Interactive World Models

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13 papers in the last four weeks, up 30% on the four weeks before. 0.1% of all new papers.

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Latest papers 86

May 29, 2026cs.CV

Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models

Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time game simulation, virtual scene navigation, and embodied AI training. However, scaling to long interactive trajectories is prohibitively expensive due to growing context memory, quadratic attention complexity, and repeated denoising steps. We present Light Interaction, a training-free inference acceleration framework for interactive video world models. Our key insight is that interaction naturally enables trajectory-dependent adaptive computation: retrieved spatial memory can be discarded during novel exploration, temporal context can be adjusted according to local latent dynamics, and early-step model outputs can be reused when the camera revisits familiar regions. Based on this insight, Light Interaction combines adaptive context management, denoising cache acceleration, and hardware-software co-designed 3D block sparse attention with fused Triton kernels. Evaluated on HY-WorldPlay and Matrix-Game-3.0, Light Interaction achieves up to 2.59x speedup without model retraining while maintaining competitive visual quality.

Jiacheng Lu, Haoyi Zhu, Sipei Yi +3

1Zhejiang University · 2NVIDIA

Real-Time Interactive Video Generation Interactive World Models Diffusion Model Inference Acceleration Block-Sparse Attention Video World Models

May 28, 2026cs.LG

World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations. Despite rapid progress across reinforcement learning, robotics, autonomous driving, and video generation, the field lacks a unified framework integrating its diverse architectural choices, training methods, reasoning mechanisms, and application settings. This survey addresses that gap with a multi-axis taxonomy organized along four dimensions: (i) architecture, encompassing representation format, dynamics formulation, input modality, learning paradigm, and downstream application; (ii) methodological family, including state-space and recurrent approaches, transformer-based models, diffusion-based generators, physics-informed networks, and language-augmented multimodal systems; (iii) reasoning strategy, covering imagination-based planning, latent policy learning, counterfactual reasoning, and planning under uncertainty; and (iv) application domain, spanning robotics, autonomous driving, video prediction, multimodal agents, reinforcement learning, scientific modeling, medical imaging, educational measurement, and business and finance. Tracing the field from early cognitive-science foundations to milestone systems such as PlaNet, the Dreamer family, MuZero, Sora, Cosmos, and Genie, we examine how these dimensions interact and highlight the recent convergence of chain-of-thought reasoning with world-model imagination. We review evaluation protocols and benchmarks, identify persistent challenges such as compounding prediction errors, sim-to-real transfer, and fragmented evaluation, and outline future directions toward unified multimodal world models, foundation-scale interactive simulators, and safe deployment in safety-critical domains.

Arif Hassan Zidan, Yi Pan, Hanqi Jiang +23

School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA · School of Computing, University of Georgia, Athens, GA, USA · Tandon School of Engineering, New York University, Brooklyn, NY, USA +9

World Model Learning World Model-Based Planning Latent World Models World Model Evaluation Interactive World Models Embodied World Models

May 28, 2026cs.CV

minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models

Recent video diffusion foundation models have achieved remarkable progress in high-quality video generation, yet turning them into real-time interactive video world models remains challenging. Interactive world models require controllable, causal, and low-latency rollout, which in practice demands a full pipeline spanning data construction, controllable fine-tuning, autoregressive training, few-step distillation, and streaming inference. In this work, we present minWM, a full-stack open-source framework for building real-time interactive video world models. minWM provides an end-to-end pipeline that converts existing bidirectional T2V/TI2V video foundation models into camera-controllable few-step autoregressive world models. Specifically, minWM first fine-tunes a bidirectional video diffusion model with camera control, and then applies the Causal Forcing / Causal Forcing++ pipeline, including AR diffusion training, causal ODE or causal consistency distillation, and asymmetric DMD, to distill it into a few-step autoregressive generator for low-latency rollout. The framework is modular and architecture-extensible: we instantiate it on representative open backbones, including Wan2.1-T2V-1.3B and HY1.5-TI2V-8B, covering both cross-attention-based condition injection and MMDiT-style architectures. minWM also supports adapting existing video world models, such as HY-WorldPlay, to new data distributions, training recipes, and latency targets. Beyond releasing runnable scripts, checkpoints, documentation, and inference code, we provide practical ablations on camera trajectory quality, controllability training steps, and minimal batch-size requirements. We hope minWM serves as a reproducible and extensible recipe for building and adapting real-time interactive video world models. Project Page: https://github.com/shengshu-ai/minWM

Min Zhao, Hongzhou Zhu, Bokai Yan +9

1ShengShu 2THU · 1ShengShu 3RUC · 1ShengShu +4

Camera-Controlled Video Generation Video Diffusion Models Causal World Models Real-Time Interactive Video Generation Streaming Video Generation Autoregressive Video Generation+1

May 27, 2026cs.CV

Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players

World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many generated environments require multi-agent interaction: multiple players, robots, or embodied agents act simultaneously within a shared space. Scaling world models to such settings requires a principled multi-agent design: agents should remain independently controllable, permutation-symmetric, and support efficient inference while maintaining consistency across time and perspectives. In this paper, we present our generative multi-agent world model for interactive simulation. It introduces Simplex Rotary Agent Encoding, a parameter-free extension of 3D RoPE that represents agents as vertices of a regular simplex in rotary angle space. This gives each agent a distinct phase while making all agents permutation-equivalent, enabling scalable agent identity without learned per-slot identities or a fixed agent ordering. To avoid dense all-to-all attention across agents, we further propose Sparse Hub Attention, where learnable hub tokens mediate token interaction across agents, reducing cross-agent attention cost from quadratic to linear in the number of agents. For real-time rollout, we distill a full-context diffusion teacher into a causal student that generates temporal blocks sequentially with KV caching, enabling action-responsive generation at 24 FPS. Experiments in multiplayer virtual environments show that our model improves video fidelity, action controllability, and inter-agent consistency over slot-based and dense-attention baselines, while generalizing from two to four players without additional training.

Fangfu Liu, Kai He, Tianchang Shen +7

NVIDIA · Tsinghua University · University of Toronto +1

Interactive World Models Video World Models

May 27, 2026cs.LG

LEIA: Learned Environment for Interactive Architected Materials

World models have enabled interactive exploration of game environments and robotic manipulation, but physical engineering remains beyond their reach: real materials exhibit nonlinear constitutive laws, carry history-dependent internal state, undergo inertial dynamics, and may possess hierarchical structures spanning multiple length scales. We present LEIA (Learned Environment for Interactive Architected materials), a world model that lets engineers apply boundary conditions step by step and observe the resulting deformation and stress fields in real time. LEIA handles large three-dimensional unstructured meshes and generates autoregressive responses to user-specified loading. We introduce MicroPlate, a benchmark of architected plates spanning two regimes of microstructure modeling: architected lattices that resolve microstructure explicitly through three-dimensional geometry, and a homogeneous plate where microstructural change is modeled implicitly through internal degrees of freedom. MicroPlate is used to assess LEIA alongside four baseline methods across both regimes. Finally, we demonstrate that LEIA enables efficient candidate generation and ranking for fast surrogate-guided search for de novo designs of architected materials, with stress-accurate candidate ranking validated by finite element ground truth.

Haiqian Yang, Yuan Cao, Markus J. Buehler

Unreasonable Labs · Mountain View, CA 94043, USA

Neural Surrogate Modeling World Model Learning Structural Mechanics Materials Property Prediction Interactive World Models Computational Materials Science

May 25, 2026cs.CV

WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill this gap, we introduce WBench, a comprehensive multi-turn benchmark for interactive world model evaluation along five dimensions, namely video quality, setting adherence, interaction adherence, consistency, and physics compliance. WBench contains 289 test cases and 1,058 interaction turns, where each case specifies a world setting and a multi-turn interaction sequence, covering diverse scenes, styles, subjects, and both first- and third-person perspectives, together with four interaction types, including navigation, subject action, event editing, and perspective switching. For navigation, WBench unifies text, 6-DoF pose, and discrete-action control, enabling evaluation of models with different native input interfaces. Evaluation uses 22 automatic sub-metrics that combine specialist vision models with large multimodal models, and all metrics are validated against human judgments. Across 20 state-of-the-art models, we find that no single model performs strongly across all dimensions. We provide detailed diagnostic insights into the characteristic strengths, weaknesses, and open challenges of each model. Code and data are available at https://github.com/meituan-longcat/WBench.

