# Understanding and Mitigating Errors of LLM-Generated RTL Code

arXiv: https://arxiv.org/abs/2508.05266 · HTML: https://mlsanity.com/paper/2508.05266/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 27 of 27)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [BetterV: Controlled Verilog Generation with Discriminative Guidance](https://mlsanity.com/paper/2402.03375/) (2024)
- [VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool](https://mlsanity.com/paper/2408.08927/) (2024)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution](https://mlsanity.com/paper/2312.08617/) (2023)
- [OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection](https://mlsanity.com/paper/2407.16237/) (2024)
- [DAVE: Deriving Automatically Verilog from English](https://mlsanity.com/paper/2009.01026/) (2020)
- [Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction](https://mlsanity.com/paper/2406.00115/) (2024)
- [AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs](https://mlsanity.com/paper/2407.18333/) (2024)
- [QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation](https://mlsanity.com/paper/2505.24183/) (2025)
- [HDLdebugger: Streamlining HDL debugging with Large Language Models](https://mlsanity.com/paper/2403.11671/) (2024)
- [MEIC: Re-thinking RTL Debug Automation using LLMs](https://mlsanity.com/paper/2405.06840/) (2024)
- [HLSPilot: LLM-based High-Level Synthesis](https://mlsanity.com/paper/2408.06810/) (2024)
- [DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model](https://mlsanity.com/paper/2502.15832/) (2025)
- [AIvril: AI-Driven RTL Generation With Verification In-The-Loop](https://mlsanity.com/paper/2409.11411/) (2024)
- [ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model](https://mlsanity.com/paper/2504.14560/) (2025)
- [VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction](https://mlsanity.com/paper/2504.19099/) (2025)
- [VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency](https://mlsanity.com/paper/2502.00028/) (2025)
- [ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation](https://mlsanity.com/paper/2407.12022/) (2024)
- [CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation](https://mlsanity.com/paper/2412.11014/) (2024)

## Built on by (most cited first, 8 of 8)

- [An Empirical Study on Failures in Automated Issue Solving](https://mlsanity.com/paper/2509.13941/) (2025)
- [Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead](https://mlsanity.com/paper/2512.00020/) (2025)
- [UCAgent: An End-to-End Agent for Block-Level Functional Verification](https://mlsanity.com/paper/2603.25768/) (2026)
- [HAVEN: Hybrid Automated Verification ENgine for UVM Testbench Synthesis with LLMs](https://mlsanity.com/paper/2604.27643/) (2026)
- [LEGO: An LLM Skill-Based Front-End Design Generation Platform](https://mlsanity.com/paper/2604.23355/) (2026)
- [EstRTL: Functional Estimation Guided RTL Code Generation](https://mlsanity.com/paper/2606.09867/) (2026)
- [How LLMs Fail and Generalize in RTL Coding for Hardware Design?](https://mlsanity.com/paper/2606.19347/) (2026)
- [SpecRead: A Benchmark for Measuring Whether Language Models Understand Hardware Specifications](https://mlsanity.com/paper/2609.33699/) (2026)

## Abstract (arXiv)

Despite limited success in large language model (LLM)-based register-transfer-level (RTL) code generation, the root causes of errors remain poorly understood. To address this, we conduct a comprehensive error analysis, finding that most failures arise not from deficient reasoning, but from a lack of RTL programming knowledge, insufficient circuit understanding, ambiguous specifications, or misinterpreted multimodal inputs. Leveraging in-context learning, we propose targeted correction techniques: a retrieval-augmented generation (RAG) knowledge base to supply domain expertise; design description rules with rule-checking to clarify inputs; external tools to convert multimodal data into LLM-compatible formats; and an iterative simulation-debugging loop for remaining errors. Integrating these into an LLM-based framework yields significant improvement, achieving 98.1% accuracy on the VerilogEval benchmark with DeepSeek-v3.2-Speciale, demonstrating the effectiveness of our approach.