mlsanityData to 14 Oct 2026

Towards Unlocking Insights from Logbooks Using AI

arXiv 2406.12881 ↗First posted 25 May 2024physics.acc-ph cs.CLcited by 5

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

Abstract (arXiv)

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.

Key facts. Cited by 5 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 16 of its 26 references that are in the map. First posted on arXiv 25 May 2024.

Builds on

16 of 26 references.

References · 16

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  4. 2017Billion-scale similarity search with GPUscited by 2,497
  5. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  6. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  7. 2014Distributed Representations of Sentences and Documentscited by 678
  8. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  9. 2023AnglE-optimized Text Embeddingscited by 99
  10. 2024Exploring LLM-based Agents for Root Cause Analysiscited by 61
  11. 2024TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learningcited by 52
  12. 2020The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizescited by 48
4 more
  1. 2023Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Modelscited by 38
  2. 2024Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Languagecited by 9
  3. 2024GAIA: A General AI Assistant for Intelligent Accelerator Operationscited by 8
  4. 2023PACuna: Automated Fine-Tuning of Language Models for Particle Acceleratorscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024PhysBERT: A Text Embedding Model for Physics Scientific Literaturecited by 8
  2. 2024Towards Agentic AI on Particle Acceleratorscited by 5
  3. 2025eLog analysis for accelerators: status and future outlookcited by 0
  4. 2025Application Of Large Language Models For The Extraction Of Information From Particle Accelerator Technical Documentationcited by 0
  5. 2026A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collider
  2. 2024Towards a RAG-based Summarization Agent for the Electron-Ion Collider
  3. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  4. 2026MITRA: An AI Assistant for Knowledge Retrieval in Physics Collaborations
  5. 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
  6. 2026A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations
  7. 2025Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance
  8. 2024RAG Does Not Work for Enterprises