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REVIEW 4 major objections 5 minor 1 cited by

Benchmarking Large and Small MLLMs

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A four-model benchmark of multimodal language models finds GPT-4o the strongest overall, with small models competitive only in narrow recognition and prompt-specific tasks.

desk verdict A broad but statistically fragile capability map; the qualitative pattern may hold, but a copied table row and tiny samples undermine the quantitative rankings. read the letter →

arxiv 2501.04150 v1 pith:ULMO4LPU submitted 2025-01-04 cs.CV

Xuelu Feng , Yunsheng Li , Dongdong Chen , Mei Gao , Mengchen Liu , Junsong Yuan , Chunming Qiao This is my paper · ORCID
classification cs.CV
keywords multimodallargelanguagemodelsbenchmarkingGPT-4oGPT-4VLLaVA-NeXTPhi-3-Visionvisualreasoningevaluationcapabilityboundaries
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

This paper tries to map where small multimodal language models (MLLMs) can substitute for large ones and where they cannot. It benchmarks GPT-4V and GPT-4o against two small open models, LLaVA-NeXT and Phi-3-Vision, across 48 tasks organized by four input formats and by capability. The central finding is that small models match large ones on narrow recognition and prompt-specific jobs, such as logo, emotion, and celebrity identification, but fall far behind on tasks that require deeper reasoning, including visual math, Raven's progressive matrices, chart and table analysis, temporal ordering, and spatial localization. A sympathetic reader would care because the result gives a practical rule of thumb: choose small models for fast, cheap, on-device recognition and inspection, but do not expect them to replace large models on reasoning-heavy multimodal inputs. The paper also documents that even GPT-4o fails badly at precise object localization and abstract visual reasoning, so the benchmark is as much about a capability ceiling as about model size.

What carries the argument

The mechanism that carries the argument is a multi-part evaluation protocol rather than a single theorem. It organizes 48 tasks into four input formats—single image-text pairs, multi-image with text, multi-frame with text, and interleaved image-text—and maps each task to a capability such as understanding, reasoning, assessment, or interaction. Quantitative evaluations are anchored to existing datasets (MSCOCO, FSC-147, BLINK, ChartInsights, MVTec AD) plus a new TableInsights set with 166 table questions, while correctness is judged by three annotators; when a model's output was unsatisfactory, the authors refined the prompt themselves. This protocol is what lets the paper claim a systematic comparison instead of an anecdotal one.

What would settle it

Run the same comparison on a pre-registered set of 200 chart-reasoning and 200 spatial-reasoning questions with fixed prompts and independent blind scoring by three or more annotators; if LLaVA-NeXT or Phi-3-Vision match GPT-4o within a few percentage points on those tasks, the paper's central boundary claim would collapse.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the capability boundary between large and small MLLMs is real and task-dependent. In most understanding and reasoning tasks, GPT-4o ranks first, with GPT-4V close behind; in specific recognition tasks, LLaVA-NeXT and Phi-3-Vision can reach comparable accuracy, sometimes beating the large models, as with celebrity recognition for LLaVA-NeXT and several chart and prompt-format tasks for Phi-3-Vision. When the task asks for multi-step inference or fine-grained interpretation, the small models' accuracy falls sharply, in several reasoning tasks to zero. The paper also identifies common failure cases shared by all four models, especially in spatial localization, where F1 scores stay below 0.1, and in abstract visual reasoning such as Raven's progressive matrices.

Load-bearing premise

The load-bearing assumption is that small, hand-collected sample sets (sometimes 5-28 items per task) and the authors' own correctness judgments, made without reported inter-annotator agreement, are representative and fair enough to rank the four models.

