Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation
Authors: Chen-Chia Chang, Wan-Hsuan Lin, Yikang Shen, Yiran Chen, Xin Zhang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that La MAGIC2 achieves 34% higher success rates under a tight tolerance of 0.01 and 10X lower MSEs compared to a prior method. La MAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5% improvement. These advancements establish La MAGIC2 as a robust framework for analog topology generation. |
| Researcher Affiliation | Collaboration | 1Duke University 2University of California, Los Angeles 3MITIBM Watson AI Lab 4IBM T. J. Watson Research Center. Correspondence to: Chen-Chia Chang <EMAIL>, Xin Zhang <EMAIL>. |
| Pseudocode | No | The paper describes methods and formulations but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code available at https: //github.com/turtleben/La MAGIC. |
| Open Datasets | Yes | We utilize the same dataset in La MAGIC (Chang et al., 2024). It contains 3, 4, 5-component circuits with 120k data points for training and 12k for evaluation. To assess the transferability of models to more complex circuits, the dataset has 76k unique 6-component circuits and split 9k data points for evaluation. |
| Dataset Splits | Yes | It contains 3, 4, 5-component circuits with 120k data points for training and 12k for evaluation. To assess the transferability of models to more complex circuits, the dataset has 76k unique 6-component circuits and split 9k data points for evaluation. In our experiments, we randomly select subsets of 500, 1k, and 2k 6-component circuits to fine-tune models initially trained on the 120k 3, 4, 5-component circuits. |
| Hardware Specification | Yes | Training runs on one NVIDIA V100 GPU using Adam W with the following hyperparameter: learning rate 3 10 4, cosine scheduler with 300 warm-up steps, batch size 128, L2 regularization 10 5, dropout 0.1, and epochs |
| Software Dependencies | Yes | We run simulator NGSPICE (Nenzi P, 2011) on each generated circuit to get its actual voltage conversion ratio and efficiency for real-world applications. Nenzi P, V. H. Ngspice users manual version 23., 2011. URL https://pkgs.fedoraproject.org/ repo/extras/ngspice/ngspice23-manual. pdf/eb0d68eb463a41a0571757a00a5b9f9d/ ngspice23-manual.pdf. |
| Experiment Setup | Yes | Training runs on one NVIDIA V100 GPU using Adam W with the following hyperparameter: learning rate 3 10 4, cosine scheduler with 300 warm-up steps, batch size 128, L2 regularization 10 5, dropout 0.1, and epochs |