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[2602.22225] SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG
Summary
The paper presents SmartChunk Retrieval, a query-aware framework that enhances retrieval-augmented generation (RAG) by adapting chunk sizes for improved accuracy and efficiency in document question answering.
Why It Matters
SmartChunk Retrieval addresses limitations in traditional document retrieval methods by dynamically adjusting chunk sizes based on query context. This innovation is crucial for enhancing the performance of AI systems in real-world applications, where diverse document types and query styles are common. The framework's ability to improve retrieval accuracy while reducing costs makes it a significant advancement in the field of information retrieval and machine learning.
Key Takeaways
- SmartChunk Retrieval adapts chunk sizes dynamically based on query requirements.
- The framework incorporates a planner using reinforcement learning to optimize retrieval accuracy.
- SmartChunk outperforms existing RAG methods across multiple QA benchmarks.
- The approach demonstrates strong scalability with larger datasets.
- It effectively balances retrieval accuracy and efficiency, reducing operational costs.
Computer Science > Information Retrieval arXiv:2602.22225 (cs) [Submitted on 17 Dec 2025] Title:SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG Authors:Xuechen Zhang, Koustava Goswami, Samet Oymak, Jiasi Chen, Nedim Lipka View a PDF of the paper titled SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG, by Xuechen Zhang and 4 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented generation (RAG) has strong potential for producing accurate and factual outputs by combining language models (LMs) with evidence retrieved from large text corpora. However, current pipelines are limited by static chunking and flat retrieval: documents are split into short, predetermined, fixed-size chunks, embeddings are retrieved uniformly, and generation relies on whatever chunks are returned. This design brings challenges, as retrieval quality is highly sensitive to chunk size, often introduces noise from irrelevant or misleading chunks, and scales poorly to large corpora. We present SmartChunk retrieval, a query-adaptive framework for efficient and robust long-document question answering (QA). SmartChunk uses (i) a planner that predicts the optimal chunk abstraction level for each query, and (ii) a lightweight compression module that produces high-level chunk embeddings without repeated summarization. By adapting retrieval granularity on the fly, SmartChunk balances accuracy with efficiency...