AI评估需从模型测试转向系统信任机制
Towards More Standardized AI Evaluation: From Models to Agents
- 评估从静态指标转向动态系统行为监测
- 高分不代表可信，现有评测易掩盖系统缺陷
- 适合关注AI治理与可信部署的研究者
评估已不再是机器学习生命周期的终点。随着AI系统从静态模型演变为使用工具的复合体代理，评估成为核心控制功能。问题不再是‘模型有多好？’，而是‘我们能否信任系统在变化中、大规模下按预期运行？’然而，多数评估实践仍沿用模型时代的假设：静态基准、综合分数和一次性成功标准。本文指出，这些方法正变得越来越模糊而非清晰，揭示评估流程本身引入了隐性失败模式，高基准分数常误导团队，且代理系统从根本上改变了性能衡量的意义。我们不提出新指标或更难的基准，而是旨在厘清评估在人工智能时代，尤其是对代理系统的作用：不是性能秀场，而是一种测量学科，为非确定性系统建立信任、支持迭代与治理。
原文摘要 · Abstract (English)
Evaluation is no longer a final checkpoint in the machine learning lifecycle. As AI systems evolve from static models to compound, tool-using agents, evaluation becomes a core control function. The question is no longer "How good is the model?" but "Can we trust the system to behave as intended, under change, at scale?". Yet most evaluation practices remain anchored in assumptions inherited from the model-centric era: static benchmarks, aggregate scores, and one-off success criteria. This paper argues that such approaches are increasingly obscure rather than illuminating system behavior. We examine how evaluation pipelines themselves introduce silent failure modes, why high benchmark scores routinely mislead teams, and how agentic systems fundamentally alter the meaning of performance measurement. Rather than proposing new metrics or harder benchmarks, we aim to clarify the role of evaluation in the AI era, and especially for agents: not as performance theater, but as a measurement discipline that conditions trust, iteration, and governance in non-deterministic systems.
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