Circuit-MLLM：面向电路原理图理解的自顶向下逻辑引导的潜在空间视觉推理
Circuit-MLLM: Topological Logic-Guided Latent-Space Visual Reasoning for Circuit Schematic Understanding
浏览论文内容
中文总结 AI 辅助
针对多模态大模型难以理解电路原理图拓扑逻辑的问题，提出Circuit-MLLM框架，通过潜在空间内器件定位、路径追踪和拓扑引导的顺序推理，显著提升电路分析性能，平均得分比GPT-5.1高25%。
中文摘要 AI 辅助
通过在大量文本和图像数据集上进行预训练，当前的多模态大语言模型（MLLMs）在通用任务上取得了强劲的性能。然而，电路原理图因其密集的元件布局和独特的拓扑逻辑，对MLLMs构成了独特挑战，需要细粒度的结构解析以提取电气语义。为解决这一问题，我们提出了Circuit-MLLM，一种多模态推理框架，将电路拓扑分析重新表述为潜在空间内的器件定位、路径追踪和顺序推理过程。我们引入了一种电路知识挖掘机制，该机制将模型的潜在表示与从多粒度电路视觉专家中提取的结构丰富特征进行深度对齐，使模型能够有效内化拓扑语义。基于这些内化的语义，我们设计了一种拓扑引导的排序策略，将推理从僵化的光栅扫描顺序中解耦，强制在潜在空间中沿着电路的拓扑逻辑进行逐步推理。在多样化的电路分析任务中，Circuit-MLLM持续优于强基线，尤其在平均得分上比GPT-5.1高出25%，这证明了我们框架在电路原理图拓扑分析中的有效性。代码可在以下网址公开获取：此https URL。
英文摘要
Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tasks. However, circuit schematics present a unique challenge for MLLMs due to their dense component layouts and distinct topological logic, demanding fine-grained structural parsing to extract the electrical semantics. To address this, we propose Circuit-MLLM, a multimodal reasoning framework that reformulates circuit topology analysis as a process of device localization, path tracing, and sequential reasoning within the latent space. We introduce a circuit knowledge mining mechanism that deeply aligns the model's latent representations with structurally rich features derived from multi-granularity circuit vision experts, enabling the model to effectively internalize topological semantics. Building upon these internalized semantics, we devise a topology-guided sequencing strategy that decouples reasoning from the rigid raster-scan order, enforcing stepwise inference along the circuit's topological logic in latent space. Across diverse circuit analysis tasks, Circuit-MLLM consistently outperforms strong baselines, notably achieving a 25% higher average score than GPT-5.1, which demonstrates the effectiveness of our framework in circuit schematic topology analysis. Code is publicly available at https://github.com/IC-Yuan/Circuit-MLLM.
发表机构
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
