SuRe-EM: Subspace-Routing and Residual-Corrected Expert Model for Domain-Adaptive Retrieval

Abstract

As the de facto standard for knowledge-intensive tasks, Retrieval-Augmented Generation (RAG) has significantly enhanced the reliability of Large Language Models by incorporating external non-parametric knowledge. However, adapting general-purpose retrievers to specific vertical domains often triggers catastrophic forgetting, severely degrading performance on open-domain queries. Additionally, existing mitigation strategies, such as linear model fusion, are mathematically constrained within a linear geometric manifold, limiting their ability to effectively rectify complex non-linear semantic drifts caused by domain shifts. To address these limitations, we present an effective approach, SuRe-EM (Subspace-routing & Residual-corrected Expert Model), designed to resolve the issues of domain specialization and Cross-Domain generalization in domain-adaptive retrieval. SuRe-EM enhances Cross-Domain representation by integrating fine-grained subspace routing with non-linear residual correction. Specifically, SuRe-EM first employs subspace routing to dynamically decouple high-dimensional features for maximizing domain specialization, followed by a residual module that generates non-linear semantic compensations. We validate our model on a vertical domain dataset (AHD) and general domain datasets (CMRC, SQuAD). Quantitative results demonstrate the effectiveness of SuRe-EM, which maintains strong In-Domain precision while improving Recall@10 by up to 4.46 points over state-of-the-art linear fusion baselines in Cross-Domain scenarios. Furthermore, comprehensive ablation studies validate the non-redundant synergy of the key design elements within SuRe-EM.