Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Abstract: This paper proposes a novel Viterbi-Like successive cancellation (VL-SC) decoding algorithm for polar codes. The algorithm employs the bit log-likelihood-ratio as the “penalty value” within ...

Abstract: The federated learning (FL) paradigm aims to distribute the computational burden of the training process among several computation units, usually called agents or workers, while preserving ...