The abstract for AgentEvolver opens with a pointed observation: an agent can complete a task without improving how it works. The authors argue that turning task experience into reusable capability requires connecting the changed component to its evaluation and subsequent use. They then introduce AgentEvolver, a system designed for this purpose, though the abstract cuts off before detailing the mechanism.
The core idea is to distinguish between performing a task and becoming better at performing future tasks. Without a link between a modification, its evaluation, and later reuse, experience is lost. AgentEvolver is positioned as a system-level solution to that problem, rather than a task-specific tweak.
Because the abstract is truncated, the specific architecture and evaluation results are not available from this source. The paper's title suggests a focus on system-wide self-evolution through task execution.