DeepEyesV2: Toward Agentic Multimodal Model
Authors: Jack Hong
Chenxiao Zhao
ChengLIn Zhu
Weiheng Lu
Guohai Xu
XingYu
Venue: ICLR 2026 Poster (2026)
Keywords: DeepEyesV2
Agentic Multimodal Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Agentic multimodal models should not only comprehend text and images, but also actively invoke external tools, such as code execution environments and web search, and integrate these operations into reasoning. In this work, we introduce DeepEyesV2 and explore how to build an agentic multimodal model from the perspectives of data construction, training methods, and model evaluation. We observe that direct reinforcement learning alone fails to induce robust tool-use behavior. This phenomenon motivates a two-stage training pipeline: a cold-start stage to establish tool-use patterns, and reinforcement learning stage to further refine tool invocation. We curate a diverse, moderately challenging training dataset, specifically including examples where tool use is beneficial. We validate DeepEyesV2 across real-world understanding, mathematical reasoning, and search-intensive benchmarks, demonstrating that systematic tool integration enables reliable and extensible multimodal reasoning behaviour. Moreover, DeepEyesV2 exhibits task-adaptive tool invocation, tending to use image operations for perception tasks and numerical computations for reasoning tasks. Reinforcement learning further enable complex tool combinations and allowing model to selectively invoke tools based on problem context. We hope our study can provide guidance for community in developing agentic multimodal models.

Published as a conference paper at ICLR 2026
DEEPEYESV2: TOWARD AGENTIC MULTIMODAL MODEL
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Jack Hong", Chenxiao Zhao", ChengLin Zhu', Weiheng Lu, Guohai Xul, XingYu Xiaohongshu Inc.
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Project Page: https://viiiii suall waaouallaggeen agent .eveet.gtthie wit.t.thhu.i... io/
ABSTRACT
Agentic multimodal models should not only comprehend text and images, but also actively invoke external tools, such as code execution environments and web search, and integrate these operations into reasoning. In this work, intro- duce DeepEyesV2 V2 and explore how to build an agentic multimodal model from the perspectives of data construction, training methods, and model evaluation. We observe that direct reinforcement learning alone fails to induce robust tool- use behavior. This phenomenon motivates a two-stage training pipeline: cold- start stage to establish tool-use patterns, and reinforcement learning stage to fur- ther refine tool invocation. We curate diverse, moderately challenging training dataset, specifically including examples where tool use is beneficial. We vali- date DyepEyes V2 across real-world understanding, mathermtical reasoning, and search-intensive benchmarks, demonstrating that systematic tool integration en- ables reliable and extensible multimodal reasoning behaviour. Moreover, Deep- Eyes V2 exhibits task-adaptive tool invocation, tending to use image operations for perception tasks and numerical computations for reasoning tasks. Reinforce- ment learning further enable complex tool combinations and allowing model selectively invoke tools based on problem context. We hope our study can provide guidance for community in developing agentic multimodal models.
INTRODUCTION
agentic multimodal model should not only be capable of understanding text and images, but can also actively invoke tools (e.g., a code execution environment web search interface) and seam- lessly integrate these operations into its advanced reasoning process. For example, as illustrated in Figure D when asked for the mean value of the black dots in the figure, agentic multimodal model first crops each subplot to inspects the numeric values for each black point, and then emits exe- cutable Python code to compute the average values. Likewise, when query requires stock quote, agentic multimodal model can issue image- or text-based search queries and invokes an external API to obtain the quote. By integrating programmatic image operations, computation, and retrieval into a single reasoning loop, these models enable fine-grained visual perception, support complex mathematical reasoning, and continually incorporate updated external knowledge, thus achieving more reliable and comprehensive multimodal inference.
Although existing multimodal models demonstrate strong perception and interpretation abilities, they remain largely passive and lack the ability to autonomously invoke external tools, which is essential for agentic multimodal models. These tool-use capabilities can be categorized into two types: (i) Operation tools: Current models cannot perform complex operations on visual or numer- ical data, including fine-grained image manipulations (e.g., cropping, measuring) and quantitative computations. This limits their capacity to reason about detailed visual content or solve mathemat- ical problems. (ii) Information retrieval tools: Models cannot proactively access up-to-date exter- nal knowledge, which often leads to outdated conclusions statements without verifiable sources.
"quual Contribution. Corresponning author.

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Published as a conference paper at ICLR 2026
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Figure/ Case reasoning trajectory of DeepEyesV2. DeepEyesV2 seamlessly integrates code exccution and web search within its iterative reasoning process. Notably, in the right case, the behavior of accessing webpages via code does not exist in cold start data and is spontancously acquired during reinforcement learning.
While 03 (OpenAil 2025) has explored "thinking with image" reasoning pattern, how realize
such capabilities remains unclear. Existing approaches are either restricted to perception tasks with- out support for complex reasoning or web search (Zhang et al., 2025b), or rely on limited tools (e.g. (Zheng et al. (0255)), leaving substantial gap to truly agentic multimodal models.
To explore how construct such agentic multimodal models, we introduce DeepEyesV2, which seamlessly integrates tool invocation within the dynamic reasoning loop. DeepłyesVV V2 actively de- cides when and how to invoke tools, enabling a dynamic process of evidence acquisition and ver- ification. Then, tool outputs are iteratively incorporated into reasoning process, allowing model to refine its hypotheses, validate intermediate results, and ultimately arrive at more reliable and inter- pretable conclusions. In this work, we systematically investigate key aspects of building an agentic MLLM, including model training strategies, dataset curation, and evaluation protocols.
We first follow the setup of DeepEyes (Zheng et al. 2025b) and apply reinforcement learning di- rectly on Qwen2.5-VL (Bai et al., 2025). but find that limited inherent tool-use capability prevents stable tool invocation. This highlights the need for a cold-start stage to establish reliable tool-use patterns. Thus, we curate a high-quality dataset that spans diverse scenarios, including perception, reasoning, and search tasks. After cleaning, we apply two filters: (i) difficulty filtering, retain- ing only questions unsolvable by the base model, and (ii) tool-benefit classification, keeping cases where tool use improves accuracy. Data are split into two subsets: tool-solvable examples for RL and harder unsolved cases for cold start, further augmented with long chain-of titt-off-fhoue thought trajectories. Supervised fine-tuning on this cold-start dataset enables the model acquire basic tool-use patterns and deeper reasoning, after which RI. further strengthens tool invocation. Notably, we rely only on two simple rewards, accuracy and format, without complex reward engineering (Su et al. (025).
We assess DeepEyesV2 on benchmarks spanning real-world understanding, mathematical reason- ing, and search-intensive tasks. Dee2EyesV2 outperforms both general-purpose MLLMs and prior specific reasoning approaches. Specifically, on real-world understanding benchmarks, DeepEyesV2 surpasses even owen2.55-V VLL32B in some benchmarks through effective tool use. On reasoning tasks, DeepEyesV2 shows performance gains across multiple benchmarks, including +7.1 on Math- Verse (52.7% accuracy). On search benchmarks, DeepEyes delivers strong advantages, reaching 63.7% on MMSearch (Jiang et al. 2024), far beyond the MMSearch-R1 (Wu et al. 2025) (53.8%).
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Published as a conference paper at ICLR 2026
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Figure 2: Pipeline of DeepEyesV2. DeepEyesV2 invokes tools and incorporates execution results into subsequent reasoning steps, enabling iterative and tool-augmented multimodal inference.
These results demonstrate that by reliably invoking tools, DeephyessV2 extends its comprehensive capabilities, achieving accurate and advanced reasoning.
We observe the task-dependent tool invocation patterns in DeepEyesV2. For perception tasks, Deep- EyesV2 primarily uses image operations, such as cropping, to extract fine-grained visual details, whereas for reasoning tasks, DeepEyes favors numerical analysis. Moreover, reinforcement learn- ing can further enhances tool-use behavior, enabling more complex tool combinations and adaptive decision-making DeepEyesV2 learns selectively invoke tools based on the problem context, re- flecting the emergence of autonomous, agentic reasoning.
The main contributions timodal model that unifies code execution and web search within single reasoning loop, enabling reliable and complex reasoning. (ii) We construct a carefully curated training corpus through rigor- ous data filtering and cleaning. The resulting dataset is diverse in task types, of appropriate difficulty, and explicitly designed to ensure the beneficial integration tools. Based on this, we build both cold-start SFT data and RI. data that complement each other. (iii) Extensive experiments across real-world understanding, mathematical reasoning, and search-intensive benchmarks demonstrate the strong reasoning and tool-usage ability of DeepEyesV2. (iv) We analyze the dynamics of tool- use behavior in DeepEyesV2 V2, revealing task-adaptive patterns. Besides, we also find reinforcement learning can enable more complex tool combinations and adaptive, context-awan tool invocation.
summarized as follows: (i) We introduce DeepEyes an agentic mul-
RELATED WORKS
Multimodal Large Language Models. The field of multimodal large language models (MLLMs) has witnessed rapid progress recent years. Early efforts mainly focus on combining pretrained visual encoders with large language models through lightweight adapters or projection layers, en- abling basic Assion-languae
alignment and simple multimodal understanding (Li et al. 2023 Liu
et al. 2023 2024a Bai et al., 2023] Chen et al. 2024). Subsequently. more powerful architectures such as Qwen2.5-Vi VL (Bai et al.) 2025), LLaVA One Vision (Li et al.) 2024a). and Intern ZZuu et al. 2025), expand the training scale and integrated more diverse visual data, significantly improv- ing performance on benchmarks of visual question answering, captioning, and general perception tasks. Recently, some OmniMLLMs (Li et al., 2025b Zhao et al. 2025a Fu al. 2024 Jain al.) 2024 [Hong et al.) 2025) are capable of processing a mix of modalities like speech, video, and images simultaneously. However, existing MI..Ms remain largely passive: they interpret multimodal
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Published as a conference paper at ICLR 2026
w/o Tool Bonus
W: Toal Bonus
200
400
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100
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120
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200
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340
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Figure 3: Pioneer Experiments reveal that existing multimodal models cannot directly acquire reliable tool use ability through RL, demonstrating the necessity of a cold start phase.
inputs and generate answers, but lack the ability to actively invoke external tools for computation or knowledge retrieval, which limits their reliability complex reasoning tasks.
Thinking with Images. The paradigm of "Think with Image" is first introduced by o3 (OpenAl, 2025), which demonstrated that multimodal models can interleave reasoning with iterative visual analysis, actively manipulating images support step-by-step problem solving. Many works at- tempt to reproduce such capabilities. Most approaches (Su et al. 2025 Lai et al. 2025 Liu et al.) 2025| Fan et al.] 2025 Jiang et al., 2025 Zhang al. (025a) adopt two-stage training pipeline,
where a cold-start phase is followed by reinforcement learning. In contrast, DeepEyyes (Zheng et al.)
2025b) only adopts reinforcement learning alone and incentivizes the "Think with Image" behav- iors, leading to strong reasoning performance. However, the majority of these efforts employ a rather limited tool set, typically restricted region cropping for fine- seee-graied perception. To improve gen- erality, Vyvision (Zhao et al. (0025)) and Thyme (Zhang et al., (025b) utilize code execution to enable more flexible visual operations. Despite this progress, these models remain constrained to image manipulation only, and are unable to handle knowledge-intensive questions where access to up-to-date external information is essential.
Search-oriented Reasoning. To mitigate the inherent knowledge limitations of large multi- modal language models, a growing line of work explores augmenting them with external knowl- edge acquisition. Early approaches commonly adopt the retrieval-augmented generation (RAG) paradigm (Song et al., 2025; Jin et al.) 2025). where relevant information is retricved from a pre- constructed knowledge base and fed into the model. While effective, this paradigm remains strained by the static and finite nature of the underlying corpus. To overcome these limitations, more recent studies attempt to leverage online search to dynamically access broader and up-to-date information (Zheng et al., (022a)). Beyond purely textual queries, some efforts extend search into the multimodal domain, enabling retrieval of not only documents but also images, charts, other media forms relevant to the task (Wu et al. 2025 Wang et al. (222d)). These advances highlight the potential of gearch-augmented reasoning to complement perception and tool-use capabilities, ultimately broadening the scope of problems that multimodal models can effectively address.
3 DeepEyesV2
We explore how to construct agentic multimodal models from the perspectives of training strat- egy, dataset design, and evaluation. We begin in Section [3. presenting the overall pipeline of V2 which integrates tool invocation into the reasoning loop. Then, we conduct pioneer experiments in Section 3.2 reveal the limitations of existing models in reliably using tools, un- derscoring the necessity of a cold-start stage. After that, we describe the curation of a high-quality training dataset and the principles behind its construction in Section 3.3 Finally, building on the cold-start foundation, we apply a reinforcement learning stage to further enhance the efficiency and flexibility of tool-use behavior, which is described in Section 3.4
3.1 OVERALL PIPELINE
Similar to DeepEyes (Zheng et al. 2025b), DeepEyesV2 is agentic multimodal model, but with extended tool-use capabilities beyond simple cropping. In DeepEyesV2, programmatic code execu- tion and web retrieval are treated complementary and interleavable tools inside a single reasoning trajectory (see Figure 2) Given an image input and the corresponding user query, DeepEyes V2 first generates an initial reasoning plan, and explicitly determines whether this question can be solved
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Table 1: Results on real-world & OCR & chart understanding Benchmarks.
Model
Tool
Param Size
V Bench
HRBennc
Real- World Understanding
HRBench
MME- RealWorld
OCR
SEED
CharXiv descriptive
Char
CharXiv reasoning
Chart
4K
SK
Beach
Beach
2 Plus
QA
Open-tounes Models
LLaVA-OV
7B
75.4
Qwen2.5 V-
7B
78.5
71.6
67.9
$7.3
37.0
864
70.4
72.7
40.2
86.2
Qwen2.5-VL.
