评估论文图表质量？SciFigQual-Bench这个新基准让GPT-5.6-Sol驱动的SFQ-Agent打分，误差比直问模型低43%，更靠谱。
SciFigQual-Bench是一个全新的科学图像质量评估基准，涵盖2020至2025年间顶级计算机科学会议的6308张图像，由多个领域专家在清晰度、布局、标题匹配、上下文相关性和误导风险五个维度进行独立评分并聚合为黄金标准。该基准将每张图像与其标题、引用句和论文上下文绑定，不同于仅做视觉表面比较的以往研究。在eval1200测试子集上，配备GPT-5.6-Sol的SFQ-Agent(F3)取得了最低平均绝对误差0.418和最高一致率93.4%，显著优于直接评估和辅助VLM方案。
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers. However, existing image quality assessment (IQA) methods are predominantly designed for natural photographs or AI-generated content, which cannot be directly applied to scientific papers. The few existing studies on scholarly charts remain confined to visual-surface comparisons, failing to verify caption alignment, citation relevance, or visual misleadingness. To address this, we propose SciFigQual-Bench, a full-text contextual benchmark that evaluates scientific images across five dimensions (clarity, layout, caption fit, context relevance, and misleading risk). The data covers top computer-science conferences from 2020 to 2025; 6,308 images were independently scored by multiple domain experts in five dimensions and aggregated into gold-standard annotations. Unlike previous scientific figure benchmarks, our dataset binds each image to its caption, citing sentence, and manuscript context. To enable automated evaluation on this benchmark, we designed a staged cross-modal evaluation framework SFQ-Agent to achieve auditable and refined scoring through the collection and fusion of modal evidence. Multiple mainstream large models were evaluated on the test subset eval1200, and SFQ-Agent (F3) equipped with GPT-5.6-Sol achieved the lowest overall average absolute error (0.418) and the highest consistency rate (93.4%), consistently outperforming both direct evaluation and auxiliary (Sidecar) visual language model evaluation schemes.