InSight-o3: Empowering Multimodal Foundation Models with Generalized Visual Search
Authors: Kaican Li
Lewei Yao
Jiannan Wu
Tiezheng YU
Jierun Chen
Haoli Bai
Lu Hou
Lanqing HONG
Wei Zhang
Nevin L. Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: thinking with images
o3
visual search
multi-agent framework
reinforcement learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The ability for AI agents to "think with images" requires a sophisticated blend of reasoning and perception.
However, current open multimodal agents still largely fall short on the reasoning aspect crucial for real-world tasks like analyzing documents with dense charts/diagrams and navigating maps.
To address this gap, we introduce O3-bench, a new benchmark designed to evaluate multimodal reasoning with interleaved attention to visual details.
O3-bench features challenging problems that require agents to piece together subtle visual information from distinct image areas through multi-step reasoning.
The problems are highly challenging even for frontier systems like OpenAI o3, which only obtains 40.8\% accuracy on O3-bench.
To make progress, we propose InSight-o3, a multi-agent framework consisting of a visual reasoning agent (vReasoner) and a visual search agent (vSearcher) for which we introduce the task of generalized visual search---locating relational, fuzzy, or conceptual regions described in free-form language, beyond just simple objects or figures in natural images.
We then present a multimodal LLM purpose-trained for this task via reinforcement learning.
As a plug-and-play agent, our vSearcher empowers frontier multimodal models (as vReasoners), significantly improving their performance on a wide range of benchmarks.
This marks a concrete step towards powerful o3-like open systems.
Our code and dataset can be found at https://github.com/m-Just/InSight-o3.

Published as a conference paper at ICLR 2026
INSIGHT-03: EMPOWERING MULTIMODAL FOUNDA- TION MODELS WITH GENERALIZED VISUAL SEARCH
Kaican , Lewei Y Jiannan Wu Tiezheng Jierun Chen', Haoli Bai?, Lu Hou?, Lanqing Hong", Wei Zhang", Nevin L. Zhanglt
'Hong Kong University of Science and Technology, "Huawei
ABSTRACT
The ability for Al agents to "think with images" requires a sophisticated blend of reasoning and perception. However, current open multimodal agents still largely fall short on the reasoning aspect crucial for real-world tasks like analyzing doe- uments with dense charts/diagrams and navigating maps. To address this gap, we introduce O3-BENCH, a new benchmark designed to evaluate multimodal reason- ing with interleaved attention visual details. O3-BENCH features challenging problems that require agents to piece together subtle visual information from dis- tinct image areas through multi-step reasoning. The problems are highly chal- lenging even for frontier systems like OpenAl o3, which only obtains 40.8% ac- curacy on O3- To make progress, we propose INSIGHT- multi-agent framework consisting of a visual reasoning agent (vReasoner) and visual search agent (vSearcher) for which we introduce the task of generalized visual search- locating relational, fuzzy, or conceptual regions described in free-form language, beyond just simple objects or figures in natural images. We then present a multi- modal LLM purpose-trained for this task via reinforcement learning. As plug- and-play agent, our vSearcher empowers frontier multimodal models (as vReason- ers), significantly improving their performance on wide range of benchmarks, This marks a concrete step towards powerful 03-like open systems. Our code and dataset can be found https: / i thub. com / Just ISSSinnSiiigg Inttigtt-S....
INTRODUCTION
Thinking with images an important and very useful skill for multimodal agents (OpenAI, 2025c). The skill rests on two crucial and fundamental cognitive abilities: reasoning and perception. Recent efforts at developing such a skill based on open models mainly focus on the perception component, e.g., searching for a particular object or figure in natural images and then answering simple visual query about them (Wu & Xie, 2024; Shen et al., 2024; Li et al., 2025: Zhang et al., 2025b; Su et al., 2025a; Zheng et al., 2025; Wang al., 2025c; Zhu et al., 2025b; Wang al., 2025a; Lai et al., 2025). While this feature is useful, it is still far from being able to handle many real-world tasks that require deeper and more abstract reasoning. Typical examples include extracting information from complex reports and navigating through intricate maps. Solving these tasks often require both organized reasoning and focused attention to visual details scattered across an image (or images).
Currently, the reasoning capability of open multimodal models is still relatively weak in comparison with frontier proprictary models (Yue al., 2024a; Yuan al., 2025; Hao et al., 2025). This makes it very difficult to replicate the kind of reasoning- driven image- thinking behavior demonstrated by OpenAl o3 (OpenAI, 2025c). In this work, we take a concrete step towards building such an intelli- gent system with open models. First, we propose a new multimodal benchmark, O3-BENCH, to help better evaluate the general capability of multimodal models to think with images. Complementary to most of the existing benchmarks which only deal with object attributes and spatial relations in natu- ral images (Wu & Xie, 2024; Wang et al., 2025g; Lai et al., 2025; Wang et al., 2025a), O3-BENCH consists of set of high-quality, reasoning -rriented questions on images of high information density. The questions involve real-world tasks such as map navigation and choo/daaar/ analysis that are highly challenging even for frontier systems like OpenAl o3. Compared with benchmarks like
"Equal contribution.
'Corresponding author (1 11zaag@sss.... ust. hk).

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Published as a conference paper at ICLR 2026
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Region Description
A multi-step visual reasoning example of INSIGHT-O3 on O3-BENCH For clarity, the internal reasoning processes are omitted. More examples can be found in Appendix D.2.
Figure I:
MME-RealWorld (Zhang et al., 2024b), O3-BENCH is significantly harder, requiring the evaluated system to collect detailed visual information from multiple distinct image areas while performing complex, interleaved reasoning using the information collected in the process.
To make substantive progress on O3- BE-BENCH, we introduce a multi-agent framework, INSIGHT-O3, that comprises visual reasoning agent (vReasoner) and visual search agent (vSearcher). The former is responsible for high-level reasoning and general image understanding, while the latter is to help vReasoner locate specific regions of interest and collect the visual information therein. As such, INSIGUT--0 reduces the burden of a single agent, allowing us to build an 03-like system via divide-and-conquer This kind of specialization has been shown to work in prior art (Dayan & Hinton, 1992; Zeng et al., 2023; Shen et al., 2023; Li et al., 2023a; Hong et al., 2023; Castrejon et al., 2024). In this work, we focus on vSearcher and how should interact with a given vReasoner. Different from current practices (Wu & Xie, 2024; Lai et al., 2025) for natural images and discrete object references, we aim to solve generalized visual search, where the input image can arbitrary, e.g., map, poster,
screenshot; and the referring description may specify relational, fuzzy, or conceptual region, e.g., "the area to the left of the wooden chair," " and "the chart showing the company's 's revenue in the last decade," rather than specific object or figure. Such fuzzy descriptions are more in line with how humans reason and direct their attention to a general region of interest.
To address this broader challenge, we present InS,ght-O3-vS, vSearcher model specialized in gen- eralized visual search through reinforcement learning. InSight-o3-v3 combines multimodal under- standing with spatial reasoning localize regions described in completely free-form language. The
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name of our model, InSight-o3- reflects its dual role: providing deeper insight into multimodal semantics while bringing the target region in sight through precise localization. Our model empow- existing multimodal foundation models (as vReasoners) in a plug-ann looug-and--aa and-plaa fashion, significantly improving the performance of frontier models across a wide range of benchmarks, e.g. from 39.0% to 61.5% 03-BENCH for GPT--- (OpenAI, 2025a), and from 80. 1% to 87.6% on V*-Bench for Demini-2.55-Faas (Comanici et al., 2025).
To summarize, we make the following key contributions in this work:
We propose a new benchmark, O3-BENCII, to evaluate complex, reasoning-oriente visual tasks. This benchmark features challenges like map navigation and cross-chart analysis, which require collecting information from multiple image areas and performing interleaved reasoning, making it significantly harder than existing benchmarks.
We introduce INSIGHT-O3, multi-agent framework that divides the task of "thinking with im- ages" between a high-level reasoning agent (vReasoner) and a visual search agent (ySearcher). This divide-and-conquer design greatly simplifies the complex interleaved reasoning, allowing us to build 03-like systems that surpass OpenAl o3 across variety of benchmarks.
We present InSight-o3-vS, a specialized vSearcher model that excels at generalized visual search. It is designed to be "plug-and-plas component that empowers existing multimodal foundation models, demonstrably and significantly improving the performance of fronticr systems on a wide range of benchmarks including O3-BENCH.
2 RELATED WORK
We provide a brief overview of the most relevant related work in this section. For more compre- hensive discussion, please refer to Appendix A.
Multimodal benchmarks. Classical multimodal benchmarks (Goyal et al., 2017; Saikh et al., 2022: Liu et al., 2023; Ge et al., 2024) mainly test coarse image-level or salient-attribute recog- nition, where modern MLLMs are near-saturated (Bai et al., 2025b; Wang et al., 2025e). Recent multimodal reasoning benchmark split into (i) cognition-centric STEM benchmarks (Lu et al., 2023; Yue et al., 2024a;b) that emphasize sollli-tep//orld-kknoow/// images, and (ii) perception-centric datasets (Wu & Xie, 2024; Zhang et al., 2024b; Lai et al., 2025) that require fine-grained recognition in high-resolution, text-rich scenes yet often limited single- region lookups. Motivated by the "think with images" paradigm (OpenAI, 2025c), 03-BENCH jointly evaluates search/localization and higher-level reasoning on
reasoning but visually simple
information-dentt charts and maps, requiring cross-region evidence aggregation via interleaved, multi-hop reasoning.
Multimodal reasoning models. Reinforcement learning (RL) has long been used to align model behavior with human preferences (Schulman et al., 2017). DeepSeek-RI applies group relative policy optimization (GRPO) (Guo et al., 2025a; Shao et al., 2024b), reliably eliciting planning. reflection, and long chain-of-thought reasoning under simple rewards. Building this idea, cent multimodal models (Yang et al., 2025c) adopt GRPO-style training and report strong gains, while cascaded RI. stages (e.g., Intern (Wang al., 2025e), V1.155 (Yang et al., 2025a)) further push reasoning, approaching proprietary models. There are also pioneering work utilizing Python code exccution to call various vision tools solve tasks via divide-and-conquer or help with reasoning (Gupta & Kembhavi, 2023; Surís et al., 2023; Ke et al., 2024). Nevertheless, most multimodal reasoners remain text-centric, overlooking the distinctive demands of visual reasoning.
Visual search models. Visual search is a core multimodal capability, requiring active region per- ception for fine-grained understanding. Early methods relied on external detectors or scripted work- flows to localize regions and triggered tools via instruction tuning, leading to rigid outputs and typically single-round search (Wu & Xie, 2024). The "think with images" paradigm (OpenAI, 2025c) internalizes zoom/crop operations and has inspired end-to-end search (DeepEyes (Zheng et al., 2025)), synthetic strm-starts (Pixel-Reed Reael-Re (Su et al., 2025a)), and multi-turn RL (Mini- o3 (Lai et al., 2025)). Nonetheless, most systems still emphasize finding single region in natural images, with limited support for multi-hop reasoning. We broaden this scope by decoupling visual search from visual reasoning and enabling multi-region search on arbitrary images.
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3 O3-BENCE
We conceptualize "thinking with images" as an iterative perception-reasoning process. Perception focuses searching and localizing task-relevant visual details, while reasoning needs to organize these cues into structured facts and performs higher-order inference (e.g., planning, arithmetic, use of world knowledge) to complete the task. These two critical skills should be executed effectively and cooperate tightly to achieve strong performance. Existing benchmarks (Wu & Xie, 2024; Zhang al., 2024b; Lai et al., 2025) primarily emphasize perception, where their questions hardly re- quire multi-step step reasoning and thus induce short reasoning chains. To bridge this gap. we introduce O3-BENCH, a benchmark that jointly assesses high-resolution perception and multi-hop visual rea- soning. O3-BENCH is designed with two principles:
High resolution & high information density. Images are large, righ-resolution, cluttered, and information-dense, making evidence gathering genuinely non-trivial.
Multi-hop solution paths. Questions require decomposing the goal, retrieving evidence from multiple regions, and composing it via intermediate steps before answering.
To instantiate these principles, O3-BENCH comprises two complementary domains: (1) Composite charts. Each image contains multiple heterogeneous charts (c.g., bar/tine//iectablle)). Our crafted questions demand cross-chart retrieval (series, axes, units), lightweight calculations (differences, ratios, aggregates), and consistency checks (scale, legend, time ranges) to derive the final answer. (2) High-resolution digital maps. The images typically include map along with auxiliary components such as legends and building indices. We meticulously design questions that require visual search for targets (e.g,, matching symbols, categories, or toponyms) and spatial reasoning about relations and routes (e.g., proximity constraints or shortest paths), conditioned on the provided context.
Overall, O3-BeNCH comprises 204 images (117 charts, 87 maps) and 345 QA samples (163 for charts, 182 for maps) in total. The majority of samples fall into the more challenging map category, underscoring our prioritization of complex visual perception and multi-hop reasoning. The questions of -3-BENCH are multi-choic questions with six choices and one correct answer. Among the six choices, there are four distractors that appear in the image or look similar the correct one. We also include an option F as No Right Choice if there are no correct options provided. Below, we present the construction process of O3-BENCH. For other details about O3-BENCH, see Appendix B.
3.1 SOURCE DATA COLLECTION
Chart. The chart images in O3-BENCH curated from the "Diagram and Table" subset from LIME-RealWorke (Zhang et al., 2024b) and the Internet. To ensure high information density, we run layout detection model, PP- PP-DoeLayout plus-L (Cui et al., 2025), the candidate images and only keep those with at least detected layouts. As a result, 256 of 2,539 images (from AMME-Rea/Worlde that contain sufficient number of sub-figures and rich recognizable texts are left.
Map. We manually collect high-resolution digital maps from the Internet via keyword search. We center on the venue-level maps that require reading the provided legend/index and visually locating entities within the image to answer the question. We exclude all the country-, state-, or city-scale cartography that could be potentially answered with world knowledge. Through this process, we end up with 87 high-density map images spanning the categories over bus routes, campus, park, etc.
3.2 ANNOTATION PIPELINE
After the collection and initial filtering process, all images then undergo further manual screening to ensure clarity and completeness of key visual cues (e.g., axes, units and legends). Next, we combine automated machine pre-annotation with human verification and authoring to generate the question-answer (QA) instances. The detailed process of data annotation is presented as follows.
Machine pre-annotation. To relieve the burden of human annotators and increase the data diver- sity, first apply three-step automated data pipeline to generate five questions for each image. (1) Layout detection. We divide the high-resolution images into several structured layouts (e.g., ta- bles, charts, legends) using PP-DocLayoou plus-L. (Cui et al., 2025). For map images, we review
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the predictions, correct erroneous regions, and supplement missing areas via manual annotation. (2) Information extraction. For each layout, we prompt Qwen2.5-VE --- (Bai et al., 2025b) to produce detailed caption for the layout and extract OCR text from it. In addition, by generating a caption and extracting the OCR text for the full image. (3) Automated question synthesis. For each image, we provide the layout set (with captions and OCR texts) and the global context to GPT-5 (OpenAI, 2025a) to generate five questions (with answers and explanations) that compose evidence from the provided layout regions. Note that we do not provide the full image to GPT-5, compelling it to focus on region-level details and encourages multi-hop composition. More details about the whole mee-nnntttioon process can be found in Appendix B.2.
obtain global context
Human annotation. (1) Filtering and validation. Annotators start by discarding ill-posed or low- quality machine-generated QAs (e.g., those with factual inconsistencies, ambiguous prompts, or spurious multi-hop reasoning). For the retained QAs, annotators verify that the six-option set con- tains exactly one unambiguous correct answer and confirm that the explanation faithfully, step by step, justifies the choice. The annotators also ensures that the target layouts are relevant to answering the question; these layouts are either derived from the explanation or added via manual annotation. (2) authored questions. For information-dent tain logical errors, wrong answers, or missed visual details. These QAs are reworked or completely rewritten by the annotators, adhering to our design principles: requiring fine-grained detail retrieval and multi-hop reasoning. Each QA includes a detailed explanation to aid verification and have exactly one unambiguous correct choice among the six.
images, machine- -egnerated QAs often con-
Difficulty filtering and secondary review. We evaluate all candidate items with three strong pro- prietary MILMMs, i.e., GPT-5-mini (OpenAI, 2025a), Gemini-2.5-Flash (Comanici et al., 2025) and Doubao-Seed-1 1.6 (Bytedance, 2025), using the same evaluation prompt. We discard any items solved by all three models to ensure difficulty. Subsequently, independent reviewers (distinct from the original annotators) conducts cross-verification: a final pass over the QAs and the explanations to confirm factual correctness, clarity, and formatting consistency. Finally, we confirm with experi- ments that attention visual details vital to good performance on D3-BENCH (see Appendix B.3).
INSIGHT-03
In the previous section, we introduced O3-BENCH, a meticulously-crafte crafftd benchmark that require problem-solving systems to have both good reasoning and perception capabilities, well as the ability to integrate them in a natural, synergetic manner. Recent approaches towards such systems mostly build upon a single MILM agent which handles both reasoning and perception workloads within a single context window (Su et al., 2025a; Zheng et al., 2025; Lai et al., 2025). While this is reasonable for tasks primarily focusing on either reasoning or perception, the agent may struggle when the workloads are heavy and intertwined.
To address the issue, propose INSIGHT-O3, two-agent system that largely decouples the afore- mentioned burden by a visual reasoning agent (yReasoner) and a visual search agent (ySearcher). The former specializes in high-level, abstract reasoning (with some general image understanding), while the latter is mainly responsible for locating detailed visual information and presenting them to vReasoner. For instance, given a question, vReasoner first decomposes the question via reasoning, and, if needed, issues relevant image region descriptions to vSearcher; vSearcher then localizes the requested evidence (with help from tools like image cropping) and returns it for subsequent rounds until a final answer is produced. This process is illustrated in Figure 2(a). However, jointly train- ing both agents in a system like INSIGHT-O3 is notoriously difficult their objectives differ yet are highly interdependent, causing difficulties such as credit assignment across calls and non-stationary updates when both policies learn. Additionally, in our case, even the frontier open MI.LMs, egg., VL (Bai et al., 2025b), tend to produce overly concise replies (Lai et al., 2025).
To avoid overcomplication, we consider a simpler, more manageable setting in this paper. Specif- ically, we delegate higher-order reasoning at training time to strong external model (e.g., GPT-5- mini as vReasoner) and focus on training vSearcher to cooperate with the given vReasoner effec- tively. This separation helps simplify optimization and improve data efficiency. Our experiment shows that a well-trained vSearcher can significantly improve the performance of a wide range of vReasoners plug-and-play callable agent. Furthermore, the resulting system may help syn-
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Query
vReazoner prompt
Full
Full image boxed region
User
vSearcher system prompt
Cropped image
Feedback to the prev. crop
1
Respond
<Desct>
CoT2
<Deac2>
CaTS
VReasoner
Locate
CoT1
<Boki'>
Locate
CoT2
<Box2>
Correct?
vSearcher
In-loop sub-agent with region descriptions generated on-the-fly during training
Box
<Desck
User
User
1
Describe
CoT
<Diac>
Locale
CoT
<Bok>
loU
VReasoner
vSearcher
(b.1) Pre-generating region descriptions
(b.2) Out-of-loop sub-agent RL with pre-generated region descriptions
Figure 2: Training pipeline. We use hybrid RL algorithm to train vSearcher. (a) In the in-loop component, vReasoner generates visual search tasks on-the-fly during training as tries to answer query. We use vReasoner's 's feedback and final answer correctness supervision (denoted by dashed arrows) for vSearcher. (b) In the out-of-loop component, we use pre- pre-generated descriptions with ground-truth bounding boxes, allowing to train vSearcher efficiently via IoU supervision.
thesize multi-turn supervised-finetuniin (SFT) traces with interleaved reasoning and visual search, paving the way for a larger, potentially unified model. While prior works (Su et al., 2025a; Jiang et al., 2025a) have explored role-playing to construct similar SFT traces, the data quality is often not very high as the role-playing agents are only loosely coordinated. In this respect, our approach helps strength this coordination with reinforcement learning (RL), detailed next.
4.1 TRAINING ALGORITHM
We propose hybrid sub-agent RL algorithm that consists of an in-loop component and an out-of- loop component train vSearcher (see Figure 2). the out-of-loop component, we pre-generate region descriptions with predefined bounding boxes, allowing us to train vSearcher very efficiently via direct IoU supervision. In the in-loop component, we use real descriptions generated on- on-the- fly during training by vReasoner. Compared with pre-generated tasks, these dynamically generated tasks are more aligned in nature with the tasks that vSearcher will see during inference time.
Reward design. For the out-of-loop RL, we use the following reward function for vSearcher:
r= [[mooot > 0] (Atormat Tformat + Ato Tiou),
(1)
where Thool is the number of tool calls made by vSearcher, Tfomaa 1) is the format reward, and Tiou E (0, is the loU reward (Atomaa and Atau are weighting coefficients for the rewards). In Eq)(1), the IoU reward riu encourages vSearcher propose accurate region that matches the description, In addition, we encourage vSearcher to use image cropping' at least once to verify that the returned region matches the given region description. For every predicted box and the ground- truth box b*, we define the IoU reward as ToU = max IoU b*) aa/(11 ar) where a E (0, controls the reward threshold. Boxes with IoU less than a are not rewarded.
For the loop RL component, we replace the IoU reward FloU in Eq.(1) with a pseudo IoU reward ÂIO E {0, 13. We obtain Áuo by asking vReasoner rate each vSearcher's prediction, with the rating criterion being whether the prediction is relevant to the assigned task and can help vReasoner answer the user query. The rating s E (0, 1) binary score indicating if the prediction helpful. However, as this rating is not always reliable (vReasoner may sometimes make mistakes), we further
Foor simplicity, only allow vSearcher to use the most essential image cropping tool. Our framework, however, does not impose such constraint. It easy incorporate other kinds of tools for vScarcher use.

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incorporate outcome supervision as safeguard. Let E {0, 1} stand for whether the final answer of vReasoner is correct. The pseudo IoU reward is defined as foo = I[s = c= 1].
Advantage estimation. We follow GRPO (Shao et al., 2024b) to estimate the advantages for the out-of-loop RL component. As for the in-loop component, we normalize the rewards with respect to the global mean and standard deviation instead of the group mean and standard deviation since the concept of "group" no longer exists for the dynamically generated tasks. Formally, the advantage of an output token at time step t is computed as A, = [r mean(r)]/std[( out-of-loop component, r [r.][= with G being the group size; and for the in-loop component, r / with N being the total number of visual search tasks generated on-the-fly by vReasoner.
where for the
Objective fuucction. The objective function we use is based on GRPO (Shao et al., 2024b), with some modifications (e.g., global advantage estimations) to incorporate the in-loop RL component. Given policy model no. the old policy To and a reference policy Tee the objective function for a batch of M vSearcher outputs (including both in-loop and out-of-loop ones) defined as
M
lo.1
1 M
(00 =
min (,))) clip (7,(0),1-,,,l +) 3L [Tol (2)
i=1
where (e))) xo xo(os,ll.o5.cc)
nu (0,,1) is the importance ratio of the output token 0,, given query q and all previous output tokens 0j including tool-response tokens. During training, we mask the loss for tool-response tokens they are not generated by the policy model.
4.2 TRAINING DATA CONSTRUCTION
As high-resolution information-denss images with challenging questions are difficult to collect, we construct training data by synthesizing collages (for in-loop RI.) and generating pseudo visual search targets (for out-of-loop RL). The source data are mostly from existing VQA training datasets, which follow a largely different distribution from evaluation benchmarks we consider in our experiments.
In-loop RL data. A key criterion for in-loop RI. data is that they must be difficult enough to incentivize visual search; otherwise vReasoner could simply answer on its own and vSearcher would receive reward. To raise search difficulty and ensure meaningful credit assignment, we build image collages by stitching multiple -o--tto-mmdiu---soottttii
images into a canvas. We construct collages from filtered combination of Visual CoT (Shao al., 2024a) and the V training data (Wu & Xie, 2024), where each item provides a QA pair and a target bounding box. For each collage, choose one target image (carrying the QA) and add several filler images as distractors. After difficulty filtering, we obtain 15,303 hard problems that vReasoner must rely on vSearcher to solve reliably. For more construction details and visualizations of the data, see Appendix C.I.
Out-of-loop RL data. We use Infographic (Mathew et al., 2022) as the image source of the out-of-loop RL data. Most Infographic images have high information density and feature more organic and diverse layouts than collages. We detect layout components in the source images with LP-Doelaaoout.plus-- (Cui et al., 2025). The candidate layout boxes are filtered and further pro- cessed, resulting in 10.186 high-quality layout boxes. We then GPT-5-nano to generate concise, high-level region descriptions for each box, as illustrated in Figure 1b. Through prompting, we make GPT-5-nano mimic the style it would use when invoking vSearcher in the in-loop setting. This process yields a set of (image, region description, bbox) which enables the out-of-loop RL. More construction details and visualizations are provided in the Appendix C.2.
EXPERIMENT
(Bai et al., 2025b) vSearcher un- der 10155-min--2025--0 2025-08-07 (OpenAI, 2025a) as vReasoner for balanced efficiency and reasoning capability. The resulting vSearcher, named InSight-O3-vS, is evaluated under various vReasoners including Semini-2.55-Flasee (lushhCoouue (Comanici et al., 2025). For comparison, we evaluate these vReason- normally standalone models and as vReasoner with the untrained ----e2.5-VL-----nnntte as
In our main experiments, we train Qwen2.5-VL-7B-Ine
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Table I: Performance comparison with frontier models/systems.. All models/systems are eval- uated under their default configurations unless specified otherwise. Performance of open models are mostly cited from the literature (Wu & Xie, 2024; Zheng et al., 2025; Wang et al., 2025a; Lai al., 2025). Other results are averaged over trials, except MME-RWIse (single-trial). Small-size numbers indicate performance gaps between vReasoners w/ and w/o access to vSearchers. For more comprehensive benchmark results on O3-BENCH, see Appendix B.6.
Model/System
V*-Bench
HR Benchux
Tree- Beache
VProbeaus
MME-RWLa
O3 03-Bench
Average
LLaVA-OV-7B
70.9
62.0
37.3
13.4
48.5
20.2
42.1
1n3emBLL3.588
64,0
64.5
40.5
11.0
24.3
42.1
Owen2.5-VL-7B
75.5
68.2
37.0
23.9
46.7
27.4
46,5
Qwen3-VL-8B
86.4
78.9
48.3
31.6
53.0
43.6
57.0
Qwen3 VL-32B
48.2
28.6
60.4
59.8
Pixel Rensoner
86.3
74.0
28.8
28.8
DeepEyes
83.3
73.2
37,5
35.1
27.0
Mini-o3
88.2
77.5
48.0
29.1
OpenAI
76.4
74.3
52.3
23.6
55.2
40.8
54.0
GPT-4o
68.6
65.1
47.4
26.4
51.2
28.0
47.8
Qwen2,5-VLL7B
75.2 16.6
69.7 |46
45.9 1.6
15.4 11.0
44.6 6.6
29.5 1.5
46.7 111
InSight-o3-vS
76.2
49.5
25.5
36.4
53.0
GPT-5-nano
64.0
60.6
45,4
21.7
47.7
26.5
44.3
(0we2255-VL-7
70.1 +6.1
67.3 +6.7
45.7 -0.3
18.2
44.9 -28
25.3 -1.2
45.3 -1
InSight-o3--VS
75.1 +11.1
72.3 -11.7
47.7 -23
31.4-99
48.4 -0.7
34.6 -8.1
$1.6 -73
GPT-5-mini
72.0
54.6
26.4
39.0
53.7
Qwen2.5-VI. VL-7B
80.6 +68
83.2 -11.2
53.1 -1.5
37.7 -1.33
58.1 -20
47.5 -85
55.4
InSight-o3--S
86,9 +1M1
86.7 -147
54.1 -8.5
41.2 -146
59.0 -29
61.5 -22.5
64.9 111.
InSightto3-vSSt
86.2 1124
85.7 -137
55.0 -0.4
39.6 -132
58.4 223
59.9 99.9
64.1 1184
Gemini-2.5-Flasht
72.8
75.0
48.9
49.8
53.4
-wen2.5-VL-77
76.3 135
76.7
51.3
16.7
50.9
47.9
53.3
InSight-o3-v8
80.8 48,
80,2 +8.2
52,1 -32
19,8-13
55.1 -0.
58.0 -82
$7.7 --433
InSightto3--S!
85.5 1127
82,7 +77
52.6 -37
26.4 -x5
56.1 u..
61.1 -11,3
60.7 +73
80.1
83.5
49.9
39.6
56.5
60.4
61.7
@wen2.55VVD
VL-77B
.0.3
79.0
49,1
31.4
55.6
58.0
InSiggh--3--- 03-ght--3-v55
87.6 +7.5
82.3 1.2
50.1 -0.2
-3.2-34
56.3 -0.2
69.7 -9.3
63.7 -2.0
InSSighto3--SS'
88.3 18.2
83.0 0.5
53.6 3.7
38.3 -3
56.4 0.1
68.3 1.7
64.7 3.30
Trained with Gemini-2 2.5-Flash as vReasoner.
Image-size constraint set to x 1280px. roughly the maximum supported size for OpenAl modela/systems via API.
VSearcher. We use the default configuration for proprietary models/systems (per official API) except from setting image detail to high". More implementation details can be found in Appendix D.I.
Evaluation datasets. We evaluate range of open and proprietary models/systems on the follow- ing benchmarks: (1) Natural-image benchmarks: V*-Bench (Wu & Xie, 2024), Tree-Bench (Wang al., 2025a), and VisualProbe- Hard (Lai al., 2025). (2) Mixed benchmarks: HR-Bench (Wang et al., 2025f) and MME-RealWorld (Zhang et al., 2024b). For efficient evaluation, we use the lite version of MME-RealWorkke which has 1,919 questions, still much heavier than the other bench- marks. (3) Our O3-BENCH. More information about the benchmarks can be found in Appendix G.
5.1
MAIN RESULTS
performance improvement for frontier models. As shown in Table INSIGHT- 03 significantly improves frontier models such as GPT-S-mini and Gemini-2.5-FFan on most bench- marks. On average, the performance of GPT-5-mini has improved by 20.9% (relatively) with the help of our vSearcher (InSight-o3-vSS, In particular, the accuracy of GPT-5-mini on O3-BENCH has improved from 39.0% 61.5%. Meanwhile, INSIGHT-03 also significantly outperforms their Pre-RI. counterparts, i.e., vReasoner Qwen2.5-VIL-7B, across all the benchmarks. The results suggest that InSight-o3-vS is able to generalize dut----distribuution across various domains since its training data distribution is distinct from the evaluation data distributions, To gain more insight on how INSIGUT--3 improves the baselines, see Appendix D.3 for a comparative analysis.
When image detail is high, OpenAl scales down oversize images to ~1280 1280px. as per OpenAl API (https: httppttttttt/////// form. openai com/ docs guides/ des/ images-viss www.iiiiiiiiiiooo www.neaallllaaaaaaaaaaaaaaaaaaaa www.llllattinnn aallatinggcoss costs). This is the maximum supported image resolution for OpenAl models/ysttmss via API. Gemini-2.5-Flan does not impose such constraint, so use much larger, 3500 35000x budget, which is ample for the task.
API
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Published as a conference paper at ICLR 2026
Table Performance of Gemini-2.5-Flash (+ InSight-o3-vS") under different maximum train- ing/test image resolutions. All results are averaged over 3 trials, except for MME-RV (which based on a single trial). Small-size numbers indicate performance gaps between settings with and without vSearcher. Trained with Gemini-2.5-Flaan as vReasoner.
Train res.
Test res,
V*. Bench
HR- TR--Benchax
Tree-Bench
VProbeaud
MME-RWLae
O3-Bench
Average
1280
72.8
75.0
48.9
17.9
55.6
49.8
53.3
1280a
1280
85.5 -12
82,7 -1.
$2.6
26.4
56.1 +0s
61.1 +11.3
60.7 +74
3500
1280
85.3 -125
81.3 -63
53.1 442
22.6 447
55.1 -us
58.8 49.9
59.4 46.1
3500
80.1
83.5
49.9
39.6
56.5
60.4
61.7
1280
3500
87.8 47.7
84.3 +88
52.1 422
39.6 Hau
56.4 -LE
67.8 (74
64.7 13,0
3500"
3500
88.3 -8.2
83.0 -a5
53.6 +37
38,3 1.3
56.4 -..
68.3 479
64,7 +3,0
Table 3: Ablation study reward design and ad- vantage estimation. All results are averaged over tri- als. Small-size numbers indicate performance changes w.r.t. the proposed setting.
Table 4: Sensitivity analysis w.r.f.
max. input resolution of vSearcher. "#ySearch" is the number of vSearcher calls made by vReasoner per QA.
Setting
V*-B.
VPHaed
03-B.
Avg.
Max. pixels
V*-B.
O3-B.
fivSearch
Proposed
86.9
41.2
61.5
63.2
0.8M
85.3
56.0
2.82
w/o tool cond.
86.4
0.5
39.3
1,9
60.6
0.9
62.1
L11
1.6M
86.7
60.5
2.75
w/o feedback
86.5
0.4
37.1
4.1
58.1
3.4
60.6
26
3.2M
89.4
62.3
2.69
w/o outcome
86.9
0.0
38.7
2.5
60.9
62.2
1.0
6.4M
86.4
61.9
2.66
w/o GN
87.3
0.4
36.8
4.4
61.3
0.2
61.8
1.4
12.8M
86.9
61.5
2.58
Generalization under differrnt vReasoners. We observe that InSight-o3-vS, which was trained a sub-agent under GPT-5-mini, generalizes under other vReasoner models well. As shown in Table 1, InSight-o3-vS improves the performance of a much smaller model, GPT-5-nano, from 21.7% to 31.4% VisualProbe-Hart from 26.5% to 34.6% 51.6% overall. Under Gemini-2.5-Flash (a different model family), the advantage remains signif- icant, showing about 7-10% lead over the baselines on V*-Bench and O3-BENCII. We have also explored training InSight-o3-vy under 2.mini-2.5-1 5-Flash instead of GPT-5-mini, and observed sim- ilar generalization (see "+ InSight-o3-vSi" rows in Table 1). In few cases where InSight-o3-vS fails to improve the performance of the vReasoner, e.g., GPT-4o and Gemini-2.5-Flass on VisualProbe- Hard, we see sharp decrease performance as we allow these models to call Qwen2.5-VLL-7B. This suggests that these models are relatively weak tool calling and multi-turn reasoning. In Ap- pendix D.4, we present typical failure cases of INSIGHT-03; we find that even GPT-5-mini (despite the good performance) still makes a lot of mistakes.
O3-BENCH and from 44.3% to
Performance gaps on 03- -3-BENCH. Interestingly, Flash has a huge edge over GPT-5-mini (when they have no access to vSearcher), but on the other benchmarks, the edge is not so mini on Free-Bench. This suggests that O3-BENCH is indeed quite different from the other bench- marks, and Gemini-2.5-Flaas is particularly good solving the kind of tasks in O3-BENCH on its own. Notably, with InSight-o3-vS, GPT-5-mini is able to drastically reduce the gap (from 21.4% to 8.2%) with Gemini-2.55Flaan demonstrating the importance of thinking with images for addressing O3- O3-BENCH, and also highlighting the effectiveness of our approach.
03-BENCH, we observe that Gemini-2.55
is even slightly worse than GPT-5-
Effect of input image resolution. Comparing the results of Gemini-2.5-Flaas in Table I under different maximum input image resolutions, we see that much higher resolution offers clear ad- vantages. However, the improvement brought by vSearcher is less when vReasoner can see clearer. In addition, we find that training under one resolution and evaluating under another seems to have little impact on the performance (see Table 2). Meanwhile, the input image resolution for vSearcher has less impact. Table 4 shows the performance of GPT-5-mini + InSight-o3-vS under varying maxi- mum image resolution of InSight-o3-vS during evaluation. We can see that InSight-o3-vS is not sen- sitive to the resolution, maintaining decent performance on V*-Bench and 03- 03-BENCH even when the maximum image resolution is only 0.8M (25% of that during training). When the resolution is low, the average number of vSearcher calls is relatively high. This is expected as low-resolution images often obscure fine details, making it harder for vSearcher to locate the targets.

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Published as a conference paper at ICLR 2026
vSearcher reward"
VSearcher IoU
V VReasoner accuracy
of vReasoner calls
0.60
2.2
0.3
propteed
0.45
vil foccback
wel
0.15
0.50
1.8
100
150
30
100
150
100
150
30
100
150
Step
Step
Figure 3: Training dynamics of INSIGHT-O3, The rightmost chart, "# of vReasoner calls", shows the average number of times vReasoner calls vSearcher per QA. For fair comparison, the reward curves are plotted under the same setting ("w/o feedback") for all the settings.
5.2 ABLATION STUDY
Hybrid RL training. Table shows the re- sults of ablating the in-loop and the out-of-loop sub-agent RI. components. Without the in-loop RL component, training is much faster (80%+ reduction time per training step) but the final performance is worse average. Dropping the out-of-loop RL. component also hurts the per- formance; moreover, the training time increases due to more in-loop training. As mentioned in Section 4.2, the RL components use differ- ent training data. The better performance of hy- brid RL training can be partly explained by the combined use of the training data. Another con- tributing factor is the combination of two dif- ferent sources of supervision (high-level vRea- soner feedback and low-level IoU with ground- truth boxes). Overall, combining the two com- ponents leads to the best result.
Table 5: Ablation study on hybrid RL train- ing. "I." and "O." stand for the in-loop and out- of-loop RL components, respectively. "T/step" is the average time per training step. All re- sults are averaged over 3 trials. Small-size num- bers indicate performance changes w.r.t. the un- trained baselines.
O. V--B.
VPilad
O3-B.
T/step
GPF- GPT-5-mm0 -0200
70.1
18.2
25.3
74,9
4.8
23.9
15.7
27.9
12.6
846s
73.7
3,6
25.1
669
31.5
16.2
130s
74.5 4.4
27.4
19.2
32,4
17.1
693s
80.6
37.7
47.5
CPT-5-min
86.4
39.0 +1.3
59.6 +12.1
1223s
84.8
41.2 +3.5
58.8 +11.3
105s
86.9
41.2
+3.5
61.5
+14.0
941s
Reward design and advantage estimation. In Table 3, compare the setting we proposed in Section 4.1 on GPT-5-mini + InSight-o3-vS with the following ablated variants: "w/o tool cond." drops the tool condition in the reward function; "wlo feedback" removes vReasoner feedback, only using outcome supervision for pseudo IoU reward: "w/o outcome" is the opposite of "w/o feedback"; and "w/o GN" drops the global normalization for advantage estimation. The originally proposed set- ting outperforms all the variants with a small average lead on the three benchmarks. The training dynamics under these settings are shown in Figure 3. As vSearcher learns better locate the re- gions described by vReasoner, we observe that both the out-of-loop localization IoU and the in-loop vReasoner accuracy improve. The con-monoto "# of vReasoner calls" shows two RL phases of INSIGHT-O3. vSearcher first learns to obey the formatting instructions, and then learns to localize more accurately (so vReasoner could solve the same problem with less vSearcher calls).
Although we encourage vSearcher to use the image-cropping tool, we find the average tool call count often ends up close to 1. There are two underlying reasons for this behavior. First, as mentioned by Zheng al. (2025); Lai al. (2025), Dwen2.5--VL-7B-In does not seem to know how to use the tool properly. Second, vReasoner usually describe a rough region that is not very hard for vSearcher to locate, the tool has little utility for vSearcher.
is often reluctant to call the tool. and
6 CONCLUSION
In this work, we introduced O3-BENCH, high-information-density benchmark that jointly eval- uates visual localization and multi-hop reasoning. To advance the research on this challenging benchmark, we proposed INSIGHTT-3,, multi-agent framework that decomposes the "think with images" workflow into high-level reasoning (vReasoner) and visual search (vSearcher). We focus on the training of vSearcher via reinforcement learning to seamlessly cooperate with vReasoner. The specialized InSight-o3-w can be used as a "plug-and and--and--lay component for existing multimodal foundation models and helps significantly improve the performance of frontier models.
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Published as a conference paper at ICLR 2026
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A ADDITIONAL DISCUSSION ON RELATED WORK
A. MULTIMODAL BENCHMARKS
Classical multimodal benchmarks (Goyal et al., 2017: Hudson & Manning, 2019; Gurari et al., 2018; Saikh et al., 2022; Li et al., 2023b; Fu et al., 2023; Liu et al., 2023; Ge et al., 2024) primarily focus coarse image-level understanding or target at salient-object attributes, multimodal models (Bai et al., 2025b; Wang et al., 2025e; Yang et al., 2025a) show near-saturated performance. With growing attention to multimodal reasoning, more challenging benchmarks have emerged, which could be categorized as two groups. (1) Cognition-cemi centrrc benchmarks. STEM (sci- ence, technology, engineering, and mathematics) benchmarks (Lu et al., 2023; Wang et al., 2024; Zhang et al., 2024a; Yue et al., 2024a;b) evaluate the model's multi-step reasoning, integration of world knowledge, and complex calculations to solve scientific problems, whereas the accompany- ing images are generally straightforward to interpret.
which current
(2) Perception-centrie benchmarks. These benchmarks (Wu & Xie, 2024; Wang et al., 2025g; Zhang et al., 2024b; Lai et al., 2025) require fine-grained perception in high-resolution images and strong OCR recognition on text-rich scenes. Nevertheless, many questions become routine once the model precisely localizes the target region, allowing for single-glance solutions. Though the recent TreeBench (Wang et al., 2025a) evaluates second-order reasoning over object spatial transformations, depth ordering, and etc, it still centers on single region in natural images, leaving cross-region evidence aggregation largely underex- plored. With the emergence of the "think with images" paradigm (OpenAI, 2025c), we argue that a well-designed benchmarks should evaluate the joint perceptual and cognitive skills. Our proposed O3-BENCII fills the research gap by meticulously constructing hard questions on high-informationn density images (e.g., composite graphs, maps), therefore requiring models gather information from multiple, spatially distinct regions and to perform complex, interleaved reasoning.
A.2 MULTIMODAL REASONING MODELS
Reinforcement learning (RL) (Schulman al., 2017: Rafailov et al., 2023; Hu et al., 2025) has long been used to align the response of large language models (LLMs) and multimodal LLMs (MLLMs) with human preferences. Recently, DeepSeek-R1 (Guo et al., 2025a) creatively applied group rela- tive policy optimization (GRPO) (Shao et al., 2024b) to LLMs,, estimating the mean and variance of advantages across response groups under a simple reward signal. This strategy reliably elicits behav- iors such as planning, thinking, and self-reflection, enabling long chain-of-thought (CoT) reasoning and moving toward more general-purpose reasoning capabilities. Building on this success, several works (Huang et al., 2025; Yang et al.. 2025c; Meng et al., 2025; Chen et al., 2025a; Wang et al., 2025b; Shen al., 2025a; Chen et al., 2025b; Deng et al., 2025; Wei et al., 2025; Peng et al., 2025; Wang et al., 2025d; Shen et al., 2025b) explore cold-start initialization and GRPO-based RI. train- ing for multimodal models (e.g., Qwen2.5-VI. (Bai et al., 2025b)) and report substantial gains on science- and math-oriented benchmarks. Concurrently, Intern VL3.5 (Zhu et al., 2025a; Wang al., 2025c) and VL1.5 (Yang et al., 2025b;a) further leverage cascaded, iterative RL stages to push the frontier of reasoning ability, achieving performance competitive with proprietary models (Ope- nAI, 2025a: Comanici et al., 2025). Nevertheless, current multimodal reasoning models (Xiaomi, 2025; Hong et al., 2025; Guo et al., 2025b; Du et al., 2025) still focus text-centric reasoning, neglecting the distinctive demands of visual reasoning in multimodal scenarios.
A.3 HIBRARCHICAL AGENT FRAMEWORKS AND TOOL-USING MULTIMODAL AGENTS
Recent works have shown that hierarchical collaboration among specialized agents can significantly improve performance on complex tasks. Socratic Models (Zeng et al., 2023) and Hugging (Shen et al., 2023) demonstrate early examples of LLM-based orchestration over expert models via lan- guage. More structured frameworks like CAMEL (Li et al., 2023a), MetaGPT (Hong et al., 2023), and HAMMR (Castrejon et al., 2024) explore rose-based or modular specialization with coordinated task decomposition. Others, including AutoGen (Wu et al., 2024), HALO (Hou et al., 2025), Pup- peteer (Dang al., 2025), and AgentOrchestra (Zhang et al., 2025a), further extend this paradigm with explicit planning hierarchies, adaptive execution, and learned orchestration, consistently out- performing flat-agent baselines across diverse domains.
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There are also recent works exploring equipping multimodal models with tool-use and program- matic reasoning to tackle complex visual tasks. VisProg (Visual Programming) (Gupta & Kemb- havi, 2023) and ViperGPT (Surís et al., 2023) are two pioncering approaches that use large language models to generate and execute code for orchestrating vision modules. Beyond static program gen- eration, other agents use L.MSs as high-level controllers. For example, HuggingGPT (Shen al., 2023) demonstrates an LLM (ChatGPT) orchestrating numerous specialized models (for vision, language, etc.) More recently HYDRA (Ke et al., 2024) introduces dynamic multi-stage frame- work for visual reasoning: it integrates an LLM-based planner and reasoner with reinforcement learning based controller that adapts the sequence of operations via feedback loops, yielding more reliable step-by-step reasoning. These tool-augmented systems highlight the power of combining learned vision-language models with external modules or code execution to improve flexibility and compositional reasoning. In contrast, our work (INSIGHT-03) targets a complementary gap by introducing a dedicated visual search agent that can be invoked by reasoning agents to locate fine- grained, conceptually described regions within images. This specialized capability, absent in prior tool-using frameworks, allows an INSIGHT-03-enablle system to pinpoint relevant visual details based on free-form descriptions, thereby enhancing multimodal reasoning with more precise visual understanding.
A.4 VISUAL SEARCH MODELS
Visual search is an important functionality in the multimodal domain, requiring the models to per- form active perception over regions of interest (Rols) for fine-grained visual understanding. Early approaches (Wu & Xie, 2024; Shao et al., 2024a; Qi et al., 2024; Hu et al., 2024; Li et al., 2025) rely on external tools or predefined workflows for region localization and use instruction tuning to trigger tool use. These models exhibit rigid output patterns and typically support only a single round of visual search, which limits their effectiveness in complex scenes. Recently, the milestone OpenAI o3 (OpenAI, 2025c) established the "think with images" paradigm, in which image manipulations (e.g., zooming, cropping) are internalized as intrinsic capabilities, enabling image- text interleaved reasoning. The community has rapidly turn the attention to the promising field. DeepEyes (Zheng et al., 2025) exploits the inherent grounding ability of MLLMS and incentivizes visual search via end-to-end reinforcement learning. Pixel-Reasoner (Su et al., 2025a) improves search accuracy by warm-start instruction tuning on synthesized data with error-induced self-correction trajectories. Mini-o3 (Lai et al., 2025) introduces an over-turn masking technique during RL to encourage multi- turn interaction, markedly enhancing reasoning adaptability and diversity. Other lines of work (Zhao et al., 2025; Zhang et al., 2025c; Liu et al., 2025) resort to write codes for executing multiple im- age manipulations (e.g., cropping, rotation, enhancement), pointing to an open-ended toolkit for visual reasoning. Although effective, recent advanced methods (Zhu et al., 2025b; Fan et al., 2025; Zhang et al., 2025b; Su et al., 2025b; Ni et al., 2025; Cheng et al., 2025; Jiang et al., 2025a;b; Wang et al., 2025c;h) still largely prioritize locating a single region on natural images, which sidelines the model's 's capacity for reasoning. This paper broadens the capability scope of visual search models by decoupling visual reasoning and visual search agents, allowing the receiving of any images and searching of multiple distinct regions.
B ADDITIONAL INFORMATION ON -E-BENCH
B. BENCHMARK STATISTICS
Layout distribution
Target layout distribution
Image resolution distribution
10000
of samples
map
of samples
75
map
Height
50
5000
10
0
10
5000
10000
of layouts
of sarget layoots
Wsdh
Figure 4: Distribution of layout numbers in O3-BENCH.
Figure 5: Resolution distribution.
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We summarize the benchmark statistics from following three aspects. (1) Distribution of layouts.
The benchmark features 8.7 layouts and 2.4 target layouts for each sample on average, indicating high information density and the need for multi-step reasoning. And the layout distribution by category is displayed in Figure 4. It can be seen that chart images typically exhibit a larger set of total layouts, whereas map images require more target layouts for reasoning.
(2) Distribution of resolution. We collect high-resolution imagery in O3-BENCH. As shown in Figure 5, most images have side lengths in the 2K-5K range, while some images reach up 100 pixels on the longer side, yielding high information density. On average, image height and width are 3,967 and 4,602 pixels, respectively. (3) Distribution of options. We randomly shuffle options A-E, ensuring an approximately uniform distribution of correct-answer positions. And a small portion (7.2%) of samples use option F (No Right Choice) as correct answer, which compels models to aggregate evidence across the entire image and determine that none of the other options is valid.
B.2 DETAILS OF MACHINE PRE-ANNOTATION
(1) Layout detection. We first divide the high-resolution images into several structured layouts (e.g.- tables, charts, legends). We use PP-Docl Aaooelaayouppuuu--.... (Cui et al., 2025) detect layout bounding boxes in the image and construct the set C = [4]=1 where 4 E R denotes layout coordinates. For chart images, we directly use the detector outputs. For map images, we review the predictions, correct erroneous regions, and supplement missing areas via manual annotation.
(2) Information extraction. For each detected layout 4 in image we obtain the cropped image It, according to its coordinates. We then prompt Swen2.5-VL-322 (Bai et al., 2025b) to produce a de- tailed caption c; and extract OCR text o, for IT,. thereby forming the layout triplet T (Ii,, Cr oz). And then we aggregate all region triplets into T {T; mm In addition, we obtain global context by generating a caption and OCR text for the full image, denoted as G (Cg. 0g). The prompts we use provided in Appendix E.I.
(3) Automated question synthesis. We provide the layout set T and the global context G to GPT- 5 (OpenAI, 2025a) and explicitly prompt it to generate five questions that compose evidence from multiple regions. For each question, GPT-5 must produce six options (A-F) with exactly one correct answer, and option F is reserved for No Right Choice. It also need to supply step-by-step explana- tion that interprets the reasoning chain. is noted that we do not provide the full image to GPT-5, which compels the model focus region-level details and encourages multi-hop composition. The prompts we use are provided in Appendix E.2.
B.3 MORE EXPERIMENTAL RESULTS FOR O3-BENCH
We present additional results for 03- BENCH in this section. Because our annotation pipeline supplies the target layouts most relevant to each question, we can provide these region crops alongside the original full image at test time. We evaluate GPT-5-Mini (OpenAI, 2025a) and Qwen2.5-VL-7B (Bai et al., 2025b), with re- sults shown in Table 6. Both models exhibit significant performance gains when given the additional target layouts, underscoring the need for models to actively locate task-critical re- gions and perform interleaved visual reasoning.
Table 6: Ablation on target layouts in O3-BENCH.
O3-BENCH
Model
chart
map
overall
GPT-5-mini
34.4
43.2
39.0
+ target layouts
74.2
61.5
67.5
Qwen2.5-VLL-7B
30.9
24.4
27.4
+ target layouts
39.3
31.9
35.4
B.4 VISUALIZATION OF O3-BENCH
In Figures 6-11, we present six representative visualizations (four map items and two chart items), showing how 03-BENCH couples high-resolution perception with multi-step reasoning. Each anno- tation includes: (i) the multiple-choie question and answer; (ii) highlighted target layouts that mark the regions consulted along the solution path; and (iii) a concise, ordered explanation that composes the evidence, which allows readers verify the answer quickly.
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Annotation
Split: Map
Q6A: What is the building opposite the nain entrance of the School of Business Postgraduate Annexe?
MLnT Study: Comeecee Building: C. Psycho Darvin
Philosophy, Union East: Pacific
Islands Contre;; None of above
Target Layouts:
Explanation: Step Locate School Business Postgraduate Aonexe in building tist identified No. 26 at F7. Step 2. Find this building F7 on the East Cazpus map Step 3, Refer to the Hap Legend to confim that the triangle represents the Main Entrance to the Building. observe that the nain entrance of the Schanl of Business Postgraduate Annexe faces Building No. 27. Stop 4. Look Building No. 27 the building List identifies led the Conrerce Building, thus the
is B.
CLosed views of key regions:
Step 3
Step
Main Entrance to 3uildings
Step 4
Business School of Commerce Buiding
Step
26 School of Business Postgraduate Anneve
Figure 6: Example from 03- 03-BENCH (Map-1). Each annotation comprises six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain in large images where fine details may hard see.
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Annotation
Split: Maр
Q6A: Which floor has the Prayer room, and near which zone is it located?
South zone;
2F, North zone; C. North zone; D. North zone;
4F, West zone; F. None of above
Target Layouts:
Explanation: Step 1. Refer to the Legend to identify the synbol for the Prayer room. Step 2. Use Floor
Prayer located on 2F. Step 3. the map locate the Prayer roon to the right of the H&M store in the North zone. Select option accordingly.
notton-right
5F
FLOOR SUIDE
Restaurants & Food court
AN GARDEH
FLOOR GUIDE
ARIAKE DLIDEN
Closed views of key regions:
Step 2
Step 3
Taclh Roor
Step
H&M left Tarmed's
HM
Prayer room
Torods
?FBLO
Figure 7: Example from O3-BENCH (Map-2). Each annotation comprises six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain in large images where fine details may be hard to see.
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Annotation
Split: Maр
Q&A: Which restaurant has an ATM located next to it?
A. Burger Federation; B, Fulton Street Café; C. Wolfgang Express; D. Goose Is land Beer Company; E. Facades Bar;
The Bronze Tap
Target Layouts:
Explanation:
Step 1. Locate the ATM synbal PASSENGER SERVICES Step 2. Hatch the boarding gate
numbers of the restaurants Listed the options using the restaurant list Burger Federation (L28), Fulton Street Cafe (H14), Wolfgang Express (K12), Goose land Beer Conpany IL18A), Facades Bar (K15), The Bronze Tap (KS). Step 3. Compare the gate locations the terminal nар and identify which gate symbol,
symbol nearby. K5
Bronze Tap,
TERMINAL
O'HARE ORD NTERNNATTIN AIRPORT
3
INFORMATION
Eat, Shop More
Oriani
EAT
1
PCOW
PRI CECURITY
SERVICES
Closed views key regions:
Step
Step 2
Step 1
L20
Burger Federation
120
n
Automated Teller Machine (AIM)
Ho
Fulton Street Café
Wolfgang Express
K12
M
Goose Island Beer Company
L10A
Line
Facades Bar
K15
kis
The Bronze Top
K5
Lr
Figure 8: Example from 03- -3-BENCH (Map-3). Each annotation comprises a six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain large images where fine details may be hard to sec.
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Annotation
Split: Map
Q6A: After having Brookfield Cafe, take visit Boote Library. Which should I take?
202; 205; 288;
209;
Target Layouts:
Locate Brookfield Cafe the Catering list 1 building in Grid C7. Step 2, Locate Boote Library in ; it is building 11 in Grid F6. Step 3. On the nap find that Route 205 bus fron Callege Road be below building 13 leads the vicinity of building 11.",
Explanation:
UCC Campus Map
bock
Closed views of key regions:
Step
7E
C
Step
Brookfield
Step 2
Boole Library
11
F6
LCC
Figure 9: Example from O3-BENCH (Map-4). Each annotation comprises six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain in large images where fine details may hard see.
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Annotation
Split:
Chart
Q6A: If the Condo unit's Purchase Cost is depreciated evenly over its stated operation persidd, what percentage does this depreciation anount account for of its Avg. Annual Rental Fee (annua Lized based the Year 10 Avg. Daily Rental Fee and 30 days/month)?
A. 35.8%; B. 36.0%; C. 37.5%; D. 38.5%; E. 39.0%
F. No right choice
Target Layouts:
Explanation: Stop 1: Fron Condo Purchase Costs 150,080 USD. Step 2: Fron deprectation period years. Therefare, Annual depreciation 150, 080 15 10,808 USB/year. Step 3: Fros Conda Year 10 Avg. Daily Rental Fer 1114 USD/day. Step 4: Annual rental incane at Year 10 rate with 38 /oyy//onthh// 114 * 30 x 12 =
41,,,, 41,840 USD/year. Step 5: Percentage = (10,000/ / 41,048) * 100 = 24.37%
Step 6: 24.37%in1 not Listed options A-E, the correct choice is F.
41
44
4.5
CLosed views of key regions:
Step
Step: 2
Step
Intial CAPEX
Rernel Unit Type
Intial
Aug. Daily Rencal Fea
Inflrtio
Year
Rernal Unit Type
Apertmentt
#
Peried
CAPEX hems
Purchase Costs
Condo
USD
Room
15
Refurtsshments & Renovations
2,500
Room
1.5%
85
Cordo
15
Interior Design
Condo
100
1.5%
118
Willa
15
Furniturs and Fistures
5,000
Villa
250
1.5%
294
150
MenMenti' Rostel Zoee
no.Mentth Ang. Dediy www.Renttal.oooo por
Figure 10: Example from 33-BENCH (Chart-1), 1). Each annotation comprises six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain in large images where fine details may be hard to see.
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Annotation
Split: Chart
Q6A: What is the LTV in the first year when Net Income exceeds 700, 000 USD?
A. 65; B. 62.18;
V C. 58.5%; D. 54.8%; E. 51.2%; F. No right choice
Target Layouts:
Explanation: Step 4,731,355 is the earliest that exceeds. Stop 2: Fron Bank Ratios LTV in 2828F is shown as 58.5%
Incone Statenent table find first year Incone 4, 700, 080: 2028F
Ph
PRETS
Step
Closed views of key regions:
Statemeet
Step 2
Protert
Year 5
Year
Year Qperationa!
Revenues
8147133
9346932
8540711
Bank Ratios
2027F
2028F
DSCR
3.66x
3.73x
-877239
-893441
Financial Deb//BIITAA
2.45x
2.31x
Decomm Decomm/sdoning istoning Proviginn
.80000
40000
10900
Debt Service Coverage (DSC
3.66x
3.73x
7850926
EBIT//meres
9.61x
>10.0.
28/7TM Mariee
89. 25.
89.00 0%
8%
LTV
1%6
Net Debu/ BquitE Ratio
22.5%
141.7%
Capiral Gain
Depeeciation
F321952
-1321952
.1321952
Equity as % of Invested Cap
90.3%
40.8%
Intered
-719922
-611652
4447733
Inceme Tanes
1719788
-11237797
-11777588
inceme
4711333
Figure 11: Example from 03- D3-BENCH (Chart-2). Each annotation comprises six-choice QA and brief explanation with highlighted target layouts for quick verification; additionally, we also provide step-wise close-ups (outside the annotation) to reveal the evidence chain in large images where fine details may hard to see.
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B.5 COMPARISON WITH AME-REALWORLDD (CHART)
The chart images of O3-BENCH are mostly selected from the "Diagram & Table" subset of MME- RealWorld. The original questions from MME-RealWorld are relatively simple, usually focusing on single value in chart, which do not require any kind of multi-hop reasoning. For example, the original question of Worlld for the chart we show in Figure 10 is:
what is the cost inflation rate in the General Settings section of the General Assumptions table?
In comparison, the new question in O3-BENCH is:
If the Cendo unit's Purchase Cost is depreciated evenly over its stated operation period, what percentage does this depreciation amount account for of its Avg. Annual Rental Fee (annualized based on the Year 10 Avg. Daily Rental Fee and 30 days/menthi
As illustrated in Figure 10, answering this question requires piecing together detailed information from three different tables through multi-step reasoning and arithmetic.
Overall, the questions of O3-BENCH (chart) are much more challenging than the MME-Real counterparts. This can also be seen from the following statisties:
The average accuracy of GPT-5-mini on O3-BENCH (chart) is about 38.2%, whereas on MME- RealWorld (chart), the accuracy is about 82.4%.
The average number of vSearch steps of InSight-o3 O3-BENCH (chart) is about 3.1, whereas on MME-RealWorld (chart), the number is about 1.1.
The average multi-turn response length (including reasoning tokens) of GPT-5-mini vReasoner on D3-BENCE (chart) is about 1942.3 characters, whereas on RealWorld (chart), the average response length is about 730.0 characters.
B.6 FULL BENCHMARK RESULTS
Table shows our full benchmark results of frontier multimodal models/systems on 03-BENCH. For OpenAl models/systems, oversize input images resized to 1280x (this is roughly the maximum supported size OpenAl API, as mentioned in the main paper) and image detail to high. For other models/systems, oversize input images are resized to 3500x (this translates to about 16K tokens/image for Qwen2.5-VL) All models/systems are given a 16K to- kens/response budget (including reasoning tokens), which should be more than enough to solve the problems in
Incomplete responses beyond this budget are considered wrong without checking. Open models are evaluated via self-hosted vLLM instances. Proprietary models are eval- uated via official APIs. Our evaluation code can be found at https: //gi thub. com Just/ InSight-o3.
C TRAINING DATA CONSTRUCTION DETAILS
C.I IN-LOOP RL DATA
Our collage sources come from the training split of Visual CoT (Shao et al., 2024a) and V* (Wu & Xie, 2024). We first filter both datasets by target bounding box size. retaining items with area(bbox)/aree (abbx)aarea(image) < 0.04. From Visual CoT, we keep all Chart/OCR-centric subsets (dude, cub, ttxtvvaa docvga, infographi csvqa, sroie, vsr, taxt and treat the natural- image subsets (flickr30k, gqa, open ipenimages, 77w) together with V as a separate stream due to lower QA reliability (e.g., weaker question-image alignment and non-unique answers). To ensure stable RL rewards, we filter this stream with an MLLM check using Qwen2.5-VL-77 and GPT-5- nano under deterministic prompt. An item is retained only if both models return correct answer; otherwise is discarded, including ambiguous or poorly aligned cases. After this pipeline, we retain 100K items the source pool D for collage synthesis.
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Table Benchmark of frontier multimodal models/systems on O3-BENCH. Default model/system settings are used unless stated otherwise. All results are averaged over 3 random trials.
Chart
Map
Overall
MaVVA-OneVision-78 (Li et al., 2024)
21.1
19.4
20.2
IntemVL3.5-8B (Wang et al., 2025c)
26.2++2.
