AI RESEARCH
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
arXiv CS.AI
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ArXi:2505.18728v2 Announce Type: replace-cross The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modules to sequences extracted from graphs, often compromising core properties such as permutation equivariance, message-passing compatibility, and computational efficiency. In this paper, we