Mamba-3 shows that linear models can match Transformer quality on real tasks by using complex-valued state tracking and better architectural design, opening a path to cheaper inference without sacrificing capability.
Mamba-3 improves linear sequence models by using state space principles to handle tasks that require tracking information over time. Unlike Transformers that are slow to run, Mamba-3 maintains constant memory and linear compute while matching quality on language tasks—making it faster and cheaper to deploy.