---
name: drrtl-autonomous-agentic-rtl-optimization-through
description: 'Research paper: Dr.~RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement'
metadata:
  openclaw:
    emoji: "📄"
    tags: ["research", "arxiv", "ai-agents", "2026-04-16"]
---

# Dr.~RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement

**arXiv ID:** [2604.14989](http://arxiv.org/abs/2604.14989)
**Published:** 2026-04-16
**Authors:** Wenji Fang, Yao Lu, Shang Liu
**Categories:** cs.AI, cs.AR
**Utility Score:** 1.00

## Abstract

Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area (PPA). However, existing methods are still far from realistic RTL optimization. Their evaluation settings are often unrealistic: they are tested on manually curated small designs rather than production-scale circuits. We present Dr. RTL, an autonomous agentic system that performs RTL optimization through tool-grounded self-improvement. The system combines an LLM-based agent with commercial EDA tools in a closed-loop optimization framework. Dr. RTL achieves competitive PPA results on industrial designs up to 10 million gates, demonstrating practical applicability for the first time in LLM-based hardware optimization.

## Key Contributions

- High-utility research paper relevant to AI agent systems
- Matched keywords: multi-agent, agentic, tool, tools, evaluation, autonomous

## Quick Reference

```bash
# View on arXiv
open "http://arxiv.org/abs/2604.14989"
```

---
*Auto-generated from arXiv paper tracker*
*Generated: 2026-04-19 22:10*

## Activation Keywords

- "drrtl-autonomous-agentic-rtl-optimization-through"
- "drrtl autonomous agentic rtl optimization through"
- "use drrtl autonomous agentic rtl optimization through"
- "drrtl autonomous agentic rtl optimization through help"
- "drrtl autonomous agentic rtl optimization through tool"

## Tools Used

- `Read` - Read existing files and documentation
- `Write` - Create new files and documentation
- `Bash` - Execute commands when needed

## Instructions for Agents

1. Identify user's intent and specific requirements
2. Gather necessary context from files or user input
3. Execute appropriate actions using available tools
4. Provide clear results and suggest next steps

## Examples

### Basic Drrtl Autonomous Agentic Rtl Optimization Through usage
```
User: "Help me with drrtl autonomous agentic rtl optimization through"
→ Understand requirements → Execute actions → Provide results
```

### Advanced usage
```
User: "I need detailed drrtl autonomous agentic rtl optimization through assistance"
→ Clarify scope → Provide comprehensive solution → Follow up
```