Adaptive DNA Sequence Modeling via Synergistic Plasticity Units

Effective DNA modeling demands the integration of complex patterns such as local motifs, long-range dependencies, and periodic signals. Yet, architectures like CNNs, Transformers, and SSMs are hindered by static or time-domain-exclusive designs, which limit their representational flexibility. To address this, we introduce the Synergistic Plasticity Unit (SPU) , a scalable architecture that achieves multi-level plasticity through three synergistic layers. Specifically, SPU integrates a Locus Plasticity Layer (LPL) to capture fine-grained local motifs via token-specific convolution operations, while utilizing a Domain Plasticity Layer (DPL) to form multi-domain global features by concurrently modeling sequential (time) and spectral (frequency) patterns. Furthermore, it incorporates a Saliency Plasticity Layer (SPL) to optimize information flow through dual-axis saliency scoring. Supported by theoretical analysis, extensive empirical validation, and in-depth biological interpretation, this unified design enables SPU to achieve state-of-the-art performance with quasi-linear complexity, establishing a robust and principled paradigm for DNA modeling.