Constructing a Pareto set is pivotal for navigating the capability-efficiency trade-offs in Large Language Models (LLMs); however, existing merging techniques r...
Constructing a Pareto set is pivotal for navigating the capability-efficiency trade-offs in Large Language Models (LLMs); however, existing merging techniques r...
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dra...
Welcome to the end of 2025. Given all the AI announcements in supercomputing, data centers, and company investments, one might say this was 'the year of AI,' bu...
The tools are available to everyone. The subscription is company-wide. The training sessions have been held. And yet, in offices from Wall Street to Silicon Val...
Equivariant neural networks encode symmetry as an inductive bias and have achieved strong empirical performance in wide domains. However, their expressive power...
We describe SynthPix, a synthetic image generator for Particle Image Velocimetry (PIV) with a focus on performance and parallelism on accelerators, implemented ...
Engineering teams are generating more code with AI agents than ever before. But they're hitting a wall when that code reaches production. The problem isn't nece...
A San Francisco-based startup has demonstrated what it calls a breakthrough in hardware development: an artificial intelligence system that designed a fully fun...
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers ...
OpenAI is investing in stronger safeguards and defensive capabilities as AI models become more powerful in cybersecurity. We explain how we assess risk, limit m...
Eye tracking for wearable systems demands low latency and milliwatt-level power, but conventional frame-based pipelines struggle with motion blur, high compute ...
