Can we build neural architectures that go beyond Transformers by leveraging principles from dynamical systems? In this talk, I will introduce a novel approach to sequence modeling that draws inspiration from the emerging paradigm of online control to achieve efficient long-range memory, fast inference, and provable robustness.

At the core of this approach is a new method for learning linear dynamical systems through spectral filtering. This method eliminates the need for learned convolutional filters, remains invariant to system dimensionality, and offers strong theoretical guarantees—all while achieving state-of-the-art performance on long-range sequence tasks.

I will present theoretical insights, empirical results on both synthetic and real-world benchmarks, and recent advancements in fast sequence generation and provable length generalization. The talk will be self-contained and accessible to researchers across STEM disciplines—no prior background in control theory or sequence prediction is required.