VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence

• URL: http://arxiv.org/abs/2508.04852v1
• Date: Wed, 06 Aug 2025 19:59:42 GMT
• Title: VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence
• Authors: Chenhui Qiang, Zhaoyang Wei, Xumeng Han Zipeng Wang, Siyao Li, Xiangyuan Lan, Jianbin Jiao, Zhenjun Han,
• Abstract summary: VER-Bench is a novel framework to evaluate MLLMs' ability to identify fine-grained visual clues.<n>Each question in VER-Bench is accompanied by structured evidence: visual clues and question-related reasoning derived from them.
• Score: 24.872901965956604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of MLLMs, evaluating their visual capabilities has become increasingly crucial. Current benchmarks primarily fall into two main types: basic perception benchmarks, which focus on local details but lack deep reasoning (e.g., "what is in the image?"), and mainstream reasoning benchmarks, which concentrate on prominent image elements but may fail to assess subtle clues requiring intricate analysis. However, profound visual understanding and complex reasoning depend more on interpreting subtle, inconspicuous local details than on perceiving salient, macro-level objects. These details, though occupying minimal image area, often contain richer, more critical information for robust analysis. To bridge this gap, we introduce the VER-Bench, a novel framework to evaluate MLLMs' ability to: 1) identify fine-grained visual clues, often occupying on average just 0.25% of the image area; 2) integrate these clues with world knowledge for complex reasoning. Comprising 374 carefully designed questions across Geospatial, Temporal, Situational, Intent, System State, and Symbolic reasoning, each question in VER-Bench is accompanied by structured evidence: visual clues and question-related reasoning derived from them. VER-Bench reveals current models' limitations in extracting subtle visual evidence and constructing evidence-based arguments, highlighting the need to enhance models's capabilities in fine-grained visual evidence extraction, integration, and reasoning for genuine visual understanding and human-like analysis. Dataset and additional materials are available https://github.com/verbta/ACMMM-25-Materials.

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