Additionally, real-time inference and generalization to large probe sets remains challenging.

Specifically, we learn the data representation as a task-specific composition of pre-trained token representations shared across all tasks. Then the correlations between different tasks' data representations are grounded

Current methods for identifying adversarial prompts aimed at "attacking" LLMs and eliciting undesirable outputs are limited by several factors.

The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.

Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames.

Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model.