activity
20232026
most citedThe Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

21 citations · 22 across the 14 of their papers we have counts for

collaborators

17 papers

cs.AR2026

LevelSyn: Physical-Aware Logic Synthesis via Level-Asynchronous Graph Neural Networks

Jingyi Zhou, Zhengyuan Shi, Ziyang Zheng +1

As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic synthesis and physical design has led to significant PPA (Power, Perform…

cs.LG2026

Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits

Jingyi Zhou, Zhengyuan Shi, Jiaying Zhu +2

Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the…

cs.IR2026

HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning

Zeju Li, Ziyang Zheng, Yizhou Zhou +1

Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-…

cs.LG2026

Context Distillation as Latent Memory Management

Ziyang Zheng, Zeju Li, Xiangyu Wen +5

Context distillation compresses contextual information into model parameters, yet existing methods often ignore how multiple distilled latent memories should be stored, retrieved,…

cs.LG2025

DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior

Ruiyang Ma, Yunhao Zhou, Yipeng Wang +9

There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these model…

cs.LG2025

Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning

Zhengyuan Shi, Jingxin Wang, Wentao Jiang +5

Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast st…