Sequence Modeling

Sequence modeling asks how information in an ordered stream—such as words, sensor readings, user actions, or DNA—can be used to predict what comes next or interpret the sequence as a whole. Common approaches include recurrent networks such as LSTMs, Transformer attention, state-space models such as Mamba, and Markov or hidden Markov models; hybrids and diffusion methods are also used for long or structured sequences. Applications range from language and time-series forecasting to recommendation, biological analysis, and action recognition, with evaluation often testing prediction quality, long-range recall, and generalization to new lengths or domains. A central tension is balancing expressive memory and reliable long-context behavior against computation, latency, and memory costs, while ensuring benchmarks measure genuine sequence understanding rather than leakage or repetition.