MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
Authors: Yuncong Yang
Jiageng Liu
Zheyuan Zhang
Siyuan Zhou
Reuben Tan
Jianwei Yang
Yilun Du
Chuang Gan
Venue: NeurIPS 2025 poster ()
Keywords: Spatial Reasoning
World Models
Test-time Scaling
Primary area: applications

Abstract: Spatial reasoning in 3D space is central to human cognition and indispensable for embodied tasks such as navigation and manipulation. However, state-of-the-art vision–language models (VLMs) struggle frequently with tasks as simple as anticipating how a scene will look after an egocentric motion: they perceive 2D images but lack an internal model of 3D dynamics. We therefore propose SpatialNavigator, a test-time scaling framework that grants a VLM with this missing capability by coupling it to a controllable world model based on video diffusion. The VLM iteratively sketches a concise camera trajectory, while the world model synthesizes the corresponding view at each step. The VLM then reasons over this multi-view evidence gathered during the interactive exploration. Without any fine-tuning, our SpatialNavigator achieves an average 7.7\% performance boost on the representative spatial reasoning benchmark SAT, showing that pairing VLMs with world models for test-time scaling offers a simple, plug-and-play route to robust 3D reasoning. Meanwhile, our method also improves upon the test-time inference VLMs trained through reinforcement learning, which demonstrates the potential of our method that utilizes world models for test-time scaling.

MindJourney Test-Time Scaling with World Models for Spatial Reasoning
Yuncong ; Jiageng ; Zheyuan Zhang", Siyuan Zhou Reuben Tan', Jianwei Yang"! Yilun Du Chuang Gan
UMass Amherst, 2JHU, *HKUST, 'Microsoft Research, Harvard
yuncongyang@umass.ee edu
http://ummaa--mmbbdieeee -giiieddd-iiieee aacilagiiiiii. gwtthb.bi//iinJouuui..
Abstract
Spatial reasoning in 3D space is central to human cognition and indispensable for embodied tasks such as navigation and manipulation. However, sfate-of-the- art vision-languse language models (VLMs) struggle frequently with tasks as simple as anticipating how a scene will look after an egocentric motion: they perceive 2D images but lack an internal model of 3D dynamics. We therefore propose MindJourney, test-time scaling framework that grants a VLM with this missing capability by coupling it to controllable world model based on video diffusion. The VLM iteratively sketches a concise camera trajectory, while the world model synthesizes the corresponding view at each step. The VLM then reasons over this multi-view evidence gathered during the interactive exploration. Without any fine-tune fine-tuning, our MindJoumey achieves average 7.7% performance boost the representative spatial reasoning benchmark SAT, showing that pairing VLMs with world models for test-time scaling offers simple, slug-and-pil route robust 3D reasoning. Meanwhile, our method also improves upon the test-time inference VI.Ms trained through reinforcement learning, which demonstrates the potential of our method that utilizes world models for test-time scaling.
Introduction
Humans inhabit-aan and effortlessly reason about a dinenssioa world. Our innate 3D spatial understanding allows to plan routes, manipulate objects, and make decisions in cluttered environ- ments. Spatial intelligence is a fundamental component of human cognition, developing progressively throughout early life [Gardner, 2011, Vasilyeva and Lourenco, 2012, Tommasi and Laeng, 201 2, Lohman, 2013, Moore and Johnson, 2020, Lauer and Lourenco, 2016]. As such, reasoning underpins almost every physical task, and endowing embodied agents with comparable 3D intelligence has become a central goal of embodied AI research [Koh et al., 2021, 2023, Yang et al., 2023, Wang et al., 2024b, Du al., 2024, Zhu et al., 2025a]. Recent advances in vision-language models (VLMs) have produced systems that are already comparable to human performance in visual recognition and comprehension, even solving simple spatial-reasoning problems [Chen al., 2024a, Zhang et al., 2025b]. However, these models still fall short of reasoning about the actual 3D world that lies behind a 2D image as humans do. Recent spatial reasoning benchmarks [Yang et al., 2024, Ray et al., 2025, Zhang et al., 2025b,a] reveal that VLMs struggle on tasks that require imagining the consequences of egocentric movements. Given a question shown the upper-left corner of Fig. 1, state-of are
Equal Contribution
Work done while at Microsoft Research
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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touch right and chors, kitchmn to right or left?
Gathered Helpful Trajectories from Exploration
20 1 1o
1 15m 6 20
1 1.0m
6 60'
Test Pist-Time Scaling with World Models
World Model
Figure I: MindJournny, Given spatial reasoning query, our method searches through the imagined 3D space through world model and improves VLM's spatial understanding through generated observations during test-time.
VLM easily fails on such perspective-shift question, while given the same view, humans can solve it effortlessly by mentally simulating a short walk and visualizing the next view. This gap highlights critical bottleneck: current VLMs do yet treat an image as interactive world, limiting their usefulness for embodied agents operating in 3D space.
Encouragingly, recent controllable video diffusion models hint solution. Systems such as CamCtr33 [Popov et al., 2025], CameraCtrl [He et al., 2024], Stable- Virtual-Camrraa [Zhou et al., 2025a], and the History-Guided Transformer [Song al., 2025] take a single RGB frame plus a pose trajectory and synthesize a coherent, egocentric video that faithfully follows the specified motion. In effect, they serve world models: given "where the agent will go", they imagine "what the camera will see".
Leveraging this capability, we introduce MindJourney, test-time scaling framework that enables a VLM to reason by interactively searching and exploring the imagined 3D space through a world model. Confronted with spatial-reasoning question, MindJourney does not ask the VLM to answer immediately. Instead, the VLM iteratively plans a short exploratory trajectory, with the world model rendering the corresponding egocentric video each step. The VLM then its gathered imagined rollouts in the world to reason and answer the spatial-reasoning question. As shown in Fig. 1, the world model helps scale up the observation space given the image provided in the question, and helpful trajectories with descriptions and observations are gathered for the VLM to answer the question easily. By combining the high-level reasoning ability of VLM with detailed 3D scene understanding of world model, we are able to boost the VLM's significantly.
performance
In comprehensive evaluation on the SAT benchmark, SpatialNavigator achieves substantial gains on multiple spatial reasoning tasks. Our method improves top-1 accuracy performance across four very different VLM back-ends with two distinct world models by an average 7.7%, with the largest single gain over 10%. Our method also outperforms and improve upon the ol-like test-time scaling VLMS, which demonstrates the compatibility of our method and showcases the potential of the idea of test-time scaling with world models.
In summary, our key contributions are as follows:
We introduce MindJourney, the first test-time scaling framework that couples VLM with controllable video world model, enabling searching through imagined 3D space improve 3D spatial reasoning without finetuning.
We empirically demonstrate that our model offers significant improvement on multiple spatial reasoning tasks.
We demonstrate that our method is model-agnostic: it boosts four different VL.Ms with two distinct world models.

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2
Related work
World Models Video generation models have shown their potential as promising world models for gaming and robotic upplications [Yang et al., 2023, Bar et al., 2024, Bruce et al., 2024, Gao et al., 2024]. Early works [Du et al., 2023, Bruce al., 2024, Zhou et al., 2024a, Du et al., 2024] leverage strong video generation models to imagine the future frames for decision making. However, previous works primarily focus on static cameras and simple environments. More recently, several studies [Bar et al., 2024, Parker-Holder et al., 2024, Zhou et al., 2025b, Team et al., 2025] have explored the simulation of 3D environment dynamies using controllable video models that are conditioned on the actions camera movement, thereby enhancing the spatial imagination capabilities of world models. In our work, we leverage the imagination abilities of world models to help the spatial reasoning ability of vision-language models.
Vision-Language Models and Spatial Reasoning The growing advancements vision- -aiguue models (VLMs) have rapidly progressed on various downstream tasks [Radford et al., 2021, Li et al., 2022a, Alayrac et al., 2022, Achiam al., 2023, Team et al., 2023, Driess al., 2023]. Many strong open-sourced models have been developed from visual instruction tuning on paired text-image data [Dai al., 2023, Liu et al., 2023b, 2024, Dong et al., 2024, Yao et al., 2024]. More recent works explored region-level and pixel-level grounding [Li et al., 2022b, Ma et al., 2023, Wang et al., 2024c, Rasheed et al., 2024, Zhang et al., 2024], more training strategies, and test-time scaling [Chen et al., 2024d,c, Wang et al., 2024a, Dong et al., 2025, Zhu et al., 2025b]. Recently, spatial intelligence has gained attention the VLM community [Liu et al., 2023a, Kamath et al., 2023]. However, evaluation benchmarks SpatialRGPT, SAT, COMFORT, SPAR show that offt--ofthhe-ar VLMs still fall short on spatial understanding and reasoning [Cheng et al., 2024, Ray et al., 2024, Zhang et al., 2025b,a].
Test-Time Scaling for Reasoning A growing line of work boosts large-model reasoning by allocating extra compute after training. Early analyses show that properly budgeted TTS consistently raises accuracy across tasks [Snell et al., 2024]. Concretely, three main strategies have emerged. (i) Best-of- n reranks multiple sampled chains of thought (CoTss, as BoNBoN and related variants [Gui et al., 2024]. (ii) Guided decoding steers beam search with learned value functions: outcome-supprrisee value models (OVM) [Yu et al., 2023] and seff-evaluaiion-uideee beams [Xie et al., 2023], are prominent examples. (iii) Tree search uses MCTS- roll-outs-e.3 AlphaMath [Chen et al., 2024b]-to explore expansive reasoning spaces. Parallel efforts focus on verifier signals: Calibrated- CLIP augments dision-language models with paired positives/neçatives [Zhou et al., 2024b], while LLa learns an open-source multimodal grader [Xiong et al., 2024]. Our approach departs from these text-centric methods by adding physically consistent world model. The model renders imagined egocentric views, supplying geometry- evidence that guides search through a latent 3D scene, which help perform TTS in opatial-reasoning tasks.
3 Approach
Our method targets test-time enhancement of vision-language models (VLMs) in 3D spatial reasoning by exploiting the predictive power of a world model. Our framework, MindJourney, achieves the test-time scaling through two tightly coupled components:
Video-Diffusion Models as World Models. Given a single RGB frame and an egocentric action sequence defined by camera pose, the world model synthesizes coherent egocentric video that follows the given trajectory, effectively turning the still image into an explorable 3D world.
Spatial Beam Search. Guided by the spatial question, the VLM and the world model interactively search for helpful trajectories in the virtual 3D space defined by the given image.
Sec. 3.1 provides an overview of our test-time scaling pipeline. In Sec. 3.2, we define our formulation of the world model in MindJourney. In See. 3.3, we will introduce our iterative search algorithm, Spatial Beam Search, which uses VLM and a world model to interactively explore the imagined 3D space. Finally, in Sec. 3.4, we introduce training and achitecture details of our world model, Search World Model (SWM), one of the world model candidates in our experiments.
3

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Spatial Reasoning Query
Answer: your right
couch on my right face: the chairs, will kittchen be my right or left?
Image
Question
VLM (Question Answering)
Spatial Beam Search
steps
Evidence Buffer
Bearn nodes
World Model
999
VLM (Search)
imagined Observation
Traeectory Expansion
Figure 2: Journey Pipeline. Our pipeline starts with Spatial Beam Search for n steps before answering the question. The world model interactively generates new observations, while VLM constructs the evidence buffer for Q&A and guides the search during the process.
3.1 Pipeline Overview
Fig. illustrates our pipeline: given spatial reasoning query, the world model and the VLM collaborate through Spatial Beam Search to generate and filter novel viewpoints that facilitate question answering. Up to three components participate: World Model W, a Search VLM V search. and Question-Answerinn VLM VQA (The VLMs may share the same network).
