Papers

GLaM: Training a Latent World Model over Global Spatiotemporal Memory for Active Exploration and Navigation

GLaM is a goal-conditioned latent world model over an online spatiotemporal map that jointly predicts future map representations and robot-centric waypoint latents, with GLaM Nav adding observation-based verification, retrieval, and geometric control. On a controlled 50% HM3D-ObjectNav subset, it raises success rate from 78.50% to 86.89% and SPL from 47.70% to 48.35%; qualitative Astribot S1 demonstrations cover exploration and memory-based return in an unknown office.

Embodied AIObject Navigation

I-Tak Ieong, Ruizhi Feng, Zhaoyang Lu · 172 views · Sep 13, 2026

L3G-World: Real-Time Infinite World Generation with Latent 3D Gaussian Memory

L3G-World maintains a compact latent 3D Gaussian memory that adds primitives only in unseen regions and refines existing ones. Rendering the memory directly into diffusion latent space avoids VAE re-encoding and sustains long-horizon consistency at 9.02 FPS on one H100.

World Models3D Gaussian Splatting

Jinhyeok Choi, JoungBin Lee, Jongmin Lee · 300 views · Oct 29, 2026

MorphMimic: Morphology-Conditioned World Model and Policy Sharing Across Humanoids

MorphMimic conditions a shared differentiable world model and tracking policy on a 125-value morphology descriptor, enabling three humanoids to learn from 128,786 common motion clips and share one controller.

HumanoidCross-Embodiment

Lizhi Yang, Fengze Xie, Yuyang Wang · 421 views · Oct 25, 2026

UniAction: Unified Action Conditioning for World Models

Actions that condition world models are highly heterogeneous, and thus existing action-conditioned world models are typically built for individual action types, fragmenting already limited action-annotated data across separate models. We introduce UniAction, a unified action-conditioning framework that represents any action as a sparse set of action elements, each with a discrete semantic identity and a continuous spatiotemporal grounding coordinate. By mapping these coordinates directly into the pretrained video model's native positional space, UniAction allows heterogeneous actions to condition a shared world model within the same spatiotemporal structure. We further introduce cardinality-calibrated attention to address the bias from differing action representation granularities. We evaluate UniAction on four representative action forms: force, camera control, hand pose, and robot pose. UniAction outperforms conditioning alternatives in action following, and a single jointly trained model surpasses the action-specific experts, showing that heterogeneous action data benefit from unified model and joint training.

World ModelsAction Conditioning

Bowen Xue, Zizhang Li, Hong-Xing Yu · 302 views · Oct 23, 2026

RynnUnify: Towards Unified Embodied Brain

We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video–language reasoning. With capabilities spanning spatial reasoning, physical prediction, and grounding, RynnUnify connects an understanding of the physical world with visual anticipation of how it evolves across successive subtasks. Building on these capabilities, we further develop an agentic extension through reinforcement learning to selectively use video predictions for reasoning about unobserved states and interaction outcomes. We also develop a world-action extension of the full RynnUnify model, translating its understanding and prediction capabilities into robot control. To evaluate these capabilities, we introduce Rynn-VP and Rynn-GEU for long-horizon visual planning and anticipatory reasoning. Experiments show that RynnUnify achieves the highest average embodied understanding score among the evaluated unified multimodal models and improves the Rynn-VP overall score by 9.75 points over Cosmos 3 Nano. Its agentic extension improves the Rynn-GEU average by 6.0 points over direct reasoning, while the world-action extension achieves 51.5% success on 20 selected composite RoboCasa365 tasks and 46.7% average success across three real-robot tasks. Together, these results establish RynnUnify as a shared foundation for embodied understanding, visual planning, and robot control, taking a step toward a unified embodied brain.

RynnUnifyEmbodied AI

Minghao Zhu, Bohan Hou, Kun Xiang · 288 views · Oct 23, 2026

LeWAM: End-to-End World and Action Modeling with JEPAs

Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representations and action-conditioned dynamics before adding a control policy or solver. We introduce LeWAM, an end-to-end JEPA for control with: (1) a joint training objective over next latent state prediction and action flow matching, and (2) an isotropic Gaussian constraint on the latent state, enabling a single model to support direct policy execution and efficient planning by evaluating sampled actions with its learned latent dynamics. Compared with prior JEPAs in visual planning, LeWAM improves success rate from 28.6% to 89.7% on contact-rich tasks and from 10.9% to 46.2% on long-horizon tasks, and plans 32.3 times faster. On behavior cloning tasks in simulation and physical robots, LeWAM is also competitive with established methods. Interpretability analyses show that LeWAM learns control-relevant representations and action distributions effectively guiding test-time planning and control.

JEPAWorld Models

Minghao Fu, Tavis Siebert, Eryk Halicki · 227 views · Oct 8, 2026

Agentic World Action Models

Recent world action models provide visual foresight and future prediction for robot manipulation, but lack explicit mechanisms for compositional skill reuse. Agentic robotics combines code-based planning and reasoning with reusable skills, but often lacks predictive grounding in physical dynamics. We introduce AW-0, an agentic world action model framework that integrates code-based planning with joint video and action prediction. We jointly model code, future video, and robot actions as three coupled generative streams within a unified mixture-of-transformers architecture. Within this architecture a code expert generates parameterized skill routines and control logic while video and action experts generate future visual states and low-level motor commands. Code serves two complementary roles: it structures multi-step prediction and control within the world model and represents skills as reusable programs for subsequent agentic execution. We further introduce an automated curation pipeline that labels robot and human video trajectories with aligned programs and consolidates recurring routines into reusable skill libraries. Evaluations on RoboTwin 2.0 and physical bimanual manipulation tasks show improved task performance over VLA and WAM baselines, alongside gains in language following, semantic reasoning and robustness, demonstrating that agentic code enables connection between planning, prediction and execution within WAMs.

