SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
Score
82首次提出针对科学论文图像的五维全文本评估基准，突破了以往仅视觉表面的局限，提供了新数据与框架。
论文方法描述清晰，实验设计严谨，数据集规模大且标注质量高，但部分细节如跨模态融合策略可进一步展开。
为学术出版质量控制、AI辅助审稿和科学图表自动评估提供了可直接使用的标准基准与高效方案。
论文发表于2025年7月，覆盖2020-2025年最新会议论文，且针对当下AI生成内容评估热点，时效性极强。
来源为arXiv知名子领域（AI/CL/LG），可公开访问，数据集和代码将开源，信源可靠。
Base 91 × T2 Source weight ×0.9 = 82
Curation line 70Selected
Why selected
The paper features a large-scale, high-quality annotated dataset, a novel cross-modal evaluation framework with strong results, and provides a reproducible public benchmark and code.
AI commentary
「This paper precisely targets the gap in scientific figure quality assessment: existing methods either focus on natural photos or only perform surface-level comparisons. SciFigQual-Bench not only provides a large-scale expert-annotated dataset but also binds each figure to its full manuscript context, making evaluation more aligned with real peer-review scenarios. The staged cross-modal design of SFQ-Agent is also insightful—collecting evidence from each modality before fusion, which is more interpretable than direct end-to-end reasoning. This benchmark is worth attention for academic publishing platforms, paper review systems, and teams working on scientific visualization.」
Quick overview
Existing image quality assessment methods are mainly designed for natural photos or AI-generated content, and cannot be directly applied to scientific figures. This paper introduces SciFigQual-Bench, a full-text contextual benchmark containing 6,308 scientific images from top computer science conferences (2020-2025), annotated by experts across five dimensions: clarity, layout, caption fit, context relevance, and misleading risk. A staged cross-modal evaluation framework SFQ-Agent is proposed to achieve auditable and refined scoring by collecting and fusing modal evidence. Experiments show that SFQ-Agent (F3) with GPT-5.6-Sol achieves the lowest overall mean absolute error (0.418) and highest consistency rate (93.4%) on the eval1200 subset, consistently outperforming direct evaluation and auxiliary VLM schemes.
- Constructed a full-text benchmark with 6,308 scientific images, independently scored by multiple domain experts across five dimensions to form gold-standard annotations.
- Each image is bound to its caption, citing sentence, and manuscript context, overcoming previous limitations of visual-surface-only comparisons.
- Proposed SFQ-Agent, a staged cross-modal evaluation framework that achieves auditable refined scoring through evidence collection and fusion.
- SFQ-Agent (F3) with GPT-5.6-Sol achieves the lowest MAE of 0.418 and highest consistency rate of 93.4%, outperforming direct evaluation and auxiliary VLM schemes.
Full text
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