MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs

Lun Zhan; Feng Xiong; Huanyong Liu; Feng Zhang; Yuhui Yin

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arXiv · 2602.23632

MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs

Lun Zhan·Feng Xiong·Huanyong Liu·Feng Zhang·Yuhui Yin

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Abstract

Synthesizing high-quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long-tail knowledge coverage, effectiveness verification, and interpretability. Knowledge-graph-based approaches still fall short in functionality, granularity, customizability, and evaluation. To address these issues, we propose MMKG-RDS, a flexible framework for reasoning data synthesis that leverages multimodal knowledge graphs. It supports fine-grained knowledge extraction, customizable path sampling, and multidimensional data quality scoring. We validate MMKG-RDS with the MMKG-RDS-Bench dataset, covering five domains, 17 task types, and 14,950 samples. Experimental results show fine-tuning Qwen3 models (0.6B/8B/32B) on a small number of synthesized samples improves reasoning accuracy by 9.2%. The framework also generates distinct data, challenging existing models on tasks involving tables and formulas, useful for complex benchmark construction. The dataset and code are available at https://github.com/360AILAB-NLP/MMKG-RDS

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Lun Zhan, Feng Xiong, Huanyong Liu, Feng Zhang, Yuhui Yin. 2026-02-27. MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs. https://arxiv.org/abs/2602.23632

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