In-Context MIL with Perceiver Architecture Solves Bag Tasks in One Forward Pass

Pretraining a Perceiver-style in-context learner on synthetic bag-structured data enables multiple instance learning on new tasks from a handful of labeled bags, with no gradient updates at inference. This addresses the low-label regime where existing MIL algorithms overfit or fail to generalize, with applications in computational pathology and satellite imagery.