A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

This brute-force scaling approach is slowly fading and giving way to innovations in inference engines rooted in core computer systems design.

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...