5 citations · 5 across the 11 of their papers we have counts for
11 papers
CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
Zihao Ye, Yingyi Huang, Hongyi Jin +11
GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce. Agents usually treat the compiler as a fixed black box and…
CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
Hejia Zhang, Sheng Lu, Zhongming Yu +3
Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification i…
CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents
Zhongming Yu, Hengjia Yu, Boqin Yuan +12
An incremental code index can complete every update without error and still return the wrong graph half the time. Coding agents depend on such reused views for search and navigatio…
Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
Zhongming Yu, Naicheng Yu, Hejia Zhang +5
As LLM agents evolve into collaborative multi-agent systems, their memory requirements grow rapidly in complexity. This position paper frames multi-agent memory as a computer archi…
SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution
Gangda Deng, Zhaoling Chen, Zhongming Yu +11
Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-running systems, are now entrusted to drive…
Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs
Wentao Ni, Kangqi Zhang, Zhongming Yu +7
As long-context inference becomes central to large language models (LLMs), attention over growing key-value caches emerges as a dominant decoding bottleneck, motivating sparse atte…
AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
Yujie Zhao, Boqin Yuan, Junbo Huang +9
Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long-horizon applications, where effective memory is critical for sustained performance. Yet exi…
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 (…
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…
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)…