标题： MDK12-Bench：多模态大语言模型在多学科考试中的全面评估 显示英文标题

标题： MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams

摘要： 多模态大语言模型（MLLMs）通过整合语言和视觉提示进行问题解决，在推动人工通用智能（AGI）方面至关重要。 然而，目前用于衡量MLLMs智能的基准测试存在规模有限、覆盖范围狭窄和知识结构不清晰的问题，仅提供静态且无差别的评估。 为了弥补这一差距，我们引入MDK12-Bench，这是一个大规模的跨学科基准测试，源自涵盖六个学科的真实K-12考试，包含141K个实例和6,225个知识点，按六层分类法组织。 涵盖五种题型并带有难度和年份标注，它能够进行全面评估，以捕捉MLLMs在四个维度上的表现： 1）难度等级，2）时间（跨年度）变化，3）情境变化，和4）知识驱动推理。 我们提出了一种新颖的动态评估框架，引入了不熟悉的视觉、文本和问题形式变化，以挑战模型的泛化能力，同时通过减少数据污染来提高基准的客观性和持久性。 我们进一步评估了知识点参考增强生成（KP-RAG），以研究知识在问题解决中的作用。 关键发现揭示了当前MLLMs在多个方面的局限性，并为提高模型鲁棒性、可解释性和人工智能辅助教育提供了指导。 显示英文摘要

摘要： Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, current benchmarks for measuring the intelligence of MLLMs suffer from limited scale, narrow coverage, and unstructured knowledge, offering only static and undifferentiated evaluations. To bridge this gap, we introduce MDK12-Bench, a large-scale multidisciplinary benchmark built from real-world K-12 exams spanning six disciplines with 141K instances and 6,225 knowledge points organized in a six-layer taxonomy. Covering five question formats with difficulty and year annotations, it enables comprehensive evaluation to capture the extent to which MLLMs perform over four dimensions: 1) difficulty levels, 2) temporal (cross-year) shifts, 3) contextual shifts, and 4) knowledge-driven reasoning. We propose a novel dynamic evaluation framework that introduces unfamiliar visual, textual, and question form shifts to challenge model generalization while improving benchmark objectivity and longevity by mitigating data contamination. We further evaluate knowledge-point reference-augmented generation (KP-RAG) to examine the role of knowledge in problem-solving. Key findings reveal limitations in current MLLMs in multiple aspects and provide guidance for enhancing model robustness, interpretability, and AI-assisted education.