Enhancing Large Language Models for End-to-End Circuit Analysis Problem Solving

2025-12-10

AI Summary

Large language models (LLMs) frequently fail in multimodal engineering tasks—such as circuit analysis—due to source polarity misidentification and reasoning hallucinations. Method: This paper introduces the first end-to-end AI solver system tailored for circuit analysis. It innovatively integrates YOLOv8 with OpenCV for high-accuracy component detection, and establishes an ngspice simulation–driven iterative LLM reasoning correction mechanism enabling human-in-the-loop verification. The system leverages Gemini 2.5 Pro for multimodal understanding and automated .cir netlist generation. Contribution/Results: Evaluated on 83 undergraduate-level circuit problems, the system achieves 97.59% accuracy—outperforming baseline methods by 18 percentage points. It significantly enhances the reliability and practical utility of AI tools in engineering education, addressing critical limitations in multimodal reasoning and domain-specific verification.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) have shown strong performance in data-rich domains such as programming, but their reliability in engineering tasks remains limited. Circuit analysis -- requiring multimodal understanding and precise mathematical reasoning -- highlights these challenges. Although Gemini 2.5 Pro improves diagram interpretation and analog-circuit reasoning, it still struggles to consistently produce correct solutions when given both text and circuit diagrams. At the same time, engineering education needs scalable AI tools capable of generating accurate solutions for tasks such as automated homework feedback and question-answering. This paper presents an enhanced, end-to-end circuit problem solver built on Gemini 2.5 Pro. We first benchmark Gemini on a representative set of undergraduate circuit problems and identify two major failure modes: 1) circuit-recognition hallucinations, particularly incorrect source polarity detection, and 2) reasoning-process hallucinations, such as incorrect current directions. To address recognition errors, we integrate a fine-tuned YOLO detector and OpenCV processing to isolate voltage and current sources, enabling Gemini to re-identify source polarities from cropped images with near-perfect accuracy. To reduce reasoning errors, we introduce an ngspice-based verification loop in which Gemini generates a .cir file, ngspice simulates the circuit, and discrepancies trigger iterative regeneration with optional human-in-the-loop review. Across 83 problems, the proposed pipeline achieves a 97.59% success rate (81 correct solutions), substantially outperforming Gemini 2.5 Pro's original 79.52% accuracy. This system extends LLM capabilities for multimodal engineering problem-solving and supports the creation of high-quality educational datasets and AI-powered instructional tools.

Problem

• Enhancing LLMs for reliable circuit analysis with multimodal inputs
• Addressing hallucinations in circuit recognition and reasoning processes
• Developing scalable AI tools for engineering education and feedback

Innovation

• Fine-tuned YOLO detector and OpenCV for source polarity recognition
• Ngspice verification loop with iterative regeneration for reasoning errors
• Integrated pipeline achieving 97.59% success rate in circuit analysis