arXiv Computation and Language August 28, 2026 By Atahan Dokme, Larry Heck

Selective State-Space Adaptation and Retrieval for Language Model Reasoning

Read the original on arXiv Computation and Language →

The paper introduces a family of adapters that enhance language model reasoning by adding selective state-space control at token and context levels. The token-level MaLoRA makes the adapter’s scaling factor dynamic and recurrent, improving over static low‑rank adaptation. The context-level MaRA tracks cross‑segment reasoning state and retrieves relevant segments, outperforming an eight‑billion‑parameter dense retriever and boosting reasoning accuracy by an average of +6.4 F1 over LoRA.

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