Can AI Handle Your Finances? A New Stress Test for Large Language Models

Researchers have created a rigorous benchmark to assess how vulnerable large language models are to manipulation in real-world financial contexts.

Researchers have created a rigorous benchmark to assess how vulnerable large language models are to manipulation in real-world financial contexts.

A new full-stack system, Kisan AI, is demonstrating how machine learning can optimize crop choices for both yield and economic return.

As artificial intelligence increasingly powers autonomous systems, ensuring their safety, security, and dependability is paramount.

As artificial intelligence data centers grow, accurately forecasting their dynamic power consumption is crucial for efficiency and cost savings.
![The study demonstrates that a time-varying Structural Causal Index [latex] \mathrm{SCI}(t;w=60\text{ min}) [/latex] effectively captures dynamic relationships, as evidenced by a well-defined persistence ratio [latex] \mathrm{PR}(t,w) [/latex] calculated across a rolling window, thereby providing a robust measure of system behavior.](https://arxiv.org/html/2604.27041v1/fig5.png)
A new index helps distinguish genuine signals from market manipulation and random fluctuations in the increasingly popular world of prediction markets.

A new wave of techniques combining artificial intelligence with established mathematical models is reshaping the landscape of modern portfolio management.

Researchers are harnessing the power of neural operators to solve notoriously difficult equations that underpin financial modeling, offering improved accuracy and interpretability.
This review examines how artificial intelligence is accelerating combustion research by enabling faster, more accurate, and scalable modeling of complex phenomena.

A new wave of computational modeling is bringing personalized cardiovascular care closer to reality by creating dynamic, patient-specific simulations of heart disease.
![Across one hundred simulations with [latex]q=500[/latex], boxplots reveal the distribution of Matthews Correlation Coefficients for both IVGL and IVL, demonstrating their performance consistency under identical conditions.](https://arxiv.org/html/2604.24969v1/Plots/MCC_q500_si3_New_2.jpeg)
A new statistical approach combines network structure with instrumental variable analysis to pinpoint causal relationships within high-dimensional datasets.