Network Resilience Under Uncertainty
New research provides tools to quantify and mitigate the risk of cascading failures in multi-agent systems grappling with unpredictable delays and network conditions.
Science
The Limits of Efficient Error Correction
New research establishes fundamental constraints on how compactly data can be encoded for reliable, localized decoding.
Mirroring Security: Digital Twins Fortify IoT Networks
A new framework uses digital twin technology and game theory to enhance secure service selection in the complex landscape of space-air-ground integrated networks.
Securing Medical AI: A New Approach to Collaborative Learning
![Clients employ a privacy-preserving federated learning approach by extracting $768$-dimensional $[CLS]$ tokens from a Vision Transformer, encrypting them with the CKKS scheme, and enabling the server to aggregate information across numerous clients while performing encrypted inference.](https://arxiv.org/html/2511.20983v1/x1.png)
A novel framework combines the power of vision transformers with lightweight encryption to enable privacy-preserving medical image analysis across distributed datasets.
The Evasion Game: Finding the Perfect Zigzag
New research proves an optimal strategy for dodging pursuers relies on sharp, on-off maneuvers, and presents a practical method for implementing it in real-time.
RDMA Gets a Memory Boost: Handling Faults on the Fly
A new approach efficiently manages memory page faults during Remote Direct Memory Access, eliminating the need for pre-pinned buffers and unlocking performance gains.
Bridging the Language Gap: Smarter SQL Queries in Arabic
Researchers have developed a new dataset and technique to significantly improve the accuracy of converting natural language questions into SQL queries for Arabic databases.
Taming Attention: Stabilizing Transformers for Faster Training
A new approach to controlling attention logit changes enables higher learning rates and improved performance in transformer models.
Reinforcement Learning’s Safety Net: Predicting the Unpredictable
A new approach safeguards Dyna-Q reinforcement learning agents against unexpected environmental shifts by proactively evaluating potential outcomes.
Ask Anything: Building Conversational AI with Knowledge Graphs
Researchers have developed a new system that combines the power of large language models with structured knowledge to deliver more accurate and adaptable conversational question answering.