论文

研究发现，recall@k 并非已部署 KB-VQA 的正确评价指标，且弥合差距需要 reader 侧介入；本文首次对多模态 KB-VQA 中 reader 侧位置依赖性进行了受控探查，设计了一种 gold-position 协议——在问题提示中仅改变 gold passage 所在的槽位。The findings indicate that recall@k is the wrong metric for deployed KB-VQA and that closing the gap requires reader-side intervention; the first controlled probe of reader-side position dependence in multimodal KB-VQA is designed, a gold-position protocol in which only the gold passage's prompt slot varies within question.

本文论证了稀疏组合监督与动宾学习的不对称性会助长物体驱动的捷径学习，并指出减少捷径诊断可提升组合泛化能力。This work argues that sparse compositional supervision and verb-object learning asymmetry can promote object-driven shortcut learning and reduces shortcut diagnostics and consequently improves compositional generalization.

本文从玩家动作控制、游戏状态动态、状态-观测持久性与实时交互生成四个维度审视交互式游戏世界建模，并针对《Black Myth: Wukong》提出可扩展的数据引擎，采集超过 90 小时的游戏画面作为状态感知型游戏世界建模的资源。This paper examines interactive game world modeling along four dimensions: player action control, game state dynamics, state-observation persistence, and real-time interactive generation, and presents a scalable data engine for Black Myth: Wukong that collects over 90 hours of gameplay as a resource for state-aware game world modeling.

提出了ShotPlan，一个基于视频扩散基础模型构建的、用于显式多镜头电影级视频生成的框架，显著优于现有的电影级视频生成方法，提供更灵活的镜头管理和更强的跨镜头一致性。ShotPlan is proposed, a framework for explicit multi-shot cinematic video generation built upon a video diffusion foundation model that significantly outperforms existing cinematic video generation methods, offering more flexible shot management and stronger inter-shot consistency.

提出结构化动态模型（SDM），通过未来特征预测，显式地将时间变化的主导来源与残差动态分离开来，而非使用单一纠缠的隐变量或非结构化的、空间密集的转移 token 来表示视频变化。The Structured Dynamics Model (SDM) is proposed, which explicitly separates the dominant source of temporal change from residual dynamics through future-feature prediction, rather than representing video change with a single entangled latent or with unstructured, spatially dense transition tokens.

将 Positive--Direction Matching (PDM)——一种分支感知的 OPD 目标，分别约束正预测方向与 CFG 条件方向——引入 dense-to-sparse 视频控制；由于朴素的 guided matching 对推理 guidance 尺度极为敏感，分支感知监督可实现更鲁棒、更有效的知识迁移。Positive--Direction Matching (PDM), a branch-aware OPD objective that separately constrains the positive prediction and the CFG conditional direction, is introduced to dense-to-sparse video control, where naive guided matching is highly sensitive to inference guidance scales, while branch-aware supervision enables more robust and effective knowledge transfer.

提出一种自适应推理时引导框架，利用VLA Latents上的强化学习，发现推理时引导在成功与失败状态下遵循根本不同的scaling laws：动作多样性在基础VLA可能失败时最为有益，但在成功可能性高时可能不必要地扰动已准确的动作。This work introduces an adaptive inference-time steering framework that leverages Reinforcement Learning on VLA Latents, and discovers that inference-time steering follows fundamentally different scaling laws under success and failure states, revealing that action diversity is most beneficial when the base VLA is likely to fail, but can unnecessarily perturb already-accurate actions when success is likely.

EffectLearner 是一个语义推理增强框架，结合基于 VLM 的 Object-Effect Reasoner 与基于 DiT 的 Video Eraser，在 EffectWorld-Eval 和具有挑战性的 EffectWorld-Wild 上均取得明显优势，证明其能在复杂真实场景中实现高质量的视频物体擦除。EffectLearner is proposed, a semantic-reasoning-enhanced framework that combines a VLM-based Object-Effect Reasoner with a DiT-based Video Eraser that achieves clear advantages on both EffectWorld-Eval and the challenging EffectWorld-Wild, demonstrating its ability to deliver high-quality video object removal in complex real-world scenes.

在客观与主观指标上的重建和生成结果匹配或超越前沿开源 tokenizer，包括 Wan-2.2、HunyuanVideo-1.5、FLUX.2、MovieGen、StableAudio 和 MMAudio 的 VAE。It is demonstrated that reconstruction and generation results on objective and subjective metrics matches or surpasses frontier opensource tokenizers, such as VAEs from Wan-2.2, HunyuanVideo-1.5, FLUX.2, MovieGen, StableAudio and MMAudio.