AI RESEARCH
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
arXiv CS.CL
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ArXi:2603.11650v1 Announce Type: new The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks.