When the Martingale Never Stops Firing: Anytime-Valid Gating on Real Forecast Streams
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鞅持续触发时：真实预测流中的随时有效门控
- 研究问题
- 依赖数据及监控器反馈改变学习器的环境中，基于可交换性保证的随时有效监控，能否可靠决定在线预测模型的更新时机？
- 方法与思路
- 作者在预先设定的案例中，用共形检验鞅控制 Kalman 适配器更新，对照合成与真实预测流，并考察 Huber 式更新门控。
- 材料陈述的贡献
- 摘要称真实清洁流中监控持续触发并放大瞬态扰动，而 Huber 式门控减轻孤立尖峰损害，建议报告零假设校准与机制轨迹。
- 阅读关注点
- 全文应核对清洁流与零假设的定义、分数流可交换性、反馈路径，以及触发如何影响适配器和尖峰损害的评估方法。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；结论来自特定部署案例，摘要不足以否定其他满足条件的随时有效方法。
原文摘要
Machine learning systems are increasingly corrected while they run, and the decision of when to intervene is increasingly delegated to statistical monitors. Anytime-valid inference promises evidence that can be acted on at any moment, exactly the guarantee this setting needs, and it is moving from theory into deployed monitoring. Conformal test martingales are the change-detection instrument, and Ville's inequality caps their false-alarm probability on exchangeable data. The guarantee is conditional. A deployment inherits it only if the stream it monitors behaves exchangeably. The premise is hardest to satisfy where these monitors are most useful, on dependent data and inside loops where the monitor modifies the learner whose scores it reads. It is also rarely measured. We measure it in a pre-specified case study, where such a monitor gates the online updates of a Kalman adapter correcting frozen time-series foundation models on five forecasting streams. On exchangeable synthetic streams, the same implementation fires in at most 1 of 60 runs. On the real streams, at alpha = 0.05, 135 of 135 clean-stream runs fired. The construction does not explain the firing; the failure comes from the deployed score stream itself. Repeated fires hold the gate's drift response active, and the gated filter amplifies the very transient it was designed to prevent. The component worth keeping makes no validity claim. Huber-style gating of the filter's own updates cuts isolated-spike degradation by an order of magnitude with no dataset specific tuning. Anytime-valid methods proposed for dependent data should therefore be accompanied by null-calibration controls and mechanism traces.