Perturbation Modeling via Signed Propagation and Localized Conditioning

Single-cell perturbation modeling is formulated as conditional generation of a perturbed cell population. Recent models use gene co-expression graphs to pretrain static gene embeddings or to mask dense attention backbones, which score every gene pair. The response direction among gene pairs is an important factor in perturbation modeling, and graphs built from absolute correlation values discard the sign that sets response direction. We present SPARC, an attention-free flow matching velocity field in which a signed graph represents the gene-mixing operator. Genes exchange information through separate positive and negative edges, normalized independently and gated at every layer by a cell state. A shared recurrent block extends propagation depth at the parameter cost of one block. Every perturbed genetic target gets a conditioning vector and is kept in every training batch. Evaluated on genetic, drug, and cytokine benchmarks, SPARC improves Spearman correlation of log-fold changes over the strongest baseline in all settings by up to 0.31, and leads in direction match by up to 0.039. Generated cells also recover more known transcription factor-target pairs and perturbed pathways than the strongest baseline.