How 2025 Recalibrated AI Models Race

In 2025, large language models moved beyond benchmarks to efficiency, reliability, and integration, reshaping how AI is ...
Tech Xplore

Helping AI agents search to get the best results out of large language models

Whether you're a scientist brainstorming research ideas or a CEO hoping to automate a task in human resources or finance, you'll find that artificial intelligence (AI) tools are becoming the ...
eLife

Hierarchical Bayesian modeling of multiregion brain cell count data

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...
IEEE

Variational Bayesian Inference-Inspired Network for Compressive Spectral Imaging

Abstract: In Coded Aperture Snapshot Spectral Imaging (CASSI) systems, model-based approaches highly rely on the handcrafted priors, while data-driven methods overlook the physical degradation process ...
IEEE

Bayesian Inference-Aided Large Language Model Agents in Infinitely Repeated Games: A Dynamic Network View

Abstract: The rapid expansion of large language models (LLMs) has led to increasingly frequent interactions between LLM agents and human users, motivating new questions about their capacity to form ...
Frontiers

Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference

Objectives: We aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD). Methods: We included 18 people with PD and 18 controls.
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...