arXiv:2606.16661v1 Announce Type: cross Abstract: Fixed-length chunking in Retrieval-Augmented Generation (RAG) often leads to boundary fragmentation, where critical evidence is split across segments, degrading retrieval recall. While static windowing and parent retrieval improve recall, they introduce significant token overhead. We propose SCAR (Semantic Continuity-Aware Retrieval), an adaptive retrieval policy that selectively expands neighboring chunks by weighing query-neighbor relevance against a structural continuity penalty. SCAR uses a relative expansion threshold tied to each retrieve
The proliferation of RAG systems highlights the limitations of current chunking methods and the need for more efficient and accurate retrieval to improve model performance and reduce costs.
Improved RAG efficiency and accuracy via methods like SCAR will enhance the practical application and cost-effectiveness of AI systems, impacting their adoption across various industries.
Retrieval-Augmented Generation models can now incorporate more relevant context without incurring disproportionate token overhead, leading to higher quality outputs and potentially lower operational costs.
- · AI developers
- · RAG-based application providers
- · Enterprises deploying GenAI
- · Inefficient RAG systems
- · Fixed-length chunking methods
Retrieval performance in RAG systems will significantly improve by addressing boundary fragmentation and context expansion challenges.
More sophisticated and reliable AI agents and applications become feasible as their underlying retrieval mechanisms are enhanced.
The overall cost and computational intensity of deploying large-scale RAG models could decrease, democratizing access to powerful AI capabilities.
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Read at arXiv cs.CL