Pith. sign in

REVIEW 3 major objections 4 minor 25 references

Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features

T0 review · 3 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Prompt wording that names the target object and demands structured output shifts a vision-language model's attention and raises counting accuracy by up to 0.4.

desk verdict Useful exploratory study with a plausible prompt-specificity result, but the attention-shift evidence is contaminated by an output-length artifact and the quantitative claims are under-reported. read the letter →

arxiv 2509.08266 v1 pith:CMBMQHDP submitted 2025-09-10 cs.CV

Saurav Sengupta , Nazanin Moradinasab , Jiebei Liu , Donald E. Brown This is my paper
classification cs.CV
keywords visionlanguagemodelsobjectcountingpromptspecificitypriorknowledgebiasattentiondistributionmultimodalevaluationsyntheticimagedatasetsvisualquestionanswering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the well-known failure of vision-language models to count altered, familiar objects—like a flag with extra stars—is not fixed only by training; it can be partially corrected by how the question is phrased. Using synthetic images that vary one visual feature at a time and altered real-world images, it shows that prompts which name the target object and require structured output raise counting accuracy across two open-source model families. It also argues that these accuracy gains are accompanied by a measurable internal change: attention shifts away from input text tokens and toward tokens the model itself generates. The broader point is that VLM behavior should be characterized along both image and text dimensions simultaneously, because small prompt changes can matter as much as image changes.

What carries the argument

The central mechanism is the paper's attention-proportion measurement. For each generated token, attention vectors are averaged across all heads and layers, then partitioned into three regions: image tokens, input prompt tokens, and previously generated tokens, yielding the proportions Aimg, Aprompt, and Agen_token. This turns 'where the model is looking' into three numbers, allowing the paper to tie prompt wording and counting accuracy to an observable internal change.

What would settle it

On the Flag Stars data, run a three-way comparison: the paper's generic prompt, a prompt that names stars without JSON, and the full JSON prompt. If the JSON prompt beats the name-only prompt by the reported 0.4 gain, structured output is the driver; if name-only matches it, object specification is the driver. In the same runs, record Aimg, Aprompt, and Agen_token: if accuracy improves while these proportions stay flat, the claimed attention shift is not the mechanism.

Watch

Extended reading notes

Core claim

The paper's central claim is that the prior-knowledge bias documented in earlier work can be partially overcome at inference time by making the prompt more precise. Across Qwen2.5-VL 7B/32B and Kimi-VL-A3B-Instruct, prompts that name the shape or object and require structured output reduce counting error on both synthetic images and altered real-world images; on the flag-star task, accuracy rises by up to 0.4. The paper also reports that more specific prompts shift attention from input text tokens toward previously generated tokens, and that higher counting error coincides with less attention on input tokens and more on generated output. Its broader assertion is that VLM behavior should be s

Load-bearing premise

The load-bearing premise is that the paper's averaged attention numbers really show where the model is focusing; if those numbers mostly reflect mechanical factors of text generation rather than task-relevant focus, the attention-shift explanation loses its meaning.

Editorial extensions

If this is right

  • Prompt specificity becomes a cheap, test-time lever for reducing prior-knowledge bias in counting tasks: rewriting 'how many' as 'detect each target and output coordinates' can recover accuracy without retraining or fine-tuning.
  • Attention proportions can serve as a diagnostic: larger counting error coincides with less attention on input tokens and more on generated tokens, so shifts in these proportions under a prompt change are evidence that the model is reorienting.
  • Image-side variation matters less than text-side variation for attention in this setup: object shape moves attention proportions only slightly, while prompt wording moves them substantially.
  • Model identity shapes error patterns—Qwen undercounts, Kimi overcounts, and the 32B Qwen is more stable than the 7B—so evaluations that average across models can hide systematic counting biases.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's design confounds two changes in its most specific prompt: the target object is named and the output format becomes structured JSON. A natural next experiment would hold object naming constant and vary only the format to see which component carries the 0.4 gain.
  • If attention shift is causal, an untested prediction follows: intervening on attention during decoding—for instance, suppressing attention to input prompt tokens—should reproduce the accuracy gain of precise prompts even with a generic question.
  • Because all three models devote little attention to vision tokens, the counting bottleneck may lie in the visual pathway itself rather than in prior-knowledge bias; testing with novel shapes that have no memorized count would help separate these two explanations.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a multi-dimensional evaluation framework for vision-language models (VLMs), varying image features (object shape, object count) and prompt features (specificity) and measuring counting accuracy plus an aggregated attention decomposition (Aimg, Aprompt, Agen_token). It uses synthetic shape images (1–50 objects), three handcrafted prompts of increasing specificity, three open-source VLMs (Qwen2.5-VL-7B, Qwen2.5-VL-32B, Kimi-VL-A3B), and two real-world datasets from Vo et al. (animal legs and flag stars). The central claim (Section A.7) is that bias from prior knowledge can be partially addressed by specifying prompts more precisely, which shifts attention and improves performance; the paper also reports several secondary observations about attention allocation, counting error trends, and model-specific error biases.

Significance. If the central claim holds, the paper provides a useful practical insight: explicit, structured prompts can mitigate prior-knowledge bias in VLM counting, and the proposed attention decomposition could serve as a tool for studying VLM internals. The paper's strengths are its use of multiple open-source models, hand-crafted synthetic data, and replication of a known bias phenomenon. However, the main contribution is empirical and currently rests on qualitative figure inspection rather than quantitative evidence. The most fragile premise is the interpretation of A_gen_token as task-relevant attention; as discussed below, that interpretation is not established, which weakens the paper's advertised mechanism even though the accuracy improvement may be real.

major comments (3)
  1. [Table 2, Section A.6 item 1] The three prompts are described as differing in 'specificity', but Prompt 3 also changes the task from counting to detection with JSON bounding-box output. For synthetic and flag data, Prompt 3 asks the model to 'Detect all distinct <shape>... output valid JSON format', while Prompts 1–2 request a single number in curly brackets. Thus the accuracy gain (e.g., up to 0.4 in Flag Stars) cannot be attributed solely to prompt precision; it may be caused by the structured-output format, the localization instruction, or the model's detection training. The causal claim 'specifying prompts more precisely improves performance' requires a control that isolates specificity from output format, e.g., a JSON prompt without specificity increase, or a specificity increase without a format change.
  2. [Section A.4, Figures 8–9] The attention-shift conclusion is undermined by a length artifact in the definition of A_gen_token. A_gen_token is the mean over generated tokens of the proportion of attention each generated token pays to previously generated tokens. In an autoregressive transformer, later generated tokens can attend to more generated tokens, so longer outputs mechanically inflate this proportion. Prompt 3 produces long JSON outputs (bounding-box lists), while Prompts 1–2 output one number. Consequently, the reported increase in A_gen_token under Prompt 3 is consistent with an output-length effect, not necessarily a shift in task-relevant focus. The paper provides no position-matched or length-matched control. Aggregating over all layers and heads also risks masking attention-sink behavior, which the paper itself cites in [10].
  3. [Section A.5, Figures 5–12] All quantitative evidence is presented graphically, with no numeric tables, repeated runs, error bars, or statistical tests. Claims such as 'accuracy improves... up to 0.4' (Section A.6 item 1) and 'Qwen-7B shows low and unstable accuracy' cannot be verified or compared across models from the figures alone. For an empirical paper whose main message is about differences between conditions, at least per-condition counts, standard deviations, or confidence intervals are needed. This is load-bearing for the central accuracy claim, not merely a presentation issue.
minor comments (4)
  1. [Section 2 / References] There are formatting issues: 'Qwen2.5 VL(both 7B and 32B Instruct variants) and [11]' should be cleaned up; footnote markers 1 and 2 appear as '12' in the body; and 'V o et al.' should be 'Vo et al.' throughout.
  2. [Section A.4] The terms 'vision tokens' and 'image tokens' are used interchangeably; define once and use consistently. Also specify how image tokens are identified in the concatenated sequence after the VLM's vision encoder.
  3. [Section A.5 / Figures] Figures 5–12 are extremely dense and appear to be screenshots; they are hard to read in print. Provide vector graphics, larger fonts, and numerical annotations so the reported distributions can be inspected.
  4. [Section A.3.3] Decoding hyperparameters (temperature, top_p, max_new_tokens, etc.) are not reported; these affect the length and content of generated outputs and are necessary for reproducibility of both accuracy and attention measurements.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the paper reports externally grounded measurements and does not fit parameters or import its conclusion from self-citations.

full rationale

The paper's central claim is empirical: changing prompt specificity improves counting accuracy against ground-truth counts (e.g., Flag Stars accuracy up to 0.4) and changes measured attention proportions. Counting accuracy is compared to external labels, not to any quantity fitted from the model's own outputs. The attention proportions (Aimg, Aprompt, Agen_token) are operationally defined in Section A.4 as averages over layers/heads of attention to three token regions; no parameter is fitted and no prediction is generated from those proportions. The paper does not invoke a uniqueness theorem or ansatz from prior work by the same authors; citations to Vo et al. are for datasets and replicated baseline results, not for the paper's own conclusions. A skeptical concern that A_gen_token is inflated for long JSON outputs is a validity/confound issue about the attention metric, not a circularity: the reported increase in A_gen_token is not logically entailed by the metric's definition alone (the model could in principle attend only to image/prompt tokens even in long outputs), and the accuracy claim is independently grounded. No self-referential definitions, fitted-input predictions, or load-bearing self-citations are present. Hence circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numbers are fitted to data; all experimental choices (shapes, count buckets, prompt wording, attention averaging) are inspection metrics rather than free parameters. The results rest on domain assumptions about ground truth, surrogate validity, and the attention operationalization; the main unstated confound is that Prompt 3 conflates specificity with structured JSON output.

assumptions (4)
  • domain assumption Object counting accuracy is a valid surrogate for how a VLM responds to a prompt.
    Used throughout Section A.5 and Figures 5-12; the paper's conclusions about prompt specificity depend on counting error being representative of relevant model behavior.
  • domain assumption The averaged attention proportions Aimg, Aprompt, Agen_token (Section A.4) are meaningful measures of where the model focuses.
    The attention-shift conclusion rests on this operationalization; means over all layers and heads may hide systematic variation or attention-sink effects.
  • ad hoc to paper The three prompts differ mainly in specificity, despite Prompt 3 also requiring JSON structured output.
    Prompt 3 adds a JSON localization requirement along with specificity, so accuracy gains cannot be cleanly attributed to specificity alone; the paper treats them together.
  • domain assumption The synthetic images are generated correctly and ground-truth counts are exact.
    Counts come from the image generation pipeline described in Section A.3.1, but no verification or code is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features." pith.science (2026). https://pith.science/paper/CMBMQHDP

@misc{pith2026250908266,
  author       = {Pith},
  title        = {Pith review of: Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CMBMQHDP}},
  note         = {Machine review of arXiv:2509.08266}
}
read the original abstract

Recent research on Vision Language Models (VLMs) suggests that they rely on inherent biases learned during training to respond to questions about visual properties of an image. These biases are exacerbated when VLMs are asked highly specific questions that require focusing on specific areas of the image. For example, a VLM tasked with counting stars on a modified American flag (e.g., with more than 50 stars) will often disregard the visual evidence and fail to answer accurately. We build upon this research and develop a multi-dimensional examination framework to systematically determine which characteristics of the input data, including both the image and the accompanying prompt, lead to such differences in performance. Using open-source VLMs, we further examine how attention values fluctuate with varying input parameters (e.g., image size, number of objects in the image, background color, prompt specificity). This research aims to learn how the behavior of vision language models changes and to explore methods for characterizing such changes. Our results suggest, among other things, that even minor modifications in image characteristics and prompt specificity can lead to large changes in how a VLM formulates its answer and, subsequently, its overall performance.

Figures

Figures reproduced from arXiv: 2509.08266 by the authors.

Figure 1
Figure 1. a. We create a set of images and prompts varying features over the vision and text [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example of prompt specificity changing model results. Guiding the model to focus on [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example of prompt specificity changing model results. Guiding the model to focus on [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Example Images from our synthetic dataset [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Results on Qwen-2.5-VL-7B [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Results on Qwen-2.5-VL-32B 8 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Results on Kimi-VL-A3B [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Results on Qwen-2.5-VL-7B for data from ‘VLMs are Biased’ paper from Vo et al. [1] [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Results on Qwen-2.5-VL-32B for data from ‘VLMs are Biased’ paper from Vo et al. [1] [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: Counting errors on Qwen-2.5-VL-7B 10 [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Counting errors on Qwen-2.5-VL-32B [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: Counting errors on Kimi-VL-A3B 11 [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

25 extracted references · 17 canonical work pages

  1. [10]

    See what you are told: Visual attention sink in large multimodal models.arXiv preprint arXiv:2503.03321, 2025

    Seil Kang, Jinyeong Kim, Junhyeok Kim, and Seong Jae Hwang. See what you are told: Visual attention sink in large multimodal models.arXiv preprint arXiv:2503.03321, 2025

    arXiv 2025

  2. [1]

    Vision language models are biased, 2025

    An V o, Khai-Nguyen Nguyen, Mohammad Reza Taesiri, Vy Tuong Dang, Anh Totti Nguyen, and Daeyoung Kim. Vision language models are biased, 2025. URL https://arxiv.org/ abs/2505.23941

    arXiv 2025

  3. [2]

    Open ai: Introducing openai o3 and o4-mini, 2025

    OpenAI. Open ai: Introducing openai o3 and o4-mini, 2025. URL https:// openai.com/index/introducing-o3-and-o4-mini/ . https://openai.com/index/ introducing-o3-and-o4-mini/

    work page 2025

  4. [3]

    Google deepmind: Gemini 2.5 pro, 2025

    Google. Google deepmind: Gemini 2.5 pro, 2025. URL https://deepmind.google/ models/gemini/pro/.https://deepmind.google/models/gemini/pro/

    work page 2025

  5. [4]

    Do vision-language models really understand visual language?arXiv preprint arXiv:2410.00193, 2024

    Yifan Hou, Buse Giledereli, Yilei Tu, and Mrinmaya Sachan. Do vision-language models really understand visual language?arXiv preprint arXiv:2410.00193, 2024

    arXiv 2024

  6. [5]

    Vlind-bench: Measuring language priors in large vision-language models

    Kang-il Lee, Minbeom Kim, Seunghyun Yoon, Minsung Kim, Dongryeol Lee, Hyukhun Koh, and Kyomin Jung. Vlind-bench: Measuring language priors in large vision-language models. arXiv preprint arXiv:2406.08702, 2024

    arXiv 2024

  7. [6]

    Vhelm: A holistic evaluation of vision language models.Advances in Neural Information Processing Systems, 37:140632–140666, 2024

    Tony Lee, Haoqin Tu, Chi H Wong, Wenhao Zheng, Yiyang Zhou, Yifan Mai, Josselin S Roberts, Michihiro Yasunaga, Huaxiu Yao, Cihang Xie, et al. Vhelm: A holistic evaluation of vision language models.Advances in Neural Information Processing Systems, 37:140632–140666, 2024

    work page 2024

  8. [7]

    Probing the visualization literacy of vision language models: the good, the bad, and the ugly.arXiv preprint arXiv:2504.05445, 2025

    Lianghan Dong and Anamaria Crisan. Probing the visualization literacy of vision language models: the good, the bad, and the ugly.arXiv preprint arXiv:2504.05445, 2025

    arXiv 2025

Show all 25 references
  1. [8]

    Don’t miss the forest for the trees: Attentional vision calibration for large vision language models.arXiv preprint arXiv:2405.17820, 2024

    Sangmin Woo, Donguk Kim, Jaehyuk Jang, Yubin Choi, and Changick Kim. Don’t miss the forest for the trees: Attentional vision calibration for large vision language models.arXiv preprint arXiv:2405.17820, 2024

    2024 arXiv

  2. [9]

    Mitigating object hallucinations in large vision-language models with assembly of global and local attention

    Wenbin An, Feng Tian, Sicong Leng, Jiahao Nie, Haonan Lin, QianYing Wang, Ping Chen, Xiaoqin Zhang, and Shijian Lu. Mitigating object hallucinations in large vision-language models with assembly of global and local attention. InProceedings of the Computer Vision and Pattern Re...

