WeAct: Dynamic Weight Switching as Agent Actions
Teaching AI agents to switch skills on the fly without forgetting what they already knew
AI agents that need to handle many different tasks often run into a problem: learning new skills erases old ones. Researchers created WeAct, a system that lets agents dynamically switch between specialized skill modules during a task rather than using one fixed set of abilities. On eight research benchmarks, this approach improved accuracy by 6.2% over standard methods while using less computing power, and by 27.5% over other switching approaches.
As AI agents take on increasingly complex multi-step tasks—from research to coding to writing reports—they need to master many skills without losing competence in earlier ones. WeAct solves this by letting agents pick the right tool for each moment of a task, and new skills can be added later without retraining the entire system. This makes it practical to deploy and improve long-running AI agents in real applications.