DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
Authors: Ziwei Zheng
Michael Yang
Jack Hong
Chenxiao Zhao
Guohai Xu
Le Yang
Chao Shen
XingYu
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Large Language Models
Multimodal Reasoning
Reinforcement Learning
Primary area: foundation or frontier models, including LLMs

Abstract: Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, a key challenge in mirroring human cognition. To address this, we introduce \nameshort{}, a model that learns to ``think with images'', trained end-to-end with reinforcement learning without requiring pre-collected reasoning data for cold-start supervised fine-tuning (SFT). Notably, this ability emerges natively, leveraging the model's own grounding capability as an intrinsic function rather than relying on external specialized models or APIs. We enable this capability through active perception, where the model learns to strategically ground its reasoning in visual information, guided by a tailored data selection and reward strategy. \nameshort{} achieves significant performance gains on general perception and reasoning benchmarks and also demonstrates improvement in grounding, hallucination, and mathematical reasoning tasks. Interestingly, we observe the distinct evolution of active perception from initial exploration to efficient and accurate exploitation, and diverse thinking patterns that closely mirror human visual reasoning processes. Code is available at \url{https://github.com/Visual-Agent/DeepEyes}.

Published as a conference paper at ICLR 2026
DEEPEYES: INCENTIVIZING "THINKING WITH IM- AGES" VIA REINFORCEMENT LEARNING
OF
Ziwei Zheng .2- Michael Yang" Jack Hong' Chenxiao Zhao!*?
Guohai Xu Le Yang?1, Chao Shen?, Xing Yu!
Xiiahhongshu Inc., Xi' an Jiaotong University
Equal contribution, Random order
Main Code Contributor
Corresponning Author
Project Homepage
(chenxiao2, xuguohai) /xiachoogiicccccchhh.uoaau com, yangle15exjts edu. en, ziwei. aweii.zhen@@tu... cn, (yangminghacl 99, jaaackhong)
gma [yangaihnhhaol9g...aaaaooog
com
ABSTRACT
Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, key challenge in mirroring human cognition. To address this, we introduce DeepEyes, a model that learns to "think with images", trained end-to- end with reinforcement learning without requiring pre-collected reasoning data for cold-start supervised fine-tuning (SFT). Notably, this ability emerges natively, leveraging the model's 's own grounding capability relying on external specialized models or APIs. We enable this capability through active perception, where the model learns strategically ground its reasoning visual information, guided by a tailored data selection and reward strategy. Deep- Eyes achieves significant performance gains on general perception and reasoning benchmarks and also demonstrates improvement in grounding, hallucination, and mathematical reasoning tasks. Interestingly, we observe the distinct evolution of active perception from initial exploration to efficient and accurate exploitation, and diverse thinking patterns that closely mirror human visual reasoning processes. Code is available at pretps://gile
intrinsic function rather than
com /iimm//isuaa sual Agget/DeeepPe
INTRODUCTION
Recent advances in Vision-Language Models (VLMs) have enabled deeper reasoning over multimodal inputs by adopting long Cfain-of-Thought (CoT) approaches (Team 2022a b Guo et al. (025b), allowing these models to handle more complex tasks. However, these models still primarily rely on text-based reasoning, with their thought processes largely confined to the language modality. In contrast, human reasoning naturally combines vision and cognition, thinking with images by extracting information through sequential visual fixations, which support more accurate perceptual docision-making which was essential for survival in early human evolution (Najemnik & Geisler 2005). While some recent works have proposed pre-defined workflow-based strategies to incorporate visual information into CoT reasoning (Shao et al. (2024a] Sun et al. 2024), the modular designs suffer from suboptimal performance (Ross et al. 2011).
In a recent milestone, the OpenAI o3 model (OpenAl 2025) has successfully integrated visual information as dynamic element the reasoning process. The o3 transcends the Language-modality confinement by extending reasoning capability to "thinking with images" like humans. Additionally, it resolves the coordination limitations by combining textual CoT and image manipulation tools in naturally interleaved fashion during the CoT process. This approach enables a new axis for test-time
Work done during Ziwei's 's internship Xiaohongshu. The specific contribution of co-first authors is shown the Appendix.C.

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Published as a conference paper at ICLR 2026
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(o) Visual Search
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Figure I: Interleaved Multi-modal Chain-of-Thoughh (iMCoTT). DeepEyes is incentivized to
perform active perception throughout the reasoning process with end-to-end reinforcement learning.
compute scaling by seamlessly integrating visual and textual reasoning, representing meaningful advancement toward true multimodal reasoning. However, the inner mechanism remains undisclosed to the open-source community.
In this paper, we introduce DeepEyes, a model with "thinking with images" ability, which is incentivized via end-to-end reinforcement learning. This capability emerges natively without relying on separate specialized models and is directly guided by outcome rewards, eliminating the need for cold-start supervised fine-tuning used in previous methods. Specifically, grounding ability in an active perception mechanism, enabling it to gather information from the original image within an agentic framework. As shown in Figure [ the model adaptively generates image grounding coordinates and crops relevant regions, which are then incorporated into the ongoing reasoning trajectory. This supports an interleaved Multimodal Chain-of of-in-of-Thhuuht (iMCoT), where visual and textual reasoning are seamlessly integrated.
encapsulate the model's
In early attempts, we observe that the model struggles to effectively utilize its active perception capability. Specifically, it is reluctant to perform image zoom-ins and even when it does, the exploration often selects suboptimal regions. This results low rewards and unstable training dynamics. To address these issues, propose data selection mechanism to choose training samples based on their potential to encourage active perception behavior. Additionally, we design a reward strategy that assigns conditional bonus to the trajectories that successfully complete their tasks through active perception. Our ablation studies validate that these two strategies are crucial for optimizing the efficiency and accuracy of active perception.
Without supervised fine-tuning (SFT) for intermediate reasoning steps, we observe the model's active perception strategy evolving through three distinct stages during RI. training: (1) initial, ineffective
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exploration; followed by (2) frequent and effective application of the capabiliiy; and finally, (3) mature, selective, and efficient approach yielding high performance. This progression demonstrates the model's growing mastery of its visual reasoning capabilitiis through active perception. Addition- ally, diverse iMCoT reasoning patterns emerge, such as visual search for small or hard-to-recognize objects, visual comparisons across different regions, visual confirmation eliminate uncertainty, and hallucination mitigation by focusing on details. These diverse reasoning behaviors closely resemble human cognitive processes, thereby enhancing the system's 's overall multimodal capabilities.
Experimental results show that DyepEyes can significantly boost performance on multiple visual perception and reasoning tasks. For high-resolution benchmarks, DeepEyes with a 7B model achieves an accuracy of 90.1% 1% (+18.9 %) on V* and improves HR-Bench-4K and 1--Bencc-8K by 6.3% and 7.3%, respectively. In addition, DeepEyes also improves multimodal capabilities on a wide range of tasks such as visual grounding, hallucination mitigation, and mathematical problem solving. The main contributions are summarized as follows:
We incentivize and enhance the ability of thinking with images via end end-to-end reinforcement learning. forming iMCoT that seamlessly blends visual-textual reasoning without requiring cold- start SFT or separate specialized models as external tools.
To better incentivize the model's interleaving reasoning, introduce an petteeeteenettoonn data selection mechanism and a tailored reward strategy that promote grounding-assistee problem solving. Experiments show that both components significantly advance iMCoT.
We reveal the intriguing RL training dynamic of iMCoT, where active perception behavior under- goes distinct stages, evolving from initial exploration to efficient and accurate exploitation. We also observe diverse reasoning patterns, such as visual search, comparison, and confirmation.
RELATED WORK
Multi-modal Large Language Models. Multimodal large language models (MLLMs) have evolved from early systems that loosely combined vision encoders with language models into more integrated architectures through joint training. Methods such as BLIP-2 (Li et al. (023b) and LLaVA (Liu et al.) 20236 a) align visual and linguistic modalities by projecting image features into the latent space of frozen LLMs using query transformers or lightweight projectors, enabling tasks like visual question answering and instruction following. To address resolution constraints, approaches like AnyRes
(Liu et al. 2024a Chen et al. 2024a) allow for flexible image sizes and enhanced visual fidelity.