Given an input image and a spatial-reasoning query, we launch an n-step Spatial Beam Search instead of producing answer directly. For every trajectory in the current beam, the world model expand each trajectories (Fig. 3), yielding candidate trajectories and their imagined observations. Conditioned on the query text, the search VLM evaluates the imagined observations and (i) writes enjjector-oobserattee pairs that are highly relevant to the answer into Helpful Observation Buffer, and (ii) selects trajectories worth further exploration form the next beam layer.
After the search, the question-ansee answering VLM consumes the original image together with the buffered observations to deliver the final answer to the spatial-reasoning query. This imagine - select answer loop equips a frozen VLM with the world model's physical priors and motion forecasts, yielding substantial gains in 3D spatial reasoning without additional training.
3.2
World Model Formulation
We treat the world model as an egocentric simulator that rolls out a sequence of actions starting from reference image.
Action Space. our paper, we define the set of primitive actions to be
A (move-forward d, turn-left 0;, turn-right 0,)
where is moving distance meters and 0z and 0, are rotation angles in degrees. Further, we define action trajectory an ordered tuple
T T= (a),.... am) E
with length m. Therefore, we denote the search space of all trajectories of length at most by To U mm where A {Ø) contains the empty trajectory.
Action Representation. Each primitive action A is mapped to relative camera- porte transfor- mation (p)) = c SE(3). Hence a trajectory T = (aj,..., am) is deterministically translated into the pose sequence
C (cI...,,mm) c₁ p((a)).

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Beam Node
Local Observation Set
Initial Reference Image
T x
6 10"
4 20
6 30
World Model
T x3
0.25
0.5
1 0.75
xx
Previous Teajectory T 1 1 6
10
P 20
e 30
Figure 3: Trajectory Expansion Illustration. The Figure illustrate a Trajectory Expansion process with k 3, d = 0.25, and 10 In this case, the world model generates observations given the Beam Node.
We condition the video-diffusion world model on each e; ensure that the i-th generated frame reflects the intended egocentric motion.
pose expressed by its intrinsic matrix K and extrinsic matrix E t..
Action-Driven Video Generation. Let Xo E R be the reference image and C (ci...., Cm E (E the pose sequence induced trajectory Our pond-cond video diffusion model,
W: (Xo, C)
(x1 X
x; x
maps the pair (xo, C) to an egocentric rollout that follows the intended motion. As an outcome, the world model produces the synthetic video V, as an imagined walk in the 3D space defined by the reference image.
3.3 Spatial Beam Search for Action Space Exploration
Our world model W can roll out an egocentric trajectory 7-a sequence of primitive actions in order to render the corresponding video frames. Because the discrete trajectory space TT grows exponentially with its horizon, we employ beam-search procedure that alternates between question- agnostic expansion and question-aware pruning. The search runs for at most n steps; At each step we expand each current beam node and then invoke a vision-language model (VLM) to score the resulting candidates with respect to the spatial-reasoning question.
Trajectory Expansion. A beam node at depth stores trajectory r (aI...., am E Tm (T Ø for the root). To search the action space starting from the beam node, we adopt the following strategy. For each primitive action a' E AA we permit up to k consecutive repetitions, denoted a'r <1 < k). The candidate set generated from т
C(T) (T@a'r r l E 1srsk).
[C(T) 3k,
where @ denotes concatenation. Each 7/ fed to the world model W, yielding the next egocentric frame X,, ((x0, r'). We collect the resulting local observation set
(
((") + C(r)).
The global observation set for the current search step the union of O(T) over all beam nodes. In practice, because the world model W renders an entire video clip in one pass, we roll out only the candidates in C(T) whose length is exactly m + k-that is, trajectorics that extend the current node by the full & actions. A single call to W with this batched set then produces the complete local observation set ((7) for the node. Fig. illustrate this process with an example.
"To avoid wasted rollouts we discard any candidate that (i) immediately reverses the previous action (e.g. turn left and then right) or (ii) exceeds pre-set translation/rotation budget.

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Algorithm Spatial Beam Search for Action Space Exploration
Require: Initial frame Xo. world model W, VLM Vsearch, VLM VQA. primitive actions A, spatial question 9. parameters n,, B, H, Texpp "help
Ensure: Final answer to question q Initialize beam B {Ø), evidence &
2: for m = to do
3:
+
4: for a E B do
5:
Generate and prume candidates ((r) of length [r| +
6:
O [(T) W((x, r')) 1 C(T)}
7:
For each (r', X,,)
O. generate desc(T')
8:
Query Vsearch for (xxp(T'), shelp(T") using (g, desc(r'), X71)
9:
Prune candidates below Texp. Thelp
10:
Update beam B + top-B by Sexp Add top-H H by Sneip evidence 3
12:
if B = then
13:
break
14:
Prepare evidence set [(T((),x,, (x), (esc(r(()))) from &
Return final answer from VQA given q and the full evidence
VLM-Based Heuristics. At each search step, For every pair (r' x",) the global observation set, we create a natural-languae description desc(T') (e.g., "move forward 0.2 m, then turn right 30"") and feed the spatial reasoning question and all the tuples (q. desc(r'), x,') from the observation set to the VLM. The model returns two scalar logits for each 7' through two different criteria:
Sexp Sexp(T)) (T') ("how useful is it to keep exploring this trajectory?"),
(nel((')) ("how useful is this view for answering now?"".
We first discard any pair whose score falls below fixed thresholds "esp or Thelp respectively. Among the remaining candidates we (i) retain the top B by Seep as the next-step beam (beam width B), and (ii) cache the top H by Shep "helpful" viewpoints and save them to the evidence buffer for the final answer. We thus use VLM drive question-ave aware pruning of the search tree and accumulates evidence views that will later be supplied to the VLM for answer generation.
Answer Generation. After the search step limit n or running out of beam node due to thresholding, we collect all cached helpful trajectories {f( h= together with their associated frames and natural- language descriptions. The VLM receives the original question and this multi-view evidence in single pass and outputs the final answer. We present our algorithm in Algorithm
3.4 Search World Model Details
We trained our world models, Search World Model (SWM), sepecifically for the defined action space in Sec. 3.2. Please refer to the Appendix for more details.
Architecture. SWM is based on the Wan2.2-T12V.. [Wan et al., 2025], following ReCamMas- ter [Bai et al., 2025]. Specifically, we represent the camera transform by camera extrinsic matrices and directly add the embeded camera matrices to the video latent in pixel-wise manner.
Training Dataset. The world model only has execute the limited set primitive egocentric actions used by MindJoumnyy, so we synthesise most of its training corpus with the Habitat 2.0 navigation simulator [Szot al., 2022]. Habitat provides mixel-accurate renderings for forward, backward and rotational motions in indoor environments, making it ideal for learning precise camera control. To bridge the appearance gap between synthetic interiors and real imagery, we blend the Habitat clips with two large-scale, view-consistent video Saaieets--RelEEaatteeee 10K and DL3DV-10K [Ling et al., 2024]. The resulting mix couples Habitat's geometric fidelity with the visual diversity of real indoor and outdoor scenes, allowing the world model to generalise beyond its synthetic training domain.

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accuracy as Table 1: SAT-Real. Accuracy for large proprietary and open-source MLMS on SAT-Real. Specifically. OpenAl ol has test-time scaling capability, MJ refers to MindJourney, augmented on both SWM and SVC. Results marked with * are from [Ray et al., 2025].
SAT Real
Avg
EgoM
ObjM
EgoAct
GoalAim
Pers
GPT4-V*
50.7
Gemini1.5 I.5-flash*
57.6
Geminil -Spro*
64.8
Robopoint 13B*
46.6
GPT-40
60.3
56.5
85.0
50.0
64.0
45.0
+ +MI (SWM)
70.6
60.9
75.7
85.3
66.7
+ MJ (SVC)
69.3
78.3
60.9
78.4
70.6
57.6
GPT-4.1
74.0
95.7
73.9
78.3
88.2
39.4
+ MJ (SWM)
80.6
100.0
78.3
89.2
91.2
48.4
IntemVLL-114
59.3
56.5
69.5
54.0
73.5
45.4
+ MJ (SWM)
66.6
82.6
60.9
67.5
82.4
42.4
OpenAl ol
74,6
78.3
82.6
73.0
73.5
69.7
MJ (SWM)
84.7
95.7
82.6
83.8
88.2
75.8
+ MJ (SVC)
77.3
100.0
65.2
78.4
82.4
63.7
4 Experiment
4.1 Experiment Settings
Benchmarks. Our main benchmark is the Spatial Aptitude Training (SAT) benchmark, which probes an agent's ability to reason about both egocentric motion and object motion-key skills for embodied AI. SAT is split into SAT-Synthesized, 4000 synthetic questions rendered in A12-THOR [Kolve et al., 2017] indoor scenes, and SAT-Real, real images spanning indoor and outdoor environments. The two splits therefore cover wide distribution shift, letting us evaluate both in ni-iistribution accuracy and real-world transfer.
Evaluation Metrics. As the SAT benchmark are all multiple choices questions, our evaluation metric accross all tasks.
Vision-Language Models. We pair our pipeline with four representative VLMs: the closed-source GPT-4o and GPT-4.1 I (strong ceneral-purpose multimodal baselines), IntermVL3114B (one the most capable open-source VLMs to date), and ol, test-time chain-of-thought scaling. This mix spans both proprietary and fully open ecosystems as well as models with and without explicit reasoning scaffolds.
model that performs
World Models. Our experiments use two distinct video world models. (i) Search World Model (SWM). the world model we trained, introduced in Sec. 3.4; and (ii) Carbll-Viirtal-Caae (SVC), recently released, publicly available generator that produces geometrically stable novel views.
Spatial Beam Search Configurations. Unless noted otherwise, we use the same search configuration for every experiment: search depth steps; up to h 3 consecutive repetitions per primitive action during each expansion; exploration and helpfulness thresholds "exp = 8, "help 8.
4.2 Experiments Results
The SAT benchmark comprises five spatial-reasoning tasks ego movement (FgoM), object move- ment (ObjM), action consequence (EgoA), and perspective shifts (Pers) mirroring challenges an embodied agent routinely faces in 3D space. We evaluate our method both SAT-Real and SAT-Synthesized and observe a clear performance boost over all baselines on both splits.
Baselines. Our baselines are the four VLMs: GPT-4o, GPT-4.1, VL3-11V.3-11B, and ol. Despite their diversity-clee source vs. open source, standard training vs. RL-based test-time scaling- expect each to benefit from our approach, MindJourney, For the ol experiments, for saving computa-

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Table 2: SAT-Synthesized. Accuracy for large proprictary and open-source MLMs on SAT- Synthesized. Specifically, OpenAI ol has test-time scaling capability. MJ refers to MindJourney, augmented on both SWM and SVC.
SAT Synthesized
Avg
EgoM
ObjM
EgoAct
GoalAim
Pers
GPT-4o
61.0
64.7
86.8
$1.9
68.7
43.4
MJ (SWM)
70.8
77.6
82.6
70.1
84.5
45.8
MJ (SVC)
72.3
80.0
84.8
65.0
89.3
51.4
GPT-4.1
66.4
75.3
89.0
57.8
78.3
41.5
MJ (SWM)
75.4
88.2
92.4
70.8
89.3
45.8
IntemVL3-
61.4
74.6
85.9
53.3
84.5
20.6
+ MJ (SWM)
66.6
69.4
58.2
68.1
81.9
58.0
OpenAT
72.4
78.0
85.9
65.4
86.0
54.6
+ MJ (SWM)
76.8
82.4
80.4
76.6
88.1
60.7
+ MJ (SVC)
78.6
87.1
80.4
72.3
89.3
70.1
tional cost, we use only as the question -anttiinnnnserringggg VLM and GPT-4o handle the search phase. In every other experiment the same VLM is used for both search and the final Q & A.
