RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision
Quick Take
RTL-BenchMT is an automated framework for maintaining RTL generation benchmarks, addressing flaws and overfitting.
Key Points
- Utilizes agent-assisted analysis for benchmark maintenance.
- Identifies and revises flawed benchmark cases automatically.
- Produces a refined, open-sourced benchmark suite.
📖 Reader Mode
~2 min readAbstract:This paper introduces RTL-BenchMT, an agentic framework for dynamically maintaining RTL generation benchmarks. Large Language Models (LLMs) assisted automated RTL generation is one of the most important directions in EDA research. However, current RTL benchmarks face two critical challenges: (1) flawed cases in the benchmarks and (2) overfitting to the benchmarks. Both challenges are difficult to resolve purely by manual engineering effort. To address these issues and systematically reduce human maintenance costs, we propose an automated agentic framework, RTL-BenchMT. RTL-BenchMT focuses on two key applications: (1) automatically identifying and revising flawed benchmark cases and (2) automatically detecting and updating overfitting cases. With the assistance of RTL-BenchMT, we conduct a thorough, in-depth analysis of flawed and overfitting cases and produce a refined benchmark suite that will be open-sourced to the community.
| Comments: | This paper has been accepted by DAC 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.15537 [cs.AI] |
| (or arXiv:2605.15537v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15537 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jing Wang [view email]
[v1]
Fri, 15 May 2026 02:17:46 UTC (847 KB)
— Originally published at arxiv.org
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