Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory
Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones. In our diagnostic suite, the strongest efficient baselines tend to solve only one side well. Continuous-time-parameterized state-space models (SSMs) such as Mamba obtain their discrete recurrence by zero-order-hold discretization of a continuous-time system; we argue that this detour is unnecessary for memory tracking and parameterize the discrete transition directly. Naju (Native Adaptive Junction Unit) factorizes the recurrent update, schematically $x_n = f_n\odot x_{n-1} + i_n\odot(B_n u_n)$, into an explici...
| Authors | Hyuk Lim, Seunghyun Yoon |
|---|---|
| Primary category | cs.AI |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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