SAT-Real. The SAT-Real split comprises 150 real-image queries spanning indoor and outdoor scenes. Table shows that augmenting each VLM with MindJourney results uniform and sizeable gains. Average top-1 accuracy rises by 7.7 percentage points, and GPT-4o achieves the largest single boost at over 10%. Remarkably, G with our method already surpasses vanilla ol; when ol itself is paired with MindJourney it sets state of the art on SAT-Real. These findings confirm that world-moddl-drivenn test-time scaling complements RL-based scaling and our method generalizes to real, outdoor scenarios.
SAT-Synthesized. Because the synthetic split contains 4 000 questions, we evaluate on a random 500-question subset to keep the ol runs tractable. Results Table reproduce the pattern seen on real images: mean accuracy improves by 8.0% in average. Again, the performance of GPT-4.1 with our method surpasses vamilla ol, and ol with our method improves its performance. Morcover, across all five SAT question types the highest score is always achieved by Minn/oorney-augmenti model, underscoring that the proposed framework consistently improves reasoning across all question types.
4.3 Ablation Study
In ablation study, ablate the hyperparameters of our search method. Specifically, for gpt- 4o augmented with SWM, we ablate with search step n E 2,,2,3 and VLM pruning threshold y (4,6, 6,8). We experimented on these values both SAT-Real and SAT-Synthesized. Accuracy is reported on both SAT-Real and the 500-question SAT-Synthesized subset, allowing us to evaluate how each setting affects performance in real-image and synthetic scenarios.
Search Depth. Panel (a) of Fig. 4 shows that accuracy for threshold 4 and on SAT-Real peaks at search steps 2 and then drops slightly at step 3. The decline stems from the limits of our world model, SWM: trained predominantly on indoor synthetic data, it can struggle to faithfully simulate outdoor views once the imagined trajectory strays too far from the reference frame. On SAT- Synthesized- scenes are closer to the SWM's training distribution- cerributioo-perffrmannc continues to rise through step for both threshold 8 and 6, confirming that deeper exploration remains beneficial when the world model can faithfully render long roll-outs. More visual results can be found in the Appendix.
Pruning Threshold. Panels (a) and (b) also show the importance of VLM score threshold. A lenient threshold lets many low-quality views into the evidence buffer, diluting the signal and lowering accuracy. The effect stronger on the Real split, where generation quality is lower, so the threshold has a larger impact there.

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Inforence Steps
Inforence
(a) Accuracy SAT-Real.
(b) Accuracy on SAT AAT-Syythesized.
Figure 4: Inference Steps vs. Accuracy. Accuracy on SAT-Real and SAT-Synthesized with different
VLM Thresholds and inference steps.
4.4 Analysis
Test-time Scaling with World Models vs. Test-time Scaling with RL. On both SAT splits, plain VLM augmented with our world-model search already surpasses the RL-fine-tuner ol. When the same world-model search is applied on top of ol, accuracy climbs still higher, yielding the best results overall. These two observations imply that the exploratory roll-outs supplied by the world model provide information that largely orthogonal to the inductive bias learned through RL chain-of-thought. The phenomenon highlights an exciting potential: giving a reasoning engine physically consistent imaginary workspace can enhance, rather than replace, other forms of test-time self- celf-improvement.
World Model Capability. The ablation results reveal clear bottleneck: once the imagined trajectory strays too far from the initial frame, today's 's world models begin to break down in terms of generation quality. Fig. 4a shows that this degradation not only lowers the fidelity of rendered views but also feeds noisy evidence to the VLM, ultimately capping the benefits of deeper search. Although our world model SWM and the sfate-of-the-ar art Stable-Virtual Caauua (SVC) perform similarly within the three-step regime explored here, further gains will require world models that can sustain geometric and photometric consistency over much longer roll-outs.
5 Conclusion
We have presented MindJourney, the first framework that equips a vision-language model with world model for imagination test-time Through Spatial Beam Search, the VLM actively explores the latent 3D scene behind single image, caching the most informative imagined views for spatial reasoning. This simple, training-free free procedure improves four heterogeneous closed-source GPT-4o to the RL-scaled ol-to new state-of-the accuracy the SAT benchmark. Gains are robust across both synthetic and real images, across all SAT task categories, and across two different world-model generators, underscoring the model-agnostic nature of our approach.
LLMs-ranging from
Beyond its empirical performance, MindJourney offers conceptual advance: it shows that for spatial reasoning, giving reasoning engine physically consistent simulator at test time can complement, and even surpass, complex RL-based self-refiection pipelines.
Limitations and Future Works Our current pipeline assumes a single reference view. When a spatial-reasoning query supplies multiple images, MindJoumey fails to treat the extra views entry points into the scene. An ideal system would regard each image as a separate "portal," " launch an exploration from every portal, and fuse the resulting evidence. Extending our Spatial Beam Search to a multi-source setting is therefore a natural next step.
A second limitation lies in the question- Because the generator unaware of the downstream query, it can hallucinate views that are irrele- vant-or even contradictory- to what the question implicitly assumes. Future work should develop query-conditioned world models or incorporate lightweight constraint mechanisms so that imagined roll-outs remain consistent with the task at hand.
nature of today's controllable video world models.

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Acknowledgments and Disclosure of Funding
We are grateful to Anushka Agarwal for assistance with the baseline code, and to Jiaben Chen, Zeyuan Yang, Lixing Fang, Haoyu Zhen, and many other friends for their helpful feedback and insightful discussions.
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Answer: [Yes]
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A Experiment Details
A.1 Inference Details
A.1.1 Search Configurations
We use the same search configuration for every experiment: a search depth of n steps, a beam size of 2, exploration and helpfulness thresholds "eep = 8 and "help = 8, and maximum trajectory length of 8 starting from the given reference image. During each expansion, we allow up to = consecutive repetitions per primitive action; each forward step moves the agent 0.25m and each rotation step turns it by 9º. We prepared more visual examples about the Trajectory Expansion process under our experiment settings at Fig. 5 and Fig.
A.1.2 Computational Resources
All inference experiments were run on high-performance NVIDIA GPUs: when using Search
World Model the world model, we employed A40 GPUs with 40GB of VRAM; when using Stable- Virtual-Camera as the world model, we ran on H100 GPUs with 80GB of VRAM; and for all experiments combining the Intern VL3-14B VLM with the Search World Model, we also used H100 GPUs to accommodate the larger memory footprint of the Assion-language model.
Local Observation Set
Reference Image
9"
18
6 27
0.25 m
1 0.5
0.75
9*
3 18"
3 27
Figure 5; Trajectory Expansion example on SAT-Real.
Local Observation Set
Reference Image
9"
S 18"
27"
0.25 m
1 0.5
0.75 m
9
18*
27
Figure 6: Trajectory Expansion example on SAT-Synthesized.
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A.2
Search World Model Training
A.2.1
Dataset
The training set for Search World Model (SWM) comprises three components: HM3D, DL3DV-10K, and RealEstate 10K [Szot et al., 2022, Ling et al., 2024, Zhou et al., 2018].
HM3D. We generate 50K simulated navigation clips using Habitat on HM3D scenes. For each episode, we sample a random start and goal position and follow the shortest path for up to 500 steps. To improve robustness to camera tilt, we uniformly draw an initial pitch from [-30%, 20 and in 10% of episodes we hold the agent at a fixed location and vary only its pitch by rotating up and down.
RealEstatc10K: This collection of 1OK real indoor videos enriches our data with diverse residential environments, balancing the simulated HM3D distribution.
DL3DV-10K. Similarly, we incorporate 10K real outdoor videos from DL3DV-10K to capture natural scenes and broaden model's generalization to exterior settings.
To ensure consistent camera dynamics across all sources, we normalize each clip's 's frame rate and spatial resolution before training.
A.2.2 Implementation Details
We adopt Wan2.2-T12V--. [Wan et al., 2025] as our backbone. We followed the implementation of ReCamMaster, which conditions video generation the target camera poses by encoding each frame's s extrinsic matrix (3x4 toration-translation) with a small learnable camera encoder and adding this signal the visual features inside every diffusiiontransformee block, right after spatial attention and before 3D (spatio-temporal) attention. During the training, we only finetuned the camera encoder and newly introduced projector. For more details, please refer ReCamMaster's open-sourced codebase.
A.2.3 Training Details
We subsample training clips with a frame-skip stride uniformly drawn between and 3 to expose the model to varied camera motions. Optimization is performed with Adam and a linear warmup schedule to peak learning rate 3e - 5, using 66 precision for efficiency and clipping gradients to a maximum norm of 1.0 for stability. All video-diffusion models are trained on eight NVIDIA H100 GPUs over approximately three days.
B
Failure Case Analysis
Despite the strong overall performance of our model, we conducted detailed analysis of the failure cases to uncover its limitations. The following examples highlight typical scenarios where the model does not perform as expected.
Unlike the previous figures, the action labels in Fig. 7 represent delta action at each step.
B.1
World Model Capabilities
B.1.1 Case 1: Inaccurate Forward Movement
In group (a), the imagined trajectories systematically under- over-estimate forward translations: the actual step lengths no longer match the intended distances, and the displacement between successive frames varies unpredictably. As result, the agent repeatedly overshoots its targets and exhibits jittery, erratic motion within the simulated environment.
We hypothesize that these errors arise from scale inconsistencies across our training sources. In the Search World Model, only HM3D provides metrically accurate movement distances, whereas Stable Virtual Camera relies on datasets with more heterogeneous scale calibrations. When these conflicting scale conventions are combined during training, the model learns incompatible motion manifestin exactly the misaligned, crratic forward movements in group (a).
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(a)
Ref Image
0.25 m
0.25
0.25
(b)
Ref Image
1 0.25
a 0.25
K 0.25
(c)
Ref Image
$ ge
< 9"
f ge
S g'
(d)
Ref Image
599 9r
9
0.25
(e)
Ref Image
3 g2
3 9*
9a
3 g'
(f)
Ref Image
b gb
b 9"
S 0.25
Figure 7: Failure Cases of World Models. Group (a) shows inaccueate forward movement; group (b) shows unintended roll movement leading to a tilted scene; group (c) shows the unstable egocentric rotation that introduces viewpoint movement; group (d) shows the model generate artifacts for unseen regions; group (e) shows misinterpretation when inference on real-world scene; group (f) shows failure case out-of -o-main animal data.
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B.1.2 Case 2: Unintended Roll Movement
In group (b), the predicted images exhibit an unnatural tilt of the scene, where the horizon line is significantly misaligned. This indicates that the model sometimes introduces unintended roll movements, resulting in distorted camera orientation.
B.1.3 Case Unstable Egocentrie Rotation
In group (c), the predicted images exhibit unstable viewpoints during egocentric rotation. The transitions between consecutive frames are inconsistent, and the visual perspective appears to undergo rightward translation while simultaneously rotating.
This issue happens more often for world model SWM, which happens because blend some Realestate 10K data when fine-tuning SWM. RealEstate 10K contains numerous segments which the camera trajectory exhibits simultaneous translation motion and rotation, leading distributional bias in training and causing the model to develop systematic prediction errors.
