![The study draws a parallel between the complexities of human societal structures and the emerging dynamics of agentic AI systems, proposing a four-component ([latex]4C[/latex]) mapping to understand how these artificial societies might evolve and interact.](https://arxiv.org/html/2602.01942v1/x3.png)
As artificial intelligence becomes more autonomous, securing these systems requires moving beyond traditional defenses and focusing on the behavior and governance of AI agents within complex social environments.
A new approach to Bitcoin price prediction leverages the combined strengths of multiple machine learning models to achieve significantly improved accuracy.
Researchers are integrating advanced mathematical tools with machine learning to forecast critical transitions in complex systems before they occur.
![The study demonstrates an inherent tension in financial artificial intelligence, revealing that increased model accuracy frequently correlates with diminished interpretability-a phenomenon where the complexity required for higher predictive power compromises the ability to understand the reasoning behind those predictions, effectively establishing an accuracy-explainability frontier that limits simultaneously optimizing both qualities, much like the principle expressed by [latex] \Delta x \Delta p \geq \frac{\hbar}{2} [/latex] in quantum mechanics.](https://arxiv.org/html/2602.01368v1/x1.png)
Deploying artificial intelligence in finance demands more than just predictive power, requiring a delicate balance between performance, regulation, and genuine understanding.
![The system architecture details a multi-agent approach to real estate investment trusts (REITs) trading, suggesting that even complex financial strategies are ultimately vulnerable to the unpredictable forces of a market mirroring humanity’s own self-deceptions - a system built on assumptions that, like light, can disappear beyond a point of no return [latex] \lim_{r \to \in fty} f(r) = 0 [/latex].](https://arxiv.org/html/2602.00082v1/diagram-en.png)
A novel multi-agent system powered by large language models is demonstrating promising results in the automated trading of Chinese public Real Estate Investment Trusts.
A new analysis demonstrates the power of automated machine learning in predicting mortgage defaults, revealing key strategies for building robust and reliable models.
A new benchmark reveals that even the most advanced language models struggle to grasp the subtle nuances of Japanese financial text.
![The study demonstrates the BlendedNet dataset’s capacity for accurate force coefficient ([latex]C_{f_{z}}[/latex]) prediction, as evidenced by the close alignment between test data and B-INN predictions, with normalized absolute error providing a quantitative measure of this performance.](https://arxiv.org/html/2601.22860v1/fig/blendednet_cfz.png)
A new Bayesian model combines the strengths of both approaches to deliver accurate and reliable predictions for complex physical systems.
A new dataset reveals the surprisingly short lifecycles of meme coins and offers tools to identify potential scams in the volatile world of cryptocurrency.
Researchers have developed a method to significantly improve the accuracy and reliability of machine learning models used to forecast complex fluid dynamics over extended periods.