New research reveals that Chinese primary school students are more likely to embrace social robots for English language practice when they perceive them as warm and engaging companions, rather than simply helpful tools.
![The work details an active inference generative model-implemented as a dynamic Bayesian network-for digital twin applications, wherein state inference at time [latex] t_{c} [/latex] in the physical space propagates to policy inference at a future time [latex] t_{p} [/latex] within a digitally inferred space, utilizing conditional dependencies encoded by directed edges between random variables, actions, generative model operators, and prior preferences, with bold nodes representing observed variables and thin nodes denoting latent ones.](https://arxiv.org/html/2604.12657v1/x3.png)
A new framework combines digital twins with advanced machine learning to create adaptive models capable of anticipating change in multi-agent environments.
As robots venture into increasingly complex and critical environments, ensuring they can reliably detect and recover from errors is paramount to safe and effective operation.
Researchers have developed a multi-agent system capable of autonomously designing and refining neural network architectures for image recognition, pushing the boundaries of automated machine learning.
New research demonstrates that virtual soft robots can rapidly improve their performance by sharing successful control strategies, offering a powerful alternative to individual learning.
![A rigorous Reproduce-Review-Reflect pipeline, when applied to a 2016 study of nanoscale contacts, revealed critical discrepancies - including a source-degeneration contact model yielding break-even resistances of 31, 77, and 125 [latex]\Omega{\cdot}\mu m[/latex] versus state-of-the-art values around 50-800 [latex]\Omega{\cdot}\mu m[/latex], a 68.7% gap shift induced by HSE06 prior updates with minimal impact on device metrics, and inconsistencies in Sb/As ratios (verified pipeline showing 2.15× As and 4.11× Sb compared to the paper’s 1.47×) - ultimately demonstrating robustness at 7nm gate length, marginal performance at 6nm, and failure at 5nm.](https://arxiv.org/html/2604.12198v1/x3.png)
Researchers have created an artificial intelligence agent capable of independently reproducing, analyzing, and building upon existing computational physics studies.
Researchers are exploring how to imbue teams of robots with consistent personalities and long-term memory to foster more natural and engaging interactions with humans.
New research reveals that even advanced artificial intelligence systems can be misled by cleverly designed synthetic data, raising questions about their reliability in detecting extraterrestrial life.
Generative artificial intelligence is rapidly transforming materials science, offering unprecedented tools for the creation of novel inorganic compounds with targeted properties.
New research explores how equipping robots with human-like memory capabilities can lead to more natural and engaging social interactions.