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NVIDIA introduces Cosmos Framework, an open, modular platform for training, post-training, optimizing, and deploying world foundation models for physical AI, covering RL pipelines like DPO, GRPO, PPO, and serving via Cosmos NIM microservices.
论文提出 Agentic World Modeling（AWM），通过只读接口与真实环境交互式获取证据来模拟动作结果，实现无需环境副本的并行 agent 测试时扩展，在 τ-bench、AppWorld 和 OfficeBench 上显著优于单次模拟与训练式世界模型基线。
The paper introduces SafeWebWorld, a benchmark for evaluating 'safety sufficiency' in web world models, showing that plausible prediction, evidence preservation, and latent authorization recovery are each insufficient to establish robust EXECUTE/ASK/BLOCK authorization decisions.
SPT-Flow 提出一种连续时间世界模型的训练方法：每条轨迹采样一个隐变量，并将自由运行的 ODE 展开与多个读出时刻的演示状态对齐，从而克服监督捷径问题。在 LIBERO 上，该方法相对 ODEWorld 基线在中段与最终帧的 best-of-10 PSNR 分别提升 2.0 和 2.4 dB，平均 LPIPS 降低 23%。
The paper frames continual reinforcement learning with a contextual MDP that splits context into behavioral and physical components, and studies zero-shot reuse of world models in FourRooms tasks with Dyna-Q and TD-MPC, finding that physical-context changes substantially degrade reuse depending on the observation representation. It identifies representation generalization as a key bottleneck for reliable world-model reuse in continual RL.
The paper introduces density-guided bootstrap boundaries, an adaptive mechanism for controlling imagined-return depth in latent world-model RL, proving contraction of the gated Bellman operator and showing improved sample efficiency within DreamerV3 across continuous-control and Atari tasks.
A new paper by Randall Balestriero and Yann LeCun argues that paired-view training (e.g., image-caption pairs), not sheer scale or next-word prediction, is what produces unified world representations—showing a 109M-parameter model outperforming a 72B language model in representational alignment.
Hugging Face weekly roundup of top research papers covering world models, streaming video agents, robot policies, and efficient diffusion VLMs, highlighting works like Rollback, Hy-Nomas, OmniShowcase, PDRVLA, and StreamVibe.
Han Junlin announces joining AMI Labs as an MTS to work on world model pre-training and capability emergence, expressing excitement about building AI for the real world with a scaling mindset.
CausalWAM proposes adding a sparse routing path to a pretrained World Action Model's action head so that generated actions depend only on action-relevant latent variables, with provable identification of causal structure up to permutation without temporal independence assumptions. Experiments on synthetic data and real robots show improved closed-loop success efficiency.
Ex-Palantir executives Andy Alder and Arjun Mather launched Dream, a startup building AI world models for building design and engineering, with $17 million in seed funding led by Andreessen Horowitz. The company's 'structural cognition' model, trained on over 100 million square feet of building permit data, is already deployed across 10 projects in Texas and California.
A report examining the intersection of video generation and agent harnesses, covering agentic video generation, agentic world modeling, and recursive self-improvement for autonomous multi-shot video production.
Roboflow announced a partnership with NVIDIA and joined the Cosmos Coalition as a premiere provider of multi-modal data and post-training capabilities to advance frontier world model research for the physical economy.
Benjamin Bai unveils NanoDreamer, a 750K-parameter Dreamer-inspired world-model and actor-critic agent trained entirely on a MacBook M5 Pro, complete with his own autograd engine, tensor library, and transformer, playing all 5 MinAtar games from imagination.
The paper introduces ImagiRep, a simulator-grounded benchmark showing that spatial reasoning benefits from diverse representations beyond RGB, and proposes GEARS, a lightweight test-time strategy for adaptively selecting representations to improve reasoning about unobserved states.
Google DeepMind 发布 Genie 4 世界模型，利用 Gemini 3.8 Flash 作为引导智能体，在物理模拟、人物表现和音频支持上较 Genie 3 全面提升，可实时生成可交互的虚拟世界。DeepMind 认为世界模型是通往 AGI 的关键一步，可用于机器人训练环境与创意原型设计。
This NeurReps workshop extended abstract evaluates whether pretrained LeWorldModel preserves local action geometry, measuring action sensitivity via Gram matrices across perturbation scales on Reacher and OGBench Cube, finding significant directional discrepancies at small scales and recommending scale-specific evaluation.
Rollback 提出了一种利用世界模型回溯到过去的决策点、探索反事实动作并生成细粒度动作级监督的策略改进范式，同时通过真实机器人反馈持续优化世界模型动力学，在三项真实任务上显著提升了机器人策略成功率。
World-in-Context introduces an interactive video world modeling framework that unifies view, action, and camera controls as explicit tokens within a shared self-attention space via In-Context Conditioning, along with Interaction-Transport RoPE for temporal continuity. Its 5B model matches the navigation performance of the 22B EchoWM on WBench while achieving a 93.8% perspective-switching success rate versus 18.8% for existing 5B baselines.
Kyutai Labs releases CADRE, open causal diffusion representation autoencoders that remove the latency typically inherited from text-to-video generators, enabling real-time world models to react faster on a single GPU.