B.1.4 Case 4: Visual Artifacts
In group (d), the predicted images contain noticeable visual artifacts, particularly in regions that occluded or unseen in the input view. These artifacts manifest as texture distortions, unnatural edges, or inconsistent object boundaries, which significantly degrade the visual realism of the generated images.
This issue may stem from the model's limited ability hallucinate plausible content in with insufficient visual context or out of domain data. In particular, when the target view includes regions not visible in the source image, the model may rely weak priors or overfit to spurious patterns seen during training.
B.1.5 Case 5: Out of domain data scene misinterpretation
In group (e), the model exhibits clear failures when processing scenes that fall outside the distribution of the training data. The predicted images demonstrate significant misinterpretation of scene structure, such as incorrect boundary extension shown the example. These failures especially prominent in complex real-word wolld environments with lighting, textures, or layouts not observed during training.
We attribute this behavior the model's limited generalization ability when confronted with out- of-distribution inputs. Without adequate exposure to diverse scene types during training, the model tends rely learned priors that do not transfer well, resulting in hallucinated semantically inconsistent content.
B.1.6 Case 6: Out of domain data human or animal
In group (f), as shown in the images, the body of the dog is missing. The model fails to generate plausible predictions when encountering humans or animals, which are underrepresented or absent in the training data. The generated images often exhibit severe distortions in body shape or texture consistency, making the predictions semantically incorrect.
This failure be attributed to the model's lack of exposure articulated and deformable entities during training. Humans and animals involve complex structures and dynamic poses that require specialized representation and learning. Without sufficient domain-specifie data, the model struggles to generalize, leading to implausible reconstructions or complete semantic failures.
B.2
VLM Capabilities
B.2.1
Case 1: VLM Q&A
As illustrated in Fig. 8, although the world model generates great visualizations that would intuitively help the VLM with spatial reasoning, the VLM can still be confused and cannot answer the question correctly. Therefore, for spatial reasoning question, the question answering ability of the base VLM is still very important.
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Question:
If turn left by 16 degrees, will I be facing away from GarbageCan (near the mark in the imagej?
Answer Choices:
[yes', 'no]
Correc: Answer: yes
Response: (wmang)
These are the images that pair with the question.
image
Imago your egocentric
Below are imagined views you would obtain if you tock the corresponding actions. Thess are provided help you the question.
turn left 18 degrees
degreeo
Figure 8: Failure case VLM's Q&A ability is not sufficient.
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Question:
If turn left by 16 Gegrees, will facing away from GarbageCan (near the mark the imagej?
Answer Choices:
[yes' 'no]
Correc: Answer: yes Response: (wmong)
These are the images that pair with the question,
degrees
degrees
left degrees
Figure 9: Failure
VLM's scoring ability is not sufficient.
B.2.2 Case 2: VLM Scoring
Given the question mentioned in Case 1, the VLM is not able to keep one of the most informative image after the scoring process. As illustrated in Fig. 9, the "turn left 9 degrees" the most informative image it contains the garbage scoring process does not keep the image in the final evidence buffer, which leads to a wrong answer. The improvement of VLM capability will also benefit the VLM scoring process and improve the overall performance implicitly.
mentioned in the question. However, the VLM
C
More Ablation Studies
C.1
World Models
We evaluated the performance of two world models, Search World Model (SWM) and Stable Virtual Camera (SVC), on dataset generated through the A12-THOR simulator, as A12-THOR is out-of- domain for both world models. The evaluation includes both quantitative metries, measuring the accuracy of predictions and the quality of the generated images, and qualitative comparisons through visualizations of representative samples.
During inference, both models are executed with 50 diffusion steps. Specifically, to get the metrics of generated quality, generated 10 episodes for each of the 208 scenes in AI2-THOR. Each episode consists of an action sequence of 8 steps, where at each step, an action is randomly selected from the primitive action set: (move forward 0.25 meter, turn left degrees, turn right 9 degrees)
C.1.1 Quantitative Comparison
More specifically, following the previous work of stable virtual camera, we tested the prediction result of our world models using standard metrics-peak signal-to-noise ratio (PSNR), learned perceptual image patch similarity (LPIPS), and structural similarity index measure (SSIM). Results are shown in
26

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Table 3: Video Generation Results. consistency.
Comparison of SWM and SVC in both visual quality and
Method
PSNR 1
SSIM 1
LPIPS(1e-4)
SVC
64.51 66.59=0.21
0.994=0.01 0.997=0.01
0.49#0.01
SWM
0.31
Table quantitative comparison between two video generation models, SWM and SVC. These metrics jointly assess both visual fidelity and perceptual consistency.
SWM outperforms SVC in terms of PSNR (66.59 vs. 64.51) and SSIM (0.997 vs. 0.994), indicating more accurate and structurally consistent predictions. also achieves a lower LPIPS score (0.31 0.49), suggesting that SWM generates images that more perceptually similar to the ground truth. Overall, SWM demonstrates superior performance terms of visual accuracy and perceptual similarity. This suggests that SWM more effective generating coherent and visually faithful video sequences for the primitive actions we defined.
C.1.2 Qualitative Comparison
SWM
Ref
SVC
Ref
F 0,25
>
SWM
Image
$ gr
$ ga
m
0.25
Case
SVC
Ref Image
6
T 0.25
Figure 10: Comparison of World Models. Case comes from validation split of SAT dataset; Case 2 comes from real-world test split of SAT dataset.
We present a qualitative comparison between the two models, SWM and SVC, using repre- sentative examples from the synthesized validation set and real-world test set of SAT dataset. As shown in Case 1 from Fig. 10, SVC sometimes performs inaccurate forward motion. After moving forward by 0.25m, while the visual consequence from SWM seems reasonble, the outcome from SVC shows its inconsistency. In Case 2 Fig. 10, the SVC shows better capability of keeping object-level details. In the generated results from SWM, the objects become blurry the video extends, but SVC successfully keeps details of each existing object. Generally, we observed that while SWM is more consistent in the scale of movement, SVC preserves more details during the camera movements.
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C.2 Ablation on Trajectoy Description
In our current method, for each observation in the evidence buffer, also provide natural language trajectory description that explains its relationship with the initial reference image. We demonstrate that the trajectory description necessary for our method in Table 5 and Table According the tables, we observe that the performance of our method drops on both SAT-Real and SAT-Synthesized for all VLMs and world models.
C.3 Ablation on Time Consumption
To assess the time efficiency of our method, we evaluated it with GPT-4o as the VLM while varying the number of inference steps. We also reduced the SWM'ss video-generation iterations from 50 to 20 to examine the trade-off between runtime and accuracy. The results show that fewer SWM iterations markedly speed up inference but degrade the quality of the generated video, which in turn lowers accuracy. Conversely, increasing the number of inference steps forces the SWM to predict more frames and provides more images the VLM, thereby increasing both the SWM time and the VLM search/QA times.
However, also observe that method does not degrade significantly with 2 search steps and 20 iterations. Therefore, while our current setting optimizes for performance, step 2 and iter 20 is much more cost-effective and recommended setting practically.
Table 4: SWM/VLM Time Consumption and Accuracy
Setting
SWM time(s)
Search time(s)
Q&A time(s)
Total(s)
ACC(%)
step:1 iter:20
11.49
3.04
1.74
16.27
64.7
step:2 iter:20
19.68
5.53
1.71
26.93
68.6
step:3 iter:20
29.37
8.00
3.24
40.60
69.3
step:1 iter:50
30.57
2.94
1.84
35.36
65.3
step:2 iter:50
63.62
5.52
1.79
70.93
69.3
step:3 iter:50
149.75
8.30
3,43
161.48
70.6
D Prompts
Here we provide 4 different prompts used in our method. The baseline prompt is shown in Fig. 11.
The exploration Scoring prompt is shown in Fig. 12. The helpful Scoring prompt is shown in Fig. 13. The question-answering prompt using MindJourney is shown in Fig. 14.
System Prompt:
Task:You are Al assistant designed kelp with spatial reasoning 3D indoor scene. You must analyze provided observations and question.
Content Prompt:
These are the images that pair with the question. (Image)
Image (Image)
(question)
Answer Choicem: (answer choices)
the from the choices.
Answer:
Figure 11: Prompt for Baseline Q&A.
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System Prompt:
assistant designed help spatial reasominng any provided images and score imagined images based. on how suitable they are for exploring these action consequences in order to answer the question from the choices.
must analyze
Rules:
be provided images (including imagined imagus), queation,
amswer choices.
ahould score all imagined images.
2. You should output a list scores from separated by For example: Output: 3,5,2,9,,11
Content Prompt:
These are the imagen that pair with the question.
Image 1: (image)
Image (image)
Image is your current egocentric view.
Question: (question)
Answer Choices: (answer choices)
imagined Imagined imagu (index) if you (action suquence): (image)
taking actions.
imagined taking
Output list Output:
Figure 12: Prompt for Exploration Scoring.
System Prompt:
Task: You Al assistant designed to help with spatial reasening 3D indoor You must any images
imagined images
helpful they
quectione.
Hint: They may not be correct for answering the questions, but they will be helpful for excluding the wrong The should also consider augnsented.
quality. the quality very should receive
Otherwise, score
Rules:
provided images (including imagined images), question,
score imagined images.
2. You should output a list of scores from to separated by For example: Output: 3,5,2,900,1".
Content Prompt:
images that pair question. Image 1: (image)
Image 2: (image)
Image it your current agocentric view.
Question: (question)
(answer choices)
Below the imagined views after taking aclions. Imagined
(index) you (action sequence).
(image)
Below imagined views after taking actions.
Output a list of acores. Output:
Figure 13: Prompt for Helpful Scoring.
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Table Ablation on Trajectory Description. Accuracy for large proprietary and MLMs on SAT- Real.
SAT Real
Avg
Ego.
ObjM
EgoAct
GoalAim
Pers
GPT-40
60.3
56.5
85.0
50.0
64.0
45.0
MJ (SWM)
68.0
73.9
69.6
75.7
73.5
48.5
MJ (SWM), w/o Traj. Desc.
66.8
60.0
76.7
71.0
70.0
48.3
MJ (SVC)
69.3
78.3
60.9
78.4
70.6
57.6
MJ (SVC), w/o Traj. Desc.
66.5
73.5
65.0
74.5
66.3
53.1
GPT-4.1
67.3
81.0
76.4
69.5
73.9
36.0
MJ (SWM)
82.6
95.0
78.2
89.0
85,0
66.6
MJ (SWM), w/o Traj. Desc.
73.0
100.0
78.2
67.7
75.8
53.1
Table 6: Ablation on Trajectory Description. Accuracy for large proprietary MLMs on SAT- Synthesized.
SAT Synthesized
Avg
Ego.
ObjM
EgoAct
GoalAím
Pers
GPT-4o
61.0
64.7
86.8
51.9
68.7
43.4
MJ (SWM)
70.8
77.6
82.6
70.1
84.5
45.8
MJ (SWM), w/o Traj. Desc.
64.1
67.9
83.1
63.0
71.8
42.3
MJ (SVC)
72.3
80.0
84.8
65.0
89.3
51.4
MJ (SVC),
Traj. Desc.
65.8
64.7
68.4
65.8
47.8
GPT-4.1
66.4
75.3
89.0
57.8
78.3
41.5
MJ (SWM)
75.4
88.2
92.4
70.8
89.3
45.8
MJ (SWM), w/o Traj. Desc.
