FreeChunker: A Cross-Granularity Chunking Framework

• URL: http://arxiv.org/abs/2510.20356v1
• Date: Thu, 23 Oct 2025 08:57:00 GMT
• Title: FreeChunker: A Cross-Granularity Chunking Framework
• Authors: Wenxuan Zhang, Yuan-Hao Jiang, Yonghe Wu,
• Abstract summary: Chunking strategies significantly impact the effectiveness of Retrieval-Augmented Generation (RAG) systems.<n>This paper presents FreeChunker, a Cross-Granularity Framework that transforms the traditional chunking paradigm.
• Score: 16.790630771624162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chunking strategies significantly impact the effectiveness of Retrieval-Augmented Generation (RAG) systems. Existing methods operate within fixed-granularity paradigms that rely on static boundary identification, limiting their adaptability to diverse query requirements. This paper presents FreeChunker, a Cross-Granularity Encoding Framework that fundamentally transforms the traditional chunking paradigm: the framework treats sentences as atomic units and shifts from static chunk segmentation to flexible retrieval supporting arbitrary sentence combinations. This paradigm shift not only significantly reduces the computational overhead required for semantic boundary detection but also enhances adaptability to complex queries. Experimental evaluation on LongBench V2 demonstrates that FreeChunker achieves superior retrieval performance compared to traditional chunking methods, while significantly outperforming existing approaches in computational efficiency.

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