Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

This important study describes long-range serial dependence of performance on a visual texture discrimination training task that manipulated conditions to induce differing degrees of location transfer ...

Abstract: Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

ABSTRACT: This is the first paper to be written on the theory of structural learning. The first section outlines the overall concept; the second section proposes the logical universe as the ...

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews. This manuscript by Xie and colleagues presents an intriguing ...

We are grateful for the many thoughtful comments and feedback from the community regarding DFT, ranging from discussions of related ideas to reports of its application in different scenarios. We have ...

How do humans manage to adapt to completely new situations and why do machines so often struggle with this? This central question is explored by researchers from cognitive science and artificial ...

In a rapidly digitizing world, where facial recognition is becoming the cornerstone of identity verification, a new comprehensive survey underscores the escalating battle against spoofing attacks.

Language models (LMs) have great capabilities as in-context learners when pretrained on vast internet text corpora, allowing them to generalize effectively from just a few task examples. However, fine ...