5 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…
Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Zizhang Luo, Yuhao Luo, Youwei Xiao +3
Large language models are increasingly deployed as complex agentic systems that scale with task complexity. While prior work has extensively explored model- and system-level scalin…
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…
Cement2: Temporal Hardware Transactions for High-Level and Efficient FPGA Programming
Youwei Xiao, Zizhang Luo, Weijie Peng +2
Hardware design faces a fundamental challenge: raising abstraction to improve productivity while maintaining control over low-level details like cycle accuracy. Traditional RTL des…