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arxiv: 2605.26380 · v1 · pith:EBKY2LJWnew · submitted 2026-05-25 · 💻 cs.CV · cs.AI

VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes

Jingru Chen , Yiming Liu , Mingtao Chen , Sijie Chen , Richeng Xuan , Liang Yang , Zhichao Hu , Fanyang Lu This is my paper

Pith reviewed 2026-06-29 22:11 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords benchmarkmultimodal modelsvisual searchfine-grained perceptionactive visionablation studyinformation-dense scenestool use
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The pith

VisualNeedle reveals that multimodal models still fail at active fine-grained visual search even with tool access.

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

The paper creates VisualNeedle, a benchmark of scenes packed with information where the key details sit in tiny spots that cannot be spotted without zooming in or searching actively. It tests frontier multimodal models both without tools and with tools that return image crops, plus a crop-black version that swaps those crops for blank images to check if answers depend on seeing the evidence. No-tool performance stays under 20 percent while the strongest tool-using model hits 56 percent, short of the 63 percent human level. The crop-black tests show that genuine visual evidence is required for success on these tasks.

Core claim

VisualNeedle establishes a benchmark for active visual search in information-dense scenes where critical evidence is confined to minute regions. Evaluations of prominent multimodal large language models show no-tool accuracy below 20% and tool-enabled accuracy up to 56.01%, compared to 63% for humans. The crop-black ablation, replacing tool-returned crops with black images, confirms that performance depends on actual intermediate visual evidence rather than shortcuts.

What carries the argument

The VisualNeedle benchmark paired with the crop-black counterfactual setting, which isolates whether tool-enabled answers rely on genuine visual input from cropped regions.

Load-bearing premise

The scenes and questions are built so linguistic or global cues cannot solve them without active fine-grained visual search, and the crop-black condition tests visual evidence use without new confounds.

What would settle it

A model achieving high accuracy on the crop-black setting comparable to the standard tool setting would indicate that it is not relying on the actual visual evidence from the crops.

Figures

Figures reproduced from arXiv: 2605.26380 by Fanyang Lu, Jingru Chen, Liang Yang, Mingtao Chen, Richeng Xuan, Sijie Chen, Yiming Liu, Zhichao Hu.

Figure 1
Figure 1. Figure 1: Overview of VisualNeedle and the interactive search workflow. Left: an example query in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples from VisualNeedle showing how questions require moving from a information [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Stability and efficiency analysis of tool use. The left panel shows Gain/Harm analysis: [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of models’ tool-use efficiency and calling behavior. The left panel shows the [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Category overview of VisualNeedle. The left panel shows the data distribution across the [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of four-model search trajectories on an OCR example. This example requires [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of four-model search trajectories on an Entity example. This example requires [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of four-model search trajectories on an Occluded Object example. This [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
read the original abstract

Frontier multimodal large language models (MLLMs) have been reported to achieve over 90% accuracy on fine-grained perception benchmarks. However, such scores do not necessarily imply faithful use of visual evidence. Prior studies have identified three shortcuts that inflate benchmark performance. First, linguistic priors and lexical cues in questions often enable models to infer plausible answers without seeing the image. Second, coarse global semantics from the visual encoder can bypass fine-grained local details. Third, in some ``think-with-images'' benchmarks, corrupting the intermediate images returned by visual tools barely affects the final answer. These findings suggest that higher input resolution or larger question pools alone do not elicit genuine active visual search. To address this, we introduce VisualNeedle, a challenging, information-dense, and fine-grained benchmark for scenes where critical evidence is spatially constrained to minute regions and not discernible at a glance. We further propose a counterfactual crop-black setting, which replaces crops returned by tools with black images of the same size, to test whether tool-enabled performance truly relies on intermediate visual evidence. We evaluate 9 promninent MLLMs across three settings: no-tool, standard tool-enabled, and crop-black. No-tool accuracy stays below 20\%, and the best tool-enabled model reaches only 56.01\%, still trailing the 63.00% human majority-vote accuracy. These results reveal persistent limitations in fine-grained visual search, while the crop-black ablation confirms that success on VisualNeedle hinges on genuine intermediate visual evidence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces VisualNeedle, a benchmark for active visual search in information-dense scenes where critical evidence is confined to minute regions. It evaluates 9 MLLMs in no-tool (<20% accuracy), standard tool-enabled (best at 56.01%), and crop-black settings, compares to 63% human majority-vote accuracy, and uses the crop-black ablation to argue that tool-enabled gains require genuine intermediate visual evidence rather than tool presence or shortcuts.

Significance. If the central results hold, the work supplies a benchmark explicitly designed to block linguistic priors, global semantics, and non-visual tool strategies, with concrete accuracy gaps and a human baseline that could guide improvements in multimodal models. The attempt at a counterfactual ablation is a methodological strength worth preserving.

major comments (2)
  1. [Crop-black ablation section] Crop-black ablation section: the manuscript does not address whether models could detect uniform black inputs via pixel statistics, absence of expected texture, or meta-knowledge of the experimental condition and consequently alter chain-of-thought or tool-calling behavior. This potential confound directly weakens the load-bearing claim that performance drops in the crop-black condition demonstrate reliance on genuine intermediate visual evidence.
  2. [Dataset construction and question design section] Dataset construction and question design section: explicit validation that questions cannot be solved by linguistic priors or coarse global features (e.g., zero-image human accuracy or controlled leakage tests) is required to support the interpretation of the <20% no-tool result; without such checks the central performance gap remains vulnerable to alternative explanations.
minor comments (2)
  1. [Abstract] Abstract contains the typo 'promninent'.
  2. [Results] Results tables or figures should report whether accuracy differences include statistical significance tests or confidence intervals.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on VisualNeedle. We address each major comment below, indicating where we will revise the manuscript to strengthen the work.

read point-by-point responses

  1. Referee: [Crop-black ablation section] Crop-black ablation section: the manuscript does not address whether models could detect uniform black inputs via pixel statistics, absence of expected texture, or meta-knowledge of the experimental condition and consequently alter chain-of-thought or tool-calling behavior. This potential confound directly weakens the load-bearing claim that performance drops in the crop-black condition demonstrate reliance on genuine intermediate visual evidence.

