KMMG-Agent: A Knowledge–Memory Multi-Graph Agent for Multi-hop Egocentric Question Answering

Large pretrained language models can store vast amounts of factual knowledge and leverage it to answer user queries. However, they remain limited when handling information beyond their knowledge boundaries. Equipping agents with external knowledge interaction and memory mechanisms has proven to be an effective strategy, enabling them to dynamically acquire explicit knowledge and incorporate it into the reasoning process, thereby improving answer accuracy. Nevertheless, existing memory retrieval and information matching methods often overlook temporal information in memory and lack effective modeling of the connections between dynamic memories and large-scale external knowledge. To address these limitations, we propose the Knowledge Memory Multi-Graph Agent (KMMG-Agent), which preserves repeated memory events as distinct timestamped edges and combines query-specific temporal filtering with intermediate-entity-based chained retrieval to support two-hop question answering over personal memories and Wikipedia knowledge. To systematically evaluate the effectiveness of the proposed framework across different cross-source reasoning scenarios, we further construct a multi-hop question-answering benchmark that integrates memory and external knowledge and covers four distinct reasoning paths. Experimental results show that KMMG-Agent outperforms existing baselines on all three memory-related tasks, achieving approximately 11.8%-33.2% improvements in Exact Match (EM) accuracy. These results demonstrate the effectiveness of KMMG-Agent in temporal memory retrieval and cross-source multi-hop reasoning.