In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation

The paper presents an in-context learning framework for system identification that replaces traditional two-step model estimation with a meta model mapping training data and query inputs directly to outputs. It introduces a probabilistic Transformer-based architecture capable of handling long training sequences and producing calibrated predictions over extended horizons. Additionally, the authors demonstrate that a pre-trained meta model can generate uncertainty-aware synthetic data to aid classical identification methods and validate their approach on standard nonlinear benchmarks and challenging out-of-distribution scenarios, achieving competitive accuracy and strong generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• benchmarks