StructKE: Mitigating Structural Degradation in Multi-Hop Knowledge Editing

Knowledge editing aims to update specific facts in large language models efficiently. Although non-parametric in-context editing is robust for single-fact correction, it degrades severely on complex reasoning tasks such as multi-hop question answering. Through fine-grained empirical analysis, this paper identifies structural degradation as a central bottleneck. Under parametric priors, a model often deviates from the required factual-transition structure by taking shortcuts or stopping early, preventing injected knowledge from propagating along a valid reasoning skeleton. To address this problem, we propose StructKE, a structure-aware in-context editing framework. StructKE reformulates multi-hop editing as a controllable four-stage search process with structure prediction, structure-constrained generation, conflict-aware filling, and SDPR selection. It first trains StructEst, a lightweight estimator, to predict the target hop count, then filters candidate reasoning paths under the target-hop constraint, performs conflict-aware filling, and finally selects the answer through a Structure-Diagnostic Pairwise Ranker (SDPR) over structural and consistency signals. Experiments on multiple benchmarks show that StructKE mitigates structural degradation in multi-hop knowledge editing and achieves strong gains over the compared baselines by preserving structural integrity.