These advances have led to strong open-source models, including the LLaVA (Liu et al.
2024b Guo
al. 2024 /Zhang al. 20256 Lin et al.] [2023] [i et al.] 2023a). Qwen VL (Bai al.
2023 Wang
Gao series. Concurrently, large-scale models like Flamingo (Alayrac et al. 2022), mPLUG-Owl (Ye
al. 2024b Yang et al. 2024). and Intern VL (Chen al.) 2024c; Gao et al. 2024 Lu et al.
2025)
2023) aim unify vision- vision-language language understanding, ct al. [223 2024b a). and GPT-4V (Yang al.
incorporating mechanisms such as mixture--ofcepprrts (hhu et al. 2024; Li et al. 2025c Shen et al.
2024b) image generation (Xie et al., 2024 Xu et al.) 2025). However, these models lack reasoning
capabilities like Shain-oofTThuugee and test-time scalability (Muennighoff et al. 2025 Zhang et al.
2025a Chen et al. 2024b), and still decouple perception from reasoning.
Vision-language Model Reasoning. Existing Multimodal Chain-of-Thought (MCoT) reasoning
methods fall into two main categories. Early approaches rely on predefined workflows or auxiliary models (Liu et al. 2024c Mondal et al. 2024, Luo et al.] (024), often focusing on region-of-intered localization (Wu & Xie
2024; Fu et al. 2025| Wei al.] (025 Li et al., (025b). latent feature 2024| Bigverdi et al. 2024). and external knowledge integration (Sun et al.)
regeneration (He et al.
2024 Li et al. (025a) to improve interoperability. Inspired by the extensive research on the long CoT in LLMs (Guo et al. 2025a), RL-based reasoning approaches have been increasingly explored in MLLMs (Meng et al. 2025 Peng et al., 2025 Shen et al. 2025). These methods predominantly extend text-only reasoning capabilities to range of multimodal tasks such spatial reasoning (Zhou et al. 2025), object recognition (Liu et al., 2025b), semantic segmentation (Liu et al.) 2025a). and video tasks (Zhao al.] 2025a b). Unlike methods that hard-code pipelines simply extend text-only
CoT, our approach lets the model autonomously decide when and how to use visual input. Guided by outcome rewards, it adapts visual exploration for more flexible reasoning process.
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Original Image do
In
Question the cell phone the right of the backpack?
Cropped Image
Cropped Image
12
Zoom-in Invoke
End iMCoT
Interleaved MCoT
Final Answer
Text-CoTI
Text-CoT2
Text-CoT3
GT
Question
Large Language Model
Original
Reward Manager
Vision Encoder & Projection
Image
J
rOO1 I1
J
Race + Jurmat
1o
+BRacc>0 Rooo
Figure 2: Overview of DeepEyes. Our model itself decides whether to perform second perception via zoom-in by generating grounding coordinates and cropping relevant regions, or to answer directly.
METHOD
3.1 DEEPÉYES
DyepEyes unified multimodal large language model that is capable of "thinking with images" through an iMCoT reasoning process. The ability is inherited from the model's 's native capability of visual grounding and action decision planning, and further incentivized and enhanced via end-to-end RL training using outcome reward signals, eliminating the need for cold-start supervised fine-tuning.
As illustrated in Figure given user question and an image To as input, DeepEyes can autonomously decide, after each textual CoT reasoning step, whether to generate an answer directly or perform an image zoom-in for further inspection. The zoom-in operation takes list of bounding box coordinates as input and outputs the cropped images within the specified regions. The returned crops, such as In and I/2, are appended the ongoing trajectory, enabling the model to reason over all previous context. DeepEyes answer. This iterative interaction enables fine- fine-grained perception, especially when the relevant object in the image is small, blurry, difficult recognize. During the RI training stage, the reward optimization policy gradient is applied to the entire trajectory, allowing all textual Co'Ts and action decision planning be jointly optimized in an end-to-end manner.
perform active perception many times needed before concluding final
Compared to previous works based on workflows or pure text reasoning, our iMCoT offers several significant advantages. (1) Simplicity in Training. Previous workflow-based methods Wu Xie (2024): Li et al. (0025)) depend on substantial SFT data, which is challenging to acquire, while our IMCoT only requires question-aie answer pairs, reducing data collection complexity. (2) Enhanced Generalizability. Workflow- based models are constrained by their task-specific manual design, which hinders their generalization to other tasks. In contrast, our iMCoT exhibits robust generalization capabilities as it learns to dynamically select optimal reasoning processes across diverse tasks through reinforcement learning. (3) Global Optimization. Our iMCoT enables joint optimization through end-to-end training, which allows the system to be optimized towards global optimum. In contrast, optimizing each component separately typically leads to sub-optimal performance. (4) Multimodal Integration. Compared to pure text- thinking, textual information, combining visual elements with textual reasoning to achieve more accurate perceptual decision-making, (5) Native Tool Calling. We encapsulate the model's native grounding
iMCoT naturally interleaves visual and
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capability as internal tool to enable active perception, allowing implicit optimization that previous external-tool paradigms cannot achieve.
3.2 AGENTIC REINFORCEMENT LEARNING
Rollout Formulation. In traditional RL with text-only CoT, the Markov Decision Process (MDP) defines the state as the input prompt tokens together with all tokens generated by the model up to the current step. The action is defined as the next token in the sequence. In contrast, agentic RL extends this formulation by introducing observation tokens, which come from external function calls rather than the model itself. These observation tokens are appended to the ongoing rollout sequence and fed back into the model as input for the subsequent step. We formalize the MDP definition for iMCoT as follows. At each step t, the state s, of iMCoT is defined as:
St (Xo, Iu), 4), 1,) = (X
(1)
where Xst = (X1,.... X represents the accumulated sequence of text tokens before step I, and Lee (T1....: } represents the image observation tokens before step t. We omit other related special tokens that are not generated by VLM itself for simplicity. Given the state St, the action Oe Ta st) is sampled from the VLM policy Te. serving as the next input token. This iMCoT continues to interleave until either an answer is generated or the maximum number of active perceptions is reached. Note that text tokens St and image tokens Içe are interleaved in the states.
Reward Design. In multimodal environments, sparse, outcome- rewards are essential for guiding vision-language models toward effective reasoning and decision-making Because intermedi- ate visual actions lack step-level supervision, we evaluate the entire reasoning trajectory based on the final outcome and the presence of meaningful active perception.
The total reward consists of three parts: conditional bonus Reoo-- Accuracy measures whether the final answer is correct, while formatting penalizes poorly structured outputs. The conditional bonus is granted only when the answer is correct and at least one active perception step is triggered:
an accuracy reward Rass a format reward Reormat and a
R(T) Race (T) + Rooomat (T) + RRa())>> (T)
where (T>00 equals 1 if the accuracy reward is positive. Conditioning this bonus on correct an- swer promotes perception-aware reasoning while discouraging unnecessary actions (see Section 4.3)
Optimization. We adopt Group Relative Policy Optimization (GRPO) (Shao et al. 2024b). which has been proven to be effective for diverse tasks. For multi-turn reasoning trajectories, we apply a token-wise loss mask to ignore loss observation tokens not generated by the model.
3.3 TRAINING DATA CURATION
A key challenge in training our model via RL is ensuring initial sampling efficiency without an SFT cold start. To address this, we designed a data curation strategy to construct a corpus that both diverse and specifically targeted to bootstrap effective active perception behavior from the outset.
Data Collection. To construct robust training corpus, we combine three complementary sources targeting key capabilities: the V training set (Wu & Xie 2024) for fine-grained perception, chart data from ArxivQA (Li et al. (024b) for task and image diversity, and the ThinkLite- VL (Wang) let al. 2025b) dataset to strengthen challenging reasoning. This combination provides a multifaceted foundation for our iMCoT framework, with further details available in Appendix B
We employ multi-stage filtering pipeline to curate a dataset aimed strengthening grounding-assisted visual reasoning. The process begins with difficulty curation, where we use Qwen2.5-VL-7B (Bai al., 2025) assess question difficulty, removing samples that are either too trivial (100% Acc.) or overly challenging (0% Acc.). Next, we standardize all questions into an open-ended format and perform data verification to eliminate incorrectly labeled samples. The final stage applies perception-utilty filter, retaining only samples solvable via active perception with ground-truth regions, thereby maximizing informational gain and boosting initial RL sampling efficiency without an SFT cold start. This last filter is applied only to the fine-grained perception data; chart and general reasoning data are preserved in their original, rigorously processed form. The resulting dataset is well-suited for training models with strong interleaved reasoning capabilities.
Data Selection.
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Table Results on Righ-Resslution Benchmarks. E2E indicates whether the model is end-to-end, requiring no manually defined workflo. denotes reproduced results.
Param Size
V- Bench Spatial
HR-Beuch 4K
HR-Bench 8K
Model
E2E
Attr
Overall
PSP
FCP
Overall
ESP
ECP
Overall
GPT Acchiam
66.0
49.0
03 OpenAT K0A6
95.7
SEAL Xie
x
7B
74.8
75.4
DyFo
4125h
X
7B
80.0
82.9
81.2
Zoomlzy Sheni
93.9
90.6
55.0
50.0
LLaVA-OneVision et
2024a
7B
75.7
75.0
75.4
72.0
54.0
63.0
67.3
52.3
59.8
Qwen2.5.VI
7B
73.9
71.2
51.8
Seaeo-Re al 20243
7B
83.5
76.3
80.6
86.0
60.3
72.9
80.0
54.3
66.9
Qwen2.5-VL
32B
87.8
87.9
56.3
DeeplEye
7B
91.3
88.2
90.1
91.3
59.0
75.1
86.8
58.5
72.6
(vs Qwen2.5-VL. 7B)
+17.4
+18.9
Table 2: Results on General Perception and Reasoning Benchmark MME-RcalWorde
Model
Param Size
Overall
Perception DT
Reasoning
OCR
RS
MO
AD
OCR
DT
MO
AD
L.aVA-OneVil Visinn ei al.
2024n
7B
43.7
80.0
40.0
56.0
31.7
39.4
65.0
33.0
38.0
32.0
Qwen2.5-VL Bai Wi 2025
7B
42.3
32.7
83.0
27.3
72.0
62.0
28.7
23.0
Qwen2.5-VI. Bar
32B
45.6
87.2
40.7
83,0
29.5
40.7
74.0
60.0
27.3
29.5
Pixel-Reusoner Sü 2025
7B
49.7
89.6
52.0
86.0
38.9
30.9
71.0
72.0
46.0
32,5
DyepEyes
7B
53.2
90.0
52.7
89.0
43.3
33.4
76.0
69.0
44.0
35.0
A (wr Qwen2.5 VL. 7B)
+10.9
+2.4
+20.0
+6.0
+16.0
+3.4
+4.0
+7.0
+15.3
+12.0
Table 3: Results on Grounding and Hallucination Benchmarks. denotes reproduced results.
Param Size
POPE
Model
refCOCO refCOCO+ refCOCOg ReasonSeg
Adversarial Popular Random Overall
LLAVA-OneVision Li al. 2024a
7B
88.4
Qwen2.5.VL. Bai cf 2025 Qwen2.5-VI.
7B
90.0
84.2
87.2
7B
89.1
82.6
86.1
68.3
85.9
86.5
87.2
85.9
DeepEyes
7
89.8
83.6
86.7
68.6
84.0
87.5
91.8
87.7
A (us Qwen2.5-V 5-VVL. 7B)
+0.7
+1.0
+0.6
+0.3
-1.9
+1.0
+4.6
+1.8
Table 4: Results on Challenging Reasoning Benchmarks.' denotes reproduced results, and denotes results taken from (Zhu al. 2025).
Model
MathVista
MathVerse
MathVision
WeMath
DynaMath
LogieVista
Size
LLaVA LLaVA-OneVisie VieVisson
Li Li al. 2024a
7B
58.61
19.31
18.31
20.9
33.3/
Owen2. 5-VL. al
68.2
25.1
35.2'
Qwen2.5-V2 [5al at.
2016
7B
68.3
45.6
25.6
34.6
53.3
45.9
DyepEyes
7B
70.1
47.3
26.6
38.9
55.0
47.7
A (ur Qwen2.5
+1.9
+1.7
+1.0
+4.3
+1.7
+1.8
4
EXPERIMENT
4.1 SETUPS
Baselines and Benchmarks. To comprehensively assess the effectiveness of DeepEyes, we compare it against three categories of baselines: (1) advanced proprietary models, including OpenAl GPT-4o (Achiam et al., 2023) and o3 (OpenAl 2025); (2) state-of-the-as open-source models, such as
L.LVVA-One Vision (Li et al. 2024a) and Qwen2.5-Vi VL (Bai et al.) 2025) and (3) approaches explicitly designed with workflows, such SEAL (Wu Xie 2024), DyFo (Li et al.] (2025)) and ZoomFEe (Shen et al., 2024a). Since tasks requiring fine- sinenerained visual understanding naturally highlight the strengths iMCOT, we first evaluate DyepEyes on high-resolution benchmarks. Then, we assess DeepEyes grounding and hallucination benchmarks to show improvements brought by iMCoT on general visual capabilities. We also adopt general reasoning benchmarks to verify its effectiveness.
Training Details. We train (iven2.5-VL-7B with GRPO for 80 iterations on H100 GPUs. Each batch samples 256 prompts, with 16 rollouts per prompt, up to a maximum of times of active perceptions. We set the KI. coefficient to 0.0 and define the maximum response length as 20480 tokens.

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4.2
MAIN RESULTS
High- Righ-Resolution Benchmarks. High-resolution benchmarks, such V Wu & Xie, 2024) and HR-Bench [Wang et al., 2025a), contain very large images (2K (2K-KK) with small target objects, making accurate localization challenging for VLMs. As shown in Table our model significantly outperforms existing open-source methods, including complex pipelines (Wu & Xic 2024 Li et al. Shen al., 2024a), achieving 18.9% and 7.3% gains
2025b
Qwen2.5-VL 7B V and
HR-Bench 8K, respectively. This demonstrates that simple RL can effectively unlock high-resolution visual reasoning without elaborate pipelines.
General Perception and Reasoning Benchmark. As shown in Table 2 our 7B model delivers top performance on MME-RealWorrd-LLite (Zhang et al., 2024b). It surpasses both the 7B and even 32B versions of Qwen2.5-VV VI, demonstrating superior real-world perception and reasoning.
Grounding and Hallucination Benchmarks.
Furthermore, the multimodal CoT enhances gen- eral visual capabilities. Evaluated on grounding (re/COCO///COCOO+ (Caesar et al., 2018). re- ICOCOg (Kazemzadeh al. 2014), ReasonSeg (Lai al. 2024 and hallucination (POPE
et al., 2023c)) benchmarks, our model achieves higher grounding accuracy and substantially reduces hallucinations (Table B3). This improvement stems from our model's ability to focus on regions of interest during visual reasoning and analyze cropped areas in detail, enabling more confident verification of object presence. These results show that iMCoT not only boosts high-resolution perception but also enhances overall visual reliability with more thorough verification mechanism.
Challenging Reasoning Benchmarks. We further evaluate our model on Math Vista (Lu et al. 2023 Math Verse ZZhng et al. (0224)), Vision (Wang et al.] 2024aa.. WeMath (Qiao et al. 2024), DynaMath Zou et al. 2024). and Logic Vista (Xiao et al. 2024)) in Table Benefiting from the integrated chain-of- tfouthttttt mechanism, model achieves consistent performance improvements
across these challenging multimodal reasoning benchmarks, including mathematical problem-solwee solviig...
