OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
Authors: Mengdi Jia
Zekun Qi
Shaochen Zhang
Wenyao Zhang
XinQiang Yu
Jiawei He
He Wang
Li Yi
Venue: ICLR 2026 Poster (2026)
Keywords: Spatial Reasoning
Vision-Language Models
Benchmark
Primary area: datasets and benchmarks
TL;DR: OmniSpatial is a large-scale comprehensive benchmark that reveals persistent gaps in VLMs’ spatial reasoning across dynamic, logical, interaction, and perspective-taking tasks, while PointGraph and SpatialCoT offer promising improvements.

Abstract: Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or improve VLMs' understanding of basic spatial relations, such as distinguishing left from right, near from far, and object counting, these tasks cover only the most elementary layer of spatial reasoning and are largely approaching saturation in the latest reasoning models. In this work, we introduce OmniSpatial, a comprehensive and challenging benchmark for spatial reasoning, grounded in cognitive psychology. OmniSpatial covers four major categories: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking, with 50 fine-grained subcategories. Through careful manual annotation, we construct over 8.4K question-answer pairs. Extensive experiments show that both open- and closed-source VLMs exhibit significant limitations in comprehensive spatial reasoning. We also explore two strategies—PointGraph (explicit scene graph cues) and SpatialCoT (novel-view chain-of-thought)—to bolster spatial reasoning.

Published as a conference paper at ICLR 2026
OMNISPATIAL: TOWARDS COMPREHENSIVE SPATIAL REASONING BENCHMARK FOR VISION LANGUAGE MODELS
Mengdi Jia
Zekun Qi
Shaochen Zhang²
Wenyao Zhang Li Y
Xingiang Yu
Jiawei He*
He Wang 455
Tsinnhuu University Galboo
Xian Jiaotong University
Shanghai Jiao Tong University Shanghai Qi Zhi Institute
SPeking University
@
Project Page
Evaluation Code
Dataset
Existing Spatial Bench
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Figure 1: Overview of OmniSpatial Benchmark.
ABSTRACT
Spatial reasoning is key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed basic spatial relations, such as
to evaluate or improve VLMs' understanding distinguishing left from right, near from far, and object counting, these tasks cover only the most elementary layer of spatial reasoning and are largely approaching saturation in the latest reasoning models. In this work, introduce OmniSpatial, a comprehensive and challenging benchmark for spatial reasoning, grounded cognitive psychology. OmniSpatial covers four major categories: dynamic rea- soning, complex spatial logic, spatial interaction, and perspective-taking, with 50 fine-grained subcategories. Through careful manual annotation, we construct over 8.4K question-answer pairs. Extensive experiments show that both open- and closed-source VLMs exhibit significant limitations in comprehensive spatial reasoning. We also explore two strategies PointGraph (explicit scene graph cues) and SpatialCoT (novel-view chain-of ofain-o--thoughht) to bolster spatial reasoning.
Equual contribution. 'Corresponding author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Spatial reasoning plays crucial role in bridging visual observation to robotic action (Huang et al., 2024b; Qi al., 2024; 2025), autonomous driving, and AR/VR. For models to execute tasks effectively, they must understand spatial relationships to determine appropriate actions. To enhance the spatial understanding of Vision-Language Models, prior works (Chen al., 2024; Cheng et al., 2024; Cai et al., 2024; Ma et al., 2024; Song et al., 2024; Yuan et al., 2024; Yang et al., 2024; Qi et al., 2025) have integrated spatial information into datasets, enabling basic forms of spatial reasoning. Various benchmarks (Du et al., 2024; Szymanska et al., 2024; Shiri et al., 2024; Fu et al., 2024b; Song et al., 2024; Yang et al., 2024) have been introduced to systematically evaluate such capabilities, focusing on tasks like recognizing left and right, estimating depth, and constructing cognitive maps (Yang et al., 2024; Tolman, 1948; Momennejad et al., 2023). Additionally, spatial reasoning has been applied manipulation tasks (Driess et al., 2023; Qi al., 2025; Yuan al., 2024), allowing systems to position objects according to specified spatial rules.
However, existing benchmarks still target basic spatial understanding, such as position relationship (left, right, front, back), proximity (near, far), and object counting. We use the latest reasoning models and agents evaluate these benchmarks. such as 03 (OpenAI, 2025b) and Gemini-2.5-Pro (Reid et al., 2024). The results are shown in the lower left of Fig. 1. These models have achieved over 90 % accuracy on previous benehmarks such as SpatialBot SpathchBot-Bench (Cai et al., 2024) and EmbSpatial (Du et al., 2024), suggesting that these basic tasks are approaching saturation.
We believe complex spatial reasoning remains significant challenge (Gardner, 2011; Baddeley, 1998; Previc, 1998; Kosoy et al., 2025; Pothiraj et al., 2025; Chen et al., 2025a). Human interaction with the physical world often involves interpreting ambiguous, dynamic, and context-dependent spatial relationships (Bar-Anan al., 2006; Trope & Liberman, 2010; Ramalho et al., 2018). For example, in an emergency. knowing that an AED is "to the right of the door" is insufficient without understanding schematic diagrams, correlating maps with real-world environments, and planning an efficient route. Similarly, tasks like inserting a knife into a rack or flattening a box demand reasoning about object rotation, deformation, and spatial compatibility-faa far beyond static object placement.
From the perspective of cognitive psychology, complex spatial reasoning goes beyond simple rela- tional judgments, encompassing dynamic world-knowledse reasoning, interactive spatial behavior with environments or agents, logical analysis of ftree-dimensional structures, and trrspective-taking abilities. Motivated by these challenges, we introduce OmniSpatial, comprehensive benchmark designed to capture the breadth and depth of spatial cognition. OmniSpatial systematically cate- gorizes spatial reasoning into four core dimensions dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking thus providing a principled foundation for developing next-generation spatially- and physically-award Al systems.
Table 1: Comparison with other spatial reasoning benchmarks. A comparison between OmniSpa- tial and other existing spatial reasoning benchmarks. OmniSpatial avoids template-bast based annotations, features highly diverse data, and includes a significantly larger number of tasks.
Dataset
Embodied Task Caregories
Data Domain
Data Amnotation
Data Scale Spatial QAs
EmbSSattill Bench (Du et al., 2024)
6
Indior (ScanNet, etc.)
Template
2.2K
3.6K
Space3D-Bench (Szymanska et al., 2024)
Indoor (Roplica) MSCOCO
Manual
1K
Visunl Spatial (Liu et al., 2023a)
7
Template
10K
10K
SpatialRGPT-BBo (Cheng al., 2024)
Urban, Indoor, Sim
Template
1.4K
What's up (Kamath al,, 2023)
6
Houschold, GQA, COCO
Template
SK
5K
Spatial-MM (Shiri al., 2024)
Internet
Template
2.3K
2.3K
RoboSpatial (Song el al., 2024)
Indour; tabletop
Template
IM
3M
SpatialVLM (Chen et al., 2024)
WebLi
Template
546
546
SpatialBot-Benae (Cai et al., 2024)
COCO,VG.RTX
Manual
200
360
VSI- Bench (Yang al., 2024)
Indoor
Template
288
5K
OmniiSpatial (Ours)
Internet
Manual
6.5K
8.4K
The OmniSpatial benchmark includes images or video frames across diverse scenes, resolutions, lighting conditions, and weather patterns, collected from multiple countries across different continents. We evaluate state-of-the-ar VLMs on our benchmark. Our findings indicate that, while current models perform well on conventional benchmarks, OmniSpatial presents significantly greater challenge due to its comprehensive and complex task design.
Our key contributions follows:
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Published as a conference paper at ICLR 2026
We categorize visual-spatial reasoning into four key dimensions- dynamic reasoning, complex spatial logic, spatial interaction, and perspective-takinn
broadening the scope of evaluation and guiding future research spatial cognition in embodied & physics intelligence.
We develop the OmniSpatial dataset, which offers a diverse and challenging set of spatial tasks, serving as comprehensive benchmark for assessing VLMs' spatial reasoning capabilittiss
We explore the enhancement of spatial reasoning in VLMs by incorporating auxiliary models in chain-of ofttthughtttttttth manner, demonstrating improved reasoning performance through this approach.
2 PRELIMINARIES: VISUAL-SPATIAL REASONING
Spatial reasoning constitutes the cognitive bridge between visual perception and geometric understanding. We define visual-spatial rea- soning as the capacity of an artificial system to infer, predict, and reason spatial properties of the world from visual observations. Formally, let RGB observation stream be I:TT and task-specific query be q. A model possesses visual-spatia reasoning that learn mapping:
Perspective Taking (37%)
Dynamic Reasoning (27%)
Allocentric (25%)
Egocentric (7%)
Monipulation((54)
Motion Analysia (23%)
Hypothetical Perspective Taking (5%)
Oyeostio cwwaaaa
pontectiv boking
Omni
Spatial
J (II:T Q) a,
where belongs to a well-defined action or answer space whose correctness can be veri- fied in the physical or simulated environment. This definition excludes non-visual priors so that improvements can be attributed to visual reasoning itself, yet remains compatible with multi-modal extensions discussed in $1.
Geospatiol Strategy (7%)
Localization (79))
Geometric Reasoning (100%
Traffic (69)
Patter (ecognition(69%)
Spatial
(20%)
Complex Logic (16%)
Figure 2: Benchmark Statistics OmniSpatial:
The distribution of tasks across 4 main categories.
2.1 TAXONOMY OF VISUAL-SPATIAL REASONING
Our taxonomy is motivated by two complementary perspectives. (i) Cognitive psychology founda- tions. Prior research spatial cognition highlights partly independent faculties tion, mental rotation, perspective taking, and spatial updating
visualiza-
can be systematically assessed in humans (Chabris al., 2006; Meneghetti et al., 2022). These constructs provide principled scaffold for analyzing how agents perceive, reason about, and act within space. (ii) Moving beyond basic spatial relations. Existing benchmarks are nearly saturated on simple tasks like left-right discrimination, front-back identification, and object counting (Chen et al., 2024; Cai et al., 2024; Du al., 2024; Szymanska al., 2024). Yet worl-world embodied tasks demand richer reasoning (Kosoy et al., 2025; Pothiraj et al., 2025; Chen et al., 2025a; Stogiannidis et al., 2025; Lee et al., 2025) about scene dynamics, multi-step logic, physical interaction, and viewpoint transformation.
Guided by these considerations, we partition visual-spatial reasoning into four complementary dimensions: dynamic reasoning, complex spatial logic, spatial interaction, and perspective taking. Each dimension corresponds to specific cognitive faculty and targets under-explored challenges in prior work, enabling us to probe a broader spectrum of spatial cognition while remaining grounded in psychological theory.
Dynamic Reasoning concerns inferring motion and temporal change from visual evidence. While our benchmark primarily uses static sparsely sampled frames, such inference is crucial for adaptive decision- making in domains like robotics and navigation.
Complex Spatial Logic involves higher-order reasoning about relations, transformations, and ge- ometric structures. It underpins problem-solvin in design, engineering, and manipulation, where anticipating structural or relational changes is essential.
Spatial Interaction emphasizes reasoning guided by environmental constraints and task goals, covering skills such as path planning, obstacle avoidance, and context-aware action selection.
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Egocentric
Manipulation Dynamic Reasoning
Hew mony robots are actually in the water?
8.1 2.2 0.3
Allocentric Perspective-Taking
In which direction the hand next? A. to to right eony the left
Are the sou phures on the left or right side of the munmiee?
A. 6o7
S. dight
Spatial Compatibility
Size Analysis
48om (8.3
Can o im wice electric vahicla pass between people and cor?
ir takes 2s to the drower eventy whott the xpeed?
Hypothetical Perspective-
1f te driver's how sere?
20,77/5m/5
10. 10.7bem/s
Traffic Analysis
Space Folding
(2, 2.275////
entering
sign
23, 23.75s
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Geometric Reasoning
Embodied Agent
Interaction
Which oprion con not be filled in?
appronching
Click which button to odjust the temp?
that shou
raturning
(too-dows, left-right)
Answert 1
1
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2r 2c. 2r le
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Spatial Imagination
Choose
chair
Geospatial Strategy
Orientation Observation
route takes advantage terroin to reduce clinbing?
Which women the some the one shown above?
Answer: A
Answer:
Embodied Navigation Analysis
Mation Analysis
What the minimum number of doors you need pase through go from chair
the o-h arte turning left right?
A.
8.2 0.3
D..
lefe
right
Figure 3: Tasks Demonstration of OmniSpatial. Several representative subtasks selected for demonstration in each category. Note that the questions above are slightly simplified for clarity.
Perspective Taking captures the ability adopt alternative viewpoints, supporting navigation, social cognition, and multi-agent coordination. It enables understanding relations from diverse perspectives and fosters flexible goblem-solvving
2.2 RATIONALE FOR CLASSIFICATION
This taxonomy balances theoretical comprehensiveness with practical applicability. Dynamic reason- ing highlights motion inference, complex logic captures abstract transformations, spatial interaction addresses real-time engagement, and perspective taking reflects cognitive flexibility. Together, these dimensions provide framework for evaluating spatial reasoning AI, robotics, and human cognition.
OMNISPATIAL: COMPREHENSIVE SPATIAL REASONING BENCHMARK
3.1
OVERVIEW
We present OmniSpatial, comprehensive benchmark designed evaluate VLMs on spatial reason- ing. Rather than pursuing sheer data volume, OmniSpatial emphasizes diversity, structure, and rigor. now consists 8.4K carefully curated QA pairs, substantially larger than earlier prototypes, and covers broad spectrum of scenarios that demand reasoning beyond pattern recognition.
The dataset integrates heterogeneous sources web imagery, standardized cognitive tests, driving- exam questions, and prior dataset images such as MME (Liang et al., 2024) and HOI4D (Liu et al., 2022). This mixture enriches both realism and complexity: natural images capture everyday envi- ronments and architectures; psychology-inste driving exams provide safety-critical dynamic reasoning; and embodied datasets contribute varied resolutions, viewpoints, and human -bjject interactions.
tasks introduce scientifically grounded challenges;
Tasks are organized into major categories and 50 fine-grained subtypes as shown in Figs. and 7, spanning from basic perspective-taking to dynamic motion prediction and spatial interaction in cluttered scenes. Each item is manually designed and reviewed through multi-round annotation, ensuring accuracy, consistency, and minimal ambiguity. This careful curation yields benchmark
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Task design
Filtering bad Data
OmniSpatial
volenie
odtracton
images
position count
Iow-sualiyy
recognition
QA Annotation
route HONE selection
Dertort
Ad 0000 Killag
Manual Data Collection
GA Pairs
Q:17 you are sitting one the brewn seats, in your parspection, what direction the pyromid?
From the perapeetire of the anfiltt in red, which direction in oppooeer calnging the exned?? lower right
Online imoges
Driving teats
A. from upper Bi Fioes deft to siaht
C. Frov bottor to top
Pepppee Tosks
Extating dotasets
O.Thssot
Figure 4: Data Construction of OmniSpatial. The pipeline collects images from multiple sources and ensures their quality and relevance through manual selection. Precise annotations are then applied, ensuring each question has clear, unique answer while maintaining natural, conversational expression. This process supports the effective training of VLMs in spatial reasoning tasks.
that not only broadens the coverage of spatial reasoning but also establishes a reliable ground for evaluating and advancing future multimodal intelligence.
3.2
BENCHMARK CONSTRUCTION
3.2. 1
DATA COLLECTION
As described in Section we define four spatial categories and corresponding task types. To build diverse and information-rich dataset, we design targeted search strategies for each type, optimizing for relevance, diversity, and complexity.
Web Images. Web images form a major part of our data, especially for saapeective-aaknnn dynamic reasoming, and spatial interaction. We use task task-spec search terms (e.g., "indoor layout," "furniture arrangement") and append filters ("-ai," "-generated") to reduce synthetic content. Images are retrieved via Google's Custom Search ISON API, Web RPA, and manual search, followed by strict filtering to remove irrelevant, low-resolun resolutio.. spatially trivial cases (e.g., isolated static objects). The resulting set balances realism and complexity, ensuring broad task coverage. All images are under MIT CC-BY 4.0 license.
Exam- Based Test Questions. To capture abstract spatial logic, such as 3D transformations, rotations, and perspective shifts, collect public spatial cognition tests through web scraping and manual curation. We categorize questions by focus and difficulty to maintain balance, removing redundant or knowledge-heavy items in favor of those targeting pure spatial reasoning. This refinement increases challenge diversity and improves benchmark quality.
Driving Test Questions. To evaluate reasoning in dynamic environments, we source tasks from three channels: (i) image-based multiple-choice questions from driving exam websites across at least three countries, (ii) online banks of standardized tasks like turning, lane changing, and parking, and (iii) interactive U.S. driving test videos, from which we extract frames, annotate bounding boxes, and design contextual queries (e.g., "Which bounding box indicates potential traffic hazard?"), This combination yields realistic and challenging traffic scenarios, enhancing VLM adaptability to safety-critical reasoning tasks.
Existing Dataset Images We integrate two key data sources: MME (Liang et al., 2024) and HO14D (Liu et al., 2022). MME provides RGB-D data, allowing depth-based spatial inference. We leverage its depth information and manually propose physics-based questions such as "If a red car passes me in seconds, what speed should maintain?" This ensures realistic distance and motion- based reasoning. HO14D contains extensive human-object interaction videos. We extract sequential frames to create motion prediction tasks, such as "Where will the hand holding the kettle move next?" By incorporating these datasets, we introduce real-world motion and interaction complexities,
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Scene Graph with Location & BBox
CoT with Novel Views
SAM
NVS
Figure 5: PointGraph: Enhance Spatial Rea- soning through Additional Scene Graphs.
Figure 6: SpatialCoT: Enhancing Spatial Imagi- nation through Novel View Synthesis.
further strengthening VLMSs dynamic reasoning capabilities. Furthermore, extend our dataset model training, we partition the dataset into 1.5K test set and 6.9K training set. The test set is entirely human-annotated and the train set further integrates samples from several existing datasets, including SpatialViz (Wang et al., 2025), PhysBench (Chow et al., 2025), ViewSpatial (Li et al., 2025), and Driving (Corbière et al., 2025), which substantially enhance the diversity.
3.2.2 QUESTION-ANSWER ANNOTATION
We design multiple-choice questions, including binary (true/false) and four-option formats, to enable standardized evaluation while minimizing annotation bias. To ensure naturalness, questions phrased conversational and context-rich styles (e.g., "If you are entering the classroom, on which side are the students?") rather than rigid templates (e.g., "Is [A] on the right of [B]?"). This encourages models to rely on contextual and relational reasoning rather than memorized patterns.
To guarantee clarity and answer uniqueness, we perform multiple rounds of validation and resolve reffreccssreerrencess through cross-checking among annotators. Six trained annotators- -initially the authors-wee involved in labeling. Each annotator underwent task-specific training, and annotations were cross-validated to reduce subjectivity. The inter-annotator agreement reached Krippendorff's 0.84, indicating high level of consistency.
references
3.3
IMPROVING VISUAL SPATIAL REASONING ABILITIES
3.3.1
POINTGRAPH: EXPLICIT MODELING OF OBJECT RELATIONSHIPS
To strengthen models' understanding of spatial relations, we introduce PointGraph, which constructs structured representations of object relationships within an image. Concretely, we employ open- vocabulary grounding models such Florence-2 localize multiple objects and extract their centers and bounding boxes. These detections are then assembled into a JSON-style scene graph encoding object identities and relative positions. By concatenating this structured spatial description with the original query, VLMS are provided with explicit geometric cues that facilitate more accurate reasoning about distances, directions, and configurations (see Fig. 5).
3.3.2 SPATIALCOT: STIMULATING SPATIAL IMAGINATION VIA NOVEL VIEWS
Human spatial reasoning often relies on mental imagery the ability to imagine how a scene looks from different viewpoints. Inspired by this, design SpatialCoT, which augments visual inputs with 3D novel views to enrich spatial imagination (Shi et al., 2023; Qi et al., 2023b; Liu et al., 2024d). Specifically, adopt InstantMesh (Xu al., 2024a) synthesize six additional perspectives for each input image and compose them into a multi-view collage. This collage is then fed, along with the question, into the VLM as part of chain-of -f-toughttt prompting pipeline. The additional perspectives provide strong geometric priors, helping models disambiguate occlusions, perspective-taking and other view-dependent reasoning tasks.
4 EXPERIMENTS
4.1 EVALUATION SETUP
To systematically assess spatial reasoning, we evaluate models on the OmniSpatial benchmark under unified protocol. Our evaluation covers both proprietary and open-source systems, spanning general-

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Published as a conference paper at ICLR 2026
Table 2: Evaluation on OmniSpatial-test. All models were tested 5 times and averaged to reduce randomness. Dark green indicates the best result and light green indicates the second-best result within the group. Gemini-2.5-Pro (Reid et al., 2024), InternVL3--88 (Zhu et al., 2025), and SoFar (Qi et al., 2025) achieve the best performance within their respective groups.
Dynamic Reasening
Sputial Interaction
Complex Logic
Perspective Taking
Method Aug. Rank
Manigulati
Motian Analysis
Truliic Analysis
Lecate
Geoopatial Stutegy
Puneen Recogaation Reasoning Ceurri Centric"
Geoonericc
Ego
Allo
Hyysolantical
Hlind Evaluation
Random Chosse
24.98
24.56
26.30
25.85
23.43
27.27
21.44
24.77
22.55
24.84
25.78
GPT 3.5 tuboo (Roumeliotis Tselikas, 20231 30.67
39.38
29.19
38.25
28.76
36.91
0.82
24.00
42.15
33.67
35:90
GPT carbo (OpenAL, 2023)
34.06
42.97
37.40
41.18
28.95
40.00
22.27
26.32
31.37
33.99
35.42
Preprisbary Models
al. 2024] 42.6 64
55.05
50.20
$4.59
43.43
44.91
22.47
29.42
61.57
36.76
34-22
GPI-4e 2024 11:20 (lasst et 10
2024)
47.51
65.5
57.25
56.47
52.38
54,09
26.29
25.48
75.96
39.49
39.76
GPT-4 L-mano-2025005 2025-04-18
(OpenAl.
2025g)
42.62
50.90
53.85
54.90 13.50
40.55
42.43
24.40
30.01
53.59
$7.25
35.73
GPT- 1-min--225--4--0 (OpenA.
2025a]
48.87
64.32
58.55
59.06
60. 40.19
56. 56
20: 208
30 19
72.55
99.57
99.28
(anl-->--ommm-2000 (DpenAt,
GPT-4 1-305--44-------
(-200-----4440pmmn.. 2025.]
51.78
66.22
64.74
60.00
68.33 39
60. 18
11:75
30.06
70.96
481.4
99 14
(Antlerpic, 2024]
46 46.36
54.05
$4.57
58. 18.12
688 38
$3.00
36.60
31.74
70.00
$4.79
99.52
2rmul-2--amms-3222000 (Antlenpic, 2024]
47.5%
57.57
55.05
56.71
63.51
59.00
20.48
28.33
72.16
36.06
36.63
Gemini-2.-4lall-iiieee 15 (Auil al.. 2023)
44.03
59.19
46.71
60.24
49.52
$3.27
21:65
31.23
66.47
36.81
38.80
1ommi---.-ffshher (Amil et 2023)
48:40
61 39
56.01
51. 76
63.43
59.00
20.80
35.81
72.75
39.20
39 26
Gemini-3 S-hba--pmiiii-------00 (Amil et 2023)
52.12
67.57
62:72
68.24
73.33
60.91
38.14
34.19
75.49
35.90
35.75
Ressoning Model
ol 2024-12221 Dlachh al. 2024)
50.36
71.62
61.9%
57.65
63.81
60.00
29.18
27.10
71.57
38,03
36.14
of minl 2025 04 16 (OpenAA, 2025b)
32.77
72.97
39.85
60.00
73.33
61.82
34.00
36.77
23.53
40169
40.96
oó 2015 16 (OpenAl, 2025b)
$6.33
71.59
68.18
61.18
68.57
65:45
40.21
29.68
77.06
48.30
48.19
Cludds T-aolss---- 21050 (oalinkig (Antlespic, 2024)
48.62
57.21
59.75
$3.75
67.94
57:27
20:34
28.17
68.63
37.94
36.95
Genini 2.5 flah-ttt 20 2i-iiiiinn (Aull al. 2025)
53.16
70.27
64.74
61.18
72.38
58.
35.05
36.13
74.12
42196
32.55
Gemeai 2.5 pro previge 05 06 (Anil al. al. 2025]
55:19
67.57
71.39
62.35
75.24
64.55
43.30
34.84
74.51
38.03
37:35
Open-sorce Model
Mava-- 5-igemm-78 (1
X034c]
15
31.23
16.19
33.04
19.01
18
$5.60
36
2034g]
15.15
32.94
39.52
41.5
22.58
47.06
37:35 17:35
LLaVA onesision qwen2 728 (Li al. 2024g]
45.66
62.16
$1.29
$4.12
60.55
56.36
22.58
25.81
76.47
37.25
33:75
Gemmo 3-40 (Kamath al. 2025)
39.79
11
41.89
19.71
36.47
27.62
36.36
23:71
26.52
59:80
36. 17.17
38.55
Gemma-3-1200 (Kamath 2025)
4377
54,05
$6.91
54.12
47.62
45:45
16.49
30:32
63.75
38,
33.75
Gemma-3-278 (K al. 2025)
44:75
56.76
55
$7.68
50,48
52.73
17. 84
29.05
64:71
33.51
32.5%
ethu
50.00
40.58
40.55
966
35.11
33.01
IstenV11.330 1.38.3888.... e7
41:60
40.87
48.0 13.94
44-77
34.95
28.63
64.20
38.62
40.96
IncmmVL.3.148..o (2hu 2025)
45.04
61L17
$1,81
51.45
38.04
28.36
35.37
34 46.46
ImeemLL3-2888
2022)
48:48
63.42
63.58
$4.39
58.29
50:55
29.00 90-90
28.52
72.16
36.
33.49
ImemVV1.3-000 (7ho al. 20055
49.33
63.78
63.12
56.24
59.24
51:45
33.63
19
24:51
35.00
Owen- V1.2.5.1.5. (Wing al., 20044]
40
47.51
46
44.73
32.16
23.87
50.41
Quen- Do.tn-V1.1.520 5.78 (Wing
pl., 20344]
44.00)
29.42
64.51
37
Qwen-V1.2 5-328 (Wang si., 20346]
47.
$5.09
66.29
56.91
27.48
37.50
Qwcn-VL25:7288 (Wang si., 20244]
47.85
58.38
60.12
30.12
39.81
53.64
26.19
33.03
71.37
36.81
36.39
Sperialized Spatial Reasonnn Modes
SpaceMMttis1333 (Chenes 2024]
33.09
22.27
SpaccQuen2:.V1.B8 (Chen al. 20241
58.31
39.88
40.91
29:30
38.83
39.76
Space Thinker Qwen2.5VL 39
al. 20241
53.06
39.73
24.33
35.11
31.08
SpuniaBBor 38 (Cal al. 20241
35.68
43.24
38.15
29.52
41.82
28.87
22.58
36.17
37.35
RobaPoint v1.5 (Yuan al.. 2024)
35:35
57.03
28.61
40.55
29.90
RoboFoont vicuna 130
35.68
32.55
24.12
SoFue 3ffu--0ee..55LL333
(Q) al. 2025)
45.34
56.49
51.16
54.12
53.14
52.73
31.75
22.58
71.60
36.56
Human Evaluution
Human 92.63
92.15
$9.02
94.30
90.26
purpose VLMs, reasoning-oriented LLMs, and spatially specialized models, thereby ensuring broad coverage and fair comparison. We consider four groups of state-of-the-ar models:
Proprietary Models. These include the GPT-4o and GPT-4.1 families (Hurst et al., 2024), the Claude series (Anthropic, 2024), and Gemini series (Anil et al., 2023). All are accessed through APIs under zero-shot settings with standardized system prompts (see Section D).
Reasoning Models. We categorize models that explicitly employ long chain-of-thought reasoning, often enhanced through reinforcement learning, such as o1, o3, 04-mini (Jaech et al., 2024; OpenAl, 2025b), and Gemini-2.5 (Anil et al., 2023; Reid et al., 2024). Because their outputs are less amenable to strict parsing, we additionally rely on an automatic judge to compare answers against ground truth, following the auddee paradigm (Zheng et al., 2023).
Open-Source Models. This set includes Qwen-VL (Bai et al., 2023), InternVL (Zhu et al., 2025), Gemma (Kamath et al., 2025), and VLe Vision (Liu et al., 2023b), These are locally deployed with standardized prompts to ensure reproducibility.
Specialized Spatial Models. We further benchmark models designed specifically for spatial rea- soning, including SpatialVLM (Chen et al., 2024), RoboPoint (Yuan et al., 2024). SpatialBot (Cai et al., 2024), and SoFar (Qi et al., 2025). These systems incorporate explicit spatial signals such as metric 3D information, point affordances, semantic orientation to improve reasoning.
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Table Camparison of textual Chain-of-Thoughh and PointGraph on OmniSpatial-test
Dynamic Reasoning
Spatial Inferaction
Complex Logle
Perspective Taking
Method
Avg. Improve
Manipulate
Motion Analysis
Traftic Analysis
Locate
Geospatial Siralegy
Pattern Recognition Reasoming Centric Centric Hypothetical
Geometric
Ego
Allo
GPT-4,I-mini
48.86
56.91
(w/ Zero-shot CoT)
49.81
+0.95
62.97
58.96
59,06
62.48
58.55
27.63
32.52
69.41
40.11
40.96
Manual
49.76
+0.90
56.91
28.45
32.13
(w/ PointCrraph
50.49
+1.63
67.57
62.14
$7.65
64.76
58.18
28.87
30.32
70.59
38.83
42.17
Gemini-2.5-Flach
(w/o CoT)
$1.47
66.22
65.90
63.53
71.43
66.36
32,99
34,84
70.59
31,91
38.55
shot 51.53
42.27
32.53
(w/ Manual CoT)
$2.12
+0.65
67.57
62.72
68,24
73.33
60.91
38.14
34.19
75.49
35,90
33.73
PoimtCraph)
53.23
62.16
Qwen-V1.2,53BB
37.35
(wl Zero-hoo CoT)
40.64
-0.81
59.73
43.87
46.12
48.38
43.27
25.36
22.84
59.61
36.54
37.59
Manual
40.07
40.57
24.39
(w/ PointGraph)
24.36
+2,91
55.68
55.20
48.94
$2.19
52.36
29.90
25.55
66.08
35.11
31.08
4.2 EVALUATION METRICS
We measure accuracy on multiple-choice questions. For standard proprictary and apen-source models, we test four output protocols: direct answer, regular-expression parsing, JSON parsing, and LLM- as-a-Judge (Zheng et al., 2023). For reasoning- reasoning-oriented oriented models with unstructured CoT outputs, correctness is assessed by GPT-4.1-min against ground truth. Ablations of these evaluation strategies are provided in Section C.I.
4.3 MAIN RESULTS
Overall Model Performance As illustrated in Table 2, we observe the following findings: (i) Proprietary reasoning models, such as ChatGPT o3 (OpenAI, 2025b) and Gemini-2.5-pro (Anil et al., 2023), achieve the highest performance, surpassing a 56% overall success rate; however, there remains a significant gap compared to human-level understanding, and they require lot of inference time and tokens. (ii) Open-source models also demonstrate competitive results. with large-scale models like Intern VLB-78B (Zhu et al., 2025) and Qwen- -Len-VVL2.5-7BB (Wang et al., 2024c) achieving comparable performance to GGPT-4.1minn and Gemini-2.0-flashheee
(iii) Specialized Spatial Reasoning Models, due to limitations in dataset coverage and model capacity, struggle to achieve substantial improvements on comprehensive benchmarks.
Category-wise Analysis We observe notable performance differences across spatial reasoning categories: (i) Leveraging their extensive world knowledge and local understanding capabilities, proprietary models have demonstrated strong performance in Dynamic Reasoning and Spatial Inter- action, indicating that reasoning models possess high proficiency in temporal understanding, spatial relationship analysis, and map-based comprehension. (ii) For Pattern Geometric Reasoning, which involves spatial imagination in planar geometry, ing can only achieve an accuracy of around 30% to 40%, slightly surpassing the random baseline. (iii) Current models exhibit limited perspective-taking abilities, predominantly analyzing scenarios from an ego-centric viewpoint while struggling to imagine perspectives from others' viewpoints.
reasoning models designed for extended think-
Impact of PointGraph & Spatial CoT To test whether structured segmentation improves performance, we apply PointGraph as pre- processing step for GPT-4.1, Gemini-2.5-faan and Qwen-V V1.2.5V....--B.. Results in Table show clear accuracy boost, particularly in the Dy- namic Reasoning and Perspective-Talee Taking Track, validating the benefits of integrating structured object representation, while traditional textual CoT difficult to bring about significant improve- ment. Fig. 6 and Table 4 further demonstrates the effectiveness of our proposed Spatial CoT the OmniSpatial Perspective- track. Through
Table 4: Performance of Spatial CoT on OmniS- patial Perspective-Takingg track.
Method Avg. Improve Centric Centric Hypothetical
Ego
Allo
GPT-4.1mmni
(w/ Zero-shhtt CoT)
45.56
69.41
40.11
40.96
Spatial CoT)
47.58
+2.02
69.43
42.37
44.34
Qwen-VLL..53B
(w/ Zero-shot CoT)
40.89
$9.61
36.54
37.59
Spatial CoT)
+2.01
60.80
39.25
37.44
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Published as a conference paper at ICLR 2026
Table
Training Exploration on OmniSpatialltest.
Method
Avg. Improve Manigulate
Motion Analysis Analysis
Traffic
Locate
Geuspatial Siralegy
Puttem Recugnition Reusoning Centric Centric
Geometric Ego
Allo
Hypothetical
Qwen-VLL2.533
Zero-shot shot-sht
40.30
55.41
47.51
46.12
42.29
44.73
32.16
23.87
59.41
33.30
30.84
OmniSpatiallraam (6.9K)
51.16
50.12
51.81
45.45
31.74
36.76
35.90
Template coepus (200K)
41.59
+1.29
50.90
46.71
40.94
45.33
40.55
28.87
24.39
72.55
35.11
33.01
Table 6: Generalization on VSI-Bench. Training with OmniSpatial yields consistent gains.
Method overall
Appearance Order
Obj Abs Distance
Obj Counting
Obj Distance
Obj Size
Room Size
Route Planning
Ohj Rel Direction
Qwen- Qwen-VLL2553B
Zero-shot 34.06
34.63
11.39
40.18
36.20
46.11
38.58
31.66
36.92
+ Sp 7B 41.68
46.60
19.63
55.27
34.23
58.32
35.17
34.54
42.84
OmniSputial
43.68
15.13
57.36
60.99
41.11
34.02
novel view synthesis facilitated by InstantMesh, both GPT-4.1 1 and Qwen-VL.2.5- VL.2.5-7B exhibit significant performance improvements, validating the effectiveness of explicit spatial imagination.
4.4 TRAINING EXPLORATION
Supervised Training on OmniSpatial-trann. We further study whether OmniSpatial- train can effectively teach spatial skills instead of merely fitting templates. Starting from strong open-source baseline, supervised fine-tuning on 6.9K samples yields a substantial +7.82 point average gain over zero-shot, with consistent improvements across dynamic, interaction, and perspective-taking oriented tracks. In contrast, training on a much larger 200K template-style corpus that follows the construction process of VSI-Bench (Yang et al., 2024) brings only marginal +1.29 average gain, underscoring the value of diverse, manually curated spatial tasks over synthetic templates.
Generalization to VSI-Bench. To examine benssbbenccmar generalization, we adopt the SpaceR- 7B pipeline and compare supervised training with and without OmniSpatial. Adding OmniSpatial improves the overall score on VSI-Bench from 41.68 to 43.68. Notably, boosts categories re- quiring ordering, counting, and metric/room-sizz reasoning (e.g.. appearance cettrranceeeddr.r.aeeee obj_ counting. obj/room_size These results indicate that OmniSpatial provides complementary supervision that transfers to external spatial tasks rather than overfitting to in-benchmark patterns.
5 RELATED WORKS
5.1 BENCHMARKING SPATIAL REASONING
Various studies have introduced innovative benchmarking methodologies (Szymanska et al., 2024; Chen et al., 2024; Cheng et al., 2024; Cai al., 2024; Song et al., 2024; Qi et al., 2025; Ray et al., 2024) to advance spatial reasoning evaluation. Spatial VQA (Du et al., 2024) was among the first to incorporate spatial information into vision-language models, enabling fundamental spatial relationship reasoning. SpatialBot (Cai et al., 2024) categorized spatial reasoning into various hierarchical levels, extending its applicability robotic manipulation tasks. RoboSpatial (Song et al., 2024) proposes a large-scale template-based spatial relationship dataset, focusing on positional relationships from different perspectives. VSI-Bench (Yang et al., 2024) combined video data (Chandrasegarra et al., 2024; Liu et al., 2023c; Fu et al., 2024a; Li et al., 2024b; Fang et al., 2024b) with cognitive maps (Momennejad et al., 2023; Apostolopoulos & Groumpos, 2023) to simulate human-like spatial cognition and optimize reasoning in dynamic environments. Recently, SoFar (Qi et al., 2025) introduced the 6-DoF SpatialBench to evaluate the understanding of orientation.
While these benchmarks have made significant contributions, unified framework encompassing wide range of complex spatial reasoning tasks remains lacking. Inspired by prior spatial reasoning research (Xu et al., 2024b; Wang et al., 2024a; Lin et al., 2014; Nwankwo et al., 2024; Wang et al., 2024b), we identified several limitations in existing benchmarks, such as reliance on generated images (Szymanska et al., 2024; Fu et al., 2024b; Kamath et al., 2023; Liu et al., 2023a; Rajabi & Kosecka, 2023; Shiri et al., 2024), LLM-generated templates (Linghu et al., 2024; Du et al., 2024; Rädsch et al., 2025), and domain-specific focus (Xie al., 2025; Chow al., 2025; Danish et al.,

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Published as a conference paper at ICLR 2026
2024).
These issues hinder their comprchensiveness and real-world applicability. To address these gaps, our study proposes a comprehensive and integrative spatial reasoning benchmark.
5.2 SPATIAL VISION-LANGUAGE MODELS
Spatial vision-langues models integrate computer vision (He et al., 2022: Oquab et al., 2024; Radford et al., 2021) and natural language processing (Bai al., 2023: Wang et al., 2024c; Brown et al., 2020; Anil et al., 2023; Touvron et al., 2023a;b; Zhang et al., 2024b) to enhance machine understanding of spatial relationships and mental intelligence. Recent research (Qi et al., 2024; Chen et al., 2024; Cheng al., 2024; Cai et al., 2024; Song et al., 2024; Yuan et al., 2024; Qi et al., 2025; Chen et al., 2025b) has increasingly focused on extending vision-language models to support spatial reasoning in dynamic and 3D environments. One of the pioneering works in this domain, SpatialVLM (Chen et al., 2024), has significantly advanced spatial reasoning by constructing an RGBD-baaee visual muestion-answering dataset. GS-Reasoner Chen al. (2025b) introduces 31) grounding as a chain-of-thought for spatial reasoning tasks. These models (Hurst et al., 2024; Reid al., 2024; Wang et al., 2024c) effectively process multimodal data containing spatial information, serving as a bridge between visual perception and linguistic reasoning.
Further advancements (Liu et al., 2024b;a; Ramakrishnan al., 2024; Tang al., 2024; Yang et al., 2023; Rozanova et al., 2021; Wu et al., 2024; Stogiannidis et al., 2025) include SpatialRGPT (Cheng al., 2024), which extends RGB-D spatial understanding by incorporating 3D scene graphs and spatial data to improve inference capabilities. Similarly, SpatialBot (Cai et al., 2024) explores hierar- chical deep reasoning mechanisms handle depth and spatial structures complex environments. Recently, SoFar (Qi et al., 2025) proposed semantic orientation and trained an open-world orientation model to enhance the orientation understanding of vision-language models, significantly improving spatial understanding and robotic operation capabilities (Cho et al., 2024; Driess al., 2023; Mu al., 2023; Huang et al., 2024b; Nasiriany et al., 2024; Huang et al., 2024a; Qi et al., 2025; He al., 2025a; Zhang et al., 2025b). However, existing research remains limited in addressing the full complexity and comprehensiveness of spatial reasoning tasks. To bridge this gap, our study aims to develop a more comprehensive benchmark that rigorously evaluates spatial reasoning capabilities.
6 CONCLUSION
We introduce OmniSpatial, benchmark for comprehensive visual-spatial reasoning. OmniSpatial distills spatial cognition into four primary categories-dynante dynamic reasoning, complex logic, spatial interaction, and perspective-taking -aanninnnnnnnnnngggggggggn-a 50 fine-grained subtasks and 8.4 K manually-curate question-answer pairs. Extensive experiments show that state-of-the-art proprietary and open-source VLMs peak 57% accuracy-ove over 30 points below human performance serrormanee-strugglinng especially with geometric reasoning and non-egocentric perspective taking. To bridge these gaps, we introduce PointGraph for structured scene-graph reasoning and Spa for viewpoint-aware CoT, both yielding consistent gains and underscoring the value of structured and multi-view reasoning.
ETHIC STATEMENT
This work complies with the ICLR Code of Ethics. All datasets are publicly available and used under their respective licenses. The research raises no direct ethical legal concerns, and the authors committed to responsible and fair use of the proposed methods.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure the reproducibility our work. The proposed model, implemen- tation details, and evaluation protocols described in detail in the main paper and appendix. All datasets used are publicly available and properly referenced. To further support reproducibility, we release the source code in the supplementary materials.
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Contents
1
Introduction
2
2
Preliminaries:
Visual-Spatial Reasoning
2.1
Taxonomy Visual-Spatial Reasoning
2.2
Rationale for Classification
3
OmniSpatial: Comprehensive Spatial Reasoning Benchmark
3.1
Overview
3.2
Benchmark Construction
3.2.1
Data Collection
3.2.2
Question-Answer Annotation
3.3
Improving Visual Spatial Reasoning Abilities
3.3.1
PointGraph: Explicit Modeling of Object Relationships
3.3.2
SpatialCoT: Stimulating Spatial Imagination via Novel Views
Experiments
4.1
Evaluation Setup
4.2
Evaluation Metrics
4.3
Main Results
4.4
Training Exploration
5
Related Works
5.1
Benchmarking Spatial Reasoning
5.2
Spatial Vision-L.anguage Models
10
Conclusion
10
A Detailed Task Design
20
A.1
Dynamic Reasoning.
20
A.1.1
Manipulation
................................
20
A.1.2
Motion Analysis
21
A.2
Complex Logic
21
A.2.1
Pattern Recognition
21
A.2.2
Geometric Reasoning
A.3
Spatial Interaction
22
A.3.1
Traffic Analysis
22
A.3.2
Localization...
22
A.3.3
Geospatial Strategy
23
A.4
Perspective Taking
23
A.4.1
Egocentric
23
A.4.2
Allocentric
24
A.4.3
Hypothetical Perspective Taking
24
Additional Related Works
24
B.1
Spatial Reasoning in Psychology
24
Additional Ablation Studies
25
C.I Evaluation and Format Strategy
C.2 Evaluation the full OmniSpatial benchmark
................................
26
C.3 Details of Human Baseline Inter-Annotator Agreement
26
System Prompts
27
Additional Visualization
27
F Additional Experiments
27
E.I The Synergy of PointGraph and SpatialCoT
27
F2
Failure Case Analysis
27
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Published as a conference paper at ICLR 2026
F33
Main Result with Standard Deviation
29
F.4 SpatialCoTT on other track
30
G Limitation & Future Works
31
H Broader Impacts
31
A DETAILED TASK DESIGN
OmniSpatial aims comprehensively evaluate the spatial reasoning capabilities of Vision-Language Models, covering four major categories: Dynamic Reasoning, Complex Logic, Spatial Interaction, and Perspective Taking. Each category only focuses on different types of spatial reasoning tasks but also includes challenges based on real-world application scenarios. This approach helps researchers better understand and enhance models' multi-domain spatial reasoning abilities. The following presents the underlying considerations and practical value behind each task design.
A.1 DYNAMIC REASONING
The Dynamic Reasoning category focuses on the model's understanding of object movement and its changes, assessing the ability to make accurate judgments in uncertain rapidly changing environments. Spatial dynamics are critical not only in robot control but also have broad applications fields like autonomous driving and intelligent surveillance.
A.1.1 MANIPULATION
Operational Position Selection This task evaluates how models determine the optimal interaction point with objects in complex environments. Selecting the best grasping point can prevent tilting or damage to objects and improve the efficiency and precision of operations. This task is crucial in robotic grasping, especially when environmental conditions are unstable.
