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

We develop statistical methods for drawing causal inference from observational data, with particular focus on Swedish register-based studies. We have developed methods for estimation of attributable ...

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 ...

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 ...

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 ...

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.

SAN FRANCISCO--(BUSINESS WIRE)--Today, Causal Labs, the AI company pioneering physics-based models to address civilization-scale challenges, is announcing it has raised $6 million in seed funding, led ...

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...