SpecAlign: A Semantic Alignment Framework for SystemVerilog Assertion Generation
The paper introduces SpecAlign, a framework designed to enhance the semantic alignment of SystemVerilog Assertions (SVAs) generated by Large Language Models (LLMs). It addresses the challenges of ensuring that generated assertions align with natural language specifications, which is crucial for reducing debugging efforts. The framework employs iterative alignment loops and a quantitative alignment score to improve assertion accuracy without relying on golden RTL.
- ▪SpecAlign focuses on semantic evaluation and refinement of LLM-generated SVAs.
- ▪The framework uses entailment-based classification to assess assertions against design specifications.
- ▪Experimental results indicate that SpecAlign effectively detects semantic inconsistencies and enhances assertion alignment.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25181 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.25181 (cs) [Submitted on 24 May 2026] Title:SpecAlign: A Semantic Alignment Framework for SystemVerilog Assertion Generation Authors:Jaime Rafael Imperial, Hao Zheng View a PDF of the paper titled SpecAlign: A Semantic Alignment Framework for SystemVerilog Assertion Generation, by Jaime Rafael Imperial and Hao Zheng View PDF HTML (experimental) Abstract:Existing Large Language Model (LLM) approaches to SystemVerilog Assertion (SVA) generation primarily focus on syntactic validity and formal verification outcomes, while semantic alignment between generated assertions and natural language specifications remains difficult to quantify.
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