mlsanityData to 7 Oct 2026

RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models

arXiv 2604.03291 ↗First posted 27 Mar 2026cs.AR cs.AIcited by 0

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Abstract (arXiv)

This paper introduces RAGnaroX, a resource-efficient ChatOps assistant that operates entirely on commodity hardware. Unlike existing solutions that often rely on external providers such as Azure or OpenAI, RAGnaroX offers a fully auditable, on-premise stack implemented in Rust. Its architecture integrates modular data ingestion, hybrid retrieval, and function calling, enabling flexible yet secure deployment. Our evaluation focuses on the RAG pipeline, with benchmarks conducted on the SQuAD (single-hop QA), MultiHopRAG (multi-hop QA), and MLQA (cross-lingual QA) datasets. Results show that RAGnaroX achieves competitive accuracy while maintaining strong resource efficiency, for example, reaching 0.90 context precision on single-hop questions with an average response time of 2.5 seconds per request. A replication package containing the tool, the demonstration video (https://www.youtube.com/watch? v=cDxfuEbcoM4), and all supporting materials are available at https://github.com/genius-itea/RAGnaroX.git.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 27 Mar 2026.

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14 of 21 references.

References · 14

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  3. 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 339
  4. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253
  5. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrieverscited by 83
  6. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  7. 2025Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applicationscited by 23
  8. 2025Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agentscited by 12
  9. 2025MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Modelscited by 7
  10. 2025Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunkingcited by 7
  11. 2023China and the U.S. produce more impactful AI research when collaborating togethercited by 6
  12. 2022Rust: The Programming Language for Safety and Performancecited by 2
2 more
  1. 2025Trends in Frontier AI Model Count: A Forecast to 2028cited by 1
  2. 2025Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Modelscited by 1

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  2. 2026Securing LLM-as-a-Service for Small Businesses: An Industry Case Study of a Distributed Chatbot Deployment Platform
  3. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAG
  4. 2025ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generation
  5. 2025Design and Implementation of a Secure RAG-Enhanced AI Chatbot for Smart Tourism Customer Service: Defending Against Prompt Injection Attacks -- A Case Study of Hsinchu, Taiwan
  6. 2024FACTS About Building Retrieval Augmented Generation-based Chatbots
  7. 2024NinjaLLM: Fast, Scalable and Cost-effective RAG using Amazon SageMaker and AWS Trainium and Inferentia2
  8. 2025THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering