新闻
NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- NetlistBench benchmark evaluates how reliably LLMs recognize and edit SPICE netlists, finding simple edits reach 96-100% accuracy but complex operations drop to 41-90%.
- 为何重要
- Circuit design engineers using LLMs in automation workflows need to understand where language models fail at netlist manipulation tasks before deploying them.
- 注意
- Performance degrades sharply with edit complexity and horizon length. Enabling reasoning helps weaker models but does not eliminate structural failures in netlists.
收听本摘要
- llm
- language model
- reasoning
- eval
- benchmark
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