# Automated QoR improvement in OpenROAD with coding agents

arXiv: https://arxiv.org/abs/2601.06268 · HTML: https://mlsanity.com/paper/2601.06268/

## Key facts

- Cited by 3 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 24 of its 56 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Placed in

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

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios](https://mlsanity.com/paper/2512.18470/) (2025)
- [A Survey of Research in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2501.09655/) (2025)
- [APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning](https://mlsanity.com/paper/2505.05758/) (2025)
- [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://mlsanity.com/paper/2312.13382/) (2023)
- [Does SWE-Bench-Verified Test Agent Ability or Model Memory?](https://mlsanity.com/paper/2512.10218/) (2025)
- [DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent](https://mlsanity.com/paper/2412.05311/) (2024)
- [ORFS-agent: Tool-Using Agents for Chip Design Optimization](https://mlsanity.com/paper/2506.08332/) (2025)
- [EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD](https://mlsanity.com/paper/2405.06676/) (2024)
- [Code-Craft: Hierarchical Graph-Based Code Summarization for Enhanced Context Retrieval](https://mlsanity.com/paper/2504.08975/) (2025)

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

- [MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning](https://mlsanity.com/paper/2607.18536/) (2026)
- [AgenticECO: An Agentic Framework for ECO on 3D Integrated Circuits](https://mlsanity.com/paper/2608.03738/) (2026)
- [GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms](https://mlsanity.com/paper/2608.16733/) (2026)

## Abstract (arXiv)

EDA development and innovation has been constrained by scarcity of expert engineering resources. While leading LLMs have demonstrated excellent performance in coding and scientific reasoning tasks, their capacity to advance EDA technology itself has been largely untested. We present AuDoPEDA, an autonomous, repository-grounded coding system built atop OpenAI models and a Codex-class agent that reads OpenROAD, proposes research directions, expands them into implementation steps, and submits executable diffs. Our contributions include (i) a closed-loop LLM framework for EDA code changes; (ii) a task suite and evaluation protocol on OpenROAD for PPA-oriented improvements; and (iii) end-to-end demonstrations with minimal human oversight. Experiments in OpenROAD achieve routed wirelength reductions of up to 5.9%, effective clock period reductions of up to 10.0%, and power reductions of up to 19.4%.