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Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations
Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correctness on a single syntactic representation of an input. Such point accuracy does not reveal whether a model's correct output is stable when the same RTL behavior is written differently. This paper presents a controlled metamorphic evaluation of LLM-based SVA generation under semantics-preserving RTL transformations. Starting from the VERT dataset, we construct a quality-filtered conditional-control pool and a stratified 40-program evaluation se
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-04T18:41:58.000Z
First collected: 2026-09-20T21:52:07.471Z. This is not the publication date.