mlsanityData to 14 Oct 2026

Neuro-Symbolic Query Compiler

arXiv 2505.11932 ↗First posted 17 May 2025cs.CL cs.IRcited by 4

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 26 of its 35 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

26 of 35 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain30 Sep
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research22 May
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb

Other references · 23

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  8. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  9. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  11. 2022Visual Programming: Compositional visual reasoning without trainingcited by 516
  12. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
11 more
  1. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  2. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 168
  3. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  4. 2020Break It Down: A Question Understanding Benchmarkcited by 131
  5. 2019Multi-hop Reading Comprehension through Question Decomposition and Rescoringcited by 119
  6. 2022Semantic Probabilistic Layers for Neuro-Symbolic Learningcited by 103
  7. 2024MindSearch: Mimicking Human Minds Elicits Deep AI Searchercited by 66
  8. 2020Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Modelscited by 65
  9. 2024Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agentcited by 65
  10. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMscited by 34
  11. 2024SymbolicAI: A framework for logic-based approaches combining generative models and solverscited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learningcited by 3
  2. 2025Towards Neurocognitive-Inspired Intelligence: From AI's Structural Mimicry to Human-Like Functional Cognitioncited by 2
  3. 2026SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaborationcited by 1
  4. 2026VeriGraph: Towards Verifiable Data-Analytic Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Neurosymbolic Retrievers for Retrieval-augmented Generation
  2. 2025SymRAG: Efficient Neuro-Symbolic Retrieval Through Adaptive Query Routing
  3. 2026NeSy-RAG: Neuro-Symbolic RAG for Explainable Question Answering
  4. 2024Enhancing SQL Query Generation with Neurosymbolic Reasoning
  5. 2025LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
  6. 2024Neuro-Symbolic Query Optimization in Knowledge Graphs
  7. 2024Toward Optimal Search and Retrieval for RAG
  8. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning