Selective State-Space Adaptation and Retrieval for Language Model Reasoning
Low-rank adaptation introduces a static learned update applied identically to every input. The update provides task-level adaptation but does not explicitly represent token-level or instance-level state variation. A family of adapters is proposed that introduces selective state-space control at two complementary granularities. At the token level, MaLoRA (Mamba-modulated low-rank adaptation) makes the adapter's scaling factor a dynamic input-dependent function with recurrent state across tokens, in contrast to the stateless modulators of prior work. The token-level adapter improves over low-rank adaptation. On the other hand, it differentiates tokens by structural role but not by contextua...
| Authors | Atahan Dokme, Larry Heck |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 14 September 2026 |
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