Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns

ArXi:2602.22479v2 Announce Type: replace Large language models deployed in the wild must adapt to evolving data, user behavior, and task mixtures without erasing previously acquired capabilities. In practice, this remains difficult: sequential updates induce catastrophic forgetting, while many stabilization methods rely on external procedures that are costly, brittle, or difficult to scale. We present TRC$^{2}$ (Thalamically Routed Cortical Columns), a decoder-only architecture that makes continual adaptation a property of the backbone itself.