RED.AI Id-Pattern: First Results of Stone Deterioration Patterns with Multi-Agent Systems
Abstract
The Id-Pattern system within the RED.AI project (Reabilita\c{c}\~ao Estrutural Digital atrav\'es da AI) consists of an agentic system designed to assist in the identification of stone deterioration patterns. Traditional methodologies, based on direct observation by expert teams, are accurate but costly in terms of time and resources. The system developed here introduces and evaluates a multi-agent artificial intelligence (AI) system, designed to simulate collaboration between experts and automate the diagnosis of stone pathologies from visual evidence. The approach is based on a cognitive architecture that orchestrates a team of specialized AI agents which, in this specific case, are limited to five: a lithologist, a pathologist, an environmental expert, a conservator-restorer, and a diagnostic coordinator. To evaluate the system we selected 28 difficult images involving multiple deterioration patterns. Our first results showed a huge boost on all metrics of our system compared to the foundational model.
Cite
@article{arxiv.2508.13872,
title = {RED.AI Id-Pattern: First Results of Stone Deterioration Patterns with Multi-Agent Systems},
author = {Daniele Corradetti and José Delgado Rodrigues},
journal= {arXiv preprint arXiv:2508.13872},
year = {2025}
}
Comments
11 pages, 1 figure, 1 table. Contribution for REEACH 2025 Symposium