MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents
Authors: Xijia Tao
Teng Yihua
Xinxing Su
Xinyu Fu
Jihao Wu
Chaofan Tao
Ziru Liu
Haoli Bai
Rui Liu
Lingpeng Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Large Multimodal Model
AI Search Engine
Benchmark
Agent Framework
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We provide a challenging multimodal browsing benchmark and an agent framework with search tools and Set-of-Mark for provenance-aware zoom-and-retrieve.

Abstract: Existing multimodal browsing benchmarks often fail to require genuine multimodal reasoning, as many tasks can be solved with text-only heuristics without vision-in-the-loop verification. We introduce MMSearch-Plus, a 311-task benchmark that enforces multimodal understanding by requiring extraction and propagation of fine-grained visual cues through iterative image–text retrieval and cross-validation under retrieval noise.
Our curation procedure seeds questions whose answers require extrapolating from spatial cues and temporal traces to out-of-image facts such as events, dates, and venues.
Beyond the dataset, we provide a model-agnostic agent framework with standard browsing tools and a set-of-mark (SoM) module, which lets the agent place marks, crop subregions, and launch targeted image/text searches. SoM enables provenance-aware zoom-and-retrieve and improves robustness in multi-step reasoning.
We evaluated closed- and open-source MLLMs in this framework. The strongest system achieves an end-to-end accuracy of 36.0%, and integrating SoM produces consistent gains in multiple settings, with improvements up to +3.9 points.
From failure analysis, we observe recurring errors in locating relevant webpages and distinguishing between visually similar events. These results underscore the challenges of real-world multimodal search and establish MMSearch-Plus as a rigorous benchmark for advancing agentic MLLMs.

Published as a conference paper at ICLR 2026
MMSSAACC-PLLS:
BENCHMARKING PROVENANCE-AWARE SEARCH FOR MULTIMODAL BROWSING AGENTS
Xijia Tao"", Yihua Teng"', Xinxing Su Xinyu Fu², Jihao Wu², Chaofan Tao", Ziru Liu Haoli Bai², Rui Liu?t, Lingpeng Kong The University of Hong Kong (HKU) HHuawei Inc.
ABSTRACT
Existing multimodal browsing benchmarks often fail to require genuine multi- modal reasoning, as many tasks can be solved with text-only heuristics without vision-in-the-loop verification. We introduce MMScarch--Plus 311st bench- mark that enforces multimodal understanding by requiring extraction and propa- gation of fine-graines visual cues through iterative image-text retrieval and cross- validation under retrieval noise. Our curation procedure seeds questions whose answers require extrapolating from spatial cues and temporal traces to out-of- image facts such as events, dates, and venues. Beyond the dataset, we provide a model-agnostic agent framework with standard browsing tools and a set-of-mark (SoM) module, which lets the agent place marks, crop subregions, and launch targeted image/text searches. SoM enables provenance-aware zoom-and-retrieve and improves robustness in multi-step reasoning. We evaluated closed- and open- source MLLMs in this framework. The strongest system achieves end-to-end accuracy of 36.0%. and integrating SoM produces consistent gains in multiple settings, with improvements up +3.9 points. From failure analysis, we observe recurring errors in locating relevant webpages and distinguishing between visually similar events. These results underscore the challenges of real-world multimodal search and establish MMSearch-PPP -Puu as a rigorous benchmark for ad advancing agen- tie MI..Ms.
INTRODUCTION
Large multimodal language models (MLLMs) increasingly act as agents that integrate vision, lan- guage, and web search to answer information-seekinn questions. Several recent benchmarks study this capability by pairing images with web browsing or image search tools. However, despite their multimodal interface, existing multimodal browsing benchmarks rarely demand sustained, fine- grained visual reasoming or long-horie horizon, nool-augmented search.
This paper introduces MMSearch-PPls
benchmark that closes this gap. MMSearch-Pli is designed to require (i) localized, exhaustive visual reasoning over subtle cues, (ii) robust verification under noisy conflicting retrieval, and (iii) multi-step tool use that interleaves text and image search with region-level visual analysis. To support this, we contribute:
A challenging multimodal browsing benchmark that matches the long-horizon difficulty of text-based browsing tasks (e.g., BrowseComp) while adding visual reasoning requirements that cannot be bypassed by single strong image search.
Spatial-Temporal Extrapolation, a principled curation procedure that constructs hard questions by requiring models to extrapolate from localized spatial cues and temporal signals to out-of- image facts such as dates, events, or locations.
A general, model-agnostic agent framework that interleaves text/image search with a Ser-of- Mark (SoM) zoom-in pathway, enabling region-seeded retrieval (zoom in/image search) and systematic analysis of how cropping and "thinking with images" affect current MI.LMs.

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Published as a conference paper at ICLR 2026
Whole Image Search Only
MMSeerch Agentic
Let' zoam to see the lyrics.
zoom better image search query for tingger
BLOGAN
WE SING TILL THE LIGHTS COME ALIVE
What performance time of the singer in this event?
Thumbnall
Summary
can see lyric the lights come wlive'
Singer A's June 2024
search: cropped image
sing
Without Search
lights
Search results
haven't seen image my training- So I'll make guess.
Search results
The search results did not contain an exact image match. So will take #1 target event.
compare webpage summary and lest query.
webpage image query.
Taylor Reputation Stadium May 2018
Round
Round
A Singer A's concert in June 2024
A: Singer B's concert in March 2025
Figure 1: Three multimodal reasoning paradigms: 1) Without search: an MLLM answers a vi- sual question using internal knowledge only; 2) Whole-image search: MLLM leverages external search results retrieved from the full VQA image; 3) MMSearch- agentic frumework: an MLLM freely calls visual tools (cropping, OCR) and search engines extract fine- fine-grained cues and perform precision search. A real example shown in Figure
We next motivate why such a benchmark is needed. Multimodal browsing benchmarks such as MM- Search (Jiang al. (024) primarily test MLLM's ability coordinate and images, but many of their questions admit relatively fixed workflows. A strong image search engine often retrieves pages whose surrounding text directly contains the answer, allowing unimodal LLMsswithuut vi- sual perception- to perform competitively. In these cases, multimodality collapses to a narrow form of image-source cross validatio,,, and fine-grained visual reasoning plays only minor role.
In contrast, recent text-only browsing tasks such as BrowseComp (Wei et al. 2025 emphasize persistence, multi-step evidence gathering, and complex search strategies. Sfate-of-the-ar MLLMs score below 2% with browsing tools, indicating substantial headroom and revealing discrepancy: multimodal browsing benchmarks are far easier than their text-only counterparts, even though real- world multimodal tasks often require deeper reasoning.
Design principles for challenging multimodal browsing benchmark. Difficult web-based vi- sual tasks typically exhibit three recurring challenges:
1. Noisy or conflicting retrieval. Image search may surface near-duplicates, irrelevant scenes, or contradictory candidates; solving the task requires cross-source consistency checks and multi- modal verification.
2. Exhaustive, part-based visual reasoning. When holistic matches fail, agents must reason over subregions-ccooppnng, zooming, and re-searching-to accumulate fragmented spatial cues.
Long, tool-augmented reasoning chains. Realistic tasks require sustained coordination of vi- sual tools (cropping, OCR), textual browsing, and multimodal evidence integration.
Benchmarks such MMSearch do not consistently elicit these behaviors: many images contain single dominant entity that quickly anchors a search, and the subsequent steps are primarily text- based.
To construct tasks that inherently require fine- fine-graines grained visual reasoning, MMSearch- introduces subtle signals ignals--mecro-tex. micro-text, uniform variations, broadcast overlays, layout signatures that jointly
MMSearch-Plus.
Extrapolatio: we curate images containing multiple
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behavior and Published as a conference paper at ICLR 2026
imply facts not explicitly present in the prompt. Models must extract these cues, propagate them through iterative searches, and verify provenance amid retrieval noise. This better reflects real- world scenarios where neither the prompt
the image alone suffices; for instance, determining the date of an esports broadcast or identifying a specific match from nearly identical sports frames. Finally, we develop general agent framework with Set-of-Mark (SoM) to study how region-level vi- sual grounding influences browsing agents. SoM provides human-verifies marks and region-seeded queries, enabling "thinking with images" through structured zoom-in reasoning. Ablations quantity how cropping and localized visual extraction affect current MLLMs.
Together, MMSearch-PPP provides a high-difficulty, multimodal benchmark and analysis toolkit for evaluating the next generation of multimodal browsing agents. It differs from concurrent efforts
such as BrowseComp-Vi (Geng al. 2025) and MMM-rroseeoomp (Li et al., 2025b) in both data sources and its agent framework, which emphasizes sustained fine-grained visual reasoning.
METHOD
2.1 SPATIAL-TEMPORAL EXTRAPOLATION
Summary. We test agent's abil- ity to infer facts that are not liter- ally present in the pixels the in- stant shown, requiring
Question: Who the
Event Game of Throne S4 E7 14:05
to recover the broader event by combining par- tial visual cues with retrieval and rea- soning across time and space. This emphasis discourages purely percep- tual shortcutting and directly mea- sures uncertainty planning, pre- cise event localization, and evidence- backed browsing.
Image-Event Relation: Spatie
What was the score the final round and what was the score?
Question:
BUOLEEEY
EW/22024 6, First Phase, Group E. Winners Matchh Vxbao Hikaru
Event:
Fighter
Image-Event Relation: Temporal
central challenge in BrowseComp- like benchmarks is the ballooning in- termediate search space induced by soft, fuzzy constraints, which demands non-trivial cross-validation to identify the correct target. Rather than remixing text-only corpora, we construct multimodal problems that naturally expand this search space: from handful of visual fragments tied to real-world events (in the spirit of Ge- oGuessr AB (201)), the agent must hypothesize the underlying source event and verify against retrieved evidence. Difficulty is modulated by the richness of visual and textual cues; single crop can widen the candidate set dramatically, eliciting iterative hypothesis-test-res long trajectories that require robust context tracking.
Figure 2: Examples of spatial and temporal relations for question curation.
Once the source event is pinned down, our questions probe metadata and support multi-hop reason- ing. To elevate difficulty, introduce Spatial-Temporal Extrapolation: instead of querying what is directly visible, we ask what is contextually implied but physically absent, compelling reasoning beyond the frame and moment reconstruct the broader situation. Spatial extrapolation targets unseen entities (e.g., off-screen participants, individuals facing away, occluded signage), while tem- poral extrapolation probes events immediately before or after the depicted moment (e.g., lineups, the next play/goal, or the subsequent segment/episode). Solving these requires agents to (i) pre- cisely localize the event (time, match, or episode). and (ii) retrieve and integrate wider contextual knowledge from diverse sources to produce an evidence-grounder answer.
2.2 DATA COLLECTION
Building on the Peatial-Temporall Extrapolation framework, instantiated the data collection pro- cess as follows.
We began with set of predefined categories and generated search keywords using mix of LLM assistance and manual curation. Data were sourced from publicly accessible video-sharing platforms and open-access scholarly repositories (i.e., arXiv). Queries for consumer video content (e.g., Vlog)
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were issued to video platforms, while those in the Acad. category issued to arXiv retrieve relevant research papers (PDFs). All materials are used only for research. We also mask sensitive content (e.g., faces and vehicle license plates). In our pipeline, both videos and papers treated as events.
For videos, human annotators manually selected keyframes suitable for constructing questions. The keyframes underwent anonymization for sensitive content. For papers, annotators extracted figures tables, capturing them via screenshots. A screenshot was considered suitable if it (i) contained vague or noisy information that challenged perception, (ii) depicted less familiar entities requiring external retrieval to answer associated questions, and (iii) could not be searched with Google image search directly. This ensures that most, if not all, screenshots are not in the training dataset, and the essential information for answering the question is neither in the training corpora nor directly retrievable via direct image search.
Filtering. Although the collection focused on new or rarely seen events, we observed that cer- tain closed-source models (e.g., GPT-4o, GPT-5, Gemini-2.5-Pro) could occasionally solve ques- tions without external search. To counteract this, we employed an adversarial filtering procedure increase difficulty. Specifically, annotators removed or obfuscated questions answerable without retrieval through the following methods:
Cross-validation: Annotators designed problems outside their own knowledge base, then verified solvability by testing at least two closed-soure, MILMs.
Image perturbation: Critical visual subregions blurred masked.
Iterative relinement:
Questions that remained trivially solvable were discarded.
Dataset updates. Some problems that originally required retrieval later became solvable without search because newer closed-source MLLMs began recognize previously unfamiliar entities or figures. This temporal drift is unavoidable as models continue to incorporate more recent data. We therefore commit to regularly refreshing the benchmark to suppress such internal-knowledge shortcuts.
2.3
DATASET STATISTICS
Statistic
Number
5.5%
Vins 6.1%
Total questions w/ images
311
Gengraphyy
Primary categories
8
Secondary categories
43
9-4%
76.1%
Video Games m..4
Unique images
441
11.2%
9.1%
Unique questions
279
5.6%
Unique answers
306
7,9%
Technology
Maximum question length
42
3.9%
67.4%
Maximum answer length
20
R.4%
3.4%%
Average question length
12.5
12.5%
Average answer length
3.7
Film 12.9%
Difficulty distribution
Academic Reszarch sh.r%
Easy
94 (30.2%)
Difficult
217 (69.8%)
Figure 3: (a) Distribution of primary categories in MMSearch-Plus, with light and dark inner sectors representing the easy and difficult splits within each category. (b) Summary of statistics.
Figure 3 depicts the distribution of MMSearch- Plsuss tasks across eight primary categories, which is approximately balanced. Each task includes at least one image, with additional images provided when single view is insufficient. The benchmark primarily comprises short-answer questions,

