Arbiter-RAG: A Neuro-Symbolic Framework for Conflict Resolution and Temporal Consistency in Autonomous Knowledge Graphs

Abstract

Knowledge graphs play a vital role in grounding Large Language Models (LLMs) in verifiable facts, which matters wherever facts evolve continuously and stale information is costly, as in clinical informatics. However, existing Retrieval-Augmented Generation (RAG) systems suffer from "structural hallucination," where contradictory "ghost nodes" corrupt retrieval context. In order to resolve conflicts at ingestion and eliminate stale graph topologies, it is crucial to enforce deterministic symbolic governance over probabilistic LLM outputs. For this purpose, a neuro-symbolic framework, Arbiter-RAG, is proposed which integrates a deterministic arbitration layer with a temporal knowledge graph. Incoming predicates are classified into four types i.e. Immutable, Temporal, List, and Functional, each with a fixed resolution strategy. On the controlled generator, the full system yields F1=1.000 against 0.802 for the baseline; these values are logic-validation checks rather than empirical clinical accuracy. On the MIMIC-IV-on-FHIR Demo, 875,609 facts from 100 patients are materialised with zero duplicate active keys and zero invalid valid-time intervals. At 20% valid-timestamp late arrivals, event-time arbitration has no F1 loss while arrival-order arbitration falls by 0.067. The event-time and arrival-order systems lose the same F1 under incorrect values, units, patient references, and predicate mappings, so no content-integrity advantage is claimed.