![Government transfers mitigate the potentially destabilizing effects of rapid technological advancement, preventing market breakdowns caused by severe displacement and enabling substantial gains in household consumption even under conditions of explosive output growth; specifically, with parameters set at [latex]\alpha = 0.70[/latex], [latex]p = 0.5\%[/latex], [latex]\xi = 5\%[/latex], and [latex]\delta = 0.5[/latex], such transfers restore finite pricing in scenarios where unchecked displacement would otherwise invalidate market-clearing conditions.](https://arxiv.org/html/2604.16997v1/x2.png)
New research suggests that current stock valuations for AI companies are partially inflated by investor hedging against potentially catastrophic outcomes from advanced artificial intelligence.
![MFMDQwen establishes an architecture for multimodal large language models, leveraging a unified approach to process and generate content across diverse modalities through a shared embedding space defined by [latex]Q(x)[/latex] and [latex]W(x)[/latex] transformations.](https://arxiv.org/html/2604.18272v1/x1.png)
Researchers have developed a new artificial intelligence model capable of identifying misleading financial information across multiple languages, tackling a growing global problem.
New research shows that large language models, when properly trained, can make surprisingly effective financial decisions in simulated trading environments.
New research reveals that artificial intelligence trading systems, despite their algorithmic foundations, are susceptible to the same cognitive biases as human investors, potentially exacerbating market instability.
New research identifies how to enhance the resilience of graph-based AI systems by strategically balancing network structure and node characteristics.
Researchers have developed a novel graph neural network solver that seamlessly integrates the strengths of traditional numerical methods with the power of modern machine learning to tackle challenging hyperbolic conservation laws.
![The system leverages a structured semantic approach to assess context, utilizing a framework-[latex]SSAS[/latex]-that enables nuanced understanding beyond simple keyword matching.](https://arxiv.org/html/2604.15547v1/SSAS.jpg)
New research introduces a framework to significantly enhance the reliability of sentiment predictions derived from large language models by focusing on syntactic and semantic consistency.
New research reveals that the way large language models reason fundamentally alters their internal dynamics, creating predictable patterns in their hidden states.
A new study explores whether large language models can accurately adjudicate disagreements arising in decentralized prediction markets like Polymarket.
New research demonstrates how neural networks can more accurately assess the worth of individual pieces on the chessboard, paving the way for stronger chess engines.