arXiv Machine Learning July 28, 2026 By Sounak Dutta, Fin Amin, Sushil Panda, Jonathan Rabe, Yuejiang Wen, Paul Franzon

Can an Actor-Critic Optimization Framework Improve Analog Design?

Read the original on arXiv Machine Learning →

arXiv:2603. 24714v2 Announce Type: replace Abstract: Analog design often slows down because even small changes to device sizes or biases require expensive simulation cycles, and high-quality solutions typically occupy only a narrow part of a very large search space.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 6

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

arXiv:2608. 04999v1 Announce Type: cross Abstract: Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort.

By Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi

llms reinforcement-learning benchmarks

arXiv AI

5d ago

AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

arXiv:2608. 13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.

By Mohammed Ayman Habib, Rylan Hart, Morteza Fayazi

llms rag agents benchmarks safety

arXiv Machine Learning

Jul 16

Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

arXiv:2607. 14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing.

By Mustafa Emre G\"ursoy, Stefan Uhlich, Ryoga Matsuo, Ya\u{g}{\i}z Gen\c{c}er, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Eisaku Ohbuchi, Lorenzo Servadei

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 16

EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models

arXiv:2607. 13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands.

By Guanglei Zhou, Chen-Chia Chang, Yikang Shen, Jonathan Ku, Isaac Jacobson, Jingyu Pan, Yiran Chen, Xin Zhang

llms fine-tuning benchmarks

arXiv AI

2d ago

Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

arXiv:2608. 13767v1 Announce Type: new Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement.

By Bingyang Liu, Ziming Wei, Xiaohan Gao, David Z. Pan

llms agents

arXiv AI

Jun 26

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.

By Boyun Zhang, Chao Wang, Kai Wu

llms agents reinforcement-learning benchmarks

Related stories

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models

Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning