Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Method
Authors: Haochen Wang
Xiangtai Li
Zilong Huang
Anran Wang
Jiacong Wang
Tao Zhang
Jiani zheng
Sule Bai
Zijian Kang
Jiashi Feng
Wang Zhuochen
Zhaoxiang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: visual reasoning
benchmark
thinking with images
MLLM
Primary area: datasets and benchmarks

Abstract: Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically ref-
erencing visual regions, just like human “thinking with images”. However, no
benchmark exists to evaluate these capabilities holistically. To bridge this gap, we
propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic
benchmark built on three principles: (1) focused visual perception of subtle targets
in complex scenes, (2) traceable evidence via bounding box evaluation, and (3)
second-order reasoning to test object interactions and spatial hierarchies beyond
simple object localization. Prioritizing images with dense objects, we initially
sample 1K high-quality images from SA-1B, and incorporate eight LMM experts
to manually annotate questions, candidate options, and answers for each image.
After three stages of quality control, TreeBench consists of 405 challenging vi-
sual question-answering pairs, even the most advanced models struggle with this
benchmark, where none of them reach 60% accuracy, e.g., OpenAI-o3 scores only
54.87. Furthermore, we introduce TreeVGR (Traceable Evidence Enhanced Visual
Grounded Reasoning), a training paradigm to supervise localization and reasoning
jointly with reinforcement learning, enabling accurate localizations and explainable
reasoning pathways. Initialized from Qwen2.5-VL-7B, it improves V* Bench
(+16.8), MME-RealWorld (+12.6), and TreeBench (+13.4), proving traceability is
key to advancing vision-grounded reasoning. The code and data will be released.

Published as a conference paper at ICLR 2026
TRACEABLE EVIDENCE ENHANCED VISUAL GROUNDED REASONING: EVALUATION AND METHOD
Haochen Wang Xiangtai Li
Zilong Huang Anran Wang2 Jiacong Wang Tao Zhang
Jiani Zheng² Sule Bai Zijian Kang² Jiashi Feng" Zhuochen Wang Zhaoxiang Zhang
New Laboratory of Pattern Recognition (NLPR),
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS),
Institute of Automation, Chinese Academy of Sciences (CASIA)
University of Chinese Academy of Sciences BByteDance
wwanghacche22222, zhaoxiang. zhang) 8 Bia. ac.cn
ABSTRACT
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically ref- erencing visual regions, just like human "thinking with images". However, no benchmark exists to evaluate these capabilities holistically. To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), diagnos- tic benchmark built on three principles: (1) focused visual perception of subtle targets in complex scenes, (2) traceable evidence via bounding box evaluation, and (3) second-order reasoning test object interactions and spatial hierarchies beyond simple object localization. Prioritizing images with dense objects, we initially sample 1K high-quality images from SA-1B, and incorporate eight LMM experts to manually annotate questions, candidate options, and answers for each image. After three stages of quality control, TreeBench consists of 405 challenging visual question-answering pairs, even the most advanced models struggle with this benchmark, where none of them reach 60% accuracy, e.g., OpenAT-03 scores only 54.87. Furthermore, we introduce Tree (Traccable Evidence Enhanced Visual Grounded Reasoning). a training paradigm to supervise localization and reasoning jointly with reinforcement learning, enabling accurate localizations and explainable reasoning pathways. Initialized from Qwen2.5-VV VLL-B, it improves V* Bench (+16.8), MME-Rea RealWorld (+12.6), and TreeBench (+13,4) proving traceability key to advancing vision-grounded reasoning. The code available at https: / com/ Saaachen-aaageeee D
INTRODUCTION
Recent breakthroughs in Large Language Models (LLMs) reasoning, such as OpenAl-ol (OpenAL, 2024b) and DeepSeek-R1 (Guo et al., 2025a) with remarkable test-time scaling properties, have motivated researchers to explore reasoning for Large Multimodal Models (LMMs) (Huang et al., 2025; Wei et al., 2025a;b; Chen al., 2025). These models typically remarkable in their mathematical and scientific reasoning, particularly through text-space reasoning. However, they exhibit critical limitations when applied perception-heavy tasks (Jiang et al., 2025) or general multimodal benchmarks (Wang et al., 2024c), primarily due to accumulated language bias from their exclusive reliance on textual reasoning pathways. paradigm shift toward visual grounded reasoning emerged with models like OpenAl-o3 (OpenAI, 2025), which is able to "think with images" by dynamically referencing and amplifying task-relevant regions during reasoning, resulting in image- text interleaved reasoning pathways. Yet, despite growing interest, the community currently lacks comprehensive evaluation benchmarks for assessing these capabilities.
Classical benchmarks like POPE (Li et al., 2023c), MMBench (Liu et al., 2023b), FEED-Bende (Li et al., 2023a), and MMMU (Yue et al., 2024) usually overlook fine-grained localization and verifiable reasoning chains. Others (Wu & Xie, 2024; Zhang et al., 2024a; Wang et al., 2025f; Dong et al., 2024; Wang et al., 2025b;a; Zhang et al., 2024b) partially address localization but lack traceability
"Corresponding author.

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Published as a conference paper at ICLR 2026
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Figure I: Qualitative examples from TreeBench for each discipline. Each question requires focused visual parsing on mere objects, and some even request second-order reasoning beyond precise localization. Moreover, the bounding boxes of all target instances are provided, ensuring traceable evaluation. All these questions are challenging, as OpenAI-o3 (OpenAI, 2025) and Gemini-2.5. Pro (DeepMind, 2025b) cannol answer them correctly simultaneously.
or complex reasoning: V* Bench (Wu & Xie, 2024) is restricted to simple spatial queries (e.g., "Is A left of B?") and risks data contamination with COCO-derived images (Lin et al., 2014); MME- RealWorld (Zhang et al., 2024a), HR-Bench (Wang et al., 2025I), and document benchmarks (Biten et al., 2022; Mathew et al., 2021; Liu et al., 2023c) support high-resolution inputs but lack traceable evidence and second-order reasoning such as perspective shifts. In short, these benchmarks fail to adequately evaluate three key elements central to visual grounded reasoning: nuanced visual grounding, traceable multi-step reasoning, and dynamic cross-modal interaction through interleaved box-text reasoning pathways.
To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), designed around three foundational principles essential for evaluating true "thinking with images" capabilities:
Focused Visual Perception. It evaluates a model's ability to identify subrle targets within cluttered, real-world scenes using detailed, precise, and unique textual descriptions, which requires hierarchical scene understanding and the discrimination of extremely similar distractors.
Traceable Evidence. It not only evaluates the final accuracy but also pioneers quantifiable evaluation of reasoning chains, resulting in an explainable, reliable, and transparent evaluation.
Vision-Centric Second-Order Reasoning Capabilities. It moves beyond simple object lo- calization and primitive "what/where" queries. It focuses on complex physical interactions between objects (such contact and occlusion), well as spatial containment (inside/outside, above/below) and relative relationships with perspective transformation.
To conduct TreeBench, we sample IK images from SA-1B (Kirillov al., 2023), prioritizing images with dense objects, as SA-1B (Kirillov et al., 2023) offers high-resolution, real-world scenes with many small and varied objects, making it particularly suitable for evaluating visual grounded reasoning. Subsequently, 8 experts with solid technical backgrounds are involved in hand-crafted annotation for 10 sub-tasks, as demonstrated in Figure 1. particular, we present semi-automated pipeline. Each of OpenAl-o3 (OpenAI, 2025) and Gemmini-2.5-Pa (DeepMindd 2025b) is required to create three distinct questions belonging to a specific subtask, accompanied by multiple-choice options and the respective correct answers. Subsequently, experts curated or replaced these to ensure quality and difficulty. We additionally incorporate a cross-vent cross-verification stage for further quality control. Finally, TreeBench incorporates 405 high-quality and extremely challenging VQA pairs with accurate bounding boxes of target instances. A comprehensive comparison between TreeBench and other related benchmarks is provided in Table 1. Key advantages are:
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Table 1: Comparison between benchmarks related to "thinking with images". TreeBench features traceable evidence annotations, as well as high input resolution and challenging questions.
Traceable Evidence
Mcan Area of Target Objects (1)
Swen2.5-VL-722
Benchmark
Resolution
Annotation
Performance ())
Bench 2,246x1,583
85.9
HR-Bench Bench-4K
4,023x3,503
79.3
HR- HRR-Bench-8K
5,727x4,430
X
76,0
MME- RMr-ReelWorld
2,076x1,434
62.9
TreeBench
2,152x1,615
3.05 %
42.2
Amnotation Quality. Unlike benchmarks relying on LMM-generated labels such as MMT- Bench (Ying et al., 2024) and SEED-Bench (Li et al., 2023a), our expert-driven process ensures correctness and extreme difficulty. However, relying on models would inevitably introduce significant noise, compromising the quality of the annotations. On the contrary, our Trechench is manually designed by 8 LMM experts, ensuring the annotation correctness and ensuring the difficulty of all questions.
Small Target Objects. All questions TreeBench focus on extremely small objects in complex real-world scenes, where target instances occupy an average of 3.05% of the image.
Traceable Evidence Evaluation. Our TreeBench provides bounding box annotations of each target instance. It not only evaluates the final answer, but also reveals the quality of intermediate reasoning steps. Those predicted bounding boxes serve window into its process, helping to diagnose the source of errors, ie., whether the model misunderstood the question or failed to locate the relevant object.
Task Difficulty. While models approach saturation (>90%) on benchmarks like V Bench (Wu & Xie, 2024), even open-source state-of-the-arr performers like Qwen2.5- (Bai et al., 2025a) achieve only 42.2 on our TreeBench, implying large potential improvement.
Beyond evaluation, we further introduce TreeVGR (Trace- able Evidence for Visual Grounded Reasoning). training paradigm enhancing localization rcalizationnddrivn visual reasoning. Previous attempts like (Wang et al., 2025e; Zheng al., 2025b; Cao et al., 2025; Fan et al., 2025; Shao et al., 2024a; Qi et al., 2024; Su et al., 2025; Liu et al., 2025a) solely supervise final answers and neglect intermediate region-of- interest generation processes. It becomes hard to quantify the actual contribution of the troomddiggthen--nsserinn framework. On the contrary, we propose TreeVGR, a novel training methodology emphasizing traceable evi- dence through reinforcement learning (RL), which explic- itly supervises bounding box generation.
Material
Mt-ibuues
mtsU
Trans
Comparison
Spn.
Con Oc.
Qwan2.5-Vt-7 78
Pad Russonor-S 76
Deep
TreeVGR-71
Building on RL with conventional accuracy-based and for- matting rewards, Tree VGR leverages a novel dual IoU reward to ensure both precision and recall in localizing the ground-truth bounding boxes for each target instance. To implement this, we curate 37K samples for RL train- ing, each comprising an image, a question, an answer, and corresponding bounding box annotations for all target instances. Empirically, initialized from Qwen2.5-VIL-7B (Bai et al., 2025a), Tre brings significant improvements on various bench- marks, i.e., +16.8 on V* Bench (Wu & Xie, 2024), +12.6 2024a), and + 13.4 on our TreeBench. Morcover, as illustrated in Figure 2, compared with related approaches, TreeVGR. enables traceable and explainable reasoning pathways with more accurate localizations (mloU), and finally contributes to bootstrapped overall performance.
Figure 2:
Normalized performance comparison with our Tre and other works (Bai et al., 2025a; Zheng al., 2025b; Su al., 2025) on our TreeBench for each category.
(Zhang et al.,
In conclusion. TrecBench pioneers the evaluation of how models "think with images", while Tree VGR establishes a blueprint for training them. Together, they significantly advance the depth and utility of multimodal reasoning assessment with traceable evidence.
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2 RELATED WORK
Large Multimodal Models. Initial breakthroughs in Large Multimodal Models (LMMs), such as Flamingo (Alayrac et al., 2022) and BLIP-2 (Li et al., 2023b), achieved this by integrating visual features into the LLM backbone via cross-attention mechanisms. A significant shift towards efficiency emerged with LLaVA (Liu et al., 2023a), which introduced a much more efficient approach. It projects visual features from a pre-trained encoder (e.g. CLIP (Radford et al., 2021)) directly into the LLM's ss semantic space using a simple two-layer MLP. This paradigm of feature projection catalyzed rapid advancement. Subsequent research has dramatically scaled LMM capabilities and tackled increasingly complex tasks (Wang et al., 2025c; Liu et al., 2024a; Li et al., 2024; Wang et al., 2025d; Bai et al., 2025a; Wang et al., 2024b; Lei et al., 2025; Zhu et al., 2025; Wu et al., 2024; Wang et al., 2024a; Yang et al., b024a;b; 2025b;a; 2026). critical frontier has been handling high-resolution inputs. Models like LLaVA-NeXT (Liu et al., 2024a) and Intern (Chen et al., 2024b) adopt any resolution strategy. Qwen2-VL (Wang et al., 2024b) and Qwen2.5-Vi (Bai et al., 2025a) introduce multimodal Rotary Position Embedding (mROPE) to support arbitrary resolution inputs. Beyond resolution, scaling pretraining with high-quality data is also vital, as demonstrated by Intern VL3 (Zhu al., 2025). Collectively, these models represent the sfate-of-the-art, forming robust baselines for diverse real-world multimodal applications. Our work builds upon these advances by leveraging their strong native visual grounding capabilities. However, existing LMMs do not " process, often resulting in misaligned or
naturally perform an explicit incomplete responses. By explicitly modeling this sequential process, our approach ensures more accurate and interpretable answers through grounded reasoning.
Reasoning LMMs. The groundbreaking reasoning capabilities of LLMs, exemplified by systems like OpenAl-ol (OpenAI, 2024b) and DeepSeek-RR R1 (Guo et al., 2025a) have motivated efforts to extend similar competencies to multimodal settings using reinforcement learning (RL) (Sutton et al., 1998). Farly approaches primarily focused on equipping LMMs to solve complex math and science problems involving image inputs (Huang et al., 2025; Wei et al., 2025a;b; Chen et al., 2025). Other approaches (Shen et al., 2025; Liu et al., 2025b; Bai et al., 2025b) directly adopt GRPO (Shao et al., 2024b) to open-ended visual grounding. Moreover, some attempts (Liu et al., 2024b; Mondal et al., 2024; Shao et al., 2024a; Qi et al., 2024) focus on regions-offinteres localization before actually answering the question. A recent milestone, OpenAl-o3 (OpenAI, 2025), advanced multimodal reasoning by enabling dynamic image manipulation, e.g., cropping and zooming into regions of interest, to emulate human-like "thinking with images." " Subsequent research has sought to replicate this capability through diverse strategies: constructing SFT data (Wang et al., 2025c), vanilla RL (Fan et al., 2025), framing grounding as a function (Zheng et al., 2025b), decoupling grounding and answering (Cao et al., 2025), multi-task reinforcement learning (Liu et al., 2025a), and curiosity- driven reasoning (Su et al., 2025). Critically, these RL-baaed methods supervise only the final answer. In contrast, our TreeVGR emphasizes traceable evidence during RL training, i.e., supervising generated bounding boxes to ensure precise localization throughout the reasoning process. By doing so, Tree VGR enables more transparent, reliable, and fine-grained control over the reasoning pipeline.
Benchmarks for LMMs. Current benchmarks lack comprehensive evaluation of multimodal models' ability to "think with images", a capability demanding three core competencies: (1) focused visual perception (identifying small targets in large scenes), (2) traceable evidence (evaluating generated bounding boxes for explainability), and (3) second-order reasoning (deriving insights beyond precise instance localization). Some benchmarks may partially satisfy the first condition. While some benchmarks address isolated aspects, critical gaps persist. Classical benchmarks like POPE (Li et al., 2023e), MMBench (Liu al., 2023b), SEED-Bench (Li et al., 2023a), and MMMU (Yue et al., 2024) usually overlook fine-grained localization and verifiable reasoning chains. V* (Wu & Xie, 2024) evaluates detailed attributes and spatial relationships (e.g.. "Is A left of B?") but relies on COCO- derived images (Lin et al., 2014), introducing high contamination risk. HAME-ReaWWord (Zhang et al., 2024a) and HR-Bench (Wang et al., 2025f) support high- high-resolution inputs but lack traceable evidence, and their questions often become easy when grounded precisely. Crucially, no benchmark integrates all three requirements, particularly the need for complex reasoning conditional on precise grounding. egg. perspective transform: "From the perspective of person A, what is the relative direction of object B?". To bridge this gap, we propose TreeBench, the first benchmark designed explicitly for "thinking with images" with traceable, multistep evaluation. Beyond accuracy, TreeBench assesses: (1) region quality, i.e., faithfulness of generated oegions-offinteree in visual reasoning chains, and (2) second-order reasoning, i.e., capabilities requiring inference beyond localization. State-of-the-ar
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models, Gemini-2.5-PPe (DeepMind, 2025b) and OpenAI-o3 (OpenAI, 2025), perform poorly TreeBench (<60%), underscoring its rigor and the unmet challenges multimodal reasoning.
TREEBENCH
TreeBench is designed to address a critical gap in multimodal evaluation by establishing the first comprehensive benchmark for assessing "thinking with images" capabilities. Specifically, it mainly evaluates (1) the ability of identifying small target objects with long, detailed, and unique text captions in large, complex, and real-world scenes, (2) the explainability of reasoning pathways and traceable evidence, and (3) second-order reasoning beyond precise localization. Our TreeBench systematically evaluates 10 core competencies through 405 distinct questions, organized into two progressive protocols, i.e., "Perception" and "Reasoning", with representative examples in Figure 1. In the following, we provide a detailed exploration of task definitions. The annotation pipeline and the final statistics of TreeBench can be found in Appendix B and Appendix C, respectively.
Perception evaluates the model's ability to accurately "see" and "identify" specific content, which one of the basic capabilities of directly extracting and interpreting visual information from every detail of the provided image. These tasks primarily evaluate first-order visual reasoning capabilitiess where correct answers usually depend on the accurate localization of target questions (e.g., objects, regions, or text) and directly recognize their explicit attributes without requiring higher-level logical inference or abstract conceptualization. It includes:
1. A Attributes evaluates the ability to identify and describe specific visual properties (e.g., color, shape, material, or precise classification) of objects or elements within images, particularly requiring attention to fine details, subtle distinctions, and accurate recognition of small-scale or context-dependent features.
2. Material measures the ability to analyze and distinguish material properties (e.g., texture, surface finish, composition, physical state) through visual cues such as light reflection, transparency, wear patterns, or microscopic structural characteristies, requiring precise reasoning about tactile qualities and material-specific visual indicators.
3. Physical State assesses the ability to assess structural integrity (e.g., damage, wear, or breakage), detect positional states (e.g., open/closed, bent/straight), and interpret age-related features (e.g., freshness, decay) through precise analysis of visual cues like cracks, alignment anomalies, lighting/shadow patterns, or contextual degradation markers.
4. Object Retrieval probes the ability interpret linguistically complex, spatially explicit de- scriptions and map them to visually subtle or contextually embedded targets in images, testing the integration of natural language understanding. spatial grounding, and discriminative object recognition under high specificity constraints.
Question-Answert evaluates the ability to extract text-based questions 5. and answer options from images, requiring seamless integration of OCR, natural language understanding, and multimodal alignment to produce accurate responses grounded in both textual and visual modalities.
Reasoning evaluates the ability to analyze and infer meaningful conclusions beyond recognition. These tasks demand second-on order visual reasoning capabilities, where correct answers require not only accurate localization but also higher-level cognitive operations over aggregated visual evidence. Precise perceptual grounding is just the first step for these tasks. It includes:
1. Perspective Transform measures the capacity to perform viewpoint transformations (e.g.. aligning viewer-centric and agent-centric frames of reference) and interpret spatial relations under mirror-reversed perspective-shifted conditions, testing the ability disambiguate directional relationships that depend on the visualized entity's orientation rather than the image's literal pixel layout.
2. Ordering evaluates the ability to analyze linearly ordered arrangements of objects (e.g., left-to- right, front-to-ban boatt, or basth- sequences) and resolve ordinal relationships by integrating spatial context with discriminative feature recognition, requiring precise localization within continuous layouts and contextual comparison of positional cues (e.g., adjacency, centrality, or extremity) answer questions dependent sequential alignment and relative placement.
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(a) Cold-Start Initialization
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Figure 3: Training pipeline of Tre. including (a) a cold-start initialization stage and (b)
reinforcement learning with traceable evidence post-training stage.
3. Contact and Occlusion measures the ability to analyze physical interactions between multiple objects (e.g., direct contact, occlusion layers, shadow-based overlaps) and resolve ambiguities in object identification by leveraging spatial dependencies, requiring precise parsing of contact cues (e.g., alignment, boundary fusion), occlusion boundaries (e.g., partial/full coverage, layer stacking), and contextual constraints to answer questions that hinge understanding how objects physically coexist and obscure one another in complex scenes.
4. Spatial Containment benchmarks the ability to analyze hierarchical spatial relationships (e.g., containment, surface attachment, or regional boundaries) by parsing visual cues like object boundaries, spatial context, and contextual containment rules, requiring precise interpretation of containment hierarchies, surface dependencies, and regional constraints resolve questions dependent on explicit spatial membership rather than isolated positional attributes.
5. Comparison assesses to compare attributes across multiple objects (e.g., distance, size, color) and resolve spatial or perceptual differences, requiring precise parsing of attribute discrimination and contextual distance estimation to answer questions demanding explicit comparison of visually ro-present entities.
TREEVGR
In this section, we introduce our TreeVGR. TreeVGR.Specificaall we leverage the native grounding capabilities of pre-trained LMMs and unlock visual grounded reasoning capabilities, i.e., localizing regions-of- interest first and answering the question next, through two-stage training pipeline shown in Figure 3, i.e., cold initialization introduced in Section 4. and reinforcement learning with traceable evidence elaborated in Section 4.2.
Notably, our Tre does not require actually replaying cropped images as previous ap- proaches (Wang et al., 2025e; Zheng et al., 2025b; Su et al., 2025) do, as text-space grounding already effective. leads to much more efficient training and inference procedures.
4.1 GOLD-STARY INTIIALIZATION
While end-to-end reinforcement learning (RL) has demonstrated validity by (Zheng et al., 2025b) for visual grounded reasoning (VGR) tasks, its practical deployment remains hindered by extreme

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computational demands. Specifically, DeepEyes-7B (Zheng et al., 2025b) requests RL training 47K samples across 32 episodes, a process requiring 32 H100 (80GB) GPUs operating continuously for 48 hours. Such resource intensity creates barriers broader accessibility.
To address these limitations, we investigate a computationally efficient alternative. Initial attempts re- vealed significant training inefficiencies when applying direct RL to VGR: models required extensive iterations to autonomously identify task-relevant visual regions before generating answers. This bot- tleneck motivates our adoption of cold initialization strategy as illustrated in Figure 3a. Specifically, we introduce a supervised fine-tuning (SFT) phase using a curated dataset comprising multimodal samples: each sample includes an image, a question, reasoning trajectories with corresponding bounding boxes, and a final answer. This structured initialization ensures VGR capabilities are established prior to RI.. Details of data construction and optimization can found in Appendix 1.
4.2 REINFORCEMENT LEARNING WITH TRACEABLE EVIDENCE
We proceed to reinforcement learning (RL) to refine reasoning trajectories through traceable evidence supervision demonstrated in Figure 3b. Specifically, the bounding boxes generated are evaluated using a box infersection-over-mmee alignment between predicted and ground-trute regions. This reward ensures explicit accountability to visual evidence, guiding the policy toward spatially accurate and logically coherent reasoning pathways.
(IoU) reward, precise and interpretable metric that measures the
Reward Design. The total reward consists of three parts: an accuracy reward Race E {0,1), formatting reward Riormat E {0, and dual Intersection-ove--uneee (loU) reward RIAU E [0, 1]:
R Race + Rtormat
RIOU
(1)
where the accuracy reward assesses whether the final answer correct. We utilize exact-matching for multiple-choice questions, and leverage an online reward model, i.e., Qwen2.5-72B Instructt (Team, 2024), to judge whether the prediction correct given the question and the ground-truth answer. The formatting reward ensures the reasoning process and the final answer must be enclosed be- tween <think> and </think>, and <answer> and </answer>, respectively, The dual IoU reward measures the quality of predicted boxes against ground-truths. Specifically, for N pre- dicted bounding boxes (b.) where b, [ of 42, ū5) and M ground-truths (br) k= where by = [at, vf .r. vł the dual IoU is average of recall term and precision term.
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[ and each ground-truth by- Maximizing this term ensures each ground-truth by is matched with at least prediction. However, we empirically find that the policy model tends to enumerate all possible boxes to obtain a larger recall. Therefore, we introduce a dual term, i.e., RP to ensure the precision and discourage "empty" boxes that do not match with any ground-truths:
N Σ loU (bb)
1
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Similarly, IoU [bx 6.] max; loU b.) indicates the maximum IoU between all ground-
truths by and each prediction {b,) Maximizing this term encourages each prediction b, to be matched with at least one ground-truth. Therefore, simultaneous optimization of both recall and precision eliminates the need for exhaustive enumeration of bounding boxes, thereby contributing to more accurate reasoning pathways. Details of data and optimization can be found in Appendix E.2.
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Table 2: Selected results of different models on TreeBench. Evaluations of open-source general models are implemented using VLMEvalKit (Duan et al., 2024), while evaluations of visual grounded reasoning models are conducted by us. "Reasoning pathways of o3 (OpenAI, 2025) are unavailable, and thus traceable evaluations are not valid. Best performances for open-source models are highlighted bold. Our TreeVGI VGR-7B achieves comparable performance with InterVL3-778B (Zhu et al., 2025).
Auributes
Material
Pby. State
Obj- Retr.
Por. Trans.
Ordering
Con & Oc
Spa. Cont.
Comparison
OCR
Overall mioU
Perception
Reasoning
Private Models
Gemini-2.5-Flashh-00
45.9
53,9
69.6
68.8
75.0
19.3
56.1
72.4
GPT-4o-1120
46.9
51.7
61.5
65.2
43.8
69.1
38.6
48.8
72.4
Bemini-2.5-Pro-O0n
54.1
51.7
61.5
56.5
75.0
83.8
20.0
36.8
65.9
86.2
54.6
o3-0416
54.8
69.0
69.2
65.2
68.8
79.4
22.4
38.6
61.0
86.2
50.0
Open-source General Models
LLaVA-Oneeission-E
37.3
55.2
53,8
56.5
50.0
32.4
21.2
22.8
41.5
72.4
36.4
40.5
62.1
53.8
65.2
62.3
36.8
28.1
53.7
65.5
47.7
Swen2.5-VIL-22.
37.0
55.2
53.8
56.5
62.5
27.9
20.0
35.1
39.0
44.8
43.2
Qwen2,5-VL-
5-VL-22B
42.2
65.5
69.2
56.5
56.3
48.5
11.8
33.3
51.2
72.4
38.6
38.8
51.7
69.2
56.5
56.3
33.7
21.2
24.6
39.0
72.4
43.2
VL3-77
46.4
61.5
52.2
68.8
52.9
33.3
86.2
Open-source Visual Grounded Reasoning Models
DeepEyes-7B
37.5
30.0
62.1
53.8
65.2
68.8
51.5
11.8
24.6
36.6
51.7
47.7
Pixel- Reasoner-7B
39.0
35.7
58.6
61.5
65.2
50.0
48.5
31.6
39.0
44.8
40,9
GGR-7
50.4
44.0
65.5
53.8
82.6
68.8
63,3
22.4
36.8
61.0
69.0
45.5
Qwen2.5-VLL77h
13.4
1 11.7
1.2
6.3
1 35.4
1
1.20.0
1 24.2
1.3
5 EXPERIMENTS
Baselines. We include four state-of-the-art private models, GPT-4o-1120 (OpenAI, 2024a) and 03-0416 (OpenAI, 2025) from OpenAI, and Demini-2.5-Flash-0522 (DeepMind, 2025a) and Gemini- 2.5-Pro-0601 (DeepMind, 2025b) from Google. Additionally, representative open-source general models are incorporated, including LLaVA-One Vision series (Li ct al., 2024), Qwen2.5-VL series (Bai al., 2025a), and Intern VL3 series (Zhu al., 2025). Furthermore, two very recent visual grounded reasoning models are also included, i.e., DeepEyes (Zheng et al., 2025b) and Pixel-Reasoner (Su et al., 2025), as both of them follow "grounding then answering" pipeline, with the capability of "thinking with images". Evaluations are mainly conducted on TreeBench, V* Bench (Wu & Xie, 2024), HR-Bench (Wang al., 2025f), and HIME-Rea/Wordd-L.. (Zhang et al., 2024a).
Results on TreeBench. Table presents per-category performance of different models. Overall, OpenAl's o3-0416 (OpenAI, 2025). the state-of-the-arr visual grounded reasoning model, demon- strates the strongest perception abilities, as expected. Larger models usually contribute to better performance. Notably, our VGR-7B even achieves comparable performance with Intern VL.3- 78B (Zhu et al., 2025), demonstrating the effectiveness of the visual grounded reasoning pipeline. Moreover, compared with visual grounded reasoning models, our Tree not only achieves higher overall performance, but also obtains a larger mloU, indicating its effectiveness in precisely localizing target objects. More in-depth analysis on TreeBench can be found in Appendix D.
