Science

Researchers have developed a new dataset and technique to significantly improve the accuracy of converting natural language questions into SQL queries for Arabic databases.

A new approach to controlling attention logit changes enables higher learning rates and improved performance in transformer models.

A new approach safeguards Dyna-Q reinforcement learning agents against unexpected environmental shifts by proactively evaluating potential outcomes.

Researchers have developed a new system that combines the power of large language models with structured knowledge to deliver more accurate and adaptable conversational question answering.

New research reveals a critical threshold governing how groups of interacting agents transition from unified consensus to stable, yet fragmented, clusters.

Researchers are exploring how to better translate natural language questions into accurate database queries, achieving improved performance with a novel reasoning framework.

New research details a robust method for multi-agent systems to maintain accurate positioning even when facing malicious interference to radio signals.

Researchers have demonstrated a novel method to manipulate image generation from text prompts in Stable Diffusion by exploiting vulnerabilities in the model’s text understanding component.

Researchers have demonstrated a novel approach to controlling quantum information flow using superconducting diodes, paving the way for more robust and scalable quantum processors.

This research introduces a practical criterion for ensuring the stability of numerical solutions when using finite element discretization, connecting theoretical spectral analysis with real-world computation.