World-action modelsVision-language-action (VLA)

Liu, Zhuoyang, Niu, Dantong, Zhang, Botong · 295 views · Oct 21, 2026

World SLAM Model: Joint World Modeling for SLAM and Navigation

World SLAM Model (WSM) embeds the mechanisms of SLAM, including persistent memory, incremental state updates, and local backend refinement, directly into a navigation world model. A unified Mixture-of-Transformers jointly predicts future RGB observations, dense depth, and camera poses from RGB input, enabling closed-loop navigation with accurate localization and mapping.

World ModelsSLAM

Minghui Qin, Yijun Yuan, Weicheng Zheng · 211 views · Sep 26, 2026

Can World and Action Modeling Benefit Each Other under Unified Causal Training?

World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal training that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architecture. Using this framework, we investigate whether learning forward dynamics conditioned on specified actions improves action learning, and whether learning to generate actions in turn improves action-conditioned world modeling. We use LIBERO, LIBERO-Plus, and RoboCasa365 as embodied testbeds to evaluate both policy learning and world modeling, complemented by real-world policy experiments. Learning the visual consequences of specified actions improves policy performance and generalization across changing scenes. In turn, action learning improves multi-view world-prediction fidelity under the same architecture. Together, these results answer both questions positively, supporting reciprocal benefits between world modeling and action generation under unified causal training.

World ModelsRobot Manipulation

Yue Su, Bokai Lin, Xinkai Wang · 230 views · Oct 15, 2026

Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence

World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the functions they support. In particular, visual dynamics, geometric structure, and executable action are commonly coupled through separate interfaces or downstream modules rather than being jointly represented and evolved within a shared dynamics model. In this work, we present Thinker-WM, a unified multimodal world model that jointly models and generates RGB, metric depth, and robot action through a shared intermediate Unified Dynamics Transformer with modality-specific encoders and decoders. The model performs multimodal unification at the representation level: appearance, geometry, and action are transformed into a common dynamic representation and jointly evolved by shared parameters, while their distinct output structures are preserved. The same parameterization supports multiple functions, including joint video-action generation, world prediction, policy inference, action-conditioned generation, video-to-action generation, and inverse dynamics. To examine whether the shared representation captures action-related information, we introduce a lightweight motion probe that shows the frozen visual representation contains recoverable motion information. Thinker-WM is trained on a heterogeneous corpus comprising approximately 20K hours of egocentric video, 10K hours of real-robot data, and 1K hours of simulation data, including approximately 4K hours of real-robot data with aligned depth supervision. Extensive experiments demonstrate strong performance across embodied world-model evaluations. Thinker-WM ranks first on EWMBench, WorldOlympiad and WorldArena, while controlled studies further demonstrate the benefits of action conditioning, geometric interaction, and multi-function training within the shared representation.

World ModelsEmbodied AI

Thinker Team, Peng Gao, Jichao Jiao · 285 views · Oct 13, 2026

SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model

SuperMimic fits one differentiable world model from simulator transitions and uses short imagined rollouts to train a general humanoid tracker. It surpasses SONIC in three hours on one H100, then reaches zero failures on 129,514 motions after 9.8 hours on two H100s.

HumanoidWorld Models

Qiayuan Liao, Yiyang Shao, Junfeng Long · 1.5k views · Oct 20, 2026

Agent World Model in the Wild: Computer-Use Agent Environment Synthesis

Agent learning is shifting from learning from human data toward learning through interaction with the world. However, scaling such learning directly in real-world computer systems is challenging and can introduce substantial security risks. We therefore propose Agent World Model in the Wild (WildAWM), a scalable pipeline for synthesizing controllable environments and tasks for training general computer-use agents (CUAs). Whereas existing approaches typically specialize in a single agent interaction interface, we construct executable environments that jointly support graphical user interfaces, model context protocol services, and command-line interfaces, allowing agents to flexibly combine visual interaction, tool use, and code execution. WildAWM is entirely powered by open-source models, demonstrating that high-quality computer-use environments can be generated without proprietary frontier models. Beyond environments, we address three recurring challenges in task synthesis: ensuring solvability, maintaining sufficient difficulty, and producing reliable verifiers, by introducing execution-based quality gates that combine heterogeneous agent rollouts, cross-judge consistency checks, and counterfactual probes of judgment robustness. The resulting pipeline synthesizes 341 long-horizon environment-task pairs that can require over 100 agent steps to solve and remain challenging even for frontier models, with GPT-6-Astra solving only 60% of them. We train two Qwen models of different sizes exclusively in these synthetic worlds. Both models achieve substantial gains across 8 out-of-distribution benchmarks spanning web, claw, and computer-use agents, demonstrating that learning in WildAWM's synthesized worlds generalizes broadly to unseen computer-use tasks. The code is publicly available at https://github.com/MSR-Orchard/WildAWM.

Computer-Use AgentWorld Models

Zhaoyang Wang, Qianhui Wu, Haorui Wang · 360 views · Oct 19, 2026

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