Summary
A new paper on arXiv (2603.25737) by Yuxing Lu, Xukai Zhao, Wei Wu, and Jinzhuo Wang proposes WriteBack-RAG, a framework that treats the knowledge base of retrieval-augmented generation (RAG) systems as a trainable component. The authors observe that RAG knowledge bases are typically assembled once and never updated, even though facts needed to answer queries are often scattered across multiple documents and buried in irrelevant content. WriteBack-RAG uses annotated examples to identify where retrieval succeeds, isolates the relevant documents, and distills them into compact knowledge units that are indexed alongside the original corpus. Evaluations across four RAG methods, six benchmarks, and two LLM backbones show an average improvement of +2.14%. This summary was automatically collected from zhichai.net on 2026-03-29.
Paper Overview
Research area: NLP
Authors: Yuxing Lu, Xukai Zhao, Wei Wu, Jinzhuo Wang
Published: 2026-03-26
arXiv: 2603.25737
Summary
In retrieval-augmented generation (RAG) systems, the knowledge base is typically assembled once and never updated, even though the facts needed to answer a query are often scattered across multiple documents and buried in irrelevant content. This paper argues that the knowledge base should be treated as a trainable component.
The authors propose WriteBack-RAG, a framework that:
- Uses annotated examples to identify where retrieval succeeds
- Isolates the relevant documents involved in successful retrievals
- Distills them into compact knowledge units
- Indexes these distilled units alongside the original corpus
Results
Across evaluations covering four RAG methods, six benchmarks, and two LLM backbones, WriteBack-RAG achieves an average gain of +2.14%.
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*Auto-collected on 2026-03-29*
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