLMs with specialized capabilities may help overcome counting biases. Pointing VLMs (Moondream, 2025; Deitke et al., 20de)-models specifically trained to output coordinate locations-can potentially force visual reasoning rather than relying on memorized patterns. We investigate whether VLMs with explicit pointing abilities can better handle VLMBias as compared to larger, general-purposs VLMs without pointing capabilities.
Experiments We test three pointing VLMs: @ Moondream-222 2B (Moondream, 2025), & Molme-7B-D, and dh Molmo- 72B (Deitke al., 2025) (Sec. A.1 For 2 Moondream-222 2B, we use its counting API, which outputs only coordinate lists, and count the array length as the final answer- this ensures 100% pointing capability usage. The exception requires Y/N responses rather than counting, so we use standard reasoning APIs. For f and A,, they autonomously decide whether to invoke pointing capabilities. When used, the coordinate outputs assist in subsequent counting. We evaluate them on the same counting questions (Q1 and Q2) across all VLMBias Mss tasks using identical experimental setups as in the previous sections.
optical illusion task, which
Table 17: Pointing VLMs substantially outperform commercial VLMs across all domains (36.02% vs. 17.05% mean accuracy; Tab. 6) and regular open-source VLMs (36.02% vs. 14.06% mean accuracy; Tab. 13). Even the smallest model @Q Moondream: 2B with 2B parameters) achieves 33.27% accuracy, exceeding most commercial VLMs despite being orders of magnitude smaller.
Model
Accuracy in counting questions (QI & Q2) counterfactual images
Bias rate
a.
b.C
c
d.2
e
f.44
g.:i
Task mean
Task mean
Moondream-28
74.36
16.91
55.00
35.07
1.79
49.75
0.00
33.27
46.78
Molmo-7B:
45.79
19.57
59.58
24.31
60.71
54.29
4.46
38.39
32.80
c Molmo-78 72B
48.90
9.18
36.25
36.81
53.57
56.06
13.99
36.39
23.92
Mean
56.35
15.22
50.28
32.06
38.69
53.37
6.15
36.02
34.50
Results Pointing VLMs significantly outperform commercial VLMs (36.02% vs. 17.05% mean accu- racy; Tabs. 6 and 17) and regular open-source VLMs (36.02% vs. 14.06% mean accuracy; Tabs. 13 and 17). Most remarkably, 2 Moondream-222 23 with only 2B parameters substantially outperforms f 04 14-mini ni (33.27% 20.25%; Tab. 17) despite being orders of magnitude smaller. This suggests that training objectives matter more than model scale for overcoming biases in VLMBias, Qualitative results can be found in Sec. M.3.
However, pointing capabilities remain significantly underutilized.
Molmo-7B-1 and Mol 1 molmmm-2220 72B achieve better performance (38.39% vs. 36.39%; Tab. 17) but only use
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Table 18: Pointing-capable VLMs: frequency tool use ("pointing use") and counting performance. These models are trained to activate the pointing tool only when prompts contain specific trigger patterns; without those triggers they often do invoke the tool, even when doing so could improve accuracy.
Pointing use (%)
Accuracy (%)
Model
Q1
Q2 (A)
Avg
Q1
Q2 (A)
Avg
à Molmo-7B-D
41,59
63.00
52.30
32.16
44.61
38,39
A Mp 120
36.14
63.36
49.75
26.82
45.97
36.39
Mcan
38.87
63.18 (+24.31)
51.03
29.49
45.29 (+15.80)
37.39
Gemini-2.5 Pro
15.59
16.45
16.02
M Sonnet-34
16.81
16.36
16.59
GPT-4.1
12.55
15.20
13.88
17.33
19.67
18.50
1o4-mim
19.96
20.55
20.25
Mean
16.45
17.65 (+1.20)
17.05
Table 19: Ablation study comparing Molmo models' overall performance versus performance when pointing capabilities are activated. On both models, pointing improves counting accuracy (+5.90
mean across tasks) while reducing bias rates (-8.86 mean). The most notable performance gain occurs in 9% animals (+41.21 for 2/
Mo molmmo-2200 +37.00 for & Molmo-7B-D)
Model
h@
d.a
1
Task
Biax
Molao-72
45,79
19.57
24.31
69.71
38.39
(wi poining)
(-1664648))
65.36
(-3
No 1o-80-126
48,00
9.18
3681
53.57
13.90
36.39
23.92
Mo1mo-728 (wl pointnng)
90.11 (+41.21)
9.46 (+0.25)
44.44 (-8.19)
(3.59(116733
55.00 (+1.49)
(+0.6
45.85 (+9:46)
23.01 (0.91)
pointing 51.03% of the time (Tab. 18). This could be due to overconfidence, defaulting direct answers without utilizing their pointing capabilities. One interesting finding is that on Q2 (e.g.. "Count the legs"), & and & use pointing capabilities much more than on Q1 (e.g., "How many legs") (63.18% vs. 38.87%; Tab. 18). This leads to much higher A between Q1 and Q2 for å and i compared to commercial VLMs, which show negligible differences (+15.80 vs. +1.20; Tab. 18). This pattern suggests that explicit counting prompts (i.e., Q2) better trigger pointing verification than implicit counting questions (i.e., QI), though the underutilization indicates that even specialized VLMs struggle to recognize when their memorized knowledge might be misleading.
When pointing is activated, both Molmo models' performance noticeably improves (Tab. 19): d Mo 1oolmo-221 72B gains +9.46 accuracy with -8.09 bias reduction, while % Molmo-7B-B achieves +3.44 accuracy and -18.44 bias reduction. Most notably, on animals, pointing achieves 82.78- 90.11 1 % accuracy, demonstrating that localization overcomes memorized priors.
A.17 SAME FAILURES ACROSS MODEL FAMILIES RULE OUT IMAGE GENERATION BIAS
potential concern is that bias could arise from generating and evaluating images with the same model families.
We analyze the results on ?x animals (generated by + Gemini 1emini-2.0 Flash) and w logos (generated by @ GPT-4o) from Sec. 4.2 to investigate whether generation bias affects our findings. We examine performance differences between model families on images generated by their own family versus images generated by other families or created programmatically.
Experiments
Results GPT-family models show no substantial advantage on GPT-4o generated images (bias rates of 88.73% for Ø GPT-4. 1 vs. 98.04% for + Gemin nim.5i Pro and 96.79% for Sonnet 3. 7; Tab. 20). Similar results are shown on Gemini- 1nmini-2.0 0 Flash generated Yx images (100% bias rate for + Gemini-2.-. Pro vs. 97.25% for @ o4-mini and 100% for Sonnet-3. Tab. 20). All VLMs consistently achieve 100% accuracy on unmodified images but fail dramatically counterfactual versions (17.05% mean accuracy: Tab. 2) regardless of image generation source. This confirms that the observed bias stems from models' inherent preferences for canonical answers rather than artifacts of the image generation process.
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Table 20:
When presented with modified, counterfactual images in VLMBias, VLMs exhibit sub- stantial bias alignment in their counting responses. The mean bias rate of five state-of-ther VLMs across our seven tasks is 75.70 75%. o4-mini shows the lowest bias alignment (73.66 %) indi- cating relatively better resistance to visual biases. VLMs with thinking capabilities ( 04 -minii o3, Gemini-2 Pro) demonstrate bias susceptibility similar to non-thinking models (1) Sonnet-3. GPT-4.
Model
Bias rate J in counting questions (Q) &Q2) on counterfactual images
a. b.C
C.
d. A
c. i
g. Hii
Task mean
Gemi ni ni-2.5 -2.5 Pro
100.00
98.04
89.58
70.83
83.93
50.19
44.94
76.79
Sonnet-3.7
100.00
96.79
82.50
84.72
97.62
45.33
29.46
76.63
GPT-4..
79.67
88.73
97.08
80.21
98.81
51.39
40.48
76.62
0.3
93.77
91.18
93.33
49.65
95.24
49.62
50.89
74.81
0 14-mnni
97.25
90.20
82.08
54.17
91.67
48.74
51.49
73.66
Mean
94. 14
92.99
88.92
67.92
93.45
49.05
43.45
75.70
A.18 IMAGE RESOLUTION HAS MINIMAL IMPACT ON VLM PERFORMANCE ACROSS VLMBIAS TASKS
Since our VLMBias dataset contains images rendered at multiple resolutions (384px. 768px, 1 1152px) as part of our generation process, we analyze whether performance varies across these different image sizes to understand if resolution affects bias-driven failures in counting tasks.
Table 21: VLM accuracy (%) across different image resolutions shows minimal variation, with only 2.85- -oint mean difference between lowest and highest resolutions.
Model
384px
768px
1152px
Mean
A (1152-384)
04 mini
17.27
20.30
23.18
20.25
+5.91
o3
16.67
17.90
20.94
18.50
+4.27
14.36
17.79
17.60
16.59
+3.24
GPT-4.
13.71
13.43
14.49
13.88
+0.78
Gem Bamini: -2.5 Pro
15.13
17.76
15.17
16.02
+0.04
Mean
15.43
17.43
18.28
17.05
+2.85
Experiments We break down the accuracy results from our main experiments by the three resolutions present in our dataset: 384px, 768px, and 152px. Each image was originally generated and tested at these different resolutions, allowing us to examine whether VLM performance on counterfactual counting questions (Q1 Q2) varies with image size across all domains.
Results Performance remains remarkably consistent across resolutions (15.43% 384px + 18.28% 1152px: Tab. 21). These consistent patterns across resolutions reinforce that VLM failures stem from memorized knowledge overriding visual analysis rather than insufficient image detail.
A.19 HUMANS @ CAN COUNT ANIMAL LEGS ALMOST PERFECTLY AFTER 2 SECONDS OF ANALYZING THE IMAGE
To establish performance baselines and validate that our counterfactual images are not inherently ambiguous, we investigate human performance on V a under various time constraints. Un- derstanding human capabilities provides crucial context for interpreting VLM failures and confirms whether the visual modifications are perceivable given sufficient examination time.
Experiments We conduct a anonymous human study (consent obtained, no PII collected, minimal risk) with 78 participants (mean age 24.4 years, 82.1% with Bachelor's degree or higher, men 51.6%, women 46.2%) who completed the animal leg counting task through our project website. Each participant is randomly assigned to image viewing time condition (see Fig. 13) throughout the session to answer 10 randomly selected questions (5 original, counterfactual) from our 91 9mmeme dataset on ' animals (Sec. 3.1). We vary image viewing times (Fig. 15) across four conditions:
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0.2, 0.5, 1.0, and 2.0 seconds, while allowing unlimited time for reading questions (Fig. 14) and responding (Fig. 16).
100
Human accuracy (%)
80
images
VLMs: Acc. on unmodified images
VLMs: Blas-algred rate
40
Human: unmedifeed images
Human: Bass algged rate
0.2
0.5
1.0
2.0
Image-viewing time (seconds)
Figure 9: Human accuracy on counterfactual images significantly outperforms VLMs (reaching
93.75 75% vs 2.12%) 95 animals with longer niegeiv times, while bias-aligned responses decrease substantially with extended exposure.
Table 22: Human accuracy increases with image- viewing time, reaching 96.88% at 2.0 seconds on 'n animals. Even under severe time pressure (0.2s), humans achieve 50.00% counterfactual images significantly better than VLMSs (mean accuracy of 2.21 %) on 7%
image-viewing
Image-viewing time
Participants
Accuracy (%)
Overall
Bias
(seconds)
(#)
Counterfactual
Original
accuracy (%)
(%)
0.2
50.00
93.33
71.67
34.44
70.00
98.75
84.38
17.50
@ Human
1.0
28
82.86
97.86
90.36
14.29
2.0
16
93.75
100.00
96.88
5,00
SOTA VLMSs
2.12
100.00
$1.06
94.14
Results Human T counting accuracy on 9% animals improves dramatically with increased image- viewing time (71.67% at 0.2 seconds - 36% at 2.0 seconds; Tab, 22 and Fig. 9). On counterfactual images specifically, accuracy also rises from 50.00% at 0.2s to 93.75% at 2.0s.
Under extreme time pressure (0.2 seconds), humans exhibit higher bias-aligned responses (34.44%; Tab. 22 and Fig. 9) compared to longer viewing times. But even in this challenging condition, humans still outperform SOTA VLMs (50.00% vs. 2.12% counterfactual accuracy; Tab, 22). This confirms that counterfactual images not inherently ambiguous and complex for humans.
A.20 SOCATE-THEN-COUN PROMPTING DOES NOT SIGNIFICANTLY IMPROVE COUNTING ACCURACY
While simple counting prompts prove ineffective (Q1 & Q2; Sec. L.I). the strong performance of pointing VLMs like Moondream- 2B, Kolmo-7B-D (36.02% accuracy: Sec. A. suggests that forcing explicit localization might be key. We investigate whether more descriptive, step-by-step prompts that first locate, then count (i.e., explicitly instruct VLMs first to locate each element, then count one by one) can help VLMs overcome their bias and improve counting accuracy.
Experiments We replicate the 9% animal leg counting experiment from Sec. 4.2 but modify the prompt to encourage more procedural approach, using the following enhanced prompt: "First, locate each leg individually, count them one by one, and then state the final number in curly brackets, e.g., (9)." This prompt explicitly guides the model through a Doaalizaioo-then-countii then-counting workflow rather than asking for a direct count.
Results sreate-then-coure prompting yields only marginal improvements the original simple prompts QI & Q2 (+0.67; Tab. 23), while the bias rate remains high (-1.09; Tab. 23). These results indicate that explicitly locate-then-coum instructions are insufficient to overcome VLMs' strong
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Table 23: Locate-then-coumt prompting yields only marginal improvements over Q1 & Q2 prompts (+0.67% accuracy, - -1.09% bias rate).
Accuracy
Bias rate
Model
Q1 & Q2
Locate-then-come prompt (A)
Q1 & Q2
Locate-then-come prompt (A)
Gemini-2.5 Pro
0.00
0.00 (+0.00)
100,00
96.70 (-3.30)
Asonnet-3.7
0.00
1.83 (+1.83)
100.00
98.17 (-1.83)
GPT-4.1
10.62 (+1.10)
79.67
82.78 (+3.11)
0
0.92
1.54 (+0.62)
93.77
93.08 (-0.69)
4 mo4-mini
0.18
0.00 (-0.18)
97.25
94.51 (-2.74)
Mean
2.12
2.80 (+0.67)
94.14
93.05 (-1.09)
visual bias (see Figs. 36 and 37), consistent with findings that prompting based interventions provide only limited improvements (Sec. A.10). Instead, the correct way to help by providing tools for VLMs and ensuring that VLMs themselves know when to use them (see Secs. A.15 and A.1
A.21 ADDING SUBJECT NAME TO TEXT PROMPTS FURTHER DECREASES VLM ACCURACY
Our VLMBias uses neutral prompts (e.g. "Count the legs of this animal.") to isolate visual bias from prompt bias. However, a key question remains: does this neutral framing actually matter? To address this, we test whether injecting object-specific names into our prompts (i.e., non-neutral; similar to Sec. A.9) affects VLM counting performance and bias rates.
Experiments We modify our neutral prompts (Q) & Q2) from previous experiments by replacing generic descriptors with specific object names. For example, "the left shoe" becomes "the left Nike shoe" ( logos), "this puzzle" becomes "this Sudoku puzzle" (iff game boards). We evaluate Sonnet -3. (best non- non-thinking) and 04-mini (best thinking) using these non-neutral prompts the same counterfactual images across all 7 tasks.
Table 24: Non-neutral prompts substantially reduce counting accuracy (-4.75), with 04 -iini experiencing 3x larger degradation than ( Sonnet-3. (-7.09 vs. -2.41 points) across tasks.
Model
b.9
c
f.
Task
Sonnet 3.7 (Neutral)
0:00
2,72
13.75
1.79
Sonnet -3.7 (Non-mcotra))
0.00 (+0.00)
1.98
(-0.44)
9.58 (-1.17
(-6.6)
1.79 (-0.00)
49,87 (-142)
33.63 (-0.99)
14.18 (-284)2))
1 64 -o--mini minn (Neutral)
0.18
9.31
14.58
44.10
4.76
$1.26
17.56
20.25
1o--minn (Noe neutraly
0.18 (+0.00)
8.09
5.42
15.62 (-28.77)
0.00 (-4.6)
50.00
(-126)
12.80 (4.76)
(3.16
(Neutrali
0.09
26.04
Model Mcan (Non neutral)
0.09 (+0.00)
5.03 (.0.8)
(.667)
9.03 (-17.53)
0.89 (-2.38)
(-2.84
23.21 (2283)
13:67 (-4755)
Table 25: Non-neutral prompts increase bias rates across all tasks (+5.32), demonstrating that object-specifie names strongly activate textual priors.
Model
a.
cfft
4.8
c18
ti
Task mean
Ploonres (Neutral)
100.0
96.79
82.5
$4.72
97.62
45.33
20:46
76.63
B Sinnnee-3.. (Nin neutral)
99.52
(-0.18
97.77
(+0.95)
88.33 (+5.3)
97.57
(+1288))
98.21 (+0.60)
47.22 (+1.39)
(+1.79)
8000 (+3.40)
mini (Ncutral)
97.25
82.08
$4.17
48.74
73.66
ootmmmi (Nontneutral)
(+0.00)
(+7.50)
(+7.10)
38.06(66.55)
Model Moan (Neutral)
47.03
75.14
Model Mean (Non-acutral)
98.53 (-1.00) (...0()
88.96 (+6.7)) (6211
98.51 (6351((4388) 48.61 (+1.58)
(14.17)
80.6 (25.32)
Results Adding object names to prompts significantly degrades performance (-4.55% mean accuracy; Tab. 24) and increases bias rates (+5.32%; Tab. 25) for both [ Sonnet-3. 7 and X 04-mini. Notably, the thinking model suffers nearly 3x larger accuracy degradation than the non-thinking A when exposed to non-neutral prompts (-7.09 vs. -2.41 points). These results demonstrate that non-neutral prompts invoke stronger textual priors that override visual information, and extended reasoning capabilities overcome this bias. This confirms that neutral prompting essential for fairly assessing whether VLMs can overcome their language bias when analyzing counterfactual images.
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A.22 VLMs FAIL TO DETECT MODIFICATIONS EVEN WITH SIDE-BY-SIDE COMPARISON
Prior sections show that VLMs struggle to count legs correctly in counterfactual images. Here, we test whether providing explicit side-by-side comparisons with original images helps VLMs detect the modifications, as the reference image may make the differences more noticeable.
Experiments We present VLMs with two images simultancously: the original animal image (with canonical leg count) and its modified counterfactual version (with one extra leg). We prompt models with: "Compare the two images side by side. Do the animals in image I and image have the same number of legs? Return the final Yes/No answer in curly brackets (e.g.. [Yes] or (No]).". Here, we expect the VLMs to always answer (No) if they can distinguish the differences.
Compare the images side by side. animals image and image 2 have the same number of legs? Relurn the final Yes/No answer in curly brackets (e.g.. (Yes) or (No)).
Model
Percentage of (No) (%)
Random baseline
50
Gemini-2, Pro
9.89
Sonnet-3.7
9.89
8
GPT-4.
10.99
c3
10.99
? c4-mini
15.38
Mean
11.76
Table 26: VL.Ms fail to detect leg count differences when comparing original and counterfactual im- ages side-by-side. The ground truth "No", but models output "No" only 11.76% of the time, far below the 50% expected from random guessing.
Original
Modified
Figure 10:
The side-by-side comparison prompt and an example input image pair.
Results VLMs are biased that even when counterfactual and original images are placed side by side, they still cannot detect the modifications. The mean percentage of (No) across SOTA VLMs is only 11.76% (Tab. 26). This demonstrates that VLMs' bias toward prior knowledge is so strong that even direct visual comparison fails to surpass the random random-guessing (50%) baseline.
A.23 EVEN WHEN ATTENDING TO CORRECT REGIONS, VLMS STILL FAIL TO GENERATE CORRECT ANSWERS
Prior sections demonstrate that VLMs fail at counting counterfactual elements despite vision encoders successfully encoding visual information (Sec. A.8). Here, we investigate whether VLMs attend to the correct visual regions during inference by analyzing attention patterns when generating answers.
Experiments Our preliminary analysis of attention patterns evolution throughout the layers (Fig. 11) reveals that : Swen2.5-VL-1 progressively localizes relevant objects (c.g., legs, logo elements) in later layers. Following this, we compute the final layer's ' attention mapping of the answer token the image tokens for & Qwen2. 5-VL-7B. For example, when the model outputs "(3)" in response to counting dog's s legs, we extract the attention weights of the token "3" across the image tokens of the last layer. We visualize the attention by mapping each image token its corresponding image patch and overlay the attention heatmap on the original image.
Results Interestingly, even when the model correctly attend to the regions of interest, often produces incorrect or biased answers. For instance, when counting overlapping circles on a modified Audi logo, Qwen2. 5-VL-7B attends strongly to all five circles in the final layer yet outputs "4". This finding is consistent with prior work showing disconnects between visual attention and final model outputs (Liu al., 2025; Zhang et al., 2025). Combined with our linear probing results (Sec. A.8), this provides strong evidence that VLMs can see the correct visual information but are highly influenced by memorized knowledge priors during answer generation.
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Original
7
Layer
Layer
@: Count the visible black stripes the logo on the left shoe. Prediction 3 X, Ground truth V
Q: Couut the stars in this flag. Prediction = 5 X, Ground truth
Q: Count the legs of this animal. Prediction 4 Ground truth =
layers for Qwen2 .0122.5vv0 5-VL-7B, revealing that it progressively localizes relevant regions in later layers. Original: Input image without attention overlay. Layers 7-28: Attention heatmaps overlaid on images, with warmer colors indicating higher attention weights.
Figure 11: Attention heatmaps
Prediction = X. Ground truth 6 /
Prediction = 4 X. Ground truth =
Prediction = X. Ground truch =
Prediction = 2 Ground truth 33
Prediction = 7 X. Ground truth 3
Prodiction 8 X. Ground truch = 7
Figure 12: Attention heatmaps from the final layer of the prediction token of & Qwen2 5-VL- The model correctly attends to the visual details for flags, car logos, patterned grids, and three-legged animals when generating its token. However, still outputs incorrect or biased answers instead of the correct count.
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B HUMAN STUDY DETAILS
Al Research Study
Contributing
pattic pution halpo understand system per lisertly
Study Overview
Durvilon: foprod notely 2-5 rutos
Quection:: IDimagge recognilins
Proous: Head qurdion View imagee hrirly Apaorr
Important: hot onlysse imaqu
gpn drout imagee you see
Important
Eachimmeg Bunh
seconds Day cime attention!
Degai Shady
Figure 13: Participants are informed about the task and their randomly assigned image viewing duration (0.2, 0.5, or seconds).
Gueston tst to
Readyl
How many legs does this animal have?
Ready?
The image will appear for only 0.2 secands when you dlick Nertl
Show Image
Figure 14: Participants read the question with unlimited time before viewing the image.
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Question of
Show Image
Figure 15: The target image is displayed for the assigned duration (0.2, 0.5, 1, seconds).
Quaasion
How many legs does this animal have?
Next Question
Figure 16: Participants have unlimited time to choose their response from multiple-choice options.
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C DETAILED COMPARISON WITH EXISTING VLM BIAS BENCHMARKS
This section provides additional detailed comparison between VLMBias and related benchmarks (Tab. 1) discussed in Sec. 2, organized by key evaluation criteria.
C.1 SOURCE OF BIAS
VLMBias isolates visual bias through neutral prompts and objective counting, while other bench- marks introduce bias through their question formulations. Specifically, PhD-ces (Liu et al., 2024), VLind-Bench (Lee et al., 2025), and HallusionBench (Guan et al., 2024b) explicitly mention objects in prompts (e.g., "Does the car have square wheels 2"), priming models toward knowledge priors. ViLP (Luo al., 2025) contains two subsets: ViLP includes distractor facts that bias responses, while ViLP* omits distractors. Though ViLP" is more relevant to our work, it doesn't t directly address visual bias like VLMBias. It either identification questions (e.g., "Which animal in the image stores fat in its humps?") on modified subjects (horses with humps), which are inherently ambiguous as horse with humps arguably ceases be a "horse": questions that explicitly men- tion the object (c.g., "From the image, in which city is the Red Square located?"), priming models toward prior knowledge of these named entities (e.g., the Red Square). While there are also counting questions in ViLP, they take up only 4% of the questions (12/300), compared to our benchmark which fully focused on counting.
In contrast, VLMB i as uses neutral language (e.g., "How many legs does this animal have?") with objective counting that results in unambiguous numerical answers. This design ensures that failures indicate memorized knowledge overriding visual evidence, not susceptibility to cues in the questions.
C.2 BENCHMARK SCALE
The main dataset of VLMB i a provides 1,392 counterfactual images across 7 diverse tasks, exceeding most related benchmarks in scale. Specifically, main dataset is 1.9 times larger than PhD-ces (750 images), 2.3 times larger than ViLP (600 images), and 7.7 times larger than HallusionBench (181 images). While VLind-Bench (2,576 images) is larger than our main dataset, VLMBias" s's full evaluation suite which includes the background removal subset and in-image text injection subset totals 4,6 images, surpassing the scale of VLind-Bench. This scale enables more robust evaluation of VLMs, covering broad range of scenarios from photo-realistic animals to abstract patterns.
C.3 IMAGE GENERATION METHOO
VLMBias as systematically generates photo-realistic, subtly modified versions of familiar subjects using state-of-the-art models, while other benchmarks (1) older image generators producing surreal-looking images or (2) manually collect images. Specifically, PhD-ces and VLind-Bench rely DALL-E, while ViLP uses DALL-E and FLUX create artificial and surreal scenes. Mean- while, HallusionBench manually curates counterfactual images, achieving high-quality but lacking scalability. In contrast, VLMB as employs state-of- oftte--f--hh-arr art generators Gemini-2 Flash. @ GPT-4o) to create subtle modifications of highly familiar subjects (e.g. a 5-legged dog) that looks highly realistic.
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D MODELS AND ACCESS DETAILS
Table 27: Model specifications and access details for evaluated commercial VLMs
Model
Model
Thinking
Platform
Settings
Gemini-2.
Pro
gemini 2.5 5- pro proo-preeee--ee 05-06
Google Al Studio
temperatuze=11
Sonnet-3.7
Anthropic
Lemperatucen1
GPT-4.1
gpt-4.1
OpenAl
temperature=1..
6.
o3
OpenAI
04-mini grok-4 gpt-5
OpenAl
resaoning_effortee
0 Srok-4
AAI
GPT-5
OpenAI
ressoning ng_niinnn__eeee fmrtimidium
Table 28: Model specifications and access details for evaluated open-source VLMs
Model
Model ID
Thinking
Platform
Settings
Pixtral-128
pixtral-12b pixtral-Laxge-2111 1ren2.5-v1-7bb-iae
OpenRouter
temperature=1.0
nw Pixtral-Large-22 2411
OpenRouter temperature=1.0
OpenRouter temperature=1.0
Qwen2.5-VI
OpenRouter
tempenature=l.0
Table 29: Model specifications and access details for evaluated open-source counting VLMs
Model
Model ID
Text output
Platform
Settings
Molmo-
alleaal mM 0924
HuggingFace
temperature=1.0
Mol -27
all enla /Mool /Mol m vikhyatk/mon b.btbyatk/moccndreema2
HuggingFace temperature=1.0
Moondream-22 2B
HuggingFace
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E TASK 1: COUNTING LEGS WITH ADDED LIMB "H
E.1
TASK DESIGN
Animal suggestions known amnais with oiher lipo ar lege. The logs of these
Image Generation
100
Genente dlay, filizods, néte-ries image ain (emmer) with (numjegg) legs backyyoure the from, Jeya) animal muat look photoseeient All (nam]lege) legs must veealy-vaaile vadile
enough
Dog: legs
Add Image Editing janimal that hhe foum, Jagas lege in total The leg 1-ken photoewehetz (numplegus") lege muse dearly andile
accept
body.
reject
quality control
Figure 17: Data generation pipeline for Task 1: Counting legs with added limb.
Pretrained on the Internet data, VLMs must have colossal prior knowledge of the count of animal legs from both textual and image data. Following this hypothesis, generate images of usual animals with one additional leg (e.g., 3-legged birds or 5-legged dogs) and ask VLMs to count legs evaluate if these models biased toward their prior knowledge.
Animal types: We modify the legs of types of animals: birds and mammals.
Modification types: Each animal is modified to have additional leg.
Target animals: We select 91 well-known animals, consisting of 23 two-legged birds and four-legged mammals.
Image resolutions: We generate each animal image and rescale them 3 different pixel sizes [384, 768, px using the scaling factor in Sec. 3.1 to test resolution sensitivity
This approach generates total of 91 animals x modification type resolutions 273 total images.
E.2 IMPLEMENTATION AND IMAGE GENERATION
Implementation details Our image generation pipeline follows this sequence:
1. Use 8 c4-mini to collect list of well-known animals with clearly visible legs
2. Generate full-body and side-view images of these animals using + Gemini -2. Flash
3. For each animal image, use Gemini-2.9 Flash to add one extra leg to the animal. Each animal image edited over 4 independent trials.
4. Manually inspect and filter out unsatisfactory images
5. Render each approved image at three different resolutions
Quality control manually inspect the images to ensure that each modified animal image has exactly one additional leg. For cases that fail (e.g., more than one added leg), we remove them from our dataset.
Prompt
We the following prompts to test the VLMs:
Q1: How many legs does this animal have? Answer with number in curly brackets, e.g., (9).
Q2: Count the legs of this animal. Answer with number in curly brackets, e.g. (9).
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Q3: Is this animal with /NumModifiedLegs/ legs? Answer in curly brackets, e.g., (Yes)
or [No].
Ground truth calculation The ground truth answers are as follow:
Birds leg counting (Q1&Q2):
Correct answer: (one additional leg)
Expected bias: 2
Mammals leg counting (Q1&Q2):
Correct answer: (one additional log)
Expected bias: 4
Animal leg identification question (Q3):
Correct answer: "No" (always, since each animal has one additional leg)
Expected bias: "Yes"
E.3
QUALITATIVE RESULTS
How many legs does this animal have? Answer with number in curly brackets, e.g., (9).
(a)-(e) How many legs does this animal have? Answer with a number in curly brackets, c.g., (9).
(a) Lion
(b) Stork
(c) Elephant
(d) Duck
(e) Dog
4
X
X
4
X
4
X
4
X
4
4
5
X
6
4
X
4
x
X
6
X
4
x
X
4
x
X
4
X
Bias
4
x
X
4
x
X
4
X
GT
5
5
5
Gemi ni minini-e -2-2.5
Pro
Sonnet-33
3.7
20
GPT-4.
..1
89
o33
88
04 41-mini
Figure 18: VLMs are often biased toward the original number of legs 14 animals have, and they tend answer based prior knowledge rather than by analyzing the image.
E.4 LIST OF ANIMALS
Mammals: Four-legged animals
horse, zebra, donkey, mule, buffalo, yak, water buffalo, deer, elk, moose, reindeer, caribou, gazelle, giraffe, camel, dromedary camel, bactrian camel, Ilama, alpaca, goat, ibex, mountain goat, pronghorn, bighorn sheep, wild boar, pig. warthog, coyote, lynx, bobcat, leopard, tiger, lion, jaguar, puma, ocelot, caracal, hyena, rabbit, impala, springbok, kudu, eland, wildebeest, okapi, hippopotamus, african elephant, asian elephant, indian rhinoceros, gnu, maned wolf, arctic fox, red fox, fennec fox, red wolf, domestic dog, domestic cat, african wilddog, dingo, jackal, gray wolf, hare, cheetah, antelope, bison, sheep, serval
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Birds: Two-legged animals
ostrich, emu, rhea, cassowary, heron, stork, crane, egret, ibis, spoonbill, turkey, chicken, rooster, duck, swan, peacock, sandpiper, avocet, stilt, plover, lapwing, oystereatcher, secretary bird
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F TASK 2: COUNTING ELEMENTS IN MODIFIED BRAND LOGOS T
Logo sugpestions Genwort 2504 ind rettett with sach lrng arre logo
Venaaaaadoo
Addas stipes of
Image generation Geware wkite eni white aboe
Product suggestions inee common non common rdlkaa sport.
Gessare
Image generation
Human control
reject
souring dha stow lees
partio as thr andiscaatnn socar
Adidas Colorg Mack white red Sports: accces Tureeng
Figure 19: Data generation pipeline of shoe logos for Task 2: Counting elements in modified brand logos
suggestions of mich car teanck and S mort cammon body tyees car
Image generation photordliss: froo on inage the ricde of the
Audi Color whise Body: Sul
Logo suggestions
Gevente JISN sitte logo modifving prompt,, each cl wall known logo.
Manual editing
Image generation Gecerste logo with The utual
selectio
Audi intefooking
Figure 20: Data generation pipeline of car logos for Task 2: Counting elements in modified brand logos
F. TASK DESIGN
Our initial evaluation show that some VLMs, such as @ 04-mini, can accurately count the four stripes modified Adidas logo on white background. As such, to increase the task difficulty, we hypothesize that VLMs strongly associate @ logos with the background they typically appear on. Subsequently, examine if the visual cues from the background would be strong enough suppress counting the elements in the logos. Our task is designed as follow:
Brand types: We use different brand types: cars and shoes
Target brands: We select well-known brands with quantifiable graphical elements:
Car brands: Mercedes- Benz, Maserati, and Audi (3 brands)
- Shoe brands: Adidas and Nike (2 brands)
Background variations: Each brand logo has specific background settings:
Car logo background: Car logos always appear on cars. For each logo, we collect 5 car body types colors (white, grey, black)
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Shoe logo background: Shoe logos often the footwear of athletes. For each logo, we collect list of relevant sports (tennis, running, basketball, soccer) colors (black, red, white)
Image resolutions: We generate each image and rescale them at 3 different pixel sizes (384, 768, pp using the scaling factor in Sec. 3.1 to test resolution sensitivity
This systematic approach generates a total of [3 car brands (5 x 3) x 3 resolutions] + [2 shoe brands x x 3) resolutions] = 135 72 207 total images.
F.2 IMPLEMENTATION AND PROMPTS
Implementation details
We employ the following process to generate logo modification images:
1. Use a fo4-mini to suggest graphical modifications for each logo (e.g., increasing Adidas' three stripes to four). We then select most relevant suggestions for our benchmark.
2. Generate modified logo versions using GPT-4o.
3. Create background images:
Background images for car logos:
Use fo4-mini to suggest popular colors and body types of each car logo.
For each logo, generate and select relevant images of cars from the logo brand with the determined body types and colors.
Manually place modified logos in typical car logo positions.
Background images for shoe logos:
Use /04-mini to suggest popular shoe colors and sports affiliated with each
shoe logo.
For each logo, generate and select relevant images of athletes wearing shoes with the modified logo for each determined color and sport.
4. Render each image at three different resolutions.
Quality control To ensure high-quality images, we manually review make sure that: (1) each generated logo has the correct number of modified elements; (2) each product is clearly visible and oriented correctly; and (3) the position of the logos on the products are natural-looking.
Prompts We use the following prompts
Counting questions (Q1 & Q2):
Q1 (Adidas): How many visible [StripeColor] stripes are there in the logo of the left
shoe? Answer with a number in curly brackets, e.g., (9).
Q1 (Nike): How many visible [CurveColor] stylized curves are there in the logo of the
left shoe? Answer with number in curly brackets, e.g., (9)
Q1 (Audi): How many overlapping circles are there in the logo of this car? Answer with number in curly brackets, e.g., [9].
Q1 (Mercedes): How many points are there the star in the logo of this car? Answer with a number in curly brackets, e.g., (9].
Q1 (Maserati): How many prongs are there in the logo of this car? Answer with a number in curly brackets, e.g., (9)
Q2 (Adidas): Count the visible [StripeColor] stripes in the logo of the left shoe. Answer with a number in curly brackets, e.g., [9].
Q2 (Nike): Count the visible [CurveColor] stylized curves in the logo of the left shoe.
Answer with a number curly brackets, e.g., [9]
Q2 (Audi): Count the overlapping circles in the logo of this car. Answer with a number in curly brackets, e.g., (9).
Q2 (Mercedes): Count the points the star in the logo of this car. Answer with number in curly brackets, e.g., (9].
Q2 (Maserati): Count the prongs in the logo of this car. Answer with a number in curly brackets, e.g. (9)
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2. Y/N identification questions (Q3):
Q3 (Adidas): Are the logos on these shoes Adidas logos? Answer in curly brackets,
egg, [Yes] or (No).
Q3 (Nike): Are the logos on these shoes Nike logos? Answer in curly brackets, e.g.,
(Yes) or [No].
Q3 (Audi); the logo on this car Audi logo? Answer in curly brackets, egg,, [Yes) or
(No].
Q3 (Mercedes): Is the logo on this car Mercedes-Bent logo? Answer in curly brackets,
egg. [Yes] or (No].
Q3 (Maserati); Is the logo on this car Maserati logo? Answer in curly brackets, e.g..
(Yes) or (No].
Ground truth calculation The ground truth answers follow:
Adidas stripes counting (Q1&Q2):
Correct answer:
Expected bias: 3
Nike stylized curves counting (Q1&Q2):
Correct answer:
Expected bias: 1
Audi overlapping circles counting (Q1&Q2):
-Correct answer:
-Expected bias: 4
Mercedes-Benz points on the star counting (Q1QQ2:
-Correct answer: 4
Expected bias: 3
Maserati prongs counting (Q1&Q2):
Correct answer: 5
Expected bias: 3
Logo identification question (Q3):
Correct answer: "No" (all logos are modified)
Expected bias: "Yes"
F.3 QUALITATIVE RESULTS
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Car logos
(a), (d): How many overlapping circles are there in the logo of this car? Answer with a number in curly brackets, e.g., (9).
@ (b), (e): How many points there the star in the logo of this car? Answer with number in
curly brackets, e.g., (9).
e: How many prongs are there the logo of this car? Answer with a number in curly brackets, e.g..
{9).
(a) Audi
(b) Mercedes
(c) Maserati
(d) Audi
(e) Mercedes
3
X
3
3
X
4
X
3
X
X
3
4
X
3
X
X
3
X
4
X
3
X
3
X
Bias
4
X
X
3
X
X
3
X
GT
5
Bamini-2.5 Pro
[
Sonnet-3.7
3PT-4.
4.1
03
88
04
m-mmmmn
VLMs are completely biased and rely entirely on prior knowledge when answering questions about @ brand logos. Please zoom in to see the logo clearly.
Figure 21:
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Shoe logos
(a), (c): How many visible white stripes are there in the logo of the left shoe? Answer with
number in curly brackets, e.g., (9).
1 (b): How many visible white stylized curves are there in the logo of the left shoc? Answer with
number in curly brackets, e.g., (9).
(d): How many visible black stylized curves are there in the logo the shoe? Answer with
number in curly brackets, e.g., (9).
@ e): How many visible black stripes are there in the logo of the left shoe? Answer with number in curly brackets, e.g., (9).
(a) Adidas
(b) Nike
(c) Adidas
(d) Nike
(e) Adidas
3
3
X
3
3
X
&
3
X
3
3
X
3
X
X
4
3
Bias
3
X
3
X
3
X
GT
Gemini-2.5
Pro
[+]
Sonnet-3.
GPT-4
4.1
o3
8
04 04-mini
Figure 22: VLMs are often biased and rely on prior knowledge when answering questions about shoe logos, even with simple ones like the Nike Swoosh. Please zoom in the logo clearly.
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Flag suggestions
Generate J5ON
wellenoun flg objers with more thnn stans scrpee.
Monual lettoon
Flng of the United States num_stars: 50 num stripes:
WikiCommons API
SVG Code
SVG code modification
The Fing the Linbed States had strppe Modily eyD
strpea to
Add background m
accept
redo
Human quality control
Figure 23: Data generation pipeline for Task 3: Counting stripes/stars in modified national flags.
G TASK 3: COUNTING STRIPES/STARS IN MODIFIED NATIONAL FLAGS x
G.1 TASK DESIGN
Flags of countries contain easily recognizable patterns. To evaluate if existing VLMs overly rely their knowledge of these flags to count a certain element, we design the task as follow:
Flag types: We modify 2 commonly used elements across different flags: stars and stripes
Modification types: Each flag has 2 types of modifications:
Add: We add an additional element (star or stripe) chosen flag
Reeoov: We remove one element (star or stripe) from a chosen flag
Target flags: We select 20 well-known country flags with either 3+ stars or 5+ stripes (a total of 13 star-typed flags and 7 stripe-typed flags) to ensure the modified flags retain recognizable traits test visual bias.
Image resolutions: We generate each flag and rescale them at 3 different pixel sizes (384, 768, px using the scaling factor Sec. 3.1 to test resolution sensitivity
This systematic approach generates a total of target flags x modification types x resolutions 120 total images.
G.2 IMPLEMENTATION IMAGE GENERATION
Implementation details We modify the SVG code of a chosen flag to create new variants following this sequence:
1. Identify 20 well-known country flags (13 with 3+ stars, 7 with 5+ stripes) based the suggestions from 004mmin.
2. Retrieve original SVG code from WikiCommons for each flag.
3. Use & 8o4-mm 1o4-mini modify each SVG to create two variants:
An "Add" variant with one additional element.
"Remove" variant with one fewer element.
4. Render each modified flag at three different resolutions.
Quality control We employ the following steps ensure high-quality and consistent images:
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Manual inspection: We manually review each generated sample to verify modification quality and visual consistency
Filtering: We remove unsatisfactory samples from the benchmark and rerun pipeline on these cases to obtain new samples.
Fallback:
For rare cases (3 in total) that consistently fail automated generation, we manual modify the flags to ensure they strictly follow the modification rules.
Prompts
We use the following prompts:
1. Counting questions (Q1 & Q2):
Q1 (Star-typed flags): How many stars are there on this flag? Answer with a number in curly brackets, e.g., (9).
Q1 (Stripe-typed flags): How many stripes are there on this flag? Answer with number in curly brackets, e.g., (9).
Q2 (Star-typed flags): Count the stars this flag. Answer with number in curly brackets, e.g., (9)-
Q2 (Stripe-typed flags): Count the stripes on this flag. Answer with number in curly brackets. e.g., (9).
2. Y/N identification questions (Q3):
Is this the flag of [CountryName|? Answer in curly brackets, e.g., [Yes) [No).
Ground truth calculation
We calculate the ground truth as follow:
Direct counting questions (Q1 & Q2):
Correct answer: The actual count of the elements (stars or stripes) on the flag after modification
For Remove modifications: Standard element count minus
* For Add modifications: Standard element count plus I Expected bias: The standard element count
Flag verification question (Q3):
Correct answer: "No" (since the flag's element has been modified)
Expected bias: "Yes"
G.3 QUALITATIVE RESULTS
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National Flag
(a), (b), (c): How many stripes are there this flag? Answer with a number in curly brackets, e.g.,
(9).
(b), (c): How many stars there this flag? Answer with number in curly brackets, e.g., (9).
(a) US
(b) US
(c) EU
(d) EU
(e) Zimbabwe
13
X
12
13
/
7
X
13
X
13
X
12
X
12
X
7
X
&
13
X
12
12
7
X
13
12
12
7
13
13
Bias
13
X
GT
12
14
13
6
Gemini-2..
Pro
[
Sonnet-3. 3.77
GPT 4.1
o3
04 -inni
Figure 24: VLMs are biased when counting the stars and stripes on ~ national flags.
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H TASK 4: COUNTING CHESS PIECES ON MODIFIED STARTING POSITION C
Code generation Genern did srandard ctnting post
Remove
Replace
Randomn piece
selection
modification
A
Figure 25: Data generation pipeline for Task Counting chess pieces on modified starting position
H.1 TASK DESIGN
To evaluate if VLMs rely on expected structure or attend to actual pieces, we test their ability to count pieces on boards with subtle modifications. We design our task with careful control of visual parameters to ensure systematic evaluation:
Board types: We use 2 different game boards: (chess (Western chess), xiangqi (Chinese chess))
Modification types: Each board has types of modifications:
Remove: remove exactly one piece from the standard starting position.
Replace: We replace exactly one piece with a different piece of the same color.
Target squares: We select 12 unique occupied squares per board type, maintaining the same target squares across the Remove and Replace modifications to ensure controlled comparison.
Image resolutions: We generate each board at 3 different pixel sizes (384, 768, 1152)px to test resolution sensitivity.
This systematic approach generates total of board types modification types 12 target squares x resolutions 144 total images.
H.2 IMPLEMENTATION AND PROMPTS
Our implementation utilizes specialized libraries for each board type. For chess, leverage the Python chess library to manipulate board states and chess svg for rendering. For xiangqi (Chinese chess), we created a custom implementation using svgwrite for rendering.
Implementation details
The algorithm for both board types follows the same sequence:
1. Create standard board with all 32 pieces in their starting positions
2. Randomly select 12 target squares from the occupied squares
3. For each target square, create (a) Remove variant and (b) a Replace variant
4. Render each modified board three different resolutions
The xiangqi implementation required special handling for:
The traditional 9x10 board layout with the central river and palaces
Chinese character rendering for pieces, which requires detecting appropriate CJK fonts
Different piece distribution (Chariots, Knights, Elephants, Advisors, General, Cannons, and Soldiers)
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Quality control To ensure consistent image quality across all variants, implemente several technical measures:
SVG to PNG conversion: We used direct SVG rendering with adjustable scaling factors based on target resolution
Quality scaling: We applied a quality multiplier (5.0x base resolution factor) to ensure clear piece visibility
Prompts
We use different prompts for each modification type to test VLMs' visual attention:
1. Remove modifications:
Q1: How many [chess/xiangqi] pieces are there on this board? Answer with a number curly brackets, e.g., (9).
Q2: Count the [chess/xiangqi] pieces on this board. Answer with a number curly bruckets, e.g., (9).
2. Replace modifications:
Q1: How many [Added Piece Type] pieces are there on this board? Answer with number in curly brackets, e.g., (9).
Count the [Added Piece Type] pieces on this board? Answer with a number in curly brackets, e.g., (9).
Q2:
3. Both modification types:
Q3: Is this the [chess/xianggi] starting position? Answer in curly brackets, e.g., [Yes) or [No].
For Replace modifications, [Added Piece Type] refers the specific piece type that added the board through replacement, chosen from:
For chess: Pawn, Knight, Bishop, Rook, Queen, or King
For xiangqi: Soldier, Horse, Elephant, Chariot, Cannon, Advisor, or General
For Replace modifications, we ask about the added piece type rather than total count because this more effectively tests whether VLMs rely on prior knowledge of standard piece distributions or actually inspect the board carefully.
Ground truth calculation
We calculate the ground truth answers for each prompt type:
Total piece count (Remove modifications only):
Correct answer: 31 (one fewer than the standard 32 pieces)
Expected bias: 32 (the standard piece count)
Added piece type count (Replace modifications only):
Correct answer: The standard count for that piece type plus one
For example, if a Knight is replaced with Bishop in chess, the Bishop count would be 3 (standard + added)
Expected bias: The standard count for that piece type (e.g., 2 for Bishops chess)
This tests if VL.Ms rely on their knowledge of standard piece counts or actually inspect the board
Starting position question (Both modification types):
Correct answer: Always "No" (since the board has been modified)
Expected bias: "Yes" (since the board closely resembles the starting position)
H.3 QUALITATIVE RESULTS
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Chess Pieces
(a): How many chess pieces there this board? Answer with number in curly brackets, e.g., (9).
(b): How many Pawn pieces are there on this board? Answer with number curly brackets, e.g..
(9).
(c): How many xiangqi pieces are there on this board? Answer with number in curly brackets, e.g..
(9).
(d): How many General pieces there this board? Answer with number in curly brackets, e.g..
(9).
(a) Chess
(b) Chess
(c) Xiangqi
(d) Xiangqi
C C
C
O O
1
32
X
32
X
32
X
32
X
28
X
16
32
X
2
31
/
17
32
X
2
32
X
32
X
2
Bias GT
32
X
16
32
X
2
31
31
Gemini-2.5 Pro
Sonnet-3.7
GPT-4.1 .
133
o -mini
Figure 26: VLMs are biased when counting the pieces on chess and xiangqi.
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1
TASK 5: COUNTING ROWS AND COLUMNS OF GAME BOARDS ii
Add
+1 row
+1 column
Code generation Cenerate SVS a chess beard
SVc modification
Subtract
-1 column
Figure 27: Data generation pipeline for Task 5: Counting rows and columns of board game
1.1
TASK DESIGN
To evaluate VLMs' over-reliance visual bias versus actual counting, we adapted the row and column counting task from BlindTest (Rahmanzadehgervi et al., 2024) where Claude-3. 5-Sonnet achieved 74.26% accuracy. Instead of simple grids, leverage modified versions of well-known game boards to test whether VLMs rely on prior knowledge or perform actual visual counting. We design our task with careful control of visual parameters to ensure systematic evaluation:
Board types: We use different grid- brsee game boards: (Chess (8x8), Xiangqi (Chinese chess, 10x9), Sudoku (9x9), Go (19x19))
Modification types: Each board has to 4 types of modifications:
Remove row: We remove exactly one row from the grid.
Remove columm: We remove exactly one column from the grid.
Add row: We add exactly one to the grid.
Add column: We add exactly column the grid.
Board-specific variations:
For Chess, Xiangqi, and Sudoku boards, all four modifications (remove/add row, remove/add column) are visually distinct, with additional positional variations (first/last), resulting in variants per board. Go boards have uniform grid structure, so we produce only 4 variations.
Image resolutions: We generate each board at 3 different pixel sizes (384, 768, 1152) Jpx to test resolution sensitivity.
This systematic approach generates total of (8 variants board types (Xianggi/ChessSuudoku) + Go variants) x resolutions = 84 total images.
I.2 IMPLEMENTATION AND PROMPTS
Implementation details Our implementation utilizes specialized drawing libraries for each board type. For Chess, Xiangqi, we implement the traditional 10x9 board layout with river gap and palace diagonal lines. For Sudoku, we create 9x9 grids with bold 3x3 block boundaries and sample numbers. For Go, generate uniform line grids with traditional star points.
standard 8x8 chessboard grid generation with alternating square colors. For
The algorithm for all board types follows the sequence:
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1. Create standard board with correct dimensions and visual elements
2. Apply systematic modifications (add/remove rows/columns at specific positions)
3. Maintain visual consistency of special elements
4. Render each modified board three different resolutions
The board-specific implementations required special handling for:
Chess: Alternating light/dark square pattern preservation across dimension changes
Xiangqi: River gap positioning and palace diagonal lines adjustment for row modifications
Sudoku: Bold 3x3 block boundary lines based original 9x9 grid structure
Go: Uniform line spacing and star point positioning for various board sizes
Quality control To ensure consistent image quality across all variants, we implemented several technical measures:
SVG to PNG conversion: We used direct SVG rendering with adjustable scaling factors based on target resolution
Quality scaling: We applied quality multiplier (5.0x base resolution factor) ensure clear structural visibility
Font and layout fidelity: Automatic detection and usage of appropriate fonts, particularly critical for Xiangqi (Chinese characters) and Sudoku (numbers)
Prompts We use different prompts for different question types to test VLMSs visual counting versus prior knowledge:
1. Counting questions (Q1 & Q2):
Q1 (Chess): How many [rows/columns/ are there on this board? Answer with number curly brackets, e.g., (9).
Q1 (Xiangqi, Go): How many [horizontal/verticaa] are there on this board? Answer with number in curly brackets, e.g.. [9].
Q1 (Sudoku): How many [rows/colunns] are there on this puzzle? Answer with number in curly brackets, e.g., (9).
Q2 (Chess): Count the [rows/columns] on this board. Answer with number in curly brackets, e.g., (9].
Q2 (Xiangqi, Go): Count the [horizontal/vertical] lines on this board. Answer with a number in curly brackets, e.g., (9).
Q2 (Sudoku): Count the [rows/columns] on this puzzle. Answer with a number in curly brackets, e.g., (9].
2. Y/N identification questions (Q3):
Q3 (Chess): Is this a 8x8 Chessboard? Answer curly brackets, e.g., (Yes) or (No). Q3 (Xiangqi): this a 10x9 Xiangqi board? Answer in curly brackets, e.g., (Yes) or [No].
Q3 (Sudoku): Is this 9x9 Sudoku puzzle? Answer in curly brackets, e.g., (Yes) or (No).
Q3 (Go): Is this a 19x19 Go board? Answer in curly brackets, e.g., (Yes) or (No).
Ground truth calculation We calculate the ground truth answers for each prompt type:
Row/Column count (Q1 & Q2):
-orrect answer: The actual number of rows/columns after modification. For example, if one row is removed from a 9x9 Sudoku, the row count is 8.
Expected bias: The standard count for that board type (c.g., 8 for Chess rows, 10 for Xiangqi horizontal lines, 9 for Sudoku rows, 19 for Go horizontal lines)
Standard layout question (Q3):
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Correct answer: Always "No" (since all boards have been modified from standard dimensions)
Expected bias: "Yes" (since the boards closely resemble their standard counterparts)
I.3
QUALITATIVE RESULTS
Game Boards
(a): How many columns are there on puzzle? Answer number curly brackets, e.g.. (9). (b), (c): How many horizontal lines are there on this board? Answer with a number in curly brackets, c.g., (9).
(d): How many rows there on this board? Answer with number in curly brackets, e.g. (9).
(a) Sudoku
(b) Go
(c) Xiangqi
(d) Chess
X
13
10
X
X
10
X
8
X
10
X
X
10
X
X
12
X
Bias
X
10
X
GT
/
20
11
7
Gemini-2.5 Pro
[+]
Sonnet-3.. 3.7
GPT-4.1 .1
o3
04 -mini
Figure 28: VLMS biased when counting the rows and columns A game boards.
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J TASK 6: VISUAL TESTING WITH BOTH ORIGINAL AND MODIFIED OPTICAL ILLUSION
Select Illusion
Pyllusion code
modification
Ebbinghaus
Figure 29: Data generation pipeline for Task 6: Visual testing with both original and modified optical illusion
J.1 TASK DESIGN
Recent VLMs show improved performance optical illusion tasks, with o4-mini achieving 71.49% accuracy on IllusionVQA. However, these VLMs might have merely memorized the common optical illusions rather than truly perceiving visual information. To investigate this hypothesis, test their ability to correctly identify illusion effects on both original and strategically modified versions. We design task with careful control of visual parameters to ensure systematic evaluation:
Illusion types: We use 6 different classical optical illusions: (Ebbinghaus, MilllrrLyee,
Ponzo, Drtical-Horizonie Zöllner, Poggendorff)
Condition types: Each illusion has conditions:
Original: Standard illusion where the visual effect should occur (e.g., two identical circles appearing different sizes).
Modiifid: Reversed version where the actual measurements contradict the typical illusion effect (e.g., circles that are genuinely different sizes).
Parameter variations: We generate multiple combinations of illusion parameters:
Most illusions: 12 original 12 modified versions with varying illusion strength and difference
Vertical--orizzmtall 6 original + 6 modified versions (fixed T-shape structure)
Image resolutions: We generate each illusion at different pixel sizes (384, 768, 1152)px to test resolution sensitivity.
This systematic approach generates a total of (12 original + 12 modified) x 5 illusion types + (6 illusion) resolutions 396 total images.
original + modified)
J.2 IMPLEMENTATION AND PROMPTS
Implementation details Our implementation adapts code from Pyllusion (attps: /gi thub. com hon/Realli i Leteeniinnng//eeee......gggggiit / 1 usion) to generate consistent, parametrically controlled optical illusions. We systematically vary two key parameters: illusion strength (which controls the intensity of contextual elements that create the illusion effect, representing how strongly the surrounding context biases perceptual experience) and difference (which controls the objective, actual difference between target elements being compared, where 0 means identical elements and non-zero values create genuine physical differences).
The algorithm for all illusion types follows the same sequence:
Define parameter ranges for each illusion type (strength values, difference values).
2. Generate original versions with standard illusion parameters (diff=0 for equal elements).
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3. Generate modified versions with reversed parameters (diff= for unequal elements).
4. Render each illusion variant at three different resolutions.
The illusion-specific implementations required special parameter handling for:
Ebbinghaus: Varying surrounding circle sizes (strength) and central circle differences (difference).
Miller--Lyr: Different arrowhead angles (strength) and line length differences (difference).
Ponzo: Perspective line angles (strength) and horizontal bar length differences (difference).
Vertical-Horizontll. Fixed T-shape with varying line length ratios (difference).
Zöllner: Background line angles (strength) and main line parallelism differences (differ- ence).
Poggendorff: Interrupting rectangle positions (strength) and diagonal line alignments (difference).
Quality control
To ensure consistent image quality and valid illusion effects across all variants, implemented several technical measures:
Parameter validation: Ensured all strength and difference values produce visually mean- ingful illusions, with diffz 0 cases design to be easily recognizable by humans to distinguish actual physical differences from perceptual biases clearly.
Balanced generation: Equal numbers of diff=0 (original) and diff (modified) cases per
illusion type
Prompts We use consistent prompts across illusion types to test VLMs' visual perception versus memorized knowledge:
Main questions (Q1 & Q2):
Q1 (Ebbinghaus): Are the two red circles equal size? Answer curly brackets, e.g.. (Yes) or (No].
Q1 (Müller-Lyer, Ponzo): Are the two horizontal lines equal in length? Answer in curly brackets, e.g., [Yes] or (No].
Q1 (Mertical-Horizontaa) Are the horizontal and vertical lines equal in length?
Answer in curly brackets, e.g., (Yes) or (No).
Q1 (Zöllner): Are the two horizontal lines parallel? Answer in curly brackets, e.g.. (Yes) or (No).
Q1 (Poggendorff): Are the two diagonal line segments aligned? Answer in curly brackets, e.g., (Yes) or (No).
Q2 (Ebbinghaus): Do the two red circles have the same size? Answer in curly brackets. egg. [Yes) or (No).
Q2 (Müller-Lyer)): Do the two horizontal lines have the same length? Answer in curly brackets, e.g., (Yes) or (No).
Q2 (Ponzo): Do the two horizontal lines have the same length? Answer in curly brackets, e.g., (Yes) or (No).
Q2 Neriiical Do the horizontal and vertical lines have the same length? Answer in curly brackets, e.g., (Yes) or (No).
Q2 (Zöllner): Do the two horizontal lines run parallel? Answer in curly brackets, e.g.. (Yes) or (No].
Q2 (Poggendorff): Do the two diagonal lines form straight line? Answer in curly brackets, e.g., (Yes) or (No).
2. Y/N identification questions (Q3):
Q3: Is this example of the Hortooma//Zilnee/Pggggnne illusion? Answer in curly brackets, e.g., (Yes) or (No).
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Ground truth calculation We calculate the ground truth answers based on the actual measurements in each image:
Counting questions (Q1 & Q2):
Correct answer:
* Original illusions (diff=0): Elements are actually equal, so the correct answer is "Yes"
Modified illusions (diff>0)): Elements actually different, so the correct answer is "No"
Expected bias:
Original illusions: VL.Ms might incorrectyy say "No" expecting the illusion effect to make equal elements appear different
Modified illusions: VLMs might incorrectly say "Yes" expecting the illusion to make genuinely different elements appear equal
Y/N identification questions (Q3):
Correct answer:
*
Original illusions: "Yes" (standard examples of the specified illusion type).
*
Modified illusions: "No" (modified versions that contradict typical illusion effects).
Expected bias:
Original illusions: VLMs likely correctly identify "Yes" since they match
memorized illusion patterns
Modified illusions: VLMs may incorrectly say "Yes" if they rely on visual similar-
ity rather than recognizing the effect contradiction
J.3
QUALITATIVE RESULTS
Abstract images: Optical Iluusions
(a) Original
(b) Modified
(c) Original Zöllner
(d) Modified Zöllner
(e) Original Ebbinghaus
Modified
Müller-Lyer
Mtiller-Lyer
Ebbinghaus
Yes
Yes
X
Yes
Yes
X
Yes
Yes
X
Yes
Yes
X
Yes
Yes
No
No
Yes
Yes
Yes
X
Yes
Yes
Yes
Yes
X
Yes
Yes
X
Yes
Yes
X
Yes
Yes
X
No
Yes
X
Bias
No
x
Yes
X
No
X
Yes
X
No
Yes
X
GT
Yes
No
Yes
No
Yes
No
Gemini-22 -25 Pro
Sonnet-3.7
GPT-4.1
o3
04 04-mini
(a), (b): Are horizontal equal longth? Answer in curly brackets, c.g., (Yes) (No).
(c), (d): Are the two borizontal lines parallel? Answer in curly brackets, e.g.. (Yes) or [No]. circles equal size? carly egg. (Yes) or [No].
(e),
Figure 30: VLMSs show systematic biases, often relying prior knowledge of optical illusions rather than directly interpreting the image.
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Abstract images: Optical Illusions
(a) Original
(b) Modified
(c) Original V-H
(d) Modified V-H
(e) Original
( Modified
Ponzo
Ponzo
Poggendorff
Poggendorff
Yes
No
No
X
No
Yes
Yes
Yes
No
X
No
No
Yes
Yes
No
X
No
Yes
Yes
X
Yes
Yes
X
No
X
No
Yes
Yes
X
Yes
Yes
X
No
X
No
Yes
Yes
X
Bias
No
X
Yes
X
No
X
Yes
X
No
X
Yes
X
GT
Yes
No
Yes
No
Yes
No
Semini-2.5 5 Pro
[ Sonnet-3.7
GPT-4.1
o3
04 o4-mini
(a), (h):
horizontal equal length? Answer in curly egg. [Yes] (No).
(c), (d): Are the horizontal and vertical lines equal in length? Answer in curly brackets, egg. [Yes] (No).
(e), (f: Are the two diagonal line segments aligned? Answer in curly brackets, e.g. [Yes) or (No).
Figure 31: VLMs show systematic biases, often relying on prior knowledge of optical illusions (e.g., Ponzo and Poggendorff illusions) rather than directly interpreting the image. In contrast, in the vertical-horizonn illusion, VLMs respond like humans. They are misled by the illusion itself, leading them to answer the original question incorrectly rather than the counterfactual ones.
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K TASK 7: COUNTING CIRCLES OR LINES IN AN ANOMALY CELL WITHIN PATTERNED GRID !
Generate base
Remove
Replace
Square selection
modification
Figure 32: Data generation pipeline for Task 7: Counting circles or lines in an anomaly cell within patterned grid
K.1 TASK DESIGN
VLMs can infer patterns from nearby visual elements to answer visual questions (Huang et al., 2024). To evaluate whether VLMs rely on pattern recognition over actual visual counting, we create square grids with systematic numerical patterns (represented visually by dice faces or tally marks) where exactly one cell violates the expected pattern. We hypothesize that VLMs will prioritize the inferred pattern over the actual visual information and report the expected pattern-completing value instead of the true count. We design our task with careful control visual parameters to ensure systematic evaluation:
Grid types: We use 2 different visual representation types: (dice (circular dots in dice-face patterns), tally (traditional tally mark lines)).
Modification types per grid type: For each grid type, we apply 2 distinet types of cell-level modifications:
Dice grids: Remove (one dot is removed from a cell) and Replace (one dot is replaced with different shape, like square or star, within cell).
Tally grids: Remove (one tally line is removed from cell) and Add (one extra tally line is added to cell).
Grid Dimensions: We generate grids of different dimensions, ranging from 6x6 to 12x 12 cells.
Unique scenarios for anomaly placement (single anomaly per grid image): To create distinct base settings for placing anomalies, where each final grid image will fea- ture only a single modified cell. We proceed follows: for each of the 7 grid dimensions, we define two separate base settings. Each of these two settings for a given grid dimension involves selecting different, unique cell location to be the sole anomaly cell for images generated under that specific setting. These potential anomaly cell locations are carefully chosen to avoid edges and corners. This gives (7 grid dimensions a single anomaly cell location per dimension) = 14 distinct base settings. For each of these base settings (defined by grid dimension and the location of its single anomaly cell), then apply all combinations of grid types and their respective modifications to generate the final images, each still containing only that one pre-detemmined anomaly.
distinet choices of
Image resolutions: Each generated grid image is rendered 3 different pixel sizes (384, 768, 1152/px to assess sensitivity to image resolution.
This systematic generation process yields a total of 2 (grid types) x 2 (modification types) x 14 (unique scenarios) x 3 (resolutions) 168 distinct images.
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K.2 IMPLEMENTATION AND PROMPTS
Implementation details Our implementation generates systematic pattern grids using distance- from-edge algorithm create naturally sereasing-then-derree numerical patterns. For dice grids, we use circular dots arranged in traditional dice-face configurations (1-6 dots per cell). For tally grids, we render authentic tally marks with proper grouping (four vertical lines crossed by diagonal fifth line).
The algorithm for both grid types follows the same sequence:
1. Generate base grid with pattern-consister cell counts using distance-from wwwm-frrooeeeeeee -dge calculation
2. Organize target positions across 14 groups, with each group containing both dice and tally variants
3. For each target cell, create modification variants:
Dice: Remove dot OR replace one dot with alternative shape (triangle, square, star)
Tally:
Remove one line OR add one extra line
4. Render each modified grid at three different resolutions with consistent visual quality
The grid-specifi, implementations required special handling for:
Dice pattern consistency: Maintaining standard dice-face arrangements (1-6 dots) while allowing single-dot modifications
Tally mark authenticity: Proper grouping of marks with diagonal crosses for every fifth line
Pattern calculation: cestance-from-egee algorithm ensuring natural numerical progression across grid cells
Cell positioning: Strategic selection of anomaly cells away from edges to preserve pattern context
To ensure consistent image quality and valid pattern recognition challenges across all variants, we implemented several technical measures:
Quality control
SVG to PNG conversion: We used direct SVG rendering with adjustable scaling factors based on target resolution
Quality scaling: We applied quality multiplier (5.0x base resolution factor) to ensure clear shape and line visibility
Prompts We use consistent prompts across both grid types to test VLMs' pattern recognition versus actual visual counting:
1. Counting questions (Q1 & Q2):
Q1 (Dice): How many circles are there in cell [CellID]? Answer with a number in curly brackets, e.g., (9].
Q1 (Tally): How many lines are there cell [CellID]] Answer with number in curly brackets, e.g., (9).
Q2 (Dice): Count the circles in cell [CelllID]. Answer with number in curly brackets. e.g. (9).
Q2 (Tally): Count the lines in cell [CellID). Answer with number in curly brackets. e.g. (9).
2. Y/N identification questions (Q3):
Q3 (Dice): Does cell [CellID] contain (ExpectedCount) circles? Answer in curly brackets, e.g., (Yes) or (No).
Q3 (Tally): Does cell [CellID] contain (ExpectedCount) lines? Answer in curly brackets, e.g., (Yes) or (No).
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For all prompts, [CellID] refers to the specific anomaly cell using standard spreadsheet notation (e.g.. C3, F7), and [ExpectedCount] represents the on surrounding cells.
count that would expected based
Ground truth calculation We calculate the ground truth answers based on the actual visual content in each modified cell:
Direct counting questions (Q1 & Q2):
Correct answer: The actual count of visual elements in the target cell after modification For Remove modifications: Standard pattern count minus
For Add modifications: Standard pattern count plus
* For Replace modifications: Standard pattern count minus (since one circle is replaced with a different shape)
Expected bias: The pattern-consistet count that VLMs might infer from surrounding cells, ignoring the actual modification
Pattern-based verification question (Q3):
Correct answer: Always "No" (since the target cell has been modified to break the pattern)
Expected bias: "Yes" (if VLMs rely on pattern inference rather than direct visual inspection)
K.3
QUALITATIVE RESULTS
Abstract Images: Patterned Grid
(a) Dice
(b) Dice Replace
(c) Tally Remove
(d) Tally Add
Remove
+
2
2
/
2
3
X
X
3
X
X
/
3
X
Bias
X
3
Gemini-24 Pro
[
Sonnet-33 3.7
GPT-4.1
o3
04-mini
(a), (b): How many circles are there in cell C3? Answer with number in curly brackets, e.g. (9).
(e), (d): many lines are there cell C3? Answer number curly brackets, e.g., (9).
Figure 33: All VLMs, except E Sonnet- -3. 3. 7, fail correctly identify the abnormal cell (C3) in both the Hi patterned grids.
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L DETAILS OF PROMPTS
L.1 EXAMPLES OF Q1, Q2 AND Q3
Table 30: Some examples of questions on Yn animal, ① brand logos, and F flags
Topic
Subtopic
Q2
Q3
Animal
How many legs does this animal have? Answer with number curly brackets, e.g., (9).
Count the legs of this ani- Is this an animal with 4 legs? Answer in curly brackets, e.g.. [Yes) or (No).
mal. Answer with num- ber in curly brackets, e.g.. (9).
Adidas
How many visible stripes are there in the logo of the left shoe? Answer with number in curly brackets, e.g.. (9).
Count the visible stripes in the logo the left shoe. Answer with number in curly brackets, e.g., (9).
Are the logos these shoes Adidas logos? An- swer curly brackets, e.g. (Yes) or (No).
Logo
Nike
How many visible white stylized curves are there the logo of the left shoe? Answer with number in curly brackets, e.g.. (9).
Count the visible white stylized curves in the logo
Are the logos on these shoes Nike logos? An- on the left shoe. Answer swer in curly brackets, with number in curly e.g., (Yes) (No). brackets, e.g., (9).
Mercedes
How many points are there on the star in the logo of this car? Answer with number in curly brackets, e.g., (9).
Count the points on the star in the logo of this car. Answer with number in
Is the logo on this car Mercedes-Benze logo? An- swer in curly brackets, e.g. [Yes) (No).
curly brackets, e.g., (9).
Audi
How many overlapping circles there in the logo this car? Answer with a number in curly brackets, e.g., (9).
Count the overlapping cir- cles in the logo of this Answer with a number in curly brackets, e.g.. (9). (No).
Is the logo this car Audi logo? Answer in curly brackets, e.g., (Yes) or
Maserati
How many prongs are there the logo of this
Count prongs in the Is logo on this car Maserati logo? Answer in curly brackets, e.g. (Yes) or (No).
logo of this car. Answer with number curly
car? Answer with a num-
ber in curly brackets, e.g. (9).
brackets, e.g., (9).
Stars
How many stars are there on this flag? Answer with number curly brackets, e.g.. (9).
Count the stars on this flag. Answer with number in curly brackets, e.g., (9).
Is this the flag of [coun- try]? Answer in curly brackets, e.g., [Yes] (No).
Flag
Stripes
How many stripes are there this flag? Answer flag. Answer with try]? Answer in curly with number curly brackets, e.g., (9).
Count the stripes on this
Is this the flag of [coun-
ber curly brackets, e.g., brackets, e.g., (Yes) or (No).
(9).
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Table 31: Some examples of questions on chess pieces, |l game boards, and ti patterned grids.
Topic
Subtopic
Q1
Q2
Q3
Chess
How many chess pieces are there on this hoard?
Count the chess pieces on this board. Answer with a number in curly brackets, e.g. (9).
Is this the chess starting position? Answer in curly brackets, e.g. (Yes) (No).
Chess Pieces
Answer with number in curly brackets, e.g., (9).
Xiangqi
How many xiangqi pieces there this hoard? Answer with number in curly brackets, e-g.. (9).
Count the xiangqi pieces
Is this the Xiangqi start- ing position? Answer in
on this hoard. Answer with number in curly curly brackets, e.g.. (Yes) [No).
brackets, e.g., (9).
Chess
How many rows there this board? Answer with a number in curly brackets, e.g., (9).
this Is this 8x8 Chessboard? board. Answer with Answer in curly brackets, number curly brackets, e.g. (Yes) or (No]. e.g., (9).
Count the
Board Game
Xiangqi
How many horizontal lines there this board? Answer with a number in curly brackets, e.g.. (9).
Count the horizontal lines this board. Answer board? Answer in curly with a number in curly brackets, e.g. (Yes) or (No).
Is this 10x9 Xiangqi
brackets, e.g., (9).
How many horizontal Count the horizontal lines this 19x19 Go board? lines are there on this on this board. Answer Answer in curly brackets, board? Answer with with a number in curly e.g., (Yes) or (No). number in curly brackets, c.g. (9).
brackets, e.g., (9).
Sudoku
How many rows are there on this puzzle? Answer zle. Answer with num- with number in curly her in curly brackets, e.g., brackets, e.g., (Yes) [No].
Count the rows on this puz-
Is this a 9x9 Sudoku puz- zle? Answer in curly
brackets, e.g., (9).
(9).
Dice
How many circles are Count the circles in cell there cell C5? Answer C5. Answer with num- with a number in curly in curly brackets. e.g., brackets, e.g., (9).
Does cell CS contain 4 circles? Answer in curly brackets, e.g., (Yes) or (No).
Patterned Grid
(9).
Tally
How many lines there Count the lines in cell C5. cell C5? Answer with number in curly brackets, curly brackets, e.g., (9).
Does cell C5 contain Answer with a number lines? Answer in curly brackets, e.g., (Yes) or (No).
e.g. (9).
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Table 32: Some examples of questions on optical illusions.
Topic
Subtopic
Q1
Q2
Q3
Ebbinghaus
Are the two red circles equal in size? Answer curly brackets, e.g., [Yes) or (No).
Do the two red circles have the same size? Answer curly brackets, e.g.. (Yes) [No].
Is this example of the Ebbinghaus illusion? swer in curly brackets, Yes) or (No).
Optical Illusion
Mullerlyer
Are two horizontal lines equal in length? An- swer in curly brackets, e.g., (Yss) or (No).
Do the two horizontal lines have length? Answer in curly brackets, e.g. (Yes) [No).
Is an example Müller-LLyer illusion? swer with Yes/No. An- swer in curly brackets, e.g.. [Ye]] or (No).
same
Poggendorff
Are the two diagonal line segments aligned? An- in curly brackets, e.g., Yes) or (No).
Do the two diagonal lines form straight line? An- in curly brackets, e.g., Yes) or (No).
Is this an example of the Poggendorff illusion? An- swer in curly brackets, e.g.. Yes] or (No).
Ponzo
Are the two horizontal lines equal in length? An- in curly brackets, e.g., (Yes) or (No).
Do the two horizontal lines have the same length? Answer in curly brackets, e.g., (Yes) or or (No). (No).
Is this an example of the Ponzo illusion? Answer in curly brackets, e.g., (Yes)
VerticalHorizontal
Are the horizontal and ver- tical lines equal length? Answer in curly brackets, e.g., ( (Ye)) or (No).
Do the horizontal and tical lines have same length? Answer in curly brackets, e.g., (Yes) or (No).
Is this example of the Vertical Horizontal lusion? Answer in curly brackets, e.g.. [Yes) or (No).
Zollner
Are two horizontal Do the two horizontal this an example lines parallel? Answer lines run parallel? An- Zällner illusion? Answer curly brackets, e.g., (Yes) swer in curly brackets, in curly brackets, e.g. (Yes) (No).
(No).
e.g.. (ee) or (No).
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L.2 PROMPTS USED FOR IMAGE GENERATION AND IMAGE EDITING
Table 33: Prompts used for image generation and image editing with Gemini-2.0 Flash and GPT-4o by topic and prompt type
Topic
Prompt type
Prompt
Animal suggestions
Generate a JSON list containing 100 animal objects. Each object should represent common animal and follow the structure below:
Animals
"name": "<Common Animal Name>", "num_ Legs"
<Typical Number of Legs> }
Ensure the following for each animal: 1. the number of legs of this animal is
2. the animal's legs must be long enough to be seen easily from the body using side-view perspective. Prioritize animals whose legs are thin and/or long.
Animal generation
Generate clear, full-body, side-view image of a(n) :animal) with [num_legs] ) legs that is walking real-world natural background. The [num_legs -legged animal must look photo-realistic in nature. All (num_ legs must be clearly visible.
Animal editing
Edit this image: Add I more leg to the {animal) that it has (num_leg) legs total. The (num_leg] } -legged fanimal) must be photo realistic. All [num]ee] } legs must be clearly visible.
Flag suggestions
Generate JSON list of flags objects. Each object should rep- resent a well-known flags and follow the structure below: "namo": "<Flag Namo>", "original_stripes or "original_stars"
Flags
<Number of Stripes or Stars (whichever applicable)>
1. Ensure that the number of stars
than 3, and the number of stripes least 2. Ensure that flag does contain any other geometrically complex elements (depicting of animal, letters, etc.). 3. Prioritize well-known flags.
Flag SVG code editing
are an expert in editing SVG image code. Modify the SVG code the flag of /country according to the following instruction:
Instruction: "The flag of ccontryy! ry} has [num_ele] e (e (element) ).
Modify the SVG code that it has num_en setm_ele_ + 1 {el (element instead. Make sure the modified (el ement natural looking and integrate seamlessly on the new flag."
Base SVG code: Isvg coce) [svg_cooe)
1. Modify the base SVG by adding or removing the mentioned feature
(stars, stripes, etc.) according to the instruction above.
2. Wrap the entire SVG <code><</ code>, explain anything.
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Table 34: Prompts used for image generation and image editing with Gemini-2 ni-2. Flash and GPT -oT--oo by topic and prompt type
Topic
Prompt type
Prompt
Logo suggestion
Generate JSON list of subtle logo modification prompts and correspond- ing VLM question prompts to test visual bias. For each entry: Slightly modify visual components
Logos
well-known car or sportswear logo. The selected logo must be geometrically simple and widely recognized.
You must include a generation prompt create the altered image. Include question prompt (e.g., "How many..."), Include metadata: element being modified, actual count (ground truth), common expected count (bias).
<Ia-context learning example 1> <In-context learning example 2>
Shoe generation
Generate an (shce_brand) style running shoe but with (actual_count] fiedeeleee_ instcad of [expecte_bbias)
Shoe hackground generation
Generate side-view image an athlete wearing this pair shoes. Keep all the fine-grained details of the shoes, particularly the lactual_couut count /modified element on both shoes. The per- is playing (sports spprrss_tppe showing their sports type skills, and wearing a (sports_type) outfit. Zoom bit see their full body.
Car logo generation
Generate {car_ (car_brand) logo but with (_ount)
(modi modiifie__e ement: instead of (expected_bias:: bias]
Car background generation
Generate photo-realistic front-view image of a :color) (car [ar__rrad] (body_typa) on the road in the middle of the day. Zoom bit that we see the road.
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L.3
PROMPTS FOR SANITY CHECK
Table 35: Examples Sanity check questions
Topic
Identification questions
Counting/lllusion questions
Animal
What animal is this? Answer curly brack- cts, e.g., [Fish].
How many legs this animal have? Answer with number in curly brackets, c.g., (9).
Logo
What car logo is this? Answer in curly brack- cts, e.g., (Toyota).
How many overlapping circles there the logo of this car? Answer with number curly brackets, c.g., (9).
Flags
What country flag is this? Answer in curly brackets, c.g., (Flag of Victnam).
How many stars there in this flag? Answer with number in curly brackets, c.g., (9).
Chess Pieces
What board game is this? Answer in curly brackets, e.g.. (Shogi).
How many chess pieces are there on this board? Answer with number in curly brackets, e.g.. (9).
Game Boards
What board game this? Answer in curly brackets, e.g., (Shogi).
How many rows are there on this board? Answer with number in curly brackets, e.g., (9).
Optical Illusions
What optical illusion this? Answer in curly brackets, e.g., (Delbocuf illusion).
This image shows the Ebbinghaus illusion. What ques- tion does this illusion typically ask, and what the correct answer?
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M ADDITIONAL QUALITATIVE RESULTS
M.1 QUALITATIVE RESULTS ON THE USE OF HELPFUL PROMPTS
How many legs does this animal have? Answer with number in curly brackets, e.g., (9).
Baseline: How many legs does this animal have? Answer with number in curly brackets, e.g., (9).
w/ Double-Check: Please double-check your answer and give your final answer in curly brackets, following the format above.
w/ Debiased Prompt: Do not assume from prior knowledge and answer only based on what is visible in the image.
Ground truth: 3
Gemini-2.5 Pro (Baseline): (2) X
Gemini-2.5 Pro (w/ Double-Check): (2)
Gemini-2.5 Pro (w/ Debiased Prompt): (2)
Sonnet-3. (Baseline): The image shows rooster (male chicken) standing on grassy field. Chickens have two legs, which are clearly visible in this image. The rooster is standing upright on its two pale yellow/tan legs with its feet planted the grass. (2) X
Sonnet-3. (w/ Doublc-Check): Looking the image again, can elearly see the rooster standing on two legs. Chickens are hipedal animals, with two legs that they use for walking and standing. In this particular photo, both of the chicken's 's legs are visible beneath its hody. (2) x ? Sonnet-3.. (w/ Debiased P Prompt.): Based solely what is visible in the image, this chicken has [2) legs. The image clearly shows rooster standing grassy field with its aellowtan-colores legs and visible beneath body. X
GPT 4. (Baseline)) (4) x
GPT 4.1 (w/ Double-Check): (4) X
8 GPT (w/ Debiased Prompt): (4)
o (Baseline): (2) x
o3 (w/ Double-Checks (2) X
o3 (w/ Debiased Prompt): (4)
04 mimmmnn (Baseline): (2)
o 44-mmiin..m (w/ Double-Check): (2) X
04 04-mini (w/ Debiased Prompt): (4) x
Figure 34: VLMs fail 100% of the time, simple tasks like counting chicken legs, despite helpful prompts such as debiasing or double-checking.
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How many stripes are there in this flag? Answer with a number in curly brackets, c.g., (9).
Baseline: How many stripes are there in this flag? Answer with number in curly brackets, e.g., (9).
w/ Double- Check: Please double-check your answer and give your final answer in curly brackets, following the format above.
w/ Debiased Prompt: Do not assume from prior knowledge and answer only based on what visible in the image.
