SAKI-RAG: Mitigating Context Fragmentation in Long-Document RAG via Sentence-level Attention Knowledge Integration

Traditional Retrieval-Augmented Generation (RAG) frameworks often segment documents into larger chunks to preserve contextual coherence, inadvertently introducing redundant noise. Recent advanced RAG frameworks have shifted toward finer-grained chunking to improve precision. However, in long-document scenarios, such chunking methods lead to fragmented contexts, isolated chunk semantics, and broken inter-chunk relationships, making cross-paragraph retrieval particularly challenging. To address this challenge, maintaining granular chunks while recovering their intrinsic semantic connections, we propose SAKI-RAG (Sentence-level Attention Knowledge Integration Retrieval-Augmented Generation). Our framework introduces two core components: (1) the SentenceAttnLinker, which constructs a semantically enriched knowledge repository by modeling inter-sentence attention relationships, and (2) the Dual-Axis Retriever, which is designed to expand and filter the candidate chunks from the dual dimensions of semantic similarity and contextual relevance. Experimental results across four datasets-Dragonball, SQUAD, NFCORPUS, and SCI-DOCS demonstrate that SAKI-RAG achieves better recall and precision compared to other RAG frameworks in long-document retrieval scenarios, while also exhibiting higher information efficiency.