AutoVSR：用于从电路原理图生成符号表达式的自动视觉到符号推理

中文摘要 AI 辅助

符号表达式能有效表征和预测电路行为，但直接从电路原理图推导具有挑战性，该过程需准确的视觉到符号的电路结构构建及正确的多步符号推导。本文提出AutoVSR，一个使用视觉语言模型进行视觉到符号生成电路表达式的自动化框架。通过将电路图重建为可执行中间表示并利用符号求解器推理，显著提高了符号表达式生成的准确性。它引入了两项关键创新：基于组件规则检索和验证反馈的IR构建方法，以及作为配备符号工具库的规划代理实现的符号求解器以进行可靠的多步推导。与端到端VLM方法和专门方法相比，AutoVSR在主要符号表达式生成任务上分别实现了30.01 - 59.45%和41.96 - 51.84%的准确率提升，且在推理成本和计算效率上超越了闭源的最先进VLM。代码可在指定网址获取。

英文摘要

Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging. This process requires accurate visual-to-symbolic construction of circuit structure from images and correct multi-step symbolic derivation, both of which impose strict correctness requirements. This work proposes AutoVSR, an automated framework for visual-to-symbolic generation of circuit expressions using Vision Language Models (VLMs). By reconstructing circuit diagrams into an executable intermediate representation (Executable IR) and leveraging a symbolic solver for reasoning, AutoVSR significantly improves the accuracy of symbolic expression generation. AutoVSR introduces two key innovations: an IR construction method guided by component rule retrieval and verification-based feedback, and a symbolic solver implemented as a planning agent equipped with a symbolic tool library for reliable multi-step derivation. Compared with end-to-end VLM approaches and specialized methods on the main symbolic expression generation task, AutoVSR achieves accuracy improvements of 30.01--59.45% and 41.96--51.84%, respectively. Moreover, AutoVSR surpasses closed-source state-of-the-art VLMs in inference cost and computational efficiency. Code is available at https://github.com/LongfeiLi1/AutoVSR.

发表机构

• Hunan University, Changsha, China（湖南大学）
• Wuhan University of Technology, Wuhan, China（武汉理工大学）
• Huazhong University of Science and Technology, Wuhan, China（华中科技大学）

机构由 AI 辅助整理，请以论文原文为准。