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: Recently, the autonomous driving technology is at a critical phase evolving from typical, closed scenarios to large-scale, open driving scenarios, which is challenged by the diversity and ...

Meta Platform’s announcement on Monday that it has acquired Chinese agent startup Manus represents a big win for Manus’ ...

A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.

Abstract: This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick & place the randomly placed block ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

A biologically grounded computational model built to mimic real neural circuits, not trained on animal data, learned a visual categorization task just as actual lab animals do, matching their accuracy ...