In-Context Learning 用于系统辨识：概率化架构与长序列处理
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation
arXiv 上的新论文，把 in-context learning 搬进系统辨识，不用先估模型就能零样本预测，还能生成带不确定性的合成数据，做控制或建模的可以看看。
论文提出统一的概率化元辨识架构，用 Transformer encoder-decoder 处理长训练序列，实现长时程校准预测。预训练元模型可零样本直接从训练数据和查询输入映射到输出，跳过显式模型估计。该元模型还能充当不确定性感知的合成数据生成器，在真实数据稀缺时为经典系统辨识提供正则化数据。框架在标准非线性系统辨识基准和分布外场景上评测，准确率与 state-of-the-art 方法相当，且仅靠输入侧适配即可应对显著分布偏移。
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation
Traditional system identification typically involves two steps: initially, training data is processed to estimate a model of the system of interest; then, the model is used to predict outputs for new input queries. In contrast, recent in-context learning approaches learn a meta model that maps directly from training data and query inputs to outputs in a zero-shot fashion, bypassing explicit model estimation. This approach leverages a meta model trained offline, on large sets of synthetically generated data, to emulate the behavior of a system identification algorithm. In this work, we advance this meta-learning framework for system identification in three directions. First, we develop a unified probabilistic meta-identification architecture that combines a Transformer-based encoder-decoder with tailored mechanisms for handling long training sequences, enabling calibrated predictions over long horizons. Second, we show how a pre-trained meta model can act as an uncertainty-aware synthetic data generator for classical system identification, providing task-specific data that regularize parametric models when real data are scarce. Third, we systematically evaluate the framework on standard nonlinear system identification benchmarks and on a challenging out-of-distribution scenario, demonstrating competitive accuracy with state-of-the-art methods and strong generalization, including cases where input-only adaptation is sufficient to handle significant distribution shifts.
