arXiv Machine Learning By Kangwei Xu, Bing Li, Ulf Schlichtmann

LLM for EDA in Front-End Design: Challenges and Opportunities

Read the original on arXiv Machine Learning →

arXiv:2607. 09616v1 Announce Type: cross Abstract: As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development.

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

arXiv AI
Jul 7

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.

By Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
arXiv AI
Aug 6

PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation

arXiv:2608. 01791v2 Announce Type: replace-cross Abstract: The rapid development of photonic integrated circuits (PICs) is shifting the design flow from traditional graphical user interface (GUI)-based methods to script-based methods for higher flexibility, portability, and maintainability.

By Xiaohan Jiang, Zeyu Li, Wei Zhang, Jiang Xu