标题： 驯服未驯服的：基于图的知识检索与推理，使MLLMs征服未知 显示英文标题

标题： Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown

摘要： 知识的实际价值不仅在于其积累，更在于其被有效利用以征服未知的潜力。 尽管最近的多模态大语言模型（MLLMs）表现出令人印象深刻的多模态能力，但由于相关知识有限，它们在很少遇到的特定领域任务中常常失败。 为了探索这一点，我们采用视觉游戏认知作为测试平台，并选择怪物猎人：世界作为目标来构建一个跨模态知识图谱（MH-MMKG），该图谱结合了多种模态和复杂的实体关系。 我们还设计了一系列基于MH-MMKG的挑战性查询，以评估模型在复杂知识检索和推理方面的能力。 此外，我们提出了一种多智能体检索器，使模型能够在不进行额外训练的情况下自主搜索相关知识。 实验结果表明，我们的方法显著提高了MLLMs的性能，为多模态知识增强推理提供了新的视角，并为未来的研究奠定了坚实的基础。 显示英文摘要

摘要： The real value of knowledge lies not just in its accumulation, but in its potential to be harnessed effectively to conquer the unknown. Although recent multimodal large language models (MLLMs) exhibit impressing multimodal capabilities, they often fail in rarely encountered domain-specific tasks due to limited relevant knowledge. To explore this, we adopt visual game cognition as a testbed and select Monster Hunter: World as the target to construct a multimodal knowledge graph (MH-MMKG), which incorporates multi-modalities and intricate entity relations. We also design a series of challenging queries based on MH-MMKG to evaluate the models' ability for complex knowledge retrieval and reasoning. Furthermore, we propose a multi-agent retriever that enables a model to autonomously search relevant knowledge without additional training. Experimental results show that our approach significantly enhances the performance of MLLMs, providing a new perspective on multimodal knowledge-augmented reasoning and laying a solid foundation for future research.