5 citations · 10 across the 16 of their papers we have counts for
17 papers
EvalResearchBench: Can AI Agents Build Their Own Feedback for Recursive Self-Improvement?
Yaolun Zhang, Tianyi Xu, Yujie Zhao +3
Recursive self-improvement (RSI) relies on evaluation feedback to assess progress and guide further research, yet repeatedly running complex benchmarks is costly and slows iteratio…
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…
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning
Yaolun Zhang, Yujie Zhao, Nan Wang +6
Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only…
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Zhongkai Yu, Yichen Lin, Chenyang Zhou +12
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, re…
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 (…
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…
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…
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)…
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…
Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction
Hanxian Huang, Zhenghan Lin, Zixuan Wang +3
We explore the use of Large Language Models (LLMs) to generate high-quality Register-Transfer Level (RTL) code with minimal human interference. The traditional RTL design workflow…
GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
Zhongming Yu, Genghan Zhang, Hanxian Huang +2
In recent years, Graph Neural Networks (GNNs) have ignited a surge of innovation, significantly enhancing the processing of geometric data structures such as graphs, point clouds,…
Learning to Maximize Mutual Information for Chain-of-Thought Distillation
Xin Chen, Hanxian Huang, Yanjun Gao +3
Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment. Distilling Step-by…
Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations
Hui Shi, Sicun Gao, Yuandong Tian +2
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural language processing tasks. Theoretical results show that both are Turing-comple…