![The proposed AMPEND-LS framework leverages a learned stiffness parameterization to achieve robust and adaptable manipulation, effectively balancing positional accuracy with dynamic response through the optimization of [latex]L_s[/latex] loss-a metric quantifying the trade-off between trajectory tracking and energy expenditure-and ensuring stable, efficient control across diverse interaction scenarios.](https://arxiv.org/html/2512.21039v1/x1.png)
Researchers have developed a new artificial intelligence system that leverages multiple sources of information and simulated personas to more accurately identify and explain the reasoning behind fake news detection.
Researchers are leveraging the power of artificial intelligence to improve the detection of rare diseases in chest X-rays, addressing a critical challenge in medical imaging.
As social media conversations evolve, sentiment analysis models can quickly become unreliable, and this research details a novel method for monitoring performance without retraining.
New research tackles the challenge of reliably evaluating and improving the resilience of deep learning models against sophisticated adversarial attacks.
A new approach using deep symbolic regression is revealing the underlying equations that govern how defects interact within atomically thin materials.
![A framework assesses language model reliability by extracting latent states from a frozen [latex]Qwen2.5-7B-Instruct[/latex] model and computing hallucination probabilities with neural network probes, enabling real-time detection of fabricated content as the system processes each token.](https://arxiv.org/html/2512.20949v1/x2.png)
Researchers have developed a novel method to detect when large language models are fabricating information, moving beyond simple accuracy metrics.
![A graph neural network addresses the challenge of identifying an epidemic’s origin by analyzing the network’s adjacency matrix and one-hot encoded node states at a given observation time [latex]t\_1[/latex], ultimately outputting a probability distribution across all nodes to pinpoint the most likely source of the outbreak.](https://arxiv.org/html/2512.20657v1/x1.png)
A new review examines the power of graph neural networks to rapidly and accurately identify the source of epidemics using network data.
![The X-GridAgent system integrates four key features - [latex]F_1[/latex], [latex]F_2[/latex], [latex]F_3[/latex], and [latex]F_4[/latex] - to establish a robust framework for distributed grid navigation, acknowledging that even elegantly designed systems will inevitably encounter the unpredictable realities of production environments.](https://arxiv.org/html/2512.20789v1/Xgrid.png)
A new agentic AI system uses the power of large language models to streamline complex power grid analysis and automation.
A new framework leverages simulation and intelligent reflection to enable fully autonomous optimization of 6G radio access networks.
A new framework, AgentMath, dramatically improves the mathematical reasoning abilities of large language models by letting them actively use and learn from code execution.