VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908956570419200 |
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| author | Collini, Luca Hennesee, Andrew Yubeaton, Patrick Garg, Siddharth Karri, Ramesh |
| author_facet | Collini, Luca Hennesee, Andrew Yubeaton, Patrick Garg, Siddharth Karri, Ramesh |
| contents | Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics and prompt design choices. In this work, we provide an empirical map of recent trends in LMs for Verilog code generation, focusing on interactions among model reasoning, specialization, and prompt engineering strategies. We evaluate a diverse set of small and large LMs, including general-purpose, reasoning, and domain-specific variants. Our experiments use a controlled factorial design spanning benchmark prompts, structured outputs, prompt rewriting, chain-of-thought reasoning, in-context learning, and evolutionary prompt optimization via Genetic-Pareto. Across two Verilog benchmarks, we identify patterns in how model classes respond to structured prompts and optimization, and we document which trends generalize across LMs and benchmarks versus those that are specific to particular model-prompt combinations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08715 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation Collini, Luca Hennesee, Andrew Yubeaton, Patrick Garg, Siddharth Karri, Ramesh Hardware Architecture Computation and Language Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics and prompt design choices. In this work, we provide an empirical map of recent trends in LMs for Verilog code generation, focusing on interactions among model reasoning, specialization, and prompt engineering strategies. We evaluate a diverse set of small and large LMs, including general-purpose, reasoning, and domain-specific variants. Our experiments use a controlled factorial design spanning benchmark prompts, structured outputs, prompt rewriting, chain-of-thought reasoning, in-context learning, and evolutionary prompt optimization via Genetic-Pareto. Across two Verilog benchmarks, we identify patterns in how model classes respond to structured prompts and optimization, and we document which trends generalize across LMs and benchmarks versus those that are specific to particular model-prompt combinations. |
| title | VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation |
| topic | Hardware Architecture Computation and Language |
| url | https://arxiv.org/abs/2603.08715 |