GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention
This work proposes GRATE (Gated Rotary Attention for Temporal Encoding), an entity-side message function that adds no learnable parameters and encodes time through relative time differences by rotating each edge message according to its time gap to the query and applying a query-conditioned gate to select temporally relevant signals.