WorldProbe: When Video World Models Fall Apart under Long-Horizon Spatial Exploration

Minqi Wang; Jie Peng; Hejun Dong; Ruchang Yao; Puyi Wang; Siyuan Huang; Jihai Zhang; Yafu Li

cs.CVOct 29, 2026

WorldProbe: When Video World Models Fall Apart under Long-Horizon Spatial Exploration

Authors: Minqi Wang, Jie Peng, Hejun Dong, Ruchang Yao, Puyi Wang, Siyuan Huang, Jihai Zhang, Yafu Li,

Organizations: The Chinese University of Hong Kong · Peking University · University of Science and Technology of China · Department of Computer Science and Engineering, CUHK ·

Abstract

Video world models generate future visual observations from camera or action inputs, with the goal of synthesizing persistent 3D worlds that can be explored across time and space. Existing benchmarks typically evaluate short interactions or rely on camera trajectories fixed before generation. The performance of video world models under long-horizon exploration with extensive camera motion remains under-explored. We introduce WorldProbe, a diagnostic benchmark that uses a goal-conditioned multimodal agent to adapt camera actions online during generation. WorldProbe comprises 164 test cases across four probing families. Across 10 state-of-the-art models, we generate more than 1,600 rollouts totaling approximately 800 minutes, with horizons up to 180 seconds. Our evaluation across four dimensions reveals both the degradation of local measures and the emergence of long-range inconsistencies. We find that recent models sustain visual and content quality for longer, while long-range memory and evolution remain weak. These results expose a gap between locally stable generation and persistent world consistency that is largely missed by existing benchmarks. Code and benchmark resources are available at https://github.com/Untitled-W/WorldProbe.

Figures & tables

Figure 1: WorldProbe overview. A goal-conditioned agent adaptively probes an evolving video world through closed-loop camera control, enabling long-horizon evaluation across four task families and four evaluation dimensions.

Benchmark	Int.	Cplx.	Len.	$N$
WorldModelBench ( Li et al., 2025a )	$\times$	Low (T)	$\sim$ 10 s ∗	350
WorldScore ( Duan et al., 2025 )	$\times$	Mid (CC)	$\leq$ 10 s ∗	3000
WorldMark ( Xu et al., 2026b )	$\times$	Mid (TC)	60 s	500
WBench ( Ying et al., 2026 )	$\times$	Mid (CC)	$\leq$ 9 tr.	289
LoopNav ( Lian et al., 2025 )	$\times$	Mid (TC)	$\sim$ 1200 fr.	144
Omni-WorldBench ( Wu et al., 2026a )	$\times$	Mid (CC)	$\sim$ 5.7 s ∗	1068

Table 1: Comparison with existing video world-model benchmarks. Int. interactive control. Cplx. probing complexity: T = text prompt; CC = case-by-case design; TC = template composition; AA = agent action. ∗ inferred from the dataset.

Figure 2: Four scene-adaptive probing tasks. Each task induces a distinct spatial exploration pattern: approaching a constrained target, traversing an open path, inspecting a scene across viewpoints, or successively exploring new regions.

Model	Latency	Chunk ∗	Avg. Len	Pass Rate	$>$ 60s
Model	(per chunk)	(frames/FPS)	Avg. Len	Pass Rate	$>$ 60s
Matrix-Game 2.0	1.469s	12/12	54.4s	73.2%	26.8%
WorldPlay (HY)	14.651s	16/24	59.6s	81.1%	24.2%
WorldPlay (Wan)	3.418s	16/16	42.4s	57.9%	9.8%
LingBot-World	6.681s	12/16	39.5s	48.8%	12.4%
Matrix-Game 3.0	8.178s	40/17	49.5s	62.2%	17.6%

Table 2: Video world model inference configuration. Per-chunk latency and rollout statistics for evaluated models.

Module	Latency
	(per chunk)
Qwen reasoning	3.025s
SAM3.1 tracking	7.609s
DA3+ Voxel	0.5143s

Table 3: Additional processing latency.

