Seed Vl2 | Auto

To understand the leap, compare the old workflow to the new one:

Botanical

We presented Auto-Seed VL2, a framework for autonomous seed generation in vision-language continual learning. By synthesizing compact, cross-modal aligned seeds conditioned on task gradients, Auto-Seed VL2 eliminates the need for storing real data while achieving superior performance over replay-based methods. Our results demonstrate that synthetic embedding replay is a viable and often superior alternative to exemplar storage. Future work includes extending to online (single-pass) continual learning and exploring seed decomposition for compositional tasks. auto seed vl2

We ask a critical question: Can we generate synthetic, compact representations of past tasks on the fly, without storing any real examples? To understand the leap, compare the old workflow

This paper is a hypothetical construct for demonstration purposes. Any resemblance to existing unpublished work is coincidental. Any resemblance to existing unpublished work is coincidental