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

Thinker Team, Ubtech

Model Description

Thinker-WM is a unified multimodal world model that jointly models and generates RGB, depth, and action through a shared intermediate Transformer backbone with modality-specific input/output experts. It is pretrained on 20K hours of egocentric data and 10K hours of robot data (≈4K hours with aligned depth supervision), and ranks 1st on three public benchmarks: EWMBench, World Olympiad, and World Arena.

This repository contains the official inference release of the model weights. For full documentation, installation, and usage, see the GitHub repository.

Supported Inference Modes

mode	inputs	outputs
wam	prompt + first_frame + robot	video + actions (+depth)
wm	prompt + first_frame	video (+depth)
vla	prompt + first_frame + robot	actions
a2v	first_frame + actions + robot	video
v2a	video + robot	actions
uncond	first_frame + robot	video + actions

Evaluation Results

Thinker-WM is evaluated on embodied world-model benchmarks covering real-robot and simulated environments, together with controlled evaluations of action conditioning and geometric prediction.

Main Benchmarks

Benchmark	Setting	Metric	Thinker-WM	Rank
EWMBench	Embodied world model	Overall ↑	4.66	1st
WorldOlympiad	Robotics sub-leaderboard	Overall ↑	0.739	1st
WorldArena	Track-1, 12-metric protocol	EWMScore ↑	71.73	1st

Detailed Results

EWMBench

Model	SceneC ↑	HSD ↑	Dyn ↑	nDTW ↑	Diversity ↑	BLEU ↑	CLIP ↑	Logics ↑	Overall ↑
Veo3	0.8415	0.2130	0.1932	0.1613	0.0221	0.2139	0.8965	0.9474	3.49
Wan2.6	0.6712	0.2034	0.0900	0.1715	0.0502	0.1616	0.8743	1.0000	3.22
Kling26	0.8211	0.3272	0.1822	0.3423	0.0173	0.2591	0.9014	1.0000	3.85
LTX-2	0.7850	0.2076	0.1283	0.2443	0.0120	0.1425	0.8869	0.5000	3.01
Sora2	0.8526	0.2807	0.3494	0.2754	0.0314	0.2466	0.9100	0.9474	3.89
Cosmos	0.7963	0.2500	0.2052	0.2533	0.0803	0.1230	0.8458	0.7333	3.29
GigaWorld	0.8707	0.3050	0.0849	0.2783	0.0278	0.2048	0.8873	0.9000	3.56
LVP	0.8795	0.4248	0.0433	0.6226	0.0093	0.2179	0.8995	0.9524	4.05
Vidar	0.7341	0.1877	0.1520	0.1769	0.0653	0.1607	0.8821	0.9411	3.30
WoW	0.8866	0.2494	0.0529	0.2566	0.0266	0.1932	0.9001	0.9524	3.52
Qwen-RobotWorld	0.9142	0.5660	0.3429	0.6708	0.0114	0.2079	0.8834	1.0000	4.60
Thinker-WM	0.8877	0.5638	0.3630	0.7102	0.0117	0.2691	0.9020	0.9524	4.66

WorldOlympiad

Robotics leaderboard

Model	Physical ↑	3D Cons. ↑	CLIP ↑	Interact. ↑	All ↑
Uranus	0.925	0.511	0.304	0.730	0.722
Cosmos-Predict-2.5	0.937	0.479	0.321	0.721	0.712
LingBot-World	0.949	0.393	0.314	0.710	0.684
Rolling Forcing	0.870	0.389	0.329	0.566	0.608
LongLive	0.857	0.472	0.327	0.470	0.600
Yume-1.5	0.851	0.288	0.312	0.624	0.588
WoW	0.787	0.272	0.288	0.447	0.502
Thinker-WM	0.923	0.531	0.305	0.764	0.739

WorldArena

Track-1, 12-metric evaluation protocol

Model	12-Metric EWMScore ↑
DexWorldEngine	71.01
BWM-Fast	70.97
UNIS	70.79
MotuBrain	70.18
Wan2.6	67.43
Cosmos-Predict2.5(Action)	64.40
Thinker-WM	71.73

For the WorldArena Track-1 evaluation, the reported EWMScore is the unweighted mean of 12 metrics covering visual quality, content consistency, physics adherence, 3D accuracy, and controllability. Dynamic Degree, Flow Score, and Motion Smoothness are excluded because of their sensitivity to frame rate and temporal sampling density.

Additional Evaluations

Evaluation	Metric	Result
Action-conditioned generation on EWMBench	Overall ↑	5.03
Action-conditioned generation on EWMBench	HSD ↑	0.7369
Action-conditioned generation on EWMBench	nDTW ↑	0.8736
Generated depth on AgiBot	AbsRel ↓	0.1360
Generated depth on AgiBot	RMSE ↓	0.1790 m
Closed-loop control on RoboTwin	Success Rate ↑	75.0%
Motion Probe on held-out AgiBot windows	Action MSE ↓	0.116

Citation

If you find Thinker-WM useful, please cite:

@article{thinkerwm2026,
  title={Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence},
  author={Thinker Team, Ubtech},
  year={2026}
}

Contributors

Core Authors: Chen Liao, Jiyuan Wang, Xiongwei Zhao

Authors: Peng Gao, Yiming Ma, Daqin Luo, Xiangming Xu

Project Lead: Baiyu Pan

Advisors: Jichao Jiao, Hailin Shi