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Published as a conference paper at ICLR 2026
with answers averaging 3.7 words. In hindsight, labeled the difficulty level of task Easy it can be answered correctly without search or with only image search by o3 or Gemini-2.5 2.5-Pro. Otherwise, the task is labeled Difficult (interchangeably Hard).
2.4 BENCHMARK METRIC AND EVALUATION
We adopt accuracy as the primary evaluation metric. For each question, a set of acceptable answers provided. We LLM--as-ajjdgg compare model predictions against this set, deeming answer correct if it matches any acceptable variant. Validation through manual inspection showed complete agreement between human judgment and the assessment of GPT-4o.
2.5 SEARCH AGENT FRAMEWORK
Overview. We implement a model-agnostic web agent that interleaves text search, image search, and a zoom-in tool. Ranked results are obtained via SerpApii the surrounding framework (i.e., the system that includes the MLLM agent) performs lightweight post-processing to summarize pages, relate images to the query, and manage multi-round context. To reduce latency and token cost across repeated steps/evaluations. the framework caches image-search returns (ranked URLLs/hhumnnail) and their MLLM summaries per benchmark image. The framework maintains threaded conversa- tion state (tool calls, cropped views, summaries, and hypotheses) support long-horizon, contex- tualized reasoning. It compatible with sub-image cropping and sub-image search for fine- -reggraiedd analysis.
Text and image search. At any step, the agent may invoke text image search. SerpAPI returns ranked candidates, from which the framework forwards a small set of top results to the model (im- ages are summarized and cached; webpages are summarized). Retrieved webpages are summarized by an MILM (Gemini in our runs) into two fields:
web_info: task-conditioned semantic summaries of page content that may support an- swering the current question;
related.infor evidence linking result's 's thumbnail/lead image(s) to the query image (e.g., matching signage. layouts, or micro-text).
These summaries are designed compress interaction history for use within limited MLLM context windows; metadata such as URL/title is readily handled by SerpAPI. All retrieved and generated artifacts are integrated into the conversation state enable contextualized evaluation across turns.
Zoom-in via Set-of-Mark (SoM). To enable precise, provenance-aware inspection, we augment the agent with Met-of-Mark (SoM) module (Yang al., 2023). In this work, we refer SoM as a list of human-verified bounding boxes per task image (examples in Fig. 9). In the first turn, we present both the raw image and an overlaid version with box outlines and indices, avoiding occlusion while allowing cross-reference.
EXPERIMENT
Evaluated Models. We evaluate both closed-source and open-source MILMS on our benchmark.
Closed-source models include Gemini-2.5-Pro (hereafter Gemini) (Team & DeepMind] 2025). 03 (OpenAl 2025b) and GPT-5 (OpenAl, (025a)), representing the current frontier of proprictary multimodal systems. On the open-source side, we focus on Qwen) (Bai et al. 2025). Expert human annotators were instructed to work with Chrome browser for at least minutes for each task, and capped 20 minutes.
(hereafter
Implementation Details. To ensure a fair comparison, we standardize the search-and -nder-nnn--e pipeline across all models. We consider five search modes:
(a) Without Search. The MILM answers using only the question and image inputs.
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Published as a conference paper at ICLR 2026
Table I: End-to-end results on the MMSEARCH* benchmark, across search modes. All numbers are accuracy (%). Darker blue means higher accuracy. The best and second-best methods under each setting are shown bold and underline, respectively.
Model Search Mode
Avg
By Category
Difficulty
Gen.
Sports
Acad.
Film/TV
Tech
Games
Vlog
Music
Easy
Hard
Human
Browser
22.8
20.3
25.9
20.0
25.0
19.4
16.1
31.6
35.3
34.0
18.0
Closed-source
o3 (2025-04-16)
Without Search
15.1
31.2
14.8
6.0
17.5
13.9
3.2
5.3
11.8
50.0
0.0
Image Search
19.3
28.1
14.8
18.0
30.0
22.2
3.2
5.3
17.6
63.8
0.0
Text Search
37.0
43.8
35.2
48.0
30.0
44.4
16.1
31.6
29.4
50.0
31.3
Full Rollout
36.0
35.9
24.1
50.0
42.5
44.4
16.1
42.1
29.4
54.3
28.1
Full Rollout SoM
SoM
37.6
45.3
29.6
46.0
45.0
45.7
16.1
26.3
29.4
62.8
26.9
GPT-5
Without Search
10.3
21.9
7.4
4.0
7.5
8.3
0.0
5.3
15.8
27.7
2.8
Image Search
25.0
11.1
14.0
22.5
19.4
3.2
0.0
29.4
50.0
1.8
Full Rollout + SoM
35.4
35.9
27.8
48.0
50.0
41.7
6.5
36.8
23.5
56.4
26.3
Gemini-2.5-Pro
Without Search
10.6
15.6
11.1
6.0
12.5
13.9
0.0
15.8
5.9
35.1
0.0
Image Search
16.4
26.6
11.1
18.0
20.0
16.7
3.2
0.0
23.5
54.3
0.0
Full Rollout
23.8
39.1
14.8
12.0
27.5
33.3
6.5
26.3
29.4
46.8
13.8
Full Rollout SoM
27.7
40.6
22.2
24.0
25.0
33.3
19.4
15.8
54.3
Open-source
Qwen-2.5-VLL-7BB
Without Search
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Image Search
13.5
20.3
7.4
18.0
17.5
11.1
3.2
0.0
23.5
41.5
1.4
Full Rollout
6.1
9.5
7.4
4.0
5.0
2.8
3.2
5.3
11.8
17.0
1.4
Full Rollout SoM
7.1
10.9
3.7
4.0
10.0
5.6
6.5
5.3
11.8
18.1
2.3
(b) Image Search. For each benchmark image, the top-10 image-search results summarized by Gemini and cached. The MLLM then receives the question, the images, and these summaries as input. For multi-image questions, append each image's summaries the model context.
(c) Text Search. For text search, retrieve the top-5 websites by default. The process is capped at 20 search rounds. In each text-search round, the model generates 3-5 refined queries
(d) Full Rollout. The model runs our search-agent framework and may call search tools as needed. Image-search and text-search settings match those described before. The process is capped at 20 search rounds (combining text and image search) to balance efficiency and coverage. In each image-search round, issues exactly one query. For Qwen, pilot studies showed that more than 10 rounds yield no further gains, so we cap its search at rounds.
(c) Full Rollout + SoM. Images overlayed with Set-of-Je Mark are provided to MLLMs. In addition to capabilities provided in Full Rollout, models can (a) call the zoom-in tool examine sub- region of an image, (b) call the image search tool to search with the subregion. The maximum number of tool calls 20.
4 RESULTS
The results of MLLMs across search modes are presented in Table
Baseline performance. Under No Search the original MMSEARCU* benchmark, open-source Qwen scored 0.0%, confirming that MMSEARCH* resisted parametric shortcuts. Closed-source models remained far from saturation (GPT-5 10.3, Gemini-2.5-Pro 10.6. o3 15.1). With Image Search (o3 +4.2, GPT-5 +6.1, Gemini +5.8; Qwen + +13.5), models gained from coarse disambigua- tion, yet multi-hop composition remained the main blocker. With iterative image and text search,

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Published as a conference paper at ICLR 2026
Full Rollout produced large gains: o3 36.0 (+20.9 over no-search) and Gemini 23.8 (+13.2), while Qwen regressed to 6.1 (-7.4 over image-search). This indicates that the external information re- trieved from search tools is essential to answering the questions correctly.
Performance drop on easy split. Notably, easy split accuracy did not improve from Image Search to Full Rollout. For o3, we conducted targeted error analysis this split: there 23 "easy" questions for which the image-only setting is correct but the full-rollout setting is not, corresponding 7.4-point absolute accuracy loss our 31 1-task benchmark. We randomly sampled 10 of these 23 cases and manually inspected the trajectories. In out of 10, the full-rollout trajectory never invoked image search all: the model apparently believed had already understood the image well enough and instead relied solely text search or prior knowledge, thereby missing critical fine-grained visual cues. In the remaining case, the model did perform image search and retrieved relevant sources, but made a reasoning error in the final step.
These observations indicate that the drop in Easy-split accuracy not primarily driven by over- retrieval or exposure to distractors, but by underuse of image search in the more complex full-rollout setting: the model sometimes "skips" necessary image-search step when it incorrectly assumes that additional retrieval is unnecessary. We therefore attribute the Easy-split gap mainly to:
1. Label vs, retrieval difficulty. In MMSEARCH*. "easy" meant higher likelihood of being in- model or solvable by direct image hop, not that multi-step retrieval was trivial.
Tool-use decision errors. The dominant failure mode for o3 is a policy-level issue deciding whether to invoke image search rather than over-retrieval or inherent drawbacks of the full- rollout setting.
Overall, rollout helped where progressive nar- rowing was necessary (academic, tech, me-
MMSearch+ lite Avg Accuracy (%6)
Without Search
dia queries), but on easy items performance was limited by miscalibration in tool choice: the model occasionally declined use image search even when it was crucial for success.
Image Search
30
(Tlext Only)
Full Rollout
25
Full Rollout SoM
20
Adding SoM (Full Rollout + SoM) gave con- sistent, targeted gains. The improvements were 03 37.6 (+1.6), Gemini 27.7 (+3.9), Qwen 7.1 (+1.0) over Full Rollout. Set-of- Mark anchored reasoning on fine-grained vi-
10
03
5-
GPT-5
Gemini-2.5-Pro
sual cues (micro-text, logos, key entities) be-
Qwen-225-VL-722
fore expanding the search space.
20
25
MMSearch+ Avg Accuracy (%)
Decoupling internal knowledge. To disen- tangle the effect of models' internal knowl- edge from their use of external tools, we further evaluate on MMSEARCHI a subset of 239 tasks that unsolvable without search by any model we tested 4). The relationship between performance on the full and lite benchmarks is visualized Figure
Figure 4: The differences method performance on MMSEARCI+ and MMSEARCII The closer a point is to the diagonal line = 2, the smaller the influence internal knowledge.
On MMSEARCH the best-performing setting is full rollout with only text search (abbreviated as Text Search in the table) with the o3 model, achieving 31.4% accuracy. This suggests that, once questions solvable purely from internal memory are removed, current MLLMs still struggle to ex- ploit fine-grained image search effectively. moving from only text search to full rollout / full rollout SoM. However, there remain categories where image search clearly beneficial: for o3 Sports, accuracy increases by 6.6 (full rollout) and 10.0 points (full rollout SoM) over text-only rollout. Overall, most methods lie close to the diagonal Figure indicating that the qualitative trends observed on MMSEARCH are not driven primarily by parametric memorization, but by genuine tool-use and retrieval behavior.
many categories, we observe a drop in accuracy when

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5 ANALYSIS
We present a list of observations (Section 5.1
and an error analysis (Section 5.2
5.1
OBSERVATIONS
Gemini
o3
Qwen
TEXT
0.8
IMAGE
Toal
ZOOM
0.4
0.0
Step index
Step index
Step index
Figure 5: Stepwise tool distribution for Gemini, and Qwen under Full Rollout+SoM, capped at Step 12 for visualization. We use search round and step interchangeably, and an MLLM can make multiple search queries one round (details 3
Closed-source models retained broad geographic knowledge. They often recognized rare lo- cales and supplied plausible context; geography tasks were comparatively strong. Without search tools, o3 achieved 31.2%, while human annotators with standard browser only achieved 20.3%. On other categories like sports and Vlog, the human baseline usually outperformed tested MLLMs. We name this phenomenon the different comparative advantages of humans and MLLMs.
03 exhibited strong long-context coordination, while Gemini leveraged fewer search calls. On the one hand, o3 sustained 10+ search rounds and reasoned over 50+ retrieved items without excessive redundancy, contributing to its rollout gains. On the other hand, Figure 5 (left) shows that Gemini always answered on or before Step However, more tool calls did correlate with trajectory correctness: Figure 15 shows that incorrect trajectories typically contained more search calls than correct ones.
Format correctness of closed-source models' tool calls, and Qwen's tool-use instability. All the closed-source models consistently produced valid tool calls. For Qwen, we observed 421 invalid image-search calls across 45 of 311 tasks, inflating retries without quality improvements.
More text search calls than image search calls, and zoom-in operations were not always fol- lowed by a sub-image search call. In Figure 5 all models made more text-search calls than image-search calls. We analyze the contribution of the "zoom-in then image-search" tool-call pat- tern to the final accuracy. For Gemini-2.5- full rollout to full rollout + SoM, there are such cases in which the model zoomed in image and then performed an image search (12.8%). For o3, out of 42 newly answered correctly questions, there is 1 such case (2.4%). This indicates that Gemini utilized fine-grained image search more frequently, while o3 often zoomed in simply to view region more clearly.
out 39 newly answered correctly questions from
To empirically support this observation, examine the Markov transition probabilities between tool types (Figure (2}. In particular, Set-of-Mark allows cropping and sub-image search, so we measure how often probability P(image zoom) varies substantially across models: Gemini uses image search after zooming in 25.37% cases (34/134), Qwen in 10.56% (15/142). and o3 in only 2.87% (18/627). These statistics are consistent with the accuracy gains above above-GGeing tends to use zoomed-in re- gions as retrieval queries, whereas seldom does, often preferring further zooms or text search instead.
action is immediately followed by image search. The transition
Image search and text search benefit different task categories. The accuracies of o3 with text search (up to 10 rounds) and with full rollout are similar (37.0 36.0). However, their category-
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level accuracy distributions differ substantially. For example, image search yields better perfor- mance in Film/TV, whereas additional text search rounds provide greater benefits in Geography.
Scene-text is predominantly processed as textual evidence rather than visual evidence. Across tasks containing logos, signage, other forms of scene text, observed that MLLMs overwhelm- ingly chose to convert visual text into explicit textual queries rather than invoking image search on the corresponding subregions. By detecting scene text with DeepSeek-OCK and manually in- specting sampled trajectories, we found that both o3 and Gemini-2.5-PP5 typically relied on their built-in OCR capabilities to extract strings and issue text-search calls. For o3, 8 out of 10 sampled trajectories used only text search, while Gemini behaved similarly in 7 of 10 cases. Direct image- search calls on highlightt text regions were rare and generally appeared only when the model seemed uncertain about the exact transcription or wished to leverage additional visual cues such as layout font. These findings suggest that, within MMSearch-Plen scene-text reasoning is mediated primarily through textual grounding rather than visual retrieval, highlighting that multimodal agents implicitly choose between two tool pathways, (a) OCR + text search versus (b) image search, based on confidence and perceived utility.
5.2 ERROR ANALYSIS
We categorize failure using manually curated taxonomy and report the distribution over categories in Figure Labels are assigned by the authors (single dominant label per er- roneous prediction) for Gemini-2.5-Pro under the full-rollout setting. Examples of incorrect task trajectories are provided Figure
relevant information found
51.1%
Iralucinat on
11.95
Key Information not extracted
Relevance vorifee
image
6.1%
Retused
4.8%
Muttipic answers
3.3%
High-frequency categories. The two most prevalent failure modes are relevant infor- mation found and hallucination, responsible for 51.1% and 11.5% of errors, respectively (62.6% combined). The former typically re- flects retrieval failure: noisy overly specific queries miss on-topic pages. The latter reflects vision-driven misatribution: the model latches onto an incorrect event from internal knowl- edge and either does not invoke search or discounts contradictory evidence in the retrieved pages.
Question misurderstood
3.1%
Search not invoked
May require
understanding
Summarization 1.5%
Figure 6: Distribution of error types. Annotated on Gemini-2.5-Par (full rollout).
Other observed categories. We also observe failures from misinterpreting the multimodal con- text; hallucinations grounded in either images or parametric knowledge; refusals or multi-answer outputs when evidence insufficient disambiguate a single event; failing to invoke search (falling back on outdated priors); and errors when summarizing relevant webpages.
Qualitative takeaways and implications. Two recurring patterns emerge: (i) there is no single "golden path"-agents can succeed via varied orders of search/crop/veriff.: and (ii) stronger mul- timodal internal knowledge narrows the practical search space, enabling targeted verification over broad exploration. While augmenting multimodal internal knowledge can reduce exploration over- head, end-to-end reliability still depends on robust verification and tighter cross-modal grounding.
RELATED WORK
Classical languaee benchmarks probe compositional and commonsense reasoning in images and text (Johnson et | [017] Krishna et al. 2016 Zellers et al. 2019 Mathew et al. 2022] l). Among these, GQA (Hudson & Manning 2019) emphasizes structured compositional reasoning using real- world scenes, while OK- OK-VQA (Marino et al. 2019) requires external knowledge beyond the visual signal. More recently, InfoSeck (Chen 2023) frames VQA as an information-scelll task, but re- lies offline retrieval corpora whose contents frequently overlap with pretraining data for modern

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Published as a conference paper at ICLR 2026
MLLMs, weakening its ability to measure genuine retrieval behavior. LiveVQA (Fu et al. 2025 benchmarks live visual knowledge by deriving VQA instances from recent web content beyond mod- els' cutoff dates, and shows that multimodal search augmentation substantially boosts performance.
Beyond static images, recent bench- mark suites extend to long videos and interleaved modalities (Wu et al., 2024;
Question: What typa of anmotations constructed by the authors work?
Missing annotation type in result summary
Li al. 2024 Hu et al.) 2025 Jeong et al. 2025). Concurrent work on web agents studies planning, browsing, and evidence gathering in open worlds (Kr-
Result #1: This research introduzes DifTTacck... key
Result #2: <irrelevant result>
ishna et al. 2024
Wei et al. 2025
Event Dertrack (ar3iv papar)
evaluation framework that includes blatching Accurasy and Confidence
Geng et al.
2025 Li et al. 2025a
Tao
GT Answer: Pseuto ground-truth tracking arnolations
et al. 2025
Score...
Did not answer the question
Multimodal web search benchmarks begin to couple vision with browsing but often emphasize entity matching or template-based grounding over fine-
Question: Who won the match??
Query #1: Championship League Snooker Invitational 2021 results Query #2 Neil Robertson vs Mark Selby
Result #1: <irrelevant result-
grained visual reasoning (Jiang al.
Result the 2021 Champlonship Leegue, Kyren Wilson of England was the winner
2024] Wu et al.) 2025). Closest to setting, MM- MM--rooseComp targets srigge/vidoo evidence encountered dur-
Event: Championship League Snooker Invitational 2024
Found visually similar incorrest event Binglao won the match.
ing search and reveals substantial head-
GT Answer: Mark Solby
room even for frontier models (Li et al. 20256).
Question: What is the rame of the player unit in this level that about to die?
Query #1: Monster Train Arkion, Sclon of Savagary
Query #2:
Monster Train Spear Stew x Wrong
A complementary line promotes "thinking with images" via improved grounding or RL-style training to elicit image-firt reasoning (Wu & Xie
Result "Arkion Soion Savagory" the album "Monster Train (Orignal SounctrackT
Result #2: <insleeant result>
2024
Zheng et al.
2024
Fan et al.
The player unit that about die is Soeat Stew. x
2025
Zheng et al. 2025b Su et al.
2025a
Xia et al. 2025).
Event: Gamop video Morster
cards very bottom the scroen corraspond to these
iduntily player
We differ from prior benchmarks by targeting sparse, spatial-temporal vi- sual cues that must be transformed into targeted queries and multi-step browsing plans. Crucially, tasks in MMSearch-Plus are designed to be ex- tremely unlikely to be answerable from internal model knowledge alone, re- quiring models to (i) decide what visual regions matter, (ii) choose appropriate external tools (image vs. text search), and (iii) gather and ver- ify evidence online. This provides a focused stress test for agentic multimodal browsing systems.
GT Answer:
Misundorstood the game UI
Figure 7: Representative error case studies. Each row illustrates an example from high-frequency error type (Figure () Panels show the prompt, GT answer, and a shortened rollout. From top to bottom: (a) Key info not extracted. (b) Relevance not verified. (c) Question misun- derstood.
7 CONCLUSION
This work introduces MMSearch-Plls, a benchmark that restores the genuinely multimodal pres- sures of web browsing by requiring agents to extract weak, localized visual cues and propagate them through noisy retrieval to out-of-image facts. Our Spatial-Temporal Extrapolation cura- tion procedure preserves the persistence and difficulty profile of text-only browsing while com- pelling fine-grained visual reasoning and provenance verification. Evaluating a range of open and closed MLLMs within a aoooo--agnosiic search agent framework reveals a substantial headroom: the strongest agent we tested (o3) achieved less than 40% accuracy, while strong open-source model achieved 13.5%. These results indicate that current systems struggle to (i) decide when and how to crop and reuse visual evidence, and (ii) maintain verifiable chains of evidence across long-horizon tool use. We hope MMSearch- Plus serves as rigorous stress test and a common yardstick for progress on multimodal browsing.
(Qwen-2.5-VI.. -LV--LL----sntrre
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ETHICS STATEMENT
Principles. We follow the Code Ethies: uphold scientific rigor, avoid harm, ensure transparency, foster fairness, respect intellectual labor, and protect privacy and confidentiality.
Data sources, consent, and licensing. We curate only publicly accessible material from (i) con- sumer video platforms (keyword queries) and (ii) open-access repositories (e.g., arXiv). We comply with platform Terms of Service and licenses and preserve attributions (c.g., video URL, paper URL). Items treated as "events" and used solely for research.
Privacy, minimization, and redaction. we exclude minors and content revealing private spaces when feasible. Residual risks remain; we provide an appeals/takedown channel.
mask sensitive content (e.g., faces, license plates);
Fairness. To counter skews in public platforms, conducted manual sampling across predefined categories and regions, report measurable distributions, and forbid uses that target protected classes amplify inequities.
Limitations. While diverse, our data inherits biases from public web sources and the domains sampled; some tasks still under-represent non-English or low-visual-density pages. Our evalu- ation focuses on general-purpor agents with standard tool access; specialized systems with richer UI control domain knowledge may behave differently. Finally, we primarily target image-text browsing; videos and dynamic interfaces remain underexplored.
REPRODUCIBILITY STATEMENT
We have taken several steps to facilitate independent verification of results. Implementation details, hyperparameters, and evaluation protocols are documented in Section 3 We uploaded
randomly selected subset (seed 42, 30 task examples) supplementary material due to the 100MB space constraint, and released an anonymized archive on how to use the dataset in https:// anonymous. 4open.
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A USE OF LLMS
The manuscript text was polished for fluency using Grammarly after the authors completed a full draft. The authors then manually reviewed all suggestions to ensure their appropriateness.
B EXAMPLE FROM MMSEARCH-PLUS
REBILA
Clue
Eberg "ur the
Need determinei their driger Londlond'
The person tooie protty since the
matciess cropped intogge
Clue
Branch
Branch
following photo What was the performance time of the singer in this event?
interrata Landiords Cur in the "Droyen fouts vie Festvel 2025, through compase thair ousits
informalin about Landiard Cer the picture
Plan for solution:
Cross validation
Identify the singer through Image information.
Find the event based on the image and information.
Clue
POKEER GOT
Soda Dousy
Douyit Soda Music
Datenmine the of event:
Find information of Music Festboll
Find the performance schedule based on the event information.
Text
Octsrber bssed
the clothing
Search
Text
photos (potential
20-30 canclideeten
Core assessment points:
Cross validation
Fine rre-grained reasoning
Continuous multi-modal tool usage
Douyin Sods Music there is huge hanging
Find which
Search
Information source venfication
Clue
related information
Strategy for reducing search space
Figure 8: Example MMSearch-Plus item. Given a 2025 concert photo and the query "What was the singer's performance time?", the agent's golden path extracts localized cues-mmicro-tx//yyyic. a crop of the singer, festival/brand signage, and a distinctive stage prop-and and issues targeted image/- text searches that (i) identify the artist/outfit, (ii) resolve the event (e.g.. specific festival and city/- date), and (iii) retrieve and cross-validate the official schedule to obtain the exact performance time. The answer is not explicitly written in the prompt the image, underscoring MMSearch-Plee emphasis on fine-grained multimodal reasoning with provenance checks under retrieval noise.
C COMPARISON WITH PREVIOUS BENCHMARKS
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Table 2: Comparison of multimodal inforiation-scekking datasets and benchmarks, ordered by re- lease date.
Multi-hop
Thinking Multi- w/ Image
Benchmark/ Dataset
MM
Search
(>>2)
Image
Multi- turn
Text
Image
InfoSeek CCen
2023
X
X
x
FRAMES Krishna et al 2024
X
J
X
V
J
X
X
MMScarch Jiang et al. 2024
V
V
X
X
X
FactualVQA al. 2025
X
X
X
X
ChineseSimpleVOA Gill cl al.
2025
X
EXXIK
X
X
BrowseComp (Wei et al. 20255
J
X
J
BrowseComp-7JH (Zhou al.
2025
J
X
J
V
BrowseComp-VL (Geng
2025
MM BrowseComp L1 2025b
Ours
D RELATED WORK
We present a more comprehensive literature review in the Appendix.
Multimodal Reasoning Benchmarks. Classical vision-and- aibio-and-languuae benchmarks such as CLEVR (Johnson et al.) 2017). VCR (Zellers et al.) 2019). Visual Genome (Krishna et 2016)) and InfographicVQA (Mathew et al.] 2021) established core evaluations for compositional rea- soning, commonsense inference, and visual question answering. Recent efforts extend these set- tings to longer contexts and interleaved modalities, including Long VingeBeeenn (Wu et al.) 2024). MVBench (Li al. 2025). which highlight long-context understanding and retrieval augmentation. Other work such as MIRB (Zhao al. 2024), pixel-level VCR (Zhang et 2024). and ReasonMap (Feng et al., 2025) aim to connect perception with explicit reasoning. However, these datasets are primarily self-contained and not require dynamic interaction with the open web or tool-mediated search.
2024). Video-MMMU (Hu al., 2025), and VideoRAG (Jeong al.
Web Browsing and Search-Enhanced Agents. A large body of work explores language mod- els as web agents that plan, browse, and gather evidence. Earilier works like FRAMES (Krishnal al. 2024) focus multi-hop QAs Wikipedia-like data. More recently, benchmarks such as BrowseComp (Wei et al., 2025) and its Chinese variant BrowseComp-ZD (Zhou et al.) 2025) test multi-step persistence in text-heavy web navigation, where even frontier models struggle. Open- source agents including WebSailor et al. 2025a), WebShaper (Tao et al. (025), and Web Wathhrr (BrowseComp-Ne VL. (Geng et al. 2025) emphasize robustness, data synthesis, and limited multi- modal inputs.
In parallel, reinforcement-leamiig based frameworks push the boundaries of search adaptivity and depth. Search-R1 (Jin et al. 2025). ). R 11-Searcher (Song et al., (205)). ReSearch (Chen et al.) 2025)
Pangu DeepDiver (Shi et al. 2025). and DeepResearcher (Zheng al. (025a) train persistence and
decomposition skills through reward shaping. ASearcher (Gao et al. 2025) extends this direction with large-scale asynchronous reinforcement learning, decoupling exploration from evaluation to unlock long-horizon search over dozens of hops. Complementary agentic systems such as Open Deep Search (Alzubi et al.] (202), AutoAgent (Tang et al.] (2025). and Search-ol (Li et al. 2025c) further democratize and automate search-enhanced reasoning.
Multimodal Web Search and MM--rowseComp. Beyond text-only browsing, MM- Search (Jiang et al. 2024) and MMSearch-RI (Wu et al. 2025) introduce image-grounded queries, but many tasks still reduce multimodality to entity matching rather than genuine visual reasoning. MM-Broue BMM-BrowseComp (Li et al. 2025b) addresses this gap with 224-question benchmark designed for multimodal browsing agents where key evidence may be embedded in images videos encountered during search. The benchmark provides a verified checklist for fine-grained analysis multimodal dependencies and reasoning paths, and reveals that even top models (e.g., OpenAl o3 with tools)
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achieve only 29% accuracy, underscoring substantial headroom for native multimodal reasoning within browsing pipelines. Our setting but differs in focusing sparse visual cues that must be transformed into targeted queries rather than predominantly entity-level matching.
is closely related to, and concurrent with, MM- AMM-BrowseComp.
Thinking with Images. growing body of work argues that multimodality should go beyond tool
orchestration to think with images. Methods such as V* (Wu & Xie 2024) and instruction instruction-guided guided masking (Zheng et al.) 2024) improve visual grounding inside LLMs. RL-style training further
enforces image-first reasoning: GRIT (Fan et al. 2025), DeepHyes (Zheng et al. 2025b). Pixel Rea- soner (Su et al. 2025a), and Visionary-RI (Xia et al.
2025) encourage explicit visual deliberation
before action. Visual Agentic Reinforcement Fine-Tiee Tuning (Liu et al. 2025). OpenThinkIMe (Su
ctt al. 2025b), and One-RL-to-See ----LLL------Te------- Them-All (Ma et al.
2025) propose unified training frameworks
for multimodal agents, while broader systems like Py Vision (Zhao al. 2025 and Thyme (Zhang
et al. 2025 generalize the paradigm.
Positioning and Summary. Taken together, web-browsing benchmarks stress persistence and decomposition in open-world settings (Wei et al.) 2025 Li et al. 2025a Tao et al.] [2025] Geng
al. 20250, while multimodal benchmarks emphasize perception- heavy reasoning (Johnson al.
2017 Krishna et al., 2016: Zellers et al.) 2019 Mathew et al. (021). Recent multimodal brows- MM-BrowseComp (Li et al. 2025b) begin to unite these threads but still leave open how sparse visual evidence should drive query formulation and multi-step planning. We formulate the
ing dlatasts--incuddng
Extrapolation methodology to concretely design challenging VQAs for testing multimodal search abilities. We also stress the importance of grounding and cropping images to allow fine-grained multimodal reasoning.
E DETAILED DATASET STATISTICS
Table 3: Primary category distribution.
The distributions of primary categorics across MMSearch-PPlus reported in Table
Category
Count
%
Geography
64
20.6%
Sports
54
17.4%
F SET-OF-MARK IMPLEMENTATION DETAILS
Academic Research
50
16. 1
Film & TV
40
12.9%
Technology
36
11.6%
Video Games
31
10.0%
Figure shows a list of task images overlaid with Set-of-Mark bounding box annotations from MMSearch-PPlu.
Vlog
19
6. 1.1%
Music
17
5.5%
In context, set-of-mark for task image consists of bounding boxes of key visual entities. Each box has a unique index. In the first user turn, we present both the raw image and overlaid version with box outlines and indices, avoiding occlusion while allowing cross-refcrence. The framework exposes:
zoom_in ( (index): return cropped sub-image for localized reasoning;
image search (index): launch an image search seeded by the cropped region (com- patible with the standard image-search pathway).
We include SoM because baseline MILMS were unreliable at proposing visually precise crops from scratch. Providing accurate marks reduces the burden detection/llcalizztion and lets models focus on reading, matching, and reasoning. While automatic mark generation (e.g., strong open- vocabulary detectors) conceivable, robust coverage is challenging due to long-tail entities common in our benchmark; therefore MMSearch- Plus uses buman-curated boxes.
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Q: What is the name of the previous level?
Q: What is title paper?
A: Exercise Hall
A: MUCAR: Benchmarking Multilingual Cross- Modal Ambiguity Resolution for Multimodal Large Language Models
MA WEST
CHULD
SAPO
HAPSLES
Q: What is title this video?
Q: Paris Fashion Week, what brand the per- son in the image represent? In which year did the event occur, and what is the event's name? A: StreetStyle Winter 2025 Hermes
A: Who Can Identify as Native American?
Figure 9: Examples of task images overlaid with Set-of-Mark bounding box annotations from MMSearch-Plls.
G EVALUATION DETAILS
We initially tested both GPT-4o and Gemini-2.5-Pro judges and found their outputs consistent. The subsequent experiments used GPT-4o. further support reproducibility, we optionally provide lightweight rule-based verification methods.
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H RESULTS ON MMSEARCH
To eliminate the impact of model's internal knowledge, obtain MMSEARCH from MMSEARCH + by removing all tasks from MMSEARCH" answerable without search by any model we tested. The results are reported in Table and visualized in Figure T0 and
In MMSEARCH the best performing setting is full rollout with only text search (abbreviated as Text Search the table) with o3 model, achieving 31.4% accuracy. This suggests that the current MLLMs cannot cleverly utilize fine-grained image search. In many categories, we observed drop in accuracy from only text search to full rollout/full rollout + SoM. However, certain categories like Sports, there is great benefit of image search: for o3, the accuracy increases by 6.6 (full rollout) and 10.0 points (full rollout SoM).
Table 4: End-to-end results on the MMSEARCH benchmark. All numbers are accuracy (%). Darker blue means higher accuracy. The best and second-best methods under each setting are shown in bold and underline, respectively.
Model Search Mode
Avg
By Category
Geo.
Sports
Acad.
Film/IVV
Toch
Games
Vlog
Music
Human
Browser
18.8
11.6
26.7
17.9
33.3
22.0
20.7
6.7
26.7
Closed-source
o3 (2025-04-16)
Without Search
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Image Search
10.0
14.0
13.3
10.3
16.7
4.9
17.2
3.3
0.0
Text Search
31.4
44.2
16.7
41.0
25.0
29.3
37.9
16.7
26.7
Full Rollout
27.3
46.5
33.3
17.9
8.3
14.6
37.9
16.7
33.3
Full Rollout + SoM
30.1
44.2
36.7
30.8
16.7
24.4
34.5
16.7
20.0
GPT-5
Without Search
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Image Search
8.8
9.3
13.3
7.7
25.0
4.9
13.8
3.3
0.0
Full Rollout SoM
28.5
46.5
36.7
23.1
8.3
24.4
34.5
6.7
33.3
Gemini-2.5-Pro
Without Search
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Image Search
10.5
12.8
25.0
10.3
3.3
Full Rollout
17.2
4.7
23.3
28.2
25.0
12.2
27.6
6.7
20.0
Full Rollout + SoM
20.9
23.3
13.3
28.2
25.0
19.5
24.1
20.0
6.7
Open-source
Qwen-2.5-VL--722
Without Search
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Image Search
7.7
25.0
10.3
3.3
Full Rollout
3.3
4.7
3.3
0.0
8.3
4.9
3.4
3.3
0.0
Full Rollout + SoM
4.2
2.3
6.7
2.6
8.3
4.9
3.4
6.7
0.0
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Accuracy 1%6
-75
-100-
Without Smarch
Full Roliout (lext Search Only)
e3
Imaae Search
Full Rollouk
Full Rolizast Sold
e3
GPT:S Withoit Search
03
GPT.5 image Search
GPT:5 Sohii
Gemini-2 S-Pro Without Search
Gemini- 2-5-rr
image Search
Gemini-2 5-Pro
Full Ralaait
Germink22 2.Prro
Rolloxt Sol
Owert-2 5-VL. 728
Without Smarch
Oween 5-L-728
Image Smarch
Ownn 5-4L S--L-28
Full Rollout
Owen-2.5-4p 5-LL-715
Full Rollout Sold
Full Rallast
Figure 10: The differences method performance on MMSEARCH* and MMSEARCH More negative means larger influence of model's 's internal knowledge.
I AGENT BEHAVIOR ANALYSIS
This appendix presents detailed visualizations tool usage patterns across the three multimodal agents: Gemini, o3, and Qwen. All figures show episodes capped at 20 steps for readability.
1.1 EPISODE RASTER PLOTS
Figure 11 shows the tool usage patterns for the 40 longest episodes from each agent. Fach row represents episode, sorted by length (longest at top). Columns represent sequential steps, with colors indicating tool types: text search, image search, zoom-in, and answer. Dark blue strips represent MILM did fully utilize 20 rounds and stopped early.
1.2 TOOL TRANSITION MATRICES
Figure 12 shows the Markov transition probabilities between tool types.
particular, Set-of-Mark allows image cropping and sub-image searching. Therefore, we investi- gate whether many of the cropped images were searched in the immediate next round, i.e, whether Piimage zoom) large.
Gemini: 25.37% (34 out of 134 ZOOM actions)
Qwen: 10.56% (15 out of ZOOM actions)
o3: 2.87% (18 of 627 ZOOM actions)
1.3 ZOoM-IN BEHAVVIR ANALYSIS
Figures 13 and 14 analyze zoom-in behavior patterns across agents. o3 shows the most aggressive zooming behavior, while Qwen and Gemini more conservative.
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Gemini (top
(top 40)
Qwen (top
ANSWER
Episade (sarted by length)
20
20
20
IMAGE
25
25
25
TEXT
35
15
10
Step index
Step index
Step index
Figure 11: Episode raster plots showing tool usage patterns for the 40 longest episodes per agent. Each row is an episode (sorted by length), each column step.
Gemini
Qwen
1.0
TEXT
3.29
6.04
3.61
6.72
0.11
3.14
6.26
1.09
6.41
0.8
IMAGE
9.77
0.44
0.11
3.07
0.4
02
ANSWER
MAGE
200M
ANSWER
TEXT
IMAGE
200M
ANSWER
TEXT
IMAGE
ZOOM
ANSWER
Figure 12: Toll-call transition matrices showing probabilities P(next tool current tool). Rows and columns represent current tools and next tools, respectively. Values 2 0.05 are annotated. Note that ANSWER is terminal (bottom row is zeros).
Gemini
03
Qwen
60
120
Epicodes
40
50
40
30
60
20
10.
20
10
15
20
0
15
20
10
20
First zoom step
First 20
First zoom step
Figure 13: Distribution of first zoom-in step positions. o3 tends to zoom earlier episodes com- pared Gemini and Qwen.
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Gemini
o3
Qwen
140
200
120
150
Eoisodes
125
100
60
75
50
zoom calls per episode
z0omm calls per episode
zoom calls per episode
Figure 14: Distribution of zoom-in frequency per episode. o3 exhibits the highest zoom-in fre- quency, with some episodes containing 10+ zoom operations.
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J
ROLLOUT TRAJECTORY STATISTICS
20.0
Corectnasa conrect
175
190
Acsittat Words
125
12.5
100
10.0
75
80
2.5
20
Text Search Calla
Imsgge Search Calle
0.0
2.5
7.5
Search Calla
10.0
12.5
15.0
17.5
20.0
4.0
correct
incomrect
Poststan WosistantWWoes Words
2.5
400
2.0
g
200
1.0
0.5
Search Calls
imge Search Calls
Correctness
700
Incorrett
600
Calls per Conversation
Assrspont Mocete
500
800
300
Image
Search Calls
Figure 15: Reajoting-trajectory statistics. Rows: MLLMs (o3, Gemini, Qwen from top to down).
Columns: left distrrbbbtttiin of image search calls and text search calls per trajectory: right-number of words in an MLLM's response versus the number of search calls, stratified by correctness. Statis- ties collected full rollout mode.
Figure 15 shows the distribution of number of search calls for correct and incorrect task trajectories (left) and the relation of the number of words in model's trajectory (excluding the words for tool calls) vs. the number of search calls (right) under the Full Rollout setting.
K
GOOD CASES
Task:
Excluding the games released through the company's white-label publishing service, what games did the publisher of the game shown in the image release in 2024?
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assistant:
Tool Call: image zoom in tool
"bbox_index": 2,
"image_name": "n5x82t8dok.png"
tool:
The cropped sub-image is provided in the next user round. user:
<zoom_in..mage name> n5x82t8dok round. wondd1..rrop wwr.p...rrpppppp.... </2o0m in imaa:ommmmiimmammaaaaaa name> <image_zoom resuul?
</image zoom in result> assistant:
Tool Call: image.search
"image_name" "15xett8
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tool:
<iragesseaachrresultt
## Result
web info: Based on the provided web content, it is not possible answer your question. The text is from the Douyin (the Chinese version of TikTok) website and contains legal information, site navigation links, user agreement details, and contact information the platform itself. There is no mention of a specific game, game publisher, list of games released in 2024.
related_inie The reference image and the search result demonstrate very high degree of relevance. Visually, the central character in both images is identical, sharing the same facial features, beard, and distinctive dark coat, indicating they the same individual from the same source material. The reference image offers a more subdued profile view, while the thumbnail presents the character in dynamic action scene, which strongly suggests shared origin, likely a video game or animation. This visual consistency is further cemented by the semantic content of the search result's title. The title explicitly names work, Light and Shadow: Expedition 33, and the text overlay on the thumbnail perfectly matches the first part of the title, creating a seamless link between the visual and textual information. The search result effectively takes the ambiguous character from the reference image and places them within specific, named context. Therefore, the search result is exceptionally relevant as it not only contains the exact same visual element but also enriches it with crucial identifying information about the production it belongs to.
## Result 2
web info: The provided web page does not contain the information required to answer your question. The page announces the game Clair Obscur: Expedition 33, identifying it turn-based RPG set for release in 2025 on console and PC, and will be available on XGP from its launch day. However, the text does name the game's 's publisher. Therefore, does not provide a list of other games released by this publisher in 2024, nor does it mention anything about white-label publishing service.
related info: The visual features show an extremely high degree of matching, as the male character in the reference image is identical the one the thumbnail, from his hairstyle and beard to the specific design of his dark coat with gold accents on the shoulders. The reference image appears to be a close-up or a cropped frame, while the thumbnail provides a wider shot of the same character in desolate, ruin-like environment, suggesting they orig- inate from the same source material. The title, "Clair Obscur: Expedition 33," semantically anchors these visuals, explicitly identifying them as content from newly announced video game. This creates perfect consistency between the visual elements and the textual informa- tion. Therefore, the search result is exceptionally relevant to the reference content. The two are not just related in topic but are direct components of the same subject, with the search re- sult providing the precise name and broader context for the character shown in the reference image, making it a definitive and highly informative match. ## Result 3
web info: (Chinese results abbreviated)
related_ints innfo The analysis reveals an extremely high degree of relevance between the refer- ence content and the search result. Visually, the core subject, male video game character, perfect match. The thumbnail features the exact same character as the reference im- age, with identical dark clothing, hairstyle, and overall design, simply captured in wider shot that includes another character and subtitles. This strong visual consistency is fur- ther solidified by the semantic information in the title. The title, "Gustave Argument Scene Breakdown..... explicitly names the character as "Gustave," " which aligns with the subtitles in the thumbnail and provides crucial context that the reference image lacks. The topic over- lap is absolute, as both the reference and the result are centered on this specific character from what is clearly the same video game. The search result does not just show the same character but offers a deeper dive into specific scene he is in. Consequently, the search result exceptionally relevant, directly expanding upon the subject of the reference image by providing specific narrative context and analysis.
## Result
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Published as a conference paper at ICLR 2026
web.info: Based on the provided webpage content, there is no information about games released by the publisher 'gamescardnet" in 2024. The content is from an Instagram page for "gamescardnet" that features posts about various video games.
The primary post, from March 18th, is about the future release "Wwe 2K25". Other posts shown on the page also feature upcoming or undated games such as "NBA 2K26", "Cronos The New Dawn". "EA Sports NHL 26", "EA Sports FC 25", "Hell is Us", and "Mafia The Old Country". The page also includes post for "Spider-Man 2", which was released prior to 2024.
The provided text does not contain a list of games released in 2024. Furthermore, there is no information regarding "white-label publishing service".
related info: The analysis reveals a very strong visual match between the reference content and the thumbnail, both images clearly depict the same male video game character, identi- fiable by his facial features, hairstyle, and distinctive fantasy-style clothing. The thumbnail is simply higher-quality, more dramatic shot of the individual shown in the reference image. However, severe semantic and topical dissonance is introduced by the search result's 's title. The title claims the content is related to "Wwe 2K25" and the wrestler "Roman Reigns." subject from the sports entertainment and wrestling simulation genre. This is in complete contradiction with the visual evidence, which portrays a character from a high-fantasy set- ting. This stark mismatch between the visual content and its textual description renders the entire result misleading. Therefore, despite the perfect visual correspondence between the images, the search result as whole is highly irrelevant and deceptive, as the title fundamen- tally misrepresents the subject matter of the visuals, likely functioning as clickbait. ## Result 5
web info: The provided webpage content does not contain information about the publisher of the game "Clair Obscur: Expedition 33." While the page is for YouTube video related to this game and mentions "Sandfall Interactive" in the context of a "Behind the Voices" video, does not name a publisher. Therefore, it is not possible to identify other games released by the publisher in 2024 or any details about their white-label publishing services based on the given text.
related info: The analysis reveals very high degree of relevance between the reference content and the search result. Visually, the character depicted the reference image and the thumbnail are identical, sharing the same dark hairstyle, beard, facial structure, and dark, multi-layered attire. The somber, low-light aesthetic and cinematic quality are also consistent across both images, suggesting they originate from the same source. Semantically, the title "Clair Obscur: Expedition 33" identifies the source as a specific video game, which perfectly aligns with the high-fidelity, computer-generater appearance of the character and environment. The topic overlap is therefore complete, as both the reference and the search result pertain to the same character from the same video game. Given these strong visual and contextual connections, the search result is exceptionally relevant, as it appears to be video clip featuring the very same character highlighted in the reference image.
## Result 6
web info: Based on the provided web page, the publisher the game "Clair Obscur: Ex- pedition 33" is Kepler Interactive. However, the content does not contain any information about other games released by Kepler Interactive in 2024, nor does it mention the company's white-label publishing service.
related info: The reference image and the search result demonstrate an extremely high de- gree of relevance. The visual features show perfect match, as the male character in the reference image is clearly the same individual featured on the left side of the search thumb- nail, identifiable by his hairstyle, facial structure, and distinctive dark outfit with shoulder details. This strong visual consistency immediately establishes a direct link. Semantically, the search result's title," "Clair Obscur: Expedition 33," and the text overlay on the thumbnail explicitly name the video game from which the character originates. The topics are not just overlapping but are identical, with the reference being a character from a specific game and the result being gameplay video of that same game. Therefore, considering the identical core character and the explicit textual confirmation of the game's title, the search result is unequivocally and highly relevant to the reference image, representing direct and specific piece of content about the source material.
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## Result 7
The provided web page content is "404 Page not found" enror from the Douyin website. does contain any images or information related specific game, its pub- lisher, or a list of games released in 2024. Therefore, it is not possible to answer the question based on the given text.
info:
related info: (Chinese results abbreviated)
## Result
web_info: Based on the provided web page content, it is not possible answer your ques- tion. The text consists of a generic YouTube page layout and does not contain any specific information about the game, its publisher,
list of games released in 2024.
related info: The analysis reveals a very strong connection between the reference image and the search result. Visually, the and a distinctive layered dark outfit with gold accents, is perfectly matched between the reference and the thumbnail. The reference provides closer view of the character, while the thumbnail shows the same individual in wider scene, confirming direct correspon- dence in character design, attire, and the overall dark, cinematic art style. Semantically, the search result's 's title, "WHEN ONE FALLS WE CONTINUE Clair Obscur Expedition 33 Ep. 2," provides the specific context, identifying the source as the video game "Clair Obscur: Expedition 33." This textual information solidifies the connection, confirming that both images depict content from the same game. The topic overlap is therefore complete, as the search result is gameplay video that explicitly features the character shown in the reference image. Consequently, the search result is exceptionally relevant to the reference content, representing a direct and specific match from the exact same source material. ## Result
element, male character with dark hair, beard,
web info: Based the provided web page, the publisher of the game "Expedition 33" is Hello Games. The content is a review that details this specific game's story, strategic com- bat system, and character customization. However, the text does not contain any information regarding other games released by Hello Games in 2024, nor does it mention a white-label publishing service. The page focuses exclusively on the features and description of "Expe- dition 33".
related info: (Chinese results abbreviated) ## Result 10
web_info: Based on the provided web page, the publisher of the game Clair Obscur: Ex- pedition 33 is Kepler Interactive, and the developer is Sandfall Interactive. The game is planned for release on Xbox, PlayStation, and PC via Steam.
However, the webpage content, which is YouTube Shorts video page for review of this specific game, does not contain any information about other games released by Kepler In- teractive 2024. Additionally, there no mention of the company's white-label publishing service or a list of titles released under it. Therefore, the provided text does not contain the information needed answer your question.
related info: The analysis indicates a very high degree of relevance between the reference image and the search result. Visually, the character the reference image is perfect match for the male character prominently displayed in the search result's thumbnail. The distinctive features, including the dark hair, beard, and specific style of the layered coat, are identical in both images. Furthermore, the dim, atmospheric lighting and the overall graphical art style are consistent, strongly suggesting that both images originate from the same source. The semantic context provided by the title, "Clair Obscur: Expedition 33," and the "#gaming" tag solidifies this connection by identifying the specific video game from which these visuals taken. The reference image is essentially cropped focused shot of the same character within the same game world depicted in the thumbnail. Consequently, as the visual subject matter identical and the textual information explicitly names the source content, the search result is directly and unequivocally relevant to the provided reference. < </image search result> assistant:
Tool Call: text.search
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"queries":
"Kepler Interactive games released in 2024",
"Kepler Interactive white-label shilishing service", "Clair Obscur: Expedition 33 publisher"
tool:
stext_ssearch.resulss
## Result 11
web_info: Based on the provided web content, the Steam search page for the publisher Kepler Interactive shows results match your search." Consequently, the page does not any games from this publisher, and therefore contains no information regarding any titles released in 2024.
## Result 12
web_info: Based on the provided web content, Kepler Interactive released the game Ultros on February 13, 2024. It is an indie adventure game available on PlayStation PlayStation 4, and PC (via Steam and Epic). In Ultros, players awaken on giant, cosmic, space-drifting entity called The Sarcophagus and are trapped in time loop. The game psychedelic sci-fi journey that explores themes of life, death, and karmic cycles. Its gameplay contrasts brutal, close-quarters combat with contemplative gardening, and unique loop mechanic allows players to restart the story to unlock new areas and abilities.
The webpage also highlights Pacific Drive as being "out now," indicating a recent release. It is survival game where the player must navigate the anomaly- ffllle Olympic Exclusion Zone with a car as their only lifeline.
No other games listed on the page are explicitly mentioned having 2024 release date in the provided text.
## Result 13
ren::nfo: Based on the webpage content, Kepler Interactive released the following games
in 2024:
Pacific Drive: Released on February 22, 2024, for PC and PlayStation 5. Flintlock: The
Siege of Dawn: Released on July 18. 2024, for PlayStation 5, Xbox Series X-S, and PC. Cat Quest III: Released on August 8, 2024, for platforms including iPhone, Macintosh, PC, Xbox Series X-S, Xbox One, and PlayStation
## Result 14
web.info: Based on the provided content, Kepler Interactive released several games in 2024 under its main publishing label and its white-label service, Kepler Ghost.
Games Published by Kepler Interactive in 2024: _Ultros.: Developed by Hadoque for macOS, Windows, Nintendo Switch, PlayStation 4/5, and Xbox Series X/S. Pacific Drive
Developed by Ironwood Studios for Windows and PlayStation 5. Cat Quest III Developed by The Gentlebros for Windows, Nintendo Switch, PlayStation 4/5, and Xbox One/Series X/S. _Flintlock: The Siege of Dawn.: Developed by A44 for Windows, PlayStation 4/5, and Xbox One/Series X/S.
Games Published via Kepler Ghost in 2024: Windblown.: Developed by Motion Twin. Unnralled 2: Back on Track_: Developed by Indoor Astronaut for Windows, macOS, and Linux. Dark Hours.: Developed by Piece of Cake Studios for Windows.
## Result 15
web_infor The provided web content does not contain information about Kepler Interactive games released in 2024. The content a security notification from Cloudflare for the website ge.deals. It indicates that access to the target page was blocked with "403 Forbidden" error, requiring a security check to verify the user is human before proceeding.
## Result 16
web_info: According to the provided text, Kepler Interactive launched a white-label publish- ing service in 2023 called Kepler Ghost. This service serves as an imprint for the company. The launch of Kepler Ghost is part of the company's 's strategy to scale its overall output, in addition to bringing new studios into its core co-ownership group. In 2024, Kepler released
28

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Published as a conference paper at ICLR 2026
five titles through either publishing agreements its Kepler Ghost imprint: Pacific Drive, Ultros, Dark Hours, Unrailed 2: Back on Track, and the early access release of Windblown. ## Result 17
web info: In 2023, Kepler Interactive launched Kepler Ghost, white-label publishing ser- vice designed provide marketing and publishing support for game developers. The first video game project to utilize this program was Eternights. by Studio Sai. Other titles published under the Kepler Ghost label include _En Garde!_. -Apocllpse Party., _Battle and _Unrailed 2: Back on Track_.
## Result 18
web info: Kepler Interactive offers publishing service named Kepler Ghost, designed for developers with fully funded projects seeking expert marketing and publishing support. This service provides true AA & AAA publishing expertise, allowing studios build their brand and retain control of their marketing. It is presented as offering better terms than standard publishing partner, with base revenue share of 15%.
Kepler Ghost's 's support covers seven key areas: Brand: Creating strong, recognizable identity for the game. * Digital: Maximizing potential through targeted online marketing, distribution, and sales. Communications: Managing public relations and social media to build a loyal community. & Relationship: Leveraging Kepler's industry network to forge valuable partnerships. Data: Using analytics to provide actionable insights for game optimization. Business Development: Identifying and executing growth strategies. Platform: Managing the technical requirements to launch and maintain the game across various platforms.
Developers interested in this service get in touch via inbound form the Kepler Interactive website.
## Result 19
web_infor Kepler Interactive has launched a new white-label publishing service for curated set of games and studios. This service provides developers access Kepler's in-house marketing and publishing talent for an industry-leading 15% base revenue share.
The primary goal of this service to empower developers to remain self-published, allowing them to build their own brand and community with professional support. Projects already utilizing this white-label service include Eternights from Studio Sai and unannounced debut game from Hadoque, Swedish developer led by El Huervo, artist known for his work on the Hotline Miami series.
## Result 20
web info: Based on the provided web page, there no mention of a "white-label publishing service."
Instead, the content describes Kepler Interactive as global publisher and game developer built on unique co-ownership model. This model provides studios with a stake in the business and a leadership role in strategic decision-making while ensuring they maintain their creative independence. The goal to enable them to create fun and commercially successful games.
Additionally, Kepler Interactive runs Kowloon, gaming fund that supports developers across the world. The company also has a submission process for developers who wish to pitch their games.
## Result 21
web info: According to the provided web page, the publisher for Clair Obscur: Expedition 33 is Kepler Interactive.
The developer, Sandfall Interactive, secured a partnership and funding from the publisher Kepler Interactive in early 2023. This deal was instrumental in expanding the development team and securing professional voice actors.
For the physical version of the game, distribution handled by Maximum Entertainment in North America and Bandai Namco Entertainment in Europe.
## Result 22
web info: According to the copyright information on the webpage, Clair Obscur: Expedi- tion 33 is published by Kepler Interactive Limited. The game was developed by Sandfall Interactive SAS.
## Result 23
29

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Published as a conference paper at ICLR 2026
web Based on the provided webpage, the publisher of Clair Obscur: Expedition 33 is Kepler Interactive. The game is the debut title from the developer Sandfall Interactive.
Clair Obscur: Expedition 33 turn-based RPG with real-time combat mechanics, built on Unreal Engine 5. Set fantasy world inspired by Belle Époque France, the story follows Gustave, Maelle, and their fellow Expeditioners on a final mission. They have one year destroy the "Paintress," a mysterious entity who. once year, paints number on her monolith, causing everyone of that age to vanish. The game features an active combat system allowing players to dodge, parry, and chain combos real-time. It will be released on Xbox Series X/S, PlayStation 5, and PC (Steam and Epic Games Store).
## Result 24
According the webpage, Clair Obscur: Expedition 33 is published by Kepler Interactive. The game was developed by Sandfall Interactive.
## Result 25
web info: Clair Obseur: Expedition 33 is published by Kepler Interactive, a global publisher and game developer with unique co-ownership model. The game is the first title from the developer Sandfall Interactive.
turn-based RPG developed on Unreal Engine for new-gen consoles and PC. The game is described as single-player, narrative-driven adventure set in an original world that blends fantasy with European "Belle Époque" art influences.
assistant:
<answer> The game shown in the image is Clair Obscur: Expedition 33 [21, 22, 23, 24, 25J. The publisher this game is Kepler Interactive [21, 22, 23, 24, 25].
Kepler Interactive has white-label publishing service called Kepler Ghost [14, 16, 17, 18,
19). Excluding the games released through this service, the games published by Kepler
Interactive in 2024 are: Ultros: An indie adventure game released on February 13, 2024
[12. 14). * Pacific Drive: A survival game released on February 22, 2024 [12, 13, 14]. Flintlock: The Siege of Dawn: An action game released July 18, 2024 [13, 14). Cat Quest III: An action RPG released on August 8, 2024 [13, 14]. < answer>;
Task: Which city this?
30

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Published as a conference paper at ICLR 2026
assistant:
Tool Call: Zmage.zzom.in..tol
"bbox_indexx 8,
iimage_name" "hesmursifz.pns"" "label": "#lag"
tool:
The cropped sub-image is provided in the next user round. user:
<image in.result:
NUOD
</ </image_ zoom in result>
assistant:
Tool Call: image.search
"image_nanm name": "heSmursifz round __ png"""
tool:
<image.seaarch.resuut.
## Result
web info: According to the provided webpage, the headquarters of the Institute for Infor- mation Industry is located in Taipei City, Taiwan. Specifically, its main office is in the Technology Building in the Daan District of Taipei. The organization also has several other offices and departments located in different buildings throughout Taipei City, well lo- cations in Nantou City and Kaohsiung City. Additionally, it maintains overseas offices in Tokyo, Japan, and Chennai, India
31

