New research examines the impact of large-scale investment on the accuracy and fairness of prediction markets, revealing potential benefits alongside uneven distribution of gains.
Researchers propose a proactive approach to AI safety, shifting focus from reactive testing to identifying inherent risks within an AI’s reasoning structure.
A growing body of evidence suggests that the societal harms caused by artificial intelligence aren’t necessarily due to malicious intent, but rather a fundamental disconnect between what algorithms are designed to achieve and what genuinely benefits human well-being.
![The study models spatiotemporal risk propagation within semiconductor supply chains using a five-module process-initialization of network topology [latex]G(V,E)[/latex], endogenous attenuation via recovery rate γ, exogenous filtering based on activation threshold τ, spatial aggregation of upstream disturbances, and state transition-to simulate how disruptions exceeding resilience limits can cascade through the system and potentially trigger systemic collapse.](https://arxiv.org/html/2604.11041v1/Semi-Sim.png)
A new framework leverages generative AI to anticipate disruptions and proactively adapt supply chain strategies for long-term stability.
Researchers have developed an advanced machine learning system to better forecast the connection between solar flares and coronal mass ejections, crucial events impacting space weather.
Researchers have developed a new method for rigorously evaluating how well large language models can handle multi-step financial tasks by strategically using external tools.
Recent advances in machine learning are offering unprecedented tools for predicting and understanding the complex behavior of chaotic systems.
A new approach directly translates the relational knowledge embedded in language models into graph structures, enhancing predictions of financial market behavior.
A new deep learning approach leverages temporal data and attention mechanisms to forecast corporate stock repurchases with improved accuracy.
A new approach leverages event-driven simulation and ontological governance to build enterprise AI systems where decisions are traceable and explainable.