New research challenges the notion that large language models experience genuine insight during reasoning, finding that self-correction is rare and only reliably improves performance under conditions of high uncertainty.
New research reveals that shared investment algorithms inevitably force a choice between maximizing profits and ensuring fair access for all participants.
New research explores how machine learning models, fueled by macroeconomic data, can accurately forecast ultimate forward rates and improve the precision of bond yield predictions.
![Human strategies for preventing collusion are being mapped onto the design of multi-agent artificial intelligence systems, with the goal of creating agents that exhibit similar cooperative and competitive behaviors as humans when faced with shared tasks and limited resources-a process formalized by principles analogous to game theory, where agents maximize their individual utility [latex] U_i [/latex] while simultaneously minimizing the potential for detrimental alliances among competitors.](https://arxiv.org/html/2601.00360v1/mapping_visualization.png)
New research explores how strategies designed to prevent price-fixing and other forms of collusion among humans can be adapted to govern the behavior of multi-agent AI systems.
A new study reveals that factoring asset-specific prediction uncertainty into portfolio construction consistently improves risk-adjusted performance.
New research demonstrates a decentralized AI framework that encourages self-interested agents to collectively maximize market liquidity, even with limited communication.
New research challenges the prevailing trend toward transformer-based models, demonstrating that standard LSTMs consistently deliver superior stock price predictions.
New research unlocks efficient statistical methods for inferring the underlying motivations behind observed decisions in complex systems.
A new approach leverages frequent subgraph mining within persistent homology to improve the accuracy of graph classification tasks.
Researchers have developed a new artificial intelligence framework that allows vehicles to critically evaluate their planned actions and adjust course before executing them, dramatically improving safety and adaptability.