Neuro-Symbolic Query Compiler

Yuyao Zhang, Zhicheng Dou, Xiaoxi Li, Jiajie Jin, YongkangWu, Zhonghua Li, Qi Ye, Ji-Rong Wen

Neuro-Symbolic Query Compiler: 18 upvotes on Hugging Face Daily Papers, #18 of 43 papers on 2025-05-20. Day-by-day upvote history.

Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents QCompiler, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically designs a minimal yet sufficient Backus-Naur Form (BNF) grammar G[q] to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a Query Expression Translator, a Lexical Syntax Parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system's ability to address complex queries.

Paper page on Hugging Face · arXiv

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