Yevgeniy Vorobeychik

Malicious instruction detection can be significantly improved by adapting adversarial training to the context of the task, leading to better robustness against evolving attack strategies.

Real-time user feedback can now directly optimize diffusion models for personalized recommendations, eliminating the need for extensive preference data.

IMPFM achieves unprecedented global exploration in feedback-driven search by leveraging multi-particle interactions to prevent mode collapse and reward over-optimization.

A malicious parameter server can exploit parameter-efficient fine-tuning to implant a privacy backdoor that reconstructs training data with alarming accuracy.

Stop training in isolation: LNTrust lets decentralized models learn *who* to trust during training, so they can collaborate effectively at deployment, boosting accuracy and cutting communication costs.