SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba

Abstract:State space models provide efficient linear-complexity processing for long sequences, but their performance frequently degrades as context length grows. We propose SuPER-Mamba, a training-free framework that uses Surprisal as a model-intrinsic signal for long-range state routing in pretrained Mamba models. SuPER-Mamba computes position-wise Surprisal from next-token predictions, aggregates neighboring responses through Local Pooling, and uses the resulting scores to prioritize Global-Channel updates without query-dependent scoring or additional training. We evaluate SuPER-Mamba on three state-space models across the LongBench-E benchmark. The results show that SuPER-Mamba improves the average score from 8.06 to 15.69, corresponding to a 94.7% relative gain over the Base Models and a 13.1% improvement over the state-of-the-art prior baseline. Ablation and length-wise analyses further support the effectiveness of the proposed routing framework.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.16636 [cs.CL]

(or arXiv:2610.16636v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.16636 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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