Balancing Act: Steering Language Models Towards Safety and Helpfulness
New research introduces a method for optimizing language model behavior by carefully managing the inherent trade-offs between being both harmless and helpful.
Science
Function Flows: A New Approach to Generative Modeling
![FlowNP demonstrably surpasses TNP in modeling discontinuous functions, accurately capturing sharp transitions within random step functions-a capability attributable to its ability to represent multimodal distributions, unlike TNP’s reliance on Gaussian autoregressive prediction which inherently smooths such features, as evidenced by its performance at [latex] x=0 [/latex].](https://arxiv.org/html/2512.23853v1/figures/step3.png)
Researchers have developed a novel neural process model that leverages flow matching to generate and evaluate complex functions with improved efficiency and accuracy.
Smarter Skies: AI-Powered Beam Prediction for Drone Networks
A new deep learning framework leverages multi-sensor data to significantly improve communication reliability for unmanned aerial vehicles in complex urban environments.
Decoding the Heart’s Signals: A New Approach to Robust ECG Analysis
![The framework dissects signal generation into dual pathways-a Content Encoder extracting the core pathological factor [latex]Z_c[/latex] under physiological constraints, and a Style Encoder isolating non-causal background noise [latex]Z_s[/latex]-before recombining them via a Decoder to ensure sufficient and statistically orthogonal reconstruction of the original signal.](https://arxiv.org/html/2512.24564v1/cpr_framework_optimized.png)
Researchers have developed a novel framework that leverages causal reasoning and physiological knowledge to build ECG analysis models resilient to real-world variations and malicious attacks.
Smarter Clouds: AI for Multi-Cluster Resource Harmony
A new framework leverages artificial intelligence to dynamically optimize resource allocation across distributed cloud environments, boosting performance and reducing waste.
Bridging the Language Gap in Mental Health: A New Approach to Depression Detection
![The study dissects an ensemble learning model, demonstrating that removing individual components-as evidenced by the decrease from an original accuracy and F1-score ([latex]Acc\_Org[/latex], [latex]F1\_Org[/latex]) to modified values ([latex]Acc[/latex], [latex]F1[/latex])-reveals the contribution of each element to overall performance, effectively quantifying the system’s reliance on redundancy and specialized functions.](https://arxiv.org/html/2512.24772v1/ch4.png)
Researchers have developed a novel framework to improve the accuracy of depression detection in multiple languages, particularly for those with limited data.
Speak Security’s Language: Automating Threat Queries Across Platforms
A new framework automatically translates high-level threat descriptions into platform-specific queries for diverse security information and event management systems.
Decoding Learning’s Wobbles: A New Framework for Stable Adaptation
A diagnostic approach to understanding and correcting error dynamics-bias, noise, and alignment-promises more robust and interpretable machine learning systems.
Modeling Randomness with Neural Networks
![The study demonstrates how increasingly refined stochastic interpolation-using SINNOs with parameters [latex]n=5, 10, 20, 50[/latex]-can approximate the Ornstein-Uhlenbeck process, revealing that even simple numerical methods can converge towards the true stochastic path with sufficient refinement.](https://arxiv.org/html/2512.24106v1/SINNOsapprox.png)
Researchers have developed a new approach to accurately simulate and predict stochastic processes using a novel type of neural network operator.
Taming Turbulence: Deep Learning Predicts Drag Reduction in Complex Pipe Flows
A new study demonstrates that deep learning models can accurately forecast drag reduction in pulsating turbulent pipe flow, even with unpredictable acceleration and deceleration patterns.