AI RESEARCH
PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matching
arXiv CS.AI
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ArXi:2603.26705v1 Announce Type: cross Motivation: Generative models for protein backbone design have to simultaneously ensure geometric validity, sampling efficiency, and scalability to long sequences. However, most existing approaches rely on iterative refinement, quadratic attention mechanisms, or post-hoc geometry correction, leading to a persistent trade-off between computational efficiency and structural fidelity. Results: We present Physics-Informed Mamba (PI-Mamba), a generative model that enforces exact local covalent geometry by construction while enabling linear-time inference.