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This reproduction study independently reimplements LeWorldModel on the TwoRoom environment, reaching 94% goals rather than the reported 87%, and shows that four undocumented evaluation conventions determine the outcome. It also finds that one-step prediction error does not reliably predict long-horizon planning success and that batch normalization can inflate validation loss.
DreamGuard is a proactive runtime guardrail for LLM agents that uses a risk-aware world model to track latent state and predict future risks, enabling interventions before unsafe actions execute. It outperforms baselines on benchmarks and online evaluation with 25ms latency.
Presents UA-NWM, an uncertainty-aware latent world model for aerial image-goal navigation that decomposes prediction-goal discrepancy into uncertainty-explainable and unexplainable components, enabling robust trajectory scoring without multiple future samples.
EnvACE introduces world rehearsal, an agentic reinforcement learning method that replaces external environment interaction by having the policy rehearse environment responses internally, achieving strong performance across multiple benchmarks.
UniNav is a unified world-action diffusion model for image-goal visual navigation that jointly predicts future visual observations and waypoint trajectories in a single diffusion process, achieving strong benchmark results with efficient inference.
Introduces WCM, a World Critic Model that jointly predicts future latent states and estimates values to improve temporal modeling for Vision-Language-Action reinforcement learning, achieving state-of-the-art results across robotic manipulation benchmarks.
This paper introduces ODEWorld, a continuous-time latent world model using Physical-Time Flow (PT-Flow) that learns a latent velocity field parameterized by an ordinary differential equation, enabling arbitrary temporal resolution, backward prediction, and improved planning for video generation and robotic control.
PhiZero is a physical world model that learns a compact discrete representation called 'physical language' from videos and uses it to reason about world state transitions before rendering future videos, improving physical coherence in generation and understanding tasks.
Stanford HAI discusses the emergence of world models and spatial intelligence in AI, calling for governance frameworks that address capabilities beyond language processing.
WorldDiT is a new, small (<1B parameters) robotics model that unifies world prediction and control, achieving top performance on the LIBERO benchmark without requiring a VLM.
This paper proposes Music-JEPA, a world model that learns piano sound representations by framing audio as a state and piano roll as an action. It captures action-sound relationships and enables downstream tasks like beat tracking and piano transcription via planning.
N₀-TWAM is a tactile-native world-action model for contact-rich manipulation, trained at scale on visuo-tactile data from 6 embodiments and 450 tasks. The authors release code and pretrained checkpoints, positioning it as the first tactile world-action model trained at scale.
Opus 5 powers a live AI tutor prototype for AlphaSchool, combining chat, direction, and a custom tiny world model for real-time rendering and interactivity.
Black Forest Labs announces FLUX 3, a multimodal foundation model that jointly generates audio-visual content and, via collaboration with mimic robotics, enables video-action prediction for robot control, tested at Audi.
Lightricks releases LTX-2.5, an open-weights world model for generating synchronized video and audio from text, image, and video inputs, with features like native multishot generation and a new diffusion video decoder.
WorldWeaver (W²) introduces cross-agent world state registers to multi-agent video diffusion models, enabling shared world state persistence across agents and views, improving logical consistency in two-agent Minecraft video generation.
AlayaWorld is a full-stack, open-source video world model capable of generating 720p, 24 FPS streaming video with camera control, enabling dynamic scene creation.
This paper introduces AlayaRenderer-Flash, a real-time generative world renderer that accelerates rendering from 0.56 FPS to 31.54 FPS using a few-step autoregressive streaming model and lightweight distilled codecs, enabling interactive play with a physics engine.
NVIDIA released Cosmos 3 Edge, a 4-billion-parameter open world model for edge devices that helps robots and vision AI agents understand surroundings, reason in real time, and generate actions. It achieves best-in-class throughput and accuracy among similar-sized models.
DSWorld introduces a Data Science World Model that predicts environment state transitions to reduce costly trial-and-error in autonomous agents, achieving 14x acceleration in RL training and 3-6x in inference while maintaining competitive performance.