The field of sequence modeling and battery state prediction is witnessing significant developments, with a focus on improving accuracy, efficiency, and scalability. Researchers are exploring new architectures and techniques, such as structured linear controlled differential equations, spectral distillation, and nonlinear recurrent models, to enhance the performance of sequence models. Additionally, there is a growing interest in developing more accurate and robust methods for predicting the state of health of lithium-ion batteries, which is crucial for electric vehicles. These advancements have the potential to impact a wide range of applications, from language modeling and time-series forecasting to electric vehicle charging and battery management. Noteworthy papers in this area include:

• Structured Linear CDEs, which provide a unifying framework for sequence models with structured state-transition matrices, and
• SpectraLDS, which presents a provable method for identifying symmetric linear dynamical systems with accuracy guarantees independent of the systems' state dimension or effective memory.