Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking

arXiv cs.CL / 6/2/2026

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Key Points

The paper introduces InSemRAG, a retrieval-augmented generation (RAG) framework that improves evidence quality by addressing intent-agnostic retrieval and fragmented information in conventional systems.
InSemRAG uses an intention-aware retriever (IAR) that adaptively reweights multiple retrieval channels according to the query’s intent, improving relevance of retrieved chunks.
It also applies semantics-preserving chunking (SPC) to detect and repair damaged evidence chunks, aiming to maintain semantic integrity during retrieval.
An iterative retrieve-and-check design is employed to refine evidence selection, and the approach uses small language models (SLMs) to reduce the additional computational latency.
Experiments on multiple benchmarks show strong competitiveness with recent RAG methods, including +2.65 F1 on HotPotQA and +1.5 accuracy on FEVER, and up to 4.32× lower latency versus Multi-Hop RAG with SLMs.

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