Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and reliance on ungrounded synthetic teacher supervision. To transcend these limitations, we propose , a framework that shifts the learning focus from mimicking high-level trajectories to learning interaction dynamics from executed transitions. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and outperforms inverse and backward objectives. leverages this insight by converting low-cost autonomous exploration into high-density generative supervision to learn a semantic transition world model from executed transitions. Experimental evaluations across a series of models demonstrate consistent gains from our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7% on offline benchmarks, which amplifies to a 16.8% gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results suggest that grounding agents in forward predictive modeling offers a promising pathway to scalable GUI automation with cross-domain adaptability and compositional generalization.

We introduce PR-XAI, a feature attribution method for transformer models based on the PageRank algorithm. The proposed PR-XAI models the attention mechanism as a directed graph, with weights derived from attention weights and their gradients. Evaluations across five well-known text classification datasets and three different architectures show that PR-AG, one variant of PR-XAI, outperforms state-of-the-art attribution methods in faithfulness and classification metrics, with significant gains on long-form text.