Ye Yuan

Experiments on five dynamic graph benchmarks show that CacheDyG adopts substantially fewer trainable parameters and lower runtime to obtain more competitive predictive performance than baselines, demonstrating that cache-based decoupling provides an effective principle for scalable dynamic graph learning.

Transition realization is a game-changer for World Action Models, with LEON boosting performance and robustness beyond traditional methods.

MLLMs can now seamlessly unify perception and reasoning, achieving state-of-the-art performance in free-form multimodal grounding tasks.