80.6
74.1
69.9
42.5
72.4
83.2
48.0
LL3
81.2
73.6
Gronded Roasoning Models
Reasoee
Crop
7B
84.3
74.0
66.9
64.4
39.0
Deeplyyes
Crop
7B
85.6
Thyme
Code
78
82.2
77.0
72.0
64.8
863
86.1
Agentic Madiimodal Model
DeepPyes
General
7B
81.8
70.5
78.6
A (vr Quea255VV-7B
+3,3
+6.3
+5.9
+7.6
+3.5
+18 +0.1
+3.9
+8.7
+2.2
directly through internal reasoning or requires tool invocation. tool use is necessary, DeepEyesV2 emits executable Python code or issues web search queries. Code execution is carried out in sandboxed environment and can produce structured outputs such as transformed images, numerical measurements, computed arrays, plots, execution logs. Image queries are submitted via SerpAPI and return the top five visually matched webpages (each with a thumbnail and title). Text queries return the five most relevant webpages, along with titles and snippets. All tool outputs are converted into observations and appended to model's 's context. DeepEyesV2 then thinks further in light of these observations and may plan further tool invocations (either additional code, further searches, both), loop until a conclusive answer is produced.
iterating this reasoning- maasonng--toolig-iing www.toog-ioteerraaaiee
DeepEyesV2 2 can dynamically choose, combine, and use tools reasoning unfolds. This integration yields three main advantages: (i) it allows expanded and enhanced analytical capability through executable code; (ii) it enables active and real-time knowledge seeking by retrieving multimodal evidence from the web; and (iii) it supports iterative, interleaved multi-tool reasoning, in which code execution and search can be dynamically combined within single trajectory, rather than being isolated modules. Together, these features position DeepEyesV2 as a more general, reliable, and extensible framework for multimodal reasoning.
3.2 PIONEER EXPERIMENTS
To investigate whether MILMs can directly acquire tool-use ability through reinforcement learning, we first conduct a pioneer experiment on Qwen2.5-VL (Bai et al. 2025) following DeepEyes (Zheng al. 20255). As shown in Figure 3 during training. we observe that the early stages model oc- casionally attempts to produce Python code, but these outputs are often buggy or fail to execute, indicating that existing MLLMs struggle to generate stable and reliable code. As training contin- ues, model gradually abandons code generation and converges to producing only short reasoning chains followed by direct answers, thereby bypassing tool use. Then, to encourage tool invocation, we incorporate the tool usage bonus mechanism from DeepEyes, which explicitly rewards the gen- cration of code. With this additional signal, model is indeed able to produce correct and runnable code in the early stages, suggesting that the mechanism can enforce coding ability. However, with continued training a new degeneration emerges: model's behavior converged to emitting exactly code block per query, and this single block typically consists of non-executable, placeholder comments rather than meaningful code, revealing the phenomenon reward hacking. This pioneer experiment highlights that existing MILMMS cannot reliably learn complex tool use through direct RI. alone, motivating the need for cold start to bootstrap model's tool invocation ability.
3.3
DATA CURATION
Data Collection. Pioneer experiments have highlighted the necessity of constructing a high-quality dataset for supervised fine-tuning to explicitly guide model to learn how to generate executable code and perform tool invocations. Following DeepEyes (Zheng et al. 2025b), we collect data in accordance with the following principles: (i) Diverse tasks and image distribution. We incorporate varied data to cover a wide range multimodal challenges and visual components. (ii) Verifiability and structured format. All questions are reformulated into structured, open-ended QA format
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Table 2: Results on multimodal reasoning benchmarks.
Model
Tool
Param Size
MathVista
Vathe
MathVision
WeMath
DynaMath
LisicVista
Open-source Models
LLaVA-OV
X
58.6
19.3
18.3
20.9
33.3
VLL
X
7B
68.3
45.6
25.6
34.6
53.3
45.9
InIernVI.3
X
8B
71.6
39.8
29.3
37.1
44.1
Text-only Reasoning Models
MM-Eureka
X
7B
72.6
28.1
21.8
46.3
ThinkLite
X
7B
71.6
24.6
41.8
42.7
VL.-Rethikker
X
73.7
28.4
36.3
42.7
VLAA--Thiner
71.7
24.2
35.7
Grounded Reasoning Models
DecpEyes Thyme
Crop Code
70.1
47.3
26.6
38.9
47.7
70.0
27.6
39.3
49.0
Agentic Multimodal Model
DeepEyesV2
General
71.9
52.7
A Qwen2.5-VL-7B)
+3.6
+7.1
+3.3
+3.5
+3.9
+2.8
Table 3: Results on search-oriented benchmarks.
Model
Tool
Model Size
eeQt-test
InfoSeek
MMSearch
SimpleVQA
Open-source Proprietary Models
GPT4o
X
41.7
42.7
22.2
46.6
Gemini 2.5 Pro Qwen-2.5-VI.
X
37.2
37.0
26.9
53.4
X
20.3
20.1
12.8
38.4
Search Models
2.55-2.5-VL MMSearch-RI
Search
52.9
53.7
52.2
51.6
Search
7B
58.4
55.1
53.8
57.4
WebWatcher
Search
7B
49.1
54.3
Agentic Multimodal Model
DeepEyesV2 General
7B
60.6
51.1
63.7
59.4
A (vs Qwen2.5-V1.-77 Search)
+7.7
-2.6
+11.5
+7.8
to facilitate objective evaluation. We exclude examples that cannot be reliably verified, such as those with incorrect answers, ambiguous phrasing, or poor readability. (iii) Appreciate difficulty. We exclude examples that the base model easily solve and prioritize questions that remain challenging. (iv) Beneficial integration of tools. We categorize examples based on whether tool usage leads correct answers. Cases where model can solve correctly using additional tool calls are reserved for reinforcement learning, whereas examples that remain unsolved even with tool assistance are used for cold start.
Specially, we curate data from three major categories: perception, reasoning, and search. Besides, also include long Chain-of-Cot (CoT) reasoning data in cold start subset. Please refer to Ap- pendix A.2 for more details on data sources. All datasets are carefully cleaned, reformatted, and divided into subsets for cold start reinforcement learning subsets. To ensure sufficient difficulty, we employ Qwen2.5-VL-77 (Bai et al. 2025) as a baseline evaluator. For each question, model prompted to generate 8 responses, and we retain only those instances where it answers correctly at most two times, thereby filtering out trivial cases. To further assess tool-use effectiveness, we prompt model solve each question with tool invocation, again collecting responses per instance, and categorize examples according their success rate.
Trajectories Synthesis. We construct cold start datasets by eliciting step-by-step trajectories from models (c.g., Gemini 2.5 Pro (Comanici et al.) 2025), GPT-4o (Hurst et al.) 2024), and Claude Sonnet (Anthropic) 2025)). For each prompt, these models are prompted to produce detailed reasoning traces that explicitly include tool-invocation markers (e.g., code snippets). Each declared tool call executed, and the returned outputs are fed back to the originating model, and model continues reasoning, potentially issuing further tool calls, until it produces final answer. The entire

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Published as a conference paper at ICLR 2026
Table Ablation study on cold start data, Long CoT refers to text-only reasoning data. For more details about cold start data, please refer to Appendix A.2
V+
SEED Plus
CharXiv descriptive
CharXiv reasoning
Perception
Reason
Long CoT
Bench
Vista
Verse
Qwen-2.5-VL-7B
63.9
69.2
68.9
35.7
65.3
36.2
78.0
68.2
70.6
40.8
66.8
38.4
76.9
66.3
68.1
38.7
63.6
36.7
75.9
68.7
72.0
43.1
68.2
47.6
78.5
69.6
73.4
44.3
68.3
47.1
Table 5: Ablation study on reinforcement learning data. DeepHyesV2-SPH denotes the model after cold start. For more details about reinforcement learning data, please refer to Appendix A.2
V*
SEED 2 Plus
CharXiv descriptive
CharXiv reasoning
Math Vista
Math Verse
Info Seck
MM
Perception
Reason
Search
Bench
Search
DeePEXesV--SFT
78.5
73.4
44.3
47.1
47.9
79.3
70.2
76.0
45.6
69.5
47.6
44.6
52.6
77.4
69.3
72.3
45.2
70.4
49.8
43.0
53.7
80,9
70.4
78.2
48.7
71.2
52.0
44.2
55.0
81.8
70.5
78.6
48.9
71.9
52.7
51.1
63.7
interaction is recorded as single trajectory. Only trajectories with correct final answers and error- free code are retained, ensuring high-quality cold-start data.
