Generative video models are increasingly used as visual world simulators, yet their primary strength lies in rendering plausible pixels rather than reasoning about how a world should evolve. Coherent world evolution requires more than visual fidelity: when an event occurs, a model must infer its consequences for the entities in the world and then render the resulting state. For example, after a wild beast appears, the model should not only depict the wild beast, but also infer whether a nearby person should flee, take cover, or fight back based on the realized scene. Existing diffusion-based video models lack an explicit mechanism for maintaining world state and reasoning over such event-conditioned consequences, often producing visually plausible but behaviorally static or causally inconsistent outputs. To make this reasoning gap concrete and measurable, we propose a new problem, Out-of-Sight Causal Evolution, which requires a model to render an event, reason about its consequences for the evolving world state, and reveal a causally plausible outcome as the world continues to evolve, even out of sight. As a baseline reference solution, we introduce EvoWorld, a brain-renderer collaboration framework in which a vision-language model serves as the world brain: it observes generated segments, maintains an evolving account of the world, and reasons about plausible state transitions, while a segment-autoregressive video renderer realizes them visually. We further introduce EvoWorld-Data and EvoWorld-Bench for training and evaluating event realization, causal reactions, and revisit consistency. Experiments show that EvoWorld substantially improves event-conditioned causal evolution over recent models while preserving visual quality and consistency, establishing a reference point for reasoning-guided, autonomous world evolution beyond the current field of view.
Figures & tables
Figure 1: Challenges in Out-of-Sight Causal Evolution . The camera first observes a subject (a vendor and his handcart), moves away to reveal a newly requested event (a collapsing building facade), and then moves back to the original scene. Previous methods (rows 1–4) exhibit various failures. In contrast, EvoWorld (row 5) realizes the event, revisits the original subject, and renders a causally plausible reaction while preserving subject and scene consistency.
Figure 2: Overview of the data engine for EvoWorld. Structured scenarios are rendered into single-take out-of-sight interactions, verified against visual evidence, temporally annotated, and converted into a training supervision dataset and evaluation benchmark.
Figure 3: Examples and statistics of EvoWorld-Data and EvoWorld-Bench. The upper part illustrates representative training and benchmark examples, while the lower part summarizes their scale and scenario diversity.
Figure 4: Overview of the EvoWorld framework. (a) Stage 1 learns out-of-sight reactive rendering. (b) Stage 2 refines the renderer through online autoregressive adaptation. (c) Inference uses brain–renderer collaboration for Out-of-Sight Causal Evolution .
Task metrics
Visual quality, consistency, and motion
Method
Core
Revisit
Event
Causal
React.
Visual
Subj. Cons.
Bg. Cons.
Motion
LingBot-World ( Team et al., 2026b )
15.8
92.5
77.5
29.2
31.3
68.8
87.6
94.6
98.0
LingBot-World-v2 ( Gao et al., 2026 )
20.0
76.7
68.3
27.1
25.4
76.7
82.6
92.5
98.4
YUME-1.5 ( Mao et al., 2026 )
15.8
62.5
86.7
22.5
20.0
65.4
72.9
88.7
99.0
SANA-WM ( Zhu et al., 2026 )
13.3
88.3
67.5
26.7
24.2
53.8
67.7
90.8
98.7
Echo-WM ( Zhang et al., 2026b )
16.7
60.8
66.7
22.5
21.7
82.9
97.6
89.2
98.7
Table 1: Comparison results (%) on the EvoWorld-Bench. Best results are shown in bold and second-best results are underlined. Core denotes the proportion of cases concurrently achieving successful revisit, event realization, and full causal plausibility.
Figure 6Figure 7
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Split
Videos
Events
Scenes
Reactions
/ cases
Fam.
Types
Fam.
Types
Fam.
Types
EvoWorld-Data
12k
19
65
16
63
5
14
EvoWorld-Bench
120
17
55
16
56
–
–
Appendix
Table 5: Reviewed semantic coverage of EvoWorld-Data and EvoWorld-Bench. Reaction categories are reported only for the training data; EvoWorld-Bench does not define target reactions.
Table 6: Training and inference settings for EvoWorld. Stage 2 inherits the Stage-1 LoRA architecture and IAA configuration.
Figure 7: Case study summarizing the VLM brain’s observation, event-conditioned reasoning, prompt generation, and verification over an out-of-sight rollout.
Task metrics
Visual quality, consistency, and motion
Method
Core
Revisit
Event
Causal
React.
Visual
Subj. Cons.
Bg. Cons.
Motion
Wan2.2 TI2V-5B ( Wan et al., 2025 )
8.3
40.0
87.5
17.1
18.3
77.9
81.7
95.4
98.8
LTX-2.3 ( HaCohen et al., 2026 )
27.5
97.5
94.2
43.3
42.1
88.8
96.7
99.2
99.2
LTX-2.5 ( HaCohen et al., 2026 )
34.2
95.0
90.8
50.8
42.9
90.4
95.8
98.8
99.4
EvoWorld (ours; autoregressive)
90.8
100.0
97.5
94.2
85.0
83.8
99.2
98.3
99.5
Appendix
Table 7: One-shot general video generators on EvoWorld-Bench, with EvoWorld included as an autoregressive reference below the separator. Best results among the one-shot models are bold (%).
Method
Core
Revisit
Event
Causal
React.
MiniMax-H3 ( MiniMax, 2026 )
21.7
100.0
99.2
44.2
47.9
EvoWorld (ours; autoregressive)
90.8
100.0
97.5
94.2
85.0
Appendix
Table 8: Task metrics of one-shot MiniMax-H3 on EvoWorld-Bench.
Evolutions in the world, such as water pouring or ice melting, happen regardless of being observed. Video world models generate "worlds" via 2D frame observations. Can these generated "worlds" evolve regardless of observation? To probe this question, we design a benchmark to evaluate whether video world models can decouple state evolution from observation. Our benchmark, STEVO-Bench, applies observation control to evolving processes via instructions of occluder insertion, turning off the light, or specifying camera "lookaway" trajectories. By evaluating video models with and without camera control for a diverse set of naturally-occurring evolutions, we expose their limitations in decoupling state evolution from observation. STEVO-Bench proposes an evaluation protocol to automatically detect and disentangle failure modes of video world models across key aspects of natural state evolution. Analysis of STEVO-Bench results provide new insight into potential data and architecture bias of present-day video world models. Project website: https://glab-caltech.github.io/STEVOBench/. Blog: https://ziqi-ma.github.io/blog/2026/outofsight/
Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's field of view. Once an object leaves the observer's view, its state is "frozen" in memory, and revisiting the same region later often fails to reflect events that should have occurred in the meantime. In this work, we identify and formalize this overlooked limitation as the "out-of-sight dynamics" problem, which impedes video world models from representing a continuously evolving world. To address this issue, we propose LiveWorld, a novel framework that extends video world models to support persistent world evolution. Instead of treating the world as static observational memory, LiveWorld models a persistent global state composed of a static 3D background and dynamic entities that continue evolving even when unobserved. To maintain these unseen dynamics, LiveWorld introduces a monitor-based mechanism that autonomously simulates the temporal progression of active entities and synchronizes their evolved states upon revisiting, ensuring spatially coherent rendering. For evaluation, we further introduce LiveBench, a dedicated benchmark for the task of maintaining out-of-sight dynamics. Extensive experiments show that LiveWorld enables persistent event evolution and long-term scene consistency, bridging the gap between existing 2D observation-based memory and true 4D dynamic world simulation. The baseline and benchmark will be publicly available at https://zichengduan.github.io/LiveWorld/index.html.
Zicheng Duan, Jiatong Xia, Zeyu Zhang +7
Adelaide University · The Australian National University · Monash University +2
World models aim to simulate how complex environments evolve under actions and events, yet existing video-based world models primarily learn dynamics from visual observations, which reveal outcomes rather than the underlying knowledge, rules, and mechanisms governing world evolution. This makes it difficult to maintain persistent consequences and support coherent, open-ended evolution. We introduce Code World Model, a framework that separates world evolution from visual realization by combining the reasoning and coding capabilities of language models with the generative priors of video models. A coding agent serves as the world brain, reasoning about events and their consequences and generating executable code to maintain persistent world state and perform rule-consistent evolution. To connect executable state with visual generation, we introduce a proxy representation that encodes frame-wise spatiotemporal constraints and is compiled into a proxy video, which conditions a video model to render high-fidelity visual observations. We further develop data pipelines for constructing aligned proxy-observation pairs from gameplay and real-world videos. After fine-tuning on paired gameplay data, MiniMax-H3 follows proxy-based spatiotemporal specifications from simple interactive worlds built by the coding agent while preserving rich visual details and dynamics. These results demonstrate the potential of combining code for persistent world evolution with video models for flexible visual realization, providing a new path toward open-ended world models.
Yiwen Chen, Guosheng Lin, Chi Zhang
AGI Lab, Westlake University · Nanyang Technological University