Q-Mirror：释放纯文本科学问答对的多模态潜力
Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs
AI总结：
本文提出Q-Mirror框架与智能体系统，将纯文本科学问答对转化为多模态问答对，并通过生成—评估闭环把平均分提升至85.22、通过率提升至95%。
摘要：
高质量的多模态基准对于提升大模型的科学推理能力至关重要，但其人工创建成本高昂且难以扩展。为解决这一瓶颈，我们探索了将纯文本问答对（TQAs）转化为高质量多模态问答对（MMQAs）的潜力，工作包括三个部分：1）任务定义与评估准则：我们开发了一个TQA到MMQA的框架，并建立了全面、多维的MMQA质量准则，为转化提供原则。2）基准构建：随后，我们构建了两个大规模基准，以在MMQA生成和MMQA质量评估这两个不同任务上严格评估最先进的生成与理解模型。3）初步解决方案：我们开发了一个智能体系统Q-Mirror，通过将MMQA生成与评估整合进用于迭代改进的闭环来落实该框架。实验表明，尽管最先进的模型能够生成MMQAs，但其输出仍存在显著差距，凸显了可靠评估的必要性。我们进一步证明，顶级理解模型在MMQA质量评估中与人类判断高度一致。利用这两点洞察，Q-Mirror智能体将平均分从78.90提升至85.22，通过率从72%提升至95%，为大规模科学基准提供了一条实用路径。
英文摘要：
High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck, we explore the potential for transforming Text-Only QA Pairs (TQAs) into high-quality Multi-Modal QA Pairs (MMQAs), which include three parts: 1) Task Definition \& Evaluation Rubric: We develop a TQA-to-MMQA framework and establish a comprehensive, multi-dimensional MMQA quality rubric that provides principles for the transformation. 2) Benchmark Construction: Then we construct two extensive benchmarks to rigorously evaluate state-of-the-art generation \& understanding models on the distinct tasks of MMQA generation \& MMQA quality evaluation. 3) Preliminary Solution: We develop an agentic system (Q-Mirror), which operationalizes our framework by integrating MMQA generation and evaluation into a closed loop for iterative refinement. Our experiments show that while state-of-the-art models can generate MMQAs, their outputs still leave substantial gaps, underscoring the need for reliable evaluation. We further demonstrate that top-tier understanding models align closely with human judgment in MMQA quality assessment. Leveraging both insights, the Q-Mirror agent raises average scores from 78.90 to 85.22 and pass rates from 72\% to 95\%, offering a practical path to large-scale scientific benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