3.4 AGENTIC REINFORCEMER LEARNING
After cold-start training has equipped model with basic tool-use patterns, we adopt reinforcement learning to further enhance ability to integrate tools in dynamic environment. Unlike SFT, which relies on learning from static trajectories, agentic RL places the model in interactive environment where it must dynamically decide when and how to invoke tools in order to solve tasks. Following DeepEyes (Zheng al. 2025b), employ sparse and outcome-driven reward. The overall reward consists of two components: (i) accuracy reward Race: which evaluates whether the final answer matches the ground truth, and (ii) format reward Rforrma- which penalizes outputs that violate required formats. The total reward is defined as: R = Race + Rommat.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
We conduct training two stages: cold start SFT and reinforcement learning. The backbone model is Qwen2.5- VLL-BB (Bai et al. 2025). For SFT, we train with batch size of 128 and learning rate of x 10-5, Model optimized for epochs using Adam (Loshchilov & Hutter 2017) optimizer with cosine learning rate decay. For RL, adopt DAPO (Yu et al.) (025) the optimization algorithm, with a batch size of 256 and 16 rollouts per prompt. The KL coefficient set to 0.0, and the maximum response length is capped at 16, 384 tokens.. The learning rate is 10-6, and the upper and lower clip ratios are 0.30 and 0.20, respectively.
4.2 MAIN RESULTS
World OCR Chart Understanding. We evaluate Deepłyes across three categories of benchmarks: real-world understanding, OCR, and chart understanding. For comparison, we include two kinds of models: (i) open-source general-purge purpose MLLMs, including VAL-e--neeeiii
(Li
al. 2024a), Qwen2.5-VL (Bai et al., 2025). and Intern (Zhu al. 2025); and (ii) grounded reasoning models, such as DeepEyes (Zheng et al. 2025b) and Thyme (Zhang et al. 2025b). Deep- Eyes performs fine-grained perception by cropping the target region, while Thyme manipulates images through executable code. Compared to base model Qwen2.5-VL. V-L-BB DeepEyesV2 demon- strates substantial performance gains, and surpasses Qwen2.5-VIL-32B in some benchmarks

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Published as a conference paper at ICLR 2026
Usttibbtion
SEit Min
Chartie deep
Charttt reason
V
SPPD-3-PR
Charkwn swep
Charkiy mwvort
Figure 4: Tool distribution comparison. DeepEyesV2 demonstrates the task-specific tool-calling distribution across different tasks.
highlighting the effectiveness of -ool-aug reasoning. Moreover, consistently out- performs existing grounded reasoning models. These results indicate that dynamic tool invocation enables model to extract fine-grained details, thereby improving real-world scene comprehension.
(Table D
Multimodal Reasoning. We further evaluate DeepEyesV2 V2 mathematical reasoning benchmarks to assess its strong reasoning capability. As shown in Table 2 we compare DeepEyesV2 against existing open-source MLLMs, such Qwen2.5-VL (Bai
2025), text-only multimodal rea- soning models, including MM-Eureka (Meng et al. 2025). ), and grounded reasoning models. such as DeepEyes (Zheng al., 2025b) and Thyme (Zhang al. 2025b). DeepEyesV2 consistently outper- forms these alternatives, and notably achieves stronger results than text-only multimodal reasoning models, underscoring the benefit of tool use for enhancing mathematical reasoning.
Online Searching. To further examine the effectiveness of external information acquisition, we evaluate DeepEyesV2 on search-oriented benchmarks. These datasets encompass knowledge- intensive visual question answering, fact verification, and multimodal retrieval- reasoning, all of which require models to go beyond perceptual understanding and actively retrieve external evidence. For comparison, as Qwen2.5-VL, Gemini 2.5 Pro, and GPT4o, as well as models where search capability is incor- porated (Tinng al. 2024 Geng al. 2025). As shown in Table 3 DeepEyes demonstrates superior search capabilities, achieving consistently higher accuracy across all benchmarks.
benchmark DeepEyesV2 against both general purrase MLLMs such
4.3 TOWARDS AGENTIC MILM
Training Data. To understand how training data influences the development of tool-use ability, we investigate the impact of different dataset compositions.
Cold Start Data. We conduct ablations on the SET dataset (Table Directly evaluating Qwen2.55 VL--B brings great performance drop and confirms that existing MILMS lack robust tool-use ability. Training only on perception data helps perception benchmarks but not reasoning; training only on reasoning data yields limited or negative gains, showing perception and reasoning rely on distinct tool-use patterns, with reasoning being more complex and harder to master. Adding long CoT trajectories substantially enhances reasoning and tool use, demonstrating that stronger thinking ability directly facilitates better tool use. Combining perception, reasoning, and CoT data achieves the best overall results, highlighting the complementary benefits of diverse supervision and the value of long CoT for complex reasoning. Overall, these results highlight two key factors of cold start data: (i) diversity, as perception and reasoning rely on different tool-use patterns and data with diverse tasks should be involved improve generalization; and (ii) the inclusion of long CoT data, which strengthens reasoning and substantially improves tool use on complex tasks.
RL Data. We further conduct ablation studies different subsets of RL data. Results are shown in Table Using only perception or only reasoning data improves corresponding benchmarks but harms others. while combining both yields consistent gains. Adding search data further boosts retrieval benchmarks, leading to balanced overall performance. These results emphasize that data diversity critical for reinforcement learning in agentic multimodal models.
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Tool Inrocation Surtsaas Afber Cold Start
Tosl Invecution Staritass Albe RL
wibbet bel
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1977
7.5
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Desuzage 1h0
13.7%
66%
60%-
45%
62.70
80.1%
(8.8%)
10.0%
90%
90.15
7636
1.25
11.%
76%
21%
0%
06
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Mustiaao
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Figure 5: Tool invocation statics. Reinforcement Learning enhances DeepEyes V2's tool-calling flexibility, allowing it to decide dynamically whether to invoke tools.
550
Resporse Lengt
500
2.0
0.48
450-
Rewordd
Call
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400
1.0
350
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10
20
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Steps
Steps
Steps
Figure 6: Training dynamics of RL. Reinforcement learning improves the efficiency of Deep- EyesV2's reasoning and tool usage.
In-Deep Analysis. Then, we conduct an in-depth analysis of DeepFyes tool-use behavior after cold start and RL, comparing the two stages better understand how training shapes and alters the model's strategies for invoking tools.
Tool Distribution. To understand how the model leverages tools across various scenarios, we ana- lyze tool-use distributions over eight benchmarks before and after reinforcement learning (Figure 4 DeepEyesV2 exhibits clear task-dependent preferences: in real-world perception tasks (V*), model mainly uses cropping to obtain fine-grained visual details; in OCR tasks SSEED--encc-2-Plua additionally performs region marking and numerical computations; chart-related tasks (CharXiv) volve more arithmetic operations; reasoning benchmarks (MathVista Math Verse) are dominated by mathematical computations for intermediate verification and final answers; and search tasks (MM- Search, InfoSeek) primarily invoke search tools. Moreover, when comparing behaviors before and after RL, we observe a notable shift. After reinforcement learning, model tends to perform more numerical operations across multiple tasks, and begins to integrate image manipulation tools (e.g., cropping) with search in search benchmarks, indicating that RL helps model develop more syner- gistic of heterogeneous tools solve complex queries.
it
Adaptive Thinking. We further investigate tool-use efficiency by measuring the proportion of questions where model invokes tools before and after RL. As shown in Figure prior RL, model over-relies on tools, using them for most questions. After RL, however, tool invocation rate de- creases significantly, showing that model learns adaptive reasoning: it solves problems directly when tools are unnecessary while still leveraging them when beneficial. Combined with Figure 7 these results highlight that reinforcement learning improves both efficiency and flexibility, enabling the balance between textual reasoning and tool calls.
Training Dynamic. We further analyze model dynamics during RL. by tracking response length, reward, and tool invocation frequency throughout training (Figure (). The average number of tool calls steadily decreases over time; however, the variance remains large, indicating that model does not simply converge to fixed number of tool invocations (e.g.. one per query). Instead, model learns adaptive thinking: selectively invokes tools when necessary, while handling simpler prob- lems with minimal calls remain high, reflecting flexible and task-aware strategies. Shorter response lengths further in- dicate more efficient reasoning, allocating detailed tool-based steps only when beneficial. Together,
tool use. For more challenging queries, the number and complexity of tool

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these findings highlight that reinforcement learning not only enhances tool-use effectiieness, while fostering diversity, complexity, and efficiency in reasoning.
5 CONCLUSION
In this work, we explore how to construct agentic multimodal models that can actively invoke tools and integrate them into reasoning, from the perspectives of training, dataset design, and evaluation. We introduce DeepÊyes V2 and conduct a practical two-stage training pipeline: supervised fine- tuning on curated dataset to establish robust tool-use patterns, followed by reinforcement learning to strengthen and adapt tool invocation. Our analysis reveals task-dependent tool-use behaviors, and reinforcement learning enables more complex, context-aware tool combinations. Extensive experiments across perception, reasoning, and search benchmarks demonstrate the strong reasoning ability of DeepEyesV2 V2, highlighting the advantages of combining tool invocation with reasoning.
ETHICS STATEMENT
This work aims to advance the tool-use capability multimodal large language models (MLLMs) through reinforcement learning and carefully curated datasets. All datasets used in this study are collected from publicly available sources under appropriate licenses, and sensitive or unverifiable content is excluded during preprocessing to mitigate potential ethical risks. While our model demon- strates improved reasoning, perception, and search capabilities, also introduces risks such as mis- use for generating misleading content, over-reliance on automated reasoning, and privacy concerns when dealing with sensitive data. We emphasize that the proposed methods and released resources strictly intended for research purposes. We strongly discourage any malicious applications, in- cluding but not limited to surveillance, misinformation, or decision decision-making in high-stakes scenarios without human oversight.
REPRODUCIBILITY STATEMENT
We provide detailed descriptions of our training settings, including datasets, preprocessing pipeline, Hyperparameters for both SFT and RL stages are reported explicitly, covering batch size, learning rate schedules, optimizer configurations, and rollout param- eters. The backbone model (Qwen2.5-VL-77B) is publicly available, and all external datasets used in our work are either publicly released cited with appropriate references. Benchmark evaluations are conducted on established public datasets to ensure comparability.
and filtering strategies in Section 4.1
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A APPENDIX
A.1 THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, Large Language Models (LLMs) employed solely auxiliary tools to refine the writing and improve the readability of the manuscript. All conceptual contributions, experimental designs, and analyses are conducted by authors. Authors bear full responsibility for the content of the paper, and affirm that the use of LLMS did not involve plagiarism, fabrication, or any form of scientific misconduct.
A.2 TRAINING DATA
For perception- tasks, include v (Wu & Xie 2024), ArxivQA (Li et al. 2024c). Pixmo Counting (Deitke et al. 2024), TallyQA (Acharya et al. 2019). and SeekWorld (Tian et al. 2025), covering wide range of scenarios such as object recognition, visual counting, and chart interpre- tation. For reasoning tasks, we adopt Visusl (Chen et al.] (025b) to provide complex reasoning problems, and additionally incorporate MathCoder (Wang et al. 2023) and Retool (Feng et al.) 2025) to supplement with executable code-based problem-solving examples. Besides, we also in- clude long Chain-of-Cot (CoT) reasoning data in cold start subset. For search-related tasks, employ MMSearch-RI (Wu et al. 2025), which includes both image-based and text-based retrieval questions. We further include data from VGR (Wang et al., (025c), Chain-of-Food Focuu (Zhang al.) 2025a), and VLM-R (Jiang et al. 2025) to strengthen the reinforcement learning corpus.
We present the distributions of our cold start and RI. data in Figure The cold start data is divided into four parts: perception, reasoning, search, and Long CoT, while the RI. data includes perception, reasoning, and search.
Cold Start
Reinforcement Learning
9.0%
Porception
51.9%
41.6%
Search
81.6%
Figure Distribution of cold start and reinforcement learning data.
A.3 EVALUATION PROTOCOL
we summarize the benchmarks and models we compare across different kinds of tasks. Bench- marks cover three main categories: real-world understanding, mathematical reasoning, and search- intensive tasks, capturing the diversity of challenges faced by agentic multimodal models.
Real-World & OCR Chart Understanding. For real-world understanding, we adopt V (Wu & Xiel 2024), HRBench (Wang et al.] (025e). MME-Reel/World (Zhang et al.) (024b). and TreeBench (Wang et al. (225a)); for OCR, we use OCRBench (Liu et 2024bb and Seed-Bench
2-Plus (Li et al.] 20224b); and for chart reasoning, we evaluate on CharXiv (Wang et al. 2024b)
and ChartAA (Masry et al. 2022). For comparison, we include two kinds of models: (1) open- MLLMs, including LLaVe-OneVin
source
(Li et al. 2024a), Qwen2.5-VV VL (Bai
jet al., 2025), and Intern VL3 (Zhu et al. 2025); and (ii) grounded reasoning models, such as Pixel-
Reasoner (Su al. 2025). DeepHyes (Zheng al. 2025b) and Thyme (Zhang et al. 2025b).
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Table 6: Results on real-world OCR & chart understanding Benchmarks.
Model
Tool
Parum Size
Real- World Understanding
HeRchch
OCR
SEED
CharXiv descriptive
Chart
CharXiv reasoning
Bench
4K
8K
RealWorkd
Beach
Beach
2 Plus
QA
Open
Models
LLaVA-OV
78
75.4
59.8
$7.4
Qwen2.5-V 5-VL
7B
78.5
71.6
67.9
57.3
37,0
864
70.4
72.7
40.2
86.2
Q.
32B
80.6
74.1
69.9
42.5
73.4
83,2
48.0
Lntem
88
81.2
69.3
69.7
73.6
37.6
Proprictory Models with Tools
GPIT4o
Code
47.5
Gemini 2.5 Pro
Code
79.6
86.9
71.6
49.1
881
75.0
92.6
67.8
Grouned Reasoning Modely
Piscl
Crop
7B
Deeplyye
Crop
7B
85.6
75.1
72.6
37,5
Thyme
Code
78
82.2
77.0
72,0
64.8
863
86.1
Agentic Matiimodal Model
DeepPyesV2 General
78
81.8
77.9
73.8
64.9
42.5
882
70.5
78.5
48.9
88.4
(yr Quea2.5-VL-78))
+3.3
+6.3
+5.9
+7.6
+3.5
+18
+0.1
+5,9
+8.7
+2.2
Table 7: Results on multimodal reasoning benchmarks.
Model
Tool
Param Size
Matt
MathVision Mathe
WeMath
DynaMath
Lisic Vista
Open-source Models
LLaVA-OV
X
58.6
19.3
18.3
20.9
33.3
Qwen-2.5-VI.
X
7B
68.3
45.6
25.6
34.6
53.3
45.5
Intern VL3
x
8B
71.6
39.8
29.3
37.1
44.1
Proprictary Models with Tools
GPT-4o
Code Code
59.3
$4.0
41.6
61.9
51.2
Gemini 2.5 Pro
83.0
81.4
Text-only Reasoning Models
MM-Eureka
X
7B
72.6
28.1
21.8
46.3
ThinkLite
X
7B
71.6
24.6
41.8
42.7
VL.-etthnker
X
73.7
28.4
36.3
42.7
VI.AA-Thinker
71.7
24.2
35.7
Grounded Reasoning Models
DeepEyes Thyme
Crop Code
70.1
47.3
38.9
70.0
Agentic Multimodal Model
DeepEyesV2 Gencral
71.9
52.7
38.1
57.2
48.7
A (vs Qwen2.5-V1.-7B)
+3.6
+7.1
+3.5
+3.9
+2.8
Multimodal Reasoning, We include Math Vista (Lu et al. 2023). Math Verse (Zhang et al.
2024a
(Wang et al. 2024a). WeMath (Qiao et al. (024), and LogicVists (Xiao et al. 2024
We compare DeepEyesVA V2 against existing open-source MLLMs, such as Qwen2.5-Vi VL
Bai al.
2025). text-only multimodal reasoning models, including MM-Eureka (Meng et al. 2025), Thin-
LLite Wang et al. 2025f), VL-Rethinker (Wang et al., 2025b), and VLAA-Thinker (Chen al.
2025a), and grounded reasoning models, such as DeepEyes (Zheng et al. 2025b) and Thyme (Zhang
al.
2025b)
Online Searching.
InfoSeek (Chen al. 2023), MMSearch (Jiang et al.] (2024), and SimpleVQA (Cheng et al. 2025). We benchmark
We compare DeepEyes V2 on FVQA-test (Wu et al.
2025
DeepEyesV2 against both general-purpose MLLMs such Qwen2.5-VL (Bai et al. 2025), Gemini
2.5 Pro (Comanici et al. 2025), and GPT4o (Hurst et al. 2024), as well as models where search
capability is incorporated (Jiang et al. 2024 Geng al. 2025)
A.4 PERFORMANCE COMPARISON WITH PROPRIETARY MODELS
In Table 6 and we compare DeepEyesV2 with existing proprietary models by using the same prompt Dee, DeepEyesV2 achieves performance comparable to GPT-4o.
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Numerical Analysis
Mark
100%
7.4%%
7:59
8.2%s
8.0%
9.2%
10.0%
11.3%
8.89%
0.4%
6.7%
9.8%
5.0%
80%
Tool Usape Percentage (%)
30.1%
38.9%
40.7%
30.0%
29.14
42.0%
60%
40.0%
40.0%
40%
20%
45.0%
43.2%
46.8%
43.0%
35.4%
23.8%
0%
10
15
20
50
55
60
Training Step
Figure 8: Tool Usage Pattern during RL.
Qwen2.5-VL.78
DoepEyes
DeepEyesV2-SFT
DeepEyesV2-RL
DeepEyesV2
DeepEyes V2 w/ Rotate
16.0
17.3
18.7
20.8
33.5
38.3
Table 8: Zero-shot results on TIR-Bench
Table 9: Zero-shot results on OCR
subset of TIR- TIR-Bench with additional tools
A.5
TOOL USAGE PATTERN EVOLUTION
To further understand how tool usage patterns evolve during RL training, Figure category- wise statistics of code tools across training stages. Clear trends emerge: (i) the proportion of "Mark" tools remains largely stable, with minimal fluctuation; (ii) the proportion "Crop" tools rises ini- tially and then declines as training progresses; and (iii) the "Numerical analysis" and "Other" cate- gories exhibit the opposite trajectory decreasing at first and subsequently increasing. These trends in tool usage are broadly consistent with the changes in tool usage shown in Figure
O 444
Figure 9: Zero-shot tool utilization example of DeepEyes TIR-Bench.
A.6 ZERO-SHOP SHOT GENERALIZATION TOOL USAGE
We conduct zero-shot testing of DeepEyesV2 on TIR-Bench Li et al. 2025a). The tasks and re- quired tool categories in TIR-Bench are not included in cold-start and RL training data, therefore TIR-Bench can serve as an excellent platform for zero-shot testing of DeepEyesV2 to illustrate its generalization capability in tool invocation.
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Performance Comparison. The performance of DeepEyesV2 on TIR-Bench is summarized in Table Specifically, DeepEyes V2-SFT represents the model after the cold-start stage, and Deep- SyLsV2-RL represents the model after RL training. It is evident that DeepEyesV2 substantially outperforms the baseline (Qwen2.5-VI. 7B) thanks to tool integration. This provides compelling evidence of DeepEyes V2's strong generalization ability, confirming that great performance improve- ments persist even when applied to unseen tools and tasks.
New Tool Visualization. We present examples of zero-shot tool utilization from TIR-Bench in Fig- ure comparing model performance before and after RL across tasks such as OCR, image rotation, and maze solving. We omit the specific options for each question and only display the core code segments. Prior to RL, lacking exposure to relevant tasks, model merely represent the input im- age. Conversely, the post-RI. model demonstrates robust generalization capabílities. Specifically, for OCR task, Ve employs grayscaling and dilation to enhance character clarity, followed by cropping individual digits for recognition. For the rotation task, DeepEyesV2 rotates the im- age to determine its original orientation angle. Notably, these tasks and tools absent from both our cold-start and RL datasets; yet, without additional training, DeepEyesV2 successfully compre- hends and utilizes these novel tools. Furthermore, highlight interesting maze scenario where DeepEyesV2 V2 generates code to simulate pathfinding, thereby verifying each option. Although the code contains minor imperfections, potential with new tools. This comparison of tool usage pre- and post-RI. effectively highlights DeepEyesV2 V2's exceptional adaptability unseen tasks and tools.
think this strongly evidences DeepEyesV2's generalization
Function Call Generalization. EyesV2 demonstrates strong generalization in function calling. To evaluate this, we equip Deep- EyesV2 with a rotation tool on a subset of the Rotated OCR task from TIR-Bench, requiring model utilize the tool via function calls rather than by writing code. Since images in this task are rotated, model must perform rotation prior to OCR recognition. It is worth noting that in addition to the rotation function call, DeepEyes V22 retains the ability to invoke other tools via code generation. As shown in Table 9 providing the rotation function call leads to further performance improvements, with DeepEyesV2 V2 invoking the rotation tool in 15% of instances. This demonstrates DeepEyesV2' V2's strong generalization capability regarding newly added tools.