Kaining Ying, Hengrui Hu, Siyu Ren +6

Fudan University · Meituan Longcat Team

World Model Evaluation Interactive World Models Video World Models

May 24, 2026cs.CV

WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models

Recent video world models enable interactive camera navigation but provide limited control over individual objects. We study persistent object-state control in autoregressive video world models, where an object-trajectory action must remain effective across viewpoint changes, generation chunks, and intervals of temporary invisibility. This setting introduces an action-memory conflict because autoregressive memory records the last observed object state, whereas an intervening action may update that state off-screen. We present WorldCraft, the first camera-navigable autoregressive video world model to jointly compose camera navigation and object-trajectory control. Given a user-selected object and a 2D motion path, Normalized World Trajectory (NWT) anchors the path in a normalized world coordinate system and projects it into each camera view, producing a consistent object-action signal throughout the autoregressive rollout. Trajectory-Anchored State Persistence (TASP) combines persistent NWT guidance with selective memory refresh to maintain action-updated object states through off-screen intervals and re-entry. A pathway-selective LoRA introduces object control while retaining the pretrained camera controller. WorldCraft achieves accurate composable camera-object control and preserves camera fidelity. We further find that it maintains action-updated object states across extended intervals of complete target invisibility without off-screen trajectory supervision.

Bohai Gu, Taiyi Wu, Yueyang Yuan +9

The Hong Kong University of Science and Technology · AI Technology Center, Tencent Video, Tencent · Wuhan University +1

Camera-Controlled Video Generation Object-Centric World Models Autoregressive Video Generation Controllable Video Generation Interactive World Models

May 22, 2026cs.CV

SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models

Interactive world models for first-person shooter (FPS) games must resolve high-frequency overlapping control signals at every frame without disrupting unaffected regions. Existing methods inject actions globally and train on single titles, failing under dense FPS inputs. We observe that FPS actions are spatially selective: discrete events such as firing or reloading affect only a localized region around the weapon (the scope), while continuous camera and movement signals govern stable surroundings. We propose SCOPE, which inserts a conditioning module into each transformer block of a pretrained video diffusion model. It reshapes features into per-pixel temporal sequences so that each position computes its action response from local visual content. This separates in-scope effects from out-of-scope generation without segmentation labels. We also introduce CrossFPS, the first multi-game FPS dataset with frame-aligned action telemetry. It comprises 69K clips from 7 titles with 10-DoF controller signals, curated to remove gameplay bias. The model learns general visual-to-action mappings rather than game-specific patterns, enabling zero-shot transfer to unseen scenes. Experiments confirm strong action responsiveness, precise scope separation, and effective cross-game generalization.

Zizhao Tong, Yeying Jin, Hongfeng Lai +11

UCAS-Terminus AI Lab, University of Chinese Academy of Sciences · Tencent · National University of Singapore +6

Action-Conditioned Video Generation Video Diffusion Models Action-Conditioned World Models Interactive World Models

May 18, 2026cs.CV

Incantation: Natural Language as the Action Interface for Multi-Entity Video World Models

Modern interactive video world models have achieved impressive visual fidelity, yet lack fine-grained multi-entity control and cross-entity, cross-world generalization. We trace this gap to the action interface: standard control protocols (e.g. animation IDs, device inputs, scene-level captions) bind action semantics to specific entities or engines at design time. We propose natural language as the interface to unlock expressiveness that no prior interface can achieve, and we present Incantation, the first interactive video world model with per-latent-frame (0.25 s) natural-language conditioning that supports simultaneous multi-entity control and concept-level cross-entity transfer beyond any fixed rendering pipeline. We pair a pretrained bidirectional video backbone with frame-local text cross-attention, and enable real-time long-horizon streaming through ODE-initialized Self-Forcing distillation with a RoPE-decoupled sliding KV-cache. We surpass the Action-Index baseline on cross-entity transfer (89% vs. 43%) and out-of-vocabulary prompts (90% vs. 0%), and our 2-step student sustains 19.7 FPS at 480p with stable FVD over 2-hour rollouts. We further apply the same architecture and training recipe to The King of Fighters, changing only the per-entity action vocabulary slots. We have released a preview subset of the Incantation dataset at https://huggingface.co/datasets/zhush/incantation-elden-ring-scenes, containing manually collected Elden Ring player-boss combat clips with structured action-oriented metadata. Larger-scale Elden Ring and KOF data will be released with the full project.

Shangwen Zhu, Qianyu Peng, Zhao Pu +12

1SJTU · 8HKU · 3Matrix Team +6

Action-Conditioned Video Generation Long-Horizon Video Generation Action-Conditioned World Models Interactive World Models Video World Models

May 18, 2026cs.RO

WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform

World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about environmental dynamics. However, existing embodied world model benchmarks are still largely confined to vision-only prediction, offline embodied applications, and simulator-based evaluation, making them insufficient for assessing increasingly comprehensive world models. In this work, we introduce WorldArena 2.0, an expanded benchmark that systematically broadens embodied world model evaluation along three dimensions: modality, functionality, and platform. Along the modality dimension, WorldArena 2.0 extends evaluation from vision-only to visuotactile modalities, enabling assessment of multimodal perception and prediction. Along the functionality dimension, it extends beyond policy evaluation and planning to assess world models as interactive RL environments for policy optimization. Along the platform dimension, it moves beyond simulator-only evaluation to a diverse suite of simulated and real-world robotic settings across multiple embodiments. Under a standardized protocol, WorldArena 2.0 comprehensively evaluates perceptual quality, interactive utility, and cross-platform performance, providing a comprehensive testbed for tracking progress toward embodied world models. The benchmark is available at: https://world-arena.ai.

Yu Shang, Yinzhou Tang, Yiding Ma +22

Tsinghua University · Shanghai Jiao Tong University · Zhejiang University +7

World Models for Robotics World Models Action-Conditioned World Models World Model Evaluation Interactive World Models Embodied World Models

May 16, 2026cs.AI

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models

Executable world models can be read, edited, executed, and reused for planning, but only if the program captures the environment's transition law rather than semantic shortcuts in its surface vocabulary. We study online executable world-model learning under prior misalignment, where an agent must induce state-dependent dynamics from interaction evidence alone, without rule descriptions, reward signals, or trustworthy lexical priors. We introduce Alice, a closed-loop system that treats failed candidate updates as structural signal: when a candidate explains a new transition but loses previously explained ones, the preservation conflict reveals dynamics that the current program had conflated. Alice refines these conflicts into hypothesis classes that both provide compact, class-stratified preservation counterexamples for update and guide frontier exploration toward transitions that are novel and underrepresented with respect to the current program. We evaluate Alice on Baba in Wonderland, a prior-misaligned variant of Baba Is You that preserves simulator dynamics while replacing semantically meaningful rule-property labels with unrelated words. Experiments show that Alice substantially improves executable world-model learning under prior misalignment, and ablations show that both class refinement and class-aware exploration contribute.

SeungWon Seo, DongHeun Han, SeongRae Noh +1

Korea University · Kyung Hee University

Online System Identification World Model Learning World Models Interactive World Models

May 15, 2026cs.CV

WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes

Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models.

Jichen Hu, Jiawei Guo, Jiazhong Cen +3

1Shanghai Jiao Tong University · 2Huawei Inc.

3D Scene Editing Interactive World Models 3D Scene Understanding

May 14, 2026cs.CV

ReactiveGWM: Flexible Control and NPC Reactivity in Game World Models

Existing game world models typically adopt role-specific interactions, where player and NPC roles are bound to fixed characters. This limits their flexibility in multi-character games, where different characters may receive external control while NPCs must react to interactions triggered by players. This setting raises two key challenges: how to flexibly assign control roles to individual characters, and how to support direct player control and reactive NPC behavior within a unified model. These challenges are particularly pronounced in shared-view 2D games, where multiple, potentially visually identical characters share the same viewpoint, making camera cues insufficient to distinguish their roles. To address these challenges, we introduce ReactiveGWM, a reactive game world model that flexibly assigns control modes at initialization and jointly simulates externally controlled players and reactive NPCs. Specifically, ReactiveGWM introduces Spatial Role Binding, which grounds learned character handles to their corresponding regions in the initial frame using instance masks. Building on these handles, Unified Agency Conditioning unifies heterogeneous control signals across characters by encoding player actions and conditional NPC rules into character-specific control-token groups. Each group is then bound to its corresponding character handle, enabling the model to apply each control signal to its designated character. Meanwhile, causal self-attention restricts temporal context to the current and preceding latent frames when generating player actions and NPC responses. Experiments on two multi-character 2D games demonstrate that ReactiveGWM supports flexible character control across different player/NPC role assignments while jointly generating accurate player-controlled behaviors and reactive NPC responses, enabling more configurable and richer multi-character interactions.

