How AI Agents Spread Information and Influence Each Other
New research explores the dynamics of artificial intelligence agents interacting on networks, revealing how their behaviors and the nature of shared information shape collective outcomes.
Science
Fine-Tuning Graph Networks for Blockchain Security
Effective fraud detection in blockchain relies heavily on the performance of Graph Neural Networks, but achieving optimal results requires careful attention to initialization and normalization techniques.
Where Did That Answer Come From?
New research reveals how to trace the origins of large language model responses – whether they stem from learned knowledge or provided context.
Decoding the Heartbeat: A New Language Model for ECG Analysis
![RhythmBERT offers a novel approach to understanding temporal patterns, embedding rhythmic information directly into the BERT architecture to capture nuanced sequential dependencies beyond those identified by standard models [latex] BERT [/latex].](https://arxiv.org/html/2602.23060v1/2602.23060v1/x1.png)
Researchers are applying the principles of natural language processing to electrocardiogram (ECG) data, creating models that ‘understand’ heart rhythms and improve disease detection.
Forecasting Epidemics with Smarter Models
A new approach combines data-driven deep learning with established epidemiological principles to improve the accuracy of outbreak predictions.
Navigating the Unknown: Building Reliable AI Teams
As artificial intelligence systems become increasingly complex, effectively managing inherent uncertainty is crucial for safe and dependable operation.
Mapping Energy’s Future: AI Closes the Spatial Gap
A new approach leveraging graph neural networks and spatial clustering dramatically improves how we model and optimize interconnected energy systems.
Predicting Where We’ll Go: A More Reliable Approach to Human Motion
New research tackles the challenge of accurately forecasting human movement, even when data is incomplete or noisy.
Decoding Neural Networks: A New Framework for Reliable Circuit Discovery
![Circuit accuracy, as measured by [latex] cACC [/latex], demonstrates a correlation with circuit size [latex] KK [/latex], with certified circuits consistently outperforming baseline models across diverse datasets and scoring methods when evaluated on out-of-distribution data, suggesting a robust relationship between circuit complexity and generalization capability.](https://arxiv.org/html/2602.22968v1/2602.22968v1/x1.png)
Researchers have developed a method to rigorously verify the stability of identified neural network circuits, enhancing their trustworthiness and predictive power.
Squeezing Sparsity: Real-Time Data Compression for AI at Collider Experiments
A new hardware generator efficiently compresses sparse data streams, unlocking the potential of graph neural networks for high-speed data analysis in particle physics and beyond.