Editorial extensions

If this is right

  • Small MLLMs can serve in narrow recognition and inspection jobs—logo, emotion, and celebrity identification, and helmet counting when an external detector supplies crops—at lower cost and faster speed.
  • Large models remain the default choice for reasoning-heavy multimodal tasks such as chart and table question answering, visual math, Raven's progressive matrices, and multi-frame temporal ordering.
  • Model choice should be task-specific rather than size-based, since Phi-3-Vision beats GPT-4V on several chart-reasoning prompt formats and LLaVA-NeXT leads on celebrity recognition.
  • Even the best model in the study, GPT-4o, shows a clear capability ceiling on precise object localization and abstract spatial reasoning, so these tasks remain open for future work.
  • Adding a reference image of a defect-free product improves large models' anomaly detection but not small models' performance, suggesting a boundary in how small models exploit additional visual context.

Reading between the lines

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

  • Not tested in the paper: whether the small-model gap on reasoning tasks comes from model size or from differences in training data and alignment, since the two small models differ on both axes; a size-matched, data-matched ablation would separate these causes.
  • A practical extension the paper only implies: small models could be embedded in constrained recognition pipelines while large models handle chart/table QA, visual math, and temporal reasoning, creating a two-tier deployment strategy.
  • Because the authors refined prompts when outputs were unsatisfactory, the absolute accuracy percentages should be read as prompt-dependent; a broader prompt search on the same tasks might narrow the measured gap on specific skills.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper benchmarks two large proprietary MLLMs (GPT-4V, GPT-4o) and two smaller open models (LLaVA-NeXT, Phi-3-Vision) across 48 tasks organized by input format: single image-text pairs, multiple images, multi-frame sequences, and interleaved image-text inputs. The main claims are that small models can match large models in specific recognition or narrow scenarios but lag substantially on complex reasoning, and that GPT-4o is the best overall model. The evaluation combines reused samples from external benchmarks with newly collected samples, uses three annotators to judge correctness, and includes both quantitative tables and qualitative failure-case analyses.

Significance. If the quantitative findings were reliable, this would be a useful practical guide: it would tell deployers where small models suffice and where only large models are adequate. The paper's strengths are its broad task taxonomy, the inclusion of multiple input formats, and the qualitative failure analysis, which highlights recurring weaknesses such as spatial localization and fine-grained counting. Credit is also due for grounding parts of the evaluation in existing datasets (MSCOCO, FSC-147, ChartInsights, BLINK) rather than relying solely on self-constructed examples. However, the quantitative evidence as reported is not strong enough to support the headline ranking claims, and at least one table contains an internal inconsistency that must be resolved before the numbers can be trusted.

major comments (4)
  1. [§2.1.1, §2.1.2, §6.2.2] The positive half of the central claim—that small MLLMs achieve comparable performance in specific scenarios—is not statistically supported. The evidence for this claim consists of post-hoc selections of tasks with very small samples, such as celebrity recognition (n=15), safety inspection (n=5), logo recognition in the wild (n=18), and guided prompting (n=15). No confidence intervals, significance tests, or multiple-comparison corrections are reported; scanning 48 tasks, a few nominal small-model wins are expected by chance. For example, in the safety-inspection task with n=5, a single response flip changes Phi-3-Vision's reported 100% accuracy to 80%, and a two-response flip changes it to 60%. The authors should either provide error bars and a pre-specified or corrected analysis of which tasks test the small-model claim, or explicitly reframe these observations as descriptive rather than as confirmatory evidence.
  2. [Tables 2.4 and 2.11] The GPT-4V row in Table 2.11 (TableInsights) lists the exact same ten per-task values as the GPT-4V row in Table 2.4 (ChartInsights)—39.55, 60.29, 65.87, 32.03, 73.96, 41.73, 53.69, 67.75, 88.91, 73.44—while the reported overall accuracy differs (50.47 versus 57.20). Identical per-task percentages across two different datasets are extremely unlikely and strongly suggest that one row was copied from the other. The authors must regenerate and verify all quantitative tables, and the discrepancy must be explained before any model-ranking conclusion can be accepted.
  3. [§2.1.2, Scene Understanding] The scene-understanding evaluation uses inconsistent denominators. The text states that 12 scene images were collected and three models are scored over 12, but Phi-3-Vision is reported as [2 + 2 × 0.5]/14. Either Phi-3-Vision was evaluated on a different sample size than the other models, or the printed denominator is a typo. In both cases the reported accuracy needs correction and the evaluation protocol should be stated uniformly.
  4. [§1.1 and throughout §2] The manuscript states that three annotators judged model outputs, but no inter-annotator agreement is reported. Many tasks use 0.5 partial-credit scores (e.g., scene text recognition, multilingual translation, multimodal commonsense), so a single annotator's disagreement can shift a reported accuracy by several percentage points and can flip a claimed small-model win. The authors should report agreement statistics, release the annotation rubric and raw outputs, or at least quantify the sensitivity of the rankings to individual annotator judgments.
minor comments (5)
  1. [§1.2, §6 intro] The model name 'LLaV A-NeXT' appears with an extra space throughout; the Section 6 introduction also contains 'LLaV A-Vision', which should be 'LLaVA-NeXT'.
  2. [§2.1.2] The word 'illustarted' should be 'illustrated'.
  3. [§2.1.4] The sentence 'We also nake an interesting finding' should read 'We also make an interesting finding'.
  4. [§2.2.1] The phrase 'the mmetric evaluation' should read 'the metric evaluation'.
  5. [General] Reproducibility details are missing: the authors should report API model versions and access dates, the exact random-sampling procedure for samples drawn from MSCOCO and FSC-147, and the annotator instructions and scoring rubric.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical benchmark whose claims summarize external evaluations rather than deriving results from their own inputs.