Style
Operational
Selection
Patteen Recogriitin
Morpulatior
Amtiiuuex
Complex Logic
Dynamic Ressoning
Location
Liiifon Metton
Polyeedrcs Unfelding
Vorialie Morion
Sectiors Proj actions
Spetio Comporibiity
Manial
Omni Spatial
Aaonely Octoction
Sigr Becsgrition
Andiyfical Geomwtry
Traffcc Anslyci
Recoguition
RiA Detection:
Count
Dencstor Guidance
Contectual Anviyyis
Birection
Espcenticc
Snteuution
Order
Onjezt
Lacaliattion
Perspective Taking
Spanial Enteraction
Soant
Stee
Spetial Recognin
Draction
Locetion Recogattion
Rag faccog-tton
Interpeetation
Gecsperiai
Sount
Stretegy
Beer
Nov
Diection
Perspactive Tocing
Hypitherca
Wop (Srens Cotvendie
Legend Recogrition
Figure 7: OmniSpatial tasks. The tasks are organized into three levels, with each of the four categories of spatial abilities containing no fewer than two subtasks. The final level features a more detailed subdivision, inspired by real-life scenarios
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Movement Direction Determination This task assesses the model's ability to predict the movement direction an object or itself, providing decision support for automated systems and helping to optimize robot motion strategies.
Intent Recognition Intent recognition involves inferring the purpose or goal behind movement. This task is particularly important for contextual analysis, such as determining whether a person reaching for door handle open or close a door. This capability enhances the reasoning of human-robot interactions and optimizes the interaction experience of intelligent assistants and robots.
A.1.2 MOTION ANALYSIS
Uniform Motion
The model's ' ability to reason about uniform motion reflects its fundamental understanding of time and spatial relationships, such as estimating the speed or time required for target to move. This is applicable in areas like object tracking and path prediction, such as estimating vehicle travel time train arrival time.
Variable Motion Variable motion analysis focuses on understanding acceleration and deceleration processes. By predicting the position changes of an object during variable motion, the model can better simulate dynamic phenomena the physical world, such as calculating braking distance for vehicles. This is critical in autonomous driving and robot control.
Spatial Compatibility Tasks that assess whether object fits within a specifie space directly relate to the precision of robots and automated devices in real-world operations. For instance, determining whether luggage fit into overhead compartment is applicable in logistics and automated warehouses, helping systems make effective object adaptation decisions.
A.2 COMPLEX LOGIC
The Complex Logic category focuses on higher-order spatial reasoning, including tasks such as geometric transformations and pattern recognition, challenging the model's ability abstractly understand and reason in multi-dimensional spatial environments.
A.2.1 PATTERN RECOGNITION
Style Style recognition tasks assess the model's ability to infer visual rules in structured patterns. Typical operations include completing missing parts, combining subtracting shapes, comparing similarities and differences, and performing visual logic like black-white inversion. This skill is crucial for tasks like intelligence tests and diagrammatic reasoning.
Quantity Quantity-based tasks evaluate the model's ability to perform visual numerosity reasoning, focusing on implicit quantitative patterns such as the number of points, lines, regions, elements, or strokes. These tasks require abstract counting under diverse spatial configurations, often without explicit numerical labels or symbols.
Attributes Attribute-based tasks evaluate the model's ability to reason about non-numeric visual properties such as symmetry (axial or radial), curvature, and openness. These tasks require the recognition of structural features that do not rely on quantity but rather on geometric perceptual traits.
Location Location tasks evaluate the model's ability to reason about spatial changes such as translation, rotation, and reflection. The focus is on how objects shift space while preserving their structure. This skill is essential for grid-based reasoning, motion prediction, and geometric pattern understanding.
A.2.2 GEOMETRIC REASONING
Polyhedron Unfolding This task examines whether the model can infer the 2D net of 3D object, reflecting its ability to mentally construct spatial layouts. It is particularly relevant to applications such packaging design, industrial manufacturing, and aerospace engineering.
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Sections and Projections By evaluating how models interpret 2D cross- projections from different viewpoints, this task challenges their capacity to connect visual appearance with internal structure- -ey fields like medical imaging, architecture, and mechanical design.
Mental Rotation Mental rotation requires the model to simulate the rotation of objects in mind and track changes across views. It underpins spatial reasoning in tasks ranging from CAD modeling to object manipulation in virtual and augmented reality.
Assembly This task involves reasoning about how separate parts fit together into coherent whole, testing the model's understanding of geometric constraints. It has broad relevance in robotics, structural analysis, and physical assembly planning.
Analytical Geometry These tasks are inspired by classic geometry problems, requiring models to reason about spatial relations using angles, distances, and symmetry. They bridge mathematical logic with visual structure, supporting applications in structured reasoning and spatial abstraction.
A.3 SPATIAL INTERACTION
The Spatial Interaction category evaluates model's ability to reason about interactions with objects and environments. It includes tasks such as Traffic Analysis, Localization, and Geospatial Strategy, reflecting the model's understanding and application of spatial knowledge in real-world scenarios.
A.3.1 TRAFFIC ANALYSIS
This task focuses on identifying potential dangers or traffic violations in complex scenes, such as unsafe following distances or unusual vehicle behavior. It plays key role in ensuring safety in autonomous driving systems.
Anomaly Detection
Sign Recognition This task evaluates the model's ability to detect and interpret traffic signs, including speed limits, no-entry zones, and yield signs. Accurate recognition critical for safe and rule-compliant decision-making.
Action Recognition This task involves identifying or predicting the actions of traffic participants, such as driver gestures, police signals, or pedestrian intentions. important for understanding dynamic human behavior in traffic environments.
Risk Detection This task aims to detect immediate hazards in the environment, such as opening car door or a pedestrian crossing the road. Timely detection supports effective avoidance and control strategies.
Behavior Guidance This task provides context-aware behavioral suggestions. such as advising turn off high beams or reminding that parking is prohibited. It enhances overall driving safety and compliance.
Contextual Analysis This task assesses the model's ability to interpret spatial relations and behaviors based on environmental cues. For example, estimating wind conditions to anticipate overtaking behavior or understanding road status to infer potential risks.
A.3.2 LOCALIZATION
UT Interaction This task requires the model to determine which icon should be selected within a user interface based on contextual understanding, and accurately localize its position. It reflects the model's ability to integrate semantic interpretation with spatial reasoning, supporting applications in intelligent assistants and automated interface control.
Object Detection This task involves identifying specific target objects within an image. It is often paired with spatial localization to jointly assess what the object is and where it is located.
Spatial Localization This task focuses determining the precise position of objects within scene. is commonly evaluated alongside object detection to answer questions like "What is at this location?" or "Where is this object?"
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Pose Estimation This task estimates the orientation and spatial configuration of objects, such as detecting whether a cup is upright or tipped over. is frequently integrated with spatial localization to enable more nuanced scene understanding.
A.3.3 GEOSPATIAL STRATEGY
Spatial Recognition Assesses the model's capacity to identify spatial structures such rooms, corridors, or zones within a scene. This is essential for semantic navigation and indoor mapping.
Location Recognition This task evaluates the model's ability to identify specific locations on map or scene, such as recognizing the position marked "You are here" locating a designated landmark. It reflects the model's capacity to associate spatial markers with real-world positions.
Region Recognition Focuses distinguishing and classifying regions in broader spatial context, such as residential vs. industrial zones on map.
Route Interpretation Tests the model's ability to follow or explain a route depicted map or scene. It requires understanding directional arrows, route labels, and spatial transitions.
Route Design Involves selecting or generating an optimal path reach given goal, considering spatial constraints and possible alternatives.
Route Selection Compares multiple candidate routes and chooses the most suitable one based on efficiency, safety, or contextual requirements.
Navigation Evaluates the model's ability to understand smartphone or in-velicle navigation inter- faces, including interpreting tum-by-turr directions, identifying route segments, and understanding map overlays. This is crucial for building intelligent voice assistants and real-time guidance systems.
Map/Scene Conversion Tests the ability to mentally convert between map views and real-world scenes, which is critical in correlating schematic representations with physical surroundings.
Legend Recognition requires identifying and interpreting map symbols (e-g.. stairs, elevators, emergency exits) and foundational skill in navigation and spatial reasoning.
Terrain Identification Focuses distinguishing types of terrain (e.g.. flat, uphill, water-crossing)) which is essential for planning safe and feasible paths outdoor navigation or robotics.
A.4 PERSPECTIVE TAKING
This category evaluates the model's ability to understand spatial relationships from different view- points. Since changes in perspective directly affect what observed, the ability reason across varying angles is essential for robotic perception and interaction.
A.4.1 EGOCENTRIC
Count Counting the number of visible objects from the current perspective is crucial for dynamic interaction. For example, robotic grasping tasks, knowing how many targets are visible helps determine the appropriate operation strategy.
Size Judging the size of an object from the observer's viewpoint aids robots or virtual systems in depth perception, helping assess whether an object can be grasped or properly placed.
This refers to the direction directly seen by the observer. It is especially important in autonomous driving scenarios, where understanding object movement helps predict traffic conditions and enables timely responses.
Direction
Order Analyzing the arrangement of multiple objects in an image is essential for robotic operations, helping prioritize which objects to interact with first
Distance The distance between objects as perceived from the observer's 's viewpoint is key capability in navigation systems, enabling path planning and obstacle avoidance.
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A.4.2 ALLOCENTRIC
Count Understanding how the perceived quantity of objects changes under different viewpoints is key. Due to variations in position and orientation, occlusion often occurs- example, a driver may have blind spots, while a road surveillance camera can objects hidden in those areas. This task evaluates the model's ' ability to judge the difference in object counts from various observation points.
Size
This task involves evaluating an object's size from a specified observer's viewpoint. Due to the general principle that closer objects appear larger and distant ones appear smaller, objects of the same size may look different to different observers. The model is expected to either infer the true size based on reference objects estimate how perceived size changes with viewing position.
This task emphasizes judging directions from abstract positions, such as another agent's viewpoint or map. The answers often differ from what is directly observed, requiring one to adopt the target's s perspective-engee planning but also for understanding object's 's intrinsic orientation.
Direction
in perspective-taking It is crucial not only for large-scale path
This task requires observing the arrangement of objects from a specified viewpoint-foor example, the seating order of students in the front row as seen by a teacher on the podium, which exactly reversed from what camera the back of the classroom would capture. Only by understanding "what the target sees" can one make accurate predictions or judgments about the scene.
Order
Distance Differences in the observer's position and orientation lead to variations in perceived size and distance. example, in the same scene, photographer taking pictures of the same object from different camera angles will capture different impressions of its size and distance. Changing the camera position essentially means changing the perspective. This ability is vital for coordination and environmental assessment in human-robot interaction multi-robot collaborative tasks.
A.4.3 HYPOTHETICAL PERSPECTIVE TAKING
This category focuses imagining the scene from specified but non non- existentt viewpoint, requiring the model to mentally adopt a fictional position-an advanced form of perspective-taking in spatial reasoning.
Count Predicting how many target objects would be visible from hypothetical viewpoint-foo example, a person standing at the opposite comerr of the street may see a different distribution of objects. The model must reason about occlusion, orientation, and visibility from the imagined perspective.
Size Inferring the apparent size of objects from hypothetical location and direction. For instance, the same object may appear larger when viewed up close or smaller when viewed from above. The model needs to simulate how visual scale changes with altered viewpoints.
Direction Reasoning about how an object's direction appears from a location where observer is present. For example, a pedestrian walking toward a doorway would appear "head-on" from the entrance, but present different direction from a side view.
Order Simulating the arrangement of objects from another location helps assess how spatial sequences change across viewpoints. For example, the seating order seen from the podium may be the reverse what's seen from the back the room.
Distance Estimating relative distances between objects from a hypothetical position requires mentally adopting viewpoint. This supports effective planning and coordination in tasks such multi- robot navigation or collaborative manipulation.
B ADDITIONAL RELATED WORKS
B. SPATIAL REASONING IN PSYCHOLOGY
In psychology, spatial reasoning refers to an individual's ability to acquire, organize, utilize, and adapt spatial knowledge, recognized one of the nine primary reasonings (Gardner, 201 1). To
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Table 7: Comparative analysis of various prompting and evaluation strategies.
Dynamic Reasoning
Spatial Interaction
Complex Logic
Perspective Taking
Promopt Type Eval Type Avg.
Manipulate
Motion Analysis
Traffic Analysis
Locate
Geospatial Stralegy
Pattern Recugnition
Geometric Reasoning Centric
Ego
Allo
Hypothetical
GPT-4.1-mini
None
Direct
39.22
57.57
36.18
58.12
44.95
53.45
12.58
18.58
62.94
37.61
37.83
None
RE
48.86
64.05
58.55
57.65
59.43
56.91
28.87
34,06
68
37.18
41.20
Zero-soo CoT
RE
49.81
62.97
58.96
59.06
62.48
58.55
27.63
32.52
69.41
40.11
40,96
Manual CoT
RE
49.76
65.68
58.90
58.59
64.38
56.91
28.45
32.13
69.61
39.31
41.20
None
JSON
55.55
54.00
70.20
40.48
zero-shot CoT
ISON
48.70
58.67
57.17
$1.88
57.71
55.09
28.89
29.29
69.02
40.16
42.89
Manual
JSON
48.87
64.32
59.06
56.36
72.55
None
LLM
48.02
60.54
56.82
58.59
58.10
57.45
28.04
32.90
68.
37.55
38,31
Zero shot
L.M
48.36
56.71
58.59
57.27
27.84
67.00
38.80
Manual CoT
LLM
49.85
62.97
59.48
58.12
61.33
58.36
28.04
31.61
69.02
41.12
39.28
Gemini Gemini-2.55laast 2.5-Ilash
None
LLM
51.47
66,22
65.90
63.53
71.43
66.36
32.99
34.84
70.59
31.91
38.55
Zero-shot CoT
LLM
51.53
63.51
61.27
58.82
67.62
65.45
42.27
34.84
79.41
35.90
32.53
Manual CoT
LLM
52.12
67.57
62.72
68.24
73.33
60.91
38.14
34.19
75.49
35,90
33.73
Qwen- V1.2.5.33
None
Direct
44.04
60.27
52.20
47.53
50.48
52.73
22.68
30.32
65.49
36.49
30.84
None
RE
41.45
58.65
43.06
39.53
50.67
48.73
32.78
22.58
16.96
37.66
37.35
Zero-shot CoT
RE
40.64
59.73
43.87
46.12
48.38
43.27
25.36
22.84
59.61
36.54
37.59
Manual CoT
RE
40.07
55.68
46.65
47.29
40.57
46.00
28.04
24.39
60.39
33.35
31.57
None
JSON
38.08
62.97
32.49
46.59
48.95
47.64
24.54
23.61
62.16
35,85
27.47
Zero-shot CoT
JSON
39.20
61.35
40.40
48.94
45.52
47.27
22.27
24.00
65.29
33.51
27.71
Manual CoT
ISON
38.37
58.11
34.28
42.35
46.29
48.55
27.63
24.65
62.75
36.54
26.75
None
LLM
42.10
66,22
43.99
44.00
51.24
50.73
27.84
25.42
70.00
35.90
29.40
Zero-shot CoT
LLM
35.89
54,59
36.24
49.41
40.19
48.18
24.95
20.13
61.37
29.20
33.98
Manual CaT
36.26
53.78
50.35
10
44.00
23.51
60.39
23.86
systematically characterize this ability, Buckley al. (2018)proposed factor malysis-based frame- work, distinguishing between spatial visualization, spatial relations (such as mental rotation), and spatial orientation. Malanchini et al. (2020) further identified strong correlation between spatial orientation and object manipulation skills. Additionally, Newcombe & Shipley (2014)introduced a classification system for spatial thinking, dividing spatial reasoning into two dimensions: intrinsic- extrinsic and static-dynamic. Furthermore, many studies also have contributed to the development of frameworks (Wai et al., 2009; Lee & Bednarz, 2012; Malanchini et al., 2020; Hegarty, 2010; McGarvey et al., 2018) and evaluation methods (Eliot & Smith, 1983; Uttal et al., 2024; 2013; Hegarty & Waller, 2005) for spatial reasoning. Their framework highlights the distinetion between object-centric spatial properties and external reference frames, offering valuable insights for applica- tions such as navigation and path planning. These psychological frameworks provide complementary perspectives for understanding and assessing spatial reasoning, offering theoretical foundations for embodied intelligent systems. Inspired by these classifications, our study proposes a novel taxonomy for visual-spatial reasoning, aiming advance spatial reasoning research in vision-language models.
C ADDITIONAL ABLATION STUDIES
C.1 EVALUATION AND FORMAT STRATEGY
The choice of evaluation format and answer-extraction strategy can materially influence how fully large language model's capabilities are expressed. Because our benchmark introduces novel visual-spatial reasoning task, Table 7 reports the performance impact of different prompting and evaluation schemes. We consider three prompting paradigms-Diree -Direct QA, Zero-shot CoT, and evaluation Manual CoT-and three evaluation methods: (i) regex-based field extraction (RF), in which the VLM
instructed to place its answer in a fixed slot at the end of the response; (ii) JSON extraction, where the answer must appear in a dedicated answer field; and (iii) LLM-based free-form evaluation, for which we employ GPT-4, I-mini as an adjudicator.
We observe that reasoning-centrie models that strongly obey formatting directives (e.g., Gemini-2.5- flash) cannot be reliably scored with regex extraction, because their compulsory chain-of-thoure interferes with the required output template. For standard models, all three evaluation strategies yield comparable scores, and both CoT variants consistently outperform the Direct QA baseline by small margin. Accordingly, in the main results table, we evaluate non-reasoning models with the
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Table 8: Evaluation on the full OmniSpatial benchmark.
Dark green marks the best Avg. accuracy and light green marks the second-best Avg. accuracy in all the models.
Dynamic Reasoning
Spatial Interaction
Complex Logic
Perspective Taking
Method Avg. Rank
Manigulate
Mation Analysis Analysis
Traffic
Geospatial Strategy
Pattern Recognition Reasoning Centric Centric
Geometric
Ego
Allo
Hypotherical
Proprictary Models
4PT-40
44.47
5
60.00
57.97
62.38
55.24
$9.09
29.90
31.01
42.06
24.34
28.05
GPT-4.1
61.18
69.52
60.91
35.58
47.06
27.26
o3-04-16
54.52
71.05
63.64
70.61
69.05
61.70
42.55
47.15
53.35
30.98
46.28
Gemini-2.0-Flash
30.04
24.56
Gemini 2l5.Flash Flash
$1.80
54.84
70.43
65.33
66.00
68.75
39.53
39.46
52.70
33.49
36.84
Gemini-2.5.Pro
66.67
69.10
78.85
36.17
50.17
50.65
39.57
37.89
Open-source Models
Qwen-VL-2255.
39.76
57.65
47.34
49.07
49.52
58.18
29.90
26.97
40.62
30.47
29.88
Qwen V1--2.533BB 43,97
58.82
52.42
52.97
68.57
51.82
27.84
29.51
51.70
26.82
35.98
Manual CoT + RE setting, whereas ceasoning-orientee models are assessed with Manual CoT + LLM evaluation.
C.2 EVALUATION ON THE FULL OMNISPATIAL BENCHIMARK
To complement the main experiments, further conduct evaluation the entire 8.4K OmniSpatial test set without applying the train/test split. This setting measures model performance on all available annotated questions, thereby reducing variance due subset sampling and providing a more holistic view of spatial reasoning ability. Table 8 reports the results across all twelve fine-grained tracks.
Overall, the relative ranking of models remains stable compared the smaller-scale splits discussed in the main text. Gemini-2.5-Pro achieves the highest average score of 55.05, securing Rank among all compared systems. ChatGPT o3 follows closely with 54.52, while Semini-2.55-Flast achieves 51 51.80. Other proprietary systems, such as GPT-411 and GPT-4o, also maintain consistent performance. On the open-source side, Qwen-VL. VL--2.5-32B achieves the strongest result with 43.97, outperforming other Qwen variants and aligning with the trends observed in the evaluations.
closer per-track analysis highlights complementary strengths across models. ChatGPT o3 excels at Manipulate, Traffic Analysis, and also leads in Patlern Recognition, Ego- Ego-Centric, and Hypothetical reasoning. In contrast, Gemini-2.5-Pro dominates Locute, Geospatial Strategy, Geometric Reasoning, and Allo Ceoo-rrriicc perspective taking. 2emmini2..5-Flaae yields the highest accuracy Motion Analysis. These complementary strengths suggest that different architectures capture distinct aspects of spatial reasoning, and point towards potential benefits of ensembling multi-ex multi-expert distillation.
C.3 DETAILS OF HUMAN BASELINE & INTER-ANNOTATOR AGREEMENT
To establish an upper bound of performance, we conducted human evaluation on the OmniSpatial benchmark. Six human annotators (graduate students with backgrounds in vision and robotics) were recruited. Each participant was presented with a randomized subset of questions covering four tracks and balanced selection of fine-graine tasks. The interface was blinded: multiple-choce options were randomized and no additional context was provided. Annotators were instructed to answer independently without external resources.
Each item was labeled by three different annotators, and the final answer was obtained via majority vote. To quantify annotation reliability, report inter-annotate agreement (IAA) using Krippen- dorff's a for multi-annotator agreement. Table 9 summarizes the results across tracks. We also report 95% confidence intervals (bootstrap questions) for human accuracy.
These results indicate that human annotators achieve ~-89% average accuracy, with substantial agreement across annotators (N/a x 0.84). Even the most abstract and complex forms of spatial reasoning achieve a consistency score of 0.76. This provides a reliable estimate of the human upper bound and confirms the internal consistency of the benchmark.
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Table 9: Human baseline accuracy and inter-annotator agreement (IAA) across tracks.
Track
Accuracy (%)
Krippendorff's
Dynamic Reasoning
95.2 + 1.3
0.92
Spatial Interaction
93.5 + 1.6
0.85
Complex Logic
87.9 = 5.5
0.76
Perspective Taking
94.4 +: 2.2
0.80
Overall
92.6 +: 2.5
0.84
D SYSTEM PROMPTS
We present all the system prompts used in our experiments in Figs. 13 and 14 to facilitate repro- ducibility. We observe that some models are sensitive to the choice of system prompt, which may stem from distributional biases in their training data. In contrast, inference-oriented models generally exhibit stronger generalization capabilities and are less reliant on carefully crafted prompts. We conduct extensive experiments and iterative tuning the system prompts, with the ultimate goal of objectively and faithfully evaluating each model's actual capability without being confounded by prompt-induced bias.
E ADDITIONAL VISUALIZATION
We present more examples of question-answer pairs from the OmniSpatial, with perspective taking, spatial interaction, dynamic reasoning, and complex logic, shown in Figs. 15 to 18, respectively. Our benchmark comprises a rich collection of data samples spanning diverse scenarios, resolutions, lighting conditions, and geographical regions. It includes both absolute numerical analyses and relative spatial relationships, aiming comprehensively evaluate the spatial reasoning capabilities of dision-language models. To the best of our knowledge, our spatial intelligence benchmark is the most diverse and comprehensive date, and is entirely aunan-annotated, enabling faithful evaluation of models' visual-spatial reasoning capabilities without the confounding effects of templated patterns.
F ADDITIONAL EXPERIMENTS
F.1 THE SYNERGY OF POINTGRAPH AND SPATIALCOT
Table 10: Performance of Spatial CoT on OmniSpatial Perspective-Takkin track.
Method
Avg.
Improve
Ego Centric
Allo Centric
Hypothetical
GPT--,1-mmin
(w/ Zero-shot CoT)
45,56
69.41
40.11
40.96
(w/ Spatial CoT)
47.58
+2.02
69.43
42.37
44.34
(w/ Spatial CoT & PointGrahh)
48.70
+3.14
71.09
42.52
44.77
VL2.5-3
(wl Zero shot CoT) 40.89
59.61
36.54
37.59
(w/ Spatial CoT) 42.90
+2.01
60.80
39.25
37.44
(w/ Spatial CoT PointGraph) 43.75
+2.86
62.79
38.64
37.74
In this section, we investigate the synergy between our two plug-and-play components, SpatialCoT and PointGraph. As shown in Table 10, across both GPT-4.1-min and Qwen-Vinee VL2.5-3B, augmenting SpatialCoT with PointGraph yields an additional gain of roughly percentage point in overall performance.
F.2 FAILURE CASE ANALYSIS
Figure 8 illustrates Spatial Interaction task instance from OmniSpatial. together with failure cases of two state-of-th of-the--rrttttt art commercial models, Gemini-2.5-Pir and ChatGPT-o3. We observe that, despite their strong capabilities, these models still struggle reason complex 3D scenes and to perform
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orientation and path analysis under imagined egocentric poses. In this example, the model must first imagine itself standing in front the washing machine and then plan a path in the 3D scene graph accordingly. We hypothesize that, if the model could generate intermediate visualizations of its planned route (e.g., hand-drawn trajectories) as a form of chain-of-thought (CoT), mimicking human problem-solving. would be more effective at handling such challenging cases.
Q: If you are facing the washing
machine, how should you walk to the stove and face the stove?
A: Turn around, turn right, turn left, turn left
Tum around, turn right, turn right, turn left
C: Tum around, turn left, turn left, turn left D: Turn around, turn left, turn right, turn left
Gemini-2.5-pro: B
03-2025-04-1 (Thought for 6m 46s)
Answer C is the only set of directions that lines up with the flat's layout.
Ground Truth: D
Figure 8: Qualitative example for Failure Studies.
In Fig. 9, we present a representative Perspective- Taking instance from OmniSpatial. We observe that even these perfeffooming models struggle with frame-of-reference confusion, counterfactual viewpoints and the associated spatial relations. In this example, the model must imagine itself facing the vase and then infer the relative positions of objects under this imagined viewpoint. Because current models find it difficult to construct and manipulate such fictitious perspectives, we propose using novel-view synthesis ability. future work, more tightly integrating this capability into the VLM's internal thinking process may further strengthen the model's spatial understanding.
glug-and-ple SpatialCoT module to enhance their Perspective-Taie Taking
If the waiter is on the side of the vase, then who on left?
Gemini-2.5-pro If the waiter is the side facing the table, Jack is his left.
rose
23-2025-04--1 66: (Thought for Im 8s) Jack is on the waiter's s left.
Ground Truth: Rose
Figure 9: Qualitative example for Failure Studies.
In Fig. 10, we present an example spatio-temporal dynamics reasoning task from OmniSpatial. We observe that even state-of-the-an art commercial models struggle to understand long-horizon sequences involving multiple continuous actions. We hypothesize that performance such long-tern term temporal understanding tasks can be improved by augmenting the model's memory, for example by enriching its temporal and episodie representations of time-space relations.
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Q: What steps does this manipulation contain?
A: "Pick up the milk, pour it into the right hand cup, put down, pick up the milk, and put the milk on the left hand table".
the left side, put the milk down, pick up the milk, and put the milk right side table',
B: "Toke the milk, pour it into the
Gemini-2.5-pro: The final answer is B.
03-2025-04-166 6: (Thought for 4m 9s) Option B is the sequence shown.
Ground Truth: C
Figure 10: Qualitative example for Failure Studies.
In Figure 11, present example of Complex-Logie task from OmniSpatial. In this problem, the model must understand the folding relationships a paper-cutting process, which is highly challenging even for humans. We observe that the reasoning model produces detailed chain of analysis, including examining how each folded corner affects the boundary and attempting to reason using structured text. However, we argue that such complex logical reasoning better addressed with vision-gre goolungg chain-of -f----f-tthhhh- (CoT), i.e., a unified generative-perceen model whose generative capability used to predict the intermediate folded states step by step, gradually approaching the final correct prediction.
Gemini-2.5-pro: The final answer is A. 03-2025-04-16- (Thought for m 8s)
Option
Hos identical edge notches?
Hos single centrol diomond?
Any extro or missing cuts?
Result
Boftiom notch taller
Thon the others
Edge notches not identicol
X
Folding
Cutting
B
All four notches
the template
No extra missing features
V
C
rotored 90
Orientation
template's orientation) notches, but
Only B satisfies all requirements.
Ground Truth: C
Figure 11: Qualitative example for Failure Studies.
In addition, we further conduct quantitative analysis how depth and object orientation affect the model's accuracy. We collect all real-image samples from the viewpoint conversion task and analyze them using Gemini-Flash-2.5 Reid et al. (2024). Object depth is estimated with Metric3D v2 Yin et al. (2023) and discretized into several ranges, while object orientation is evaluated with SoFar Qi et al. (2025) and grouped by angle. As shown in Fig. 12, observe that the model's accuracy slightly decreases as the distance between the object and the camera increases. Regarding orientation, the model handles oblique views better, whereas its understanding of rear-view objects is somewhat weaker.
F.3 MAIN RESULT WITH STANDARD DEVIATION
In Table 11. we additionally report results with standard deviations. Due to space limitations, we only present the means and standard deviations of the four coarse-grained categories, keeping the
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63.6
62.5
3931
e Cereaation
Distance
Figure 12: Quantitative example for Failure Studies.
Table 11: Evaluation on OmniSpatial-test. models were tested times and averaged to reduce randomness. We report accuracy on four coarse-grained categories: Dynamic Reasoning, Spatial Interaction, Complex Logic, and Perspective Taking. Each coarse category is the arithmetic mean of its corresponding fine-grained sub-tasks.
Avg.
Dynamic Reasoning Spatial
Complex Logic Perspective Taking
Propristary
GPT-4e--ini-2004--77 (Hurst al, 2024) 42.64
53.12 + 1.1 47.64 14
25.95
0.4
44 18
166
GPT-44-2024-1 1-20 (Hurst al. 2024)
47.81
1.0
61.39
1.3
54.31 16
25.88
0.5
51.74
18
GPT-4 11-mio-2025-40 200--0225------
(OpenAl, 2025a)
42.62 = 0.8
52.38 1
46.09
27.25
0.4
41.52
14
GPT- 10mm--.-mmin--225-00011 (OpenAl, 2025a)
$8.54
50.47
GP1-4-1-022.044-1
(OpenAI, 2025a)
1aall------mmm-222220110 (Anibropic, 2024)
59.86
29.17
48.10
Clande-----nn 3-7-4mmms-20275021 (Anthropic, 2024)
47.53
56.76
90
28.94
48.28
1tt.--000-tcc--ii---222222 (Anil et al. 2023)
44.03 0.8
52.95 +
$4.34
26.44 01.
47.36
1.4
Gemini- 1.alahh-ppp (Anil 2023)
48:40
38.95
$8.09 1.6
27.32 1 0.5
5041
1.8
2023)
52.12
Reasoning
ol-2024-12- 12-1-22-17 (Jacc et 2024))
50.36
66.30
60.49
33.14
48.58
u4-mini uf-mini-2025-0--
(OpenAL 20256)
$2.77 1 1.2
66.40
65.05
35.40
51.73
19
03- 13--0025-04-66 (OpeAA, 20256)
56.33 :: 1.3
69.03
65.07
34.95
$7.88
2.0
Clande 3-7 sosnet 20250219 chinking (Anihropic, 2024)
48.62
38.47
59.65
29.20
47.84
Gemini- 3--0ab--5
2023)
Gemini-2 2.5-pro pro--pevieww-S-11 (Anil 2023)
55.19
69.48
67.38
39.07
49.96
Open-souree Models
LLav vicuna (Lin al. 2024c)
34.97 = 0.7
42.84 +: 0.9
35.14 2: 1.1
26.39 a: 0.4
42.13
LLaVA unevisooe qwent
all. 35.68
10.70
34.76 10
25.73 0.3
40.19 12
LL www.-eeiison-ueen--23555
20344)
24.24
49.14
(Kamath 46mma.3-4B
Gomma-3-12B (Kamath et al. 2025)
43.71
$4.48 11
49.06
23:41 0.4
44.72
Gemma-3-278 (Kamathh et al. 2025)
44.75
56.27
53,02
28.44 0.5
43.58
166
InteraVL3-28 (Zhu al. 2025)
37.98 0.7
45.29 0.8
41.28
25.19
41.20
InterVVL3-SB
2025)
46.65
48.25
26.79
47.93
InternV1 (2hu 2025)
$1.20
InteraVL_3-788 (2hu
2025)
49.33 1.0
63.45 A:
55.64
15
28.91
89.62
Owen VL.2.5-BB (Wang at., 2024c)
60.30
$1.66 1 1.0
44.38
12
28.02
0.4
41.18
Quen-VVL2..-B (Wang et al. 2024c)
39.18 0.9
46.73 +
46.88
31.27 #: 0.6
45.02
1.5
Owen- VL..53V....33BB (Wang 20244)
5833
48.59
Qwen Dwen-VLL25-2BB (Wang
Specialized Spatial Reasoning
SpuceMamis-133 (Choon et 2024)
36.36
41.81
36,30
23.33 0.4
42.25
SpuceQwen2555V
V-L-3B (Chenn ut al. 2024)
40.25
0.9
49,00
41.01
27.85 0.5
47.44
SatTiinnkk--Qeen25VV (Chen
20244)
40.42 0.9
50:45
30.15
26.16 0.5
41.41
14
SputialBes.38 (Cai
SahoPoint-vieua--V S.7--oor
2024)
RoboPoint-iicms--11 5-138 (Yiun 2024)
41.91
25.93
40006
(Q) al. 2025)
45.14 14
$3.83
53.33
27.31
0.6
49.95
Human Evalmation
Human 92.63 1.5
95.24
93.45
1.6
87.86 1.5
94.36 2.22
configuration consistent with the main table in the paper. We observe that the reasoning models exhibit larger variability across the five runs, whereas the smaller-paramest open-source models produce more consistent results.
