Understanding Mamba-3 Through Oscillatory Dynamics
Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear.
ProofPaper ↗
Key points
- 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.
Sources (1)
- [1]Understanding Mamba-3 Through Oscillatory DynamicsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:16 AM
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.
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