mlsanityData to 14 Oct 2026

VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation

arXiv 2603.08715 ↗First posted 4 Feb 2026cs.AR cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

23 of 28 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2023Teaching Large Language Models to Self-Debugcited by 988
  6. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  7. 2024A Survey on Large Language Models for Code Generationcited by 658
  8. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 542
  9. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 252
  10. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 200
  11. 2022DocPrompting: Generating Code by Retrieving the Docscited by 153
  12. 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 148
11 more
  1. 2023Structured Chain-of-Thought Prompting for Code Generationcited by 138
  2. 2023RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solutioncited by 71
  3. 2024Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generationcited by 45
  4. 2023Large Language Model-Aware In-Context Learning for Code Generationcited by 43
  5. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 29
  6. 2023LLM-Assisted Code Cleaning For Training Accurate Code Generatorscited by 27
  7. 2025VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generationcited by 16
  8. 2025VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verificationcited by 15
  9. 2023Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performancecited by 9
  10. 2025Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generationcited by 8
  11. 2025Assessing Small Language Models for Code Generation: An Empirical Study with Benchmarkscited by 8

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Citing papers per year: 2026: 1.

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  1. 2026LLMs for Secure Hardware Design and Related Problems: Opportunities and Challengescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning
  2. 2026Exploring the Agentic Frontier of Verilog Code Generation
  3. 2024What Makes Large Language Models Reason in (Multi-Turn) Code Generation?
  4. 2025PromptBridge: Cross-Model Prompt Transfer for Large Language Models
  5. 2026SpecLens: LLM-Based Verilog Generation with Specification-Derived Constraints via Behavioral Divergence ↗
  6. 2025VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning
  7. 2025Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback
  8. 2026Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement