Reading Minds, Shaping Markets
![The equilibrium mean control [latex]\bar{D}^{1}(t)[/latex] is demonstrably affected by signal precision [latex]p[/latex], exhibiting a heightened incentive for belief manipulation when precision is low due to sluggish opponent posteriors, and converging towards a perfect-information benchmark as [latex]p[/latex] approaches infinity.](https://arxiv.org/html/2603.12140v1/x3.png)
New research reveals how strategic players can both predict and influence the expectations of others, with significant implications for economic efficiency.
![The equilibrium mean control [latex]\bar{D}^{1}(t)[/latex] is demonstrably affected by signal precision [latex]p[/latex], exhibiting a heightened incentive for belief manipulation when precision is low due to sluggish opponent posteriors, and converging towards a perfect-information benchmark as [latex]p[/latex] approaches infinity.](https://arxiv.org/html/2603.12140v1/x3.png)
New research reveals how strategic players can both predict and influence the expectations of others, with significant implications for economic efficiency.

This review explores a novel architecture for managing decentralized AI workloads across dynamic, multi-domain environments.

A new approach leverages the power of artificial intelligence to streamline the complex tasks of managing and maintaining modern optical networks.
New research reveals how cognitive biases impact the accuracy of human-annotated data for rare-event AI, and proposes effective strategies to counteract them.
This review explores the methods used to quantify trust within the complex networks of online social platforms.

New research demonstrates how improving the accuracy of prediction probabilities can reduce the number of equally valid, yet different, model predictions.

As large language models gain agency, ensuring the stability and safety of their evolving memories becomes a critical challenge.
A new system leverages wearable technology and real-time data to understand and predict the emotional well-being of elderly individuals as they go about their daily lives.

A new framework empowers researchers to generate realistic time series data, including interventional data, for training more robust causal models.

A new approach leverages past solar wind events to refine probabilistic forecasts of speed at Earth, providing more accurate predictions and crucial uncertainty estimates.