# Marco: Configurable Graph-Based Task Solving and Multi-AI Agents Framework for Hardware Design

arXiv: https://arxiv.org/abs/2504.01962 · HTML: https://mlsanity.com/paper/2504.01962/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2605.14212/) (2026)
- [CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2508.08709/) (2025)

## Abstract (arXiv)

Hardware design presents numerous challenges stemming from its complexity and advancing technologies. These challenges result in longer turn-around-time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops. Large Language Models (LLMs) have shown remarkable capacity to comprehend and generate natural language at a massive scale, leading to many potential applications and benefits across various domains. Successful LLM-based agents for hardware design can drastically reduce TAT, leading to faster product cycles, lower costs, improved design reliability and reduced risk of costly errors. In this work, we propose a unified framework, Marco, that integrates configurable graph-based task solving with multi-modality and multi-AI agents for chip design by leveraging the natural language and reasoning abilities with collaborative toolkits. Lastly, we demonstrate promising performance, productivity, and efficiency of LLM agents by leveraging the Marco framework on layout optimization, Verilog/design rule checker (DRC) coding, and timing analysis tasks.