RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision
Quick Answer
RTL-BenchMT is an automated framework designed to dynamically maintain RTL generation benchmarks by identifying and revising flawed cases and updating overfitting instances.
Quick Take
This agent-assisted approach aims to reduce human maintenance costs and improve benchmark quality, with a refined suite to be open-sourced for community use. The framework addresses critical challenges in EDA research, enhancing the reliability of automated RTL generation processes.
Key Points
- RTL-BenchMT automates the identification and revision of flawed benchmark cases.
- The framework also detects and updates instances of overfitting in benchmarks.
- It aims to significantly reduce human maintenance costs in EDA research.
- A refined benchmark suite will be open-sourced for community access.
- The paper has been accepted for presentation at DAC 2026.
Paper Resources
📖 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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