SOPRAG: Multi-view Graph Experts Retrieval for Industrial Standard Operating Procedures

• URL: http://arxiv.org/abs/2602.01858v1
• Date: Mon, 02 Feb 2026 09:30:43 GMT
• Title: SOPRAG: Multi-view Graph Experts Retrieval for Industrial Standard Operating Procedures
• Authors: Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen,
• Abstract summary: SOPRAG is a novel framework specifically designed to address the pain points in SOP retrieval.<n> SOPRAG replaces flat chunking with specialized Entity, Causal, and Flow graph experts.<n> SOPRAG significantly outperforms strong lexical, dense, and graph-based RAG baselines in both retrieval accuracy and response utility.
• Score: 15.42553917257021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard Operating Procedures (SOPs) are essential for ensuring operational safety and consistency in industrial environments. However, retrieving and following these procedures presents unique challenges, such as rigid proprietary structures, condition-dependent relevance, and actionable execution requirement, which standard semantic-driven Retrieval-Augmented Generation (RAG) paradigms fail to address. Inspired by the Mixture-of-Experts (MoE) paradigm, we propose SOPRAG, a novel framework specifically designed to address the above pain points in SOP retrieval. SOPRAG replaces flat chunking with specialized Entity, Causal, and Flow graph experts to resolve industrial structural and logical complexities. To optimize and coordinate these experts, we propose a Procedure Card layer that prunes the search space to eliminate computational noise, and an LLM-Guided gating mechanism that dynamically weights these experts to align retrieval with operator intent. To address the scarcity of domain-specific data, we also introduce an automated, multi-agent workflow for benchmark construction. Extensive experiments across four industrial domains demonstrate that SOPRAG significantly outperforms strong lexical, dense, and graph-based RAG baselines in both retrieval accuracy and response utility, achieving perfect execution scores in real-world critical tasks.

Related papers

• GrepRAG: An Empirical Study and Optimization of Grep-Like Retrieval for Code Completion [32.17127975368661]
  Repository-level code completion remains challenging for large language models.<n>We investigate lightweight, index-free, intent-aware lexical retrieval.<n>We introduce Naive GrepRAG, a baseline framework in which LLMs autonomously generate ripweighted commands to retrieve relevant context.
  arXiv  Detail & Related papers  (2026-01-30T18:22:15Z)
• Is Agentic RAG worth it? An experimental comparison of RAG approaches [0.07777489763207261]
  "Retrieval-Augmented Generation" systems are usually defined by the combination of a generator and a retrieval component.<n>These shortcomings have motivated the development of "Enhanced" RAG.<n>The growing self-reflective capabilities of Large Language Models have enabled a new paradigm, which we refer to as "Agentic" RAG.
  arXiv  Detail & Related papers  (2026-01-12T16:43:44Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG [0.0]
  Retrieval-Augmented Generation (RAG) is a critical technique for grounding Large Language Models (LLMs) in factual evidence.<n>Existing evaluation frameworks often rely on metrics that fail to capture domain-specific nuances.<n>This paper introduces RAGalyst, an automated, human-aligned agentic framework designed for the rigorous evaluation of domain-specific RAG systems.
  arXiv  Detail & Related papers  (2025-11-06T16:22:52Z)
• Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation [0.16754194618631593]
  This paper introduces an agentic RAG architecture to address domain-specific and dense terminology challenges.<n>We evaluate our approach against a standard RAG baseline using a curated dataset of 85 question-answer-reference triples from an enterprise knowledge base.
  arXiv  Detail & Related papers  (2025-10-29T13:41:36Z)
• Automatic Building Code Review: A Case Study [6.530899637501737]
  Building officials face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity.<n>This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification.
  arXiv  Detail & Related papers  (2025-10-03T00:30:14Z)
• DRBench: A Realistic Benchmark for Enterprise Deep Research [81.49694432639406]
  DRBench is a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings.<n>We release 15 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance.
  arXiv  Detail & Related papers  (2025-09-30T18:47:20Z)
• Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search [54.987957691350665]
  Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query.<n>Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications.<n>We propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search.
  arXiv  Detail & Related papers  (2025-08-28T08:51:51Z)
• RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging [69.2230254959204]
  We propose RouteMark, a framework for IP protection in merged MoE models.<n>Our key insight is that task-specific experts exhibit stable and distinctive routing behaviors under probing inputs.<n>For attribution and tampering detection, we introduce a similarity-based matching algorithm.
  arXiv  Detail & Related papers  (2025-08-03T14:51:58Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [91.17994756436259]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search [85.12447821237045]
  HiRA is a hierarchical framework that separates strategic planning from specialized execution.<n>Our approach decomposes complex search tasks into focused subtasks, assigns each subtask to domain-specific agents equipped with external tools and reasoning capabilities.<n> Experiments on four complex, cross-modal deep search benchmarks demonstrate that HiRA significantly outperforms state-of-the-art RAG and agent-based systems.
  arXiv  Detail & Related papers  (2025-07-03T14:18:08Z)

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.