Retrieval-augmented generation (RAG) grounds large language models (LLMs) with external evidence. Dynamic knowledge tasks, however, require systems to decide not only what to retrieve but also when to refresh, how to arbitrate conflicts, and how to preserve an auditable record of the evidence used to answer a query. We present LedgerRAG, a trigger-aware retrieval chain framework that maintains an explicit claim-level evidence ledger and uses coverage, temporal validity, authority, and conflict signals to control retrieval, refresh, and stopping decisions. We expand the evaluation with a query-level BM25 baseline, a dense retriever setting, and task-aligned proxy baselines representing graph-style retrieval, temporal-only retrieval, and conflict-focused retrieval. The revised results show that LedgerRAG’s clearest advantage lies in conflict governance and auditable evidence control, achieving near-perfect ConFLICT adjudication (CRAcc = 0.993) under authority-aware routing while yielding more modest gains and explicit trade-offs in regulation-change and streaming settings.
Wang et al. (Thu,) studied this question.