Inducing Functional Locality in Linear Attention for Whole-Transcriptome Generation

Single-cell RNA-seq profiles are long, high-dimensional and sparse, imposing severe computational bottlenecks in generative modeling at the whole-transcriptome scale. Existing methods predominantly rely on Performer for linear approximation but neglect biological structural priors. We introduce PI2L, a generative framework operating across 19,295 protein-coding genes that explicitly leverages the geometry of the PPI network as a soft inductive bias. PI2L embeds the interaction confidence as a Gaussian distance decay on a unit sphere, which is reformulated into an exponential kernel of inner products. The concatenation adopts a norm-preserving mixture form, where content and prior terms are allocated one shared attention logit budget conditioned on the corruption level. Empirical results demonstrate that cells synthesized by PI2L outperform those from classical single-cell generative models in terms of gene expression correlation and distributional fidelity. Overall, PI2L provides a scalable optimization paradigm for single-cell generative modeling, exhibiting broad utility for in silico cell gen- eration and biological interpretability. Our code is anonymously available at https://anonymous.4open.science/r/pi2l-submission/.