Furthermore, beyond the code generalization capabilities, Deep-
A.7 TOOL TAXONOMY
The tools can be categorized into three major classes:
Code Execution. Code execution covers a set of operations that require Python-based execution. We further divide it into four subtypes:
Crop: extract specific region of the input image for fine analysis.
cropped image_1 ccrop (top, left, right, bottom) )
pit imshow (cropped) plt. (f1"") plt.show() ()
Numerical Analysis: perform numerical computations, formula evaluation, or quantitative reasoning.
import math height = 68
w height / math. tan (math, radi ans )37)
/ math .tan (math. radians )
print (f"w [w]")
print (E"x (x)")
Mark: annotate or highlight regions of interest in the image support reasoning.
from
import
mageelraw
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ImageDraw. DrrreDraww.Daw(aaee (imagee
box = (50, 50, 300, 200) color (255, 0, 0)
thickness
draw. rewtangee (box, outline=color, wicthsthickness)
pit imshow (image_ p plt.show
Other: other manipulation operations such as rotation, enhancement, or resizing.
from PIL import ImageEnhance
enhancer ImageEnhancs. htt.eeaahaanee..Bightteeeee (image_1)
factor 1.5
bright_img = enhancer. enhance (factor)
pit imshow (bright img)
axt.axis (ffff') plt.show ()
2. Image Search. Given an image query, we utilize SerpAPI to retrieve visually similar results from the web, returning candidate images with thumbnails.
3. Text Search. Based on a textual query, we retrieve relevant webpages and provides both titles and snippets of content.
Tool Execution Error
Tool Selection Error
What color the bicydle frame in the image?
the specific model
ofe car in the image?
Brown Dark
A: Unknown
Dongfeng Honda
<linko enntro ase the reage to Joach
All-New Inspire
Error Reasson:
nythom wctiee
The model selected the correct tool age cropping). butthe execution went wrong the target region was mislocalized
Specific mudel bike
Error Reason:
mogecnppinngnn) gitindh inishioll puo
Carout determined
wrong
Search instead Image Search)
Tool Output Parsing Error
phone
Introducing iPhone 14 Pro
model in this image re reaeseed?
Q: Which has the lowest derivative at - 0.25 plot 1?
2007
GT: 2022
A: Real
GT: FILR-RR
cutthon
ectrodo Let ee scarch the laue tiee 34
Error Reason:
sutplet
The issue that the model failed to specity "iPhane 14 Pro" in the query, retrieving genericresult for "iP hone"
crmpsemmtton ww hettll
iPhone diste
The earlest
The model called the Image Crop tool and got the relevant region, but misparsed
2007
con one
Figure 10: Error analysis.
A.8 ERROR ANALYSIS
We categorize the errors made by DeepEyesV2 into three main types (Figure 10). First, tool ex- ecution error occur when the model generates correct reasoning trajectory but fails during tool
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operation, such as cropping the wrong region using incorrect search keywords. Second, tool selec- tion errors arise when the model chooses an inappropriate tool for the task, for example selecting text search when image search is required. Third, tool result analysis emrors happen when the model correctly selects and executes a tool, but misinterprets or incorrectly analyzes the returned outputs. This categorization helps to identify the main sources of failure and guides future improvements in tool-invoked reasoning.
A.9
PROMPT
SYSTEMPPROMPT
You are an agent please keep going until the user's query is completely resolved, before ending your and yielding back to the Only terminate your turn when you that the problem solved.
Solve the following problem step by step. In your reasoning process, if the answer cannot be determined, you can write Python code Jupyter Notebook to process the image and extract more information from it. The stdout and stderr content, along with the images generated by "plt. show () " will be returned to better assist with the user query.
You MUST use the python tool to analyze or transform images whenever it could improve your understanding. This includes but not limited to zooming in, rotating, adjusting trast, computing statistics, or isolating features.
If you find you sufficient knowledge to confidently answer the question, you MUST conduct search to thoroughly seek the internet for information. No matter how complex the query, you will not give up until you find the corresponding information.
You can conduct image search, which will trigger a Google Lens search using the original image retrieve relevant information that can help you confirm the visual content, and text search, which will use Google Search to return relevant information based on your query.
You MUST plan extensively before each function call, and reflect extensively the out- comes of the previous function calls. DO NOT do this entire process by making function calls only, this impair your ability to solve the problem and think insightfully.
Additionally, you can combine python tool with search assist in answering questions. Python tool can help enhance your understanding of images, while search tools can provide the knowledge you lack. Please use python tool and search flexibly. However, you can only call one type of tool in a single round; you cannot use a python tool and perform search simultaneously.
For the provided images, in order, i-th image has already been read into the global variable "image_i" using the "PIL. Image open () function. For example, the first image can be accessed as "image_i __ When writing Python code, you can directly use these variables without needing to read them again.
## Tools
## python
Your python code should be enclosed within <code> < code> tag.
Example for calling Python code in Jupyter Notebook:
<code>
" "pythhnn
\# python code here
</code>
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Note:
1. **ythoo*** can be called to analyze the image. **python** will respond with the output
of the exccution or time out after 300.0 seconds.
2. Like jupyter notebook, you can use Python code to process the input image and use
"plt. show () visualize processed images in your code.
3. All python code are running in the same jupyter notebook kernel, which means the func-
tions and variables are automatically stored after code execution.
4. You program should always returns in finite time. Do not write infinite loop in your code.
Writing file to disk is not allowed.
##f search
You are provided with function signatures within <tools></tools> XML tags: Etcol_call>>
("type": : "function", "function";
"name": "Retrieves top 10 images and descriptions "description":
from Google's image search using the original image. Should only used once.",
"name": "search",
"description": "Performs batched web searches: supply an array 'query'; the tool retrieves the top 10 results for each query in one call.", "parameters":
"type": "object",
"properties";
"query":
"type": "string",
"description"; "Search query to find relevant information."
b,
"required"; "query"
</tool_call>
Example for calling search: Return a json object with function name and arguments within [hool_callxxttoo_ccaa XMI. tags:
<tcol_call>
("name": "image_search")
</tool_cali>
<tool_call>
("name": "search", "arguments"; ("query": "Does Cloudflare
analyze submitted data to block attacks")) e/tool_cali>
Note:
1. You MUST engage in many interactions, delving deeply into the topic to explore all possible aspects until satisfactory answer is found.
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Before presenting a Final Answer, you will ** *cross-check ** and **validate the infor- mation** you've ve gathered to confirm its accuracy and reliability.
3. You will carefully analyze each information source to ensure that all data is current, relevant, and from credible origins. Please note that you can **ony** call search once at a time. If you need perform multiple searches, please do so in the next round.
You can **nlyy* conduct image search once.
USER_PROMP3
(Question)
You must put your answer inside <answer> </answer> tags, i.c. <answer> answer here </answer>, Please reason step by step. Use Python code to process the image if necessary. You can conduct search seek the Internet. Format strictly as <think> </think> <code> </code>(i code is needed) or <think> </think> <tool_call> </tool_cal1>(i function call is needed) or <think> <think> <answer> </answer>.
RETURN.CODE.USER
Code execution result:
stdout:
*..
(stdout) }
...
stderr:
...
(stderr)
...