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Published as a conference paper at ICLR 2026
## Result 2
web_info: The provided web page content does not contain any information about city. The text is security notification from Cloudflare for the website www.cake.me. It indicates that the system was verifying the user's connection to review its security before proceeding. The content also includes a warning about "403 Forbidden" error from the target URL, suggesting potential access issue with the page. ## Result
web_info: The provided web page, which describes a stock video of the World Intellectual Property Organization (WIPO) logo, does not explicitly state a specific city as the location. However, "Ankara" is listed one of the image keywords associated with the video.
## Result
web_info: The main article discusses drug approval that occurred in Japan. However, the website's footer provides information indicating is operated by a company based in Shanghai.
## Result 5
web_info: According to the copyright information on the webpage, the company that owns the XIBERIA brand is Dongguan Desheng Industrial Co., Ltd., which is located in Dong-
guan City.
## Result 6
web_info: The provided webpage content does not mention specific city. The page is product listing for "Union of South American Nations Vertical Garden Flag" from the seller Pixelforma.
The flag is described resistant polyester with double-sided printing for visibility from all angles. Its dimensions 32 47.5 The product includes only the flag; the holder is sold separately. The regular price listed as 100, but the item currently unavailable and on backorder. The page also provides information on processing times (3 to 5 business days) and notes that delivery times are estimated based on the customer's 's location.
durable, decorative item for outdoor spaces, made of weather-
## Result 7
web_info: Based on the webpage's footer, the company operating this website, MedSci, is located in Shanghai, China. This is indicated by several pieces of information: (Chinese results abbreviated).
## Result
web_info: Based on the provided web content, the Birch Wathen Lenox School (BWL) is located in New York City (NYC). This is indicated in an Instagram post introducing the school's new clarinet teacher, Jeff Nichols. The caption for his introduction states, "Since moving to NYC post-college, he's dedicated his career to both teaching and performing." ## Result
web_info: The provided web page does not focus on a specific city. It is a page on the Japanese website APPLION that recommends various "footprint tracking" apps for the iPhone. The developers of the listed applications are based in several different cities, in- cluding Foshan, Shanghai, Xi' an, Chengdu, Wuhan, and Guangzhou.
## Result 10
web_info: Based on the provided web page content, there is no information available to identify specific city. The page is standard Facebook interface showing a "You' re Tem- porarily Blocked" message, indicating that a feature was used too quickly. The content also includes login form, options to create new account recover forgotten one, a list of languages, and numerous links to various Meta products (like Messenger, Instagram, Meta Quest), services, and policies (such as Privacy Policy, Terms, and Help Center). </ </image s/image_searcee sewrch www.cc.reeuut.......... result> assistant:
Tool Call: text search (Translated from Russian)
"queries": "NIKOO logo blue flag", "NIKOO company",
32

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Published as a conference paper at ICLR 2026
"NIKOO city"
tool:
<text.search.rresul>
## Result 11
web_info:
Nikao Technology is company providing innovative digital solutions and consulting ser- vices, specializing in wealth management and investment. It offers expert digital consulting and customized strategies aimed at driving business growth and success.
Key areas of expertise include: * Product launch: Development and launch of savings and pension products for banks and retail clients. Platforms: Creation of omnichannel web and mobile platforms for investment and product management. Migrations: Managing complex client and asset migrations between different investment platforms, including FNZ and GBST. Artificial intelligence: Using Al for market analysis, document processing, and creating financial data stores.
The company collaborates with financial institutions and asset managers, focusing on inno- vation and Long-term partnerships to provide cutting-edge solutions.
## Result 12
web info: (Translated from Russian) Nikoyo (HK) Ltd., founded Hong Kong in 1985, is part of the Nikoyo (Holdings) Ltd. The company manages commercial, financial, and logis- ties operations, signing contracts with global partners for product distribution and providing platforms for sales, software development, and technical support.
Nikoyo offers comprehensive IT solutions, including business process management (BPM), cloud services, cybersecurity, digital transformation, infrastrueture. intelligent information management, IoT, and AI, as well as robotic process automation (RPA). The company also provides professional IT services: software development, managed services, consulting, and training.
Key development milestones include obtaining exclusive agency rights for Fujitsu products in 1986, launching NetApp and EMC products on the mainland of China in the 1990s, and developing its own content management system "DocuMan uContent" in 2002. Over the years, Nikoyo has implemented numerous projects for clients from various sectors, including government agencies, global banks, and transportation companies. The company has ISO certifications and collaborates with leading technology companies such Cisco, Dell, IBM, Microsoft, NetApp, and VMware. ## Result 13
web info: (Translated from Russian) Nikko Co., Ltd. (Nikko Co., Ltd.) is a Japanese com- pany founded on August 13, 1919. The main office is located in the city of Akaashi, the Hogo Prefecture, and there is also an office in Tokyo.
Main business: The company specializes in producing asphalt mixing and concrete mixing equipment, belt conveyor systems, as well as equipment for protecting the environment, including plastic recycling equipment, beverage containers, and soil reclamation. It also develops control panels and software.
Key data: Capital: 9.197 billion yen. Net sales: 49.1 billion yen (as March 31, 2025). Employees: 1.397 people. Certification: ISO-9001 and ISO-14001.
History: Originally founded as Nihon Kogu Product Co., Ltd., the company started with producing shovels. In 1958, it produced its first asphalt mixing plant, and in 1968, it changed its name to Nikko Co., Ltd. August 2019, the company celebrated its 100th anniversary. Nikko has international presence, including subsidiary in Shanghai and sales offices world- wide.
## Result 14
web info: (Translated from Russian) CNOOC Limited (CNOOC Limited) is the largest producer of offshore crude oil and natural gas in China. It is the main subsidiary of the state-owned China National Offshore Oil Corporation (CNOOC).
33

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Published as a conference paper at ICLR 2026
The company was registered in Hong Kong in August 1999 and listed the Hong Kong Stock Exchange from February 2001. Previously, it was also listed on the New York Stock Exchange (2001-2021) and Toronto Stock Exchange (2013-2021).
The main areas of activity of CNOOC Limited in China are the Bohai Sea, the South China Sea, and the East China Sea. On the international level, the company has oil and gas assets in Asia, Africa, North America, and Oceania.
CNOOC Limited part of the "Big Three" Chinese companies alongside CNPC (special- izing in onshore production) and Sinopec (specializing in refining and marketing), although these differences have diminished, and companies compete all sectors.
## Result 15
web info: (Translated from Russian) The provided web page content does not contain infor- mation about the company "NIKOO".
The text is a system error message from Google/YouTube. It states that access to the target URL was blocked due the detection of unusual traffic from the user's computer network, which may violate the Terms of Use. Possible causes of such traffic are malicious software, browser plugins, automated scripts, or the use of common IP address with another device, violating the rules.
## Result 16
web_info: (Translated from Russian) Nikko (Japanese, meaning "sunlight") is city in Japan, located in the Totigi Prefecture, 115 kilometers north of Tokyo. As of 2020, its pop- ulation was 80,239 people. Nikko ranks third in Japan by municipal area (1,449.83 square kilometers), with 90% of its territory consisting of mountains.
The city was founded in 782. It became important religious and postal center during the Edo period, especially after the construction of the Tosho-gu Shrine in 1617. In 1954, Nikko was granted city status. The economy includes food processing, aluminum rolling. and copper foil production.
Nikko a popular resort and major tourist center. Its main attractions are the Tosho-gu Shrine and Futarasan Shrine, as well as the Rinno-ji Buddhist temple, which has been in- cluded the UNESCO World Heritage List since 1999. Natural features such as Lake Tsudzendo, Kegon Waterfall, and Senjogahara Bog are part of the Nikko National Park. The city is named after the asteroid (1 (155) Nikko.
## Result 17
web_info: (Translated from Russian) Nikko is a region north of Tokyo, known for its beauti- ful mountainous landscapes and hot springs. The "Travel to Tokyo and Nikoo" tour includes a two-day visit to this area.
The program for Nikko includes: Natural attractions: Visiting the Kegon Waterfall (97 meters high, one of the three most famous waterfalls in Japan), Lake Tsudzendo, and de- scending the famous zigzag road Iroha. Cultural and historical sites: Visiting the Tosho- gu Shrine, the mausoleum of the great Tokugawa leyasu, a XVII-century complex that is UNESCO World Heritage Site and known for its rich decoration. Entertainment: Visiting the Edo-mura theme park, where the life of Japan during the XVII-XIX centuries is recre- ated with ninja and samurai shows. * Accommodation: Staying in a traditional Japanese hotel (ryokan) at Lake Tsudzendo with a Japanese-style dinner and visiting the natural hot springs.
After the tour of Nikko, it is planned to return Tokyo on the 10th day.
## Result 18 web reb_info: (Translated from Russian) Ningguo is county-level city in the Sunchang District of Anhui Province, China. Its area is 2,466.9 square kilometers, and its population as of 2020 was 384,644 people.
The history of Ningguo began in 208 when it was founded as a city. Over the centuries, its administrative status changed multiple times, In 1997, the county of Ningguo was transformed into county-level city, and in 2000, it became part of the newly established Sunchang District.
county, prefecture, and region.
Ningguo is administratively divided into 6 street committees, 8 towns, 4 townships, and national township.
## Result 19
34

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Published as a conference paper at ICLR 2026
web_info: (Translated from Russian) The Hotel Nikoo is located in the city of Meshhed, Iran. It is in the center of the city, kilometer from the center, at Imam Reza Street, Baite Makdis Square.
This is a one-star budget hotel offering affordable accommodation. Its key advantages are its convenient location near main attractions, including the Imam Reza Shrine, and its view of it. The hotel is suitable for both tourists and business travelers.
The hotel offers comfortable rooms and quality amenities such as 24-hour reception, free breakfast, free Wi-Fi, and private bathrooms. The hotel is known for its high level of service and hospitality of the staff.
The check-in time is from 14:00, and the check-out time is before 12:00. It should be noted that the hotel does not accept international credit/debit cards. ## Result 20
web_info: (Translated from Russian) The provided Facebook web page does not contain information related the request "NIKOO city".
The content of the page is notification of temporary block of the user. The reason for the block is the misuse of the function due too fast actions. The page also contains standard elements for logging in, creating a new account, recovering access, and selecting the interface language.
</text_sea r rch_result>
assistant:
Tool Call: zoo.
"bbox_index"; 4,
"image_name" sare__name" "he5murs1fz.pnn". "label": "sign"
Tool Call: zmage.zoom..n.too)
"bbox_index"; 7,
"image_name" name": "heSmurslfz.png" "label": "sign"
tool:
The cropped sub-image is provided in the next user round.
user:
<image zoom in result>
THAI
special
</image.room_inreeuubb
tool:
The cropped sub-image is provided in the next user round. user:
sxo.ll..........meemmeeee/ozeoo. <inage.coominn.eeuub>>>>>>
35

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Published as a conference paper at ICLR 2026
//Amage.zoomin.reeuul.
assistant:
Tool Call: text.search
"queries":
"NIKOO Sochi",
"NIKOO real estate Sochi",
"builder NIKOO Sochi"
tool:
stext.search.resultt
## Result 21 web info: (Russian results abbreviated)
## Result 34
web_info: (Russian results abbreviated) </text_searchh stext.seaarc.resulle
assistant:
Thinking Process
1. Initial Analysis of the User's Question: The user wants to identify the city shown in the
image.
Visual Clues from the Image: The environment features palm trees and deciduous trees, suggesting temperate or subtropical climate. 'The cars and road infrastructure look modern. * There are several flags and signs with text. The most prominent the blue flags (e.g., bbox 8, 9, 10). The text on them appears to be in the Cyrillic alphabet.
Strategy Formulation: Step Identify the text on the most prominent objects. The blue flags (bbox 8) are the clearest starting point. will zoom in on one to read the text accurately. * Step 2: Once the text is identified, will use a text search find out what organization it represents and where it is located. The Cyrillic script suggests searching in Russian will be most effective. Step 3: To confirm the location, I will look for other textual clues in the image. The signs at bbox 4 and 7 could provide additional information. I will zoom in on them as well. Step 4: Based on the information gathered, will refine my search queries, likely by combining the identified name with potential city that
36

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Published as a conference paper at ICLR 2026
fit the visual context (a Russian- speaking city with palm trees). Step 5: Synthesize all the evidence to make final conclusion about the city.
Execution of the Plan: Round 1 (Zoom): started by zooming in on the blue flag (bbox 8). The resulting image clearly shows the text and logo. Round 2 (Search): performed an image search on the cropped flag. but the results were generic and unhelpful. I then conducted text search in Russian. The results pointed to various companies named "Nikko," "Nikoyo." or "Nikoo" in Japan, Hong Kong, and Iran, none of which the vi- sual context of Russian-speaking city. This indicated that russian-1 might be local company, and needed more context. * Round 3 (Zoom): To gather more information, I zoomed in the signs at bbox 4 and 7. The sign at bbox 4 was billboard. The zoomed- image revealed the text ("Thai Spa Salon") in Russian. This confirmed the language and that the location in Russian-speaking country. The palm trees strongly suggested southern city, like Sochi. Round 4 (Refined Search): Armed with the name russian: and strong hypothesis that the city Sochi, performed a targeted search using queries like trussian-- and "russ 3 (NIKOO real estate Sochi). "Analysis of Search Re- sults: The search results returned information about several companies named russian-- (a very close match to russi an 1 in Sochi. Specifically, search results [25, 30, 31, 33] mention that russ ian-4 was a company involved in real estate and construction in Sochi. Although the records indicate these specific entities were liquidated, their existence strongly links the name on the flags to this city.
5. Conclusion: The combination of visual evidence (palm trees, Russian- signs) and the results of the targeted text search, which linked the company name russian- or russian-1 to real estate and construction businesses in Sochi, confirms that the city in the image is Sochi, Russia.
janswer; Based the visual evidence and search results, the city in the image is Sochi, Russia.
Here step-by-step summary of how this conclusion was reached: Initially, I iddetifiedd prominent blue flags with the text russian-1 written in the Cyrillic alphabet. 2. I then zoomed on another sign in the image (a billboard), which had the Russian text ("Thai Spa Salon").. This, combined with the presence of palm trees, strongly suggested the location is southern Russian city. 3. A targeted search for the company russian- information about company named russian- construction in the city [25, 30, 33]. While some records show the company was liquidated, the presence of these flags confirms connection between the brand and the city of Sochi. :/answerg
Sochi revealed which was involved in real estate and
L PROMPTS
System Prompt
You are question-answering expert. You are skilled in flexibly using search tools and image processing tools to answer users' question. The search tools include image search and text search. The image processing tools include object grounding. In the first round user-submitted content, there is an image containing object bounding boxes and their corresponding index numbers, which denoted as the annotation image. each round, users will send you text images, and you need to:
1. Break down the question based on its complexity, analyze what information needed to answer it, and then multiple rounds of tool calls to respond to the user's question.
2. For complex questions, pay attention to combining the initial analysis strategy and planning during the reasoning process, gradually obtaining relevant information.
3. For text search, you can generate 3 to 5 queries at a time and send them to the search engine improve retrieval efficiency; the returned results are not as expected, you can try modifying the search queries to obtain more useful information.
4. Note that when you are uncertain about the image or certain concepts, try use tools to
get the answer unless you are very sure that the relevant content is correct.
37

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Published as a conference paper at ICLR 2026
5. Determine the retricval strategy needed based on the question and the information directly obtainable from the image. If the target in the image is too small unclear, you can crop the sub-image to obtain a clearer view. Specifically, you can #*obtain the sub- image by requesting the index number of specific subregion provided in the annotation image of the first round**, When the image search results are not ideal, make full use of the concepts and text information in the image find the answer and relevant information to the question.
6. After each round of tool calls, you will receive the return results of the tool calls. You need to decide whether to continue calling tools and which tools to call.
7. Note that after each round of tool calls, fully analyze the current state based on the completed execution results, appropriately review the user's s original question and reflect, determine whether the overall plan needs to be adjusted, and whether to continue calling tools.
8. When information to be returned, historical information can already answer the user' question, first #*uummarize the overall thought process and the process of obtaining the answer to ensure accuracy, and check whether your answer indeed answers the user's s question, avoiding irrelevant responses**, then output the answer, with the answer fragment written between <answer>> and </answer>>.
9. When outputting the answer, remember to include citations, using the '[quote_id]' format, where "quote id' corresponds to the numerical ID of the webpage. 10. For text search, each query must be **a keyword, phrase, or sentence*s.
38


Reviews:
[{"review_id": "VeWgT8BXW1", "reviewer": "Reviewer_xCaP", "summary": "This paper proposes a new benchmark called MMSearch-Plus (311 tasks) designed to force true multimodal reasoning by requiring Fine-grained visual cue extraction, search under retrieval noise, and multi-step visual–textual cross-validation. This addresses the drawbacks of existing multimodal browsing benchmarks, such as MMSearch, in which many tasks can be solved by text-only reasoning. It also contributes a\nmodel-agnostic agent framework with standard browsing tools and a set of mark module, which lets the agent place marks, crop subregions, and launch targeted image/text searches. The authors show that even the strongest MLLMs do not perform well on this benchmark.", "questions": "How many questions in MMSearch are solvable by text-only browsing? This is an important motivation for the current benchmark.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The core design decisions, such as requiring fine-grained, exhaustive visual reasoning for answering the question make sense.", "weaknesses": "1. The authors did not include statistics or any reference to how many questions in MMSearch are solvable by text-only browsing.", "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": "WR5Eq9T7qK", "reviewer": "Reviewer_WMkC", "summary": "The authors propose a new VQA benchmark. To answer the questions, models should understand both spatial and temporal knowledge. The authors show that existing models perform poorly on the benchmark but achieve some improvement with set-of-mark prompting.", "questions": "- The text appearing within the image (i.e., scene text) could arguably be treated as both visual and textual information. Did you apply both image and text searches on this scene-text?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The proposed benchmark, where questions require both visual and textual cues to be answered during web browsing, is interesting.\n- The experiments are comprehensive, evaluating various models across different categories and difficulty levels.\n- The proposed approach --- i.e., identifying subregions through set-of-marking and searching those regions on the website --- looks interesting and promising.", "weaknesses": "- Novelty of the benchmark: Evaluating a model’s spatial and external temporal knowledge has been widely studied in conventional VQA tasks (e.g., [1], [2]). I feel the authors simply extend these existing works to the web-browsing domain.\n\n- The paper is somewhat difficult to follow, and its core contribution is not immediately clear at first glance.\n\n- Since you call image/text search APIs for every SoM-defined subregion, the runtime could increase substantially, which may be too costly for real-world deployment. Do you have strategies to mitigate this overhead?\n\n[1] Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?, EMNLP'23.\n\n[2] Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering, EMNLP'22.\n\n[3] GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering, CVPR'19.", "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": "BjRznyMbhH", "reviewer": "Reviewer_SjaW", "summary": "MMSearch-Plus is a multimodal searching benchmark with 311 tasks that forces multimodal reasoning by requiring agents to extract fine-grained, localized visual cues and propagate them through iterative image-text retrieval. The tasks also require provenance checks under retrieval noise to reach those \"out-of-image\" facts like events, dates, venues. The authors also introduce Spatial–Temporal Extrapolation to curate questions and provide an agent framework with a Set-of-Mark (SoM) zoom-and-retrieve module for searching. Results show that: even the best closed-source system reaches only 36.0% with full rollout, and SoM yields consistent gains up to +3.9 points; dominant failures involve missing relevant webpages and confusing visually similar events. The benchmark thus serves as a rigorous stress test and common yardstick for agentic MLLMs.", "questions": "(1) You note that models sometimes “zoom in without subsequently performing region-based retrieval.” Could you report the proportion of zoom actions that are followed by a subimage search, and quantify the marginal contribution of that step to final accuracy?\n\n(2) Why does performance on the Easy subset decrease when moving from \"image-only\" search to the \"full-rollout\" setting? Is this due to distractor exposure or over-retrieval, and can you provide supporting diagnostics?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The paper clearly identifies limitations in MMSearch and proposes a carefully designed dataset-curation process to construct a more challenging benchmark where information is intentionally hidden, thereby requiring genuine visual reasoning rather than shortcut cues.\n\n(2) The experiments and evaluation are comprehensive and detailed, comparing cutting-edge open-source and proprietary models across four search modes and multiple task subsets.\n\n(3) The analysis is thorough, offering interesting observations and insights alongside a well-reasoned error analysis.", "weaknesses": "(1) The average answer length is relatively short, suggesting that many items may be closer to MCQ-style or “single-point” questions; the benchmark may under-represent open-ended QA.\n\n(2) While the benchmark is valuable for provenance-aware retrieval, it is less comprehensive for many real-world agent tasks like cross-site form/API interactions. This may bias the evaluation toward retrieval-and-verification strength while being less sensitive to interactive capabilities.", "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": "78L6wdVsmB", "reviewer": "Reviewer_sEzM", "summary": "This paper introduces MMSearch-Plus, a multimodal browsing benchmark that requires models to perform iterative multimodal retrieval and cross-validation under noise. The benchmark is constructed using a spatial-temporal extrapolation curation pipeline that constructs 311 tasks requiring models to extract localized visual cues and extrapolate to out-of-image facts. The authors also proposed an agent framework with web search tools and a set-of-mark module for the proposed task. The paper further evaluates different closed-source and open-source models with the proposed framework on the proposed benchmark and provides error analysis.", "questions": "Since the benchmark emphasizes iterative multimodal search and validation, the “easy” split includes samples that certain models can already answer correctly without any search steps. Why do the authors still keep these samples in the benchmark? Would it be more reasonable to have human annotators label the necessary reasoning or tool-use steps and then categorize difficulty based on that process?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper clearly identifies a real weakness in existing multimodal search benchmarks—tasks solvable with text-only heuristics—and systematically designs MMSearch-Plus to require true multimodal reasoning.\n\n2. The paper proposed a suitable agent framework for the proposed benchmark and conducted comprehensive evaluations for different models with multiple search modes, providing a solid empirical foundation for further research.\n\n3. The authors provide detailed error categorization, offering actionable insights into model weaknesses in long-horizon multimodal search.", "weaknesses": "1. The dataset size is relatively small, and all samples are generated through the same spatial-temporal extrapolation pipeline. This design choice may introduce human bias and constrain the diversity of reasoning patterns, leading to potentially predictable task structures. As a result, the benchmark might be vulnerable to overfitting or data-specific “hacks,” where a model trained on a small amount of similar data could achieve disproportionately high scores.\n\n2. To better support interpretability and diagnostic analysis, it would be highly beneficial to annotate fine-grained evidence—for example, marking which visual regions are essential or sufficient for solving each task. Such annotations would allow for a deeper understanding of model failures and reasoning behaviors.\n\n3. While the paper introduces zoom-in and cropping tools within the evaluation framework, it lacks experimental evidence demonstrating whether these tools are genuinely necessary or beneficial for specific task types, or whether current models can effectively leverage them. \n\n4. Some tasks can be solved using the model’s internal knowledge rather than external retrieval, which weakens the benchmark’s diagnostic focus on search-based reasoning. Future iterations might consider constructing questions from recent or dynamically updated sources and designing a reusable pipeline to continuously refresh the benchmark content, ensuring long-term relevance.\n\n5. The low human performance reported on MMSearch-Plus raises concerns about possible ambiguity or excessive difficulty in certain questions. This suggests that some samples may not have a clearly defined or uniquely inferable answer, limiting their reliability for model evaluation.", "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)