CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems
On the map: LLM agents and tool use → Software engineering coding agents
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.
Key facts. Cited by 1 paper in the map (data through 7 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.
Builds on
7 of 10 references.
Closest ancestors · 3
Same direction, newest first
- 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification17 Jun
- 2025RTLSquad: Multi-Agent Based Interpretable RTL Design6 Jan
- 2024VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool15 Aug
Other references · 4
- 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 143
- 2024Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correctioncited by 39
- 2024RTLRewriter: Methodologies for Large Models aided RTL Code Optimizationcited by 30
- 2025Marco: Configurable Graph-Based Task Solving and Multi-AI Agents Framework for Hardware Designcited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Agentic Electronic Design Automation: A Handoff Perspective18 Jun 2026
Similar, not cited
Close in wording, no citation either way
- 2026ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs
- 2026Can Agents Design Better Chips with a Higher Level Abstraction?
- 2026CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization
- 2025TuRTLe: A Unified Evaluation of LLMs for RTL Generation
- 2026VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs
- 2026Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement
- 2025ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation
- 2026LLM for EDA in Front-End Design: Challenges and Opportunities