Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workflows rely on user-specific tools and tacit conventions, so unstated instructions can produce arbitrary variations across runs. We present UI-Mate, a foundation GUI agent that integrates an environment-grounded training stack with in-context demonstration learning. UI-Mate makes three contributions: A Scalable Environment-Grounded Training Stack: A closed-loop data engine automates task generation, environment construction, rollout, filtering, capability balancing, SFT, and online RL across massively parallel environments via unified task-verifier bundles. In-Context Demonstration Learning: A mechanism that transforms multimodal demonstrations into flexible subtask-level workflows, follows relevant demonstrated steps, and re-plans from the live interface. OSWorkerBench Benchmark and Insights: A benchmark of 100 long-horizon office tasks across 41 applications that supports instruction-only and demonstration-guided evaluation. Its demonstration resources separate a 33-task self-demo setting, built from successful strong-agent rollouts of the same targets, from a 45-task variant-demo setting, built from human recordings of related but non-identical tasks. Experiments show that UI-Mate-27B sets a new open-weight state of the art on general computer-use benchmarks, scoring 77.0% on OSWorld-Verified and 66.2% on WindowsAgentArena. On OSWorkerBench, it reaches 41.0% strict success and 76.9% progress, outperforming its Qwen3.6-27B base by 17.7 and 24.5 points. On the 33-task self-demo subset, one demonstration raises strict success from 17.2% to 35.4% and progress from 67.9% to 81.1%, substantially improving long-horizon reliability. Project page: https://ui-mate.github.io.
Zihan Ding, Longxu Dou, Qixiao Gao et al.· 0 citations
Text-to-image diffusion models have achieved remarkable progress in image synthesis, but their potential misuse for generating unauthorized or harmful content has raised growing safety concerns. This has created an urgent need for safe diffusion-based image generation methods that can selectively suppress sensitive concepts while preserving the model’s general generative capability. Existing concept erasure approaches typically rely on either model fine-tuning or closed-form editing. However, they often suffer from two major limitations: (1 insufficient or excessive erasure, where the former fails to suppress target concepts and the latter disrupts benign semantics; and (2 degradation of non-target concepts, where removing target concepts undermines the generation of unrelated concepts, especially in multi-concept scenarios. To address these issues, we propose the Singular Value Eraser (SVEraser), a lightweight concept erasure module that removes specific concepts by optimizing singular-value offsets of weight matrices. Operating in a compact yet expressive singular-value space, SVEraser enables precise concept removal while reducing side effects on unrelated content. Moreover, once trained for different concepts, multiple SVErasers can be flexibly combined for multi-concept erasure. To further reduce interference, we introduce an eraser activation mechanism that adaptively selects the appropriate SVErasers during inference based on the input prompt. Extensive experiments on copyrighted objects, artistic styles, and explicit content demonstrate that our method achieves accurate target concept removal while preserving non-target semantics, providing a practical and reliable solution for safe diffusion-based image generation.
Xiaoyu Geng, Shuaixiong Hui, Yuxin Wang et al.· IEEE Transactions on Image P...· 0 citations