Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
- URL: http://arxiv.org/abs/2607.01244v1
- Date: Mon, 18 May 2026 14:57:21 GMT
- Title: Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study
- Abstract summary: This paper describes a case study of building a Retrieval-Augmented Generation system for the consultation of complex technical regulations in the railway domain.<n>Although developed for the railway sector, this testimony of an industrial experience is of particular value for technical domains.
- Score: 6.239901911708078
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The growing number and complexity of technical regulations represent an important challenge for all professionals in regulated industries. This paper describes a case study, from design to deployment, of building a Retrieval-Augmented Generation system for the consultation of complex technical regulations in the railway domain. Although developed for the railway sector, this testimony of an industrial experience is of particular value for technical domains where regulatory compliance and accurate information retrieval from complex documentation are essential requirements. It also constitutes a human-centered approach for implementing LLM-powered technical documentation consultation across various regulated industries, balancing technological capabilities with domain expertise.
Related papers
- Towards Viewpoint-centric Artifact-based Regulatory Requirements Engineering for Compliance by Design [16.99229148501035]
The goal of this paper is to report and get feedback about the synthesis and future evaluation of our Artefact Model for Regulatory Requirements Engineering (AM4RRE) for a integrated compliance by design.
arXiv Detail & Related papers (2026-03-10T10:51:40Z) - Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
arXiv Detail & Related papers (2026-01-15T18:55:03Z) - Embodied Intelligence for Flexible Manufacturing: A Survey [53.502316787299144]
In flexible manufacturing, industrial embodied intelligence faces three core challenges.<n>These include accurate process modeling and monitoring under limited perception, dynamic balancing between flexible adaptation and high-precision control, and the integration of general-purpose skills with specialized industrial operations.<n>This work reveals the key technological pathways of embodied intelligence for closed-loop optimization of perception-decision-execution in manufacturing systems.
arXiv Detail & Related papers (2025-12-24T14:26:30Z) - Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents [63.03252293761656]
This paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on large language models (LLMs)<n>We examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use.<n>We provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation.
arXiv Detail & Related papers (2025-10-20T12:46:55Z) - A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
arXiv Detail & Related papers (2025-10-10T06:56:50Z) - Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely [41.788724443376815]
The study found that these experts are commonly concerned about sensitive information leakage, intellectual property infringement, and uncertainty regarding the quality of model outputs.<n>In response, they spontaneously adopt various mitigation strategies, such as actively distorting input data and limiting the details in their prompts.<n>Our research reveals a significant gap between current NLP tools and the actual compliance needs of experts.
arXiv Detail & Related papers (2025-10-02T03:35:46Z) - MASLegalBench: Benchmarking Multi-Agent Systems in Deductive Legal Reasoning [45.37095206528033]
Multi-agent systems (MAS), leveraging the remarkable capabilities of Large Language Models (LLMs), show great potential in addressing complex tasks.<n>Previous studies have developed legal benchmarks for LLM agents, but none are specifically designed to consider the unique advantages of MAS.<n>We propose MASLegalBench, a legal benchmark tailored for MAS and designed with a deductive reasoning approach.
arXiv Detail & Related papers (2025-09-29T15:24:40Z) - The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations [3.8314877221880512]
Despite widespread interest in machine learning (ML), the engineering industry has not yet fully adopted ML-based methods.<n>This paper explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies.
arXiv Detail & Related papers (2025-07-11T13:28:32Z) - Requirements Engineering for a Web-based Research, Technology & Innovation Monitoring Tool [46.38386372048799]
We introduce a requirements engineering process to identify stakeholders and elicitate requirements for a web-based interactive and open-access RTI system monitoring tool.<n>Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers.<n>A cornerstone of this architecture is the user-facing dashboard module.
arXiv Detail & Related papers (2025-01-18T20:36:26Z) - ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural Knowledge [2.0159170788984024]
Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals.<n>Our study introduces a novel approach that decomposes ARC into two distinct tasks: rule information extraction and verification code generation.
arXiv Detail & Related papers (2024-12-10T10:37:11Z) - RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages.<n>We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents.<n>We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
arXiv Detail & Related papers (2024-09-09T14:44:19Z) - Practices, Challenges, and Opportunities When Inferring Requirements From Regulations in the FinTech Sector - An Industrial Study [1.0936851319953484]
Understanding and interpreting regulatory norms and inferring software requirements from them is a critical step towards regulatory compliance.
This study investigates the complexities of requirement engineering in regulatory contexts, pinpointing various issues and discussing them in detail.
We have identified key practices for managing regulatory requirements in software development, and have pinpointed several challenges.
arXiv Detail & Related papers (2024-05-05T09:39:08Z) - Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs) [0.0]
This paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes.
We explore various roles that LLMs can play in this context while identifying some of the challenges to address.
arXiv Detail & Related papers (2024-03-26T15:54:48Z) - Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels [47.187609203210705]
We propose a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels.
Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP) and allows for identifying possible anomalies and errors in the final product.
The experiments conducted on a real test case provided by an Italian Company demonstrate the effectiveness of the proposed approach.
arXiv Detail & Related papers (2023-05-17T10:29:02Z) - An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago.
We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.