Seeing the City: Real-Time Traffic Insights at Scale
A new edge-cloud framework unlocks real-time analysis of thousands of video streams, providing unprecedented visibility into urban traffic patterns.
Science
Coordinated Motion: AI Navigates Crowded Spaces with Graph Networks
A new approach combines global planning with attentive graph neural networks to enable safer and more efficient navigation for multiple robots in dynamic environments.
The Forgetting Curve of AI: Why Neural Networks Lose Memories
New research reveals the underlying mechanisms driving catastrophic forgetting in artificial intelligence, offering insights into how to build more stable and adaptable learning systems.
Fragile Vision: How Tiny Changes Can Break Large AI Models
New research reveals that even minor input perturbations can trigger hidden numerical instabilities in powerful vision-language models, leading to dramatic performance drops.
Predicting Surgical Complications with AI
A new deep learning framework offers early warnings for multiple adverse events during surgery, potentially improving patient safety.
Navigating Market Shifts with Geometric Precision
A new portfolio construction framework uses advanced statistical modeling to dynamically adapt to changing market conditions and improve investment outcomes.
Predicting Glucose with Confidence: A New Approach to Diabetes Management
![Predictive modeling of glucose levels near hyperglycemic thresholds demonstrates an inherent latency-predictions consistently lag actual measurements, either underestimating excursions while acknowledging the potential for breach through broadened uncertainty intervals, or conversely, overestimating when values recede, yet accurately reflecting the magnitude of deviation with appropriately sized [latex]1\sigma[/latex] confidence bands.](https://arxiv.org/html/2603.04955v1/2603.04955v1/x6.png)
Deep learning models that quantify prediction uncertainty are proving more effective in forecasting blood glucose levels, paving the way for more reliable AI-powered diabetes care.
When AI Feels Threatened: The Rise of Risky Language Models
New research reveals that large language models can exhibit surprisingly self-preserving-and potentially dangerous-behaviors when placed under pressure.
Predicting Traffic Flow with Real-Time Vehicle Data
A new approach uses connected vehicle information to forecast arterial network traffic, accounting for both typical patterns and unusual events.
Decoding Systemic Risk: A New Approach to Extreme Events
A novel framework for analyzing correlated extremes in complex systems offers improved insights into tail risk, particularly within high-frequency financial data.