full rationale

This manuscript is a benchmark study, not a derivation. Its central claims (‘small MLLMs can achieve comparable performance to large models in specific scenarios but lag significantly in complex tasks’) are empirical summaries of measured accuracies on external datasets (MSCOCO, FSC-147, ChartInsights, BLINK) and on newly collected samples. No parameter is fitted from one subset of data and then reported as a prediction of a closely related quantity; no quantity is defined in terms of another quantity in a way that makes a reported result true by construction; and no load-bearing conclusion is justified by a self-citation or by a uniqueness theorem imported from the authors’ prior work. The only experimenter involvement described is prompt refinement for tasks with unsatisfactory outputs (Section 1.2), which is a validity concern about fairness and reproducibility, not circularity. Similarly, the small sample sizes (e.g., 5 safety-inspection cases, 15 celebrity cases) and post-hoc highlighting of small-model wins are statistical-evidence concerns, not circular-reasoning concerns. The apparent duplication of the GPT-4V row in Tables 2.4 and 2.11 with different overall accuracies is a data-integrity problem, not a circularity problem. Consequently, there are no circular steps to report, and the appropriate circularity score is 0.

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

The report is empirical. Its validity rests on assumptions about annotator reliability, small-sample representativeness, API stability, and the 0.5 partial-credit rubric. These are not formal axioms but load-bearing evaluation choices.

assumptions (4)
  • domain assumption Human annotators' binary judgments are treated as ground truth for correctness, with three annotators but no agreement metric.
    Section 1.1 states three annotators assessed accuracy, but no inter-annotator agreement or adjudication rule is reported.
  • domain assumption Small convenience samples (often 5 to 28 items per task) are sufficient to rank model capabilities.
    Most task sections report performance on 5 to 28 hand-collected examples without confidence intervals or power analysis.
  • domain assumption Proprietary API model outputs are treated as stable point estimates.
    No evaluation date, API version, temperature, or repeated sampling is reported for GPT-4V and GPT-4o, though accuracy is given to one or two decimals.
  • domain assumption The scoring rubric with 0.5 partial credit is a valid ordinal measure.
    Many tasks award 0.5 for partially correct answers; the rubric is defined by the authors and not validated.

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Cite this review

Pith. "Pith review of Benchmarking Large and Small MLLMs." pith.science (2026). https://pith.science/paper/ULMO4LPU

@misc{pith2026250104150,
  author       = {Pith},
  title        = {Pith review of: Benchmarking Large and Small MLLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ULMO4LPU}},
  note         = {Machine review of arXiv:2501.04150}
}
read the original abstract

Large multimodal language models (MLLMs) such as GPT-4V and GPT-4o have achieved remarkable advancements in understanding and generating multimodal content, showcasing superior quality and capabilities across diverse tasks. However, their deployment faces significant challenges, including slow inference, high computational cost, and impracticality for on-device applications. In contrast, the emergence of small MLLMs, exemplified by the LLava-series models and Phi-3-Vision, offers promising alternatives with faster inference, reduced deployment costs, and the ability to handle domain-specific scenarios. Despite their growing presence, the capability boundaries between large and small MLLMs remain underexplored. In this work, we conduct a systematic and comprehensive evaluation to benchmark both small and large MLLMs, spanning general capabilities such as object recognition, temporal reasoning, and multimodal comprehension, as well as real-world applications in domains like industry and automotive. Our evaluation reveals that small MLLMs can achieve comparable performance to large models in specific scenarios but lag significantly in complex tasks requiring deeper reasoning or nuanced understanding. Furthermore, we identify common failure cases in both small and large MLLMs, highlighting domains where even state-of-the-art models struggle. We hope our findings will guide the research community in pushing the quality boundaries of MLLMs, advancing their usability and effectiveness across diverse applications.

Figures

Figures reproduced from arXiv: 2501.04150 by the authors.

