title: Neuro-Symbolic Query Compiler
summary: 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_authors._id: 682bf7363e041a44f23afcea
paper_authors.name: Yuyao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:38:04.060Z
paper_authors._id: 64bdfa1a1a62149c5e80ef6f
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paper_authors.fullname: Yuyao Zhang
paper_authors.type: user
paper_authors.user: KeriaZhang
paper_authors._id: 682bf7363e041a44f23afceb
paper_authors.name: Zhicheng Dou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:37:46.972Z
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paper_authors.avatarUrl: /avatars/be97941e60064e5dd806c6fe9db3c537.svg
paper_authors.fullname: Zhicheng Dou
paper_authors.type: user
paper_authors.user: douzc
paper_authors._id: 682bf7363e041a44f23afcec
paper_authors.name: Xiaoxi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:37:41.072Z
paper_authors._id: 66e03eace17fb5ff054b7686
paper_authors.avatarUrl: /avatars/2b739ff11e43dd9e701c647a92617f20.svg
paper_authors.fullname: Xiaoxi Li
paper_authors.type: user
paper_authors.user: lixiaoxi45
paper_authors._id: 682bf7363e041a44f23afced
paper_authors.name: Jiajie Jin
paper_authors._id: 682bf7363e041a44f23afcee
paper_authors.name: Yongkang Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:38:33.785Z
paper_authors._id: 62f3a590261bc5fb2e072a5f
paper_authors.avatarUrl: /avatars/d65d362ddc32aca3d6c564252d81e109.svg
paper_authors.fullname: YongkangWu
paper_authors.type: user
paper_authors.user: wuyongkang
paper_authors._id: 682bf7363e041a44f23afcef
paper_authors.name: Zhonghua Li
paper_authors._id: 682bf7363e041a44f23afcf0
paper_authors.name: Qi Ye
paper_authors._id: 682bf7363e041a44f23afcf1
paper_authors.name: Ji-Rong Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:38:51.063Z
paper_authors._id: 64b8c89052b7353d8c6a1013
paper_authors.avatarUrl: /avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
paper_authors.fullname: Ji-Rong Wen
paper_authors.type: user
paper_authors.user: jrwen
paper_title: Neuro-Symbolic Query Compiler
paper_summary: 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_ai_summary: QCompiler, a neuro-symbolic framework, improves RAG systems by compiling complex queries into ASTs using a formal grammar, enhancing precision in document retrieval and response generation.
paper_ai_keywords: neuro-symbolic framework Backus-Naur Form (BNF) Query Expression Translator Lexical Syntax Parser Recursive Descent Processor Abstract Syntax Trees (ASTs)