Science

This paper introduces a new framework for seamlessly integrating and interacting with data from multiple sources, empowering agentic systems with enhanced intelligence.

As artificial intelligence reshapes biomedical research, ensuring fairness and preventing the amplification of existing healthcare disparities is paramount.

A new framework, DETR-ViP, significantly boosts object detection performance by strategically organizing and refining the visual cues used by detection transformers.

A new framework leverages the power of multiple learning agents to dramatically improve the process of automatically generating functional code.

Researchers have developed a novel framework that combines physics-based constraints with neural networks to create more accurate and interpretable models of complex, nonlinear systems.

A new framework, OpenMobile, tackles the challenge of training robust mobile agents by generating synthetic data and incorporating error recovery mechanisms.

This article explores how a structured approach to documentation and iterative development can significantly improve coherence and traceability in AI-assisted software projects.

A new framework simplifies the creation and deployment of interactive, web-based multi-agent simulations for studying human-AI interaction.

A new study reveals that existing tools struggle to reliably distinguish between ideas originating from human peer reviewers and those generated by artificial intelligence.

A new framework dramatically improves the reliability of robotic docking tasks by intelligently expanding training data with synthetic scenarios.