Figure 1.1
Figure 1.1. Mapping of task types, input formats, and general capabilities across different [PITH_FULL_IMAGE:figures/full_fig_p007_1_1.png] view at source ↗
Figure 2.1
Figure 2.1. Examples of four MLLMs on understanding and following text instructions, to [PITH_FULL_IMAGE:figures/full_fig_p011_2_1.png] view at source ↗
Figure 2.2
Figure 2.2. Constrained prompting to return in JSON format. Images are example IDs for [PITH_FULL_IMAGE:figures/full_fig_p012_2_2.png] view at source ↗
Figures from the paper (69 more)
Figure 2.3
Figure 2.3. Figure 2.3: Examples of four MLLMs generating text based on the perceived or desired [PITH_FULL_IMAGE:figures/full_fig_p014_2_3.png]
Figure 2.4
Figure 2.4. Figure 2.4: Results on color recognition. Red highlights the wrong answer [PITH_FULL_IMAGE:figures/full_fig_p015_2_4.png]
Figure 2.5
Figure 2.5. Figure 2.5: Results on food recognition and description. [PITH_FULL_IMAGE:figures/full_fig_p017_2_5.png]
Figure 2.6
Figure 2.6. Figure 2.6: Results on landmark recognition and description. [PITH_FULL_IMAGE:figures/full_fig_p018_2_6.png]
Figure 2
Figure 2. Figure 2: , GPT-4o fails to name the logos and mistakenly assumes that a brand has only one [PITH_FULL_IMAGE:figures/full_fig_p019_2.png]
Figure 2.7
Figure 2.7. Figure 2.7: Results on celebrity recognition and description. [PITH_FULL_IMAGE:figures/full_fig_p020_2_7.png]
Figure 2.8
Figure 2.8. Figure 2.8: Results on logo recognition and description. [PITH_FULL_IMAGE:figures/full_fig_p021_2_8.png]
Figure 2.9
Figure 2.9. Figure 2.9: Results on counterfactual examples. Red highlights the wrong answer [PITH_FULL_IMAGE:figures/full_fig_p023_2_9.png]
Figure 2.10
Figure 2.10. Figure 2.10: Results on scene understanding. Red highlights the wrong answer. None of the responses explicitly convey the emotion of “embarrassment.” However, some descriptions could be indirectly associated with embarrassment in certain contexts. Overall, GPT-4V, GPT-4o and LLa…
Figure 2.11
Figure 2.11. Figure 2.11: Results on identifying and reading the emotions of people from their facial [PITH_FULL_IMAGE:figures/full_fig_p025_2_11.png]
Figure 2.12
Figure 2.12. Figure 2.12: Results on scene text recognition. Red highlights that the answer is incorrect [PITH_FULL_IMAGE:figures/full_fig_p027_2_12.png]
Figure 2.13
Figure 2.13. Figure 2.13: Results on handwriting text recognition and understanding. [PITH_FULL_IMAGE:figures/full_fig_p028_2_13.png]
Figure 2.14
Figure 2.14. Figure 2.14: Results on multilingual image descriptions. [PITH_FULL_IMAGE:figures/full_fig_p029_2_14.png]
Figure 2.15
Figure 2.15. Figure 2.15: Results on multilingual text recognition, translation, and description. [PITH_FULL_IMAGE:figures/full_fig_p031_2_15.png]
Figure 2.16
Figure 2.16. Figure 2.16: Results on multilingual text recognition, translation, and description. [PITH_FULL_IMAGE:figures/full_fig_p032_2_16.png]
Figure 2.17
Figure 2.17. Figure 2.17: Results on multilingual scene text recognition. [PITH_FULL_IMAGE:figures/full_fig_p033_2_17.png]
Figure 2.18
Figure 2.18. Figure 2.18: Results on multilingual multiculture understanding. [PITH_FULL_IMAGE:figures/full_fig_p034_2_18.png]
Figure 2.19
Figure 2.19. Figure 2.19: Results on MLLMs’ capability of writing codes in python to replicate the input [PITH_FULL_IMAGE:figures/full_fig_p036_2_19.png]
Figure 2.20
Figure 2.20. Figure 2.20: Results on spatial relationship understanding. [PITH_FULL_IMAGE:figures/full_fig_p037_2_20.png]
Figure 2.21
Figure 2.21. Figure 2.21: Results on object counting (MSCOCO). Red highlights the wrong answer [PITH_FULL_IMAGE:figures/full_fig_p039_2_21.png]
Figure 2.22
Figure 2.22. Figure 2.22: Results on object counting (FSC-147). Red highlights the wrong answer [PITH_FULL_IMAGE:figures/full_fig_p040_2_22.png]
Figure 2.23
Figure 2.23. Figure 2.23: Results on object localization (MSCOCO). We visualize bounding boxes for [PITH_FULL_IMAGE:figures/full_fig_p041_2_23.png]
Figure 2.24
Figure 2.24. Figure 2.24: Results on dense captioning. Red highlights the wrong answer. We can observe that four MLLMs cannot provide specific name of each individual in the images. 2.2.2 Logical Thinking Visual Math Mathematics is a crucial component of intelligence testing. To evaluate thi…
Figure 2.25
Figure 2.25. Figure 2.25: Results on basic-level visual math reasoning. [PITH_FULL_IMAGE:figures/full_fig_p046_2_25.png]
Figure 2.26
Figure 2.26. Figure 2.26: Results on Olympic-level visual math reasoning. [PITH_FULL_IMAGE:figures/full_fig_p047_2_26.png]
Figure 2.27
Figure 2.27. Figure 2.27: Example questions from the Raven’s Progressive Matrices. [PITH_FULL_IMAGE:figures/full_fig_p049_2_27.png]
Figure 2
Figure 2. Figure 2: and Figure 2.29 illustrates the results across various [PITH_FULL_IMAGE:figures/full_fig_p056_2.png]
Figure 2.28
Figure 2.28. Figure 2.28: Results on document understanding. Red highlights that the answer is incorrect or does not provide specific name. tions accurately, while Phi-3-Vision performs the lowest, with only 6 correct answers. These results reveal significant disparities in multimodal reason…
Figure 2.29
Figure 2.29. Figure 2.29: Results on document understanding. Red highlights that the answer is incorrect or does not provide detailed description [PITH_FULL_IMAGE:figures/full_fig_p059_2_29.png]
Figure 2.30
Figure 2.30. Figure 2.30: Results on understanding how different visual contents may arouse human [PITH_FULL_IMAGE:figures/full_fig_p060_2_30.png]
Figure 2.31
Figure 2.31. Figure 2.31: Examples of Food/Landmark/Celebrity/Logo Understanding. [PITH_FULL_IMAGE:figures/full_fig_p061_2_31.png]
Figure 2.32
Figure 2.32. Figure 2.32: Results on answering science questions. Red highlights that the answer is inaccurate [PITH_FULL_IMAGE:figures/full_fig_p063_2_32.png]
Figure 2.33
Figure 2.33. Figure 2.33: Results on joke and meme understanding. Red highlights that the answer does not get to the point to comprehend the humor embedded within memes [PITH_FULL_IMAGE:figures/full_fig_p064_2_33.png]
Figure 2.34
Figure 2.34. Figure 2.34: Results on multimodal commonsense reasoning. [PITH_FULL_IMAGE:figures/full_fig_p066_2_34.png]
Figure 2.35
Figure 2.35. Figure 2.35: Prompt large and small MLLMs to give a score from 1 to 10 on how similar the [PITH_FULL_IMAGE:figures/full_fig_p068_2_35.png]
Figure 2.36
Figure 2.36. Figure 2.36: Examples of two type of visual pointers, one is visual pointers directly overlaid [PITH_FULL_IMAGE:figures/full_fig_p070_2_36.png]
Figure 2.37
Figure 2.37. Figure 2.37: Examples of pixel space edited input. Red highlights the wrong answer [PITH_FULL_IMAGE:figures/full_fig_p071_2_37.png]
Figure 3.1
Figure 3.1. Figure 3.1: Understanding abstract visual stimuli such as ASCII text art and tangram [Ji et al. [PITH_FULL_IMAGE:figures/full_fig_p074_3_1.png]
Figure 3.2
Figure 3.2. Figure 3.2: Results on multi-view reasoning. Red highlights that the answer is incorrect [PITH_FULL_IMAGE:figures/full_fig_p075_3_2.png]
Figure 3.3
Figure 3.3. Figure 3.3: Examples of two large MLLMs judging image aesthetics based on societal [PITH_FULL_IMAGE:figures/full_fig_p076_3_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: Results on forensic detection. Red highlights that the answer is incorrect [PITH_FULL_IMAGE:figures/full_fig_p077_3_4.png]
Figure 4.1
Figure 4.1. Figure 4.1: Sequences of video frames understanding: Interpreting human poses and deriving [PITH_FULL_IMAGE:figures/full_fig_p080_4_1.png]
Figure 4.2
Figure 4.2. Figure 4.2: Short-term and long-term temporal ordering. Given a specified action, such as [PITH_FULL_IMAGE:figures/full_fig_p081_4_2.png]
Figure 4.3
Figure 4.3. Figure 4.3: Short-term and long-term temporal anticipation. [PITH_FULL_IMAGE:figures/full_fig_p083_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: Long-term and short-term temporal localization and reasoning of GPT-4V and [PITH_FULL_IMAGE:figures/full_fig_p084_4_4.png]
Figure 4.5
Figure 4.5. Figure 4.5: Grounded temporal understanding: GPT-4V and GPT-4o are capable of applying [PITH_FULL_IMAGE:figures/full_fig_p085_4_5.png]
Figure 5.1
Figure 5.1. Figure 5.1: Spot the differences. Red highlights the inaccurate description about the differ￾ences [PITH_FULL_IMAGE:figures/full_fig_p088_5_1.png]
Figure 5
