Spotting Trouble in the Stream: How AI Learns from Past Events
A new framework uses artificial intelligence to predict and mitigate risks in live streaming by analyzing patterns of behavior across multiple sessions.
Science
Mapping the Genome’s Geography: A New Foundation for Spatial Transcriptomics
Researchers have developed a lightweight model to better understand gene expression within the physical context of tissues, paving the way for more accurate biological insights.
Graphs Without the Gossip: Smarter Federated Learning with Diffusion
![The model distinguishes itself from traditional Federated Graph Neural Networks by establishing a communication structure-indicated by sequential exchanges [latex]❶[/latex], [latex]❷[/latex], and [latex]❸[/latex]-between server and clients, fundamentally altering the flow of information during the learning process.](https://arxiv.org/html/2601.15722v1/x1.png)
A new approach drastically cuts communication costs in federated graph learning, enabling more practical and privacy-preserving analysis of complex network data.
Hidden Risks in AI Emergency Triage
New research reveals that artificial intelligence systems used to prioritize patients in emergency rooms can exhibit subtle biases, potentially leading to unequal care.
Beyond Prediction: Building More Reliable AI Features
A new framework focuses on quantifying uncertainty directly in the learned representations, leading to more stable, calibrated, and robust AI models.
Clear Signals: Forecasting Lake Health with Sparse Data
A new machine learning framework pinpoints the minimal data needed to accurately predict lake water clarity, streamlining monitoring efforts and improving resource management.
Leveling the Playing Field: Boosting Rare Classes with Confidence-Guided Learning
![The weighting function [latex]\Omega(p_t, f_c)[/latex] demonstrates an asymmetric relationship between prediction confidence [latex]p_t[/latex] and class frequency [latex]f_c[/latex], utilizing a pivotal point ω to differentiate low-confidence regions and facilitate smooth transitions between patterns characteristic of both tail and head classes.](https://arxiv.org/html/2601.15924v1/wireframe.png)
A new approach dynamically adjusts training to prioritize challenging examples from underrepresented classes, improving performance on imbalanced datasets.
Skyborne Networks: Rebuilding Connectivity After Disaster
A new framework leverages high-altitude platforms and drones to rapidly restore communication and provide critical environmental data in the wake of natural disasters.
Predicting Failure: A New Approach to System Lifespan
A novel statistical framework leverages both past performance and real-time data to more accurately forecast when engineering systems will need maintenance or replacement.
The Vanishing Context: When AI Minds Lose Focus
New research reveals a surprising performance drop in large language models as they process longer sequences of text, even when architecturally capable.