We study the embedding dimension of distance comparison data in two settings: contrastive learning and k -nearest neighbors ( k -NN).

Most currently deployed LLMs undergo continuous training or additional finetun-ing. By contrast, most research into LLMs' internal mechanisms focuses on models

A new challenger has appeared. In the fiercely competitive world of video games, it's common for new contenders to fade away as quickly as they burst on to the scene. But Battlefield 6 is hoping to change that. It's the latest entry in a long-running military shooter series often framed as a grittier, more realistic answer to Call of Duty. The title's never quite managed to match its most famous rival in terms of sales or players, but there are signs the new installment could close the gap.

We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.

In contrast to previous work on this learning model, we consider scenarios in which the family of groups is too large to explicitly enumerate, and hence we seek algorithms that only access groups via an optimization oracle.

These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation.

Graph neural networks (GNNs) have recently emerged as powerful tools for addressing complex optimization problems.