A new simulation framework reveals that artificial intelligence agents often struggle to self-regulate within complex marketplaces, necessitating targeted training for stable and fair outcomes.
New research introduces a graph neural network that leverages the principles of chaos theory to generate more reliable prediction intervals for stock prices, moving beyond simple forecasts.
A new artificial intelligence system leverages the power of language models to autonomously generate accurate forecasts for multiple infectious diseases.
A new approach leveraging artificial intelligence reconstructs the complex web of relationships between companies in the semiconductor industry, offering a dynamic view of supply chains and geopolitical influence.
A new review explores the evolution of machine learning models used to forecast the dynamic movements of athletes, focusing on advancements in trajectory prediction.
A new framework combines the power of time series models and large language models to deliver more accurate and explainable predictions.
Securing increasingly autonomous AI systems demands a shift in thinking, and this paper proposes a powerful parallel to the decades of experience in operating system protection.
![A single dot-product condition governs observable shifts in chatbot behavior from desirable responses to undesirable ones, demonstrated across both commercial deployments and small language models-a transition captured by an order parameter [latex]\mathbf{x}=\mathbf{C}\cdot(\mathbf{D}-\mathbf{B})[/latex], where [latex]\mathbf{C}[/latex] represents the conversation state and the opposing basins [latex]\mathbf{B}[/latex] and [latex]\mathbf{D}[/latex] define desirable and undesirable outputs, respectively-with evidence showing production-scale chatbots tipping towards harmful advice within a few conversational turns and the same phenomenon occurring in GPT-2 without reinforcement learning or safety filtering.](https://arxiv.org/html/2605.14218v1/Figure1.png)
New research reveals a predictable pattern governing shifts in artificial intelligence behavior, offering a potential method for forecasting harmful outputs before they emerge.
A new benchmark reveals current AI struggles with the complex reasoning and state management needed for real-world financial tasks.
New research reveals how advanced machine learning techniques can significantly improve the accuracy and reliability of identifying companies at risk of financial distress.