A new analysis reveals that fragmented definitions of the same event across prediction markets lead to price discrepancies and limit their effectiveness as global information aggregators.
A new deep learning model leverages advanced image analysis to improve the accuracy and interpretability of skin lesion diagnosis.
Traditional statistical methods fall short when evaluating adaptive AI in healthcare, necessitating a shift towards quantifying and managing the inherent risks of these systems.
New research reveals that artificial intelligence systems demonstrate a clear preference for companies with strong environmental, social, and governance practices, potentially influencing financial markets.
![Synchronization is demonstrated across a range of frequencies-[latex] \omega_{1} = \omega_{2} = 0.8, 0.6, 0.3 [/latex]-with a fixed phase relationship of [latex] \pi_{1} = 0.5 [/latex], highlighting consistent behavior despite parameter variation.](https://arxiv.org/html/2601.01505v1/nuova.png)
New research reveals how interconnected bank leverage can amplify systemic risk and contribute to financial crises.
New research reveals that equipping smaller language models with the ability to self-correct factual errors dramatically boosts their performance in complex financial classification tasks.
A new framework leverages the power of autonomous AI agents to deliver faster, more transparent, and accurate credit risk assessments.
New research reveals that a select group of U.S. banks act as critical conduits for systemic risk, amplifying the impact of economic and policy changes.
A new machine learning approach leveraging routine health data can forecast the development of liver cirrhosis up to three years in advance, offering opportunities for early intervention.
![The system classifies Parkinson’s disease by fusing information from MRI cortical thickness, clinical assessments, MRI volumetric data, and demographic features, employing modality-specific encoding followed by symmetric cross-attention between cortical and clinical data, then sparse attention-gated multimodal fusion weighted by learnable parameters [latex]\alpha_{1}-\alpha_{4}[/latex] to generate a representation [latex]\mathbf{H}[/latex] for predicting disease probability.](https://arxiv.org/html/2601.00519v1/x2.png)
Researchers are leveraging artificial intelligence to better understand and profile the severity of Parkinson’s Disease using a combination of diverse patient data.