IntervalRAG: Hierarchical Interval Graphs for Time-Grounded Retrieval-Augmented Generation

IntervalRAG is a retrieval-augmented generation framework for questions whose answers depend on when a fact remains valid. Such questions often concern a persistent state, while the supporting evidence is distributed across appointments, departures, and other transitions in a narrative. Retrieving an isolated observation can therefore leave the generator to reconstruct which state applies to the queried period and how its boundaries are established. IntervalRAG organizes this evidence in a two-level temporal graph: macro units identify broad states or phases, and micro units preserve individual facts and transitions together with the temporal information needed to place them in context. Hierarchical edges retain the association between each micro-event and its source context, while local temporal edges expose neighboring evidence for assembling a short chronology. The query first locates a relevant macro context and then guides the selection of nearby micro-events and source passages around the requested period. The resulting context presents the relevant state together with the transitions that explain its temporal boundaries. We evaluate IntervalRAG on TimeQA, TempReason, and ComplexTR, where it obtains accuracies of 60.93%, 91.79%, and 68.39%, respectively. Code and data will be released upon publication.