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World Models

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WORLD MODELS · TIMELINE

World Models: 36 Years from Internal Models to WAMs

1990–2026 · Five lineages · 33 milestones

Early explorationLatent-space eraVideo generation · Physical AI · VLAWAM unification1990200020102018202020222023202420252026Model-based RL · Latent DynamicsVideo-generation World ModelsJEPA Predictive EmbeddingsVLA · World Action ModelsEmbodied / Physical World ModelsWorld Models1990 · SchmidhuberPILCO2011 · Deisenroth & RasmussenWorld Models2018 · Ha & SchmidhuberPlaNet2018 · DeepMindMuZero2019 · DeepMindDreamer2019 · Hafner et al.DreamerV22020 · Hafner et al.TD-MPC2022 · Hansen et al.DreamerV32023 · Hafner et al.I-JEPA2023 · Meta AIGAIA-12023 · WayveUniSim2023 · GoogleSora2024 · OpenAIGenie2024 · DeepMindV-JEPA2024 · Meta AI1X World Model2024 · 1X TechnologiesGenie 22024 · DeepMindCosmos2025 · NVIDIAV-JEPA 22025 · Meta AIGenie 32025 · DeepMindRT-22023 · Google DeepMindOpenVLA2024 · Stanford et al.π02024 · Physical IntelligenceGR00T N12025 · NVIDIAHelix2025 · Figure AIWAMs Survey2026 · Fudan University · Shanghai Innovation Institute · NUSACT2023 · StanfordRDT / H-RDT2024 · Tsinghuaπ0.52025 · Physical IntelligenceOpenVLA-OFT2025 · Stanford · BerkeleyGR00T N1.72026 · NVIDIAXiaomi-Robotics-02026 · XiaomiGigaWorld-Policy-02026 · GigaWorld Team
The x-axis is non-linear (1990–2017 compressed). Hover a node for details.← swipe to explore the full timeline →
WAM PHYLOGENY · 2023–2026

Branching Evolution: From VLA to World Action Models

61 site-collected papers · four lineages · taxonomy per arXiv:2605.12090

2023202420252026timeVLA · p(a|o,l)Cascaded WAMJoint WAMWorld models · p(o′|o,a)RT-2OctoHelixGR00T N1Pointmap-VLACosFly-VLAAC-VLAJoyNexusPOT-VLAFM-VLAMoE-VLAN₀-VTLAτFutureRTCRL²-VLAπR²Ego2RobotAtlasVLAG0.5HAFWAMs SurveyHyWorldVLADeVALeapBot-WARobust-WAMAeroActWorldDiTN₀-TWAMX-NavDPω-0SimWAMDynaWMSiamJEPAOrbis 2When to PlanKoopman DreamerPhysCoReWorld TranslationMusic-JEPAViTacWorldRobot-Factored WMGS-AgentFeelWorldPhyLatentPhyAITinyVLAMolmoActSpatial ForcingX-VLASmolVLAInternVLA-A1ABot-M0LDA-1BDM0Xiaomi-Robotics-0GigaWorld-Policy-0Fast-WAMHy-Embodied-0.5-VLAEventVLAInternVLA-A1.5Xiaomi-Robotics-1
Vertical axis is paper date (non-linear; the dense 2026 band is expanded). WAM unifies VLA's p(a|o,l) and world models' p(o′|o,a) into the joint distribution p(o′,a|o,l), splitting into cascaded (imagine, then act) and joint (one model generates both) families; the bottom band is the RoboDojo arena snapshot (Aug 2026, 34 models). Hover for details; click a node to open the paper.
PAPERJul 26, 2026

