MMD-TKGR: Multi-agent Multi-round Debate for Temporal Knowledge Graph Reasoning

Yadong Wu, Xuanhong Li, Po Hu

Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims to predict future events or relationships by leveraging historical facts within temporal knowledge graphs. By integrating the structured semantics of knowledge graphs with temporal dynamics, TKGR is well-suited for tasks involving time-dependent inference, such as temporal relation extraction and event prediction. In recent years, large language models (LLMs) have been increasingly applied to TKGR. However, a single LLM agent often struggles to handle complex reasoning tasks effectively, primarily due to its relatively narrow reasoning perspective and shallow reasoning depth. To address these challenges, this paper proposes a Multi-agent Multi-round Debate framework for Temporal Knowledge Graph Reasoning (MMD-TKGR). The framework optimizes the construction of query-specific historical contexts by introducing a relation-temporal cooperative retrieval mechanism, and it dynamically calibrates prediction results and enhances their interpretability through a multi-round debate structure based on multi-agent collaboration. Experimental results demonstrate that the proposed method significantly outperforms existing approaches on several benchmark datasets.

External IDs:dblp:conf/nlpcc/WuLH25