# BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis

arXiv: https://arxiv.org/abs/2506.10501 · HTML: https://mlsanity.com/paper/2506.10501/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 4 of 4)

- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [VCDiag: Classifying Erroneous Waveforms for Failure Triage Acceleration](https://mlsanity.com/paper/2506.03590/) (2025)
- [Review of Machine Learning for Micro-Electronic Design Verification](https://mlsanity.com/paper/2503.11687/) (2025)

## Built on by (most cited first, 6 of 6)

- [PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases](https://mlsanity.com/paper/2509.25238/) (2025)
- [GRPO with State Mutations: Improving LLM-Based Hardware Test Plan Generation](https://mlsanity.com/paper/2601.07593/) (2026)
- [Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems](https://mlsanity.com/paper/2605.22883/) (2026)
- [Saarthi for AGI: Towards Domain-Specific General Intelligence for Formal Verification](https://mlsanity.com/paper/2603.03175/) (2026)
- [VeriPilot: An LLM-Powered Verilog Debugging Framework](https://mlsanity.com/paper/2606.23759/) (2026)
- [Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking](https://mlsanity.com/paper/2606.29088/) (2026)

## Abstract (arXiv)

Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realistic functional bugs in RTL. BugGen partitions modules, selects mutation targets via a closed-loop agentic architecture, and employs iterative refinement and rollback mechanisms to ensure syntactic correctness and functional detectability. Evaluated across five OpenTitan IP blocks, BugGen produced 500 unique bugs with 94% functional accuracy and achieved a throughput of 17.7 validated bugs per hour-over five times faster than typical manual expert insertion. Additionally, BugGen identified 104 previously undetected bugs in OpenTitan regressions, highlighting its utility in exposing verification coverage gaps. Compared against Certitude, BugGen demonstrated over twice the syntactic accuracy, deeper exposure of testbench blind spots, and more functionally meaningful and complex bug scenarios. Furthermore, when these BugGen-generated datasets were employed to train ML-based failure triage models, we achieved high classification accuracy (88.1%-93.2%) across different IP blocks, confirming the practical utility and realism of generated bugs. BugGen thus provides a scalable solution for generating high-quality bug datasets, significantly enhancing verification efficiency and ML-assisted debugging.