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models

Controllers based on sampling and latent world models assign a predicted terminal cost to each candidate action sequence, choose the minimum, execute its first action block, and replan. This rule can fail even when the terminal cost perfectly and accurately reflects the true task objective in the physical world. Residual prediction error can give an infeasible sequence an anomalously low cost, and a larger proposal pool gives such errors more chances to outrank feasible alternatives. We call this conditional failure proposal overgeneration. In Cube candidate execution audits, increasing the total proposal budget from 72 to 288 reduces the feasibility of selection by minimum latent cost from .375 to .062 for position targets and from .344 to .031 for targets defined by position and yaw, although every larger pool contains a feasible sequence. We introduce Adjacent Set Action Reconstruction (ASAR). Among proposals with low cost, ASAR measures density from standardized early action prefixes and reconstructs a full sequence from an adjacent set with a light anchor from the sequence with minimum cost. On a Carry and Release evaluation set of 75 queries, Kernel ASAR improves event completion success over matching selection by 28.0, 24.0, and 18.7 percentage points under latent cost and by 18.7, 20.0, and 17.3 points under a trajectory reachability cost at 72, 144, and 288 proposals. Analysis of finite proposal pools characterizes selection risk from the lower tail, separation by a related radius support statistic, and sequence containment under an explicit local feasibility condition.

Liangyu Li, Qingwen Liu, Mingqing Liu

World Models
PAPERJul 26, 2026

Real-Time Human-Centric World Modeling for Upper-Body Human-Object Interaction

We present a real-time human-centric world model for upper-body interactive generation, aiming to synthesize coherent local world dynamics centered on a person, where coordinated body, hand, and facial motions evolve jointly with controllable human-object discrete interaction. To this end, we adopt a continuous-discrete joint control scheme with two complementary components: a continuous human state and a discrete interaction state. For continuous human-state control, we introduce a unified implicit representation based on multi-scale motion encoding, in which motion latents from the upper body, hands, and face are fused into a shared latent space. This multi-scale design improves expressiveness across different spatial scales, captures fine-grained human dynamics more effectively, and enables direct control without explicit retargeting. For discrete object interaction-state control, we represent object contact using a small set of language-encoded discrete interaction states, where text serves as an explicit interaction-state command, such as no contact or grasp, rather than an open-ended generation prompt, and we further construct a dedicated rendering pipeline for human-object interaction data to supervise such discrete interaction states. By combining continuous implicit human-state control with discrete interaction-state control, our model enables precise modeling of how a person moves and interacts with the local environment, including controllable changes to nearby scene states. Finally, we distill the model for efficient streaming real-time inference, achieving 25 FPS on two H100 GPUs. Experiments demonstrate improved fine-grained motion fidelity, more realistic hand-object coordination, and effective real-time interaction, establishing a practical step beyond motion reproduction toward real-time human-centric world modeling.

Chaonan Ji, Jinwei Qi, Peng Zhang

World Models
Open-set object detection recognize unseen objects
VIDEOJul 26, 2026

Open-set object detection recognize unseen objects

Open-set detection technology detects and segments unknown objects instantly using a text prompt or reference image with no task-specific pre-training

TOPReward zero-training robot reward model
VIDEOJul 26, 2026

TOPReward zero-training robot reward model

TOPReward reads the internal belief of any pretrained VLM directly from token probabilities—zero training, no fine-tuning, works on open-source models.

PAPERJul 25, 2026

Towards Dual-Brain Minimal Sufficient Representation for Vision-Language Navigation

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen scenes. Although recent multimodal large models and world-model-based methods have improved navigation, they often preserve excessive task-irrelevant detail, weakening generalization and increasing computational burden. We propose BrainNav, a navigation framework grounded in the Principle of Minimal Sufficiency. BrainNav consists of three components: a Logical Anchor Model that implements instruction-aware selective perception to suppress environmental noise, a Minimalist Constraint Alignment module that serves as a compact cross-modal bottleneck, efficiently synchronizing discrete linguistic intent with continuous latent dynamics while filtering out redundant information, and a Compression World Model that predicts action-conditioned states within a condensed, low-rank latent space. These modules align semantic intent with spatial perception, enhancing the agent's robustness and efficiency in complex tasks. Experiments show that BrainNav improves over prior SOTA by 2.0 % / 1.0 in SR/SPL on R2R-CE val-unseen and 0.94 % / 0.78 on RxR-CE val-unseen. These results indicate that minimally sufficient world representations provide an effective foundation for robust VLN.

