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[2601.04568] Neurosymbolic Retrievers for Retrieval-augmented Generation
Summary
The paper presents Neurosymbolic Retrievers for Retrieval-augmented Generation, addressing the limitations of traditional RAG systems by integrating symbolic reasoning with neural retrieval techniques to enhance interpretability and performance.
Why It Matters
This research is significant as it tackles the transparency issues in AI systems, particularly in high-stakes domains like mental health. By combining neural and symbolic approaches, it aims to improve the reliability and clarity of AI-driven decision-making processes.
Key Takeaways
- Neurosymbolic RAG integrates symbolic reasoning with neural retrieval to enhance transparency.
- Three methods are proposed to improve document selection and retrieval clarity.
- Preliminary results show improved performance in mental health risk assessments.
Computer Science > Artificial Intelligence arXiv:2601.04568 (cs) [Submitted on 8 Jan 2026 (v1), last revised 22 Feb 2026 (this version, v2)] Title:Neurosymbolic Retrievers for Retrieval-augmented Generation Authors:Yash Saxena, Manas Gaur View a PDF of the paper titled Neurosymbolic Retrievers for Retrieval-augmented Generation, by Yash Saxena and 1 other authors View PDF HTML (experimental) Abstract:Retrieval Augmented Generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. However, traditional RAG systems consist of three interconnected neural components - the retriever, re-ranker, and generator - whose internal reasoning processes remain opaque. This lack of transparency complicates interpretability, hinders debugging efforts, and erodes trust, especially in high-stakes domains where clear decision-making is essential. To address these challenges, we introduce the concept of Neurosymbolic RAG, which integrates symbolic reasoning using a knowledge graph with neural retrieval techniques. This new framework aims to answer two primary questions: (a) Can retrievers provide a clear and interpretable basis for document selection? (b) Can symbolic knowledge enhance the clarity of the retrieval process? We propose three methods to improve this integration. First is MAR (Knowledge Modulation Aligned Retrieval) that employs modulation networks to refine ...