    2025

  3. [11]

    Qwen2.5-vl, January 2025

    Qwen Team. Qwen2.5-vl, January 2025. URL https://qwenlm.github.io/blog/qwen2. 5-vl/

    2025

  4. [12]

    bbox_2d": [100, 253, 194, 437],

    Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, Congcong Wang, Dehao Zhang, Dikang Du, Dongliang Wang, Enming Yuan, Enzhe Lu, Fang Li, Flood Sung, Guangda Wei, Guokun Lai, Han Zhu, Hao Ding, Hao Hu, Hao Yan...

    2025 arXiv

  5. [13]

    Object counting accuracy over different images grouped by prompt

  6. [14]

    Prediction Error, defined by taking the difference between the ground truth object count and the predicted count over all shapes/data grouped by prompt 6

  7. [15]

    Distribution ofA img over all shapes/data grouped by prompt

  8. [16]

    Distribution ofA prompt over all shapes/data grouped by prompt

  9. [17]

    We report these metrics over Qwen2.5-VL-7B, Qwen2.5-VL-32B and Kimi-VL-A3B

    Distribution ofA gen_token over all shapes/data grouped by prompt. We report these metrics over Qwen2.5-VL-7B, Qwen2.5-VL-32B and Kimi-VL-A3B. You can find the results for our synthetic dataset in Figures 5,6 and 7. For our results on the data from V o et al. [1] refer to Figu...

  10. [18]

    In Flag Stars, specifying the target object and requiring structured output substantially increases accuracy (up to 0.4; Figures 8 and 9)

    For real-world data, the accuracy improves and the error decreases when prompts explicitly specify the target object (Prompts 2–3). In Flag Stars, specifying the target object and requiring structured output substantially increases accuracy (up to 0.4; Figures 8 and 9). Under ...

  11. [19]

    Refer Figures 5,6 and 7

    We can see that when the counting prediction error (calculated using (Ground Truth Count - Predicted Count)) is higher, the model spends less of its total attention on input tokens; instead, it focuses more on generated output tokens. Refer Figures 5,6 and 7

  12. [20]

    We can see that the proportion of attention across the same prompt for different object shapes are within a very small interval

    Shape of the object in the image leads to very minor changes in the distribution of attention. We can see that the proportion of attention across the same prompt for different object shapes are within a very small interval. Refer Figures 5,6 and 7

  13. [21]

    When the number of objects in the image is <10, the models perform relatively accurately, but counting performance becomes less accurate as we move towards the >40 bucket

    As the number of objects in the image increases, the counting errors increases. When the number of objects in the image is <10, the models perform relatively accurately, but counting performance becomes less accurate as we move towards the >40 bucket. Refer Figures 10,11 and 12

  14. [22]

    Errors for Qwen 2.5-VL are centered mostly around negative values, meaning the model often underestimates compared to ground truth

    Kimi-VL-A3B tends to overestimate counts, while Qwen2.5-VL tends to underestimate counts. Errors for Qwen 2.5-VL are centered mostly around negative values, meaning the model often underestimates compared to ground truth. While the opposite is true for Kimi-VL-A3B. Refer Figur...

  15. [23]

    Refer Figures 8 and 9

    The Qwen-7B model shows low and unstable accuracy with severe underestimation under detailed prompts, while the Qwen-32B model achieves higher and more stable accuracy with errors closer to zero, though both still struggle with fine-grained tasks like Animal Legs. Refer Figure...

  16. [24]

    Both Qwen2.5-VL and Kimi-VL-A3B models assign relatively little attention to vision tokens

  17. [25]

    [1], when using the same prompts and data as them

    We replicate the results for data from V o et al. [1], when using the same prompts and data as them. Refer Figures 8 and 9. A.7 Conclusion and Future Work Our study shows that VLMs are highly sensitive to both image characteristics and prompt specificity. Importantly, we find ...

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.