# DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

arXiv: https://arxiv.org/abs/2608.13489 · HTML: https://mlsanity.com/paper/2608.13489/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 32 of its 36 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 30 of 32)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025)
- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [Learning Interactive Real-World Simulators](https://mlsanity.com/paper/2310.06114/) (2023)
- [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Unified Video Action Model](https://mlsanity.com/paper/2503.00200/) (2025)
- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [iVideoGPT: Interactive VideoGPTs are Scalable World Models](https://mlsanity.com/paper/2405.15223/) (2024)
- [Cameras as Relative Positional Encoding](https://mlsanity.com/paper/2507.10496/) (2025)
- [IRASim: A Fine-Grained World Model for Robot Manipulation](https://mlsanity.com/paper/2406.14540/) (2024)
- [GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers](https://mlsanity.com/paper/2310.10375/) (2023)
- [RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation](https://mlsanity.com/paper/2511.17441/) (2025)
- [Vid2World: Crafting Video Diffusion Models to Interactive World Models](https://mlsanity.com/paper/2505.14357/) (2025)
- [WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models](https://mlsanity.com/paper/2602.08971/) (2026)
- [Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising](https://mlsanity.com/paper/2604.26694/) (2026)
- [InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy](https://mlsanity.com/paper/2511.16651/) (2025)
- [DreamX-World 1.0: A General-Purpose Interactive World Model](https://mlsanity.com/paper/2606.16993/) (2026)
- [AVID: Adapting Video Diffusion Models to World Models](https://mlsanity.com/paper/2410.12822/) (2024)
- [Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression](https://mlsanity.com/paper/2502.04296/) (2025)
- [FlowWAM: Optical Flow as a Unified Action Representation for World Action Models](https://mlsanity.com/paper/2607.13017/) (2026)

## Built on by (most cited first, 5 of 5)

- [DreamX-Creator: Democratizing Native Audio-Video Generation at 2K Resolution](https://mlsanity.com/paper/2608.31106/) (2026)
- [The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models](https://mlsanity.com/paper/2609.23369/) (2026)
- [HelixWorld: A Real-time Interactive Audio-Visual World Model](https://mlsanity.com/paper/2609.38123/) (2026)
- [EVEWorld: Physical Evolution Supervision for Embodied World Models](https://mlsanity.com/paper/2610.03374/) (2026)
- [KineWorld: Action-Induced Transport Fields for Embodied World Modeling](https://mlsanity.com/paper/2610.06349/) (2026)

## Abstract (arXiv)

We present DreamX-Phi 1.0, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm's commanded path, we inject per-arm SE(3) transformations into attention via PRoPE-style geometric encoding, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight depth branch for scene-level geometry and use SAM3 masks with a frozen V-JEPA teacher to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, model achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.