Efficient Sequence Modeling

This research direction tracks advances in efficient attention mechanisms and long-context modeling, covering trainable block-sparse indexers, hybrid architecture designs, KV cache compression, and long-context evaluation methodologies, with an emphasis on compute-matched comparisons against full attention and real degradation curves as key evidence. It centers on sequence modeling operators, KV states, or long-context capability per se, offering foundational insights for building efficient and reliable long-sequence models.