Ground truth: 12 /
Gemini-2. Pro (Baseline): [13)
Gemini-2.5 Pro (w/ Double-Check): [13] X
Gemini-2.5 Pro (w/ Debiased Prompt): [13) x
Sonnet-3: -37 (Baseline): The image shows the flag of the United States of America. The American flag has 13 alternating red and white horizontal stripes red and white), representing the original colonies. The flag also features stars, representing the 50 states. To answer your specific question: (13) X
rectangle in upper left corner containing 50 white
Sonnet-3. (w/ Double e-hhck): I'll double-check the American flag's design. The flag of the United States consists of:
blue rectangle in the upper left comeer (the union) containing 50 white stars
Horizontal stripes alternating between and white. Looking more carefully the image, can count 13 horizontal stripes total (7 red and 6 white).
The final answer is: (13) X
A Sonnet-3 7 (w/ Debiased Prompt): Based only what visible in the image, can the American flag with distinctive design. The flag alternating red and white horizontal stripes, image shown, can count total of (7) stripes. There are red stripes and 3 white stripes visible in this particular rendering of the flag.
GPT 4.1 (Baseline): (13)
GPT 4.1 (w/ Double-Check): (13)
GPT CPT-4.1 (w/ Debiased Prompt): (13) x
o3 (Baseline): (13)
(w/ Double-Check): (13) X
o3 (w/ Debiased Prompt): (13) x
4 mini (Baseline): (13) x
4 m-mmmn (w/ Double-Check): (13) X
4-mini (w/ Debiased Prompt): (13)
Figure 35: VLMSs fail and remain biased toward even when one stripe is removed from the U.S. flag.
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M.2 QUALITATIVE RESULTS ON THE USE OF LOCATE- LOCATE-THEN THEN COUNT PROMPTS
First, locate each leg individually, count them by one, and then state the final number in curly brackets, c.g., (9).
Ground truth: 3 /
Gemini-2.. Pro:
Here the legs of the stork:
is standing on right leg.
The stork's left leg is lifted off the ground. I have counted (2) legs. X
Sonnet-3 3.
This image shows a white stork (Ciconia ciconia) standing a grassy field. The hird distinctive white plumage with black wing feathers, long bright red bill, and long thin red legs. Looking at the legs individually:
Leftt leg: red leg visible
Right leg: red leg visible
The stork appears to be tagged with small white band on one of its legs, which is likely for tracking or
research purposes. (2) x
The stork is standing in lush green environment with some small yellow wildflowers visible in the background. These birds are known for their large nests, migratory behavior, and culturally significant many European countries where they're re associated with bringing babies.
GPT-4 1:1:
The first leg is visible the left side of the bird. The second leg visible on right side the bird.
Final number: (2)
03:
First leg: the long straight red leg supporting the bird's weight.
Second leg: the bent red leg lifted slightly behind/above the first. (2) x
o4-mini:
Leg I: the front leg visible the grass
Leg 2: the rear log supporting the body (2) X
Figure 36: Despite explicit prompting to locate and count each leg individually, all 5 VLMS fail on the 3-legged stork, incorrectly answering 2 instead of the ground truth
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First, locate each leg individually, count them by one, and then state the final number in curly brackets, e.g.. (9).
Ground truth: 5 /
Gemini-22 1ni-2.55 Pro:
Here are the four legs of the lion:
1. The front right leg is planted on the ground
The front left leg is visible behind the front right leg.
The hind leg stretched back with paw on the ground.
4. The hind right leg is visible moving forward, positioned between the other three visible legs.
[4] x
Sonnet--3 3.
This image shows a magnificent male lion standing in profile on dry savanna grassland. lion distinctive dark mane around its head and neck, contrasting with its tawny-colored body.
Looking at the legs individually:
Front left leg clearly visible
Front right leg is visible
Back left leg is visible
Back right leg is visible
The lion has [4) legs X, which is the normal number for lions and other big cats. The image captures the lion in beautiful golden lighting, likely during sunrise sunset, which enhances the rich colors of its coat and mane. The background shows a blurred savanna landscape with dry grass stretching into the distance.
GPT-4.
Let's count each leg individually:
The front right leg (closest to us).
2. The front left leg (behind the front right leg, partially visible).
3. The back right leg (middle, partially visible).
4. The back left leg (furthest back, fully visible).
There are four legs in total. The final number (4). X
o3:
Front leg closest the viewer (left side of the image).
Front leg the far side (partly visible behind the mane).
Hind leg closest to viewer (left side, extending back).
4. Hind leg on the far side (partly visible hehind the nearer hind leg).
(4) X
o4-mini:
I can identify four distinct legs:
1. Front left leg
2. Front right leg
Hind left leg
Hind right leg
(4) x
Figure 37: Locate-then-count prompting fails on the 5-legged lion, with all VLMs defaulting to the biased answer 4 rather than accurately counting the visible legs.
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M.3 QUALITATIVE RESULTS ON POINTING VLMS
Moandream 2H's successful examples
(a) How many legs does this animal have?
(b): How chess pieces are there this board?
(c): How many stars are there in this llag"
(d) How many legs does this animal have?
(e): How many visible white stripes are there the logo of the left shoc?
(a) Elephant
Chess
(c) EU Flag
(d) Stork
Adidas
S.
32
4
32
X
12
X
X
3
6
32
X
12
X
X
3
4
31
/
12
X
X
4
4
32
X
12
3
X
5
/
31
/
11
/
/
4
Bias
4
X
32
X
12
x
X
3
X
GT
5
31
/
4
Gemini-2.5 Pro
C
Sonnet-3.7
GPT-4.1
o3
04-mini
Mcondream-28
Figure 38: Moondream am 2B usually counts accurately when the distance between objects is far enough apart and large enough.
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Moondream- 2B's failure examples
(a): How chess pleces are there on this board?
(b): How many columns there this puzzle?
(c): How many lines are there in cell D4?
(d) How many cireles are there in cell C3?
(c) How many stripes are there on this flag?
(a) Chess
(b) Sudoku
(c) Tally
(d) Dice
(c) US Flag
00
AEGO
X
+
32
X
X
4
X
/
13
X
32
X
4
X
X
13
31
X
4
X
13
4
50
7
X
Bias
X
4
13
X
GT
3
Gemini-2.5 Pro
[
Sonnet-3.7
AB
GPT-4.1
o3
04-mini
Moondream-2B
Figure 39: @ Moondream- 2B often fails to count accurately when objects are too close together it doesn't 't understand what the objects (a, e). It also sometimes fails localize the object correctly (c. d).
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Mo Molmo 1moo-73'' successful examples
(a) Count the legs of this animal.
(b): Count the chess pieces on this board.
(c): Count the stars this faag
(d) legs animal.
(e): Count the visible white stripes in the logo of the shoe.
(a) Elephant
(b) Chess
(c) Australia Flag
(d) Chicken
(c) Nike
X
32
2
2
x
32
X
X
4
2
X
32
X
2
2
128
31
/
3
2
Bias
X
32
X
6
2
X
GT
11
Gemini-2.5 Pro
Sonnet-3.7
GPT-4.1
o3
o4-mini
Molmo-728
Figure 40: å Molmo- 72B usually counts accurately when the distance between objects far enough apart and large enough.
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folmo--723's failure examples
black stripes in logo the shoe. (b): Count the borizontal lines on this board.
(a):
Count
(c): Count the lines cell C3.
(d) Count the circles cell D4.
Count the stripes this flag.
(a) Adidas
(b) Go
(c) Tally
(d) Dice
(c) US Flag
$
ADCDEFORT
3
19
X
3
13
X
19
3
8
19
3
3
19
X
3
X
X
13
X
3
19
X
3
X
X
13
X
3
x
15
X
4
x
12
X
9
X
Bias
3
19
X
3
X
X
13
X
GT
4
18
2
14
Gemini-2.8 2.5 Pro
Sonnet-3.2
GPT-4..
x o3
2
o4-mini
Molmo-72H
Figure 41: å Molmo-725 72B often fails count accurately when objects are too close together or it doesn't understand what the objects are (a, b, e). It also sometimes fails to localize the object correctly (c. d).
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M.4 QUALITATIVE RESULTS ON FEW-SHOT PROMPTING
Few-shot prompting
First example: This is 5-legged animal
Second example: This is legged animal
Based on the examples provided above, please answer the following question: How many legs does this animal have? Answer with number in curly brackets, e-g., (9). After providing your answer, briefly explain your reasoning and describe what you observed in the examples.
Question:
o4-mini: [4) Explanation: In the two examples, both the giraffe and the bighorn sheep are four-legged mammals (the "5-legged" label on the giraffe was clearly mistaken). Tigers, like other large cats, also have pair of forelegs and one pair of hindlegs, for total of four. X
Ground truth: 5
Figure 42: @
doesn't trust the label of the first few-shot example.
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Few-shot prompting + strong labels
First example:
This is 5-legged animal, which has been verified.
Second example: This is a 4-legged animal, which has been verified.
Based on the examples provided above, please answer the following question: How many legs does this animal have? Answer with number in curly brackets, e.g., (9). After providing your answer, briefly explain your reasoning and describe what you observed in the examples.
Question:
o4-mini: (4) counted the visible limbs in the third photo tiger has four legs. In the examples, both the wildeheest and the antelope quadrupeds (each with four limhs), even though the first caption mistakenly claimed five legs. By actually observing their limb count, it's clear tiger, like the others, has four legs.
Ground truth: 5 /
Figure 43: Even after adding strong labels to the few-shot examples (i.e., "This is a 5-legged animal, which has been verified."), & o4 -mini still thinks it's wrong for a caption to say an animal has legs.
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Few-shot prompting strong labels
First example: This is 5-legged animal, which has been verified.
Second example: This is 4-legged animal, which has been verified.
Based on the examples provided above, please answer the following question: How many legs does this animal have? Answer with number in curly brackets, e.g., (9). After providing your answer, briefly explain your reasoning and describe what you observed in the examples.
Question:
o4-mini: (4) simply counted the visible limbs on the cat there are four paws on ground. first example (the warthog) the text matched actual count of legs, in second (the elk) the text claimed five legs even though there were really only four. That shows you can't an't trust the labels and must actually look at the image to count.
Ground truth: 5
Figure 44: Even after adding strong labels to the few-shot examples (i.e., "This is 5-legged animal, which has been verified")). 04 04-mini still thinks it's wrong for a caption to say an animal has legs. More importantly, teaches users not to trust labels and to use their own eyesight.
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Few-shot prompting strong labels + hint
First example: This is 5-legged animal, which has been verified.
Second example: This is a legged animal, which has been verified.
Question: Based on the examples provided above, please answer the following question: How many legs does this animal have? Answer with number in curly brackets, e.g., (9).
HINT: This is an animal with an unusual number of legs. After providing your answer, briefly explain your reasoning and describe what you observed in the examples.
14-mit o4-mini: [6] counted six distinct limbs on the tiger photo. The first example was deliberately shown with five legs (one extra), the second normal four-legged elephant, and this third im- age again departs from usual fougelegged pattern by adding two extra tiger legs, for total of six.
Ground truth: /
Figure 45: After adding an explicit hint to indicate that this an image of an animal with an unusual number of legs, / o4 mini still counts incorrectly (i.e., 6 legs).
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M.5 QUALITATIVE RESULTS ON 04-MINI CHAT INTERFACE WITH TOOLS
The Lsv adlin, cal
Hanrary sar ti
Thoight 20 recands
Irapeatroe diftrcl ago
mone anrtice mary cools
bad Euctigs ioca
loy.
plabs malingt rtuals tarrr hane
Qcaldty the comes
eceetel tntthe fook serta
Thoggh far aesinal
Figure 46: Successful tool use case: 04-mini autonomously crops the image to focus on legs (bottom) and correctly counts (5) legs, overcoming the bias toward the canonical answer Top: Original image. Bottom: Cropped region after tool execution. This demonstrates that vision tools can help models overcome knowledge priors when used correctly.
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Thinking
The mas aloone con voridrs frome
conndd on On
ben alr veiile On be sira thod
pnd logs coeen viuill
Thought
Simard whil BSd jon slord
thatina coaa lociad rear sndee with the
the lioe cantor nov diica the com mation
off onferrccasions
Coundre men' leco
Mestia and right log siiide
me bocke undie mncray
bust chettei futt purdonirg
volél lnte
iipeciall bisty,
infival
hocoms butt thard lone les
the wriiig regual turder cially.
anaboed eaace
Figure 47: Tool use failure: o4-mini activates cropping but incorrectly focuses on only the front legs (bottom), outputting X instead of 5 Top: Original 5-legged cow. Bottom: Incorrectly cropped region missing rear legs. This demonstrates that correct localization is crucial. The model's reasoning shows it examined the incomplete crop and concluded "all four legs are visible," revealing how poor tool execution fails to overcome bias.
