InsightX Agent: An LMM-based Agentic Framework with Integrated Tools for Reliable X-ray NDT Analysis
Jiale Liu, Huan Wang, Yue Zhang, Xiaoyu Luo, Jiaxiang Hu, Zhiliang Liu, Min Xie

TL;DR
InsightX Agent introduces an LMM-based framework for X-ray NDT analysis that enhances reliability, interpretability, and interactivity by integrating detection and reasoning tools within an agentic system.
Contribution
This work presents a novel LMM-based agentic framework that actively orchestrates detection and reasoning tools for more reliable and interpretable X-ray NDT analysis.
Findings
Achieved 96.54% F1-score on GDXray+ dataset.
Enhanced interpretability and trustworthiness in defect detection.
Demonstrated effective tool integration for active reasoning.
Abstract
Non-destructive testing (NDT), particularly X-ray inspection, is vital for industrial quality assurance, yet existing deep-learning-based approaches often lack interactivity, interpretability, and the capacity for critical self-assessment, limiting their reliability and operator trust. To address these shortcomings, this paper proposes InsightX Agent, a novel LMM-based agentic framework designed to deliver reliable, interpretable, and interactive X-ray NDT analysis. Unlike typical sequential pipelines, InsightX Agent positions a Large Multimodal Model (LMM) as a central orchestrator, coordinating between the Sparse Deformable Multi-Scale Detector (SDMSD) and the Evidence-Grounded Reflection (EGR) tool. The SDMSD generates dense defect region proposals from multi-scale feature maps and sparsifies them through Non-Maximum Suppression (NMS), optimizing detection of small, dense targets in…
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