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This paper proposes a query-based cross-modal projector that compresses visual tokens via cross-attention to improve Mamba-based multimodal LLMs, boosting both performance and throughput on vision-language benchmarks while eliminating the need for manual 2D scan order design.
This paper proposes TopoMamSurv, a Graph Mamba framework for whole-slide image survival analysis that uses topology-aware ordering to address Mamba's sensitivity to input order, and incorporates bidirectional Mamba and GCN for spatial context modeling.
MVCHead is a novel method for generating 3D Gaussian head avatars from single 2D images without multi-view data, using hierarchical state space models and multi-view consistency enforcement.
The author presents SM1, a variant of Mamba1 with d_state=1, using two native PyTorch ops to replace the selective scan, reducing memory by 16x compared to d_state=16. The closed-form solution eliminates the state dimension, enabling efficient inference with constant memory per token.
Proposes ReTAMamba, a method using reliability-aware temporal aggregation with Mamba for irregular clinical time series prediction, achieving significant AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
This paper introduces AIRA-Compose and AIRA-Design, dual frameworks using AI agents to autonomously discover neural architectures that outperform standard Transformers and scale efficiently.
This paper challenges the claim that prediction bottlenecks in models like Mamba recover causal structure, demonstrating through a new benchmark that gains are largely due to confounds and robustness artifacts rather than true causal discovery.
Introduces triattention v3, a new attention mechanism that enables safe eviction without recall loss for long-context inference, demonstrated on a hybrid mamba+attention model up to 256k tokens.