A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output ...

A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.

MiniMax-M1 presents a flexible option for organizations looking to experiment with or scale up advanced AI capabilities while managing costs.

Researchers from Nanjing University and UC Berkeley have unveiled a clustering-based reinforcement learning framework that balances novelty and ...

We propose a multi-agent reinforcement learning (MARL) algorithm, named convex-embedded transformer QMIX (CTQMIX), using the centralized training and decentralized execution (CTDE) framework for agent ...

In this article, an energy efficiency (EE) optimization problem for non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the ...

His research focuses on learning and adaptive systems that actively acquire information, reason and reliably make decisions in complex and uncertain domains. His works advances principles of online, ...

A new discovery of how bees use their flight movements to facilitate remarkably accurate learning and recognition of complex ...

Aqarios' platform Luna v1.0 marks a major milestone in quantum optimization. This release significantly improves usability, ...