Understanding Mamba-3 Through Oscillatory Dynamics

Abstract:Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear. We interpret these ro- tations as underdamped second-order dynamics, providing a framework for in- vestigating the role of oscillation in sequence modeling. Controlled comparisons show that underdamped dynamics outperform non-oscillatory damping regimes on state-tracking tasks, while language-model pretraining demonstrates benefits for long-context prediction and length generalization. Interventions in pretrained models further establish the importance of learned phase, and state analysis sug- gests that rotation regulates how retained information combines with incoming up- dates. Taken together, these findings identify underdamped dynamics as a useful mechanism for recurrent computation and suggest that oscillation supports long- context processing by regulating how information accumulates.

Machine Learning (cs.LG)

arXiv:2610.13341 [cs.LG]

(or arXiv:2610.13341v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.13341 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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