Forecasting with the Few: Improving Predictions from Intermittent Expert Input
A new Bayesian method boosts the accuracy of combined forecasts even when experts participate irregularly, offering a solution for real-world survey panels.
Science
Verifying Neural Networks at Scale: A New Approach to Robustness
Researchers have developed a hybrid verification method that significantly speeds up the process of ensuring neural network reliability and safety.
Mapping the Future of Traffic Flow
A new forecasting model uses spatial awareness and transformer networks to predict traffic patterns with greater accuracy.
THz Channel Sounding: A Faster Path to 6G
A new framework dramatically reduces data acquisition time for millimeter-wave and terahertz channel sounding, enabling more practical large-scale measurements for future 6G wireless systems.
Tracking System Health with Geometric Drift
![The study demonstrates a nuanced relationship between feature extraction (F), reliability (R), and operational toil (O) within a simplex, revealing that a Euclidean alarm can trigger even when the F/R ratio remains constant, while conversely, no alarm may sound despite an unsafe F/R ratio exceeding 1.5, indicating the limitations of a simple Euclidean threshold for accurately assessing system state when [latex] F = R [/latex] or [latex] F = 1.5R [/latex].](https://arxiv.org/html/2602.05483v1/x1.png)
A new approach leverages compositional data analysis to monitor evolving software systems and maintain reliable observability.
Beyond Trends: A New Approach to Time Series Forecasting
![Electricity load forecasting performance-measured by metrics including mean squared error (MSE), spectral divergence, autocorrelation MSE, and maximum mean discrepancy-is evaluated across varying context lengths [latex] \ell [/latex], demonstrating the comparative efficacy of LISA, ALSA, and PTST models in predicting energy demand with established statistical rigor.](https://arxiv.org/html/2602.04906v1/figures/electricity_mse.png)
Researchers have developed a novel framework that combines geometric modeling with in-context learning to achieve more accurate and adaptable predictions for complex time series data.
Beyond Single Signals: A New Era for Time Series Analysis
Researchers are harnessing the power of multimodal data and large-scale pretraining to dramatically improve the accuracy and robustness of time series forecasting and anomaly detection.
When Bigger Data Isn’t Better: Scaling Challenges in Materials Science
New research reveals that performance gains from increasing datasets aren’t guaranteed in materials modeling, challenging established scaling laws.
Tracing the Source: Understanding Information Flow in AI Systems
A new framework dissects how AI systems like those powered by retrieval-augmented generation arrive at their answers, offering unprecedented insight into their reasoning.
Beyond Summation: Neural Networks Learn to Aggregate Sets More Effectively
A new approach to set function learning, called Quasi-Arithmetic Neural Networks, boosts expressiveness and transferability by moving beyond simple summation of set elements.