5 papers · 1 filter
LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
Hejia Zhang, Zhongming Yu, Chia-Tung Ho +3
Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making online reinforcement learning (…
HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization
Souradip Poddar, Chia-Tung Ho, Ziming Wei +3
Conventional AI-driven AMS design automation algorithms remain constrained by their reliance on high-quality datasets to capture underlying circuit behavior, coupled with poor tran…
PRO-V-R1: Reasoning Enhanced Programming Agent for RTL Verification
Yujie Zhao, Zhijing Wu, Boqin Yuan +6
Register-Transfer Level (RTL) verification is a primary bottleneck, consuming 60-70% of development time. While Large Language Models (LLMs) show promise for RTL automation, their…
SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting
Lily Jiaxin Wan, Chia-Tung Ho, Rongjian Liang +3
Log schema extraction is the process of deriving human-readable templates from massive volumes of log data, which is essential yet notoriously labor-intensive. Recent studies have…
Polymath: A Self-Optimizing Agent with Dynamic Hierarchical Workflow
Chia-Tung Ho, Jing Gong, Xufeng Yao +3
Large language models (LLMs) excel at solving complex tasks by executing agentic workflows composed of detailed instructions and structured operations. Yet, building general-purpos…