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

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.

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

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 ...

Large language models have made impressive strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—essentially “thinking longer” through more detailed reasoning steps.

ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most ...

Welcome to the Braze Fiscal First Quarter 2026 Earnings Conference Call. My name is Luke, and I'll be your operator for today's call. [Operator Instructions] I'll now turn the call over to Christopher ...

What is supervised learning and how does it work? In this video/post, we break down supervised learning with a simple, real-world example to help you understand this key concept in machine learning.

Abstract: The adversarial example presents new security threats to trustworthy detection systems. In the context of evading dynamic detection based on API call sequences, a practical approach involves ...

Our training pipeline is adapted from verl and rllm(DeepScaleR). The installation commands that we verified as viable are as follows: conda create -y -n rlvr_train ...

Abstract: A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external ...