新闻
CompressionBench: How can your coding agents compress LLMs?
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- CompressionBench tests whether coding agents can autonomously compress large language models while meeting storage and accuracy constraints.
- 为何重要
- Relevant for engineers building AI systems that need efficient inference or exploring whether agents can handle infrastructure optimization tasks.
- 注意
- Best agent designs showed 3.4% perplexity increase and 77% downstream accuracy versus 79.6% for uncompressed models, indicating meaningful accuracy tradeoffs.
- agent
- llm
- rag
- inference
- self-improv
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