Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench
The paper explores the effectiveness of agentic AI systems in hardware engineering compared to software engineering. It introduces Phoenix-bench, a benchmark designed to evaluate AI agents on hardware tasks. The findings indicate significant differences in performance between software and hardware engineering tasks, highlighting the challenges faced by AI in hardware contexts.
- ▪Phoenix-bench consists of 511 verified Verilator instances from 114 GitHub repositories.
- ▪Agentic AI systems show a performance drop of 37% to 58% when transitioning from software to hardware tasks.
- ▪Localization granularity is crucial, with a single round of test case feedback improving resolved rates by 42% to 45%.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15226 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Hardware Architecture arXiv:2605.15226 (cs) [Submitted on 13 May 2026] Title:Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench Authors:Qingyun Zou, Feng Yu, Hongshi Tan, Bingsheng He, WengFai Wong View a PDF of the paper titled Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench, by Qingyun Zou and 4 other authors View PDF HTML (experimental) Abstract:We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate sub-tasks but none jointly requires repository navigation, hierarchy-aware localization, Electronic Design Automation (EDA) executable verification, and maintenance-style patching.
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