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Autonomous Evolution Of EDA Tools: Multi-agent Self-evolved ABC

·2026

Abstract

This paper introduces the first *self-evolving* logic synthesis framework, which leverages Large Language Model (LLM) agents to autonomously improve the source code of \textsc\{ABC\}, 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 benchma

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