论文

提出 Context-Matched Distillation（CMD），一种因果 DMD 框架，将 teacher 监督信号与每个 target 生成时可用信息对齐，并可自然扩展到逐帧和逐 chunk 生成、长视频蒸馏以及相机条件蒸馏。Context-Matched Distillation (CMD) is introduced, a causal DMD framework that aligns teacher supervision with the information available when each target is generated, and naturally extends to frame-wise and chunk-wise generation, long video distillation, and camera-conditioned distillation.

当将监督规模扩展到 680,000 小时的多语言、多任务数据时，所得到的模型在标准 benchmark 上泛化良好，在 zero-shot transfer 设置下常可与此前全监督方法的结果相当，且无需任何微调。When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning.

建立一个可复现的选择性 3D 定位框架，并指出跨视角对应（cross-view correspondence）是主要操作瓶颈。The study establishes a reproducible framework for selective 3D localization and identifies cross-view correspondence as the dominant operational bottleneck.

MMDiff 是一种多模态 model-diffing 框架，训练多模态 SAE 并将其转化为特征级接口，用于发现和控制多模态行为；研究表明多模态 SAE 不仅可作为可解释性工具，还可作为审计、引导和控制 MLLM 行为的机制，以实现更安全、更具能力的生成。MMDiff, a multimodal model-diffing framework that trains multimodal SAEs and turns them into feature-level interfaces for discovering and controlling multimodal behavior, shows that multimodal SAEs can serve not only as interpretability tools, but as mechanisms for auditing, steering, and controlling MLLMs behavior toward safer and more capable generations.

提出 Self-Supervised Visual On-Policy Distillation（S²VOPD），一种简单有效的方法，通过非对称增强视图构建 on-policy 学习信号，系统地探索了视觉增强的广阔设计空间，并发现非对称性至关重要。Self-Supervised Visual On-Policy Distillation (S$^2$VOPD), a simple yet effective method that constructs on-policy learning signals from asymmetric augmented views, systematically explores a broad design space of visual augmentations and uncover that asymmetry matters.

提出 UniProbe，一个轻量、统一、可学习的检测器，通过单次前向传播建模冻结 LVLM 的异构计算 trace，在 token 级和物体级幻觉检测上达到 SOTA。UniProbe is introduced, a lightweight, unified, learnable detector that models a frozen LVLM's heterogeneous computational trace from a single forward pass and achieves state-of-the-art token-level and object-hallucination detection.

AnyTalk 能够在多样化的人脸网格和 blendshape 配置下生成唇形同步动画，显著减少人工工作和数据需求；并通过将 AnyTalk 蒸馏为精简网络 $\text{AnyTalk}_{RT}$ 来提升可用性，从而实现实时性能。AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements and enhances usability by distilling AnyTalk into a streamlined network, $\text{AnyTalk}_{RT}$, thereby enabling real-time performance.

GRNEdit 是一个轻量级的两阶段指令驱动通用视频编辑框架，性能优于多个 14B 开源编辑器，同时其 8B 模型与领先的开源编辑器表现相当。GRNEdit, a lightweight two-stage framework for instruction-based general video editing that outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.

WorldRover 将长视野世界探索转化为可扩展的数据生成问题，为需要在可探索世界中构建、维护并重访一致表征的模型提供监督信号。WorldRover turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.