![The system moves beyond conventional robotic node feature computation-which relies on the network to independently learn information flow from link connectivity-by encoding the computational structure of forward dynamics through dynamics-inspired message passing, propagating and aggregating learnable inertia-related quantities [latex]I_a[/latex] from child nodes to parents, thereby forming more informed node features.](https://arxiv.org/html/2603.19078v1/figures/figure1.jpg)
A new graph neural network architecture incorporates the principles of physics into robot learning, resulting in more efficient, robust, and computationally performant control.
A new study probes whether large language models possess the capacity for ‘theory of mind’ – the ability to attribute beliefs and intentions to others.
A new framework uses generative models to create realistic robotic simulations and generate executable trajectories, drastically reducing the need for manual programming and data collection.
New research reveals that while artificial intelligence tools can help us write, they subtly but consistently change the meaning, tone, and style of our words.
New research dissects the internal workings of vision-language-action models, revealing a strong reliance on visual pathways and a surprising separation of motor programs from intended goals.
![A process-aware agent architecture achieves macro-level alignment through framing mechanisms while enabling micro-level operation via a [latex]Perceive-Reason-Act[/latex] loop over framed knowledge, facilitating interactions between agents-both human and AI-and the external environment through tools like messaging and sensors, ultimately realizing a system capable of contextualized action and dynamic adaptation.](https://arxiv.org/html/2603.18916v1/x1.png)
A new vision for Business Process Management proposes embedding autonomous agents to create systems that learn, adapt, and optimize themselves.
A new open-source library empowers robots to handle objects more reliably by accounting for real-world uncertainties in physics and perception.
A new framework uses the power of large language models to synthesize and structure information, enabling automated extraction of facts from political biographies.
New research sheds light on how Vision-Language-Action models learn to connect sight, language, and movement, revealing the underlying features that drive robotic behavior.
A new study explores how computer science students perceive the ethical and societal implications of artificial intelligence, revealing a complex landscape of concerns.