Restoring Power, Faster: AI Dispatch After the Storm
![The training process demonstrates a predictable pattern: initial loss fluctuations gradually converge, mirroring the ascent of average episodic reward-a testament to the algorithm’s capacity to learn and optimize performance over iterative refinement, as reflected in the diminishing [latex] L [/latex] and increasing [latex] R [/latex] values across epochs.](https://arxiv.org/html/2601.10044v1/dvrp_rl_eval_reward.png)
A new approach uses artificial intelligence to rapidly deploy repair crews following severe weather events, minimizing outage times.
Science
Mapping the Flow: A New Graph-Based Approach to Traffic Prediction
Researchers have developed a novel framework that leverages dynamic graph structures and meta-learning to improve the accuracy of traffic flow forecasting.
Sharper Financial Insights: Fine-Tuning Language Models for Precision
New research demonstrates that carefully instructing a powerful language model can dramatically improve its ability to identify key financial entities.
Decoding Dynamics: A New Approach to Real-Time System Identification
Researchers have developed a framework that learns the governing equations of complex, changing systems in real time, even with incomplete information.
Tracking Avian Flu’s Spread with Ecological Networks
A new framework integrates wildlife movement, genomic data, and environmental factors to improve forecasting of highly pathogenic avian influenza outbreaks.
Speaking the Language of Debt: A Vietnamese AI for Smarter Collections
Researchers have developed a specialized artificial intelligence model to better understand and process conversations related to debt collection in Vietnam.
When AI Gets Phished: Why Language Models Struggle to Spot Online Threats
A new study reveals that even finely-tuned language models can be surprisingly vulnerable to phishing attacks, highlighting critical weaknesses in how these systems learn to identify malicious content.
Resurrecting Legacy Models with AI’s Symbolic Touch
![NSR-Boost establishes a framework for enhancing performance through a novel boosting mechanism, fundamentally altering the decision boundary via weighted samples to achieve improved generalization capabilities, as demonstrated by its iterative refinement process detailed in [latex] \mathcal{L} = \sum_{i=1}^{N} L(y_i, f(x_i)) [/latex].](https://arxiv.org/html/2601.10457v1/x1.png)
A new framework combines the power of large language models with traditional machine learning to intelligently correct and enhance existing industrial applications.
The AI Skills Gap: Are Computer Science Students Feeling the Pressure?
A new study explores the rising anxieties among future computer scientists about job security in an era of rapidly advancing artificial intelligence.
Predicting the Future, One Time Series at a Time
New research reveals a method for gauging how reliably we can forecast future trends from past data, before even building a predictive model.