# Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABC

arXiv: https://arxiv.org/abs/2604.15082 · HTML: https://mlsanity.com/paper/2604.15082/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [Developing Synthesis Flows Without Human Knowledge](https://mlsanity.com/paper/1804.05714/) (2018)
- [Autonomous Code Evolution Meets NP-Completeness](https://mlsanity.com/paper/2509.07367/) (2025)
- [FlowTune: End-to-end Automatic Logic Optimization Exploration via Domain-specific Multi-armed Bandit](https://mlsanity.com/paper/2202.07721/) (2022)

## Built on by (most cited first, 6 of 6)

- [Agentic Hardware Design as Repository-Level Code Evolution](https://mlsanity.com/paper/2606.28279/) (2026)
- [GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms](https://mlsanity.com/paper/2608.16733/) (2026)
- [Arbor: Tree Search as a Cognition Layer for Autonomous Agents](https://mlsanity.com/paper/2606.12563/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [EDATracer: An Agentic Framework for Large-Scale EDA Artifact Analysis](https://mlsanity.com/paper/2608.04032/) (2026)
- [Loreley: Repository-Scale Program Evolution with Quality-Diversity Search](https://mlsanity.com/paper/2608.19703/) (2026)

## Abstract (arXiv)

This paper introduces the first self-evolving logic synthesis framework, which leverages Large Language Model (LLM) agents to autonomously improve the source code of textscABC, the widely adopted logic synthesis system. Our framework operates on the entire integrated ABC codebase, and the output repository preserves its single-binary execution model and command interface. In the initial evolution cycle, we bootstrap the system using existing prior open-source synthesis components, covering flow tuning, logic minimization, and technology mapping, but without manually injecting new heuristics. On top of this foundation, a team of LLM-based agents iteratively rewrites and evolves specific sub-components of ABC following our ``programming guidance`` prompts under a unified correctness and QoR-driven evaluation loop. Each evolution cycle proposes code modifications, compiles the integrated binary, validates correctness, and evaluates quality-of-results (QoR) on multi-suite benchmarks including ISCAS~85/89/99, VTR, EPFL, and IWLS~2005. Through continuous feedback, the system discovers optimizations beyond human-designed heuristics, effectively learning new synthesis strategies that enhance QoR. We detail the architecture of this self-improving system, its integration with textscABC, and results demonstrating that the framework can autonomously and progressively improve EDA tool at full million-line scale.