System Message
"System message" encompasses various research areas focusing on the design, optimization, and impact of information exchange within different systems. Current research explores efficient message passing in graph neural networks, aiming to improve performance in heterophilic graphs and address challenges like over-smoothing, while also investigating methods to enhance security and privacy in message transmission, such as steganography in 3D models and robust message embedding in images. These advancements have significant implications for diverse fields, including autonomous driving (via CAN bus analysis), recommender systems (via improved collaborative filtering), and large language model security (via jailbreak mitigation).
Papers
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veličković, Eva L. Dyer
Don't lose the message while paraphrasing: A study on content preserving style transfer
Nikolay Babakov, David Dale, Ilya Gusev, Irina Krotova, Alexander Panchenko