Image:
(image)
A Google image search for the image found 5 results: ## Web Results
1. < i mage>> [[title)]
2. <image> [[title)]
OETURN_TEXTS BERINNTEET.SEERAEREEE SEARCH.USER.PROO
A Google search for {query}' found 5 results:
## Web Results
1. [[title]] ((link)) (snippet) [[title]] ((link)) (snippet)
22


Reviews:
[{"review_id": "4Wqk948aVm", "reviewer": "Reviewer_VFi3", "summary": "The paper introduces DeepEyesV2, an agentic multimodal model designed to actively use external tools—such as code execution and web search—within its reasoning process. Unlike conventional multimodal large language models that passively interpret visual and textual inputs, DeepEyesV2 integrates tool invocation into a closed reasoning loop.", "questions": "- How robust is DeepEyesV2’s tool invocation under noisy or adversarial tool outputs (e.g., failed code execution or irrelevant search results)?\n- How does DeepEyesV2 compare to closed-source agentic systems like GPT-4o with “thinking with images” capabilities under controlled conditions?\n- Is there any mechanism to control when not to invoke a tool to reduce unnecessary computational overhead?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The work clearly articulates the challenge of building agentic multimodal models and distinguishes itself from prior “thinking-with-image” paradigms by integrating multiple heterogeneous tools (code + search) in a unified reasoning loop.\n- The proposed two-stage pipeline (cold-start + RL) is well-motivated and empirically justified, addressing the instability of direct RL for tool learning.\n- DeepEyesV2 consistently outperforms both general-purpose and tool-augmented baselines, often matching or exceeding larger models in accuracy.\n- The behavioral study of tool distribution and adaptive invocation provides convincing evidence that the model learns non-trivial reasoning strategies rather than merely following scripted patterns.", "weaknesses": "- The success of the approach relies heavily on the curated cold-start dataset. Details about data sources, annotation quality, and scalability are somewhat underexplored.\n- Most benchmarks are vision-centric. It remains unclear how well the approach generalizes to other modalities such as audio, video, or 3D tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ECKP4U6JQc", "reviewer": "Reviewer_UjKn", "summary": "This paper introduces DeepEyesV2, an agentic multimodal model that extends beyond traditional perception and reasoning capabilities to actively invoke external tools such as code execution environments and web search APIs. The authors observe that direct reinforcement learning alone fails to induce robust tool-use behavior, motivating a two-stage training pipeline: (1) a cold-start stage using supervised fine-tuning to establish initial tool-use patterns, and (2) a reinforcement learning stage to refine tool invocation strategies. The paper curates a diverse, moderately challenging training dataset with examples where tool use is beneficial, and validates DeepEyesV2 across multiple benchmarks including visual understanding, mathematical reasoning, and search-intensive tasks. A key finding is that DeepEyesV2 exhibits task-adaptive tool invocation, selectively using image operations for perception tasks and numerical computations for reasoning tasks, with reinforcement learning enabling complex tool combinations.", "questions": "1. Can you provide ablation studies showing performance gains from RL versus SFT only, and analyze the primary failure modes in tool invocation?\n2. What are the quantitative metrics on tool call frequency, success rates, and computational overhead (training time and inference latency)?\n3. Can the model generalize to new tools not seen during training, and if so, how much additional data is required?\n4. What specific reward design prevents reward hacking, and what makes multimodal tool use fundamentally different from text-only approaches?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Good presentaion demonstrates task-adaptive tool invocation (image ops for perception, computation for reasoning)\nPractical two-stage training approach (cold-start SFT + RL refinement)\n2. Task-Adaptive Behavior: The finding that models learn to selectively invoke different tools based on task requirements (image operations for perception, computation for reasoning) is interesting and suggests genuine understanding rather than blind tool use.", "weaknesses": "1. Insufficient analysis of what RL learns: The paper lacks detailed examination of how tool-use patterns evolve during training, what new behaviors emerge, and when the model makes mistakes in tool invocation. Learning curves and failure analysis would strengthen the claims.\n2. Inadequate efficiency analysis: Missing quantitative data on tool call frequency, success rates, and computational overhead. \n3. Unclear generalization to novel tools: Evaluation limited to a fixed tool set. Whether the model can adapt to new tools or requires retraining remains unexplored.\n4. Limited novelty in training paradigm: The two-stage SFT + RL approach is standard in LLM literature. The paper needs clearer articulation of what makes multimodal tool use fundamentally different or novel techniques beyond established methods.", "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": "8SwNhgWdMo", "reviewer": "Reviewer_zneT", "summary": "This paper introduces DeepEyesV2, an agentic multimodal model designed to actively invoke external tools like code execution and web search and integrate their outputs into its reasoning loop. The authors first demonstrate that direct reinforcement learning (RL) alone fails to teach robust tool use, often leading to reward hacking. To solve this, they propose a two-stage training pipeline: (1) a \"cold-start\" supervised fine-tuning (SFT) stage using a carefully curated dataset of difficult, tool-beneficial examples to establish reliable tool-use patterns , and (2) an RL stage to refine and adapt these skills. The experimental results showing DeepEyesV2 outperforms existing models on real-world understanding, mathematical reasoning, and search benchmarks. The analysis also reveals that the model learns task-adaptive tool invocation (e.g., image operations for perception, computation for reasoning) and that RL enables more complex, context-aware tool combinations and efficiency.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This paper is well written and easy to follow.\n- The paper goes far beyond reporting final scores by providing deep insights into the learning dynamics.\n  - A discovery of \"Adaptive Thinking\", where the SFT model over-relies on tools, and the RL stage teaches it the efficiency of when not to use tools .\n- The performance is well, the authors validate DeepEyesV2 across a wide and diverse range of benchmarks, covering real-world understanding, mathematical reasoning, and search-intensive tasks.", "weaknesses": "- A substantive weakness of the paper is its limited methodological novelty, as its core SFT + RL two-stage training paradigm and reasoning CoT data curation are well-established approaches for reasoning-based models, making the work feel more like a high-quality technical report than a novel research contribution.\n- The paper correctly observes that the SFT model \"over-relies on tools\" but fails to investigate if this is merely a statistical artifact of the cold-start data's high tool-calling density. More critically, the claim that RL fosters \"adaptive efficiency\" by decreasing tool use is presented as a raw observation without explaining the underlying mechanism, especially since the simple reward function ($R = R_{acc} + R_{format}$) provides no explicit incentive for this emergent efficiency.\n- While the paper provides strong analyses and experiments, it is questionable whether supervising only the correctness of the final answer can effectively ensure the accuracy and relevance of the tool-use process itself. For instance, if the model invokes an irrelevant or unnecessary tool but still manages to produce the correct final answer, it receives a high reward, indistinguishable from that of a model that used tools logically. This makes the reasoning process uninterpretable and unreliable.", "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": "09liJZwVaY", "reviewer": "Reviewer_xzDS", "summary": "This work presents a sound and well-executed study on building an agentic MLLM using SFT and RL, unifying code execution and web search. Through extensive experiments, the authors show that their work DeepEyesV2 presents significant gains over prior work. The analysis and ablation studies provided valuable insights into why direct RL on a base model doesn't elicit robust tool-use.", "questions": "1. Will the curated dataset be released? If not, the results would be hard to reproduce since this \"carefully curated training corpus\" is fundamental.\n\n2. SerpAPI was used for image search, but it's unclear if any API was used for text search. Does it simply use Google search? If so, did you encounter rate limiting?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Extensive experiments using RL alone to post-train Qwen2.5-VL and the thorough analysis are interesting and insightful.\n\n2. Comprehensive evaluation on 8 benchmarks and analysis. Interesting and insightful comparison between pre-RL and post-RL in terms of tool use patterns.\n\n3. Strong results across benchmarks.", "weaknesses": "1. Lack of comparison with a concurrent work WebWatcher, whose core methodologies seem very similar to this work. WebWatcher's performance is cited in this work but its methodological similarities are not discussed.\n\n2. Unclear how much of the gain on search benchmarks comes from the use of SerpAPI. Do baselines and this work use the same set of search APIs?", "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)