新闻
Measuring the Serving Stack Instead of the Model: Hidden Confounds in Local Tool-Use Evaluation
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Research shows that local serving stacks like Ollama and vLLM significantly affect measured tool-use performance in coding agents, not just model behavior.
- 为何重要
- Matters for engineers evaluating local AI models on tool-calling tasks, especially when comparing results across different serving frameworks.
- 注意
- Different serving stacks handle the same request differently; rejection and retry failures may be misclassified as model failures, inflating error rates.
- agent
- tool-use
- inference
- serving
- eval
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- Constitutional AI Evaluation Framework
- AISI Evaluation Framework
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