1 citations · 1 across the 4 of their papers we have counts for
4 papers
HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks
Fan Cui, Hongyuan Hou, Zizhang Luo +2
Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leavi…
Clover: A Neural-Symbolic Agentic Harness with Stochastic Tree-of-Thoughts for Verified RTL Repair
Zizhang Luo, Yansong Xu, Runlin Guo +6
RTL program repair remains a critical bottleneck in hardware design and verification. Traditional automatic program repair (APR) methods rely on predefined templates and synthesis,…
R3A: Reliable RTL Repair Framework with Multi-Agent Fault Localization and Stochastic Tree-of-Thoughts Patch Generation
Zizhang Luo, Fan Cui, Kexing Zhou +4
Repairing RTL bugs is crucial for hardware design and verification. Traditional automatic program repair (APR) methods define dedicated search spaces to locate and fix bugs with pr…
OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection
Fan Cui, Chenyang Yin, Kexing Zhou +9
Recent studies have demonstrated the significant potential of Large Language Models (LLMs) in generating Register Transfer Level (RTL) code, with notable advancements showcased by…