Multi-Modal Agents for Power Distribution Defect Detection: An Evaluation of Foundation Models

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

A Multi-Modal Agent framework is proposed for power distribution defect detection, addressing limitations in traditional inspection methods. This framework systematically evaluates multimodal foundation models as unified cognitive engines across three capabilities: Perception, Reasoning, and Tool Usage. Perception involves identifying equipment and generating expert-level defect descriptions, while Reasoning interprets visual findings to diagnose causes, assess severity, and plan maintenance. Tool Usage enables autonomous actions like querying knowledge bases or generating work orders for closed-loop maintenance. A domain-specific evaluation dataset, comprising 26,803 high-resolution images across 10 equipment and 31 defect categories, and a comprehensive benchmark were developed. Experimental results, evaluating models like GLM-4.5V and Qwen2.5-VL-32B, show that general-purpose models achieve less than 10% accuracy without domain adaptation. Retrieval-Augmented Generation (RAG) significantly improves performance, though text-only and multimodal retrieval have trade-offs. Cognitive planning and toolchain execution remain bottlenecks, with hallucinations and cascading failures reducing task success.

Key takeaway

For MLOps Engineers deploying AI agents in high-stakes industrial environments like power distribution, you must prioritize domain-specific adaptation. General-purpose multimodal models perform poorly (under 10% accuracy) without it. Integrate Retrieval-Augmented Generation (RAG) with domain knowledge to significantly boost recognition and reasoning capabilities. Be aware that current agent architectures still struggle with cognitive planning and robust toolchain execution, leading to potential hallucinations and cascading failures. Consider instruction tuning and advanced planning algorithms to mitigate these risks in your deployments.

Key insights

Multi-Modal Agents require integrated perception, reasoning, and tool usage for autonomous industrial defect detection.

Principles

• Domain adaptation is critical for fine-grained recognition.
• RAG significantly enhances model performance.
• Model scale positively correlates with RAG-enhanced performance.

Method

The framework uses a single foundation model as a cognitive engine, processing multi-modal inputs (images, natural language) and generating dual-modality outputs (descriptions, JSON commands) via prompt engineering.

In practice

• Implement RAG with domain-specific knowledge bases to improve defect recognition.
• Prioritize text-only exemplars for stable accuracy gains in recognition.
• Integrate self-reflection mechanisms to enhance agent robustness.

Topics

• Multi-Modal Agents
• Power Distribution Inspection
• Defect Detection
• Foundation Models
• Retrieval-Augmented Generation
• Tool Usage

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.