AIエージェントによる適応的な知識グラフ構築にむけて

抄録

As LLM-based agents become more integrated into real-world workflows, ensuringaccurate task execution through collaboration is a key challenge. To address this, applyingRetrieval-Augmented Generation (RAG) with knowledge graphs (KGs) is particularly promising.However, existing KGs are often incomplete or unreliable in specific task domains, limiting theireffective- ness in supporting accurate decision-making. We tackle this limitation with a methodbuilt on three key components: (1) Autonomous extrac- tion of structured, domain-specific knowledgefrom inter-agent discussion logs; (2) Integration of this knowledge into a shared, evolvingKG; and (3) Autonomous refinement of the KG by LLM agents during task execu- tion, ensuringconsistency and enabling real-time decision-making. Preliminary results show that our method improvesrelation accuracy from 79% to 97%, highlighting its effectiveness. Future work will explorehow refined knowledge enhances task performance.