mlsanityData to 7 Oct 2026

Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collider

arXiv 2604.02259 ↗First posted 2 Apr 2026hep-ex cs.AI physics.ins-detcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

To harness the power of Language Models in answering domain specific specialized technical questions, Retrieval Augmented Generation (RAG) is been used widely. In this work, we have developed a Q&A application inspired by the Retrieval Augmented Generation (RAG), which is comprised of an in-house database indexed on the arXiv articles related to the Electron-Ion Collider (EIC) experiment - one of the largest international scientific collaboration and incorporated an open-source LLaMA model for answer generation. This is an extension to it's proceeding application built on proprietary model and Cloud-hosted external knowledge-base for the EIC experiment. This locally-deployed RAG-system offers a cost-effective, resource-constraint alternative solution to build a RAG-assisted Q&A application on answering domain-specific queries in the field of experimental nuclear physics. This set-up facilitates data-privacy, avoids sending any pre-publication scientific data and information to public domain. Future improvement will expand the knowledge base to encompass heterogeneous EIC-related publications and reports and upgrade the application pipeline orchestration to the LangGraph framework.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 26 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

12 of 26 references.

References · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  4. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  5. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  6. 2024The Faiss librarycited by 370
  7. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 104
  8. 2024Is Cosine-Similarity of Embeddings Really About Similarity?cited by 89
  9. 2022Problems with Cosine as a Measure of Embedding Similarity for High Frequency Wordscited by 49
  10. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46
  11. 2024Towards a RAG-based Summarization Agent for the Electron-Ion Collidercited by 5
  12. 2025Considering Length Diversity in Retrieval-Augmented Summarizationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysiscited by 1

Similar, not cited

Close in wording, no citation either way

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  3. 2024Semantic Tokens in Retrieval Augmented Generation
  4. 2024Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup
  5. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  6. 2024Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
  7. 2024T-RAG: Lessons from the LLM Trenches
  8. 2026AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering