9 papers
Stronger-MAS: Multi-Agent Reinforcement Learning for Collaborative LLMs
Yujie Zhao, Lanxiang Hu, Yang Wang +4
Multi-agent systems (MAS) and reinforcement learning (RL) are widely used to enhance the agentic capabilities of large language models (LLMs). MAS improves task performance through…
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…
OrcaLoca: An LLM Agent Framework for Software Issue Localization
Zhongming Yu, Hejia Zhang, Yujie Zhao +4
Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvemen…
SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters
Yiping Wang, Hanxian Huang, Yifang Chen +3
While Large language models (LLMs) have advanced natural language processing tasks, their growing computational and memory demands make deployment on resource-constrained devices l…
MAGE: A Multi-Agent Engine for Automated RTL Code Generation
Yujie Zhao, Hejia Zhang, Hanxian Huang +2
The automatic generation of RTL code (e.g., Verilog) through natural language instructions has emerged as a promising direction with the advancement of large language models (LLMs)…
You Only Use Reactive Attention Slice For Long Context Retrieval
Yun Joon Soh, Hanxian Huang, Yuandong Tian +1
Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, ma…