72.6
89.3
92.3
65.0
88.1
33.4
E Broader Impacts
By allowing vision- language models to build and interrogate a physically consistent "mental workspace," method could accelerate progress in assistive robotics, remote inspection, and immersive training: robots that better understand 3D space can navigate cluttered homes for elder care, inspect hazardous sites without human entry, and deliver richer AR/VR experiences for ed- ucation or therapy. At the same time, safer decision-making from imagined roll-outs may reduce real-world trial-and-error, lowering both cost and risk. Yet the technology also raises concerns. More capable spatial reasoning can enhance autonomous surveillance systems or military platforms; and greater autonomy could displace certain manual-labor jobs. Finally, training large video world models consumes considerable energy and inherits any biases present in the data (e.g., under-representation of certain environments). Researchers and practitioners should therefore pair technical advances with robust provenance tracking for generated content, scenario-specific safety constraints, and Rata-diversity audits, while favouring meery-efficientt architectures and openly reporting compute footprints.
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System Prompt:
Task: You are an Al assistant designed to holp with spatial reasoning in 3D indoor scene. You must analyze provided
observations and
question.
I. You should oucput the exact answer from the choices.
2. You will be provided with multiple imagined vieus if take correspunding actiona to help
You can minimal reasoning, but your must only include the exect answer choice,
Content Prompt:
images
Image 2: (image)
Image 2: (image)
Image your egocentric
Question: (question)
Choices:
imagined views you would oblain you look corresponding actiona. provided
help you answer the question.
You include them reazoning. but you whould still only output enact at the leat line.
Actiom: (ection cstalog)
(action dequence)
Action: (action catalog)
Output the exact answer from Answer:
Figure 14: Prompt for Q&A using MindJourney.
31


Reviews:
[{"review_id": "xtiKiRPr56", "reviewer": "Reviewer_s68S", "summary": "This paper introduces SpatialNavigator, which frame a Vision reasoning task with a test-time scaling framework. This framework significantly enhances the 3D spatial reasoning capabilities of many different Vision-Language Models (VLMs). The core idea is to address the inherent limitation of VLMs in understanding 3D dynamics from static 2D images by simulating a exploration of a 3D environment. \n\nSpatialNavigator achieves this by using a VLM with a controllable video world model: the VLM iteratively proposes camera trajectories, and the world model synthesizes the corresponding egocentric views. The VLM then leverages the gathered evidences to perform spatial reasoning.", "questions": "- The current framework's effectiveness is demonstrated with a relatively small search depth. Beyond the computational cost, are there other fundamental considerations or architectural limitations that currently constrain scaling the Spatial Beam Search to significantly greater depths, and what are your ideas for overcoming these to enable more complex, long-horizon spatial reasoning?\n- Considering the observed sensitivity of the world model's fidelity to out-of-distribution scenes and extended trajectories, what specific advancements in world model training or architecture do you believe are most critical?", "limitations": "- The framework currently operates within a relatively small, predefined set of primitive egocentric actions. While sufficient for the tasks presented, this limited action space raises questions about its direct generalizability to more complex, nuanced, or interactive real-world scenarios", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- The method presents a highly intuitive approach to spatial reasoning, effectively mimicking how humans might mentally simulate movement to understand a scene from different perspectives. Starting from a static image and building a dynamic 3D understanding through simulation.\n- A strength of SpatialNavigator is its design as a test-time compute framework. This makes it inherently model-agnostic. Results show that the method generally improve performance of several baseline VLM models, including strong commercial ones.\n- It is particularly interesting to observe that the information provided by the world model appears orthogonal to the knowledge learned by RL-trained VLMs (e.g., \"O1 style\" models). This suggests that the world model provides a unique and complementary source of information\n\nWeaknesses:\n - As a test-time compute framework, the iterative Spatial Beam Search, which involves generating multiple hypothetical trajectories and synthesizing corresponding views, incurs significant computational cost at inference time.  While this is understandable, this cost should be discussed more thoroughly, and hopefully with suggested future directions on how to improve this.", "was_revised": true, "final_justification": "After rebuttal and checking other reviews, I still find that it is interesting to see the world model appears orthogonal to the knowledge learned by RL-trained VLMs. This provides a good direction for future exploration and I would vote for acceptance.", "rebuttal": "Dear Reviewer,\n\nWe appreciate the detailed comments and insightful suggestions. We are glad that you find our method both high-intuitive and effective. Here are the responses to your questions.\n\n\n### Computational Efficiency\nWe appreciate the reviewer’s suggestion and agree that analyzing computational trade-offs is important for practical deployment. To address this, we provide a structured analysis of the current computational efficiency of SpatialNavigator.\n\nAlso, we would like to clarify that SpatialNavigator is the first work on test-time scaling framework that uses VLM to control a video diffusion model. Thus, we mainly focused on optimizing the effectiveness instead of efficiency, we acknowledge that efficiency optimization is a promising direction for future work. To address this, we outline several acceleration techniques that make further speed-ups highly plausible.\n\n#### Efficiency Analysis\nWe profiled computational efficiency on a single H100-80G. We evaluated using gpt4o+SN(SWM), with beam size 2 and inference step 50 on SAT-Real. The memory consumption is~40G across the experiments. As SVC cam only generates high resolution video and therefore not efficient(around x3 world model latency), we mainly use SWM in this section.\n|Model|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|gpt4o|0|-|-|1.57|1.57|60.0|\n|gpt4o|1|14.52|4.70|1.69|20.91|66.0|\n|gpt4o|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|52.78|13.53|3.35|69.66|68.7|\n\nIn our main experiments, we focused on optimizing the effectiveness so the default setting is not the most cost-effective setting. To explore more cost-optimized settings, we further investigate reducing the number of inference step in the table below:\n\n|Iters|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|10|1|4.66|4.66|2.56|11.88|66.7|\n|10|2|7.62|8.28|2.72|18.62|66.7|\n|10|3|10.69|11.25|5.79|27.73|67.3|\n|20|1|7.22|4.44|2.48|14.14|66.0|\n|**20**|**2**|11.76|9.14|2.50|**23.40**|**68.0**|\n|20|3|23.51|10.30|4.29|38.10|67.3|\n\nThe results demonstrate that our model can maintain a >95% of the accuracy gain at ≤33% of the latency, with a latency of about 23s. We tested o1 on the SAT-Real with latency in average 19.6s, so SpatialNavigator in cost-optimized setting is in the same latency band as widely used test-time-scaling pipelines.\n\nWe further added the ablation study on beam size in the table below:\n|Model|Step|Beam|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|-|\n|gpt4o|2|1|23.24|7.51|1.60|32.35|67.3|\n|gpt4o|2|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|1|32.16|14.43|3.58|50.17|68.0|\n|gpt4o|3|2|52.78|13.53|3.35|69.66|68.7|\n\n#### Scalability & Acceleration Pathways\n\n**Backbone Model** \nRecent released Wan2.1-1.3B provides videos with quality higher than our backbone CogVideoX-5B in ≈8GB VRAM. Wan2.1 achieved ≈1.7×lower (8.7s vs.14.5s) latency and ≈½ the memory footprint relative our current backbone tested under identical frame counts and resolution.\n\n**Step Distillation**\nLatent Consistency Models produce high-fidelity outputs in 2–4 inference steps, yielding 10–20× speed-ups over 50-step schedulers. Video-specific extensions such as Distribution-Matching Distillation report similar benefits on text-to-video transformers.\n\n**Low-Bit Quantization**\nPost-training methods like SVDQuant quantize both weights and activations to 4-bit while keeping FID within ±2 points of full precision. Empirical results report ≈3× speedups and up to 3.6× memory reduction on DiTs, enabling real-time sampling on a 16GB laptop-class GPU.\n\nAll three techniques are open-source and multually compatible, which indicates that improving computational cost is both practical and well-supported by the community.\n\n### Constrained scaling the Spatial Beam Search\nOne fundamental constraint is error accumulation. With greater search depths, there is an increased likelihood of compounding errors in both the imagined views synthesized by the world model and the subsequent reasoning steps by the VLM. The lower fidelity of the subsequent generated views may hurt VLMs' ability to perform spatial reasoning. We further evaluated the world models' fidelity at different steps to confirm it.\n\nUsing the out-of-domain simulator AI2Thor, we evaluated the world model's fidelity under a sequence of atomic actions defined in our work. We report the PSNR, LPIPS, and SSIM in Tab. 1 of the supplemental. The SVC and SWM models obtain comparable SSIM scores but the latter outperforms the former by larger margins of ~2 and 0.18 for PSNR and SSIM. We refer all reviewers to Sec. B.1.1 for more information. \nTo further analyze the reliance of these world models, we evaluate the performance when extended to multiple steps(1 to 3) in the following table.\n||PSNR$\\uparrow$|SSIM$\\uparrow$|LPIPS$\\downarrow$|\n|-|-|-|-| \n|SVC-step1|67.38|0.997|0.39|\n|SVC-step2|63.98|0.994|0.54|\n|SVC-step3|62.80|0.993|0.57|\n|SWM-step1|67.35|0.998|0.26|\n|SWM-step2|66.71|0.997|0.30|\n|SWM-step3|65.98|0.997|0.35|\n\nGenerally, we observe consistent trends across the SVC and SWM models, where their effectiveness gradually decrease as the number of steps increases. This is expected since the uncertainty of predicting the exact visual changes is compounded over steps.\n\n### Critical advancements required\nGiven our observation that the fidelity of the world model can deteriorate quickly over extended imagined trajectories, we believe that addressing this challenge is important. Some possibilities include but are not limited to a long-term memory module that is capable of storing a mental model of a 3D scene as it is explored, as well as learning to generate environments based on scene-graphs. The latter is aimed at improving generalizability to spatial layouts unseen during training time."}, {"review_id": "aSjVWYSfto", "reviewer": "Reviewer_27Ro", "summary": "Summary\nThis paper introduces SpatialNavigator, a test-time scaling framework designed to enhance the spatial reasoning capabilities of vision-language models (VLMs) by pairing them with action-driven, pose-conditioned video world models. The pipeline operates by letting the VLM iteratively plan exploratory camera trajectories, which the world model renders into imagined observations. These are then used by the VLM to answer spatial-reasoning queries, leveraging multi-view synthetic evidence at inference time without any additional training.", "questions": "See weakness", "limitations": "Yes", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths And Weaknesses\n\nStrengths:\n1. The paper identifies a core challenge in spatial reasoning for VLMs, emphasizing the inability of current models to mentally simulate 3D scene dynamics from 2D inputs.\n\n2. The strategy of coupling an off-the-shelf VLM with a video world model is systematic and well grounded.\n\n3. This paper is well-written and well-organized.\n\nWeaknesses:\n1. The performance of the entire system appears to heavily depend on the quality of the world model.\n\n2. The test-time computation required for multiple simulated rollouts, per-step VLM scoring, and evidence buffer construction could be substantial, especially for deeper search settings and larger action spaces\n\n3. Although the method is compared to some RL-based TTS VLMs, more explicit analysis or comparison to recent test-time ensembling, chain-of-thought reranking, verifier-based TTS, or model-based counterfactual data augmentation could provide a clearer picture of strengths relative to the broader TTS design space.", "was_revised": true, "final_justification": "After reading the rebuttal and reviewing the provided case studies and videos, my main takeaway is that the inclusion of the world model brings negligible benefit to spatial reasoning, and in some cases, may even be unnecessary or redundant.\n\nFirst, **zero-shot generalization in video generation is inherently very limited**. When the viewpoint shift involves a significant rotation or movement, the model must hallucinate content that was not clearly visible in the first frame. This makes the generated frames unreliable, especially for reasoning tasks that demand accurate spatial understanding. **This limitation is clearly visible in the authors’ videos and qualitative results** — the world model is only capable of generating scenes under minor rotations or small translations, which aligns with current known limitations of such models.\n\nSecond, **the generation process takes several seconds**, which raises serious concerns about practicality and scalability. If the spatial reasoning gain is has limited application and unstable, it is unclear what value the additional computational burden brings to the overall pipeline.\n\nIn summary, the proposed use of the world model seems to add substantial complexity and latency while providing minimal actual benefit to spatial reasoning or downstream performance.", "rebuttal": "Dear Reviewer,\n\nWe thank the reviewer for the detailed reviews and insightful suggestions. We are glad to hear that you find our method \"systematic and well-grounded\" and our paper well motivated and well-written. Here are the responses to your questions.\n\n### World Model Limitations\nWe acknowledge that the performance of our method depends on the fidelity of the world models. Therefore, we provide metrics and visualizations to analyze this dependency both quantitatively and qualitatively. We included additional analysis of the quality of SWM and SVC, and also their failure modes in the supplementary material, which helps the community to quantify the world model quality in our application and understand the weakness of current world models for further improvement (Sec B).\n\nFor the former, using the out-of-domain simulator AI2Thor, we evaluated the world model's fidelity under a sequence of atomic actions (turn-left/right, move-forward) defined in our work. We report the PSNR, LPIPS, and SSIM in Table 1 of the supplemental. The SVC and SWM models obtain comparable SSIM scores but the latter outperforms the former by larger margins of ~2 and 0.18 for the PSNR and SSIM scores. We refer all reviewers to Section B.1.1 of our supplemental for more information.\n\n#### Quanitifying World Model Quality vs. Downstream Performance\n\nTo gauge how visual quality translates into reasoning, we ran SpatialNavigator with the original SWM and two deliberately degraded checkpoints.\n|  | PSNR $\\uparrow$| SSIM $\\uparrow$| LPIPS(1e-4) $\\downarrow$ | SAT-Real | SAT-Synthesized |\n| -------- | -------- |  -------- | -------- |  -------- | -------- | \n| SWM-original | 66.59 $\\pm$ 0.21| 0.997 $\\pm$ 0.001| 0.31 $\\pm$ 0.02  | 68.0 | 70.8 |\n| SWM-checkpoint1 | 65.06 $\\pm$ 0.28| 0.996 $\\pm$ 0.001| 0.44 $\\pm$ 0.02  | 63.3 | 66.0 |\n| SWM-checkpoint2 | 64.03 $\\pm$ 0.28| 0.995 $\\pm$ 0.001| 0.50 $\\pm$ 0.03  | 61.3 | 63.0 |\n\nAs PSNR/SSIM drop and LPIPS rises, SAT accuracy falls proportionally, showing that the three metrics, especially PSNR and LPIPS provide a practical heuristic for evaluating world models before downstream deployment.\n\n#### Future Development of World Models\n\nDespite the current limitations of learned world models, SpatialNavigator consistently boosts VLM reasoning performance. Ongoing advances—illustrated by lighter, higher-fidelity backbones such as Wan2.1 and the controllable ReCamMaster—provide a clear trajectory for further progress. As these models improve, we expect them to unlock even greater downstream performance with SpatialNavigator.\n\n### Computational Efficiency\nWe appreciate the reviewer’s suggestion and agree that analyzing computational trade-offs is important for practical deployment. To address this, we provide a structured analysis of the current computational efficiency of SpatialNavigator.\n\nAlso, we would like to clarify that SpatialNavigator is the first work on test-time scaling framework that uses VLM to control a video diffusion model. Thus, we mainly focused on optimizing the effectiveness instead of efficiency, we acknowledge that efficiency optimization is a promising direction for future work. To address this, we outline several acceleration techniques that make further speed-ups highly plausible.\n\n#### Efficiency Analysis\nWe profiled computational efficiency on a single H100-80G. We evaluated using gpt4o+SN(SWM), with beam size 2 and inference step 50 on SAT-Real. The memory consumption is~40G across the experiments. As SVC cam only generates high resolution video and therefore not efficient(around x3 world model latency), we mainly use SWM in this section.\n|Model|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|gpt4o|0|-|-|1.57|1.57|60.0|\n|gpt4o|1|14.52|4.70|1.69|20.91|66.0|\n|gpt4o|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|52.78|13.53|3.35|69.66|68.7|\n\nIn our main experiments, we focused on optimizing the effectiveness so the default setting is not the most cost-effective setting. To explore more cost-optimized settings, we further investigate reducing the number of inference step in the table below:\n\n|Iters|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|10|1|4.66|4.66|2.56|11.88|66.7|\n|10|2|7.62|8.28|2.72|18.62|66.7|\n|10|3|10.69|11.25|5.79|27.73|67.3|\n|20|1|7.22|4.44|2.48|14.14|66.0|\n|**20**|**2**|11.76|9.14|2.50|**23.40**|**68.0**|\n|20|3|23.51|10.30|4.29|38.10|67.3|\n\nThe results demonstrate that our model can maintain a >95% of the accuracy gain at ≤33% of the latency, with a latency of about 23s. We tested o1 on the SAT-Real with latency in average 19.6s, so SpatialNavigator in cost-optimized setting is in the same latency band as widely used test-time-scaling pipelines.\n\nWe further added the ablation study on beam size in the table below:\n|Model|Step|Beam|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|-|\n|gpt4o|2|1|23.24|7.51|1.60|32.35|67.3|\n|gpt4o|2|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|1|32.16|14.43|3.58|50.17|68.0|\n|gpt4o|3|2|52.78|13.53|3.35|69.66|68.7|\n\n#### Scalability & Acceleration Pathways\n\n**Backbone Model** \nRecent released Wan2.1-1.3B provides videos with quality higher than our backbone CogVideoX-5B in ≈8GB VRAM. Wan2.1 achieved ≈1.7×lower (8.7s vs.14.5s) latency and ≈½ the memory footprint relative our current backbone tested under identical frame counts and resolution.\n\n**Step Distillation**\nLatent Consistency Models produce high-fidelity outputs in 2–4 inference steps, yielding 10–20× speed-ups over 50-step schedulers. Video-specific extensions such as Distribution-Matching Distillation report similar benefits on text-to-video transformers.\n\n**Low-Bit Quantization**\nPost-training methods like SVDQuant quantize both weights and activations to 4-bit while keeping FID within ±2 points of full precision. Empirical results report ≈3× speedups and up to 3.6× memory reduction on DiTs, enabling real-time sampling on a 16GB laptop-class GPU.\n\nAll three techniques are open-source and multually compatible, which indicates that improving computational cost is both practical and well-supported by the community.\n\n\n### Test-Time Scaling Baselines\n\nAs suggested by the reviewer, we implemented two classic test-time scaling baselines for further comparisons.\n1) Self-Consistency: The model is queried n times to give answers and rationales; the majority answer is returned.\n2) Re-Ranking: The model generates n rationales and then scores them itself; the top-scoring answer is chosen.\n\n| Config | SAT_test | SAT_val |\n| -- | --- |  -- | \n| gpt-4o | 60.3|61.0|\n| + SN (SWM) |68.0|70.8|\n| + SN (SVC) |69.3|72.3|\n| + Self-Consistency 10 samples | 61.3|62.2 |\n| + Self-Consistency 30 samples | 63.3|61.4 |\n| + Re-Ranking 10 samples | 62.7| 63.6|\n| + Re-Ranking 30 samples | 59.3| 63.2|\n\nWe evaluated the two methods using gpt-4o as the base model and experimented with different n values, 10 and 30.\n\nAccording the tables, although the classic test-time scaling methods generally improve from the baselines, they are not as effective as SpatialNavigators for the spatial reasoning tasks."}, {"review_id": "BqAI5kcpjV", "reviewer": "Reviewer_Pkry", "summary": "This paper proposes a novel spatially-aware beam search algorithm that allows to improve the performance of existing VLMs at test-time by coupling a VLM with a posed-controlled diffusion models that acts as a world model.", "questions": "1. Table 1 (SAT-Real).\n    * Why are GPT-4.1 + SN(SVC) and InternVL + SN(SVC) missing?\n\n\n2. World-model quality.\n    * Can you quantify how the DM quality correlates with downstream accuracy? (An experiment where you degrade SWM (e.g. an early checkpoint) or compare longer roll-outs would help.)\n    * Could you provide a simple heuristic or automatic self-check so practitioners know when their world-model is “good enough” for the presented methodology?\n\n\n3. Runtime.\n    * Please report the average wall-clock time per VQA  and compare it to the vanilla VLM baseline.\n    * A plot of accuracy vs. inference-time budget would let readers judge whether the reported  gain justifies the extra compute.\n\n4. Hyper-parameter sensitivity.\n    * How robust is the algorithm  to the thresholds  ?\n    * What happens if you restrict the action space to {move-forward} only but allow deeper search, or conversely allow all three actions but only one step?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n* The idea of “adding” a visual/spatial dimension to beam search is thought-provoking. One can imagine extending the same principle to other search algorithms.\n* Experiments are fairly comprehensive: the authors evaluate on both proprietary  and open-source  VLMs, across synthetic and real SAT benchmarks, and with two distinct world models (their SWM and SVC). I particularly appreciate showing SVC even tho the results on o1 are not as good.\n* Reported gains are consistent.\n\n\nWeaknesses:\n* Inference cost. Even vanilla beam search is slow; adding a video-diffusion rollout at every node seems expensive for simple VQA tasks. The paper gives no wall-clock numbers or cost–accuracy plots, so practicality remains unclear.\n* Single-view assumption. The pipeline assumes exactly one reference image; the authors list this as a limitation themselves . It is unclear how SBS would handle multiple views or a short video.\n* Reliance on world-model fidelity. The approach assumes the pose-conditioned diffusion model can render a faithful scene. In practice, a single image underspecifies geometry, so hallucinations could hurt more than help. The authors also acknowledge this,.\n* Scalability knobs left unexplored. Apart from the brief ablation, the paper mostly fixes one search configuration for all experiments . It would be useful to see accuracy vs. (a) action-set size and (b) beam depth to understand the compute/accuracy trade-off. For instance, what’s the performance if the action space is reduced to only one action vs two, and the depth of the tree is increased?\n\n\nOverall, the idea is interesting and the empirical gains seem solid, but the paper needs more discussion (and data) on computational cost and on when the world-model may fail.", "was_revised": true, "final_justification": "I think the response addresses my concerns. I recommend to incorporate all new experiments to the paper. I feel this is an interesting direction and I feel positive about acceptance of this paper.", "rebuttal": "Dear Reviewer Pkry,\n\nWe appreciate your detailed comments and insightful suggestions. We are glad to hear you find our idea \"thought-provoking\" and our experiments \"comprehensive\". Here are the responses to your questions.\n\n### Computational Efficiency\nWe appreciate the reviewer’s suggestion and agree that analyzing computational trade-offs is important for practical deployment. To address this, we provide a structured analysis of the current computational efficiency of SpatialNavigator.\n\nAlso, we would like to clarify that SpatialNavigator is the first work on test-time scaling framework that uses VLM to control a video diffusion model. Thus, we mainly focused on optimizing the effectiveness instead of efficiency, we acknowledge that efficiency optimization is a promising direction for future work. To address this, we outline several acceleration techniques that make further speed-ups highly plausible.\n\n#### Efficiency Analysis\nWe profiled computational efficiency on a single H100-80G. We evaluated using gpt4o+SN(SWM), with beam size 2 and inference step 50 on SAT-Real. The memory consumption is~40G across the experiments. As SVC cam only generates high resolution video and therefore not efficient(around x3 world model latency), we mainly use SWM in this section.\n|Model|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|gpt4o|0|-|-|1.57|1.57|60.0|\n|gpt4o|1|14.52|4.70|1.69|20.91|66.0|\n|gpt4o|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|52.78|13.53|3.35|69.66|68.7|\n\nIn our main experiments, we focused on optimizing the effectiveness so the default setting is not the most cost-effective setting. To explore more cost-optimized settings, we further investigate reducing the number of inference step in the table below:\n\n|Iters|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|10|1|4.66|4.66|2.56|11.88|66.7|\n|10|2|7.62|8.28|2.72|18.62|66.7|\n|10|3|10.69|11.25|5.79|27.73|67.3|\n|20|1|7.22|4.44|2.48|14.14|66.0|\n|**20**|**2**|11.76|9.14|2.50|**23.40**|**68.0**|\n|20|3|23.51|10.30|4.29|38.10|67.3|\n\nThe results demonstrate that our model can maintain a >95% of the accuracy gain at ≤33% of the latency, with a latency of about 23s. We tested o1 on the SAT-Real with latency in average 19.6s, so SpatialNavigator in cost-optimized setting is in the same latency band as widely used test-time-scaling pipelines.\n\nWe further added the ablation study on beam size in the table below:\n|Model|Step|Beam|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|-|\n|gpt4o|2|1|23.24|7.51|1.60|32.35|67.3|\n|gpt4o|2|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|1|32.16|14.43|3.58|50.17|68.0|\n|gpt4o|3|2|52.78|13.53|3.35|69.66|68.7|\n\n#### Scalability & Acceleration Pathways\n\n**Backbone Model** \nRecent released Wan2.1-1.3B provides videos with quality higher than our backbone CogVideoX-5B in ≈8GB VRAM. Wan2.1 achieved ≈1.7×lower (8.7s vs.14.5s) latency and ≈½ the memory footprint relative our current backbone tested under identical frame counts and resolution.\n\n**Step Distillation**\nLatent Consistency Models produce high-fidelity outputs in 2–4 inference steps, yielding 10–20× speed-ups over 50-step schedulers. Video-specific extensions such as Distribution-Matching Distillation report similar benefits on text-to-video transformers.\n\n**Low-Bit Quantization**\nPost-training methods like SVDQuant quantize both weights and activations to 4-bit while keeping FID within ±2 points of full precision. Empirical results report ≈3× speedups and up to 3.6× memory reduction on DiTs, enabling real-time sampling on a 16GB laptop-class GPU.\n\nAll three techniques are open-source and multually compatible, which indicates that improving computational cost is both practical and well-supported by the community.\n\n### Multiview Evaluation\nAs additional evidence of the generalizability of our approach, we provide evaluation results on the MindCube benchmark, which evaluates VLMs' capability to perform multiview spatial reasoning in real 3D scenes. We used the Stable Virtual Camera (SVC) backbone because SVC can synthesize videos conditioned on multiple input views, whereas our current SWM lacks multiview generation support. MindCube supplies an initial frame for every question, enabling us to plug our SpatialNavigator into the multiview setting with only minor modifications.\n\n||Acc.|\n|-|-|\n|gpt4o|44.0|\n|+ SN (SVC)|51.3|\n|gpt4.1|43.3|\n|+ SN (SVC)|47.3|\n\nUnlike static and single-view image datasets, the multiview setting in MindCube requires the ability to understand 3D spatial layouts and cross-view consistency. \nThe results further demonstrate the potential of our method in real-world and multiview settings. We observe that SpatialNavigator is able to improve average accuracy by approximately 4 to 7% across different VLMs. Thus, SpatialNavigator enables VLMs to generalize to multiview settings by integrating visual cues from multiple imagined perspectives. This capability highlights its potential in embodied AI and robotic tasks. Last but not least, the results suggest that SpatialNavigator is model-agnostic and can further improve with advances in more capable world models as well as VLMs. We will include these results in the next version of the paper to make our evaluations more comprehensive.\n\n\n### World Model Limitations\nWe included additional analysis of the quality of SWM and SVC, and also their failure modes in the supplementary material, which helps the community to quantify the world model quality in our application and understand the weakness of current world models for further improvement (Sec B).\n\nFor the former, using the out-of-domain simulator AI2Thor, we evaluated the world model's fidelity under a sequence of atomic actions (turn-left/right, move-forward) defined in our work. We report the PSNR, LPIPS, and SSIM in Table 1 of the supplemental. The SVC and SWM models obtain comparable SSIM scores but the latter outperforms the former by larger margins of ~2 and 0.18 for the PSNR and SSIM scores. We refer all reviewers to Section B.1.1 of our supplemental for more information.\n\n#### Quanitifying World Model Quality vs. Downstream Performance\n\nTo gauge how visual quality translates into reasoning, we ran SpatialNavigator with the original SWM and two deliberately degraded checkpoints.\n|  | PSNR $\\uparrow$| SSIM $\\uparrow$| LPIPS(1e-4) $\\downarrow$ | SAT-Real | SAT-Synthesized |\n| -------- | -------- |  -------- | -------- |  -------- | -------- | \n| SWM-original | 66.59 $\\pm$ 0.21| 0.997 $\\pm$ 0.001| 0.31 $\\pm$ 0.02  | 68.0 | 70.8 |\n| SWM-checkpoint1 | 65.06 $\\pm$ 0.28| 0.996 $\\pm$ 0.001| 0.44 $\\pm$ 0.02  | 63.3 | 66.0 |\n| SWM-checkpoint2 | 64.03 $\\pm$ 0.28| 0.995 $\\pm$ 0.001| 0.50 $\\pm$ 0.03  | 61.3 | 63.0 |\n\nAs PSNR/SSIM drop and LPIPS rises, SAT accuracy falls proportionally, showing that the three metrics, especially PSNR and LPIPS provide a practical heuristic for evaluating world models before downstream deployment.\n\n### More Ablation Studies and Experiments\n\n\n#### Experiments\n| Config        | Avg  | EgoM  | ObjM | EgoAct | GoalAim | Pers |\n| ------------- | ---- | ----- | ---- | ------ | ------- | ---- |\n| GPT-4.1+SVC   | 77.3 | 100.0 | 82.6 | 86.5   | 79.4    | 45.4 |\n| Internvl3+SVC | 66.7 | 69.6  | 60.9 | 78.4   | 79.4    | 42.4|\n\nWe added the experiments on gpt-4.1 + SVC and InterVL3 + SVC on the SAT-Real as suggested by the reviewer\n#### Action Space Ablations\nTo disentangle the role of translation versus rotation in viewpoint acquisition, we evaluated two reduced-action variants of our policy: (i) No-Move—all move-forward commands were removed; (ii) No-Turn—all turn-left / turn-right commands were removed.\n| Config | Acc. |\n| -------- | -------- |\n| GPT-4o      |70.8|\n| GPT-4o+forward_only | 64.6 |\n| GPT-4o+Turn_only | 69.3 |\n\nAccording to the ablation results, the performance of turn_only is close to the complete action set. Rotations are potentially more important than forward for the tasks in SAT.\n\n#### Threshold Ablations\nSection 4.3 varies the pruning threshold τ controlling which roll-outs are retained. As illustrated in Fig. 4, very low thresholds (τ ≤ 5) admit numerous low-fidelity frames; accumulated error then grows with trajectory length. Performance stabilises once τ ≥ 7—the curves for τ = 7 and τ = 9 are similar."}, {"review_id": "0jbBvLYpcD", "reviewer": "Reviewer_w3Uk", "summary": "This paper proposes SpatialNavigator, a test-time framework that enhances vision-language models' (VLMs) spatial reasoning by coupling them with a controllable video diffusion world model. Without requiring fine-tuning, the method enables VLMs to iteratively explore 3D scenes by planning camera trajectories while the world model generates corresponding synthetic views; these imagined observations are then evaluated and cached to help answer spatial reasoning questions. Experiments across four VLMs and two world models demonstrate consistent improvements (average +8.1% accuracy on the SAT benchmark), showing that this plug-and-play approach complements—and sometimes surpasses—existing methods like RL-finetuned VLMs, while revealing limitations in long-horizon scene consistency that suggest directions for future world model development.", "questions": "1. **World Model Quality**: What are the FVD/LPIPS scores for generated views at 1/3/5 steps? Include metrics for both indoor and outdoor scenes.\n\n2. **Failure Analysis**: What are the top 3 failure modes of your world model (e.g., object disappearance)? What percentage of errors do they account for?\n\n3. **Compute Efficiency**: What is the end-to-end latency for SAT questions (breakdown: world model calls vs VLM scoring)? How does it scale with beam width B?\n\n4. **Real-World Generalization**: Can you show results on real embodied tasks (ScanNet in Thinking in Space)?\n\n5. What is the human performance gap on SAT after your improvements? What errors remain?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n### 1. Technical Soundness\nThe integration of video diffusion models (for 3D consistency) with VLM-guided search is well-executed. The beam search formulation is reasonable and addresses combinatorial explosion in trajectory space. Pipeline (Fig. 2) and algorithm (Alg. 1) are clearly explained. Key components (world model formulation, action space, VLM scoring) are detailed in separate subsections.\n\n### 2. Practical Impact\nAddresses a critical limitation of VLMs (static 2D reasoning) for embodied reasoning. The plug-and-play nature makes it accessible for real-world deployment.\n\n### 3. Rigorous Evaluation\nExtensive experiments on the SAT benchmark (synthetic + real images) with multiple VLMs (GPT-4o, o1, etc.) and world models (SWM, SVC), demonstrating consistent gains (+8.1% avg.). Ablation studies validate design choices.\n\n---\n\n## Weaknesses\n\n### 1. Clarify Technical Novelty  \nThe proposed use of a video world model for test-time scaling in spatial reasoning appears incremental, as conditioning video diffusion on camera poses builds directly on prior works (e.g., CamCtrl, Stable-Virtual-Camera). The core novelty seems to lie in the *interaction* between the VLM and the world model rather than the individual components. The authors should explicitly highlight and differentiate their key technical contributions to avoid misperception of triviality.  \n\n### 2. Address World Model Limitations  \n- **Long-horizon consistency**: Breakdowns in multi-step reasoning (e.g., Fig. 4a) suggest the approach is constrained by current video diffusion models’ limitations. A deeper analysis of failure modes would strengthen the discussion.  \n- **Quantitative evaluation**: Metrics like FVD or LPIPS for view synthesis quality are absent. Including such analyses would provide a more rigorous assessment of the world model’s capabilities.  \n\n### 3. Analyze Computational Trade-offs  \nThe test-time search requires multiple world model calls, but the computational costs (latency, throughput, or energy usage) are not discussed. A brief analysis of scalability—especially for real-world applications—would help readers understand practical limitations.  \n\n### 4. Expand Evaluation Scope  \nThe focus on the synthetic SAT benchmark limits the generality of claims. Evaluating on additional spatial reasoning tasks—such as real-world 3D scene understanding (e.g., [1]) or multi-view consistency (e.g., [2])—would better demonstrate the method’s robustness and broader applicability.  \n\n### 5. Include Missing Baselines  \nComparisons with alternative approaches are needed to contextualize the benefits of test-time scaling:  \n- **3D-aware VLMs**: How does the method compare to architectures explicitly designed for spatial reasoning?  \n- **End-to-end fine-tuning**: Does test-time adaptation outperform direct model adjustments?  \n\n\n[1] Yang, J., Yang, S., Gupta, A.W., Han, R., Fei-Fei, L. and Xie, S., 2025. Thinking in space: How multimodal large language models see, remember, and recall spaces. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 10632-10643).\n\n[2] Yeh, C.H., Wang, C., Tong, S., Cheng, T.Y., Wang, R., Chu, T., Zhai, Y., Chen, Y., Gao, S. and Ma, Y., 2025. Seeing from another perspective: Evaluating multi-view understanding in mllms. arXiv preprint arXiv:2504.15280.", "was_revised": true, "final_justification": "The response sufficiently addressed the concerns I raised in the initial review, particularly the technical novelty and missing baselines. I find the clarifications satisfactory and have updated my score to reflect this.", "rebuttal": "Dear Reviewer,\n\nWe thank you for the detailed comments and questions. We are glad to hear you find our method \"well-executed and reasonable\" and our evaluation \"rigorous\". We address each concern below:\n\n### Technical Novelty\nWe would like to clarify that our contribution does not lie in proposing a new video diffusion model, but rather in the test-time collaboration protocol that integrates a frozen VLM with a pose‑conditioned world model to enhance 3D spatial reasoning:\n\n**VLM‑guided imagination.** SpatialNavigator, to our knowledge, is the first framework in which a VLM actively controls a world model at inference time to bolster its own reasoning.\n\n**Beam‑search coupling.** The VLM/world-model loop is cast as beam search, balancing diversity and evidence scoring. \n\nWe will revise the introduction sections to highlight that our novelty lies in this inference-time coordination mechanism, which builds on, but is distinct from prior works using camera-conditioned video models.\n\n### World Model Limitations\nWe have already included additional analysis of the quality of our trained world model and SVC, and also their failure modes in the supplementary material (Sec. B). \nFor the former, using the out-of-domain simulator AI2Thor, we evaluated the world model's fidelity under a sequence of atomic actions defined in our work. We report the PSNR, LPIPS, and SSIM in Tab. 1 of the supplemental. The SVC and SWM models obtain comparable SSIM scores but the latter outperforms the former by larger margins of ~2 and 0.18 for PSNR and SSIM. We refer all reviewers to Sec. B.1.1 for more information. \nTo further analyze the reliance of these world models, we evaluate the performance when extended to multiple steps(1 to 3) in the following table.\n||PSNR$\\uparrow$|SSIM$\\uparrow$|LPIPS$\\downarrow$|\n|-|-|-|-| \n|SVC-step1|67.38|0.997|0.39|\n|SVC-step2|63.98|0.994|0.54|\n|SVC-step3|62.80|0.993|0.57|\n|SWM-step1|67.35|0.998|0.26|\n|SWM-step2|66.71|0.997|0.30|\n|SWM-step3|65.98|0.997|0.35|\n\nGenerally, we observe consistent trends across the SVC and SWM models, where their effectiveness gradually decrease as the number of steps increases. This is expected since the uncertainty of predicting the exact visual changes is compounded over steps.\n\nDespite the current limitations of learned world models, SpatialNavigator consistently boosts VLM reasoning performance. Ongoing advances—illustrated by lighter, higher-fidelity backbones such as Wan2.1 and the controllable ReCamMaster—provide a clear trajectory for further progress. As these models mature, we expect them to unlock even greater downstream performance with SpatialNavigator.\n\n### Expand Evaluation Scope\nWith regards to expanding the evaluation scope to real 3D scenes, we have also demonstrated the effectiveness of our method on SAT-Real in Tab. 1 of the main paper, where it achieves performance gains consistent with prior observations. As additional evidence of the generalizability of our approach, we also provide evaluation results on MindCube, which tasks VLMs to perform multiview spatial reasoning in real 3D scenes. We only evaluated with SVC, as it supports video generation conditioned on multiview images, unlike SWM which currently lacks this capability.\n||Acc.|\n|-|-|\n|gpt4o|44.0|\n|+ SN (SVC)|51.3|\n|gpt4.1|43.3|\n|+ SN (SVC)|47.3|\n\nUnlike static and single-view image tasks, the multiview setting in MindCube requires to understand 3D spatial layouts and cross-view consistency. The results further show the potential of our method in real-world and multiview settings. We observe that SpatialNavigator is able to improve accuracy by approximately 4 to 7% across different VLMs. Thus, SpatialNavigator enables VLMs to generalize to multiview settings by integrating visual cues from multiple imagined perspectives. This capability highlights its potential in embodied AI and robotic tasks. \nFinally, the results suggest that SpatialNavigator is model-agnostic and can further improve with advances in better world models as well as VLMs. We will include these results in the next version of the paper to make our evaluations more comprehensive.\n\n\n### Computational Efficiency\nWe appreciate the reviewer’s suggestion and agree that analyzing computational trade-offs is important for practical deployment. To address this, we provide a structured analysis of the current computational efficiency of SpatialNavigator.\n\nAlso, we would like to clarify that SpatialNavigator is the first work on test-time scaling framework that uses VLM to control a video diffusion model. Thus, we mainly focused on optimizing the effectiveness instead of efficiency, we acknowledge that efficiency optimization is a promising direction for future work. To address this, we outline several acceleration techniques that make further speed-ups highly plausible.\n\n#### Efficiency Analysis\nWe profiled computational efficiency on a single H100-80G. We evaluated using gpt4o+SN(SWM), with beam size 2 and inference step 50 on SAT-Real. The memory consumption is~40G across the experiments. As SVC cam only generates high resolution video and therefore not efficient(around x3 world model latency), we mainly use SWM in this section.\n|Model|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|gpt4o|0|-|-|1.57|1.57|60.0|\n|gpt4o|1|14.52|4.70|1.69|20.91|66.0|\n|gpt4o|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|52.78|13.53|3.35|69.66|68.7|\n\nIn our main experiments, we focused on optimizing the effectiveness so the default setting is not the most cost-effective setting. To explore more cost-optimized settings, we further investigate reducing the number of inference step in the table below:\n\n|Iters|Step|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|\n|10|1|4.66|4.66|2.56|11.88|66.7|\n|10|2|7.62|8.28|2.72|18.62|66.7|\n|10|3|10.69|11.25|5.79|27.73|67.3|\n|20|1|7.22|4.44|2.48|14.14|66.0|\n|**20**|**2**|11.76|9.14|2.50|**23.40**|**68.0**|\n|20|3|23.51|10.30|4.29|38.10|67.3|\n\nThe results demonstrate that our model can maintain a >95% of the accuracy gain at ≤33% of the latency, with a latency of about 23s. We tested o1 on the SAT-Real with latency in average 19.6s, so SpatialNavigator in cost-optimized setting is in the same latency band as widely used test-time-scaling pipelines.\n\nWe further added the ablation study on beam size in the table below:\n|Model|Step|Beam|SWM time(s)|vlm_search time(s)|vlm_qa time(s)|total time(s)|acc%|\n|-|-|-|-|-|-|-|-|\n|gpt4o|2|1|23.24|7.51|1.60|32.35|67.3|\n|gpt4o|2|2|39.53|8.40|1.85|49.78|70.7|\n|gpt4o|3|1|32.16|14.43|3.58|50.17|68.0|\n|gpt4o|3|2|52.78|13.53|3.35|69.66|68.7|\n\n#### Scalability & Acceleration Pathways\n\n**Backbone Model** \nRecent released Wan2.1-1.3B provides videos with quality higher than our backbone CogVideoX-5B in ≈8GB VRAM. Wan2.1 achieved ≈1.7×lower (8.7s vs.14.5s) latency and ≈½ the memory footprint relative our current backbone tested under identical frame counts and resolution, \n\n**Step Distillation**\nLatent Consistency Models produce high-fidelity outputs in 2–4 inference steps, yielding 10–20× speed-ups over 50-step schedulers. Video-specific extensions such as Distribution-Matching Distillation report similar benefits on text-to-video transformers.\n\n**Low-Bit Quantization**\nPost-training methods like SVDQuant quantize both weights and activations to 4-bit while keeping FID within ±2 points of full precision. Empirical results report ≈3× speedups and up to 3.6× memory reduction on DiTs, enabling real-time sampling on a 16GB laptop-class GPU.\n\nAll three techniques are open-source and multually compatible, which indicates that improving computational cost is both practical and well-supported by the community.\n\n\n### Missing Baselines\nFor additional baselines, we report results for (i)a recent 3D-aware VLM VLM3R [1] and (ii)end-to-end fine-tuning on SAT-Real/SAT-Synth.\n\n**3D-aware VLMs**\nVLM3R was evaluated under the same zero-shot protocol used for our method (SpatialNavigator, SN).\n|Accuracy|SAT-Real|SAT-Synth.|\n|-|-|-|\n|gpt4o|0.603|0.61|\n|+ SN (SWM)|0.68|0.708|\n|VLM3R|0.527|0.512|\n\nDespite being explicitly designed for 3D spatial reasoning, VLM3R lags behind both the GPT-4o baseline and our test-time scaling scheme, underscoring the effectiveness of coupling a strong VLM with a controllable world model at inference time.\n\n**End-to-End Fine-Tuning**\nFine-tuning requires task-specific training data, whereas our approach is strictly zero-shot, which makes the comparison unfair. Nevertheless, for context we refer the finetuning baselines from the SAT paper with our numbers:\n|Acc.|SAT-Real|Improvements|\n|-|-|-|\n|LLaVA-1.5-13B|41.6|-|\n|+ Finetuned|54.9|+13.3|\n|LLaVA-Video-7B|53.5|-|\n|+ Finetuned|63.4|+9.9|\n|gpt4o|60.6|-|\n|+ SN(SVC)|69.3|+8.7|\n\nThe absolute gains delivered by test-time scaling(+8.7%) are on par with supervised fine-tuning(+9~13%), while preserving the zero-shot assumption and avoiding additional training costs. \n[1] Z, Fan., et al. VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction. arXiv preprint, 2025.\n\n### Other Questions\n\n**World Model quality in outdoor scenes**\nAssessing world-model fidelity outdoors would require using a dedicated outdoor simulator to render GT image sequences for atomic actions, which require engineering efforts to set up. As the outdoor environments are inherently unstructured (e.g., vegetation, variable lighting), generalizing to outdoor scene will be comparatively challenging. Although SpatialNavigator also boost performance on SAT-Real with outdoor scenes, this study targets more on structured indoor domain.\n\n**Top 3 Failure Mode**\nWe manually inspected part of the rollouts and the top 3 failure modes are B.1.1 Inaccurate Forward Movement, B.1.4 Visual Artifacts, and B.1.5 Scene Misinterpretation mentioned in the supplimentary material.\n\n**Human Performance Gap**\nHumans score 92.8% on SAT-Synth.—≈15% above our best model—signaling significant headroom for future work."}]

Author rebuttal:

Meta-review: This paper presents a method for pairing a VLM with a world model, in a navigation task. The VLM estimates a path to take, and the world model synthesizes the views that would be received after these actions, and these views are then sent back to the VLM as hallucinated "evidence" for scoring. This paper received mixed reviews: 5, 2, 5, 4. The positive reviews commend the intuitiveness of the approach, the fact that it's model-agnostic, and the clarity of the results. The reviews also point out (as a negative) that this approach is very expensive. Reviewer 27Ro in particular makes a confident negative case, pointing out (in addition to the speed issue) that the results are highly sensitive to the quality of the world model, and that the datasets used in evaluation are not the best ones. Overall however, the AC finds the positive case for this paper compelling, especially thanks to the additional results added in the rebuttal (MindCube dataset, GPT4.1 baseline). Therefore, the AC recommends acceptance. The authors are highly encouraged to carefully incorporate the work done during the rebuttal, to strengthen the paper for camera-ready.
Decision: Accept (poster)