22.7=007
24.3=1.1
Intem (Wang et al., 2025e)
24.5
21.2+
22.8=2.5
GLM-4.6V (Hong et al., 2025)
51.5 1:22
38.5.12
44.6
Qwen2.55-VL-7B-Insttre (Bai et al., 2025b)
30.9
24,4
27.4=033
Qwen2.5-VV--22-Inssruu (Bai al., 2025b)
35.4
33.5
34.4=1.0
Qwen3-VL-&B- (Bai al., 2025a)
54.4 10.3
33.91:43
43.6
Qwen3--V--BB-Thinnnng (Bai et al., 2025a)
49.1
33.0#009
40.6=0.7
Qwen3--V--30B-A33--ine (Bai et al., 2025a)
49.3
32.1
40.2=0.4
Seen3--V---0B-AAB--hhin--- (Bai et al., 2025a)
51.1
36.8
43.6
Qwen3-VL-32B-Im Insttruuct (Bai ct al., 2025a)
73.7
48.5+221
60.4=1.7
Owen3-VL-32BB-hhing (Bai et al., 2025a)
52.4
40.5
46.1=13
Qwen3-V1-2255--A22B Instrucct (Bai et al., 2025a)
73.4
53.811.00
63.1 2.08
Qwen3-VL-2338--222B- Thinkknn (Bai al., 2025a)
57.3.112
17.8
52.3
GPT-4o (OpenAl, 2024)
22.1
33.3
28.0=08
GPT-5-nano (OpenAl, 2025a)
19.2+23
33.3+339
26.5=3.1
GPT-5-mini (OpenAI, 2025a)
34.4
43.2
39.0
GPT-5 (OpenAI, 2025a)
30.9.:0..
52.6
42.3 =000
GPT-5.2 (OpenAI, 2025b)
31.9
39.0
35.7 223
OpenAl o3 (OpenAI, 2025c)
27.8
$2.4
40.8
Gemini-2.8 Commanic et al., 2025)
46.6+13
52.6+3.0
49.8
Commiii-2.5-Flas (Comanici et al., 2025)
61,8+112
59.2+118
60.4=0.5
Gemini-2.5-Pro (Comanici al., 2025)
67.3+25
63.7+25
65.4=25
Gemini-3-Flash (Google, 2025)
68.1126
69.0
68.6
Gemini-3-Pro-Previeee (Google, 2025)
67.7 1200
69.6
68.7 2.7
doubao-seedd1116-556615 (Bytedance, 2025)
55.4
48.5+44
51.8=2.7
INSIGHT-03 (w/ GPT-4o)
::.7
38.3
36.4=002
INSIGHT-03 (w/ GPT-5-nano)
35.3
34.1
34.6=1.9
INSIGHT-03 (w/ GPT-5-mini)
67.3
56.412.1
61.5 =044
INSIGHT-03 (w/ Gemini-2.5-Flash)
75.6+2.
64.4
69.7-0.7
Imago-size constraint set to x 1280px, rogglly the maximum supported size for OpenAl models/ystemss via API.
Given the filtered source pool D, we synthesize collage-style training images around one primary target (the image the model should attend to) and auxiliary fills (other images used to occupy maining space and control background complexity). The full procedure is presented in Algorithm 1. Specifically, we sample and grid-quantize canvas, then determine feasible target scale by inter- secting global bounds with bbox-to-canvas cap and a minimum short-edge constraint after light aspect jitter (Steps 1-2). We plan & place the target using fit-then-shrink heuristic with a single enlarge-canye canvas fallback (Steps 3-4). Remaining area is panelized into grid-aligned regions under simple aspect/size guards (Step 5). Panels are then filled (largest-firs by sampling images & D using usage-aware weights (favoring less fessufreyy used candidates) that also roughly match panel aspect; when needed, we apply a light center crop and bounded scaling (Step 6). packing remains incomplete after brief extra fill pass, lage (Steps 7-8). To avoid ambiguity when querying the target image, we annotate each collage tile with an ID and include this ID in the question reference. Figure 12 shows representative visualizations of synthesized collages.
resample from the canvas; otherwise
finalize the col-
The canvas is sized that the target box occupies only tiny fraction of the canvas area, enforcing area area(bbox (bbox)/ /area /rreaccanvvs) canvas) < 0.0002. We filter out items that vReasoner can already solve without calling VSearcher using pass@3 check (three attempts; any success leads to removal).
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Algorithm Target-and-Fill Collage Synthesis (High-level)
Require: Metadata table D (image path, Ware Hees object bbox), target image t" E ED, grid G, min short edge M, canvas area/aspect ranges [Amin Amax] and [Amin: @max], target scale bounds [Amiur Amax]. target aspect jitter Tig. fill jitter Thu- fill scales [Ami A panel aspect range [AR M max effective source area max bbox coverage cap Peap=2x 10 place- ment retries R, max attempts T
Ensure: Canvas C and placements P = (target, fills}
1: Precompute target meta. From t*, read Wsre: Hure bbox ratio Parei set S. +
min(1, v S / (W sre si))) and Fsee k- Wate/ Hare-
2: for attempt to 7 do
b rejection loop
Step Sample canvas. Draw Acanvas [Amin Amax], a [amin 0 snap grid to obtain (W, H).
4:
Step - Compute feasible target scale interval.
Bounds: E [Amin: )max]; occupancy: XS, S Acamvas's bbox: Pare ASx
S Peap
Acanvas Choose r+ by log-jittering Jsn within Tgg. and raise the lower bound x that min(yAS,rr, /AS./r.) M.
Let Ix be the intersection of the above constraints; if IA 2, optionally enlarge Acanvas once reject + restart at Step 5: Step - Plan target box. Pick A E Tx (e.g., midpoint); set w. /
and recompute; if still empty, continue
h - (AS.(A)) snap (w., h.) to multiples of G.
6: Step 4 - Place target. Best-fit on the free list (grid-aligned)
no fit, iteratively shrink (w,, h4) and update \=w.hh,/5 keeping feasibility in Jx, up to retries; still not placed, optionally enlarge canvas once and re-plan; it fails, continue b reject - restart at Step Step 5 - Normalize free space. Recursively split free regions into grid-aligned panels subject to aspect ARp E [AR ARpare and minimal size.
7:
8:
Step 6--Fil Fill panels. For each panel (largest-fing first), sample fill image E within 7 aspect band around ARp; if needed, center-crop i to ARp; scale with A E [A place.
Ailll and
9: Step 7 Resample if needed. If residual free space remains after one extra fill pass, reject - restart Step
continue
Step Finalize. return C,P
end for return None
D no feasible collage after attempts
C.2 OUT-OF-LOOP RL DATA
For LL-DecLayouu.plu-LL, we use the following configuration: ("threshold": 0.01, wyr.outtmmrre..bbxeeeee
"layout.nms" :: True,
"union").
To construct meaningful visual search targets from the boxes produced by PP-DocLayout.plus- we start by dropping boxes of trivial layout classes such as header and footer, keeping only text, image, table, chart. figure title, and paragraph_tize title boxes. We then drop boxes that are too large as they usually merge disparate things together. Boxes with area larger than a quarter of the whole image area are dropped, except for charts which are usually clean and we use a much higher threshold (0.8) for them. Next, we merge boxes that are very close to each other, measured by the effort required to enclose them in one box. The effort computed as 1 summed_ Larea / min_encle winsinnaaa area. We start with the boxes that require the least effort to merge, and stop until the required effort reaches a threshold. For figure_tt title and paragraph, title boxes, the threshold is 0.15, while for other boxes, it is 0.1. This helps merging small, auxiliary boxes such as figure titles and chart legends with their closest neighbors in the vicinity, avoiding truncating important context and preventing trivial search targets dominating
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Pii
pavilion
Whe's
WELCOMER
ZINE ZINE-LIEAA LIBRARY
CHARER Mas MAIIE RENS! BANE
JAN Californiaa
PICKPAL
TITS HAVE
JUST 60%
TWI
SANCT
TEACHORS
THE
ISTDD
TWISTED NOSE VODKA
NO
OSE
ED GIN
We buuzu
SPACESHIP
Question: In image13, what is the sanctioned faculty strength in 13 IIMS?
Answer: 908
Target Image
Target Bbox
Figure 12: Example of synthesized collage for the in-loop RI.. Multiple low-resolution images stitched to raise visual density. The blue dashed box highlights the target tile; the magenta box marks the target bbox. Remaining tiles are distractors.
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wewish
OBAMA ENERGY AGENDA
SAMSUNG FDUCAATON SOI UTIONS TRANSFORMING THE LEARNING EXPERIENCE
GAS PRICES
CHINA TO AUSTRALIA
Viler juil oaL
58%
31%
Securn PLaty day
Virkad Nathrra
Tars entinge carh
2008 (ike
WARICIPROORY
JPO "WE AND
over years to naka evet
NEW SMARTT DEVICES AND SOFT TWARR ENABLE
THIS COLLAOOOLOOOTIO BY PROVIDING:
1542.000
1212.000
1657 300
885 000
005
THE EXOLUTION THE
Marwirale vit
snall table-like black showing quarterly visitor arrival numbers, located at the lower-left portion of the image
OPPERTNOS INCUURE:
laptop screen displaying borksheet/diap lover-right of the inage
stacked about increasing use renevable energy twind, solar, hydrwwo,))), r located in the towgr-right area of the "Me need an all-of-the-ebove app reach" section
titled
Hill
Layout
Region Description
Box
SAMSUNG
Figure 13: Examples of InfographicVQA images with pre-generated layout boxes and region de- scriptions for the ootoof-loop RL.
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the dataset. We skip merge if the new box would be too large 0.25) or have an extreme aspect ratio (not within 1:5 and 5:1). We do not merge charts and tables.
area ratio than
After merging the boxes, drop (1) charts/tables enclosing other charts/tables, (2) unmerged im- ages that do not contain any text or titles, (3) unmerged titles, (4) unmerged texts, (5) boxes that are ratio less than 0.001), and (6) boxes with extreme aspect ratios (not within 1:5 and 5:1). These boxes often contain little information (e.g., icons, short texts). In the end, an image may still have multiple visual search targets; they are treated as separate data entries.
too small (box-to-image
To generate region descriptions for the visual search targets obtained earlier, we first draw a red box around the target the image, and then prompt GPT-5-nano as follows:
[SYSTEM)
are viaual assistant. Your goal. help the user locate region indicated by the zed bounding box in an inage.
When the aska you to describe the region, you mus: Iollow the rollewing zules: Keep it super simple and short as iE you can't t see clearly what is in the region. Don't mention any details, apecific content, small text the region.
Uise conaise, viaually grounded targeta le-gu, a chart, an object, a text block, a distinet acea).
Optionelly include approximate lccation (e.g.r zop-left of the inage, bottem-right of the big chart, center column).
Optionally include the title of the region (e.g.r the table about XXX, the section titled xXx)
Awaid non-viausl ordinal reforences le.g., "she third largest bar", *the second row's number"I,
mention zed bounding box.
Output formal: region_descripe wwt.eeeetrrttto........... io.
[DSER]
neacribe be the regioon in the red hounding box.
In Figure 13, three examples of the final data are shown. Note that unlike collages, these examples are not stitched together; they are simply displayed side-by-side save space.
D ADDITIONAL INFORMATION ON INSIGHT-03
D.1 INSIGIT--33 IMPLEMENTATION DETAILS
The maximum image resolution vSearcher is set to ~3.2M pixels (4K tokens/image) during train- ing, and 12.8M pixels (16K tokens/image) during evaluation. Oversize images downsampled to meet the constraint. We allow both vReasoner and vSearcher to make at most 6 sub-agent/tool calls during both training and evaluation. Image crops returned by sub-agent/tool calls obtained from original images, and then resized they exceed the size limit. For vSearcher, we use a max- imum response length (including results returned by sub-agent/tool calls) of 9K and 32K tokens (with sampling temperature 1 and 0) for training and evaluation, respectively. Other hyperparame- ters include: training batch size 24, rollout number 8, learning rate 10 6, KL loss coefficient 0.01, reward weights Afomatt = 0.2. λyo 0.8, and IoU reward threshold a = 0.25. The composition of hoop/oottof-looy training data is 1:1. We train vSearcher fully on-policy for 150 steps. We freeze the vision tower and the adapter of Qwen2.5-VLL77B V during the whole training pro- cess. We use GPT-5-nano (OpenAI, 2025a) for evaluating answer correctness. Our code is based on verl (Sheng et al., 2024). The prompts use can be found in Appendix F.
D.2 MORE VISUALIZATIONS OF INSIGHT-03 REASONING PROCESS
Figure 14 and 15 show examples of inference process of INSIGHT-03 (GPT-5-mini as vReasoner). The vReasoner issues natural- lanural- target descriptions; the vSearcher localizes evidence and returns them. Across a few rounds, the pair composes multi-step evidence and produces the final answer, demonstrating that INSIGHT-O3-VS plugs in cleanly and supports effective reasoning.
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Question
firishes
to AD3i STYLE, shich Clevator ee Exealator
8. Excelator
Excalator
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Mone
2F
VOLO
Tulb CW
H&-
MUJI
FLOER GUIDE
mest in Clase vitee of aria
sibaa right toie Add doow
cantion the
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right lil.
fell
(1)) and of
Sitaen
Weori ad lnmtter
216 et
tiec Lakis
vñeesooer
USearcher
Region Description
Figure 14: Qualitative example with GPT-5-mini as vReasoner and INSIGUT-O3-VS as vSearcher. The reasoner requests venue-level cues (e.g., legend/index lookups); the searcher returns localized regions and snippets, iterating to a correct answer.
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Question
Zoo
the park at I an and plans to watch antaal vauze
special vseving
for ted preividials,
shoutime they choose?
6. Asinforest Kidmart to Anchitheatre, 1.38
Shan Foendation Arphitheatre, 2:30 pa
Slon foundation A ppitticatre 5.40
Ro right chelie
GOU
DI
3
ERVOIR
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meed arcadd
and te uii paure coe Luallyy
Lilll
desther
mal clrelted shatt aheelcaa toa
thet
the mener
tepond
latt the lovk the
Cleas the Foanci Zoge ttbestre
Moveet1
Acletrent E1MAU' 5up mockttiet7s
VReasoner
VSearcher
Closed View
Region Descriptiion
Qualitative example 2 with GPT-5-mini as vReasoner and INSIGHT-03-VS vSearcher. The reasoner requests venue-level cues (e.g., legend/index lookups); the searcher returns localized regions and snippets, iterating correct answer.
Figure 15:
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D.3 COMPARATIVE ANALYSIS BETWEEN INSIGHT-O3 AND BASELINES
In Figure 16-19, we compare the behavior of INSIGHT-03 (GPT-S-mini + InSight-o3-vS) with two baselines: (1) GPT-5-mini and (2) GPT-5-mini Qwen2.5-VL-77. This comparative analysis is based on three examples of O3-BENCH. We rate each crop returned by vSearcher on three levels:
High-quality crops tightly enclose the visual search targets and the relevant context.
Medium-gue quailtmmuullttyyyyy crops contain the visual search targets but include too much context.
Low-quality crops miss truncate the visual search targets relevant context.
In the most basic setting where vReasoner does not have access to vSearcher, it often uses similar reasoning patterns as follows to reach its conclusion: "I first locate ... see ... then look for There is Therefore ... (see the top parts of Figure 17-19). During this process, vReasoner often hallucinates and makes factual errors about what it sees, suggesting that it does not really see the relevant visual details clearly but still pretends so anyway.
With the vanilla Qwen2.5-VLL77 vSearcher, vReasoner is able to take closer looks at regions of interest and makes less factual errors (see the bottom part of Figure 17 and the middle part of Figure 18-19). However, the vamilla vSearcher is often unreliable, returning inaccurate/wrong crops vReasoner simply concluding that the target is not in the image, usually after minimal amount of (sometimes none) reasoning. In such cases, vReasoner would eventually give up and resort to its own perception after multiple failed visual search attempts, leading to wrong final answers.
In comparison, our visual search agent, InSight-o3-vS, would usually first reason about the visual search request and then crop the candidate region verify before returning example, in Figure 16, InSight-o3-vS first reasons about what the bounding box should cover:
vReasoner. For
Based on the description, the right section of the map includes the legend/index and the "Catering venues" list with numbered cafes. The legend/index is located at right of the map, and the "Catering venues" list is further down, under the "Catering venues" heading. The bounding box should cover these areas.
Then, after viewing the cropped region, it concludes:
Based on the tool response, the right section of the map showing the bogena/index and the "Catering venues" list with numbered cafes is already covered by the bounding bex provided. Therefore, no further zooming is necessary.
In some cases, InSight-o3-vS is able correct an initial bad crop after reviewing the crop:
Based on the response, the zoomed-in in area does not match the intended area around grid B4. The bounding box needs to be adjusted to better capture the parks and nearby cafe icons and Labels.
The returned crops are usually mediun-toohigh--quaiiyy crops as shown in Figure 16. From these cases, we can see that vSearcher mostly helps vReasoner by (i) precisely locating the regions re- quested by vReasoner and (ii) presenting them to vReasoner and directing its attention to those regions, thereby reducing hallucination and facilitating evidence-baet based reasoning.
D.4 FAILURE CASES OF INSIGHT-03
Figure 20-21 show typical failure cases of INSIGITT-03, In the first three failure cases, vSearcher (InSight-o3-vS) provided the correct crops for the search targets but vReasoner (GPT-5-mini) an- swers incorrectly due to its own errors, e.g., ignoring visual evidence due to internal knowledge bias, and jumping to conclusion without examining key visual information. This suggests that existing frontier models, even the proprietary ones, are still not very good at thinking with images in com- plex scenarios. The last failure cases is due to vSearcher failing to understand the structure of the map and/or the relatively complicated region descriptions involving multiple visual cues. Finally, we note that, at least on O3-BENCH, most wrong answers are due to vReasoner, not Searcher.
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Question
Explanation
scude
(3)
Riint 135
eaiiity
Round
GFF
IeSight-03
Round
lariett Hover
GFT
cofiee
Sabr, nariy becddnng
In5 03
Round
CPT
Loacs unitie alvrly
LeSiigh 03
Round
Sorening
1eSipit-03
Siiie
Figure 16: Example (MAP case 1) of complete reasoning trace of our model (GPT-5-mini as vReasoner and INSIGHTT-3-VS as vSearcher). The vReasoner iteratively guides vSearcher through natural-language region descriptions. INSIGHT-O3-VS precisely retrieves high-quality image crops that match the described regions, effectively supporting vReasoner in locating the target area and producing the correct answer.
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Explanation
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right saaae
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6PT-5-Min1
corettre certer
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Gin- + by Qwen2.5VV
5-4L
Nigain
Round 1
Crip
Crep
eetobeeroggaa aud En Engits
Round 2
Round
moauig priking valy salling
Round 4
London Road origus and Mure Ba
Round 5
Dertiry the cieei cife
Figure 17: Example (MAP case 1, continuation of Fig. 16): reasoning traces of GPT-5-mini and GPT-5-mini + @wen2.5-VL.... 1wre2.5-vL-7B--nsttutt GPT-5-mini exhibits misperception and reasoning drift, while wwen2.5-VL-7B-Innstee quality crops misaligned with the described regions. Consequently, both baselines yield incorrect answers.
frequently fails to follow vReasoner's 's instructions, producing low-
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tuentioon
WELCOME
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Figure 18: Example 2 (MAP case 2): reasoning traces of model, GPT-5-mini, and GPT-5-mini Qwen2.5-VEL VLV-LL--Instun Our 1IIGHT--3--5 accurately follows vReasoner's 's instructions and re- turns high-quality crops aligned with the described regions, leading to a correct answer. In contrast, Qwen2.55VLL-7B fails to return a valid crop in the final reasoning round, resulting in an incorrect answer.
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Question
Ststanee
ciatty OED S000 Aaart she Bilans beest Siiler Tital dusts
dalarts theen cartty anaa
Explanation
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2010, the Titar serts
207%, This, 2011 2011
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Round
Round
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GPn- + L35
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EnSighht
Figure 19: Example (CHART case): reasoning traces of our model, GPT-5-mini, and GPT-5-mini Siren..5-V-7B--nnsteee INSIGITT-O3-VS effectively follows vReasoner's 's guidance and retrieves high-quality crops that fully capture the described regions. In contrast, Qwen2.55VL--7 returns only partial crops in Round 2 and, in the Round 3, fails to produce a valid crop as requested by vReasoner, incorrectly concluding that the target region is absent, which leads to an incorrect answer.
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Failure case (
VReasoner
Khowledge-Perceptioo Conflict
Round
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and
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Fallure case 2 ( VReasoner Visual Misperception
MAX
Round
aniiiing 1%
Round
teanter wnded
Question
Cheetar plans deprrt rrre serrec stacise excoer
Round
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Explanation Lion
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BUS PREQUENCY
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Figure 20: Failure cases & 2 of INSIGHT-03 (GPT-5-mini + InSight-o3-vS).
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Fallure case 3
VReasoner Improper Search Delegation
Round
Round
41
sected Leveed furee fross dwsett Bulls dall
Appport
Round 3
Question
right
Explanation
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( VSearcher Region Localization Failure )
Round
Round 2
Question
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Round
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Explanation
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Figure 21: Failure cases 3 & 4 of INSIGHT-03 (GPT-5-mini + InSight-o3-vSS.
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Table Performance of Qwen3-VL-32B and INSIGHT-O3 with Qwen3-VL-32B vReasoner.
VReasoner
vSearcher
V*-Bench
VisualProbe-Hard
O3-Bench
Q VL-32B
86.0
28.6
60.4
Owen3-VI-32B VL-32B
Qwen2.5-VL-7B (before RI.)
69.1
21.7
48.5
Owen3-VLL-322
Qwen2.5-VL-7B (after RI.)
90.1
36.8
61.2
D.5 INSIGHT-O3 WITH QWEN3-VL VL VREASONER
Apart from closed-source proprietary models, we also experiment with open models like Qwen3- VL (Bai et al., 2025a) the vReasoner of INSIGHT-O3. As shown in Table 8, INSIGHT-O3 (the last row) outperforms both the base Qwen3-VL-32B and the combination of Qwen3-VL-32D (vRea- soner) and Qwen2.5-VL-7B (vSearcher) without RL. Although the advantage of INSIGIIT-03 on O3-BENCII is relatively small, we note that as open models continue to advance in tool use and general reasoning. the performance of INSIGHT-O3 with open vReasoners will further improve.
PROMPTS FOR O3-BENCH
E. PROMPTS FOR INFORMATION EXTRACTION
For both the full image and cropped images, we feed them into Qwen2.5-V1-32B (Bai et al., 2025b) for information extraction using the same prompt as below.
##4 System
You are acting as *sprecise visual information extractor.*.
Civen ONE image, you must (1) identify image sype, (2) write a +*comprehersivn, strictly factual** caption, and (3) extract ecomplete OCR text** when present (with special handling for tabies).
rollow the rules **exactiye> and the outpuz in the three sections shown under Output FormaLAY,
#44 Global Principles
1) x*No sellueinstions.tv Describe only what is visible. If scmething uncleas, write '[ illegible] 1 oz '(pertially cascured: ...l'.
labels, fostnotes, wazermarks, axis titles, subtitles, panel labels (e.g., *(a) ((h) "I, or figure notes.
exhaustive.
not
text, legends,
3) **Preserve fidelity. Copy punctuation, capitalizacion, diacritics, signa (t), 1-)), units, and specing **exactly+., Do not normalize nurbers er zewrite
4) **geading order... when listing text outside of tables, use extep-to-bottem left-to- rightzs ordar.
5) A*Language. OCR text must remain in the Averiginal language The caption should be English
the visible Ji/page langsaga clearly not English; that casa, keep captions in that language, no **not** translate ate OcR unleas the image itaelf contains translation.
6) +*ll extra sections.ev Output wonlye the three required sections and nothing else.
#*t Image Type Ident fication (Section
Clessisy the image using one or more of the following types (multiple allowed if appropriste):
'chart' , 'sabla', 'document/taxe page", Floorrar// 'mop', VUI/scroenshot rorm', involce/"ecelpt" 'poster/tlyer: 'scientitic figure (multi-parel" " "natural acens', 'legend', "infographie', lother (specify)
#it Detailed Caption (Section
Write A*dense, atructuredes caption covers eritical elements. cleat, sbottono oz foreground
db jectiive language and organize logically background). Include the relevant aub-guidelines:
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**A, Charts Ploss scientific rigures**
State the figure title (if present), chart (y), axes titles, *eunitaes, tick labels, gridlines, data series, narkers/line styles, t*legend and color/ahape mappingsws, annotations,
bars, brend lines, and astable extrema/patteras (pesks, troughé, monotenic trends, outliers).
If milti-panel: identify panel labals 'ia) (b) '
and summarizo each panal in order
Mention any insets, callouts, fostnotes, or scurces.
**B, Tables Forms Receipts Documentsrx
Summarize what the table/dbcument centains (topics/fields), appreximate dimensions '12 rows x 6 colurns", ,,, header zeus, merged cells, checkboxes, stamps, signstures, page numbers, and "oote"s/footnnn" footnotes.
Call out key sections (headings, 1ista, paragraphs), logoa, and seals.
**C, Maps**
Repert title, compass/nerth azrow, scale/scale ber, coordinate grid or lat/long, boundaries, regions, routes/lines with moclongtto-maning maypings* (ézem legend) syabols/ icona (e.g., hospitals, stations), labels for placen/rozds, insats, and any zoning/heo: ramp.
Include legend content (calegories and theiz viaual encodings).
*DD UI Screensholssz
App/site name, window title, memut/toolbars, visible controls (buttons, toggles, checkboxes, dropdowns, search fields), selerved/disaaled states, scroll position, timestamps, status bars, notifications, dialogs, vigible file potha, and version strings.
+.E Natural / Real World Scenes** knumerate
on signs/labels, relative positiens le.g., above the doozway"), counts for repeated items, conditions (day/night, indeor/outdoer), and visible brands/logos.
objects,
sign
no **not** Invent interpretations or causs) explanations. Keep to what ia viaually supported.
##t OCR Extraction (Section 3)
Extract svall visiale text**, Follow these rules:
.*General 0CR Rulesv
cnnttura reading ardervi (top->bottom, left->right).
Preserve original line bresks and spacing.
If taxt is repeated (e.g., in watermark), list it once and note ((repeata) if necessary.
If character/vord is uncertain, write it as "[illgibbee" or "[7]" without guessing,
*Ibabls Ivery important)
Khen area table, extract immedi ataly vsing vaMarkdown syntaxax.
Preserve the **xxcct** row/column structure and header if cell has lins breaks, use '<br>' or 'In',
merged cella, repeat visible text affected and '(merged) after the table.
multiple order they appear.
exist,
sequentially 'iable 'Table 2", ...
Bocuments Tex: Pages / Forms**
Extract headings, paragrapha, 1asts, captiors, footnotes, headera/foaters: exact ly as Maintain indentation and 1ist
**thrrts Maps Jiagrans /
Scenes..
Extract mall textual alementsxx present: titles, subtitles, axis labela, tick labela, legend entries, series labels, annotations, callouts, map labels iplacea/roads// rlnes/ labels (menu items, buttons, tooltips), signs, badges, and watermarks.
If
present, "Hone'.
#4 Cutpat Foznat (retuzn EXACTLY this Markdown sizuctuzer no code feacee)
# Image Identification
more types
silowed list; brief justification if mixeds
# Betailed Caption <dense, strictly factual caption covering all visible elements rulea above>
# Extracted (if
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<ETTHER: Karkdown tables remaining text reading order; all non-table in reading order; write "None" if no text>
E.2 PROMPTS FOR CONSTRUCTING O3-BENCH
For chart images, we use the following prompt for GPT-5 (OpenAl, 2025a) to automatically generate QA instances.
You are an expert ansessment-iten author who designs rigorous -miiti-hop questioons benchma-k thirk--wit--mmaess abt litias **denge diagrams, charts, tables and schemacicaw cicwe, The goal Le to generate items so challenging that today's a stronges: score 508.. without
receive:
##f INFUTS
++GLORAT__DRR-
caption describing e image/page.
(GLODA1_OCC)
**ELOBAL CAРTION+ caption describing
image/page.
CELOBAL_CAPTION
ANTAYOUTS** list eropped regiors. Rach layous contains:
"layout_id' unique numeric io (far your insernal reference only). "caption_ar__s"" 8CR or descriptive caption of the cropped region. [1AYOUTS]
importann: constraint:**
In the vxquestions and vxoptionsss, must use only natural labels/text, present in the "GI,CBAT_OCR" or #IORAT_CAPTTION or
**Nevar** mention "layout_id', 'region', 'crop', 'panel', 'box', any similar takens. tayouts are erreterenoe-nly-- to help you construct lona; they may be citeed in the * explanationse
question options.
#4 TASK
Create ++3-5 independent multiple-choice choice questicns*. that each requizes +*>=2 distinct visual hopa** across layouta or global text. These mus: be ssmulti-ster itema directlyss, derived single step only.
grounded provided OCR/capsions
## QUESTION DESSIGN RELES
1. MMllli hop inference (required) x
different layouts*x from Cross-table lookup join (match category Tayout to code/key ir Tayout B, then filter condition).
question must integrate information from
glabal local text. valid patterns include:
Table <-> chart sligament Imap series/labels from one layout to anether!.
biagram <-> table mapping luse schematic labels query corresponding roms/values). Temporal alignment (identify whea a threshold is ecossed in one chart, then fetch related inño from another).
(compute ahoras from ona segion and compare with targets in
another).
snceptiios/constaaae lapply Ioctnote condi Irom layout before interprecing velues elsewhere).
2. Annalyticel reelismAv situate each question plausible scenaric (finance, science, education, operations, product metrica, stc.] while remaining strictly grounded in the provided DCR/ captions.
3. ADlfficulty controliv
questions should require zareful scanning, crass-referencing, and light calculations differences, ratios, ranking). Avoid itema thas answered glance.
*iinits, scales, rounding..
Always follow the unita/scales given in the OCR, Tf rounding is receasary, state the counding rule in the vxexgianationss
5. ssmbiggiiy guardrsilors Ensure exactly
cholcevv among unleas 1p (Ml right chofoe") is correct, Adjuss conditions to avoid ties.
6. ++Iabel ridellsyer Copy text exactly as provided (case, spelling, diacritics), Never use external/vorid knewledge.
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#4 ANSMER OFTION ROLES
1. Provide srexactly sixve options, one per line, labeled W. .... "P."
2. 'F. must always be exaetly "No right choice'.
3. Place the correct answer randomly among 'A, 'B, <-108 of itens may have 'F' aa the
correct answer.
4. Distractors must be austble and drawn from actual text /numbera in the ocR/eaptions.
5, Make options mutually exclusive' no meta options like "All of the above".
# CAPATNOTONN RULES
Provide -estop-by-step chain 'Step ...., 'Step 2: ...., etc. Explicitly cite which Layouts were used as '[layout X] '
Show all computations (e.g., (132 35) 95 0.383 33.93 [layout 41").
# OUTPUT FORMAT
+*nly+* following JSON array no extra text, block, commentazy:
outside code
jssnn
"question": "...",
"eptions:: "A. ... AnB, ... ... AnD. ... AnE. ... AnF. right choice",
aanwwer; "C",
"explenation": *step 1: [layout 21 \nStep 2: ... [layout 51 31 3: ..."
!..
For map images, we use the following prompt for GPT-5 (OpenAl, 2025a) to automatically generate QA instances.
You are an expert evaluation-Ltee author who designs zigorous +smiti -mmiti-hep (tsual-reasonte questions benchmark "chänk-wsth-inager" sbilities нлпарае (bus/metro natworks, terminals, malls, festivals, parks, ete.). Items should be challenging enough that today's strongest schieve 508.. accuracy.
You will receive:
*#t INFUTS
1. LNCLOSAL_DCR/x
caption describing the entire map.
[GLOBAL_OCR) CELLDAAL_CAPTONNS captior describing
map.
IGT.ORAT [CPPAT]
3. ANIAVOUTS*A List cropped map regions. Each layout containe: "layout_id' unique numeric ID (for your insernsl reference only). "caption_or_oat" UCR or descriptive caption of the cropped region. [1AYOUTS]
requitement:* the s*questione. and exoptiona*., you must use only natural labels/text found in 'GL.05LL_OCR" or "GLOBAt_CAPTION" or "caption_or_oe Do axnot mention 'layout_id', 'region', 'crep', 'bex', 'panel', ' or similar. Layouts are **rofsrence-only> for your ressoning; you may cite then the veexplanatione,, but in tha question or options.
#4 TASK
Generate x43-5 independent pectiple-choiee questions*ss Each question muss require **5=2 distinct reasoning hopa** that combine information di difeettnt layouts or between global and local GCR, All facta must be image-grounded.
#4 1) QUESTION DESIGN RULES
1. **Kulti-hop reasoning (mandatory) Examples of valid hops:
Lagend <-> line color <-> stop/zone.
Grid index <>> labe <-> adjacency,
Level/flooz
facility inset. Route <-> timetable <-> destination.
Synbol <>> restriction <-> path: feasibility.
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Uistanse/scale number segments
2. **Image/OC3 grounced only, Do not use external/world knowledge,
3. ::Biversia.... Vary questior styles (souting, conditionsl reachability, trensfer logic, aejacenccyccnntaimmmn count/ count//ompare, scale-based).
+iiificuuly tazget.** Avoid "st-a-glancs" items. Require cross-checking, counting, lightweight ca culation.
5. AvLabel fidelity.
labels exectly (case, spelling, diacritics).
6. A.tiniqueness.u Ensure exactly choice"; ls deliberately correct.
among A-E, unless right
7. **Units scale, If computing length/tine/seementa use the map's scales, syabols, or counts.
#* 2) ANENER OPTION RULES
**exactyy sixv+ options labeled 'A."
2. ', must always be "No right choice',
3. Normally, the correct answer is among A-E; only zarely (c108) should 'p correct.
4. Distractors be plausible, drewn from real map cext/aunbers, and mutually exclusive.
5. No mato-options ("All of the above").
#4 3) EXPLANATION RULES
Provide a ksstep-yy-step reasoning chain Explicitly cita layouts used as '[layout x)
Make hops and computat ona explicit (e.g., "Count atops loog Red tiree (layouu 3] and :layout 5]*).
compare stepa
** 4) OUTPUT SURMAT
Return *onnyy+ the following JSON array-no commentary markdewn:
** "jjsen
"question": "...",
"eptions": "A. AnB, ...Anc, ... AnD. ... Ang. An. No right choice",
"anawer"; *B",
"explanation"; *Step 1: [layout 11 AnStep 2: ... [layout 4] \nStep 3: ..." 11,
...
E.3
PROMPTS FOR EVALUATION
For proprictary models/systems, adopt the following thinking prompt (Guo et al., 2025a) their system prompt. The prompt is used across all benchmarks except O3-Bench.
A conversatio: lon between Oser and Assistant. The use: asks question, and the Assiatant reascning process
it. assistent providas the user with the answer. The reasaning process and answer are enclosed within < thinks </thinks <answer> </answer> tags, respectivaly, 1.e., <think> ressoning process here <anser>
thinks
mind
here </answer>.
O3-Bench, we use the models' default system prompts as they usually lead better performance. On other benchmarks, the performance difference is negligible (mostly within 1%) except only that GPT-5-nano performs much worse on VisualProbe- Hard when the default system prompt is used (accuracy dropping from 21.7% 16.0%).
For open models, e.g., Intern VL3.5 (Wang et al., 2025e), Qwen2.5-VL (Bai et al., 2025b), and their default system prompts since they relatively
Qwen3-VL models (Bai al., 2025a), sensitive to the system prompt. We note that the thinking prompt above often leads to suboptimal performance of QWen3-VL. models.
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F PROMPTS FOR INSIGHT-03
F.1 VREASONER PROMPTS
We use the following prompts for vReasoner during training.
[SYSTEM]
visual assistant. Your goal is
question based
think step by step ideatify which visual facus you need image the questien. If the visual informaticn is insufficiens or unclear, call the visual search by providing consise region description: www.sallieggnnneecciiitoooo..... 3<ltoooo_cl
cropped view of the target region. may repeat this proceas until you have enough evidence to answer confidently, The tool is not incorrect off-target,
tool will search the image
always precise evaluate output eritically. refine your description and try again.
Region description guidance:
Use concise, visually grounded targeta le.gu, a chart, an object, a text block, a inst area)
Optionally include approximate location (eig.r cop left, bottom right, center)
Avoid non-visual ordinal references le.g., "she third largest bar", *the second number";
Describe only one region per toal call; request meltiple regions single description
Output
Put your reasening process inside
any Thia anould indicate whether the result returned by the previous tool call is relevant to prior region description and helpful to answering the question. If it misses the key information you are looking for, it is unhelpful. If no prev' ous tool reault extata Lurn), outout LLoo__eeddaccottc//eee Immediately aftez </zocl_feedbacie do exactly one of:
immedistely after </thinks, output your assessment formatted as <tool, <tool_
most receat
e,g.,
Call tcol; or
23 Provide the rinal answer (no toal call) -- include the result in Wboxed].... no not mix tool calls and answera in the same tuzn.
If you need to call the tes-, provide the region description using the exact Format fonn:....//te................ must strictly follow output format, otherwise your
be judged wrong.
multi-turn example: Asaistant:
<think> step by step analysis; decide if more detail is needed) </think> Ses__eefedcccaaa/ttoooo... fete//ttcoleeeeackk j</tocl_call>
cconciss, visually grounded tazget toptionally with lccation)
User:
(Zoomed-in in Image guidance te.g., "Based your description, heze is the soomed-in in Image. Please continue your anslysis; you may cell the zool again or provide your final answer if sufficient.
Assistant:
(spdaeed analysis based on the zoomed-in view; decide whether to refine or </ think>
hoooo_eeellsckuuheeittte/eoeee feedbeckk
concise targes (ep:ionslly with losation! |//tcol_call>
(Repeat the User >> Asslatent pattern as neaded until enough evidence ta gathered.)
Asaistant (final curn):
<think>(Zinal reasoning; explain why the available visual evidence sufficient s/thiink>
Answer: Wbooxed]....
[DSER]
<imestee>//<mmg>>>>>>
[ASSISTANT]
[3SRR]
Based on your description, here is the zoomed in image.
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Please contimue your analysis. After the analysis, state asseasment of the previous tool reault using stool www.eeetheellptuuomeleeltlllliie
then one of the call the teol again if you believe more visual detail is needed; or Provide your final answer if the information sufficient. <image>
following:
[ASSTSTANT]
[USER]
Rased your description, here is the zoomad-in image.
using visual tool cannet again. In this turn, after reasoning step by step, output your assesament of the previous tool result using exactly stool_ htt.eepppuuuuueellpuu///ooooo the available information, provide your final using the required format. <image>
have
and then, based on
[AGSISTANT]
In case that vSearcher is unable to find region that matches the region description provided by vReasoner, we use the following user prompt to notify vReasoner.
[USER]
The visual searchez could not locate the requested target in the image based on your description.
adjuss or refine your region description example, refer larger, clearly visible eresl and continue your analysis. Think first, then state your assessment of the previcus
using ttt..//edaaccaapp/////aaaaaaaaaa
feedbackk, rinslly,
exactly one of the folloxing:
call the teol again with a revised description; or information sufficient.
Provide your final answer if the
Occasionally, vReasoner may fail to follow the format instructions. When this happens, we use the following user prompt to ask vReasoner to generate new response:
[JSER]
In your previous response, neither tool call nor final boxed answez was deteczed, or previous tool result
didn't output your
format.
Think first, there 1a no previous result). Fina ly, do exact ly one of the fol lowing:
previous tool result using exactly include your assessment for <tool ttt__/ttaaccoo//oooooooooooo feedhackk if
IS you still need viaual detail, call the :00l using the exact Tormal: mer.llleeggon__ssrrippi.... /<t////ol..///..oo...o calls
Otherwise, provide the final
inolude
result in Vaooed(.....
During inference, we do not ask vReasoner to provide any feedback to the tool. The prompts are slightly simplified as follows:
[SYSTEM]
You are a visual assistant. Your goal is to answer a question based on an image.
Firat, think step by step to ideatify which visual faces you need fzom the Image to anawer the queation. If the visual information insuffictens or unclear, call sual search tool providing concise zeçion description: <tool_cell> regioondescciipiionr.....
</tool_cell>
The teol will search the image and return a cropped view of the target regian. You may repest proceas until you enough evidence always precise - evaluste its output critically. If is looks incorrect or off target, refine your description and try again.
confidently. tool is
Region description guidance:
coneise, visually grounded targeta le.g., chars, object, diat Inot area)
block,
Optionally include aporoximale location (e.g., cop
right, center)
Avoid son-visusl or ordinel references le.g., "the third largest bar", *the second roy's number")
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Describe only one region per teol call; request multiple regions in single description
Output format:
Put your zeascning process inside <thinks... </think>>
When you need call the tool, need provide the region deseription waing tha as//ool_oaall immediately after each </thinks, exactly one of:
format <tool_calll ball=reggoo_cearrpptooo
1) Call the tool; or
Provide the final anave: (no Lool call) include the zesult Viboxed not in the turn.
mix tool calls and
You must strictly follew the output format, otherwise your answer will be judged as wrong,
A multi-turn Cormat example:
Assistant:
<think> step-oy ana yeasy decide if more detail la needed]</think> visually grounded target (opticnally with location
<tool_call> segion_ddsc:ipiionrieee
11 </tsol_call>
[Zoomed in image guidance (e.g., "Based on your description, here is the zooned in Image. again or provide your final
Please continue your analysis; you may sucTicient. ")]
Assistant:
dthink>(spdazee) analysis based on the zoomed-in view; decide whather to refine or answer)< thinks
<Lool_call> region_descciiptooneee concise target (opticnally with location) </tocl_call
(Repeat the User -> Assistent pettern as needed until enough evidence is gathered.)
Asaistant (final burn):
<think> [inall reasoning; explain why available visual evidence sufficient htfiicient/s/hhikk </thinks Answer: Wbboeed]...?
[USER]
[question] <imggee
[ASSISTANT]
[DSER]
Based your description, here is the zoomed-in image.
Please continue your
may:
Call the Lool agaia if you believe more visual detall is needed; or information sufficient.
Provide your finel if <image>
[ASSISTANT]
[USER]
Based on your desesiption,
zoomed-in image.
You have resched the limit for usiigg visual tool cannot ea again. this based the available information, provide your final answer using the required format.
<image>
[ASSISTANT]
When vSearcher can't find the target region, the following prompt notify vReasoner:
[US2R]
The visual searches could not locate the zequested Larget in the inage based on description.
Please adjuss or refine your region description (for exarple, refer to larger, clearly continue your analysis. may: Call the tool agein with revised descriptionj or Provide your final answer
visible
information sufficient.
When vReasoner fails to follow the format instruction described in the system prompt, we the fol- lowing user prompt to ask vReasoner to generate a new response:
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[SSER]
in your previous response, neither tool call nor a final boxed answer was detested.
please do exactly one of the following:
you still need more visual detail, <too www.alllrgoooooeeerriiiooe www.eeepteeeee.............. ///tooll _oall>>
using the exact format:
Otherwise, provide the finss easwer now and include the result in Maexedf....
F.2 VSEARCHER PROMPTS
We the following prompts for vSearcher during both training and evaluation after training. The prompts are adapted from DeepEyes (Zheng et al., 2025). For the last turn, we notify vSearcher in the user prompt that it has reached tool call limit.
[SYSTиM]
You are helpful assistant.
Tools
may call one o" more funstions to assist with user query. You are provided with function signatures within <toola></Loole> XML Lage: <tools>
in apecific region of on image by aropping it based on bounding box (bbox) and an optionsl cbject label. "pazamezers" (""typs" :"cbjcct" "properties"" (""boxx__a 2d": ["type" "Marray""""""""" ": ":""typ"" "numbor"], """yye"=""ummor"" "minitams" :4, "maxitama":4 [x1, yl, x2, y2l, where (X), yl) is the top-left corner and (X2, y2) 1a
["sype":"funetiin" "function": ("" """amm"""mmm""""" ""mage_zocm zocm_in_tool", "description";
"asserip:ion":"1 hounding box of the region
zoom in, botten zight corner, "), cbject in the specified bounding box (opticnal) "),, "required": ["abox"]]]] </tools>
"stting"" "description" "on":"The name label of the
to tool Return json object with function rame and arguments within
_eall>>>>>>>>>>> XMr.
tags:
<tool_sall>
("name": <function-namet "azguments": Karge-json-otjacts)
</toal_oall
Exampla:
<tool_eall>>
{"name": *image samage_soo__nne iooo__" "azgumente"; ("bbox_2d": [10, 20, 10c, 200), "label": "the
apple the desk*)]
</tool_call>
[USER]
<image>
Locate (target).
Think first, call pese__zoo__i_too if needed, then answer with the bbex coordinates in yl, x2, y2) fornat for 10, 0, 0, 0] if you can't locate it). Format strictly thinks... <tool_coll>,, stlb..../tol_oalll. lif toola neaded) <onswer> (xl, yl, x2, y2)</ (otherwise)
[ASSTSTANT]
[USER]
<tool stooll_asppoae>-e wewpponae-ciaagpe//aaaa <tape>>>/ttol_reepsses response>
coordinates xl, yl, x2, y2] fornat for 10, 0, 0, 0] if you canft locate it). Format strictly as: < chinks..</tthnk>> ctool_call>.... sccll....<tooeeellll. lif needed) <answer> (xl, yl, x2, y21</ answers (otherwise)
first, call pege___oo_______e if needed, answer
[ASSISTANT]
[USER]
You have resched the tool. cal: limit and cannot call tools anymore. Think first, call image_ saage_zcom_ir_too if reeded, then answer with the bbox cocrdinates in y1, v2, y2] fornat (ar [0, thinko..<<tthiii <anawer> [x., yl, x2, y2] </answez>>
if you locata it). Format strictly
1A8S1STANT)
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Without RL, Qwen2.5-VL-72 has difficulty following the instructions given in the above prompts. So, for evaluating yReasoner Qwen2.5-VL-7B, we use the following simple prompt:
[SYSTEM]
You are a helpful assistant.
[JSER]
Given an image and region descriptien,
region that best matches the description and output its bounding box coordinates as (X_min, y.mir, x_max, y_max).
If the targes cannot be found, output [0, 01.
Region description: [target)
Now, cutput the cooréinates in format (X_min, y_min, x_max, y_max] <image?
[ASSISTANT]
F.3 PROMPTS FOR ANSWER VERIFICATION
For answer verification, use GPT-5-nano (OpenAI, 2025a) and feed with the following prompt.
You are given an image-based question, the ground truth (GT) answer, and a model's answer. Compare the model's answer with the GT answer:
matches the >. Tf it doesn't match, if urcertain, reply with swrong?.
1f the model's
visually semantically, reply with <correct
Only reply
<ureng?, explenations.
Question: Iquestion)
er Answer: (gt_answerr
Model Answer: imodel_answer
G EVALUATION BENCHMARKS
G.1 NATURAL-IMACE BENCIMARKS
V*-Bench (Wu Xie, 2024) designed to test a model's ability to attend to high-resolution, detail-rich images. V*-Bench consists of 191 challenging natural images (sourced from the SA- IB Segment Anything dataset (Kirillov et al., 2023)) and focuses on two fine- ane-grained visual tasks: attribute recognition (identifying specific object attributes like color material) and spatial lationship reasoning (determining the relative positions of objects). By requiring accurate visual grounding of small details, V*-Bench exposes the limitations of models that rely on coarse image understanding.
Tree-Bench (Wang al., 2025a), like V*-Bench, uses high-quality, object-dense natural images (drawn from the SA-1B dataset) to evaluate fine-grained visual reasoning. However, Bench places additional emphasis traceable evidence and complex reasoning. Each of its 405 visual question-answe pairs is annotated with bounding-box evidence for the correct answer, and many questions require second-order reasoning about object interactions spatial hierarchies rather than simple identification.
VisualProbe (Lai et al., 2025) pushes visual reasoning to even harder regime. It features high-resolution images with very small target objects and many distractors, making it "super chal- lenging" and necessitating iterative, trial-and-error search by the model. VisualProbe is organized into easy, medium, and hard subsets; VisualProbe- Hard denotes the toughest set questions (106 in total) that often cannot be solved in single glance. Compared to V* -Bench and Tree-Bench. VisualProbe. Hard scenarios demand an active visual search strategy: the model may need to zoom on different regions or sequentially explore the image find relevant details.
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Table 9: Comparison of O3-BENCH and related benchmarks.
Layout/target boxes
Detailed explanations
Avg. resolution
Benchmark of QAs
Image domains
Multi-hop
(wigthx)eiihh))
V--Bench
191
natural (100%)
2246x 1583
Tree-Rench
405
natural (100%)
X
2152x
VisualProbeHand
106
natural (100%)
X
4944x3980
natural (89%).
IRR-Benchxx
200
chart (5%)
X
X
X
x024x3503
map (6%)
natural (>60%),
MME-RealWorld
29K
chart (25%),
x
X
2708 1844
map (6%),
chart (47%),
O3-Bench
345
x602x3967
map (53%)
Roughly 200 distinct QA pairs. The original paper reported 800 but most are the same questions with scrambled options.
Table 10: Comparison of O3-BENCH and related benchmarks on chart & map.
Avg. resolution (width
Avg. of GPT-5-mini
Avg. # of vSearch steps
Benchmark
HIR-Bench4x MME-ReB RealWorld
x032x2509
79.6
2.3
1875 1269 4602x3967
83.8
1.0
03 03-Bench
39.0
2.9
Based 500 random samples.
G.2 MIXED BENCIIMARKS
HR-Bench (Wang et al., 2025f) is a benchmark deliberately designed to evaluate models on ultra high-resolution images (up to 4K-8K pixels). It addresses key limitation of prior multimodal tests (which max out at ~2 resolution) by presenting tasks that cannot be solved with down- sampled images. HR-Bench is split into two sub-task categories: (1) Fine-grained Single-instance Perception (FSP), with tasks like identifying detailed attributes of a single object, reading text via image; and (2) Fine aineigra Cross-instance Percep- OCR, responding visual prompts multi-object challenges such as analyzing maps, inter- tion (FCP), which includes more complex preting charts/graphs, and assessing spatial relationships among multiple items. Each sub-task contains 100 queries on 8K-resolution images (with downsampled 4K version also provided for efficiency).
MME-RealWorld (Zhang et al., 2024b) is a large-scale, comprehensive benchmark that evaluates models across wide spectrum of real-world visual tasks. It comprises 13,366 high-quality images (average 2000 1500 resolution) and 29, 42,429 QA pairs, spanning 43 distinct task types grouped into five real-world scenarios, curated from various datasets (Agustsson & Timofte, 2017: Liu et al., 2020; Zhang et al., 2021; Yang et al., 2023; Li et al., 2022; Sun et al., 2022: Sachdeva al., 2024; Zhu al., 2021; Jia et al., 2021). These scenarios cover diverse applications such as autonomous driving (e.g., interpreting traffic scenes), video surveillance (e.g., counting vehicles an overhead street video), remote sensing (e.g., identifying and counting tiny objects in satellite maps), sports and entertainment (e.g. reading scoreboard in broadcast image), and others. MME-Real Real World is notable as the largest fully human- annottted multimodal benchmark to date. Given the benchmark's 's scale, employ the official AMME-RealWorld lite version for efficiency, which uses a subset 50 samples per task to speed up evaluation without significantly altering the task distribution.
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G.3 COMPARISON OF 23-BENCH AND RELATED BENCHMARKS
In Table 9 and Table 10, we compare 03- 03-BENCH with related benchmarks and highlight their key differences. Compared with the most closely related AME-RealWorld, we note that on the over- lapping domains (i.e. chart & map): (i) the average image resolution of O3-BENCH is significantly higher (4602 3967 ours vs 1875 x 1269 of MME-RealWorld), (ii) the average accuracy of GPT- 5-mini on O3-BENCH is much lower (39.0% of ours Vs 83.8% of MME-RealWonn average number of visual search steps produced by InSight-o3 for O3- 03-BENCH is 2.9x that of MME- RealWorld. In addition, O3-BENCE provides layout boxes and detailed explanations for each QA pair, while most of the benchmarks do not. The explanations can help the community easily verify the correctness of the answers. These differences show that O3-BENCI is of exceptional quality and much harder to solve than the other related benchmarks.
and (iii) the
Apart from quality, the scale of O3-BENCH is par with most of the fine-grained perception multimodal understanding benchmarks, c.g., V*-Bench (191). Tree-Bench (405), VisualProbe- (106), HR- Re--Bench-4K (200*), and commonly-used benchmarks in other multimodal research
eas; to list a few: Math: Vision test-mini (304) (Wang et al., 2024), Vista test-mini (1K) (Lu et al., 2023); VQA: RealWorldQA** (765); Embodinent//patiall Understanding: ERQA (400) (Abeyruwan et al., 2025), RefSpatial-Bench (277) (Zhou et al., 2025); Agent: MIA Bench (400) (Qian et al., 2024), OSWorld (389) (Xie al., 2024), AndroidWorld (116) (Rawles et al.,
2024); Fine- Fime-grained Perception: V*-Bench. These benchmarks were used to evaluate the perfor- mance of Qwen3-VI (Bai et al., 2025a). As with O3-BENCH, these benchmarks are relatively small mainly because of the difficulty in data collection. Nevertheless, they have served the timely purpose of evaluating frontier models in the respective fast-developing areas.
O3 BE-BENCH only focuses on composite charts and digital maps for two main reasons. First, they are representative of most use cases of thinking with images in the digital domain (as opposed to the natural domain). More specifically, composite charts represent structured images (with clear delin- eations between different layout regions) and often require more abstract reasoning (e.g., computing the difference of two quantities). On the other hand, digital maps represent images with less struc- ture and more organic layouts. They usually require more visual reasoning (c.g., finding the shortest route from location A to B). Together, ing the thinking-witte with--mmae capability of current multimodal models in the digital domain. As for the natural domain, there are already a number of high-quality benchmarks for thinking with natural images (some are listed in Table 9). O3-BENCH precisely fills the gap of existing benchmarks while striking balance between evaluation efficiency and generality.
argue that O3- is generally sufficient for evaluat-
H USE OF LARGE LANGUAGE MODELS
For this paper, used LLMs to aid and polish writing. Specifically, asked LLMs paraphrase certain sentences or paragraphs to improve clarity and readability. We also used LLMs to conduct literature survey, All literature references and related discussions in this paper are manually checked and proofread to ensure correctness.
http::///uggiigaaa...//atasss//s 4//MMM-ReR worrl d- Lite hatress//ouginnias....eaaassssss wor 1d04
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Reviews:
[{"review_id": "NuAluY9ANj", "reviewer": "Reviewer_CxbX", "summary": "This paper addresses the problem of using an LLM reasoning with a specialist LLM visual reasoner which can be called as a tool. The paper makes two contributions: (1) a new, complex, yet quite small dataset of  questions which requires multi-modal reasoning. Half of these are complex questions about maps, as well illustrated in Fig 1, requiring zooming in and reading the legends of the maps. (2) optimize a special visual searcher (i.e. visual LLM) in the context of a strong LLM-based reasoner. The second contribution seems the largest. They formulate the visual reasoning problem as a cooperative task between a visual reasoner (a pre-trained LLM) and a visual searcher (a visual LLM which is called as a tool by the visual reasoner and optimized with RL for the task at hand). Since optimization in a cooperative setting is really hard, the paper chooses to fix the visual reasoner to GPT-5-mini-2025-08-07  and optimize the visual searcher (i.e.  Qw2n2.5-VL-Instruct in this paper).\n\nOn standard academic benchmarks such as V*-Bench, VisualProbe, and MME-RealWorld results demonstrate that they can adding their now-trained visual searcher to several GPT variants and Gemini Flash and obtain significant improvements on top of using those models without their visual searcher. The fact that is works on many visual reasoners shows generalization.  Additionally, their best result with GPT-5-mini and Gemini-2.5-Flash outperforms the state-of-the-art on the majority of datasets. Note that while Mini-O3 seems to be stronger, AFAIK this appeared on ArXiv only recently and should be counted as contemporary work.", "questions": "I don't really have specific questions.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper decouples visual reasoning from visual search, leading to a modular system which is more understandable and whose components can be trained independently.\n* Results are state-of-the-art.\n* Good improvement using Gemini as a visual reasoner suggests that even though their visual search model was optimized on GPT-5-mini, it generalizes to multiple visual reasoners.\n* Ablation shows good improvement of RL fine-tuning and training efficiency benefit of using their static RL setup in conjunction with the dynamic RL setup.\n* New and complex visual reasoning dataset.", "weaknesses": "* The new dataset is rather small and limited in domain.\n* Minor: Table 1 is a bit confusing: the bottom part seems the most important while the top part is hardly discussed and only used for context; I would suggest to show the bottom part either on top or maybe better shown separately as Figure 1.\n* Minor: it is unclear which tools are used by Qwen2.5-VL. Anything other than 'crop'?\n* Minor: specialization of agents and sub-agents in agentic frameworks has been shown to work in prior art. Examples are [Socratic Models, Zeng et al., ICLR'23] and [HAMMR, Castrejon et al., NeurIPS workshop 2024]. Would be good to cite some of these works.", "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": "JU7gKAernr", "reviewer": "Reviewer_EKkk", "summary": "This paper addresses the limitations of current multimodal agents in visual reasoning by introducing a new benchmark, o3-bench, which requires models to integrate fine-grained information from multiple image regions while performing complex reasoning. To tackle this challenge, the researchers developed the InSight-o3 multi-agent framework, focusing on its vSearcher module with generalized visual search capabilities. Trained via reinforcement learning, vSearcher can locate conceptual visual regions based on natural language instructions and, as a plug-and-play component, significantly enhances the performance of state-of-the-art models across multiple benchmarks.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper clearly identifies a specific weakness in current multimodal agents—their inability to perform complex reasoning that requires integrating fine-grained visual details. It presents a challenging benchmark (o3-bench) designed explicitly to measure this underdeveloped capability.\n\n2. The proposed InSight-o3 framework presents a sophisticated multi-agent architecture that decomposes the complex problem into specialized sub-tasks (reasoning and search).", "weaknesses": "The claim that the framework's search steps decrease with increasing resolution is not sufficiently supported, as the reported variations across resolutions are minimal. This suggests the search pattern may be overly reliant on the characteristics of the training data, raising concerns about its scalability and effectiveness in real-world, multi-step search-and-reasoning tasks involving high-resolution images.\n\nThe framework's performance on powerful yet tool-agnostic models like GPT-4o and Gemini-2.5 (as seen in VisualProbe-Hard and MME-RW-Lite results) is suboptimal. This indicates a strong dependency on models pre-equipped with tool-calling capabilities trained with multi-turn RL, highlighting a limitation in its general applicability. Further optimization is required to improve its stability and performance across a wider range of model architectures.\n\nThe ablation study on hybrid RL training reveals a trade-off: while combining static and dynamic RL improves performance on the proprietary O3-bench, it results in higher per-step inference latency without a clear balance of their respective advantages (static RL's speed vs. dynamic RL's adaptability). Furthermore, the performance gains appear primarily concentrated on O3-bench, questioning the hybrid method's generalization to broader tasks.", "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": "x6hLikgj8t", "reviewer": "Reviewer_Pra6", "summary": "This paper proposes to augment visual-language reasoning models with the capability to perform a visual search function. In particular, it trains an additional visual search module that predicts the bounding box coordinate related to a given description (e.g., instructions derived from the input question), and feeds the retrieved image patches to pretrained reasoning models. A new multi-modal reasoning dataset is also presented, which focuses on reasoning on complex charts and digital maps. Experimental results show that the method improves the performance of several state-of-the-art reasoning models across different datasets.", "questions": "(1) What are the advantages of visual search over other types of external tools?\n\n(2) How does the visual search agent help the reasoning agent? Please provide in-depth analysis of the decision-making process.\n\n(3) Why does the model only improve the GPT models but lead to worse performance on Gemini? Is it related to the use of GPT-nano for evaluation?\n\n(4) Please justify how the proposed training paradigm could accommodate the artifacts in synthetic data.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "(1) It is a promising direction to improve multi-modal reasoning models by incorporating external tools (i.e., the visual search module in this case), which could provide both performance boost and enhanced transparency.\n\n(2) The use of collages for visual search training reduces the reliance on large-scale naturalistic data.\n\n(3) The paper develops a new evaluation benchmark for multi-modal reasoning, and can benefit the development of subsequent models.\n\n(4) The proposed method shows generalizability across several models and datasets.", "weaknesses": "(1)  It is not a new idea to combine VLMs with external tools (e.g., some compositional reasoning models [ref1, ref2] already explore the tool usage with reinforcement learning). The paper experiments with a single tool (i.e., visual search framed as a visual grounding task), while solving real-life problems could require diverse abilities. It is unclear whether visual search (especially when trained independently) can help generalize reasoning across different scenarios.\n\n(2) One advantage of having an additional visual search agent is to provide a transparent interface of the decision-making process. Nevertheless, the paper only reports the accuracy on datasets, without any analysis of how the improvement is achieved with visual search.\n\n(3) Looking at Table 1, it appears that the proposed method only shows consistent improvement on models from the GPT family, and can have negative effects on the best-performing Gemini model. Please justify the inconsistency in performance.\n\n(4) The proposed visual search module is trained on synthetic collages created by stitching together different images. Such a paradigm ignores the contextual relationship between different regions within a visual scene, and also introduces boundary artifacts. Since the search agent is essentially trained on a visual grounding task, I wonder how it will perform when training on naturalistic grounding datasets.\n\n(5) The new dataset contains a very limited set of stimuli (~350 images), making it difficult to be used for training or comprehensive evaluation.\n\n[ref1] ViperGPT: Visual Inference via Python Execution for Reasoning. CVPR, 2023.\n\n[ref2] HYDRA:AHyper Agent for Dynamic Compositional Visual Reasoning. ECCV, 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": "gFhaY2iBKS", "reviewer": "Reviewer_7mrZ", "summary": "This paper proposes a novel multi-agent framework that decomposes multimodal reasoning into two agents: a visual reasoning agent (vReasoner) that performs CoT reasoning and provides answers, and a visual search agent (vSearcher) that outputs coordinates given a description. Given an input, the vReasoner processes the answer and, when needed, calls the vSearcher to find a relevant visual region. The authors also built InSight-o3-vS using a novel RL training pipeline and presented the O3-Bench to evaluate complex visual reasoning tasks.", "questions": "1. Could you provide results for the vSearcher using different model sizes, or paired with open-source vReasoner models (such as Qwen2.5-VL or InternVL3)?\n2. The paper states that the O3-BENCH chart images are from MME-RealWorld. Could you clarify in more detail how the tasks (i.e., the questions) for this domain differ from the original MME-RealWorld chart domain?\n3. Does the number of layouts in the O3-Bench data affect model performance? (For example, is there a correlation between a higher layout count and lower model accuracy?)", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. I think decomposing mulitmodal reasoning model into two agents can be efficient without the need of training the whole model, especially with strong closed source models we cannot train. \n2. Training pipeline and constructing collage data for vSearcher is interesting and novel.\n3. O3-Bench can be beneficial to the community.\n4. Nice addiotional observation report from the hands-on experience.", "weaknesses": "1. **Reliance on Closed-Source Models**: The experiments rely heavily on closed-source models (like GPT-5-mini), which are difficult to use for reproduction or further training. As a result, training the vSearcher based on a closed model lacks scalability and reproducibility for the wider research community. I think experiments with open-source models can improve this paper significantly\n2. **Lack of Experiments**: While the vSearcher is an interesting approach, its validity would be more convincing if it were tested more rigorously.\n3. **Limited Benchmark Coverage**: The O3-Bench has a relatively small scale (185 images, 318 QA samples). This is a limitation, especially since the chart images are sourced from the existing MME-RealWorld dataset.", "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)