提供一套标准化的AI系统日志分析流程，助力研究可复现。
Seven simple steps for log analysis in AI systems
- 分七步构建日志分析流水线，结合最佳实践
- 配套代码示例与详细指导，降低分析门槛
- 适合评估模型表现或验证实验设计的研究者
AI系统在与工具和用户交互过程中产生大量日志。分析这些日志有助于理解模型能力、倾向性和行为，或判断评估是否按预期进行。尽管研究者已开始开发日志分析方法，但尚未形成标准化流程。本文提出基于当前最佳实践的分析流水线，通过Inspect Scout库提供具体代码示例，详述每一步操作并指出常见陷阱。该框架为研究人员提供了严谨且可复现的日志分析基础。
原文摘要 · Abstract (English)
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intended. Researchers have started developing methods for log analysis, but a standardised approach is still missing. Here we suggest a pipeline based on current best practices. We illustrate it with concrete code examples in the Inspect Scout library, provide detailed guidance on each step, and highlight common pitfalls. Our framework provides researchers with a foundation for rigorous and reproducible log analysis.
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