# RTLSquad: Multi-Agent Based Interpretable RTL Design

arXiv: https://arxiv.org/abs/2501.05470 · HTML: https://mlsanity.com/paper/2501.05470/

## Key facts

- Cited by 9 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.
- First posted on arXiv 6 Jan 2025.

## Placed in

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

## Built on by (most cited first, 9 of 9)

- [Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead](https://mlsanity.com/paper/2512.00020/) (2025)
- [EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization](https://mlsanity.com/paper/2601.18067/) (2026)
- [Towards Optimal Circuit Generation: Multi-Agent Collaboration Meets Collective Intelligence](https://mlsanity.com/paper/2504.14625/) (2025)
- [CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2508.08709/) (2025)
- [Automating Hardware Design and Verification from Architectural Papers via a Neural-Symbolic Graph Framework](https://mlsanity.com/paper/2511.06067/) (2025)
- [QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation](https://mlsanity.com/paper/2602.00704/) (2026)
- [LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration](https://mlsanity.com/paper/2608.27184/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [When LLMs Over-Answer: Measuring and Mitigating Quality Issues in LLM-Based Hardware Description Language Question Answering](https://mlsanity.com/paper/2607.17063/) (2026)

## Abstract (arXiv)

Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), making it difficult for hardware engineers to trust the generated results, thus preventing these methods from being integrated into the design process. To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation. RTLSquad divides the design process into exploration, implementation, and verification & evaluation stages managed by specialized agent squads, generating optimized RTL code through inter-agent collaboration, and providing decision interpretability through the communication process. Experiments show that RTLSquad excels in generating functionally correct RTL code and optimizing PPA performance, while also having the capability to provide decision paths, demonstrating the practical value of our system.