AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This article proposes a novel causal approach to learning subgoal spaces that learns higher quality subgoal representations and outperforms current methods, and proposes a regularization method to alleviate representation collapse and improve stability.
Motif-Mamba is a structured state space model that augments Mamba with a motif-constrained low-rank recurrent pathway, Inspired by the dynamics of three-node network motifs, which enhances cross-dimensional communication while preserving the linear-time recurrent structure of Mamba.
This work introduces Progressive Multimodal Alignment (PMA), a framework that enables the projector to adapt continually while preserving previously learned alignment, and scales across diverse MLLM backbones, demonstrating robust and broadly applicable MCIT performance.
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