# CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems

arXiv: https://arxiv.org/abs/2508.08709 · HTML: https://mlsanity.com/paper/2508.08709/

## Key facts

- Cited by 1 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 7 of its 10 references that are in the map.
- First posted on arXiv 12 Aug 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, 7 of 7)

- [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (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)
- [Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification](https://mlsanity.com/paper/2506.14074/) (2025)
- [Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction](https://mlsanity.com/paper/2406.00115/) (2024)
- [RTLRewriter: Methodologies for Large Models aided RTL Code Optimization](https://mlsanity.com/paper/2409.11414/) (2024)
- [RTLSquad: Multi-Agent Based Interpretable RTL Design](https://mlsanity.com/paper/2501.05470/) (2025)
- [Marco: Configurable Graph-Based Task Solving and Multi-AI Agents Framework for Hardware Design](https://mlsanity.com/paper/2504.01962/) (2025)

## Built on by (most cited first, 1 of 1)

- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)

## Abstract (arXiv)

This paper presents CRADLE, a conversational framework for design space exploration of RTL designs using LLM-based multi-agent systems. Unlike existing rigid approaches, CRADLE enables user-guided flows with internal self-verification, correction, and optimization. We demonstrate the framework with a generator-critic agent system targeting FPGA resource minimization using state-of-the-art LLMs. Experimental results on the RTLLM benchmark show that CRADLE achieves significant reductions in resource usage with averages of 48% and 40% in LUTs and FFs across all benchmark designs.