Existing frameworks remain trapped in a passive and mechanical approach in constructing knowledge structure, which only allows them to uncover superficial associations between chunks while lacking proactive exploration of deeper semantic relationships among them. To address the aforementioned issues, we propose **Momoka-RAG** (MCTS-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation). It employs the **Momoka-Map** to utilize Monte Carlo Tree Search (MCTS) to proactively uncover connections among chunks and construct optimal semantic information paths with the objective of completing semantic relationships. On this basis, the **Momoka-Trail Retriever** further expands and filters the chunk candidate pool to retrieve the chunks most relevant to the query. Experiments on datasets including Dragonball, SQUAD, NFCORPUS, SCI-DOCS, HotpotQA, and TriviaQA demonstrate that for long-document retrieval tasks, our framework achieves higher precision while maintaining competitive recall compared to other RAG frameworks.

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.