Large language model agents are increasingly deployed in dynamic settings where new information, tools, and workflows appear regularly. This means they need to adapt on the fly, picking up new tasks while retaining what they already know.
A new paper on arXiv asks whether self-evolving agents—those that modify or improve themselves—are truly continual learners. The abstract frames the expectation: such agents should learn new tasks as they arrive without forgetting old ones.
The paper appears to probe whether current self-evolving approaches satisfy this requirement. The abstract does not yet present findings, but it highlights a potential gap between the promise of self-evolution and the demands of continual learning.