PRISM: A Unified Heterogeneous-Expert Agent Framework for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to infer missing facts and alleviate the inherent incompleteness of knowledge graphs. Existing KGC methods capture complementary information at different levels, including structural patterns, path and rule information, and local or global graph semantics, while multimodal KGC further incorporates textual and visual modalities. However, heterogeneous models differ in prediction reliability, output scales, and inference costs, and simply combining them cannot adaptively determine which models should be invoked or when further computation is necessary. We formulate KGC as an information-driven sequential decision problem and propose PRISM, a unified framework for coordinating heterogeneous reasoning models. PRISM standardizes model outputs and maintains a continuously updated shared state. The Profiler summarizes relation, entity, and modality characteristics; the Adapter normalizes model outputs; the Verifier evaluates structural support and prediction agreement; the Judge performs reliability-aware rank fusion; and the Router estimates the utility of additional model invocation and decides whether to Call or Stop. Together, these components form a traceable Call–Verify–Stop loop for sequential model selection and stopping. On the MKG-W dataset, PRISM reduces the average number of model invocations from 6 to 2.09 and lowers the configured inference cost by approximately 67.10%, while largely preserving the performance of the full-model ensemble. These results demonstrate that PRISM can effectively coordinate heterogeneous reasoning models while substantially reducing redundant computation. Our code and data are available at https://anonymous.4open.science/r/PRISM-C687