![A symmetry-aware machine learning model’s predictive accuracy hinges on its adherence to group equivariance-specifically, whether its outputs transform predictably under symmetry operations-a condition quantified by metrics assessing both the variance of back-transformed predictions [latex]A_{\alpha}[/latex] and the decomposition of internal features [latex]B_{\alpha}[/latex] using Haar integration over the relevant symmetry group.](https://arxiv.org/html/2603.24638v1/x1.png)
New research reveals that machine learning models can independently learn fundamental physical symmetries, offering insights into their internal representations and the impact of neural network design.
A new framework leverages artificial intelligence to drastically reduce the time and effort required to prepare input files for nuclear reactor modeling.
New research introduces a control strategy that optimizes mobile manipulator movements during physical human-robot interaction, resulting in a more natural and safer experience.
![An explainable deep learning framework identifies reaction coordinates by mapping candidate collective variables to a neural network - consisting of [latex]N_{\mathrm{layer}}[/latex] layers and [latex]\mathbf{N}_{\mathrm{node}}[/latex] nodes - trained to approximate the sigmoidal function [latex]p_{\mathrm{B}}(q) = [1 + \tanh(q)]/2[/latex], thereby linking the reaction coordinate [latex]q[/latex] to the committor [latex]p_{\mathrm{B}}[/latex] and enabling analysis of free-energy landscapes.](https://arxiv.org/html/2603.25237v1/x1.png)
A new deep learning framework leverages explainable AI to reveal the key factors driving changes in complex molecular systems.
Researchers are leveraging artificial intelligence to streamline and accelerate multistep computational chemistry workflows, moving beyond single-step predictions.
A new framework empowers multi-robot systems to reliably coordinate in changing environments with moving targets, even when faced with unpredictable conditions.
New algorithms enable robots to safely explore and manipulate objects in uncertain, deformable environments without prior knowledge of their properties.
A new review of the evidence reveals that the quality of code generated by artificial intelligence is far from guaranteed, and depends heavily on how humans interact with the technology.
Researchers have developed a novel framework that leverages interaction-aware modeling and reinforcement learning to significantly improve the accuracy and reliability of predicting where people will move in complex environments.
![The system translates natural language directives into structured specifications, validates them through a rigorous gatekeeping process-encompassing triad checks and compiler verifications-and subsequently reconstructs data across modalities like CT, MRI, and CASSI with performance-measured at [latex]24.824.8[/latex]dB, [latex]31.731.7[/latex]dB, and [latex]24.324.3[/latex]dB respectively-approaching expert-level quality with a mean ratio of [latex]98.1\pm 4.2[/latex]% and a theorem tightness ratio ranging from [latex]\tau\in[1.8,5.2][/latex] with a median of 2.9.](https://arxiv.org/html/2603.25636v1/x1.png)
A new framework uses natural language to automatically create computational imaging systems, opening doors to customizable and optimized image capture.