The remarkable progress of vision–language models (VLMs) has enabled computer-use agents to interact with computers in a human-like manner. Yet real-world computer-use tasks remain difficult due to long-horizon workflows, diverse interfaces, and frequent intermediate errors. Prior work equips agents with external memory built from large collections of trajectories, but relies on flat retrieval over discrete summaries or continuous embeddings, falling short of the structured organization and self-evolving characteristics of human memory. Inspired by the brain, we propose Hybrid Self-evolving Structured Memory (HyMEM), a graph-based memory that couples discrete high-level symbolic nodes with continuous trajectory embeddings. HyMEM maintains a graph structure to support multi-hop retrieval, self-evolution via node update operations, and on-the-fly working-memory refreshing during inference. Extensive experiments show that HyMEM consistently improves open-source computer-use agents, enabling 7B/8B backbones to match or surpass strong closed-source models; notably, it boosts Qwen2.5-VL-7B by +22.5% and outperforms Gemini2.5-Pro-Vision and GPT-4o.

Recent progress in multilingual pretraining has yielded strong performance on high-resource languages, albeit with limited generalization to genuinely low-resource settings. While prior approaches have attempted to enhance cross-lingual transfer through representation alignment or contrastive learning, they remain constrained by the extremely limited availability of parallel data to provide positive supervision in target languages. In this work, we introduce NeighXLM, a neighbor-augmented contrastive pretraining framework that enriches target-language supervision by mining semantic neighbors from unlabeled corpora. Without relying on human annotations or translation systems, NeighXLM exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs. The approach is model-agnostic and can be seamlessly integrated into existing multilingual pipelines. Experiments on Swahili demonstrate the effectiveness of NeighXLM in improving cross-lingual retrieval and zero-shot transfer performance.