Circuit-Think: A Multimodal Reasoning Framework for Automated Circuit-to-Netlist Translation with Trajectory-Guided Reinforcement Learning
Yuqi Jiang, Yupeng Hu, Jinyuan Deng, Xiaotian Qiu, Yucheng Cui, Xuyang He, Ruidong Li, Qi Sun
Abstract
Vision Language Models (VLMs) have shown strong performance in multimodal understanding, offering promise for the circuit-to-netlist translation task. However, the diverse component symbols and complex connections in circuit images challenge VLMs in understanding physical layouts and reasoning for electrical connection logic. To address these, we propose Circuit-Think, the first multimodal reasoning framework for the automated circuit-to-netlist translation task, which employs a Trajectory-Guided Reinforcement Learning (TGRL) paradigm for structured logical reasoning on circuit images. Circuit-Think initializes reasoning capabilities through supervised fine-tuning (SFT) on image-netlist pairs, then optimizes reasoning trajectories and netlist generation decisions using TGRL. Firstly, TGRL introduces a step-by-step reasoning paradigm, which guides the model with stepwise reward functions to simulate the human cognitive trajectory of ``identifying ports, recognizing devices, and inferring connections
BibTeX
@inproceedings{aaai2026_circuitthinkamul,
title = {Circuit-Think: A Multimodal Reasoning Framework for Automated Circuit-to-Netlist Translation with Trajectory-Guided Reinforcement Learning},
author = {Yuqi Jiang and Yupeng Hu and Jinyuan Deng and Xiaotian Qiu and Yucheng Cui and Xuyang He and Ruidong Li and Qi Sun and Cheng Zhuo},
booktitle = {AAAI 2026},
year = {2026}
}