Physical-world interaction is inherently dynamic, as environments can evolve during execution, requiring agents to adapt their plans under non-stationary conditions. We study this challenge through long-horizon embodied planning under environment deviations and execution uncertainty. Existing embodied-task benchmarks can expose such failures, but these failures are usually treated as evaluation outcomes instead of learnable signals for training agents to recover. In this work, we introduce DynamicEnvPlan, a closed-loop framework for high-level planning in dynamic environments. It extends embodied task execution with humanoid agents, high-level primitive skills, structured semantic memory, and controllable perturbations. Our data synthesis design consists of planning, perturbation, and guarded correction modules that turn dynamic execution states into recovery-oriented traces. The resulting traces are used for staged supervised fine-tuning, enabling the planner to learn from both nominal execution and perturbed recovery trajectories. Using 104 task-scene combinations spanning i.i.d., compositional generalization, and out-of-distribution settings for fine-tuning and evaluation, DynamicEnvPlan boosts success rate from 33.3% for the base planner to 76.2%, while improving across all seven evaluation metrics critical to physical-world interaction, including safety and affordance compliance.
Experiments show that UI-Mate-27B sets a new open-weight state of the art on general computer-use benchmarks, substantially improving long-horizon reliability, and makes three contributions to an environment-grounded training stack with in-context demonstration learning.
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
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