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Gemini 2.5 Pro framework boosts circuit analysis accuracy to 97.59%

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an enhanced framework for end-to-end circuit analysis problem-solving using Gemini 2.5 Pro as the core LLM. The system addresses common failure modes in LLMs for engineering tasks, specifically circuit recognition and reasoning hallucinations. By integrating a fine-tuned YOLO detector with OpenCV for improved circuit recognition and an ngspice-driven verification loop for reasoning, the framework achieved a 97.59% accuracy on undergraduate circuit analysis problems, a significant improvement over the baseline Gemini model's 79.52%. The enhanced system demonstrates substantial gains in robustness, scalability, and generalizability across different problem sets and diagram variations. AI

IMPACT Enhances LLM capabilities in specialized engineering domains, potentially improving educational tools and practical analysis.

RANK_REASON Research paper detailing an enhanced LLM framework for a specific engineering task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gemini 2.5 Pro framework boosts circuit analysis accuracy to 97.59%

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Research paper detailing an enhanced LLM framework for a specific engineering task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Liangliang Chen, Weiyu Sun, Huiru Xie, Yongnuo Cai, Ying Zhang ·

    Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving

    arXiv:2512.10159v3 Announce Type: replace-cross Abstract: LLMs have shown strong performance in data-rich domains such as programming, but their reliability in engineering tasks remains limited. Circuit analysis is particularly challenging because it requires both multimodal unde…

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