(a) Image Zoom-in Count
(b) Response Length
[c) Grounding IoU
(d) Evaluation on V" Bench
2.2
325
62
63
0.4
81
90
51
1.8
1.4
225
1.0
175
0.2
70
0.6
20
40
50
20
80
80
0
20
80
80
16
32
48
64
80
Stegs
Steps
Steps
Steps
Figure 3: Training dynamics of DeepEyes V*, s1/2/3 represent different stages.
4.3 KEY FINDINGS: FROM CASUAL USER TO PROFICIENT VISUAL REASONER
Training Dynamics. To better understand the model's behavior during end-to-end reinforcement learning, we analyze its performance on aie-ggrined data V Since fine- fineggrained data includes ground-truth bounding boxes closely aligned with target answers, we quantify the quality of the model's 's visual grounding using Intersection-ovee -ver--noonn-nn (loU). In Figure S a clear evolution emerges in how the model leverages active perception. This progression unfolds in three stages, reflecting increasingly effective integration of active perception into reasoning:
Initial Exploration (Steps 0-20) The model starts following system prompts to access additional visual cues, but lacks coherent strategy. Action count and response length rise, reflecting exploratory behavior, while low grounding IoU shows repeated attempts without successfully linking retrieved information to the visual context. A sharp drop in response length between steps 8 and 20 indicates it is streamlining descriptions while acquiring basic active perception skills.
Stage 1:
Stage 2: high-Freeuency Engagement (Steps 20-45) The model enters phase of intensive active perception, repeatedly leveraging visual information to boost accuracy and reward. Key metrics, including grounding loU, improve, while longer responses and frequent visual interactions suggest "broad sweep" strategy: the model externalizes reasoning by over-querying the environment. This stage reflects growing recognition of active perception's value, though efficiency remains suboptimal.
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(a) Active Porcaption Count
(b) Response Length
Table 5: Evaluations w.r.t. tool reward.
:50
Method
V*
HR-4k
HR-8k
w/o Tool Reward
87.4
53.4
55.4
150
Unconditional Reward
72.1
90
Conditional Reward
90.1
75.1
72.6
20
-0
63
80 20
40
80
Stapa
Staps
Figure 4: Training dynamics w.r.t. tool reward.
Table Scaling Model Size. The 32B model is trained with the same data. Resp. Len.: Average Response Length. loU measured on V*
Table 7: Scaling Challenging Reasoning Data from Chen et al. (2025) shows co-evolving per- ception (V*) and mathematical problem-solving
Model
V-
WeMath
Resp. Len.
loU
Model
MVerse
WeMath
V
Qwen2.5-VL-7B
71.2
34.6
212
DeepEyes-788
90.1
38.9
241
0.37
Qwen2.5-VL.c
45.6
34.6
71.2
DeepEyes-7B
47.3
38.9
90.1
Qwen2.5-VL.32 VL-33B
87.9
47.7
314
DeepEyes-32B
93.3
55.9
754
0.53
More Reasoning Data
51.8
43.6
91.6
Table 8: Zero-Shot Tool Generalization. HR- OCR-Rot: Random rotated subsets of HR- Bench-8K for OCR tasks.
Table 9: Ablation on iMCoT. We provide results
trained with text-only CoT the same datasets.
Model
V*
HR-OCR-Rou
Model
V*
HR-4K
HR-8K
Qwen2.5-VL-7B
71.2
76.5
Qwen2.5-VL-7B
71.2
68.8
65.3
RL w. Text-only CoT
88.5
75.4
60.8
DeepEyes (crop)
90.1
80.1
DyepEyes (crop+rotate)
90.1
83.6
DeepEyes (iMCoT)
90.1
75.1
72.6
Stage 3: Efficient Utilization (Steps 45-80) The model adopts a more selective, precise approach, reducing query frequency and response length while maintaining high grounding and task accuracy. This reveals a compact linguiiie policy: active perception is invoked only when needed, complementing internal reasoning. High IoU with fewer queries reflects implicit planning, as the model narrows the visual scope internally before selectively confirming hypotheses.
Overall, training progresses from broad exploration to targeted exploitation, showing that the model can learn to integrate active perception into reasoning effectively. The ability leverage active perception strategically co-evolves with its policy, highlighting the potential of perception-augmentee visual-language models for scalable and interpretable multimodal reasoning.
Tool Reward. The reward in Eq. 2 includes a conditional component (tool reward) that grants a bonus only when the model answers correctly while performing active perceptions. For compari- son, we train two variants: one without the conditional bonus (w/o tool reward) and one with an unconditional bonus (unconditional reward). Results are shown Figure 4 and Table Without the conditional reward, the model quickly reduces and stops performing perception actions. With an unconditional bonus, minimal engagement persists but remains static. Conditioning the reward on correctness leads to gradually increased active perceptions and more informative responses, reflecting deeper integration of visual reasoning. This setting achieves the highest accuracy, showing that rewarding actions alone are insufficient; alignment with correct outcomes is essential in DeepEyes.
Thinking Patterns. Here, we analyze diverse thinking patterns that emerged during end-to-end RL training, showing how the model performs active perceptions into its reasoning in ways that mirror human visual cogmition. Four primary patterns can be identified: 1) Visual Search: When facing complex problems that single observation can't solve, the model actively scans different image regions, gathers visual clues, and reasons through them to reach reliable conclusions (Figure 7 2) Visual Comparison: When handling understanding across multiple images or objects, the model iteratively zooms in on each one, allowing close examination and comparison before drawing final conclusion (Figure g): 3) Visual Confirmation: In some cases, the model begins with uncertainty but gradually builds confidence by zooming in image details gather evidence and resolve doubts (Figure (): 4) Hallucination Mitigation: Although VLMs can sometimes hallucinate, performing active perceptions helps the model focus on visual details mitigate hallucination. (Figure 10).
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Source Token
Highly Relevant Taken
Hallucinated Text
Relevancy rooks
The Image person walking sandy
The indivitunn's
gray hoodle, jeans sandet. are holding top, which appears to contose some items, possibly shells rocks. the background festure the accon with woves gently raking onto the shore....
oor o glase
bench
The image depicts a person stroling along a sendy shoreline beside the oceon. The person corrying rectengulor rlock to therr right hand
Relevancy clock
Now wil zoom for clsser view to checkt whether the person &s holding the clock: ("bbox,
The image 750, 652). "label"; "clock")
image ditaiil.
conswers mage depicts welling beach corrying
cleck, woves and background v/onswers
Figure 5: Analysis of hallucination mitigation. Qwen2.5 VL-7B (top) hallucinates "rocks," driven by linguistic association with "beach" rather than visual evidence, yielding low relevancy. contrast, DeepEyes (bottom) triggers iMCoT to counter this bias, zooming in re-ground reasoning and override the language prior, correctly identifying the "clock" with a focused relevancy heatmap.
4.4 ANALYSIS AND ABLATION STUDY
Scaling Model Size. Our framework exhibits strong scalability, as evidenced in Table 6 When scaling from 7B to 32B parameters, DeepEyes consistently widens its performance gap the Qwen2.5-VL. baseline. More importantly, the larger model demonstrates more sophisticated emergent behaviors. It generates substantially longer reasoning chains (Resp. Len.) and achieves higher grounding precision (IoU). This indicates that our RL paradigm not only boosts task performance but also fosters deeper and more accurate reasoning as model capacity increases.
Scaling Challenging Reasoning Data. As shown in Table 7 scaling our training set with more chal- lenging reasoning data (from 23% to 42%) demonstrates a mutual reinforcement between perception and reasoning. improving performance on both mathematical benchmarks and the perception task V as well. We hypothesize that stronger abstract reasoning enables a more sophisticated understanding of complex queries, which in turn guides more effective risual-grounded thinking process.
Zero-Shot Tool Generalization. The primary goal of DeepEyes is to explore how models can natively "think with images," using cropping simple, foundational tool. Although not aimed at building large toolset, the framework is easily extensible. To verify this, we introduced a rotate tool solely through the system prompt, requiring no retraining or architectural changes. We evaluated it on HR-OCR Rot, a benchmark we created by applying random rotations (0", 90%, 180", 270") to the HRBench-8k OCR subset. As shown in Table 8 the tool yielded 3.5% performance gain on this task while maintaining stable results on the general V benchmark, demonstrating that DeepEyes can seamlessly integrate new tools and apply them selectively for zero-shot generalization.
Ablation on iMCoT. Finally, the ablation in Table 9 isolates the contribution of our core iMCoT mechanism. Compared to an RL baseline trained with text-only CoT, iMCoT achieves superior performance across all benchmarks. The advantage is most pronounced on the ultra-high-resolutiee HR-8K benchmark, where iMCoT outperforms the text-only approach by a substantial margin. This result decisively demonstrates that for tasks requiring fine-grained visual detail, interleaving visual perception with textual reasoning is not merely beneficial, but essential for robust performance.
4.5
CASE STUDY: How DOES DeepEyes SYSTEMATICALLY MITIGATE HALLUCINATION?
Object hallucination in VLMs often stems from a strong language bias (Zhou al. 2024). where text generation detaches from the visual input to rely on learned linguistic patterns. Our "thinking with images" paradigm directly counters this. By triggering active perception, the model is forced re- engage with visual evidence, effectively fact-checking its linguistic assumptions against visual reality, To analyze this mechanism, we compute relevancy maps (Ben Melech Stan al. 2024) to quantify the grounding of the model's output, which measures the contribution of all preceding tokens to the generation specific source token. Visualized via heatmaps, high relevancy attributed to image regions indicates strong visual grounding, whereas high relevancy from purely textual priors suggests a iogguage-driven hallucination. As illustrated in Figure 5 this approach proves effective. While bascline model succumbs to linguistic bias, DeepEyes leverages active perception to re-evaluate its initial assumptions based on new visual evidence. This process breaks ungrounded reasoning, overriding the language prior and correcting the hallucination, confirmed by relevancy analysis.
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5 CONCLUSION
In this paper, we presented DeepEyes, a vision-language model that learns to "think with images" via end-to-end reinforcement learning. Unlike prior methods, this capability emerges natively, requiring neither pre-collected reasoning data for SFT nor external specialized models. To guide its reasoning behavior, we propose active perception mechanism, featuring tailored data selection and rewards, that promotes successful reasoning trajectories by incentivizing the strategic use of visual grounding. Consequently, DeepEyes achieves competitive results on multiple benchmarks, exhibiting diverse, human-like reasoning patterns such as visual search and comparison.
ACKNOWLEDGMENTS
This work was supported by the National Key Research and Development Program of China (2023YFB100700), the National Natural Science Foundation of China (62521002, U24B20185) Thanks to the New Cornerstone Science Foundation and the Xplorer Prize.
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Baining Zhao, Ziyou Wang, Jianjie Fang, Chen Gao, Fanhang Man, Jinqiang Cui, Xin Wang, Xinlei Chen, Yong Li, and Wenwu Zhu. Embodied-r: Collaborative framework for activating embodied spatial reasoning in foundation models via reinforcement learning. In Proceedings of the 33rd ACM International Conference on Muliimedia, pp. 11071-11080, 2025b.
Hengguang Zhou, Xirui Li, Ruochen Wang. Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh. R1- zero's" aha moment" in visual reasoning 2b non-sft model. arXiv preprint arXiv:2503.05112, 2025.
Yiyang Zhou, Chenhang Cui, Jachong Yoon, Linjum Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao. Analyzing and mitigating object hallucination in large visoonlanguage models. In ICLR, 2024.
Jinguo Zhu, Weiyun Wang. Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu. Yuchen Duan, Hao Tian, Weijie Su. Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504. 10499, 2025.
Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, and Huan Zhang. Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models. XrXiv preprint arXi::2411.00836 2024.
A PROMPT
A.1 SYSTEM PROMPT
SYSTEM_PROMPT PROMPT
You helpful assistant.
# Tools
You may call one or more functions to assist with the user query. You are provided with function signatures within <tools></tools> XML tags:
<tools>
"type": "function", "function";
"name": "Zoom in on a specific region of an image by "description";
cropping it based on bounding box (bbox) and optional object label.",
"parameters";
"type": "object",
"properties":
"bbox_2d"
"type": "srray", "items": "type": "number" b, "minItems"; 4,
"max temsi: 4,
"description": "The bounding box of the zegion to zoom in, as [xl, yl, x2, y21, where (x1, yi) is the top-left and (x2, y2) is the botton-rigat corner."
"label":
"type": "string",
"description": "The name or label of the object in the specified bounding box (optional)."
rrequired;
"bbex 2d"
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Published as a conference paper at ICLR 2026
</tools>
# How to call tool
Return jaen object with fanction name and argunenta within
<col_cal>><toolcalll>>> XML tags: @tool_call
:"name": <function-name>, "arguments"; <arge-json-objecta)
</tool_call>
**Examplexx
<tool_call call>
("name": "image_zoom_in__ "arguments"; ("bbox_2d", [10, 20, 100, 200), "label": "the apple on the desk" </tool_call>
A.2
USER PROMPT
USER PROMPT
Question: (}
Think first, call ++image zoom_in_ttolyy it needed, then answer.
Format strictly <taink?.. </thinkk
<tool_call>, ltt_calll.../tto_callll. </tooo_caal>>> (if tools needed)
<answer>... </answer>
B TRAINING DATA
B. DATA DISTRIBUTION
23%
Visual Search 22% (Natural image)
47%
AnxivQA 14k (Chart)
ThinkLite-VL (Reasoning)
30%
Figure 6: Distribution of Training Data.
As shown in Figure 6 our training corpus constructed from three distinct sources, each contributing unique focus:
Visual Search (47 % 22k samples): To support the model's visual grounding and fine-grained perception capabilities, we leverage the V dataset (Wu & Xiel 2024), which is derived from COC02017 (Lin al. 2014). This collection emphasizes natural image understanding, where accurate responses require identifying subtle visual cues and object-level distinctions.
ArxivQA (30% % 14k samples): To diversify the visual input types, we incorporate the ArxivQA dataset (Li et al., 2024b), which features scientific plots, diagrams, and schematic charts. These samples introduce structured visual semantics beyond natural scenes, enabling the model to better interpret abstract and symbolic visual representations.
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ThinkLite-Ve VL (23%, 11k samples): While the above datasets visual understanding and dia- gram comprehension, they are limited in reasoning variety. To address this, we include multimodal question answering examples from ThinkLite-VI (Wang al. 2025b). focusing tasks such as arithmetic reasoning, commonsense inference, and problem solving. This addition is intended to improve general reasoning robustness and mitigate modality overfitting.
B.2
IMPACT OF TRAINING DATA
Table 10 reveals the critical role of training data composition. While unfiltered data (#1) offers minimal benefit, our curated, fine-grained data (#2) substantially boosts high-resolution image handling. However, this specialization induces catastrophic forgetting of reasoning skills. We address this by incorporating reasoning data (#3), which preserves mathematical abilities without sacrificing perception gains. To further enhance the model's cognitive range, we introduce chart data (#4), which adds visual diversity and fosters complex relational reasoning. The results confirm clear synergy: high-resolution data for perception, reasoning data for cognitive retention, and chart data for relational complexity. Consequently, our final dataset (#5) combines these complementary sources to comprehensively activate the model's visual reasoning capabilities.
Table Impact of Training Data. Fine represents the fine-grained data. HR denotes HR-Bench. Row #0 is the origin score of Qwen2.5-VL-7B.
Resolueeoo
Basic VL Capability
Reasoning Math
#
Fine Reason
Chart
V Bench
HR-4K
HR-SK
ReasonSeg
POPÉ
Math
71.2
68.8
65.3
68.3
85.9
68.2
45.6
/
86.9
68.9
67.3
69.0
86.6
67.0
42.9
2
/
91.6
74.1
71.0
69.1
88.1
64.7
41.3
/
91.6
73.8
70.5
68.6
88.8
67.7
43.8
90.1
74.6
74.6
68.5
87.9
64.6
38.1
/
75.1
72.6
68.6
87.7
70.1
47.3
C CO-FIRST AUTHOR CONTRIBUTIONS
Chenxiao:
Conducted early-stage exploration, contributed the main coding, and conducted the experiments.
Jack: Conducted early-stage exploration and performed evaluation.
Michael: Contributed codebase, and conducted the experiments and analysis.
Ziwei: Performed data curation, completed the main manuscript writing, and conducted analysis.
D MORE CASES
D.1
SUCCESSFUL CASES
Visual Search
Figure After an initial observation of the whole image, the model recognized that the current visual information alone was insufficient determine whether it was wet, and acknowledged that factors such as lighting could cause misleading cues. It was therefore decided that more detailed inspection was necessary. During the first tool invocation, grounding was inaccurate, and the cropped image failed to provide some clues. The model then conducted second grounding step, this time actively focusing on the area surrounding the wetsuit an attempt to locate more direct andicators-such as water droplets or visible signs of wetness. It also incorporated contextual cues from the surrounding environment, such as reflections on wet sand and the wetsuit's 's contact with water. Ultimately, by combining zoomed-in visual details- such-suchha the wetsuit's 's dark coloration indirect environmental evidence, the model concluded that the
and how clung to the wetsuit appeared to be wet.
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state of the wetsuit wet?
Thinking with image
D0
<think>
It's not evident from the image whether the wetsuits are wet. Usually, wetsults that are not wet appear darker, but this could depend on the lighting and the photo itself. Further magnification might be needed to clarify if there are signs moisture. Calling the zoom-in tool can help explore this aspect more closely.
</think>
<tool, <tool_call> call>
("name": samage_zoom,, zoom_in_tood too""""" "arguments": (" ("bbox_2d") [37, 125,
81, 231], "label": "wetsuit"))
</tool_call>
<think>
seems like the image does not provide enough information determine the wetsuit is wet or not. Since there's a reflection on wet sand and the wetsult appears to be in contact with the water, it's plausible, hut not guaranteed, that wetsuit wet. Before confirming whether wetsuit wet, zooming in on wetsuit can give more definitive proof or indicators like droplets or moisture on the suit.
</think>
<tool_clll>
("name": "image_zoon zoom_in tool", "arguments": ("bbox_2d": [73, 182, 164, 476), "label': "the wetsuit")) </tool_call>
<think>
Based the image, wetsuit appears dark and held tightly against the body, which could indicate that is The lighting and the be wet due recent </ </think>
the sand suggest that the wetsuit might water.
Yes, the state of the wetsuit appears be wet. </answer>
Figure 7: Thinking Pattern: Visual Search.
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Based the upper grophs, which lobeled section (o-d appears to show voriability
Thinking with image
<think>>
To determine which section shows the loast variability, we need to examine upper graphs (a. b, c, d) variability can assessed by how much the data fluctuates around the mean line. Let's observe euch section:
</think>
<tool_cals
("name": "image ceceeeeoommmtt tool"""", "arguments"; ("bbox_2d"; (124.77
"label": s/tool
955, "label" 'section (a)"))
<thinkc>
From the zoomed-in view of section (4), it's clear that data Buctuates quite indicating higher variability. Let's proceed examine the other sections:
</think>
<tool call:
("name": "image cecee_ooommme tool"""" "arguments"; ("bbox_2d"; [79.321,
986, 558). "label": 'section (b]))
</tool
<thinks
Section (b) shows less fluctuation compared to section (a),
suggesting less variability. Now, to compare them more clearly.
sections and
</thinle>
<tall_call>
("name": : sargge__oom____toe "arguments" : ("bbox_d":: [1143, 30,
2003, 249), "label": "section (c]"))
</tooLcall>>
<thinks
After zooming sestion appears that the data fluctuates less
than in sections (a), (b), (d), indicating the least variability, Let's
confirm by examining section
< </think>
("name": "image sariggeeooomminneeee iool"""" "arguments": ("bbox_2d 2d [1090 317,
558), "label": "sertion (a)"))
</tool_call: call>
<answer>>
Section (4) appears to show the least variability in the data, so the answer is section </answer>
Figure 8: Thinking Pattern: Visual Comparison.
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Published as a conference paper at ICLR 2026
Is the window on the front of vehicle block square?
Thinking with image
D
<think>
Looking at the image, the window on the front of the vehicle appears to be black and square. However, the resolution and angle of the photo may make difficult to see the details clearly. we need closer look, we could use the zoom-in tool to get better view of the window.
</think>
<tool call>>
("name": "Image_zone zoom, In, tool", "arguments": ("bbox 2d" [125, 44, 471,137J. "label": "window on the front of the vehicle")) )
</too|_cal>>
<think>From the zoomed-in image of the vehicle's s window, the window appears to be booking possible diagonal from comer corner, suggesting at least one dimension might not be square. However, considering the relation of the window to the vehicle's body, which seems to conform to overall rectangular configuration, the window itself might be square, but confirmation would require closer view or more specific tool amalysis. </think>
<tool calls
("name": "image_zoor soogee_zoommme jn, tool", "arguments": : ("bbox_dd": [222, 51, 455,134), "label":: "the window")]
</tool_call>
<thinksThh front window the vehicle appears black square, judging by visible parts and typical vehicle design. </think
<tool_call>
("name": "imaag_zoom__ too"" "arguments": ("bbox_2d) 2d": [170,37,
342, 112), "label": window"])
</tool_call>
<think>
Upon examining the image more closely, it appears the window on the front of the vehicle is indeed black and square. </th e/think>
<answer>
Yes, window on the front of the vehicle and square. </answer>
Figure 9: Thinking Pattern: Visual Confirmation.
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Published as a conference paper at ICLR 2026
Is the blazer black?
Thinking with image
<think> The blazer through the windows of the building appears to be black, per the overall color tone of the image. It's a person walking away from the viewer towards the right of the frame. The blazer distinguishable from the orange shirt by another individual in the background. Considering the color contrast and the blurred features, the blazer can be identified black.
</think>
<tool_clll>
("name": "image zoom_iz. in "arguments": ("bbox_2d"; [94, 194,197, 143, 351]M
</tooLcall>>
<think> Upon closer inspection, it is clear that the blazer is not black. Instead, it seems to have black pants with the blazer, suggesting the person is wearing a red blazer. Based on the visible colors and lack black elements, the blazer the image not black, especially because contrasts starkly with the red top </think>
<answers
</answer>
Figure 10: Thinking Pattern: Hallucination Mitigation.
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Published as a conference paper at ICLR 2026
the color of the awning green?
Thinking with image
10
<think>
Yes, awning on wall in backurup of image appears be green. The distinct color evident against the other elements such the parked vehicles. Let's verify this by zooming the that includes awning.
</think>
<tool_call>
("name": "image moom_in_tonn too""" "n_.tooo"""rrgumenttss "arguments": ("bbox_2d") [146. 5,
313, 92], "label": "the awning')) e/tool_call>
<think
The awning in the image oppears green. However, confirm this with high precision, especially considering the lighting, size, and resolution of the image, may be beneficial in that region and examine it more closely. </think>
<tool_call>
{" ("name": "image z0om_ in_to""", "arguments' :: ("bbox_2d) (136.3,
311, 75), "label"; "awningss""
</tool, calls
<think
The awning clearly visible in image. The image resolution allows for clear distinction of colors, and awning distinct cobalt blue hue, No confirmation tools would needed theawning color is observable. </think>
<answer>
Yes, the awning is blue. X < </answer>
Figure 11: Grounding Limitation.
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Published as a conference paper at ICLR 2026
PR feritigne, dumitaaiion volume fromion ilfehj
fie poat leight deressss & The noight ecressoo. The peer stoite inuer R Jen////lpns ii Jacker.
The past sapte to rupeer (4, httl:////////om..... mn veliee
Thinking with image
107
<think
Observing figure (b), we need to focus on the position of the peaks of the dis distributions corresponding to different (\phi). As (Aphi) increases from 0.0 to 0.4, the peaks of the distribution shift higher [R_ [R[eee] (igma, m values. This suggests that as the volume fraction (\phi) increases, the most probable
[R_(ee) ] value increases Therefore, correct answer
</think>
<tool_call>
("uame":" zoom in_ tool", "arguments": ("bbox_2d) 2d": [1102, 707, 1520, 1057]; "label"" "peak position }}}
</tool_call>
<think
The zoomed-in view confirms that the search space on the horizontal axis for ( [ee)/'sigmaa m increases, the locations of the peaks shift to the right. This aligns with the observation in the full graph that the peaks move to higher values of [R. (sigm/ m as (Aphi) increases. The peak position for higher values of (yphi) (color-coded differently) shifts to higher (R. [ee)/\signms m values compared to lower
(hph)).Tereeorr fore,
</think
<tool_cal call>
("name": "Image_ z00m tnol""", 'arguments": ("bbox_2d") 2d" [1202, 707, 1520, 1057], Babell".""eak "oobell..peakkpostttoo"""" ")]
</tool_call>
<answer>
D. The peak shifts to higher (ccecc//sigmaaa values. X </answer>
Figure 12: Reasoning Limitation.
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Published as a conference paper at ICLR 2026
Visual Comparison
Figure 8 To determine which section exhibits the least data variability, the model sequentially zoomed in on the charts of four sections (a, b, c, and d), focusing on fluctuations around the moving average. Through comparison, found that section (a) showed significant volatility, while section (b) was relatively less volatile. However, section (c) displayed the most stable pattern, with fluctuations clearly smaller than those in the other regions. Based on this analysis, the model concluded that section (c) has the least data variability.
Visual Confirmation
In this case, the model was initially uncertain about the shape of the window. Through multiple invocations of the zoom-in tool and careful analysis of potential visual details, it gradually resolved its internal uncertainty and ultimately provided a confident answer.
Figure 2
Hallucination Mitigation Figure TO The model initially confused the colors of the pants and the blazer. However, by leveraging its perceptual capabilities and invoking the zoom-in tool examine the enlarged region, it ultimately corrected the hallucination.
D.2 FAILED CASES
Grounding Limitation
Figure 11: The model initially hypothesized that the awning was green. It then invoked the zoom-in tool for closer inspection, maintaining its assumption while noting the need for more precise verification. However, during the second zoom-in, grounding drift occurred-the awning was no longer within the selected region, and instead, blue area appeared. This misalignment led reversal in the model's judgment, ultimately resulting in an incorrect answer.
Reasoning Limitation
Figure 12 Although the model was able to accurately locate the position of figure (b) and invoke the tool for detailed inspection, still lacked fine-grained understanding and reasoning capabilities. It failed to thoroughly analyze the trend changes in the zoomed-in curves, ultimately leading to an incorrect answer.
E LIMITATIONS
Although the simple end-to-end RL can elicit visual reasoning abilities, there still exist shortcuts, such as insufficient richness in the reasoning process and inaccurate target localization. We think these issues stem from limitations in the foundation model's poor capabilities. We only utilized - which has relatively weak fundamental capabilities due to its small model size.
F BROADER IMPACTS
Our exploration of interleaved multimodal chain-of-thoughh reasoning provides valuable insights for the future development of the Al community. By investigating how models can engage in step-by- step visual reasoning through interactive dialogues, advance understanding of more transparent and interpretable systems. This research direction may inspire new architectures and training methodologies that better align with human reasoning processes.
G FUTURE WORK
Currently, our visual reasoning process only includes the crop operation. However, in real-world scenarios, a wider range of tools is needed, such as search and drawing auxiliary lines. We will explore the integration of additional tool utilization in future work.
24


Reviews:
[{"review_id": "pNj3HqI4ZP", "reviewer": "Reviewer_8hFE", "summary": "This paper introduces DeepEyes, a vision–language model (VLM) endowed with active perception capabilities through the integration of an image zoom-in tool. DeepEyes exhibits substantial performance gains over existing open-source and workflow-based baselines across a range of high-resolution visual tasks and reasoning benchmarks. Furthermore, the paper provides an in-depth analysis of the model’s training dynamics and emergent reasoning behaviors, uncovering patterns reminiscent of human visual cognition.", "questions": "1.Can the authors clarify how the model parameterizes and samples coordinates for zoom-in actions? Additionally, is any mechanism employed to prevent the repeated selection of non-informative or overlapping regions during active perception?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. The paper is technically solid. The results (Tables 1–9) demonstrate consistent and notable improvements over relevant baselines in high-resolution perception (e.g., HR-Bench-8K), general reasoning, visual grounding, hallucination mitigation, and multimodal mathematical reasoning. The empirical rigor is further supported by comprehensive ablation studies and scaling experiments.\n2. The paper conducts an insightful analysis of the training dynamics and emergent reasoning behaviors of **DeepEyes**, offering readers a deeper understanding of how active perception shapes visual reasoning.", "weaknesses": "1. Within the broader context of Agentic Reinforcement Learning (RL), this work can be regarded primarily as a **multimodal agents** equipped with a zoom-in tool, contributing limited methodological novelty.\n2. The current implementation falls short of fully realizing the concept of **“Thinking with Images.”** This paradigm should encompass not only zoom-in operations but also more diverse capabilities—such as image editing, spatial manipulation, or compositional visual reasoning.\n3. **Limited tool generalization.** Section 4.4 briefly asserts that the method generalizes to new tools; however, only a trivial extension to rotation is quantitatively validated. No systematic exploration of tool compositionality or multi-tool chaining—both central to agentic multimodal systems—is presented.", "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": "qcFrZIno8Y", "reviewer": "Reviewer_3FYG", "summary": "This paper proposes a new CoT pipeline by integrating both the text and visual cues into the intermediate reasoning steps. Specifically, without SFT training, this paper firstly curates a post-training dataset and then leverages the RL-like GRPO algorithm to incentivize the model's internal grounding abilities to crop and embed the local visual information into the next reasoning trajectory. Trained by several reward functions, this paper then achieves powerful performances on general perception tasks and also the reasoning benchmarks, which imitates our human beings' zoom-in ability to investigate the mostly related visual regions and does not solely rely on once visual embedding sequencers and text-based reasoning trajectories.", "questions": "I am also curious what if using these thinking with images idea into video tasks or 3D scenarios, by adopting and improving the similar pipeline into other more difficult tasks?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Combing both the textual and visual cues into the MLLM's intermediate reasoning trajectories sounds like an interesting and critical exploration, despite the openai-o3 has applied the very similar methods to empower the MLLM thinking with images abilities. \n\n2. The fully released pipeline including codes and datas contributes the community which can be a good point for the community to develop the O3 frameworks.\n\n3. The overall pipeline is not that complicated and somehow simple but effective, in which the authors also conduct various experiments to explore and exploit the overall RL training dynamics.", "weaknesses": "1. Regarding the iterative MCoT steps, does this paper explore multiple object grounding at the same time?\n\n2. If the model needs more than twice MCoT, then the model needs more than twice visual embedding extraction, which sounds like a computational overhead. Can the authors also provide more inference latency analysis?\n\n3. What if the model predicts all possible grounding objects' bounding boxes and extracts all visual embeddings, then concatenates all these new local visual tokens together, saying all in once?\n\n4. What about the hard image-text samples, but with a small resolution? Does the author study these low-resolution scenarios?