# LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation

arXiv: https://arxiv.org/abs/2506.10235 · HTML: https://mlsanity.com/paper/2506.10235/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability.
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Placed in

- Mechanistic interpretability / [Circuits and mechanistic analysis](https://mlsanity.com/mechinterp/circuits-and-mechanistic-analysis/)

## Builds on (most cited first, 6 of 6)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://mlsanity.com/paper/2305.02309/) (2023)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits](https://mlsanity.com/paper/2407.18269/) (2024)

## Built on by (most cited first, 6 of 6)

- [AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs](https://mlsanity.com/paper/2508.02518/) (2025)
- [CircuitFormer: A Circuit Language Model for Analog Topology Design from Natural Language Prompt](https://mlsanity.com/paper/2605.05773/) (2026)
- [PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters](https://mlsanity.com/paper/2601.21984/) (2026)
- [AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic](https://mlsanity.com/paper/2607.11338/) (2026)
- [Validation and Simulation Catch Different Errors: Four Levels of Evaluation for LLM-Generated Circuits](https://mlsanity.com/paper/2609.26830/) (2026)
- [Long-Horizon Analog Design Bench: Benchmarking Agents on Hours-Long Analog and Mixed-Signal Circuit Design Tasks](https://mlsanity.com/paper/2609.33356/) (2026)

## Abstract (arXiv)

Automation of analog topology design is crucial due to customized requirements of modern applications with heavily manual engineering efforts. The state-of-the-art work applies a sequence-to-sequence approach and supervised finetuning on language models to generate topologies given user specifications. However, its circuit formulation is inefficient due to O(|V |2) token length and suffers from low precision sensitivity to numeric inputs. In this work, we introduce LaMAGIC2, a succinct float-input canonical formulation with identifier (SFCI) for language model-based analog topology generation. SFCI addresses these challenges by improving component-type recognition through identifier-based representations, reducing token length complexity to O(|V |), and enhancing numeric precision sensitivity for better performance under tight tolerances. Our experiments demonstrate that LaMAGIC2 achieves 34% higher success rates under a tight tolerance of 0.01 and 10X lower MSEs compared to a prior method. LaMAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5% improvement. These advancements establish LaMAGIC2 as a robust framework for analog topology generation.