English(EN) Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving

Gemini 2.5 Pro 框架将电路分析准确率提升至 97.59%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一个增强型框架，用于使用 Gemini 2.5 Pro 作为核心大语言模型来解决端到端电路分析问题。该系统解决了大语言模型在工程任务中常见的故障模式，特别是电路识别和推理幻觉。通过集成经过微调的 YOLO 检测器和 OpenCV 以改进电路识别，以及一个由 ngspice 驱动的验证循环来进行推理，该框架在本科电路分析问题上达到了 97.59% 的准确率，相比基线 Gemini 模型 79.52% 的准确率有了显著提升。增强型系统在鲁棒性、可扩展性和跨不同问题集及图变体的泛化能力方面均取得了实质性进展。 AI

影响增强了大语言模型在专业工程领域的应用能力，有望改进教育工具和实际分析。

排序理由研究论文，详细介绍了针对特定工程任务的增强型大语言模型框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Liangliang Chen, Weiyu Sun, Huiru Xie, Yongnuo Cai, Ying Zhang · 2026-09-15 04:00

增强基于大型语言模型的系统以解决端到端电路分析问题

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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增强基于大型语言模型的系统以解决端到端电路分析问题

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