Instead of permanently merging all skills into one model, let agents choose which skill module to activate at each step—this prevents forgetting old capabilities while enabling flexible, modular skill composition.
WeAct lets AI agents dynamically switch between specialized skill modules (LoRA adapters) during task execution, treating adapter selection as an action the agent can take. This solves catastrophic forgetting—where learning new skills degrades old ones—by keeping a frozen backbone and training separate adapters for different capabilities.