FedSocket is a recipient-executable knowledge exchange framework for heterogeneous multimodal federated learning. It enables recipient models to execute exchanged knowledge directly, improving accuracy and efficiency. FedSocket combines local and exchanged predictions to achieve better results than independent ensembles or local models alone.

Researchers extended Reinforcement Learning with Verifiable Rewards to Large Vision-Language Models and found that visual claims are not always supported by images. They introduced a diagnostic to measure the persistence of visual claims and proposed Persistence-Aware Credit Gating to attenuate credit for persistent claims.