{"slug": "mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning", "title": "MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning", "summary": "MMKG-R1, a reinforcement learning framework that trains a multimodal large language model to construct multimodal knowledge graphs using quality-aware rewards, improves downstream question-answering accuracy across multiple datasets, according to the arXiv paper 2610.19341v1. The framework evaluates both the quality of information extracted from individual knowledge sources and the quality of the assembled graph, addressing the lack of explicit graph-quality guidance in existing construction methods. The authors report that MMKG-R1's constructed graphs outperform prior approaches on downstream QA tasks.", "body_md": "arXiv:2610.19341v1 Announce Type: new \nAbstract: Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG), which aims to improve the performance of multimodal large language models (MLLMs). The effectiveness of these multimodal Graph RAG methods largely depends on the quality of the constructed MMKGs. However, most existing methods construct MMKGs without explicit guidance on graph quality, while reinforcement learning (RL)-based methods that rely on final-answer correctness may provide only indirect feedback on MMKG quality. To address these limitations, we propose MMKG-R1, a novel MMKG-quality-driven RL framework for training an MLLM as a multimodal knowledge constructor. To be specific, MMKG-R1 introduces multiple quality-aware rewards to directly evaluate both the quality of information extracted from individual knowledge sources and the quality of the assembled MMKG. These rewards are then used to guide the RL training. Experiments across multiple datasets show that MMKG-R1 constructs MMKGs that improve downstream question-answering accuracy.", "url": "https://wpnews.pro/news/mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning", "canonical_source": "https://arxiv.org/abs/2610.19341", "published_at": "2026-10-15 04:00:00+00:00", "updated_at": "2026-10-15 05:56:23.551513+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "natural-language-processing"], "entities": ["MMKG-R1", "arXiv", "multimodal large language models", "multimodal knowledge graphs", "Graph Retrieval-Augmented Generation"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning", "markdown": "https://wpnews.pro/news/mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning.md", "text": "https://wpnews.pro/news/mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning.txt", "jsonld": "https://wpnews.pro/news/mmkg-r1-constructing-multimodal-knowledge-graphs-via-reinforcement-learning.jsonld"}}