Model	Visual		Content
Model	Mean	Slope	Mean	Slope
Matrix-Game 2.0	0.565	$-0.77$	6.09	$-0.061$
WorldPlay (HY)	0.542	$-3.30$	6.08	$-0.072$
WorldPlay (Wan)	0.603	$-2.91$	6.29	$-0.092$
LingBot-World	0.532	$-2.27$	6.61	$-0.026$
Matrix-Game 3.0	0.556	$+0.76$	5.76	$-0.051$

Table 4: Local quality and temporal degradation across models. Mean measures average rollout-level quality, while Slope measures its change over time; more negative slopes indicate faster degradation. Visual scores are normalized to 0–1, and content scores retain their 0–10 scale. Visual slopes are reported in

10^{-3}

/s; content slopes are in points/s.

Figure 3: Representative local-quality behaviors. Metric degradation aligns with visible quality loss, while some long rollouts sustain rich content without evident collapse.

Figure 4: Local quality degrades over long-horizon interaction. Per-rollout visual and content trajectories show stronger collapse in earlier models, while more recent models generally maintain a higher quality floor. Models are ordered by release time from left to right.

Figure 5: Extended closed-loop rollout. Sampled observations from a 367.9 s DreamX rollout driven by 491 agent actions.

Figure 6: Representative cases for spatial memory. Long-range revisits show strong inconsistencies across spatially separated viewpoints, while short-range revisits better preserve scene structure; memory scores align with visible consistency across revisitation.

Figure 7: Representative evolution behaviors. Low-scoring cases exhibit entity and scene drift, implausible dynamics, and action-inconsistent changes, while high-scoring cases preserve substantially more coherent evolution.

	Memory						Evolution
Model	Scene	Layout	Entity	Texture	Overall	VP/seq.	Entity	Scene	Physics	Causality	Overall	Cxt/seq.
Matrix-Game 2.0	4.73	3.66	3.47	4.19	4.01	2.14	3.78	3.59	3.60	3.28	3.56	12.68
WorldPlay (HY)	6.14	4.99	4.96	5.37	5.37	1.74	3.56	3.86	3.37	3.49	3.57	13.62
WorldPlay (Wan)	4.90	3.61	3.56	4.57	4.16	1.88	2.72	2.40	2.48	2.43	2.51	12.03
LingBot-World	4.59	3.22	2.92	4.37	3.78	2.66	4.30	3.79	3.77	3.21	3.77	12.23
Matrix-Game 3.0	7.14	5.75	5.81	6.54	6.31	2.24	3.41	3.62	3.07	3.08	3.29	12.67

Table 5: Long-range memory and evolution fidelity across models. Memory evaluates scene, layout, entity, and texture consistency across revisited viewpoints; evolution evaluates entity, scene, physical, and causal consistency over extended rollouts.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Visual (normalized)						Content (0–10)
	MUSIQ		MANIQA		Q-Align		Geometry		Object Validity		Scene Richness
	Mean	Slope	Mean	Slope	Mean	Slope	Mean	Slope	Mean	Slope	Mean	Slope
Matrix-Game 2.0	0.667	$-0.08$	0.378	$-0.22$	0.649	$-2.01$	6.14	$-0.060$	6.07	$-0.063$	6.07	$-0.059$
WorldPlay (HY)	0.622	$-1.87$	0.346	$-1.13$	0.657	$-6.89$	5.97	$-0.077$	5.90	$-0.082$	6.38	$-0.057$
WorldPlay (Wan)	0.687	$-0.24$	0.400	$-0.92$	0.722	$-7.57$	6.06	$-0.102$	5.94	$-0.117$	6.87	$-0.058$
LingBot-World	0.606	$-1.70$	0.331	$-2.21$	0.660	$-2.90$	6.88	$-0.021$	6.66	$-0.031$	6.28	$-0.026$

Appendix

Table 6: Per-dimension frame-level visual-IQA and content scores across the 327 rollouts with complete scores in both groups. For each model and dimension, Mean is the equal-weighted average of rollout-level frame means; Slope is the equal-weighted average of per-rollout least-squares slopes against elapsed time in seconds. Visual scores are normalized to their theoretical ranges (MUSIQ: 0–100, MANIQA: 0–1, Q-Align: 1–5); their slopes are reported in

10^{-3}

/s. Content dimensions retain their 0–10 scale and slopes are in points/s. Best mean and slope in each column are in bold.

