![The study demonstrates a transformer model’s capacity to accurately predict the short-term dynamics of a charge density wave (CDW) order parameter [latex]\Delta\_{\rho}(t)[/latex], effectively mirroring exact simulations, though inherent error accumulation within the chaotic regime leads to divergence over extended timescales-nevertheless, the model successfully captures the statistical characteristics of the system’s dynamic behavior.](https://arxiv.org/html/2601.19080v1/x4.png)
Researchers are leveraging the power of transformer networks to model and predict the collective behavior of chaotic many-body systems, opening new avenues for understanding complex phenomena.
A new framework efficiently captures the complex interplay between connections in signed graphs, improving the accuracy of link sign prediction.
A new deep learning framework leverages the power of Kolmogorov-Arnold Networks to predict species co-occurrence and improve joint species distribution models.
New research reveals the behavioral cues that make people vulnerable to online job scams, leading to significant financial loss.
New research shows how machine learning can forecast time-to-injury for elite female soccer players, offering a proactive approach to athlete health.
A new framework combines Hahn polynomials and Kolmogorov-Arnold networks for more accurate and efficient time series forecasting.
As artificial intelligence systems grow in complexity, pinpointing the source of errors becomes increasingly difficult, and this research introduces a new framework for statistically attributing failures to specific components.
A thought experiment explores how over-dependence on generative AI tools could subtly undermine core technical competencies and erode the foundations of trustworthy research in software engineering.
A new deep learning framework dramatically accelerates the prediction of blood flow dynamics within brain aneurysms, offering a faster path to risk assessment.
New research demonstrates that statistically rigorous economic modeling can rival the predictive power of machine learning in energy markets.