    Authors: We acknowledge this as a valid concern not addressed in the original manuscript. The crop-black setting was introduced to test whether tool-enabled gains depend on actual visual content rather than tool presence. To directly respond, we will add a new analysis in the revision examining model behavior on black inputs, including inspection of tool-calling rates and chain-of-thought patterns when black images are provided. If detection occurs, we will report it and discuss implications for the ablation. revision: yes

  2. Referee: [Dataset construction and question design section] Dataset construction and question design section: explicit validation that questions cannot be solved by linguistic priors or coarse global features (e.g., zero-image human accuracy or controlled leakage tests) is required to support the interpretation of the <20% no-tool result; without such checks the central performance gap remains vulnerable to alternative explanations.

    Authors: The questions in VisualNeedle were constructed to require fine-grained details from spatially constrained regions that are not recoverable from global semantics or linguistic cues alone, consistent with the observed no-tool accuracies below 20%. We agree, however, that explicit validation would make this more robust. In the revised manuscript we will include zero-image human accuracy results and any controlled tests for question leakage to confirm that the performance gap cannot be explained by priors. revision: yes

Circularity Check

0 steps flagged

Purely empirical benchmark; no derivations or self-referential reductions

full rationale

This is an empirical benchmark paper introducing VisualNeedle scenes and evaluating MLLMs in no-tool, tool-enabled, and crop-black conditions against a human majority-vote baseline. The abstract and provided text contain no equations, no parameter fitting presented as prediction, no uniqueness theorems, and no self-citations invoked to justify core claims. The crop-black ablation is an experimental control whose interpretive validity can be debated on confound grounds, but it does not reduce any claimed result to a definitional or fitted input by construction. The central findings (accuracies <20% no-tool, 56.01% best tool-enabled vs 63% human) are direct measurements, not derived quantities that collapse to the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are invoked; the work is an empirical benchmark introduction.

pith-pipeline@v0.9.1-grok · 5825 in / 1076 out tokens · 21890 ms · 2026-06-29T22:11:35.568515+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

12 extracted references · 4 canonical work pages · 2 internal anchors

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    work page doi:10.48550/arxiv.2401.06209 2024

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    Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation

    doi: 10.48550/arXiv.2504.14988. URLhttps://arxiv.org/abs/2504.14988. 11 Xiang Yue, Yulan Ni, Kai Zhang, Tianyu Zheng, Ruichen Li, Ge Liu, Weiming Huang, Huan Sun, and Yu Su. MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI.arXiv preprint arXiv:2311.16502, 2023. doi: 10.48550/arXiv.2311.16502. URL https://arx...

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  5. [5]

    First, look closely: describe the image in detail, identify what information you need to answer the question

  6. [6]

    Identify regions of interest: if the answer depends on small/unclear details (text, numbers, fine print, charts, tables, small objects), identify the approximate region to zoom in

  7. [7]

    For tiny/unclear details, use a zoom/crop tool

    Use an appropriate image tool: call the most suitable tool from the provided tool list. For tiny/unclear details, use a zoom/crop tool. For mirrored/upside-down content, use a flip/rotate tool. For blurry text, use sharpening/contrast tools. If existing tools are not sufficient, use code_interpreter to write Python for custom image processing and related ...

  8. [8]

    Analyze the tool result: carefully read the processed/zoomed view and extract relevant information

  9. [9]

    When operating on a derived image, ensure you select the correct image index (if applicable) and use coordinates relative to that image

    Repeat if needed: you may apply tools multiple times. When operating on a derived image, ensure you select the correct image index (if applicable) and use coordinates relative to that image. Tool-use Policy.Use tools proactively whenever you are not 100% confident about text, numbers, or small details. Prefer the least invasive tool that resolves the unce...

  10. [10]

    First, examine the full image and identify what information you need

  11. [11]

    For tiny/unclear details, prefer zoom/crop tools

    If the answer depends on text, numbers, small objects, or any detail that is not clearly visible, use the available image tools from the provided tool list to inspect/verify the relevant details. For tiny/unclear details, prefer zoom/crop tools. For mirrored/upside-down content, use flip/rotate tools. For blurry text, use sharpening/contrast tools. If mul...

  12. [12]

    答案” equals答案; (工商舖) equals工商舖. Extra explanation or preamble before/after the answer; whitespace and punctuation differences; case differences such as “hello

    After verifying the details, provide your answer. Do NOT guess if you are uncertain; use the available image tools to verify. Output ONLY the final answer text on the last line. 21 Crop tool interface Tool name.image_zoom_in_tool_reason Model-facing description. Zoom in on a specific region of an image. REQUIRED params: reason (string), img_idx (number; 0...