mlsanityData to 7 Oct 2026

FreeChunker: A Cross-Granularity Chunking Framework

arXiv 2510.20356 ↗First posted 23 Oct 2025cs.CLcited by 0

On the map: Retrieval-augmented generation → Multimodal RAG

Abstract (arXiv)

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 avoids the computational overhead required for semantic boundary detection, but also enhances adaptability to complex queries. Experimental evaluation on LongBench V2 demonstrates that FreeChunker possesses significant advantages in both retrieval performance and time efficiency compared to existing chunking methods. The pre-trained models and codes are available at https://github.com/mazehart/FreeChunker.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 23 references that are in the map. First posted on arXiv 23 Oct 2025.

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12 of 23 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking4 May
  2. 2025MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System12 Mar
  3. 2024Is Semantic Chunking Worth the Computational Cost?16 Oct

Other references · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  5. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  7. 2024LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitaskscited by 367
  8. 2024CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Modelscited by 50
  9. 2024Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generationcited by 23

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  1. 2025Passage Segmentation of Documents for Extractive Question Answering
  2. 2026Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
  3. 2026Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations
  4. 2024Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
  5. 2026Adaptive Chunking: Optimizing Chunking-Method Selection for RAG
  6. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
  7. 2025SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG
  8. 2026CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents