EXPLORE: Search-Enhanced Framework for Analog Circuit Topology Generation Using Language Models

Jul 16, 2026

Researchers introduce EXPLORE, a framework that integrates simulator-guided Monte Carlo Tree Search with transformer-based language model decoding to improve analog circuit topology generation. On a 6-component benchmark with tight tolerance, EXPLORE achieves a 65% success rate, outperforming one-shot generation (12%) and sampling-and-filter baselines (33%). The framework also reduces mean squared error by over 20% compared to sampling-and-filter under the same search budget.

Why it matters: This work demonstrates a significant advance in automating analog circuit design by enabling language models to generate complex topologies more reliably through structured search.

Full story at: arXiv Machine Learning ↗