Results High- Resoluteso Benchmarks. In Table 3, VGR achieves open-source state-of- the-art on V* Bench (Wu & Xie, 2024). On HRRBBench (Wang et al., 2025f) and MME-Rea RealWorld Lite (Zhang al., 2024a) illustrated in Table 3 and Table 4, respectively, significant improvements over our base model, Qwen2.5-VL-77 (Bai et al., 2025a). Results on other general benchmarks can be found in Appendix F.I.
brings
Ablation Studies. The core contribution TreeVGRE is the traceable training pipeline, where Ria is incorporated in conventional RI. training. The effectiveness of this design is ablated in Appendix F.2.
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Published as a conference paper at ICLR 2026
Table Comparison with state-of-the-art alternatives on V* Bench (Wu & Xie, 2024) and HRBench (Wang et al., 2025f). All results are self-collected. Best performances of visual grounded reasoning models are highlighted in bold.
v Bench
HR MRR-Bench-4K
IR-Bench-8K
Overall
Attr.
Spatial
Overall
Single
Cross
Overall
Single Cross
Private Models
GPT-4o-
66.0
o3-0416
95.7
Open-source General Models
ILaVA-OOnViision-77
70.7
73.0
60.5
64.3
74.8
53.8
59.8
65.3
54.3
LLaVA-One Vision-72B
73.8
80.9
76.5
56.0
60.9
53.0
InterVVL3-8B
72.3
73.0
71.1
70.8
79.3
62.3
62.0
64.3
59.8
InternVL3-78P3
76.4
75.7
77.6
75.5
84.5
66.5
67.3
71.8
62.8
Qwen2.5 VVL-7B
74.3
77.4
69.7
72.1
88.8
55.5
68.8
83.5
54.0
Qwen2.5-VL-72B
84.8
90.8
80.9
79.4
88.8
70.0
76.3
84.3
68.3
Open-source Visual Grounded Reasoning Models
Pixel-Reasoner-7B
80.6
83.5
76.3
72.9
86.0
60.3
66.9
80.0
54.3
DeepEyyes-BB
90.0
92.1
86.8
75.1
91.3
59.0
72.6
86.8
58.5
Tree
91.1
94.0
87.0
77.1
90.3
64.0
73.1
86.5
59.8
Qwen2.5-VIL-7B
16.8
16.6
17.3
5.0
1.5
1 8.5
4.3
3.0
5.8
Table 4: Comparison with state-of-the-art alternatives MME-RealWorld-Lnn (Zhang et al., 2024a).
All results are self-collected. The best performance is highlighted in bold.
Perception
Reasoning
Overall
OCR
RS
DT
MO
AD
OCR
DT
MO
AD
General Models
Qwen2.5-VL..2
42.3
87.6
32.7
83.0
27.3
30.0
72.0
62.0
28.7
23.0
Qwen2.5-VLL-72
43.7
90.8
34.0
87.0
27.9
30.6
74.0
61.0
26.7
25.5
LLaVA-One Oneennionn-78
43.7
80.0
40.0
56.0
31.7
39.4
65.0
33.0
38.0
32.0
LLaVA-One Vision
48.7
79.2
50.7
67.0
37.9
40.0
76.0
41.0
38.7
39.3
IntemVL3-8B
47.9
83.6
49.3
75.0
34.5
36.9
70.0
44.0
40.0
37.0
Intem
52.3
87.6
54.7
77.0
42.6
36.6
76.0
56.0
46.0
40.3
Visual Grounded Reasoning Models
Fixel-Reasoner-
49.7
89.6
52.0
86.0
38.9
30.9
71.0
72.0
46.0
32.5
DeepEyes-7B
53.2
90.0
52.7
89.0
43.3
33.4
76.0
69.0
44.0
35.0
VRR-VG V.S. Qwen2.5-VL--B
54.9
87.6
50.7
83.0
47.0
43.4
74.0
66.0
51.3
39,0
12.6
0.0
1 18.0
0.0
19.7
f 13.4
2.0
4.0
1 22.6
1 16.0
CONCLUSION
This paper introduces TreeBench, benchmark designed to rigorously evaluate visual grounded reasoning (VGR) "thinking with images" in large multimodal models, and TreeVGR. two-stage training framework that enhances VGR methods through traceable evidence supervision.
TreeBench addresses critical gaps in existing benchmarks by focusing on three principles: focused visual perception (identifying subtle targets in cluttered scenes), traceable evidence (quantifiable reasoning chains via bounding box annotations), and vision-centric second-order reasoning. Con- structed through expert-driven annotation and multi-stage quality control, TreeBench features 405 high-difficulty visual question-ansee answer pairs with precise bounding boxes, emphasizing small objects in real-world world scenarios. It reveals the limitations of ofate-of-the-att models, e.g., OpenAl-o3 (OpenAI, 2025) scores 54.8%, while setting a new standard for assessing nuanced visual grounding, multi-step reasoning transparency, and cross-modal interaction.

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Published as a conference paper at ICLR 2026
VGR advances VGR training through reinforcement learning guided by dual IoU rewards, which explicitly supervise bounding box generation to ensure both precision and recall. This approach enables explainable reasoning pathways and achieves significant improvements across benchmarks.
Limitation and future works. The current implementation of Tree VGR is based on a 7B parameter model, which may limit scalability compared to larger architectures. TreeBench contains only 405 rigorously curated question-and -nnweer pairs. Expanding the benchmark with additional samples across broader domains would further challenge model capabilities. Scaling up would be future work.
ACKNOWLEDGEMENNS
This work was supported by the Beijing Natural Science Foundation (No. L257015) and the National Natural Science Foundation of China (No. 62320106010).
ETHICS STATEMENT
Our research is grounded in ethical practices, with particular attention paid to the responsible use of data. All datasets employed in this study are publicly available and well-establisedd within the computer vision community. Specifically, our benchmarking was conducted on SA-1B (Kirillov et al., 2023). Our use of this data is in accordance with their provided licenses and intended academic purpose.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of the research presented in this paper. To this end, comprehensive implementation details for our models and experiments are provided Appendix E, including the training procedures and all hyperparameters used. Furthermore, upon acceptance of this paper, all source code, datasets, and trained model checkpoints will be made publicly available.
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APPENDIX
A OVERVIEW
Here, we provide table of contents:
First, in Appendix B, we provide the annotation pipeline detail, which includes three rounds of quality control.
In Appendix C, we introduce statistics of our TreeBench.
In Appendix D, perform in-depth analysis TreeBench.
In Appendix E, we provide implementation details of our two-stage training pipeline, including cold-start initialization and reinforcement learning with traceable evidence.
In Appendix F, we provide more experiments of our TreeVGR, including results on general multimodal benchmarks and ablation studies.
In Appendix G, we discuss our limitations in detail. Finally, in Appendix H, we provide qualitative examples and failure cases of our Tree VGR.
B ANNOTATION PIPELINE
TreeBench was constructed through systematic pipeline combining automated sampling. LMM- assisted generation, and three rounds of human validation. The annotation team contains eight human experts in LMMs, including six Ph.D candidates and two senior research scientists.
1. Image Selection. A total of IK images initially sampled from the SA-IB (Kirillov et al., 2023), with deliberate prioritization of images containing high-density objects (e.g., scenes with overlapping or clustered items), it offers high-resolution, real-world scenes with large number of small and varied objects, making it particularly suitable for evaluating visual grounded reasoning. To ensure balanced representation across categories, 100 images are initially allocated per category.
2. First Round Quality Control. The annotation team manually evaluates the relevance and quality of each image for its assigned category. This step is critical for addressing category- requirements, e-g., the "Ordering" category necessitates images with visually similar or repetitive objects for practical reasoning tasks. Following this review, 647 images meet the criteria.
3. Automated Question Generation. Question-option-anssee trios are then generated using two advanced LMMs, i.e., OpenAI-o3 (OpenAI, 2025) and Gemini-2.5-Pa (DeepMind, 2025b), each tasked with producing three diverse, high-quality questions per image. Prompts are designed to emphasize task-specific complexity and sesual-semantic alignment.
4. Second Round Quality Control. Human experts then manually review all six model-generated questions per image. For each image, annotators selected the most semantically coherent and task-relevant question from the pool of six, prioritizing: (1) alignment with the target subtask, (2) avoidance trivial or ambiguous object referring, and (3) clarity and unambiguous answerability. none of the six questions met these criteria, annotators manually constructed a new question. This step ensures that only high-quality, human-vetted questions advance to the next stage.
5. Difficulty Filtering. Questions deemed insufficiently challenging removed through model- based consensus screening. Specifically, any question answered correctly by all four state-of- the-art lanion-language models (Qwen2.5-VL-72) (Bai al., 2025a), InternVL3-78B (Zhu et al., 2025), GPT-4o (OpenAI, 2024a), Gemini-2.5-Flaa (DeepMind, 2025a)) was excluded to ensure the benchmark retained meaningful difficulty.
6. Third Round Quality Control. The final cross-verification phase engages independent human annotators cross-validate the accuracy and relevance of each question-optioo--assee pair. The final dataset comprised 405 rigorously validated questions.
C BENCHMARK STATISTICS
Distribution of Each Subtask. As demonstrated in Figure 4, TreeBench emphasizes advanced reasoning tasks, accounting for 63% of the total subtasks (256 questions), while basic perception-
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5 (1
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A
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Ponzption
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C
41.5%
37%
(113)
2 (182)
27.9%
B (113)
44.9%
27.9%
Distribution each discipline in TreeBench, which prioritizes reasoning over perception.
Figure 4:
Figure 5: The ground-truth distribution of TreeBench with 3 instances E and in- stances of
Distribution of the number of instances in TreeBench, with one question with target instances.
Figure 6:
related tasks constitute 37% (149 questions). Within the reasoning category, key subtasks reflect a focus on complex spatial and relational understanding. This structure underscores a deliberate prioritization of higher-order reasoning foundational perceptual tasks, aligning with the goal of challenging models to process nuanced relationships and transformations rather than mere object recognition attribute detection.
