MapPFN: Learning Causal Perturbation Maps in Context

ArXi:2601.21092v2 Announce Type: replace Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single-cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their