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Reviews:
[{"review_id": "h3sWN1Zaqb", "reviewer": "Reviewer_CH8x", "summary": "This paper investigates how prior knowledge biases vision-language models (VLMs) rather than visual evidence when performing objective counting and identification tasks. The authors propose VLMBias, a benchmark of 1,392 counterfactual (CF) images across seven domains (animals, logos, flags, chess pieces, game boards, optical illusions, patterned grids), where well-known objects are systematically modified by adding or removing signature elements. They evaluate 5 SOTA VLMs (Gemini-2.5 Pro, Sonnet-3.7, GPT-4.1, o3, o4-mini), showcasing that they achieve 100% accuracy on unmodified images but drop to 17.05% on CF images, and answers align with the predefined bias option 75.70% of the time. On yes/no identification, accuracy collapses from 100% on originals to 25.11% on CFs. The authors show further analyses, demonstrating that this bias is exacerbated by contextual cues in the image background and is not easily overcome by simple prompting strategies like few-shot examples or double-checking the answer, resulting in minimal improvements.", "questions": "- Unifying the benchmark leaderboard? The paper could be strengthened more by combining the results of all the models, including GPT-5/Grok and open-source models (Molmo, Moondream, Qwen, Pixtral) - Tables 2, 12, 13, and 16 into a single main-paper table. This would greatly improve readability and comparison, avoiding appendix hunting.\n\n- Although linear probing the vision backbone of the VLMs helps to state that it contains information but it is still being trained (Section A.8). The paper can benefit from using interpretability methods like attention/attribution visualization from the (frozen) vision backbone to show whether models still attend to the edited regions, yet output wrong answers.\n\n- The paper presents perplexing results. For instance, the zero or near-zero accuracy of Gemini-2.5 Pro and other models on several tasks (Table 2) is striking. Is this due to answer-formatting, decoding policy, or genuine content failure? A short diagnostic breakdown (format vs. content error) would help interpretability.\n\n- How do the models perform when provided with both the original and counterfactual images together? Are the models able to identify when asked to compare the two images side by side?\n\n- For original vs counterfactual experiments. Why does the original set contain 66 unmodified images (line 291) compared to a bigger set for modified images? How was this subset chosen? \n\n**Minor Comments:**\n\n- Line 291 - Table 2 is wrongly referenced as Table 21\n\n- Line 1430: performnace -> performance", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well written and is easy to follow. The authors also report consistency in findings across 5 runs for select models. Do the numbers in the table show an average of 5 runs or just 1 of 5 runs?\n\n- The papers show a comprehensive evaluation of 5 SOTA closed-source models (Gemini-2.5 Pro, Sonnet-3.7, GPT-4.1, o3, o4-min) in the main paper. In the appendix, they show 2 more recent versions of closed-source SOTA models (Grok and GPT-5) and 4 open-source models (Qwen, Pixtral, Molmo, and Moondream) to strengthen the generality of the findings\n\n- This paper highlights a critical vulnerability: a tendency to \"hallucinate\" based on priors rather than perceive what is actually present. It also exhibits actionable insights, such as simple pre-processing of background removal, which reduces the bias.", "weaknesses": "- The paper's primary weakness is that while the benchmark dataset is novel, the core finding that large models rely on learned priors and can fail on out-of-distribution or counterfactual inputs is well known, as highlighted in related works. The title highlights general bias for VLMs, but the paper shows only for two specific tasks - counting and identification. Also, VLMs cannot count reliably is also being explored. It is a well-known bias that the VLMs fail at counting problems [A, B].\n\n- The authors mention it is an automated pipeline for creating the benchmark, but there is a human evaluation at the end of data generation, where each data is either accepted or rejected. Seeing table 1, it seems that benchmark creation is fully automated, but it would be a human-in-the-loop system. Full automation could yield to bigger benchmark dataset than 1,392 images.\n\n- The paper demonstrates that simple prompts are ineffective, which is useful. However, the strong performance of pointing VLMs (e.g., Moondream) suggests that forcing localization is key. The authors could have explored more descriptive prompting, chain-of-thought, or other prompts (e.g., \"First, locate each stripe individually, count them one by one, and then state the final number\") to encourage a more procedural approach.\n\n- The current benchmark focuses on additive modifications, for example adding a leg to the animal (2 legs to 3 legs, 4 legs to 5 legs) and adding stripes (3 stripes to 4 stripes), which often result in visually implausible subjects as mammals don’t have 5 legs but the image is more likely to have 3 (or 2) (visibly) legged mammals due to occlusion photographic angles. How do the VLMs perform then?\n\n**References:**\n\n[A] Yin, Zhenfei, et al. \"Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark.\" Advances in Neural Information Processing Systems 36 (2023): 26650-26685.\n\n[B] Xu, Peng, et al. \"Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models.\" IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).", "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": "ZINP26Q7Iu", "reviewer": "Reviewer_nh3P", "summary": "In this paper, the authors evaluate the degree to which vision-language models are biased towards prior knowledge learned during pre-training when encountering images that exhibit counterfactuals.  In particular, they focus on images where the count of a particular object (an animal’s legs, the stripes in a well-known logo, etc) are different than expected in the canonical case.  In addition to counting, they include modified optical illusions (though there are only a handful of these).  They find in all of these cases, state-of-the-art foundation models struggle to actually look at the images they’re presented, often defaulting to responses that reflect the canonical or dominant count / illusion.  Curiously, they find that dropping the background of counterfactual images dramatically reduces bias.  Finally, they show that while reasoning tokens can help, it results in a ceiling performance of ~40% after which point additional reasoning hurts.", "questions": "The pattern matching results reads differently than the other counting results – a bias towards in-context knowledge rather than learned knowledge.  I wonder if the authors can quantify the relative strengths of these somehow – perhaps through images with multiple animals with extra legs and a final animal with the expected number of legs (or vice versa).  \n\nCan the authors expand on their background removal result at all?  As mentioned above, it seems at odds with their claims that the LLM is driving the numeracy bias given that the change is primarily in the visual encoding.  Are models less good at recognizing what the object is when the background is removed (and as such, can’t rely on knowledge in its weights to answer the counting question?).", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The writing in this paper is very clear and the figures / results are presented in a way that makes their takeaways easy to grasp.  Overall, it is extremely polished in appearance.\n\nI appreciate the authors’ rigor in using images generated from multiple different models, testing with multiple image resolutions (and, for their visual illusions, varying illusion strength) and testing with multiple runs.  \n\nI also find the background removal & inverse scaling results quite interesting and a nice contribution to the existing body of research that explores common knowledge biases in VLMs.", "weaknesses": "### Conflation of knowledge prior and counting deficiencies \n\nThe authors argue that existing commonsense knowledge benchmarks with synthetically generated counterfactuals (like Zhou et al. 2023 and Bitton-Guetta et al. 2023) exhibit language prompt biases which set VLMs up to fail.  While this seems plausible, it is also well documented that VLMs struggle with counting (see “Teaching CLIP to Count to Ten” by Paiss et al, ICCV 2023 or “Understanding the Limits of Vision Language Models through the Lens of the Binding Problem” by Campbell et al, Neurips 2024).  As model counting is a core prerequisite for the tasks the authors present, it seems important to disentangle counting deficiencies from the knowledge prior bias for the authors to make their claims.  I suspect that counting fails at a higher rate when there’s a counterfactual with a canonical count at play but can the authors show this?\n\n### Narrow focus on counts\n\nNearly all the tasks the authors choose (with the exception of optical illusions, of which there are only 6 types) probe the reliance of VLMs on canonical counts they may have seen during pre-training.  While this is sufficient to demonstrate a knowledge bias in VLM numeracy for the class types they select (animals, logos, flags, game boards), this is quite narrow.  This work would be strengthened with an expansion to other kinds of knowledge biases models may exhibit (for example, color, shape, material, size, relative position, etc). \n\n### Novelty\n\nAs the authors note, prior work from 2023 (Zhou et al and Bitton-Guetta et al) have already explored synthetically generated counterfactuals for commonsense knowledge in a way that has broader coverage than the focus on numeracy in this work (as discussed above).  The authors argue that their work improves on this prior work as they have carefully constructed neutral statements to test model performance while prior work prompt models with questions where the answer according to the language prior is wrong.  Can the authors quantify the effect of this neutral framing on their results?  How much do their results change with non-neutral prompts?  How important is this – after all, the existing benchmarks are biased towards generating the wrong answer so to do well on them models really do need to “look at” their images (which is the goal after all?).  That doesn't seem like that much of a deficiency, if at all?\n\n### Source of bias\n\nThe authors claim the source of the numeracy bias they observe is predominantly from textual pre-training (lines 25-31, 90-92, 287) but I think this merits stronger motivation through experimentation.  For example, the background removal results seem to suggest that the bias might be an artifact of the training of the vision encoder.  In line 363, they reference linear probing results on CLIP that they perform and while this does suggest that numerical information is expressed in CLIP’s vision embeddings, it does not mean that the bias is from the LLM.  For example, the issue might be in multimodal alignment (the numerical information is not properly mapped into the model’s text embedding space so the LLM is not ignoring the count information so much as extrapolating based off the coarse visual information it is receiving).  The authors might better tease these results apart by training similar probes on visual representations after mapping to the LLM’s space.  Additionally, it would be illuminating to 1) compare probe performance here on images with no canonical counts (to account for issues with counting in general in these models) and 2) see if a probe trained to count logo elements can generalize to animal legs or vice versa.\n\n### Nitpicks\n\n\"Are VLMs biased?\"  is quite broad/vague title that goes well beyond the experiments / claims in the paper which are more narrowly focused on biases in VLM numeracy.  I think the paper would benefit with a narrower / more specific title.\n\nAdditionally, “these tasks cover the most common subjects natural and manmade” reads like a bit of an overclaim.", "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": "MfCPPK4ulw", "reviewer": "Reviewer_6zwz", "summary": "This paper introduces VLMBias, a benchmark designed to systematically measure visual biases in Vision-Language Models (VLMs) when objective visual facts conflict with common textual priors. The authors show that current state-of-the-art VLMs—including Gemini-2.5 Pro, GPT-4.1, o3, o4-mini, and Claude 3.7 Sonnet—perform very poorly on simple counting and identification tasks when the images contradict “common sense” visual facts (e.g., a four-striped Adidas logo or a three-legged bird).\n\n## Key findings\n\n1. Mean accuracy across seven domains (animals, logos, flags, chess, board games, optical illusions, and patterned grids) is only 17.05%, with 75.7% of answers biased toward prior knowledge.\n2. Removing background context roughly doubles accuracy (+21 points).\n3. Longer reasoning (more tokens) initially improves performance, but “overthinking” degrades it.\n4. Bias persists even in the newest “thinking” models and tool-using agents, suggesting the bias originates in the language priors of the underlying LLM, not vision encoders.", "questions": "1. **Extremal counterfactual realism**: How do models behave when the counterfactual modification becomes visually implausible, such as an animal with only one leg or no legs at all? Including such non-realistic or extreme manipulations could reveal whether VLMs fail gradually as realism decreases or exhibit a sharp threshold where visual absurdity triggers default, biased responses.\n2. **Mitigation Experiments**: It remains unclear whether the authors have explored any debiasing interventions to evaluate how VLMBias might function beyond diagnostics—as a tool for improving model training. Given that the paper convincingly identifies where and how visual bias manifests, it would be valuable to test whether counterfactual data augmentation, explicit visual grounding objectives, or reweighting of chain-of-thought reasoning could reduce the bias measured by the benchmark.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. **Clear empirical evidence**: strong empirical evidence through a broad and systematic evaluation across seven visual domains—ranging from naturalistic (animals, logos) to abstract (flags, chess boards, optical illusions, patterned grids)—and five major VLM families (Gemini-2.5 Pro, GPT-4.1, Claude 3.7 Sonnet, o3, o4-mini). \n2. **Novel benchmark framing**: benchmark framing is particularly novel in how it redefines the study of bias in multimodal models: rather than focusing on social, linguistic, or demographic biases—as most prior work does—it isolates visual prior bias by designing neutral, objective tasks that require models to rely solely on perceptual evidence rather than memorized world knowledge. \n3. **Investigating the source of the problem**: the authors train a logistic regression probe on frozen features from the SigLIP 400M vision encoder used in LLaVA-OneVision-S to predict leg counts (4 vs. 5). The probe achieves 95.26 % accuracy, while the full VLM using the same encoder performs at random chance (49.7 %) and outputs “4 legs” for 99 % of samples. This striking dissociation confirms that visual representations are accurate, but the language or fusion components override perceptual evidence, providing a clear mechanistic insight into where the bias emerges.", "weaknesses": "1. **Counterfactual design asymmetry**: The benchmark tests animal images modified to have one additional leg (e.g., 5-legged mammals, 3-legged birds) but does not include cases with one fewer leg. This one-sided manipulation limits interpretability of the bias direction—whether VLMs are simply drawn to the most familiar visual prior (e.g., “four legs”) or whether they exhibit asymmetric sensitivity to feature additions versus deletions. Including both types of counterfactuals would provide a more balanced and diagnostic test of how VLMs handle deviations from canonical visual knowledge and help disentangle whether bias strength depends on the magnitude or direction of the visual alteration.\n\n### Minor Comment:\n\nPlease reduce the amount of model and task icons in the text, which—while visually distinctive—make the text visually cluttered and hard to parse. In several key pages, the abundance of small brand logos (e.g., for datasets or models) and overlapping color elements distracts from what's being discussed.", "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": "6TuWGyPypG", "reviewer": "Reviewer_pUPY", "summary": "This work introduces VLMBias, a new benchmark that tests whether VLMs over-rely on memorized prior knowledge instead of actual visual evidence, by asking them to perform objective counting and identification on counterfactual images where subtle but crucial details (e.g., an Adidas logo with 4 instead of 3 stripes) have been altered. It shows that leading VLMs achieve only 17.05% mean accuracy on these counterfactual visual questions and default to biased, knowledge-aligned answers about 75% of the time, despite performing perfectly on unmodified versions of the same objects.", "questions": "- The evaluation largely treats current model behavior as monolithic averages. It reports mean accuracies and bias rates but gives limited fine-grained error analysis/explanation by failure mode, which would help figure out why models fail beyond \"they’re biased.\"\n- Have you conducted any quantitative evaluation of the visual quality or realism of the edited images compared to the original ones, beyond manual inspection and filtering?\n- While these findings are interesting, I find it difficult to determine how they and these benchmarks can realistically drive LLM research and applications.\n- The benchmark intentionally tests minimal edits to expose perceptual bias, and does not explore performance under larger, more salient differences (like a six-stripe Adidas logo).", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Novel Task\n- Easy to Follow\n- Comprehensive Coverage\n- Interesting Findings", "weaknesses": "- No Detailed Explanation\n- Unclear Influence", "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)