6 citations · 6 across the 4 of their papers we have counts for
5 papers
Evidence-Guided Repository-Level RTL Repair
Yuxin Du, Juxin Niu, Zhe Jiang +1
Repository-level RTL repair must localize a failure that spans files, modules, and clock cycles, then propagate the fix consistently. Existing methods reason over source code, whic…
SeqFeed: Improving Agentic RTL Code Generation with Sequential Behavior Feedback
Yuxin Du, Juxin Niu, Tao Hu +3
RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct RTL code, ag…
VerilogMonkey: Exploring Parallel Scaling for Automated Verilog Code Generation with LLMs
Juxin Niu, Yuxin Du, Dan Niu +3
We present VerilogMonkey, an empirical study of parallel scaling for the under-explored task of automated Verilog generation. Parallel scaling improves LLM performance by sampling…
ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection
Juxin Niu, Xiangfeng Liu, Dan Niu +3
Coding with hardware description languages (HDLs) such as Verilog is a time-intensive and laborious task. With the rapid advancement of large language models (LLMs), there is incre…
Grade Like a Human: Rethinking Automated Assessment with Large Language Models
Wenjing Xie, Juxin Niu, Chun Jason Xue +1
While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading com…