[2602.10218] ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs

Computer Science > Hardware Architecture arXiv:2602.10218 (cs) [Submitted on 10 Feb 2026 (v1), last revised 21 Mar 2026 (this version, v2)] Title:ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs Authors:Chenhui Deng, Zhongzhi Yu, Guan-Ting Liu, Nathaniel Pinckney, Brucek Khailany, Haoxing Ren View a PDF of the paper titled ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs, by Chenhui Deng and 5 other authors View PDF HTML (experimental) Abstract:Recent advances in LLMs have sparked growing interest in applying them to hardware design automation, particularly for accurate RTL code generation. Prior efforts follow two largely independent paths: (i) training domain-adapted RTL models to internalize hardware semantics, (ii) developing agentic systems that leverage frontier generic LLMs guided by simulation feedback. However, these two paths exhibit complementary strengths and weaknesses. In this work, we present ACE-RTL that unifies both directions through Agentic Context Evolution (ACE). ACE-RTL integrates an RTL-specialized LLM, trained on a large-scale dataset of 1.7 million RTL samples, with a frontier reasoning LLM through three synergistic components: the generator, reflector, and coordinator. These components iteratively refine RTL code toward functional correctness. We further analyze a parallel scaling strategy that reduces wall-clock iterations to first success by exploring diverse debugging trajectories concurrently. On the CVDP be...