Beyond General Prediction: Testing AI’s Expertise in Real-World Domains

A new benchmark assesses how well AI agents can apply future prediction capabilities to critical sectors like finance, healthcare, and disaster response.

A new benchmark assesses how well AI agents can apply future prediction capabilities to critical sectors like finance, healthcare, and disaster response.
![Forecast accuracy is demonstrably linked to the distribution of out-of-sample volatility, suggesting that predictive models perform best when calibrated to the inherent uncertainty present in dynamic systems-a relationship quantified by [latex] \sigma^2 [/latex].](https://arxiv.org/html/2601.13014v1/x4.png)
New research demonstrates that machine learning models are significantly improving the accuracy of volatility forecasts, challenging established econometric methods.

Researchers are leveraging the power of artificial intelligence and structured knowledge to move beyond traditional stock market analysis and generate explainable investment insights.

New research introduces a framework for aligning artificial intelligence with real-world experience, enabling more robust planning and physical interaction.

A new analysis of millions of online forum posts reveals a surge in concerns about digital privacy, security threats, and the growing need for tailored support.

A new framework integrates neural networks into the OPM Flow reservoir simulator, promising faster and more accurate modeling of complex near-well behavior.

New research demonstrates a pathway to autonomous marine vessel navigation that prioritizes both efficiency and safety through advanced reinforcement learning.
New research reveals how to maintain a large language model’s core abilities while updating its knowledge with new information.

A new approach leverages deep reinforcement learning to intelligently adapt search strategies, boosting performance on complex vehicle routing problems.

New research explores whether artificial intelligence can anticipate human decisions in everyday conversations, even when we’re influenced by subtle biases.