Distribution of Answers. As illustrated in Figure 5, the ground-truth distribution of TreeBench is dominated by four main categories: (28.6%, 116 instances), (27.9% 113 instances), C (27.5 27.9%, 113 instances), and D (13.8%, 56 instances). These account for 98.2% of the total 405 instances. The remaining 1.8% (7 instances) includes E (3 instances) and instances). This structure highlights balanced emphasis categories A, B, and C, with D as a notable secondary group, while E and F represent minor but distinct components.
Distribution of the Number of Target Instances. Figure 6 shows the distribution of the number of target instances per question. The majority of questions TreeBench require identifying 1 or 2 target instances, accounting for 41.5% (168 questions) and 44.9% (182 questions) of the total, respectively. Questions requiring 3, 4, 5, or targets constitute smaller fractions: 4.2% (17 questions), 4.0% (16 questions), 4.0% (16 questions), and 1.2% (5 questions), respectively. Notably, a single question (highlighted in gray) demands 8 target instances, representing an extreme case. Overall, 86.4% of questions focus on 1-2 targets, suggesting balance between simplicity and complexity in task design while incorporating rare multi-target scenarios for comprehensive evaluation.
Distribution of Target Instance Area. We compute the relative area for each target in- stance using its bounding box, i.e., area HW (322 y1 (r2 21). where H and W are the imput resolution. Figure is the histogram of the mean area for each question. It illus- trates that the majority of target instances in TreeBench are extremely small, with sharp peak near 0.0 and long tail extending to larger areas (up to 0.7). The mean area across all ques- tions is 0.0305, confirming that targets are pre- dominantly tiny. Most questions (highest fre- quency bin) involve target instances with areas clustered around 0.0 0.05, while only small fraction require identifying larger objects. This distribution highlights the importance of addressing challenging scenarios where small-scale object detection and reasoning are crucial, potentially compromising model performance.
200
Frequency
150
50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Area for Each Target Instance
Figure 7: The histogram of mean target instance areas per question with a low average of 0.0305 (indicating small target instances).
D ANALYSIS TREEBENCH
Correlation between Localization and Performance. Importantly, for visual grounded reasoning models, our traceable evaluation demonstrates a positive correlation between localization preci-
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0.14
0.12
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Cornet Predictions Wrong Pred adainiiona
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Whong Predictions
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Figure 8: Distribution of Figure 9:
Distribution of
Quentiio
Figure 10:
Distribution of IoU
toU Each Question
IoU for each question in TreeBench.
IoU for each question in TreeBench- Seeeeenc-Perreptiin
for each question in TreeBench- Reasoning.
Treetennh Perforannce
%
Treettench Performance
Treeteench Performance
22
82
70
06
63
70
ADD Nelfomancc
MatrVisiia Performance
Wibhtr Perffrr mento
Figure 11: coupling with A12D (Hiipala al., 2021).
Performance de-
Figure 12: Performance decou- pling with Vista (Lu et al., 2023).
Figure 13: Performance decou- pling with MMStar (Chen et al., 2024a).
sion and the overall performance, as illustrated in Table 2. This positive correlation between precise localization (mloU) and overall performance is evident in the progressive improvement from DeepEyes-7B (Zheng et al., 2025b) Pixel-Reasoner-7B (Su et al., 2025) to our final TreeVGR-7B. As mIoU increases, the overall scores rise correspondingly, with Tree achieving the highest mloU and strongest overall performance the same time.
Beyond global analysis, we further plot the histogram of IoU for each question in Figure 8, where blue bars represent wrong predictions and orange bars are correct predictions. Overall, wrong predictions tend to have smaller loU values. However, by going deeper through the lens of perception and reasoning, the relationship between mloU and performance diverges. Precise localization (mloU) aligns closely with perception performance demonstrated in Figure 9. In contrast, as shown in Figure 10, reasoning performance reveals a weaker correlation with mIoU, as improvements in localization alone fail to fully translate to complex reasoning tasks. This disconnect suggests that reasoning questions of TreeBench require second-order cognitive capabilities that go beyond precise spatial localization.
Correlation with Other Multimodal Benchmarks. We systematically compare our TreeBench with three existing multimodal benchmarks: A12D (Hiippala al., 2021), MathVista (Iu et al., 2023), and MMStar (Chen et al., 2024a), in Figure 11, Figure 12, and Figure 13, respectively, to investigate potential performance correlations. Our analysis reveals decoupling of performance characteristics. For instance, while OPT-40-1120 (OpenAI, 2024a) ranks among the top performers on BrecBench, it lags significantly behind alternatives on other benchmarks. This dissociation underscores the unique emphasis on "thinking with images" of our TreeBench.
The Quality of Visual Evidence in TreeBench. First, in Table we mask all instances during inference on TreeBench. The results show a significant performance drop across all models. This confirms that the bounding boxes TreeBench are not only high-quality but also indispensable for accurate visual grounded reasoning, directly validating the importance of the annotated evidence. Moreover, we conduct dual experiments in Table 6. When we provide ground-truth bounding boxes as explicit evidence hints models, all models achieve consistent performance gains. It indicates that bounding boxes indeed help models explicitly anchor their reasoning to visual evidence: without boxes, models may rely on ambiguous textual biases or global image impressions, while with boxes, they are forced to align their answers with the specific visual content in the evidence region, which reflects shift from "heuristic reasoning" to "evidence-based reasoning".
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Table 5: Performance comparison with masked target instances. When masking out all target instances on TreeBench, we observe a significant performance drop across all models, confirming that the annotated bounding boxes are not only high-quality but also indispensable for accurate visual grounded reasoning.
Masking Qwen2.5-VL-7B InternVL3-8B
GPT-4o
o3
Gemini-2.5-FFaas
Gemini-2.5-PP
37.0
38.8
46.9
54.8
45.9
54.1
31.8 5.2
4.2
29.1 4 17.8 33.8
4 21.0 29.9 4 16.0
33.1 4 21.0
Table 6:
Performance comparison with explicit bounding boxes-based textual hints. When we provide ground-truth bounding boxes explicit evidence hints models, all models achieve consistent performance gains.
Textual Boxes Qwen2.5-VL-7B IntemVL3-8B
GPT-4o
o3
Gemini Fla-hlash Gemini-2.5-Prr
37.0
38.8
46.9
54.8
45.9
54.1 61.0 6.90
43.7 f 6.7
43.5 1 4.7
49.4 1 2.5
58.3
3.5
51.9 f 6.0
E IMPLEMENTATION DETAILS
E. COLD-START INITIALIZATION
Data Construction. We base supervised fine-tuning (SFT) dataset on VGR-158K (Wang et al., 2025e), which provides pseudo-chain of thouhttttt annotations paired with bounding boxes for visual reasoning tasks. However, to align with the grounding capabilities of our base model (Qwen2.5-VL series (Bai et al., 2025a)), which outputs absolute coordinates rather than the normalized coordinates (ranging from 0 to 1) used by LLaVA-NeXT (Liu et al., 2024a) in (Wang al., 2025e), perform coordinate system conversion. Specifically, for each bounding box, we transform normalized coordi- nates [Ta yu Tag r22] into absolute coordinates via [Ir, ,,, y/] = [Wr. H WTa2 lrr where H x W is the resolution of the input image. Next, we filter samples to prioritize complex reasoning pathways, retaining only entries with multiple bounding boxes (i.e., more than one box per reasoning trajectory). This yields 35K samples, as multi-box interactions demand stronger spatial-temporte reasoning compared to single-box tasks. Subsequently, we construct a reflective subset of 4.7K samples among them by introducing controlled perturbations: for each sample, we (1) inject a synthetic error by inserting a randomly generated incorrect bounding box into the reasoning sequence, and (2) append the meta-cognitive prompt "Wait, this box seems to be wrong" immediately afterward, resulting in our TreeVGR-SFT-3S. This design explicitly trains the model to detect and correct erroneous visual grounding, which is critical skill for robust real-world deployment.
Optimization. Initialized from Qwen2.5- VL-BB -L--B-Instruct Instrucct (Bai et al., 2025a), we train Tree VGR-7B- CI ("CT" here stands for Cold Initialization) with 8 GPUs using LLaMA-Factor (Zheng et al., 2024), where the AdamW optimizer (Loshchilov & Hutter, 2017) with a learning rate of 5e-6 and a global batch size of 256 is utilized. The learning rate is decayed following a cosine schedule (Loshchilov & Hutter, 2016) with a warmup ratio of 0.1.
E.2 REINFORCEMENT LEARNING
Data Construction. Tree VGR incorporates a novel dual IoU reward, which means each sample should contain ground-truth bounding boxes during the RL phase. To this end, we filter hard samples from the original 191K training set of V (Wu & Xie, 2024) using Swen2.5-VL--7B-Inttre et al., 2025a), resulting in 30K samples. Additionally, we incorporate the VisDrone dataset (Zhu et al., 2021), which is originally designed for detection and tracking under UAV images, which offers extremely high-resolution, real-world scenes with large number of small and varied objects and their corresponding bounding box annotations. We reformulate the training set and the validation set into 38K multiple-choice counting problems, and only retain samples with the ground-truth number ranging from 5 to 10, contributing to the final 7K samples. Finally, our Tree of 30K open-ended anesstoonansseringg samples from V* (Wu & Xie, 2024) and 7K multiple-choice problems from VisDrone (Zhu et al., 2021).
(Bai
consists
Optimization. Initialized from Tree VGR-7B-CI. train our final with another 8 GPUs serving the reward model, i.e., Qwen2.5-72B-Instte (Team, 2024), using LLLM (Kwon et al., 2023). We adopt Group Relative Policy Optimization (GRPO) (Shao al.,
with 8 GPUs,
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Table Comparison with state-of-the-ar alternatives on other multimodal benchmarks, including CV-Bench (Tong et al., 2024a), MMVP (Tong et al., 2024b), MMBench (Liu et al., 2023b), POPE (Li al., 2023c), Al2D (Hiippala al., 2021), and ChartQA (Masry al., 2022). *Results are obtained from (Guo et al., 2025b), otherwise are self-collected.