Zeqing Wang, Danze Chen, Zhaohu Xing +4

Tencent · National University of Singapore · The Hong Kong University of Science and Technology (Guangzhou) +3

World Models Multi-Agent Control Interactive World Models Video World Models

May 12, 2026cs.CV

The DAWN of World-Action Interactive Models

A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action generation as either isolated parallel branches or rigid predict-then-plan pipelines. We formalize this perspective as World-Action Interactive Models (WAIMs), and instantiate it in autonomous driving with \textbf{DAWN} (\textbf{D}enoising \textbf{A}ctions and \textbf{W}orld i\textbf{N}teractive model), a simple yet strong latent generative baseline. DAWN operates in a compact semantic latent space and couples a \emph{World Predictor} with a \emph{World-Conditioned Action Denoiser}: the predicted world hypothesis conditions action denoising, while the denoised action hypothesis is fed back to update the world prediction, so that both are recursively refined during inference. Rather than eliminating test-time world evolution altogether or rolling out the full future in pixel space, DAWN performs a short explicit latent rollout that is sufficient to support long-horizon trajectory generation in complex interactive scenes. Experiments show that DAWN achieves strong planning performance and favorable safety-related results across multiple autonomous driving benchmarks. More broadly, our results suggest that interactive world-action generation is a principled path toward truly actionable world models.

Hongbo Lu, Liang Yao, Chenghao He +6

1COWARobot Co. Ltd · 2Shanghai Jiao Tong University · 3Hohai University

Diffusion-Based Planning World Model-Based Planning World Action Models World Models for Autonomous Driving Latent World Models Autonomous Driving Planning+1

May 11, 2026cs.AI

How Mobile World Model Guides GUI Agents?

Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable prediction of action consequences remains critical for long-horizon and high-risk interactions. Existing mobile world models provide either text-based or image-based future states, yet it remains unclear which representation is useful, whether generated rollouts can replace real environments, and how test-time guidance helps agents of different strengths. To answer the above questions, we filter and annotate mobile world-model data, then train world models across four modalities: delta text, full text, diffusion-based images, and renderable code. These models achieve SoTA performance on both MobileWorldBench and Code2WorldBench. Furthermore, by evaluating their downstream utility on AITZ, AndroidControl, and AndroidWorld, we obtain three findings. First, renderable code reconstruction achieves high in-distribution fidelity and provides effective multimodal supervision for data construction, while text-based feedback is more robust for online out-of-distribution (OOD) execution. Second, world-model-generated trajectories can provide transferable interaction experience in the training process and improve agents' end-to-end task performance, although these data do not preserve the original distribution. Last, for overconfident mobile agents with low action entropy, posterior self-reflection provides limited gains, suggesting that world models are more effective as prior perception or training supervision than as universal post-hoc verifiers.

Weikai Xu, Kun Huang, Yunren Feng +10

1Nanyang Technological University · 2MiLM Plus, Xiaomi Inc. · 3Independent Researchers +3

World Model Learning GUI Agents Mobile GUI Agents World Model Evaluation Interactive World Models

May 10, 2026cs.CV

DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos

World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and material behavior. Learning such a model from real videos is challenging because deformable linear, planar, and volumetric objects evolve under high-dimensional deformation, noisy interactions, and complex material response. The model must therefore infer a physical state from visual observations, roll it forward under new interactions, and render the resulting dynamics with high visual fidelity. We present DeformMaster, a video-derived interactive physics-neural world model that turns real interaction videos into an online interactive model of deformable objects within a unified dynamics-and-appearance framework. DeformMaster preserves structured physical rollout while using a neural residual to compensate for unmodeled effects, grounds sparse hand motion as distributed compliant actuator for hand-continuum interaction, represents material response with spatially varying constitutive experts, and drives high-fidelity 4D appearance from the predicted physical evolution. Experiments on real-world deformable-object sequences demonstrate DeformMaster's ability to roll out future dynamics and render dynamic appearance, outperforming state-of-the-art baselines while supporting novel action rollout, material-parameter variation, and dynamic novel-view synthesis. Project page: https://can-lee.github.io/deformmaster-web/

Can Li, Zhoujian Li, Ren Li +4

Nankai University · Rightly Robotics, A4X · Zhejiang University +2

World Model Learning World Models Interactive World Models Deformable Object Simulation

May 7, 2026cs.AI

AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites

In model-based learning, the agent learns behaviors by simulating trajectories based on world model predictions. Standard world models typically learn a stationary transition function that maps states and actions to next states, when an action and an outcome frequently co-occur in training data, the model tends to internalize this correlation as a general causal rule while ignoring action preconditions. In interactive environments, however, agent actions can reshape the future affordance space. At each timestep, an action may becomes executable only after its prerequisites are met, or non-executable when they are destroyed. We term such events structure-changing events (SC events). As a result, a conventional world model often fails to determine whether a given action is executable in the current state, especially in multi-step predictions. Each imagined step is conditioned on an incorrect affordance state, and therefore the prediction error compounds over the rollout horizon. In this paper, we propose AGWM (Affordance-Grounded World Model), which learns an abstract affordance structure represented as a DAG of prerequisite dependencies to explicitly track the dynamic executability of actions. Experiments on game-based simulated environments demonstrate the effectiveness of our method by achieving lower multi-step prediction error, better generalization to novel configurations, and improved interpretability.

Qinshi Zhang, Weipeng Deng, Zhihan Jiang +4

University of California, San Diego · University of Hong Kong · Columbia University +2

World Model Learning Interactive World Models

May 5, 2026cs.CV

iWorld-Bench: A Benchmark for Interactive World Models with a Unified Action Generation Framework

Achieving Artificial General Intelligence (AGI) requires agents that learn and interact adaptively, with interactive world models providing scalable environments for perception, reasoning, and action. Yet current research still lacks large-scale datasets and unified benchmarks to evaluate their physical interaction capabilities. To address this, we propose iWorld-Bench, a comprehensive benchmark for training and testing world models on interaction-related abilities such as distance perception and memory. We construct a diverse dataset with 330k video clips and select 2.1k high-quality samples covering varied perspectives, weather, and scenes. As existing world models differ in interaction modalities, we introduce an Action Generation Framework to unify evaluation and design six task types, generating 4.9k test samples. These tasks jointly assess model performance across visual generation, trajectory following, and memory. Evaluating 14 representative world models, we identify key limitations and provide insights for future research. The iWorld-Bench model leaderboard is publicly available at iWorld-Bench.com.

Jianjie Fang, Yingshan Lei, Qin Wan +8

Tsinghua University, China · Northeastern University, China · South China University of Technology, China

World Model Evaluation Interactive World Models Video World Models

Apr 24, 2026cs.AI

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that manipulate objects, navigate software, coordinate with others, or design experiments require predictive environment models, yet the term world model carries different meanings across research communities. We introduce a "levels x laws" taxonomy organized along two axes. The first defines three capability levels: L1 Predictor, which learns one-step local transition operators; L2 Simulator, which composes them into multi-step, action-conditioned rollouts that respect domain laws; and L3 Evolver, which autonomously revises its own model when predictions fail against new evidence. The second identifies four governing-law regimes: physical, digital, social, and scientific. These regimes determine what constraints a world model must satisfy and where it is most likely to fail. Using this framework, we synthesize over 400 works and summarize more than 100 representative systems spanning model-based reinforcement learning, video generation, web and GUI agents, multi-agent social simulation, and AI-driven scientific discovery. We analyze methods, failure modes, and evaluation practices across level-regime pairs, propose decision-centric evaluation principles and a minimal reproducible evaluation package, and outline architectural guidance, open problems, and governance challenges. The resulting roadmap connects previously isolated communities and charts a path from passive next-step prediction toward world models that can simulate, and ultimately reshape, the environments in which agents operate. Code and resources are available at: https://github.com/matrix-agent/awesome-agentic-world-modeling.

Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47

World Model Learning Action-Conditioned World Models World Model Evaluation Interactive World Models

Apr 23, 2026cs.CV

WorldMark: A Unified Benchmark Suite for Interactive Video World Models

Interactive video generation models such as Genie, YUME, HY-World, and Matrix-Game are advancing rapidly, yet every model is evaluated on its own benchmark with private scenes and trajectories, making fair cross-model comparison impossible. Existing public benchmarks offer useful metrics such as trajectory error, aesthetic scores, and VLM-based judgments, but none supplies the standardized test conditions -- identical scenes, identical action sequences, and a unified control interface -- needed to make those metrics comparable across models with heterogeneous inputs. We introduce WorldMark, the first benchmark that provides such a common playing field for interactive Image-to-Video world models. WorldMark contributes: (1) a unified action-mapping layer that translates a shared WASD-style action vocabulary into each model's native control format, enabling apples-to-apples comparison across six major models on identical scenes and trajectories; (2) a hierarchical test suite of 500 evaluation cases covering first- and third-person viewpoints, photorealistic and stylized scenes, and three difficulty tiers from Easy to Hard spanning 20-60s; and (3) a modular evaluation toolkit for Visual Quality, Control Alignment, and World Consistency, designed so that researchers can reuse our standardized inputs while plugging in their own metrics as the field evolves. We will release all data, evaluation code, and model outputs to facilitate future research. Beyond offline metrics, we launch World Model Arena (warena.ai), an online platform where anyone can pit leading world models against each other in side-by-side battles and watch the live leaderboard.

Xiaojie Xu, Zhengyuan Lin, Kang He +5

Alaya Studio, Shanda AI Research Tokyo · The University of Tokyo · Shanghai Innovation Institute

Action-Conditioned World Models World Model Evaluation Interactive World Models Video Generation Evaluation

Apr 10, 2026cs.CV

Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory

With the advancement of interactive video generation, diffusion models have increasingly demonstrated their potential as world models. However, existing approaches still struggle to simultaneously achieve memory-enabled long-term temporal consistency and high-resolution real-time generation, limiting their applicability in real-world scenarios. To address this, we present Matrix-Game 3.0, a memory-augmented interactive world model designed for 720p real-time longform video generation. Building upon Matrix-Game 2.0, we introduce systematic improvements across data, model, and inference. First, we develop an upgraded industrial-scale infinite data engine that integrates Unreal Engine-based synthetic data, large-scale automated collection from AAA games, and real-world video augmentation to produce high-quality Video-Pose-Action-Prompt quadruplet data at scale. Second, we propose a training framework for long-horizon consistency: by modeling prediction residuals and re-injecting imperfect generated frames during training, the base model learns self-correction; meanwhile, camera-aware memory retrieval and injection enable the base model to achieve long horizon spatiotemporal consistency. Third, we design a multi-segment autoregressive distillation strategy based on Distribution Matching Distillation (DMD), combined with model quantization and VAE decoder pruning, to achieve efficient real-time inference. Experimental results show that Matrix-Game 3.0 achieves up to 40 FPS real-time generation at 720p resolution with a 5B model, while maintaining stable memory consistency over minute-long sequences. Scaling up to a 2x14B model further improves generation quality, dynamics, and generalization. Our approach provides a practical pathway toward industrial-scale deployable world models.

Zile Wang, Zexiang Liu, Jiaxing Li +20

Skywork AI

Real-Time Interactive Video Generation Long-Horizon Video Generation Memory-Augmented Video Generation Streaming Video Generation Diffusion Model Acceleration Interactive World Models+1

Apr 2, 2026cs.CV

LivingWorld: Interactive 4D World Generation with Environmental Dynamics

We introduce LivingWorld, an interactive framework for generating 4D worlds with environmental dynamics from a single image. While recent advances in 3D scene generation enable large-scale environment creation, most approaches focus primarily on reconstructing static geometry, leaving scene-scale environmental dynamics such as clouds, water, or smoke largely unexplored. Modeling such dynamics is challenging because motion must remain coherent across an expanding scene while supporting low-latency user feedback. LivingWorld addresses this challenge by progressively constructing a globally coherent motion field as the scene expands. To maintain global consistency during expansion, we introduce a geometry-aware alignment module that resolves directional and scale ambiguities across views. We further represent motion using a compact hash-based motion field, enabling efficient querying and stable propagation of dynamics throughout the scene. This representation also supports bidirectional motion propagation during rendering, producing long and temporally coherent 4D sequences without relying on expensive video-based refinement. On a single RTX 5090 GPU, generating each new scene expansion step requires 9 seconds, followed by 3 seconds for motion alignment and motion field updates, enabling interactive 4D world generation with globally coherent environmental dynamics. Video demonstrations are available at paper.pnu-cvsp.com/LivingWorld.

Hyeongju Mun, In-Hwan Jin, Sohyeong Kim +1

Department of Electrical and Electronic Engineering, Pusan National University

3D Scene Generation Interactive World Models 4D Scene Generation

Nov 20, 2025cs.CV

SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios

Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments. Interacting with these interfaces requires agents not only to ground language in visual observations,but also to execute actions, track temporally evolving state changes, and verify whether intended outcomes have been achieved. However, existing benchmarks predominantly evaluate open-loop perception or single-step action execution, failing to capture this continuous cycle of interaction, feedback, and correction. We introduce SWITCH, a benchmark for closed-loop interactive reasoning with TCIs in realistic egocentric environments1. SWITCH comprises 1,170 temporally interactive videos across diverse functional categories, providing structured annotations of instructions, actions, state transitions, outcomes, and recovery behaviors over time. To probe generative world modeling, SWITCH also evaluates video generation models on interaction-centered tasks using both LLM-as-judge and human evaluation2.Experiments with frontier proprietary and opensource multimodal models reveal persistent weaknesses in fine-grained visual-temporal perception, outcome verification, and error recovery, highlighting SWITCH as a testbed for closed-loop embodied intelligence.

Juntao Cheng, Wanyue Zhang, Zhiwei Yu +7

Beijing Academy of Artificial Intelligence (BAAI) · Shanghai Jiao Tong University · School of Artificial Intelligence, University of Chinese Academy of Sciences +3

Long-Horizon Agent Evaluation Egocentric Video Understanding AI Agent Benchmarks Interactive World Models

Oct 10, 2025cs.RO

RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation

Learned world models hold significant potential as neural simulators for robotic manipulation. However, prevalent 2D video-based models inherently lack the spatial and kinematic reasoning crucial for physical interactions. We introduce RoDyn, a novel Robot-Dynamic 2.5D World Model that formulates environmental dynamics within a highly efficient, geometry-aware latent space. Through the proposed Robot-Dynamic Tokenizer, we explicitly couple semantic visual appearances with spatial and agent-centric priors via an RGB-dominated cross-attention mechanism and dynamic mask guidance. Furthermore, by injecting these mask priors directly into sequence transitions, our Mask-guided Autoregressive architecture drives the model to focus on active robot-object interaction regions. Extensive experiments demonstrate that RoDyn establishes SOTA generation fidelity across large-scale datasets. Crucially, it translates these predictive capabilities into substantial downstream gains, accelerating model-based reinforcement learning and achieving a 42% improvement in real-world imitation learning success rates over pure 2D baselines.

Chuanrui Zhang, Zhengxian Wu, Guanxing Lu +2

Nanyang Technological University, Singapore · Tsinghua University, Beijing, China

World Models for Robotics World Models Robotic Manipulation Interactive World Models

Aug 18, 2025cs.CV

Matrix-game 2.0: An open-source, real-time, and streaming interactive world model

Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamics and interactive behaviors. However, existing interactive world models depend on bidirectional attention and lengthy inference steps, severely limiting real-time performance. Consequently, they are hard to simulate real-world dynamics, where outcomes must update instantaneously based on historical context and current actions. To address this, we present Matrix-Game 2.0, an interactive world model generates long videos on-the-fly via few-step auto-regressive diffusion. Our framework consists of three key components: (1) A scalable data production pipeline for Unreal Engine and GTA5 environments to effectively produce massive amounts (about 1200 hours) of video data with diverse interaction annotations; (2) An action injection module that enables frame-level mouse and keyboard inputs as interactive conditions; (3) A few-step distillation based on the casual architecture for real-time and streaming video generation. Matrix Game 2.0 can generate high-quality minute-level videos across diverse scenes at an ultra-fast speed of 25 FPS. We open-source our model weights and codebase to advance research in interactive world modeling.

Xianglong He, Chunli Peng, Zexiang Liu +16

Skywork AI

Action-Conditioned Video Generation Real-Time Interactive Video Generation Action-Conditioned World Models Streaming Video Generation Autoregressive Video Generation Interactive World Models

Date pendingcs.AI

GameWAM: A World Action Model for Video Games

Modern video games combine first-person perception, rapid visual changes, persistent world state, and heterogeneous native controls. Existing game agents map visual and task context directly to actions but lack explicit world dynamics modeling, whereas interactive game world models predict visual futures from supplied actions but do not serve as task policies. World-Action Models (WAMs) unify these objectives, but remain largely unexplored under the dynamics and open-ended interaction of video games. We introduce GameWAM, to our knowledge the first WAM for native closed-loop gameplay and GUI control. GameWAM jointly generates future visual observations and executable keyboard-mouse trajectories through parallel visual and action generative processes with block-causal conditioning and flow matching. To support joint world-action learning, we construct synchronized gameplay and GUI trajectories. To handle heterogeneous native control, GameWAM predicts a gameplay/GUI mode per action step and generates actions with mode-specific prediction distributions and continuous-action normalization. For long-horizon interaction, block-cycle control coordinates prediction, execution, and temporal context: it predicts beyond the committed horizon, executes short action blocks, replans from new observations, and hierarchically structures context from fine-grained within-cycle history to persistent cross-cycle history. Experiments demonstrate competitive task success with fewer executed native actions than the compared agents. We further uncover Low-Frequency Action Source Imprinting (LASI), in which low-frequency components of the sampled action source systematically steer coarse generated camera motion under fixed conditioning, revealing a source-sensitivity failure mode in generative control. Project page is available at https://yunncheng.github.io/GameWAM/.

Yuncheng Guo, Zhanqiu Zhang, Yiwen Guo +1

Fudan University · LIGHTSPEED · Independent Researcher +1

Action-Conditioned Video Generation Game-Playing Agents Action-Conditioned World Models World Action Models Interactive World Models