Foundation AI model uses MRI data to predict multiple brain disorders

Artificial intelligence (AI) systems are computational models that can learn to identify patterns in data, make accurate predictions or generate content (e.g., texts, images, videos or sound ...
Frontiers

AI & Mechanistic Neuroscience: Hybrid Models Beyond Prediction

The intersection of artificial intelligence and mechanistic neuroscience is rapidly transforming our understanding of neural systems. While AI ...
Frontiers

Hybrid Mechanistic–AI Modeling with Physics-Trained Neural Networks for Patient Digital Twins

Patient digital twins aim to create computational replicas of an individual’s physiology that can predict disease trajectories and treatment response.
IEEE

A Multi-Objective Path Planning Framework in Off-Road Environments Based on Deep Reinforcement Learning

Abstract: Efficient and safe path planning is a key navigation requirement for intelligent vehicles, particularly in off-road environments characterized by complex terrain. Traditional methods focus ...

The Global Sportswear Design Award Enters a New Phase of Global Collaboration with ‘Linglong’ AI Integration

Artificial intelligence is profoundly reshaping the design paradigm of sportswear, driving the industry’s transition from traditional experience-driven methods to a digital model characterized by data ...
IEEE

Stop Rumors Fast: A Multi-Objective Blocking Approach with Deep Reinforcement Learning in Social Networks

Abstract: The rumor blocking approaches in social networks aim to identify a small set of counter-rumor seed nodes to quickly stop the propagation of rumors. Existing solutions predominantly focus on ...

Exclusive: DeepSeek withholds latest AI model from US chipmakers including Nvidia, sources say

DeepSeek has not shown U.S. chipmakers its upcoming flagship model for performance optimization, breaking from standard ...

Spanish ‘soonicorn’ Multiverse Computing releases free compressed AI model

Spanish startup Multiverse Computing has released a new version of its HyperNova 60B model on Hugging Face that, it says, ...

Manifold-Constrained Hyper-Connections: The Architectural Breakthrough That Might Redefine LLM Training

If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...
InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...