Capability
Benchmark
Qwen2.5-VL-77
Tre-VGR-7B
Qwen2.5-VL-72B
Vision-centric
CV-Bench-2D
74.1
76.9 2.2
77.7
question
CV-Bench-3D
72.6
77.6 5.0
87.0
answering
MMVP
66.7
75.3 8.6
66.7!
MMBench
83.1
84.4 1.3
88.61
General VQA
POPE
86.7
87.2 0.5
84.9
Document and chart
Al2D
84.9
84.8 4 0.1
88.71
ChartQAus
85.6
85.8 0.2
89.51
Table 8: Ablations of each component of our TreeVGR. "MME-RW" stands for MME-Real Lite (Zhang et al., 2024a), and "Acc" represents the multiple-choice accuracy. This improvement mainly comes from the training set, as many training samples from V* (Wu & Xie, 2024) are included in RI. The model enumerates boxes to obtain larger IoU recall, and fails to produce final answers.
Rewards
TreeBench
V*
MME-RW
Cold-Start
Race + Risemar
R
RP
Acc
mloU
Acc
Acc
Qwen2.5-VIL-7B
37.0
71.2
42.3
Cold-Start
39.0
23.4
76.4
Tree VGR
50.4
44.0
91.1
54.9
w/o Traceable Evidence
38.0
27.2
87.9t
$1.6
w/o Precision
0.0
78.3
0.0
0.0
w/o Recall
45.4
20.6
89.5
52.6
Text-Only
39.0
86.91
46.3
2024b), which has been proved to be effective and efficient for diverse tasks. We have also tried DAPO (Yu al., 2025), but we find unstable compared with GRPO. Therefore, we simply utilize the original GRPO (Shao et al., 2024b). We implement using EasyRI (Zheng et al., 2025a), which is clean fork of veRL (Sheng al., 2024). We train our Tree VGR-7B with 5 epochs on Tree VGR- RL-37K, which is significantly less than DeepEyes-7B (Zheng et al., 2025b) (which is trained on 47K samples with 32 epochs).
F MORE EXPERIMENTS
F.1 RESULTS ON OTHER MULTIMODAL BENCHMARKS
In Table 7, we compare our TrR with its base model Qwen2.5-VL-78 (Bai al., 2025a) a variety of conventional multimodal benchmarks. Specifically, we select CV-Bench (Tong et al., 2024a) and MMVP (Tong al., 2024b) evaluate vision-centric question-amswering capabilities. MMBench (Liu et al., 2023b) and POPE (Li et al., 2023c) are selected for evaluating general VQA capabilities, and A12D (Hiippala al., 2021) and ChartQA (Masry et al., 2022) for comprehension with document and chart. We observe significant improvements in most cases, especially for vision- centric benchmarks. Notably, VGR-7 achieves 75.3 MMVP (Tong et al., 2024b), surpasses Qwen2.5-VL-77. (Bai et al., 2025a) by significant margin.
F.2 ABLATION STUDIES
The core contribution of Tree is the traceable training pipeline, where the dual IoU reward Rio is incorporated in conventional RL training. Therefore, we aim to evaluate the effectiveness of including this traceable term. As demonstrated in Table FreeVGR. including the cost-start initialization and reward functions.
ablate each component of our
The cold-start stage is quite beneficial for visual grounded reasoning, when compared with ! and ② This means the formatting of outputting bounding boxes of target instances useful for conventional visual grounded reasoning benchmarks like V* Bench (Wu & Xie, 2024) and MME-
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RealWorld-Lite (Zhang et al., 2024a). Note that these benchmarks can be regarded as -fl-off-Dmminn (OOD) samples for the SFT dataset.
Traceable visual grounded reasoning is more effective than untraceable one, when compared with ) and ① Starting from the same cold-start checkpoint, integrating dual IoU rewards into the RL framework yields substantial performance gains. particularly on our TreeBench and MMME-ReaWWorldLLAn (Zhang et al., 2024a), which rep- resent -fstiiiibbuttii (OOD) scenarios relative to the RL training data. Notably, on TreeBench, our VGR demonstrates significant enhancements both overall ac- curacy and mloU. This dual improvement suggests that precise and interpretable reasoning pathways are critical for achieving optimal performance, underscoring the value of structured reward design in complex, real-world tasks.
932-
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630
522
230-
232
20
Figure 14: Mcan response length with different IoU rewards. The precision term is crucial for alleviating the repeti- tion problem.
The precision term is crucial for alleviating the repetition problem, when compared with @ and @ As illustrated in Figure 14, without precision, the mean response length grows rapidly. When evaluating this model, we find that it tends enumerate candidate bounding boxes to obtain larger IoU recall and thus always fails to produce final answers.
The recall term is crucial for precise and complete localization, when compared with a and O. On TreeBench, without the recall term, the model achieves significant accuracy improvements, but the localization accuracy (mloU) remains limited, usually grounding incomplete target instances.
Vanilla text-only RL is not so effective as visual grounded reasoning, when compared with @ and ①. Vanilla RL in text-based tasks demonstrates value through its text-space reasoning capabilities. However, when integrating visual grounded reasoning with traceable evidence, the performance gains become more significant. This highlights the critical role of two factors: (I) pre-answer contextual grounding to anchor responses in multimodal evidence, and (2) accurate spatial localization to refine decision- decision-making precision.
G LIMITATIONS AND FUTURE WORKS
One possible limitation of VGR is the model scale and architecture, which is limited to Qwen2.55 VL-7B (Bai et al., 2025a). Experiments with other base models and larger model scales could be future work. Furthermore, Tre is not general multimodal reasoner, as it is not designed to perform ultra-long reasoning processes in math, sciences, and coding. How to effectively unify vision-centric reasoning models with standard text-centric models could be future work.
As for TreeBench, we find that the "perspective transform" protocol becomes one of the major bottlenecks, which means current state-of-ther art multimodal models, even including visual grounded reasoning models, have not effectively modeled the ego-view 3D awareness. Moreover, basic perception capabilities under complex scenes are also limited, leading to relatively low scores on "attributes" and "material". How to effectively let LMMs perceive any details of the dense visual world becomes critical challenge.
H QUALITATIVE EXAMPLES OF TREEVGR
We first provide qualitative examples of the correlation between the predicted bounding boxes and the model's internal attention maps in Figure 15, where the attention values are the mean attention of generated tokens related to bounding boxes (highlighted red). Strong correlations are observed across examples, indicating that the model's focus is truly guided by those textual bounding boxes- based hints. This explains why visual grounded reasoning pipelines could be such effective. Note that sometimes the visual attention is still being overwhelmed by textual domain knowledge even if the grounding is accurate (see the failure case in Figure 15). Explicitly enforcing the model's 's focus beyond attention may be one potential solution.
Next, we provide qualitative examples and failure cases of our Tree VGR on each category of TreeBench in Figures 16 to 25.
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Question: Wiat the cokor the candled!
(A) Red
Yellow
(C) Onyy
<tinic lins, identify the candles in the image. They are located near the top left
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Question: Wlat the coker of womanns shirt?
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(8o Purple
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look at her shirt which is clearly vieble she is swinging The shirt has
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Question: What the color of the flng?
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Question: Whet the coler of the Apple logo? (A) Pulychrumatir
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(C) White
(D) Silver
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it knowa sycabol that has been white many years. There are no other
logos or colors that could change this conclasion. So, the coler of the Apple logo Stthank..
Figure 15: Qualitative examples (first three rows) and failure cases (the last row) V* Bench (Wu
Xie, 2024) with attention maps.
Question: the the top of tall pageds lornted the
Question: shape of ornament very park the tall, sharply pointed weoden spire that rises from the shinglel rnd the foreground wooden bailding!
A_ Yellow
B. White
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Figure 16: Qualitative examples (left) and failure cases (right) on the "Attributes" protocol of TreeBench.
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Queetion:
likely primary gooeeneck kichen faucet peominanlly positoned the cetter forggoondd above the stainles steel sink?
Bloor reflections
The white plate loroground, adorned light brown goometric pattern and holding the falafel, most likely has which type surface finish?
Question:
moterial finish combirusioo
A. Polished
Brushed stainless Otl-mbbeed bronze
High-gless.
R.
C. conted alley
C. Textured non-shlip sillicone.
D. Glass like.
TreeGGR-7D
Target Instances:
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Find, identiff the tall, groseneck krtchen firortt
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the cente--oregruudd
stainles-
steel sink. Succet has a shiny, reflective surface The staineess sorel sink how a refleerive surface. The lighting the image is hright, which enhonzzss the re fllettive quality. Cansidering the optians: Polished brass would love waem, Brushed stainless has light, mesallic which matehes faucet's uppeanacee. Marte
Fins, identify the white plate in the foreground with the falafel <hos- <box>|Ju620 (1.621.1.66620 1536,2016///bsss The plate las light brows geometric pullers. diflused, shuap glossy, show any glare
reclection plate are
golden
diffevent
couted alloy
or reflections that would indicate high gloss finish. The surface of the plate smooth and does not have the appeomece of sextured, non-slip vilicene surface. Alen the plate glooollke plass would have differeet reflective quality <ithinks
have dack, non reflctivve finish, which doesalt march. Oil rabbed bronze has dack, masic linish, also nos musching, The flurets finish is consistent with brmshal stainless stred which common and dumble moterial kitchen faucess,
canewrr- Bechswrr-
Figure 17: Qualitative examples (left) and failure cases (right) on the "Material" protocol of TreeBench.
SCOTIAAL
Question: the plysical posture bay the rod shin the grassy bull at the fur top led?
Question: current state soccer portion of field?
lower-right
A. He is standing and watching the performance.
B. biigg down tlat the the midele sunning the hill.
A. Rolling along the grass B. Resting matioulls on the ground Suspended
D. He is sitting with his knees beat.
D. Tropped under unimmmed player's
TreVGGR-7R
TreeVGR-7R
<think-
<think>
idcutity boy
shiri grassy sitting on grass with logs
Fast, Alestily the ball
paction the patems.. Then.
