Reincarnate is pleased to announce the publication of the paper “LLM-Driven Multi-Agent Inspection Planning via Semantically Enriched Knowledge Graphs for Non-Destructive Testing,” issued in the CEUR Workshop Proceedings as part of the 2nd International Workshop on Semantic Materials Science (SeMatS 2025), co-located with ISWC 2025 held on 2-6 November in Nara, Japan.
Authored by Ghezal Ahmad Jan Zia, Andre Valdestilhas, Benjamin Moreno Torres, and Sabine Kruschwitz, the paper introduces a new multi-agent framework that combines semantic knowledge graphs with large language models to support inspection planning in Non-Destructive Testing (NDT). The approach enhances engineers’ ability to select appropriate NDT methods, plan inspection sequences, and understand system recommendations through transparent graph-based reasoning.
The framework consists of three coordinated LLM-driven agents: a PlannerAgent that generates inspection strategies, a ToolSelectorAgent that identifies relevant NDT techniques based on material–defect relationships, and a ForecasterAgent that schedules inspection timelines. These agents operate on an enriched NDT knowledge graph that captures materials, deterioration mechanisms, defect types, and testing methods in a structured, queryable format.
Through case studies on concrete infrastructure, the researchers show that the system improves the relevance, consistency, and explainability of inspection plans. The integration of SHACL validation, OWL/RDF export, and a real-time visualization interface further strengthens traceability and decision support for practitioners.
This work aligns with Reincarnate’s mission to advance intelligent, data-informed processes for extending the lifetime and reliability of infrastructure and building materials.