HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) at inference by retrieving external documents as context. However, retrieval becomes increasingly time-consuming as the knowledge databases grow in size. Existing acceleration strategies either compromise accuracy through approximate retrieval, or achieve marginal gains by reusing results of strictly identical queries. We propose HaS, a homology-aware speculative retrieval framework that performs low-latency speculative retrieval over restricted scopes to obtain candidate documents, followed by validating whether they contain the required knowledge. The validation, grounded in the homology ...
| Authors | Peng Peng, Weiwei Lin, Wentai Wu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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