<box- 1120,492,175, 17,,77777771 bboss under him, which is typical sitting posture with knnos bast. There is so
132-111201098,16640 (12)bo0
alecrve its posiiion relative to the playes. The player in the white unifonn (number 891 the process of licking a. The bell cleatly on the gi ground and murioa resting. or trapped graes <hhinkc
indication that be stnddnng lylnn down, running. So, physical posture that mutches the description is siting his sait<<think>>
being kicked. There indication the players So, hall rolling along
mid
-amowar--D
<anewrr> wewes--///nneee </nnowrr>
Figure 18: Qualitative examples on the "Physical State" protocol of TreeBench.
22

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Published as a conference paper at ICLR 2026
PEL IN PLACE
Question: Which object located dinectly the right of the man werning a black long-eloeve shirt, black parts, and black cup ho standing the left side of strce??
Question:
What color of the umbrella the wemon in the right corner of houee
holding?
white. medium commercial parked
gate
B. A Seanis P450 tanker track with a red and white flgg on top
B. Green
C. A healthy, medium comiferous neir road and checkkpoint streetlight
C. Black
moad,
D. Red
Target Instances:
<hink
First, identify the man wearing a black long sleeve shirt black pants, and black
TnwVGR--RR
who standing the left side the <hose (454,340,04 (6,0066,111) <//os>..>.
<think>
right. The top <box-[1112 (1172,110,1666,0000 -o0) directly to right. The other options
flag
<bosr <box=1140015500 |1400]550, 1294 She is holding an unbrella. The unbrelia is pink <bose 1900, 1266,66 -/4)-/bbcc There are green, blck,, red unbrella she hokling
Fins,, identify the
right
are not diseelly to right. Oytion A behlnd hum. Option fariher back, and Option D the right side of the stroct So the best anvour is R-Sttiikk- -answer-B<sanswes
umbrellas
pink-<think>> -answgr-Aslaneweer
Figure 19: Qualitative examples (left) and failure cases (right) on the "Object Retrieval" protocol of TreeBench.
marge Satirns
Recognize
options image
Recognize
options image
Tre-VG
TreVGR--70 <thirk>>
<think>>
Fist, identity the pattemned whise building <bex- 11311188111111100000 and
Fins, identify the faryy libeled "Souk Kiel" <hev--11164 (112111111112117111 1711 They
brick building <bos- 18556594,2300.11 -ioss. The white building
ship "ColorLine CARGO" <box- [1435, 894,2008,111 Sichbox the pier. feny cargo ship is in the bacisround The image shows that the two ships are side by. side and there no significant distance between them The options A, B. and alll suggest separation, bit the image clerrly shows they part the fony oachslad which is true as cargo ship not obstucting the ferry ottink- <anewor- [>>aawwer>>
image orange building right side.
foreground
So. the white building on the left the orange brick, buildin. <othink>
contact Option is
Figure 20: Qualitative examples on the JOCR-Integrated TreeBench.
protocol of
23

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Published as a conference paper at ICLR 2026
From the the wearing mask brown and- which dinection werring long-sloeve lops brown trousers, and carrying bug shoubles loraied?
Question:
Question: From the par the third person riding clectri scooter fiom. which direction compoet SUV located!
A. From right
A. Fromt lett
B. Fromt left
Left rear
D. Rightt rear
Target Instances:
<think>
TreeVGR-17
Tre-GRR-7B
<bou- 1584,10144556, 156,1140)=-bors Then, woman in longedoreve lepy dark brown trosees, and carying bag her leh shoulder
wearing
stiped
which is parked near the building the right side of the image Then, consiber the of chind-penoon rillnn electric scooter from left right The SUV is behind the right right.
Finst, identify the rod compact SUV <box-[1084, 1194,,0944,1164,19444
<bos> (724,854,784, 1027788444-200000 =/bee, The woman positioncd behind and to the lell perspective, lefleeer dircetion relative the
from
rider.
right-rear
ciecerion. <think-
Camswero -answr-CCSumieerr
Figure 21: Qualitative examples (left) and failure cases (right) on the "Perspective Transform" protocol of TreeBench.
Counting right person black
Question: behind pink sign, counted right!
A. The lirst one
A. The first one
B.
B. The socond
The ene
D. The founh one
D. The ane
TrecVGR-7B
Target lastances:
<think>>
<box- [16-11,944,2227 (2442027.1224)bboo This person wearing a black
penson
image
<thikk>
their left land Then, check the person to their left
First, identify the pink sign <baxc (814,832,110 113321101,,9122221111 -1)- in the image. Then,
<hos> (1432,450, 1371 iolbest, who sloo has back watch on their lell hand. Since one sequence right
look the trees behind it. The firs
left tall, numrow evergreen
penson
and has more rounded shape. The fourth tree taller than the chird one. So, the third tree is the shortest smong thom when counted from left right <itinic>
narnww
Colanswar
Figure 22: Qualitative examples (left) and failure cases (right) the "Ordering" protocol of TreeBench.
24

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Published as a conference paper at ICLR 2026
Question: ccclusioo relationalip cxists between wentan central picture wearing tradttiona green clothing and woman weuting a peacock cloak behind her?
Question: any for right the pieture the rowering power polos?
relationship mailing
A. The hase of railins contact with the gawer polt. part the railing
B.
the power no contact between the twa.
A. Complecely blocked
B. Partially blocked
C. Completely wtblocked
D. Overlapping but visible
D. camnot determined.
Tre-GGR-1B
Target Instances:
<think>
Fins, idlenitt the
contral picture weasing traditional grees clothing
TnwVGR-TB
15,5,
Then, lind the woman wearing peacock closk
<think>
behind ber choup (2240-2222222323333 4,224,435.56666 (3556,66-6/b The woman in green partially
Fins, idantify the milling the far right of picture
obsoured
peacock clock, peacock cloak covering relationship as
<bos- (2526,475,2777 475,2772.675)-11
<hos- 2,340,0,2416,0ll 2406,,00)////bxx The milling is power poles
the
fence rumning slong
edye of the sotting srca, and the power pole is tall, vatical strucurc. By observing the image, the railing and the significntt disance
pole uee panalicl and there touch
them. The railing connection or
any point, correct answer that there no contact between <ithink> <answers CunwerrCC<laaeee C-lanswer
indication
Figure 23: Qualitative examples (left) and failure cases (right) the "Contact and Occlusion"
protocol of TreeBench.
Question: golden oenament the pupple complenely enclosed by the transparent plastic sheec?
Where pray
white rectongular luminsire sgroen
(positioned just
pas (llon) relation
A. No, the omoemene entirely outside the sheat
A. Fully bebind (inside) the fence, reeted in the garden area.
Ves, fully inside the
B. Scraddiigg the with its outside
inside.
C. Only the lower part ornament inside sheet
coserete sidewalk.
D. Only the upper part of ormument inside the sheet
D. Hanging from the tade of the nearest sower.
TreVGR--TR
Target lastances:
<think>
Fins, idencitt the golden omament <hox- (b0x=1200.345...0m (200_355.448..))))) (48
TreVGR-TB <think.>
purple robed statue on the float, The golden cenament part the statue. Then,
look at the trmnpaaent plantic sheet -bex- 1s 180,896,1000 (000)-/bog> which covers the The plastic sheet wrapped
Fint, identify the teal pevilion <bosc (416, 264, 1832)=/bex>> which left of
and goldea cotride
prey lang
rectangular
filly
lamiusiee positiened just
dask-green
the sheet. So, the omament fully inside <ibinkk>
<bose (8.1800, 1240, 2072 27/2-//bu>-- is flou baiidings The street-lanp is oa the sidewalk and in froar of the fence, and is fully behind the fence. from the So, it from the fence concrete sidewalk. <hhink>
<ancwer- C-lanwwr--
Figure 24: Qualitative examples (left) and failure cases (right) on the "Spatial Containment" protocol of TreeBench.
25

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Published as a conference paper at ICLR 2026
Question: distunt skyline, the octual height slender, gray shaped skyscraper ering chimney not for right, milky white buikdnng to the left the frume. Which object the photo is actulllyy tiller?
Question: the picture, left side the extends from the back of the front
hand strops, counting 10, which actual distance littl girl's head the closest!
A.2
B.
smokestack appears
among buildings
C.4
Themilky
appears ansong buildings
D. All objects are not visible clearly enough to judge beight.
C. The blue-glass skyseraper appears stallest among the buildings
First, identity the left side hand stras in the image.
<thikk>
Tre-GGR-7B
<box- 10n0-119,755288. 15.)888 <boss
<bos2-4408,10 (408,1 67, (75,475555)44.... 475)
<bos- <bux-1150,2200
(72.285524555)-bbbb
Fint, identity the three key buildings in the image. The slender, blue glass, cone
<boxt (904,360, 550,550)-bbb3
from <boc- (962.22810003235570
shaped skysoraper -bor- (II 44,354, 1283, 1224, 1224)-/bbx> the tallest among the three. The narnow, dark chimney <bere (1688,592, (62,8166 <chor the milky building (0.1 fmue aborier sillid smong the three <thrnn->
(1024-(102A,0 10, 1015, (75555555-40 b05 <hoc- 1352,335, 1432,515) (5)cho
<hosch 4424, 1500 hho) Counting these, there hand The
littl girl's head
zeong unis long,
skyscraper. So, the skyseraper
little girl's the closeet. The question anks for the option that matches this distance and the position of the little git's head. The option 5 the closest match it the clasest among given options. <thisk>
back car
CCcluteeer?
-answge-Bohanseee
Figure 25: Qualitative examples (left) and failure cases (right) on the "Comparison" protocol of TreeBench.
