发表机构
University of Pisa; National Research Council (CNR)（比萨大学; 国家研究委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现实世界预测的非平稳性，提出黑曼巴测试时自适应预测架构，通过证据门控状态跟踪及动态内存更新实现选择性、事件驱动的自适应，在多基准测试中提升性能并减少内存更新，证明累积意外可区分漂移与噪声。
AI 中文摘要
在现实世界条件下进行预测本质上是非平稳的，因为未来观测的条件分布会随时间演变。近期的测试时自适应序列模型通过在推理过程中更新内部状态来应对这一挑战，但将自适应与瞬时预测误差或意外联系在一起。这种耦合会将持续的分布变化与随机创新混为一谈，导致不必要的更新和低效的自适应。我们引入了黑曼巴，一种测试时自适应预测架构，它将在线自适应表述为分布漂移下的证据门控状态跟踪。该模型通过一个动态内存增强基础预测器，当时间累积意外提供足够的状态变化证据时更新动态内存。这将自适应转变为一个选择性的、事件驱动的过程而非连续过程。在多个具有非平稳动态的预测基准测试中，与现有的测试时自适应方法相比，黑曼巴实现了具有竞争力或更优的预测性能，同时显著减少了推理过程中的内存更新次数。结合数学分析和生物学证据，这些结果表明累积意外为区分持续漂移和瞬态噪声提供了一个有原则的信号，从而产生更高效、更稳健的自适应。
英文摘要
Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over time. Recent test-time adaptive sequence models address this challenge by updating internal states during inference, but tie adaptation to instantaneous prediction errors or surprise. This coupling can conflate persistent distribution shift with stochastic innovations, leading to unnecessary updates and inefficient adaptation. We introduce Black-Mamba, a test-time adaptive forecasting architecture that formulates online adaptation as evidence-gated state tracking under distribution drift. The model augments a base predictor with a dynamic memory updated when temporally accumulated surprisal provides sufficient evidence of a regime change. This turns adaptation into a selective, event-driven process rather than a continuous one. Across multiple forecasting benchmarks with non-stationary dynamics, Black-Mamba achieves competitive or improved predictive performance compared to existing test-time adaptation methods while significantly reducing the number of memory updates during inference. Together with mathematical analysis and biological evidence, these results suggest that accumulated surprisal provides a principled signal for distinguishing persistent drift from transient noise, yielding more efficient and robust adaptation.
