LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

What if every decision you made left behind an echo – an imprint of your past actions, repeating endlessly? In Causal Loop, players don’t just solve puzzles—they navigate a fractured reality that is ...

Over a decade ago, when I was first starting to pretend I could write about quantum mechanics, I covered a truly bizarre experiment. One half of a pair of entangled photons was sent through a device ...

class (aliased as ``IPTWGEEModel`` for backward compatibility).

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

The creators of the open source project vLLM have announced that they transitioned the popular tool into a VC-backed startup, Inferact, raising $150 million in seed funding at an $800 million ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...

Large Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair.