Author

Kun Wang

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Jul 2026

RTLCurator: Label-Efficient Data Curation for RTL Generation

RTLCurator is presented, which learns a behavior-aware compatibility prior by contrasting each specification with implementations that fail simulation, and calibrates it to a new corpus using a small number of validated pairs, and constructs the retained subset by balancing alignment, representation coverage, and RTL structural richness.

Siyang Cai, Cangyuan Li, Wenjing Chang et al. · arXiv.org · 0 citations

Preprint Jul 2026

When Fuzzing Meets Understanding: LLM-Driven Semantic Test Generation for RTL Verification

ChipFuzzer is proposed, a hardware fuzzing framework that leverages the semantic reasoning capabilities of large language models (LLMs) to improve fuzzing effectiveness and improves average condition coverage and bug detection rate over the strongest baseline.

Kun Wang, Cangyuan Li, Kaiyan Chang et al. · 0 citations