Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and
  Methodology
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically
referencing 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 visual 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 is available at https://github.com/Haochen-Wang409/TreeVGR.
68706dcdc8391850d60977fb
Haochen Wang
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2025-07-11T08:00:30.986Z
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HaochenWang
user
HaochenWang
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Xiangtai Li
claimed_verified
2025-07-11T08:00:28.856Z
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Xiangtai Li
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LXT
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Zilong Huang
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Anran Wang
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Jiacong Wang
claimed_verified
2025-07-11T08:00:26.581Z
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wang
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stormthunder
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Tao Zhang
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Jiani Zheng
claimed_verified
2025-07-11T08:00:33.749Z
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zjn
user
garlicisnotmyfavor
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Sule Bai
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Zijian Kang
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Jiashi Feng
68706dcdc8391850d6097805
Zhuochen Wang
68706dcdc8391850d6097806
Zhaoxiang Zhang
Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and
  Methodology
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically
referencing 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 visual 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 is available at https://github.com/Haochen-Wang409/TreeVGR.
TreeBench evaluates visual grounded reasoning with a focus on subtle targets, traceable evidence, and second-order reasoning, while TreeVGR enhances this reasoning using reinforcement learning.
visual grounded reasoning TreeBench bounding box evaluation second-order reasoning TreeVGR reinforcement learning Qwen2.5-VL-7B V* Bench MME-RealWorld