Large Language Model(LLM) inference demands massive compute and energy, making domain-specific tasks expensive and unsustainable. As foundation models keep scal...
As healthcare increasingly turns to AI for scalable and trustworthy clinical decision support, ensuring reliability in model reasoning remains a critical challe...
The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for solving combinatorial optimization problems on near-term quantum processors. How...
Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inferen...
Overview Imagine binge‑watching your favorite TV show and having an AI predict how much you’ll enjoy each upcoming episode based on your viewing history and pr...
A new NeurIPS 2025 paper shows how self-supervised learning imbues ViT with better image understanding than supervised learning The post Do Labels Make AI Blind...
Spiking neural networks offer event driven computation, sparse activation, and hardware efficiency, yet training often converges slowly and lacks stability. We ...
This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by f...
The landscape of AI agent development is shifting fast. We’ve moved beyond prototyping single‑turn chatbots. Today, organizations are deploying sophisticated, a...
Overview For 25 years, the NVIDIA Graduate Fellowship Programhttps://research.nvidia.com/graduate-fellowships has supported graduate students doing outstanding...
Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover comple...
The diverging path of China’s two leading AI players shows where the country’s artificial intelligence industry is headed....
