CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality

Recent research highlights challenges faced by Mamba-based models in effectively forgetting earlier tokens, even with built-in mechanisms, due to training on contexts that are too short for their state size. This leads to performance degradation and incoherent outputs when processing longer sequences.

The introduction of SfMamba marks a significant advancement in source-free domain adaptation (SFDA), addressing the challenges of adapting models to unlabeled target domains without access to source data. This framework enhances the selective scan mechanism of Mamba, enabling efficient long-range dependency modeling while tackling limitations in capturing critical channel-wise frequency characteristics for domain alignment.

The introduction of HiFi-Mamba, a dual-stream Mamba-based architecture, aims to enhance high-fidelity MRI reconstruction from undersampled k-space data by addressing key limitations of existing Mamba variants. The architecture features stacked W-Laplacian and HiFi-Mamba blocks, which separate low- and high-frequency streams to improve image fidelity and detail.