Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and
  Methodology
Authors: name: Haochen Wang; name: Xiangtai Li; name: Zilong Huang; name: Anran Wang; name: Jiacong Wang; name: Tao Zhang; name: Jiani Zheng; name: Sule Bai; name: Zijian Kang; name: Jiashi Feng; name: Zhuochen Wang; name: Zhaoxiang Zhang
Published: 2025-07-10 17:59:58

Abstract: 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.