SAGE: Semantic Ambiguity Guided Capacity Expansion for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) mitigates Large Language Model (LLM) hallucinations by grounding generation in external knowledge. Although structure-augmented RAG methods improve multi-hop reasoning, they incur high indexing costs and degrade on simple queries. We argue that this robustness gap is driven by capacity bottlenecks in fixed-dimensional inner-product scoring. As corpora grow, semantically close passages create local regions where a base scorer cannot maintain sufficient separability. Reorganizing documents into trees or graphs can expose or partially mitigate this issue, but it does not by itself provide a stronger local scoring function. We propose Semantic Ambiguity Guided Capacity Expansion (SAGE), a lightweight RAG framework that detects such regions using document-side Local Separability Deficit and expands capacity only where needed. SAGE builds a two-layer semantic index, derives atomic query views from corpus passages, and calibrates a parameter-efficient hypernetwork to generate the local Ambiguity-Conditioned Scorers for selected nodes. Extensive experiments show that SAGE improves retrieval and QA performance over dense retrievers and structure-augmented baselines, achieving a better balance between single-hop, multi-hop retrieval accuracy, and robust scalability. Anonymous Code: https://anonymous.4open.science/r/SAGE-1676.