When Algorithms Tell Stories
A new wave of generative art is challenging our understanding of narrative, pushing beyond plausible storytelling to explore the unique potential of machine creativity.
A new wave of generative art is challenging our understanding of narrative, pushing beyond plausible storytelling to explore the unique potential of machine creativity.

Researchers have developed a framework that uses predictive guidance and persistent object tracking to enable more robust and adaptable interactions between humanoid robots and the objects around them.

A new system combines artificial intelligence with biomedical knowledge to suggest promising drug combinations guided by patient biomarkers.

New research reveals how a robot’s responsiveness can foster trust during collaborative tasks, but consistent communication is key.

A new artificial intelligence system is streamlining scientific discovery by automating everything from data analysis to experiment design and iterative refinement.

New research demonstrates how multi-objective reinforcement learning can optimize tote allocation in fulfillment centers, improving efficiency and space utilization in collaborative human-robot systems.
As the demand for robust data analysis grows, researchers are increasingly turning to simulated datasets to augment limited experimental data in Magnetic Resonance Spectroscopy.
A new framework leverages agentic AI to move beyond simple automation in Open RAN, enabling networks to dynamically adapt to changing demands and optimize performance.
![Across 1010 repetitions of a 90/10 train-test split, the [latex]\mathsf{VaSST}[/latex] model-configured with [latex]K=3[/latex] and [latex]D=3[/latex]-demonstrates robust performance in learning the function [latex]\mathbf{y}=\mathbf{x}\_{0}^{2}-\mathbf{x}\_{1}+\tfrac{1}{2}\mathbf{x}\_{2}^{2}[/latex] across varying noise levels, as evidenced by consistently low out-of-sample Root Mean Squared Errors (RMSE).](https://arxiv.org/html/2602.23561v1/2602.23561v1/figures/in-sample-rmse_boxplots_3noise_sim1.png)
A new framework leverages variational inference and soft symbolic trees to automatically discover interpretable mathematical relationships within complex datasets.

Researchers have developed a new framework that allows humanoid robots to seamlessly track a wider range of complex motions with a single control system.