Panel of Figure 8 — Figure 8: WorldProbe’s service and data flow. The runner mediates the task agent, auxiliary perception modules, and WMI. Bidirectional arrows indicate request and response; the final artifact flows to rollout screening and metric evaluation.

Model / driver	GPUs	Configured chunk bound	Native step, cache, and adaptation
Matrix-Game 2.0	1	180	3 latents per action; a 4- or 6-latent visual-KV window by game; 32-slot causal-VAE feature cache; the token cursor advances by 2,640 per step.
WorldPlay (Hunyuan)	2	270	4 latents per action; geometry-aware history retrieval from up to 20 frames.
WorldPlay (Wan)	4	180	4 latents per action; a 16-latent visual context composed of 12 recent and 4 field-of-view-selected remote latents.
LingBot-World (v1 Fast)	2	240	3 latents per action; local attention 18 with 6 sink latents; four denoising evaluations per chunk.
Matrix-Game 3.0	2	77	Initial segment of 15 latents; subsequent segments add 10 latents with 4 preceding latents for image conditioning; field-of-view memory selects 5 latents.
SANA-WM	1	120	24 pose updates per action; 2 cached Stage-1 blocks; refiner block size 3 and KV limit 11 frames; denoising schedule $[1000,960,889,727,0]$ .

Appendix

Table 7: Code-derived streaming capacities and model-specific state.

Model	Largest recorded native run	Public-path control and memory behavior
Matrix-Game 2.0	10 blocks; 117 frames, 9.75 s	3-latent loop with rolling visual/action KV and persistent causal-VAE cache; each block waits for an action on stdin. The standalone entrypoint has no HTTP session API.
WorldPlay (Hunyuan)	10 chunks; 157 frames, 6.54 s	CLI takes the full pose and target length. Its 4-latent loop retains text KV, rebuilds visual KV from selected history, then decodes the accumulated latents.
WorldPlay (Wan)	10 chunks; 157 frames, 9.81 s	Default: 4 chunks (about 61 frames, 3.81 s). num_frames=961 preallocates 241-latent noise, but num_chunk sets generated chunks. Pose and decode remain inside one invocation.
LingBot-World (v1 Fast)	10 chunks; 117 frames, 7.31 s	The CLI takes a complete camera trajectory and owns the 3-latent loop. It commits clean-history KV per chunk, then assembles latents for whole-sequence VAE decoding.
Matrix-Game 3.0	10 segments; 417 frames, 24.53 s	Normal mode constructs a full action/pose trajectory from num_iterations ; interactive mode reads one stdin action per segment but fixes the total count. Optional async VAE uses a separate GPU id.
SANA-WM	40 chunks; 960 frames, 60.00 s	Native 24-frame callbacks use three CUDA streams for Stage-1, refiner, and decoder on one GPU. The 60-s run completed without OOM; the camera/action plan is supplied to the invocation.

Appendix

Table 8: Recorded horizons and control or memory behavior of the public inference paths. Durations are computed from output frames and the corresponding public-path frame rate.

Figure 9: Representative rollout trajectories with local, memory, and evolution diagnostics. Each row shows sampled rollout frames (left), Content and Visual metric curves, and Memory and Evolution comparison frames with dimension scores and failure explanations. Gray headers report the probing task, sample count, and frame layout.

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PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives

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Video world models simulate future states conditioned on current observations and user actions. Recent systems have demonstrated impressive video consistency and action controllability over long sequences. However, fairly comparing these interactive models remains challenging. In practice, a human player typically evaluates a world model by pursuing long-horizon objectives through interaction. For example, a user may turn around 360 degrees to see whether the environment remains consistent, or walk into the water and inspect whether realistic water ripples are generated. The action sequence required to achieve the same objective may vary substantially between models, making fixed action-conditioned evaluation unsuitable for cross-model comparison. To address this, we employ multi-modal Agent Players to interact with world models toward specified long-horizon objectives. Building on this paradigm, we introduce PlayWorld, a benchmark providing 171 scenarios, each with a specified objective. To evaluate performance thoroughly, we assess models along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution. In addition, we incorporate basic ability metrics for video quality and controllability. Experiments across nine state-of-the-art world models reveal that current models remain unreliable on long-horizon interactive objectives, particularly in maintaining spatial consistency and persistent state evolution. Code and data are available at https://github.com/kxding/PlayWorld.

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