Beyond Single Minds: How Group Dialogue Can Unlock Smarter AI
A new approach leverages the power of multi-agent conversation to improve an AI’s ability to tackle complex reasoning tasks.
A new approach leverages the power of multi-agent conversation to improve an AI’s ability to tackle complex reasoning tasks.
Researchers have developed a system leveraging artificial intelligence to automatically generate preliminary radiology reports from chest X-rays, bridging the gap between machine analysis and clinical interpretation.

New research identifies key psychological principles governing how people arrange objects, paving the way for robots that understand and adapt to our personal preferences.

A new study reveals Brazilian teachers’ enthusiastic yet cautious embrace of artificial intelligence in K-12 education, highlighting urgent needs for support and equitable access.
Researchers have developed a new method for translating AI-generated video into real-world robotic actions, bypassing the need for extensive task-specific training.

A new declarative intermediate language aims to bridge the gap between diverse data analytics approaches, paving the way for more efficient and reusable optimization techniques.

Researchers have developed a new framework that enables robots to construct detailed 3D representations of environments by learning from human demonstrations of how to manipulate objects.
![The BatteryAgent framework establishes a tiered architecture-encompassing physics-based perception, gradient-boosted detection with SHAP-driven attribution, and large language model reasoning-to diagnose battery faults and propose maintenance through a [latex] \text{Numeric-to-Semantic} [/latex] bridge, thereby translating data into actionable insights.](https://arxiv.org/html/2512.24686v1/BA2.png)
Researchers have developed a framework that combines physical insights with the power of artificial intelligence to pinpoint battery faults with unprecedented accuracy and clarity.

A new system delivers high-fidelity 3D models from single images in under a second, unlocking the potential for truly responsive robotic interactions.

A new approach uses collaborative AI to predict software flaws by accounting for how code changes over time, overcoming limitations of traditional methods.