Task-Aware Self-Evolution for Resource-Constrained AI Appliances

How should a resource-constrained AI appliance learn to serve its tasks better? For multi-step agents, the work to be served is itself an outcome of execution: model responses trigger tools, change context, and determine subsequent requests. Optimizing isolated inference requests therefore leaves a gap between system performance and useful task delivery. We present a task-aware self-evolution framework that addresses this gap through a persistent cycle of execution, outcome modeling, and resource decisions. The framework couples a conditional three-level action space—instance structure, inference configuration, and serving policy—with task-level probabilistic feedback and cost-aware experiment selection. A cohort model separates task heterogeneity, configuration effects, and batch variation, allowing incomplete executions to inform later decisions. We instantiate the framework on a three-node AI appliance and study 54 experimental attempts on 50 Office tasks. The study reveals quality–time trade-offs, substantial queueing exposure, and changing execution work even under the same nominal configuration. Together, the system design and empirical findings establish a task-centered approach to adaptation: learn from what the appliance delivers, reason about what another experiment would teach, and adjust how resources serve the next task cohort.