These universal truths form the shared foundation upon which a great deal of rigorous and robust merit-based inquiry is ...

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see ...

anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a ...

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: Cognitive task analysis methods have been extensively applied across various fields since the 1980s. Among these, the GOMS (Goals, Operators, Methods, Selection rules) model stands out as ...

Virtual Reality, Ideological and Political Education in Colleges and Universities, Red Culture, Teaching Optimization Zhang, Q. and Yu, Y. (2026) Research on the Optimization Strategy of Integrating ...

A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the ...

The rise of the AI gig workforce has driven an important shift from commodity task execution to first-tier crowd contribution ...

The 2025 SANS SOC Survey shows AI use is rising, but many SOCs lack integration, customization, and clear validation ...

Abstract: Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, ...

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