Self-Conditioned Positional HNSW for Overlap-Aware Retrieval in Chunked-Document RAG Systems: Method and Industrial Evidence-Quality Audit
arXiv:2606.01542v1 Announce Type: cross Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems. Documents are split into overlapping chunks, embedded, and indexed with approximate nearest-neighbor search such as hierarchical navigable small world graphs (HNSW). Overlap improves boundary coverage but induces a practical failure mode: top-k retrieval often returns near-adjacent chunks that repeat evidence and waste prompt budget. We propose Self-Conditioned Positional HNSW (SCP-HNSW), a lightweight modification that appends a low-dimensional po
The proliferation of RAG systems highlights the need for more efficient and accurate retrieval methods to optimize performance and reduce computational waste.
Improving chunked-document retrieval directly enhances the efficiency and quality of RAG systems, which are foundational for many advanced AI applications.
RAG systems can now retrieve more relevant information with less redundancy, leading to better prompt utilization and potentially more coherent outputs.
- · AI developers
- · RAG system providers
- · Enterprises deploying RAG
- · Cloud computing providers
- · Inefficient RAG systems
- · Excessive prompt budget waste
RAG systems become more cost-effective and produce higher-quality responses by reducing redundant information retrieval.
This efficiency gain could accelerate the adoption and sophistication of RAG-based AI applications across various industries.
Improved RAG systems contribute to the overall maturation of AI agents, enabling them to perform complex tasks with greater accuracy and autonomy.
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Read at arXiv cs.CL