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

Abstract: In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to ...

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...

🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

With its playlist chatbot, Spotify says you could ‘curate your next Discover Weekly, exactly the way you want it.’ With its playlist chatbot, Spotify says you could ‘curate your next Discover Weekly ...

HeteroRL is a novel heterogeneous reinforcement learning framework designed for stable and scalable training of large language models (LLMs) in geographically distributed, resource-heterogeneous ...

The rapid growth of AI is projected to push global data center power demand to 2,200 terawatt-hours (TWh) by 2030, an "always-on" load that threatens to overwhelm the world's aging electrical grids.

Abstract: This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control ...