Energy Efficiency: Doing More with Less

K-MAF is designed to minimize energy consumption while maximizing output. By optimizing computation and task execution, agents operate sustainably, even in resource-constrained environments.

• Batch Processing: Agents group similar tasks to reduce computational overhead:

This reduces redundant operations and allows agents to process multiple tasks simultaneously.

• Adaptive Execution Frequency: Agents dynamically adjust their execution cycles based on task urgency and priority: This formula ensures that high-priority tasks are executed immediately, while lower-priority tasks are queued for later.

• Energy-Aware Learning: During training, agents focus on low-complexity models initially and gradually increase complexity as needed: where Etraining is energy expenditure, and C(θ) is the computational cost of model parameters.