26


Reviews:
[{"review_id": "39YnrUjxwq", "reviewer": "Reviewer_PkSa", "summary": "This paper aims to enhance the \"thinking with images\" capability of large multimodal models (LMMs). To this end, we introduce a benchmark named TreeBench and a training pipeline called TreeVGR. Specifically, TreeBench comprises 406 visual question-answer pairs, each accompanied by trace evidence that serves as a verifiable grounding instance. The proposed TreeVGR method extends the reinforcement learning algorithm GRPO to incorporate relevant visual instances (e.g., bounding boxes) as a form of Chain-of-Thought. Extensive experiments on TreeBench and the V* benchmark demonstrate the effectiveness of TreeVGR.", "questions": "1. How is the causal relationship between the evidence and the final answer validated? A critical test would be to see if the model's performance drops significantly when the key evidence instances are masked.\n\n2. Should the individual reward components ($R_{acc}, R_{iou}, R_{format}$) be normalized to the same scale? If not, there is a risk that the term with the largest magnitude could dominate the entire training process.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The TreeBench Benchmark: They construct a novel VQA benchmark wherein each question is explicitly linked to a groundable instance that serves as traceable evidence, ensuring verifiability.\n\n2. The TreeVGR Method: They propose a GRPO-based training pipeline designed to enhance the groundedness of LMMs in VQA. The TreeVGR method guides the model to identify and utilize relevant evidential instances as a Chain-of-Thought.", "weaknesses": "1. The paper does not explicitly measure the quality of the groundable evidence in TreeBench. Given the semi-automatic annotation process (Lines 91-101), how is the correctness of this evidence guaranteed? A compelling way to validate the importance of the evidence would be to observe if masking the critical instances (e.g., the bounding boxes) leads to a significant drop in VQA performance.\n\n2. The overall reward $R=R_{acc} + R_{iou} + R_{format}$ combines terms from different scales. Were these reward components normalized to a common range to prevent any single term from disproportionately dominating the optimization?", "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": "3yHpxv81mK", "reviewer": "Reviewer_rrGV", "summary": "This paper presents TreeBench, a benchmark for evaluating traceable visual grounded reasoning, and TreeVGR, a reinforcement learning framework that jointly supervises reasoning and localization using a dual IoU reward. TreeBench includes 405 expert-curated VQA samples with bounding box annotations to assess perception, reasoning, and evidence traceability. TreeVGR improves visual reasoning through a two-stage training process with supervised initialization followed by reinforcement learning, achieving notable improvements across multiple benchmarks and contributing to more explainable multimodal reasoning.", "questions": "Please see the weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Studying visual grounding in the reasoning process is very meaningful because most models may ignore intermediate results in decision-making and fail to learn the true causal relationships.\n- A new benchmark has been constructed, which allows researchers to consider a wider range of factors.", "weaknesses": "- Although the paper emphasizes achieving traceability through bounding boxes, the \"intermediate interpretability\" of the inference chain is still weak if it relies solely on box localization measure (mIoU).\n- Although the paper includes extensive comparisons with open-source multimodal models such as LLaVA-OneVision and Qwen2.5-VL, these models are not specifically designed for reasoning or reinforcement learning–based visual grounded reasoning. As a result, the experimental comparison may not fully demonstrate TreeVGR’s advantage over other reasoning-oriented approaches.\n- In the *Reinforcement Learning with Traceable Evidence* stage, the paper assumes that reasoning chains should explicitly include bounding boxes to ensure traceability. However, the necessity of this design is not fully justified. Reasoning transparency could also be achieved through implicit or attention-based grounding without inserting explicit box tokens. The paper lacks an analysis or ablation to clarify whether explicit box supervision is essential for reasoning quality.\n- It is recommended to discuss work related to the interpretability of visual grounding [1].\n\n[1] Interpreting Object-level Foundation Models via Visual Precision Search. CVPR 2025.", "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": "6Okxj2b9T7", "reviewer": "Reviewer_qSgY", "summary": "This paper tackles the challenge of making vision language models (VLMs) not only answer visual questions correctly but also show where in the image their reasoning comes from. Current VLMs like Qwen2.5-VL or GPT-4V often produce correct answers without verifiable visual grounding, leading to untraceable or hallucinated reasoning.\n\nTo address this, the authors propose TreeVGR, a two-stage training framework built on Qwen2.5-VL-7B, and introduce TreeBench, a new benchmark for evaluating traceable visual grounded reasoning.\n\nTreeBench contains manually verified image–question–answer triplets with annotated bounding boxes marking the visual evidence, covering ten sub-tasks spanning perception (e.g., color, attribute, OCR) and reasoning (e.g., ordering, contact, spatial containment).\n\nTreeVGR first performs supervised fine-tuning to teach the model to produce structured reasoning with bounding boxes, then applies reinforcement learning with a reward that combines answer correctness, reasoning format, and bounding-box IoU.\n\nThe resulting TreeVGR-7B achieves nice gains over the base Qwen2.5-VL-7B on TreeBench (+13.4 points) and shows moderate improvements on other multimodal reasoning benchmarks such as V*Bench and MME-RealWorld. The authors claim that TreeVGR enables more transparent and verifiable visual reasoning by linking model predictions to explicit image evidence.", "questions": "1. How do the authors envision scaling TreeBench to larger or more diverse datasets without compromising annotation quality or requiring extensive human effort, since this is one of the reasons the authors claim their dataset to be superior?\n\n2. The paper assumes that correct reasoning must manifest through accurate bounding-box prediction, yet models may still attend to the correct region internally without explicitly outputting coordinates. Have the authors analysed attention maps or other internal signals to verify that bounding-box accuracy genuinely reflects visual grounding?\n\n3. Since both the training data and TreeBench share nearly identical QA structures and output formats, to what extent do the reported gains represent task adaptation rather than generalizable reasoning improvements? Have the authors tested transfer to unseen reasoning styles or datasets?\n\n4. The RL component seems to add only marginal improvements. Can the authors clarify what qualitative or behavioural differences emerge after RL fine-tuning compared to supervised fine-tuning alone, and whether these differences justify the added complexity?\n\n5. Given that TreeVGR fine-tunes the full Qwen2.5-VL model, have the authors evaluated whether general tasks such as captioning or VQA are preserved, or does the model overfit to the traceable reasoning format?\n\n6. The paper claims that the method enhances “traceable reasoning,” but without analyzing the alignment between predicted evidence and model attention. Can the authors provide evidence—such as attention heatmaps or token-level visualization—that the model truly focuses on the localized regions it predicts or what was the before vs after effect of their method on the base model?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. TreeBench is carefully constructed and manually verified for correctness and visual traceability, providing one of the first benchmarks that explicitly links reasoning answers to bounding-box evidence.\n\n2. The paper identifies a gap in current multimodal research: the lack of verifiable, evidence-grounded reasoning, and frames the need for “traceable visual reasoning” in a straightforward way.\n\n3. The two-stage TreeVGR pipeline (supervised fine-tuning followed by RL with evidence-based rewards) is simple, reproducible, and effectively demonstrates that incorporating bounding-box supervision can improve visual reasoning performance.\n\n4. The experiments cover diverse perception and reasoning sub-tasks, results are consistently reported, and the writing is clear, making both the dataset and the approach easy to understand and potentially useful for future follow-up work.", "weaknesses": "1. TreeBench, though high quality, relies heavily on manual verification of image–question–evidence triplets, making it difficult to scale to larger or more diverse data. The process is partly automated but still human-dependent, which restricts reproducibility and extensibility.\n\n2. The paper equates correct reasoning with the ability to predict accurate bounding boxes, but provides no empirical evidence that models failing to output boxes are not attending to the correct regions internally. This makes the “traceability” assumption more procedural than cognitive, and potentially misleading.\n\n3. The model is trained and tested on tasks with nearly identical structures and output formats (question + bounding-box evidence). As a result, the large reported gains likely reflect adaptation to the curated data and benchmark design rather than a general improvement in reasoning ability.\n\n4. The RL stage yields only minor improvements over supervised fine-tuning and is insufficiently analysed. The paper does not demonstrate that reinforcement learning meaningfully enhances reasoning depth or grounding beyond improving output formatting.\n\n5. The study evaluates only on reasoning-style benchmarks and does not test whether TreeVGR retains the broader multimodal skills of the base Qwen2.5-VL model. This leaves open the possibility of overfitting or catastrophic forgetting of non-reasoning capabilities.\n\n6. Although the method claims to enhance traceable reasoning, the paper never validates whether the predicted evidence regions align with the model’s internal attention patterns or decision process, limiting the interpretability claims it makes.", "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": "tmLM0O50fO", "reviewer": "Reviewer_o6mU", "summary": "This paper aims to assess and improve the grounding reasoning of Vision-Language Models. To this end, it initially introduces a small, high-difficulty benchmark targeting detailed visual queries. Furthermore, the authors present a two-stage training method, which is demonstrated to boost the performance of Qwen2.5-VL-7B on their self-constructed benchmark.", "questions": "Please refer to the issues detailed in the Weakness part.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The proposed benchmark fills a gap in existing open-source benchmarks by specifically testing a model's ability to attend to fine-grained details\n2. The proposed method significantly improves the performance of Qwen2.5-VL-7B on their self-constructed benchmark", "weaknesses": "1. Regarding the ground truth for the intermediate reasoning: 1) How was this data obtained or generated? 2) What measures were taken to ensure its correctness and accuracy? 3) What was the underlying method and rationale for its construction?\n\t2. The authors have collected a 37k-sample dataset for RL, but it is unclear how the contributions of the data and the proposed method are disentangled. The paper would be strengthened by an ablation study that isolates the impact of each component. To provide a clearer picture of the method's efficacy, I suggest including a baseline that applies conventional RL algorithms to the same 37k dataset. This would help clarify whether the performance gains stem from the novel method itself or the curated data.\n\t3. The paper would benefit from a clearer justification for the evaluation dimensions presented in Section 3. Could the authors elaborate on the rationale behind their selection and explain the method used to ensure their comprehensiveness? Furthermore, providing data distribution across these different dimensions would be crucial.\n\t4. The paper only provides results on a self-constructed benchmark that focuses on small regions. Provide results on public benchmarks. Besides, does this specialized training approach compromise the model's generalization ability?\n\t5. Does focusing on a single small point per image create bias? This approach may allow the model to succeed without understanding the global context of the image.", "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)