AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning

ArXi:2605.07121v1 Announce Type: new Temporal knowledge graphs (TKGs) represent time-stamped relational facts and a wide range of reasoning tasks over evolving events. However, existing methods produce entity representations that are static at the entity level, in that each representation is a function of learned parameters only and retains no trace of the interactions in which the entity has participated. In this paper, we depart from this static view and propose that each entity be modeled as an adaptive process whose representation is refined every time the entity participates in a fact.