Figure 5. Figure 5: , enables users to continuously refine their edits until they achieve the desired [PITH_FULL_IMAGE:figures/full_fig_p089_5.png]
Figure 5.2
Figure 5.2. Figure 5.2: Example questions from the Wechsler Adult Intelligence Scale (WAIS). [PITH_FULL_IMAGE:figures/full_fig_p090_5_2.png]
Figure 5.3
Figure 5.3. Figure 5.3: Improving the editing prompt: Given the original image, the editing prompt, and [PITH_FULL_IMAGE:figures/full_fig_p091_5_3.png]
Figure 5.4
Figure 5.4. Figure 5.4: Examples on generating pointing output to ground the textual or visually queried [PITH_FULL_IMAGE:figures/full_fig_p093_5_4.png]
Figure 6.1
Figure 6.1. Figure 6.1: Customized captioner for photo organization: reference images are cropped from [PITH_FULL_IMAGE:figures/full_fig_p096_6_1.png]
Figure 6.2
Figure 6.2. Figure 6.2: Understanding part-object association in abstract and natural images. We calculate [PITH_FULL_IMAGE:figures/full_fig_p097_6_2.png]
Figure 6.3
Figure 6.3. Figure 6.3: Dense captioning w/ segmentation cut-outs from SAM [Kirillov et al. 2023]. [PITH_FULL_IMAGE:figures/full_fig_p098_6_3.png]
Figure 6.4
Figure 6.4. Figure 6.4: Anomaly detection results comparison between a single image and an interleaved [PITH_FULL_IMAGE:figures/full_fig_p100_6_4.png]
Figure 6.5
Figure 6.5. Figure 6.5: Application Highlights on Safety Inspection: Personal Protective Equipment [PITH_FULL_IMAGE:figures/full_fig_p101_6_5.png]
Figure 6.6
Figure 6.6. Figure 6.6: Application Highlights on Grocery Checkout. [PITH_FULL_IMAGE:figures/full_fig_p103_6_6.png]
Figure 6.8
Figure 6.8. Figure 6.8: Additionally, in some samples, GPT-4V and Phi-3-Vision fail to provide assistance [PITH_FULL_IMAGE:figures/full_fig_p104_6_8.png]
Figure 6.7
Figure 6.7. Figure 6.7: Application Highlights on Auto Damage Evaluation. [PITH_FULL_IMAGE:figures/full_fig_p105_6_7.png]
Figure 6.8
Figure 6.8. Figure 6.8: Application Highlights on Insurance Reporting. [PITH_FULL_IMAGE:figures/full_fig_p106_6_8.png]
Figure 6.9
Figure 6.9. Figure 6.9: Acting as an embodied agent to navigate through a house to fetch something from [PITH_FULL_IMAGE:figures/full_fig_p108_6_9.png]
Figure 6.10
Figure 6.10. Figure 6.10: Results of four MLLMs on predicting the action upon receiving a notification. [PITH_FULL_IMAGE:figures/full_fig_p109_6_10.png]
Figure 6.11
Figure 6.11. Figure 6.11: Four MLLMs navigate through GUI to shop for a facial cleanser for women. [PITH_FULL_IMAGE:figures/full_fig_p110_6_11.png]
Figure 6.12
Figure 6.12. Figure 6.12: Four MLLMs navigate through GUI to browse the web to search for the guidance [PITH_FULL_IMAGE:figures/full_fig_p111_6_12.png]
Figure 6.16
Figure 6.16. Figure 6.16: However, as shown in the second case in Figure 6.16, both models struggle to [PITH_FULL_IMAGE:figures/full_fig_p112_6_16.png]
Figure 6.13
Figure 6.13. Figure 6.13: Four MLLMs navigate through GUI to shop for a facial cleanser for women. [PITH_FULL_IMAGE:figures/full_fig_p113_6_13.png]
Figure 6.14
Figure 6.14. Figure 6.14: Four MLLMs navigate through GUI to shop for a facial cleanser for women. [PITH_FULL_IMAGE:figures/full_fig_p114_6_14.png]
Figure 6.15
Figure 6.15. Figure 6.15: Four MLLMs navigate through GUI to shop for a facial cleanser for women. [PITH_FULL_IMAGE:figures/full_fig_p115_6_15.png]
Figure 6.16
Figure 6.16. Figure 6.16: Watching web videos: We present GPT-4V and GPT-4o with screenshots of video [PITH_FULL_IMAGE:figures/full_fig_p116_6_16.png]

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    Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2901–2910. D. Kang, Z. Ma, and A. B. Chan. 2018. Beyond counting: comparisons of density maps for crowd analysis tasks—counting, detection, and tracking. IEEE Transactions on Circuits...

    arXiv 2018

  4. [2023]

    Computer Science

    Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf, 2(3): 8. A. F. Biten, R. Tito, A. Mafla, L. Gomez, M. Rusinol, E. Valveny, C. Jawahar, and D. Karatzas. 2019. Scene text visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4291–4301. L. B...

    arXiv 2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.