\n\n5. I also notice that the implemented codes adopted a stronger MLLM models using vllm to serve as the judger, but the paper claims internal grounding post-training, which raises confusions.", "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": "SRW2P4c7JK", "reviewer": "Reviewer_BabE", "summary": "The paper introduces DeepEyes, a vision-language model trained end-to-end with reinforcement learning to “think with images” by interleaving textual chain-of-thought with active visual perception. The model autonomously decides when to zoom into image regions by generating bounding boxes; the resulting crops are fed back as observation tokens and used for subsequent reasoning. Training uses agentic reinforcement learning with GRPO, sparse outcome rewards, and a conditional bonus that incentivizes correct answers achieved via active perception. A data curation pipeline selects samples likely to benefit from grounding-assisted reasoning. DeepEyes achieves strong gains on fine-grained, high-resolution benchmarks, improves general perception and reasoning, reduces hallucination and slightly improves grounding, and yields consistent math reasoning gains. The paper analyzes training dynamics, showing a three-stage evolution from exploration to efficient use of active perception, validates the conditional tool reward, demonstrates scalability to larger models, positive data scaling effects, and zero-shot tool extension.", "questions": "- Reward design and stability:\n  - What are the exact numerical weights for R_acc, R_format, and the conditional R_tool? How sensitive are results to these weights and to the correctness indicator threshold (Eq. 2)? Please provide a sensitivity study or at least ranges (Section 3.2; Table 5).\n  - Why was KL set to 0? Did you observe policy collapse or reward hacking at any point? Would a small KL or reference model improve stability/generalization (Section 4.1)?\n- Observation/crop handling:\n  - How are cropped images preprocessed and encoded (e.g., AnyRes vs fixed resolution), and how many visual tokens do they add? Is there an explicit cap on total image tokens across multiple crops? Any latency analysis (Section 3.1)?\n  - Do you condition bounding-box generation on a learned coordinate head or pure text token decoding? How is the bbox format enforced and validated during rollouts (Appendix A)?\n- Data and splits:\n  - For V* and HR-Bench, please detail train/val/test splits used for RL vs evaluation to rule out leakage (Sections 3.3, 4.1). Are any evaluation images seen during RL?\n  - Can you release the curated indices and the perception-utility labels (Section 3.3; Table 10)?\n- Comparisons and metrics:\n  - For Table 1, can you report confidence intervals or variance over multiple seeds? Similarly, could you add a compute-normalized comparison against Pixel-Reasoner (Su et al., 2025) and ZoomEye (Shen et al., 2024a)?\n  - For grounding, can you report IoU distributions and success vs failure breakdown for cases where iMCoT invoked cropping vs not, to substantiate causal benefits (Section 4.2; Table 3)?\n- Generalization and tools:\n  - Beyond rotate, have you tried text-conditioned detection/segmentation or simple measuring tools (e.g., rulers/lines)? Any no-retrain extensions besides rotation (Table 8)?\n  - What are the failure modes that lead to grounding drift (Figure 11)? Could a recurrent state or memory help prevent drift across sequential crops?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Originality:\n  - Proposes an interleaved multimodal CoT (iMCoT) that natively integrates active perception into reasoning with end-to-end RL, avoiding pre-collected reasoning SFT and external specialized models/APIs (Abstract; Sections 1, 3.1). \n  - Conditional reward that only bonuses correct tool-using trajectories effectively incentivizes perception-aware reasoning while discouraging gratuitous tool calls (Section 3.2; Table 5; Figure 4).\n  - A targeted data curation pipeline that filters for perception-utility to bootstrap active perception without SFT (Section 3.3; Appendix B; Table 10).\n- Quality:\n  - Comprehensive evaluations across high-resolution, grounding/hallucination, and reasoning benchmarks with strong improvements (Tables 1–4).\n  - Ablations isolate key components: conditional tool reward (Table 5), iMCoT vs text-only RL (Table 9), data composition (Table 10), model scaling (Table 6), and zero-shot tool generalization (Table 8).\n  - Insightful training-dynamics analysis revealing emergent stages of active perception and behavioral patterns (Figure 3; Section 4.3), plus qualitative case studies (Figures 7–10).\n- Clarity:\n  - Clear agentic RL formulation with observation tokens (Section 3.2), system/tool interface (Appendix A), and pipeline overview (Figure 2).\n  - Training details provided (Section 4.1) and code link for reproducibility.\n- Significance:\n  - Large, consistent gains on fine-grained high-res understanding (Table 1) and reductions in hallucination (Table 3), suggesting a practical path to multimodal test-time scaling via native visual thinking.\n  - Demonstrates scalability to larger models with stronger grounding IoU and longer reasoning chains (Table 6), and extensibility to new tools at inference (Table 8).", "weaknesses": "- Technical detail gaps:\n  - Reward magnitudes/normalization, exact coefficients for accuracy/format/tool rewards, and sensitivity analyses are not fully specified (Section 3.2; Table 5 references but no hyperparameters), limiting reproducibility and insight into stability.\n  - Limited description of how image crops are tokenized/encoded, how many visual tokens per crop, and how observation tokens are interleaved with text for different tools (Section 3.2 mentions loss masks but not encoder specifics).\n- Evaluation scope and fairness:\n  - High-resolution gains are compelling, but comparisons to workflow baselines may be confounded by different training data and compute; stronger apples-to-apples controls are desirable (Table 1). Statistical significance and variance are not reported.\n  - Grounding improvements on refCOCO family are modest (+0.6–1.0; Table 3); ReasonSeg gains are small. It would help to quantify how often crops improve IoU versus distract.\n- Generality of “thinking with images”:\n  - The system currently centers on cropping; while rotate shows zero-shot extensibility (Table 8), the toolset remains narrow. Some failure cases highlight grounding drift and reasoning limits (Figures 11–12), suggesting brittleness for complex visual workflows.\n- Compute and efficiency:\n  - Training uses 80 iterations on H100 with 256 prompts × 16 rollouts and max 6 perceptions, KL=0 (Section 4.1). Sample efficiency and cost-benefit vs SFT or hybrid methods are not deeply analyzed; KL=0 may risk policy drift/collapse.\n- Claims around “no cold-start SFT”:\n  - Although no reasoning SFT is used, the base model (Qwen2.5-VL) is already instruction-tuned; clarifying the exact initialization and any prompt engineering dependencies (Appendix A) would temper the claim.", "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": "Bk2F4YBVGj", "reviewer": "Reviewer_gPB1", "summary": "This paper presents DeepEyes as one of the first open-source solutions that follow the thinking-with-images paradigm and uses image cropping as a tool to incentivize fine-grained visual perception capabilities in modem VLMs.\n\nTwo key contributions of this paper are: \n- Interleaved Multi-modal Chain-of-Thought (iMCoT) that reasons beyond text-only trajectories and provides seamless integration of zoom-in operations and textual reasoning. \n- Conditional tool reward that promotes the usage of zoom-in actions. \n\nWhile those key contributions are common practice in agentic literature, I think it is not a trivial attempt to make them work in the thinking-with-images paradigm. Considering the comprehensiveness of ablation studies in this paper, I lean to recommend acceptance pending the discussion with fellow reviewers/ACs.", "questions": "1. Can you anticipate the generalization beyond image crop? Say various image manipulation tools in related work, such as scale, contrast, denoise, or keypoint heatmaps.\n2. Can you quantify the benefit of (i) difficulty curation, (ii) verification, and (iii) perception-utility filtering in your data curation pipeline?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. DeepEyes comes with clear motivation – using RLVR-style post-training methods to activate the precise grounding capabilities of VLMs.\n2. The proposed iMCoT and conditional tool reward prove to be effective in ablation studies in various high-res & visual reasoning benchmarks. \n3. The staged evolution (exploration -> high-frequency engagement -> efficient utilization) is compelling and supported with metrics like tool-call counts, response length, and grounding IoU curves.", "weaknesses": "1. Both iMCoT and conditional tool incentives are now fairly standard in agentic RL literature (interleaving actions + CoT, rewarding tool use). The novelty is mainly in making them work cleanly for “thinking with images”. \n2. While the authors claim to put DeepEyes in an agentic framework, the available tools are relatively limited – seems to be zoom-ins only. See the following Q1.", "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)