# IncreRTL: Traceability-Guided Incremental RTL Generation under Requirement Evolution

arXiv: https://arxiv.org/abs/2603.25769 · HTML: https://mlsanity.com/paper/2603.25769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [ChipGPT: How far are we from natural language hardware design](https://mlsanity.com/paper/2305.14019/) (2023)
- [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)
- [AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs](https://mlsanity.com/paper/2407.18333/) (2024)
- [MAGE: A Multi-Agent Engine for Automated RTL Code Generation](https://mlsanity.com/paper/2412.07822/) (2024)
- [Semantically Enhanced Software Traceability Using Deep Learning Techniques](https://mlsanity.com/paper/1804.02438/) (2018)
- [Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning](https://mlsanity.com/paper/2312.17582/) (2023)
- [TVR: Automotive System Requirement Traceability Validation and Recovery Through Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.15427/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and struggle to adapt to evolving design requirements, potentially causing structural drift and costly full regeneration. We propose IncreRTL, a LLM-driven framework for incremental RTL generation under requirement evolution. By constructing requirement-code traceability links to locate and regenerate affected code segments, IncreRTL achieves accurate and consistent updates. Evaluated on our newly constructed EvoRTL-Bench, IncreRTL demonstrates notable improvements in regeneration consistency and efficiency, advancing LLM-based RTL generation toward practical engineering deployment.