Yihao Wu, Chenyi Xu, Liqi Yan

World Models
PAPERJul 25, 2026

False Prophets: On the Security of World Models in Agentic Systems

Large language models now power autonomous agents capable of complex, multi-step tasks in different environments. Accurate and reliable execution of these tasks requires the agent to predict the results of its actions. Recent research proposes to enhance predictive capabilities via specially trained environment simulators-world models. While world models can improve performance, they can also mislead agents into executing harmful actions, creating significant security and privacy risks. In this paper, we raise security concerns regarding the usage of world models in agentic systems. We discover a range of world model specific vulnerabilities, which can be exploited in terminal-based agents to execute malicious code or extract sensitive data. To facilitate future development, we introduce a security benchmark dataset designed for text-based world models. We argue that some risks are intrinsic to approximate world modeling, and show that attackers can induce mispredictions in agentic pipelines with up to 95% success rate, possibly resulting in unintended command execution, denial of service, drainage of wallet and private information extraction. Finally, we provide practical recommendations for practitioners to mitigate the discovered harms and harden agentic systems.

Erik Imgrund, Anna Wimbauer, Klim Kireev

World Models
VLK: Vision-Language-Kinematics for Humanoid Loco-Manipulation
VIDEOJul 25, 2026

VLK: Vision-Language-Kinematics for Humanoid Loco-Manipulation

VLK model learns humanoid loco-manipulation from synthetic scenes, combining vision, language, and kinematics.

PAPERJul 24, 2026

Robot-Factored World Models via Robot Rendering

Action-conditioned video world models predict future observations from an initial observation and an action signal. In robotics, actions influence future observations through two distinct processes: they are first realized into robot motion by the robot body and controller, and the scene then responds through contact and object motion. Conditioning directly on action commands asks the world model to learn the realization process itself, while conditioning on logged future states leaks the interaction outcomes it is meant to predict. We propose robot-factored world models, which move two robot-specific factors outside the world model. First, action realization: each command is rolled through the robot's own controller and kinematics into a deployment-available nominal trajectory, a middle signal that avoids both action-realization learning and future-state leakage. Second, robot rendering: this nominal trajectory is rendered through the robot URDF, factoring the robot's geometry, kinematics, and appearance out of the model and into explicit rendered robot geometry. To resolve depth ambiguity, we pair end-effector depth with scene depth, giving geometric cues for contact and occlusion beyond image-plane overlap. Together, camera-aware static RGB/depth context and rendered robot geometry form a shared visual world-model interface that stays consistent across viewpoints and robot embodiments, so the model sees the action only as visible robot geometry and learns how objects respond to it. Our experiments show that the rendered interface outperforms vector-conditioned baselines and generalizes to unseen robot embodiments at inference. We further demonstrate that our model generates robot manipulation videos from human demonstrations by retargeting and rendering the hand motion as robot geometry.

Byungjun Kim, Taeksoo Kim, Hyunsoo Cha

World ModelsRobot Rendering
PAPERJul 24, 2026

ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust control. However, scaling visuo-tactile robot learning remains difficult because real tactile interaction data are expensive to collect, hardware-dependent, and limited in task and scene diversity. We present ViTacWorld, an action-conditioned visuo-tactile world model for scalable contact-rich robot manipulation. ViTacWorld leverages public real tactile datasets and a constructed simulation environment to scale visuo-tactile-action data, exploiting the fact that tactile signals are directly grounded in physical contact and can exhibit a smaller simulation-to-real gap than purely visual observations. The model is first pretrained with large-scale real and simulated visuo-tactile trajectories, and then finetuned with real-world policy rollouts to better match downstream manipulation behaviors. Given robot actions, ViTacWorld predicts temporally aligned visual observations and tactile feedback, enabling visuo-tactile-action rollout generation. To the best of our knowledge, ViTacWorld is the first framework that uses a world model for robot visuo-tactile-action trajectory generation and policy evaluation. It serves two roles: synthesizing rollouts to improve downstream tactile policies, and evaluating policies by predicting action-conditioned visuo-tactile outcomes under controlled action sequences. Experiments on contact-rich manipulation tasks show that ViTacWorld generates physically meaningful rollouts, improves policy performance through scalable data augmentation, and enables action-conditioned policy evaluation. Project page: https://vitacworld.github.io/

Yunao Huang, Shiyu Sang, Haotao Lu

World ModelsVisuo-Tactile
PAPERJul 24, 2026

On the Identifiability of Controlled World Models

Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control. Joint-Embedding Predictive Architectures (JEPAs) provide a compelling framework for learning such models in representation space. Recent action-conditioned extensions perform promisingly in visual control and latent-space planning, but leave a fundamental question unresolved: when does controlled latent prediction identify both the underlying state and the controlled dynamics? This is challenging under nonlinear observations and behavior policies with limited conditional action variation, where state-dependent evolution and action effects can be statistically confounded. We establish a joint identifiability theory for controlled world models with Gaussian latent states under state-dependent Gaussian behavior policies. We identify two policy-dependent conditions: spectral separation of the predictable signal governs representation identifiability, while non-degenerate conditional action variation governs transition identifiability. We prove that when both conditions hold, every global minimizer of the JEPA objective identifies the latent state and controlled transition up to an orthogonal transformation. We further derive quantitative bounds on representation and transition identifiability under approximate optimization. Finally, we construct predictor perturbations along weakly excited action directions whose counterfactual-to-on-policy error ratio is the inverse transition-identifiability margin, revealing the cost of limited action coverage. Experiments across nonlinear observation maps and behavior policies corroborate the theory and demonstrate implications for transition identifiability, counterfactual prediction, and goal-conditioned latent planning.

Xiangteng Zhang, Yang Guan, Bo Zhang

World ModelsIdentifiability
PAPERJul 24, 2026

Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents

Multimodal Large Language Models (MLLMs) are emerging as core reasoning modules for embodied agents, yet it remains unclear how well general-purpose models can solve long-horizon embodied tasks from a single high-level instruction. We introduce MissionBench, a benchmark for mission-level evaluation of MLLMs in aerial 3D environments. It comprises 120 missions across five simulated 3D environments and four task families. Agents must autonomously plan, navigate, and report outcomes using only egocentric observations and its action history, without aerial-specific fine-tuning. Across 22 open- and closed-source MLLMs, the strongest model succeeds on fewer than 35% of missions compared to 84.4% human performance, highlighting the difficulty of multi-step embodied tasks. Despite large variations between model families, we observe gains from scaling, indicating that larger general-purpose models possess stronger zero-shot embodied capabilities. Our analysis shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception, including multi-step planning and adaptive reasoning. This motivates closed-loop evaluation and highlights both the promise and risk of scaling-driven improvements for embodied AI.

Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt

AerialMLLM
PAPERJul 24, 2026

Music-JEPA: Learning a World Model of Sound from Action

Joint Embedding Predictive Architectures (JEPA) have recently emerged as a paradigm for learning world models by predicting latent representations, offering a promising direction for self-supervised learning. While initial attempts have applied JEPA to the music domain, it remains unclear how such frameworks can naturally support the formation of a world model for music. In this work, we propose to learn a world model of piano sound using JEPA by framing music as an action-conditioned system: the audio is treated as the state, and the pianoroll as the instrument action. Given a current audio state and an action, the model predicts the resulting future audio state, mirroring how humans learn musical sound through interaction. The model is trained in a fully offline setting using paired audio-pianoroll data, without environment interaction. Experiments show that the learned model captures the relationships between musical actions and their resulting sound. The resulting representations support downstream tasks, including beat tracking, composer identification, and key estimation, and enable piano transcription via planning, by searching for actions that best explain a target sound.

Ziyu Wang, Kun Fang, Yann LeCun

World ModelsSound

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