A novel semi-supervised learning method called OFD-Net is introduced to improve medical image segmentation by utilizing orthogonal feature disentanglement without teacher models.
DMFNet is a proposed dual-backbone network architecture aimed at improving the classification of complex urban scenes in remote sensing imagery.
This paper explores the application of AI to analyze urban mobility data for the purpose of improving transportation management in smart cities.
A field study evaluates visitor acceptance of an autonomous multilingual android robot deployed in a public museum.
Ferrovial is investing one billion euros to develop a new data center campus in Madrid with an initial 60MW capacity.
Baidu's general-purpose AI agent, Baidu Dazi, has been recognized at the World Artificial Intelligence Conference 2026 for its productivity and task-automation capabilities.
This article provides a summary of key insights and discussions from industry leaders and researchers at the World Artificial Intelligence Conference.
MirrorSense showcased new hardware and software tools at WAIC 2026 aimed at integrating spatial perception and robotics for industrial applications.
Researchers have developed Holi-Spatial, an AI method for 3D automatic annotation that significantly improves detection accuracy without the need for manual labeling or expensive hardware.
This article explores the technical complexities and challenges involved in implementing model routing systems for AI applications.
China Mobile has invested in Youji Technology to advance the development of AI-integrated industrial infrastructure.
Researchers introduced Beaver, an agent framework designed to extract and synthesize structured information from complex, multimodal scientific documents.
Researchers have introduced a new benchmark designed to evaluate how language models handle ambiguous instructions, policy conflicts, and adversarial commands.
A randomized field experiment evaluated how professional and playful emails rewritten by GPT-5 affected recipient engagement and behavior in workplace communications.
The authors present MorphologyFM, a foundation model designed to learn clinical waveform representations while preserving the structural morphology of ECG and pulse oximetry data.
The paper presents the NVAITC AI Scientist, a governed end-to-end agentic research system designed to coordinate complex scientific workflows in institutional biomedical environments.
The proposed T2MLR architecture introduces temporal recurrence in middle layers to improve reasoning persistence in transformer models.
This research utilizes mechanistic interpretability to identify and improve the robustness of World Action Models against distribution shifts.
This paper investigates why large language models struggle with complex context learning and proposes a method using self-discovered specifications to improve performance.
The GRASP framework improves agentic retrieval-augmented generation by optimizing when to retrieve information and controlling the granularity of the retrieved context.
Researchers propose a new method for detecting AI-generated text by analyzing the latent trajectories of the autoregressive generation process.
The proposed RAD framework improves offline reinforcement learning by retrieving high-quality demonstrations to help models generalize beyond static training datasets.
This paper models the value of connectivity within networks of AI agents to determine optimal collaboration protocols.
Researchers introduced a benchmark designed to evaluate the accuracy and traceability of LLM agents performing complex structural engineering workflows.