F.4 SPATIALCOT ON OTHER TRACK
Besides the perspective-taking tasks, SpatialCoT also improves performance many problems that require viewpoint transformation. We further evaluate SpatialCoT on the Complex-Lope track; as shown in Table 12, SpatialCoT consistently achieves better performance than Manual CoT.
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Table 12: Performance of Spatial CoT on Complex-Logie track.
Pattern Recognition
Geometric Reasoning
Method
Avg.
Improve
GPI-225--lasb--Tanaee
Manual CoT) 35.59
35.05
36.13
(w/ Sputial CoT)
37.19
+1.60
35.30
38.28
D.ven-VT.2.5-BB (w/ Manual CoT) 28.02
32.16 29.01
23.87
(w/ Spotial CoT)
29.27
+1.25
28.52
G LIMITATION & FUTURE WORKS
Although OmniSpatial includes some image clips with dynamic information from HOI4D (Liu et al., 2022), the complexity of the operational tasks still lags behind that of long videos (Yang et al.. 2024; Chandrasegaran et al., 2024). Moreover, while PointGraph & Spatial CoT enhances VLM's spatial understanding through point cues, the improvement not fundamental in nature. Spatial reasoning tasks are more like mathematics and coding tasks, require longer and more complex reasoning (DeepSeek-A) AI al., 2025; Jaech et al., 2024).
3D information is crucial for spatial reasoning, and future work involves introducing 3D representa- tion (Dong et al., 2023; Qi al., 2023a; 2024; 2023b) and perception (Qi al., 2017a;;; Zhang et al., 2024a; Fan et al., 2024), as well as 3D VLMS (Peng et al., 2024; Dong et al., 2024; Guo et al., 2023; Qi et al., 2024; 2025), reasoning model (Zhang al., 2025a; DeepSeek-Al et al., 2025; OpenAI, 2025b) and knowledge distillation (Zhang et al., 2023; Hinton et al., 2015; Dong et al., 2023; Qi et al., 2023a). The ultimate goal of spatial reasoning is empower robots, and future work also involves robot execution tasks (Huang et al., 2024a; Fang et al., 2024a; Qi et al., 2025; Huang et al., 2024b; He et al., 2025b).
H BROADER IMPACTS
OmniSpatial promises several positive societal benefits. By pushing models reason about motion, collision risk and traffic scenes, it can hasten the arrival of safer autonomous vehicles and service robots that foresee hazards and navigate crowded spaces responsibly. fasssfrrom polyhedron unfolding to assembly- offfer data that can streamline product design, packaging and manufacturing, lowering material use and energy waste. The benchmark's localization, Ul-interaction and perspective-taking challenges cultivate spatially aware assistants that improve AR/VR experiences, access tools for visually impaired users and more natural human-com computrr interfaces.
geometric-reasoning
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[Default System Prompt]
You are spatial--easoningg assistant.
Task
You will receive
**Image** single RGB frame depicting
44 **Question** natural-language query about spatial relationships between objects in the image.
3. **Options** 22 answer candidates, each tagged by a capital letter (A, B, C, D...)
Based on the image and question, provide your answer. Always ground your answer in the visual evidence; do not hallucinate unseen objects. If uncertain, pick the most plausible option-never refuse reply "Insufficient information."
[Zero-shot CoT System Prompt]
You spatial-reasoning assistant.
Task
You receive
1. **"maae*** a single RGB frame depicting scene.
2. **Question** natural-language query about spatial relationships between objects in the image.
3. **Options** 22 answer candidates, each tagged by capital letter (A, B, D...)
Think step by step and provide the
Always ground your answer visual evidence; hallucinate unseen objects. uncertain, pick most plausible option- never refuse or reply "Insufficient information."
[Manual CoT System Prompt]
You spatial-reasoning assistant.
Task
You will receive
**Image** single RGB frame depicting scene.
** **Question** natural-language query about spatial relationships between objects in the image.
**Options** 22 answer candidates, tagged a capital letter (A, B, D...).
Guidelines
Please follow these steps to analyze the image and answer the question: First, carefully observe the image and identify all relevant objects and their spatial relationships.
Next, break down the question key components that need addressed.
Think through spatial reasoning step-by-step arrive your answer. may be necessary to transfer perspective better understand the scene.
4. Finally, select the most appropriate option (A, B, C, or D) based on your analysis.
Always ground your answer in the visual evidence; do not hallucinate unseen objects. If uncertain, pick the most plausible option never refuse reply "insufficient information."
Figure 13: System prompts used in OmniSpatial evaluation.
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[Blind Evaluation System Prompt]
You are spatial-reasoning assistant.
Task
You will receive "'*uutition*** natural-lanuuage query about spatial relationships, "OOtions** * 22 answer candidates, tagged a capital letter (A, B, D...)
Based on question only, provide your answer.
[LLM Judgement System Prompt]
judge
The user will provide:
Question: The original question. Pred: The predicted answer.
GT: The ground truth answer.
You need judge whether the predicted If the predicted the predicted answer incorrect, respond with "False".
not; just judge the final correct, respond with "True".
[Direct System Prompt]
You are a spatial--easoninng assistant.
Task
You will receive
1.*'|mae***. single frame depicting scene.
2. **Questton** natural-language query about spatial relationships between objects in the image.
3. ".**Opiios*** 22 answer candidates, each tagged by a capital letter (A, B, C, D...).
Note: You only need respond with A, B, without providing additional information.
[RE Format]
End your answer with separate line formatted exactly as:
Answer:
where (A, B, C, D).
[JSON Format]
You need to respond with the answer in JSON format:
"json
"analysis": "The analysis the image question", aanswer":" "A"
ILLM Format]
Your must clear and accurate.
Figure 14: System prompts used in OmniSpatial evaluation.
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[Subtask Type: Motion Analysis]
[Task Type: Dynamic Reasoning)
[Question]: If one of the three women in kimono walked toward the traffic light m/s, how long would it take her reach
[A]: "L3s"
[B]: "1.9s"
[C]: "12.7s"
[D]: "26.5s"
[Answer]:
[Suhtask Type: Manipulation]
[Task Type: Dynamic Reasoning)
[Question]: it takes you seconds to fully open the drawer, is your average drawer opening speed?
[A]: "4.33inchows"
[B]: "9.4inchea/s"
[C]: "16.3inchess"
[D]: 725.7inches/
[Answer]:
[Subtask Type: Spatial Compatibility]
[Task Type: Dynamic Reasoning)
[Question]: gap between the construction vehicle the cement container least 0.6 meters wide, enough for person pass through?
[A]: "Yes" [B]: "No"
[Answer]: A
(Subtask Type: Allocentiric]
NEW
[Task Type: Perspective Taking)
[Question] Which direction are they facing, their own perspective?
[A]: "their left"
[B]: "their right"
[C]: "forward"
[D]: "backward"
[Answer]:
Figure 15: Visualization example of OnniSpatial data samples.
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[Subtask Type: Hypothetical]
[Task Type: Perspective Taking!
[Question]: riding the tricycle, which direction is the white electric car from your perspective?
[A]: "left"
[B]: "right"
[C]: "forward"
[D]: "backward"
[Answer]: B
[Subtask Type: Egocentric]
[Task Type: Perspective Takingl
[Question]: How many dancers have their left foot in front?
[A]: "0"
[B]: "10"
[Answer]: A
[Subtask Type: Traffie Analysis]
Type: Spatial Interaction]
am driving the highway. The safety distancc sufficient.
[Question]:
[A]: "Yes"
[B]: "No"
[Answer]: B
[Subtask Type: Traffic Analysis]
[Task Type: Spatial Interaction]
[Question]: While driving forward, what potential danger should you he aware of?
[A]: '0"
[B]: "1"
[C]: 12"
[D]: "3"
[Answer]:
Figure 16: Visualization example of OnniSpatial data samples.
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[Subtask Type: Localization]
[Task Type: Spatial Interaction]
10:20
[Question]: case of emergency, like a fall, what should you
this interface?
[A]: "Click the first button from the left"
[B]: "Click
button from [C]: "Click the third button from the loft"
Übenwechung
505
[D]: "Can not determine"
[Answer]:
[Subtask Type: Localization]
[Task Type: Spatial Interaction]
[Question]: Where are the colored sweet peppers located on the sholf?
[A]: "Count the fourth from the top and the first one from the right"
[B] "Count the first line from the and the second one from the right"
[C]: "Count the second line the top and the third one from the right"
[D]: "Count the third line from the and the fourth one from the right"
[Answer]: D
[Subtask Type: Geospatial Strategy]
[Task Type: Spatial Interaction]
[Question]: How can 1 get from the coffee shop on first floor to the second floor?
[A]: "Go north and take the clevator"
[B]: "Go south and take the clevator"
[C]: "Go west and take the elevator"
[D]: cast and take the clevator"
[Answer]:
ISubtask Type: Geospatial Strategy]
[Task Type: Spatial Interaction]
[Question]: Which utilizes road support the run?
[A]: green route"
[B]: "red route"
[C]: "purple route"
[D]: "orunge route"
[Answer]:
Figure 17: Visualization example of OnniSpatial data samples.
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[Subtask Type: Pattern Recognition]
[Task Type: Complex Logie]
[Question]:ar hamster placed in maze. The hamster turs first to the right, then left, to the right. Where hamster und up?
[A]:
[B]: "Q"
ICI:
[D]: "S"
[Answer]: A
[Subtask Type: Puttern Recugnition]
[Task Type: Complex Logiel
[Question]: Given the outer surface of the carion on the left, which of following can folded from
[Answer]: B
[Subtask Type: Geometrie Reasoning)
[Task Type: Complex Logie]
[Question]: From rose's point of view, which side is the vase on?
[A]: "On ber left side"
jack
[B]: her right side"
[Answer]:
[Subtask Type: Geometric Reasoning]
[Task Type: Complex Logie]
[Question]: Which of the fillowing shapes rotated version the shape on the left!
[Answerj: D)
Figure 18: Visualization example of OnniSpatial data samples.
37


Reviews:
[{"review_id": "WnL22uzw2m", "reviewer": "Reviewer_Qifc", "summary": "This paper introduces OmniSpatial, a large-scale benchmark designed to evaluate comprehensive spatial reasoning in vision-language models (VLMs). It organizes tasks into four key categories, including dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking, covering 50 subtypes and 8.4K manually curated QA pairs. The benchmark integrates multiple data sources (web, cognitive tests, driving exams, and prior embodied datasets) with high annotation consistency (Krippendorff’s α = 0.84).\n\nThe authors further propose two methods to enhance VLM spatial reasoning:\n1. PointGraph – providing explicit scene graphs for spatial structure.\n2. SpatialCoT – enabling multi-view reasoning using novel-view synthesis (InstantMesh).\n\nThey benchmark 36 VLMs (GPT-4.1, Gemini-2.5, Qwen-VL, InternVL, etc.) and show that while leading reasoning models (e.g., o3, Gemini-2.5-pro) achieve ≈56% accuracy, human performance reaches 92%. Fine-tuning on OmniSpatial improves performance (+7.8 points) and transfers modestly to other spatial benchmarks (e.g., VSI-Bench +2 points)", "questions": "1. How do PointGraph and SpatialCoT interact? Are their improvements additive or overlapping?\n2. Can the authors provide qualitative examples illustrating typical model errors (e.g., misinterpreting object orientation, inconsistent frame of reference)?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Focused and Systematic Scope\nThe paper maintains a clear focus on spatial reasoning, defining it precisely, covering its cognitive dimensions, and avoiding unnecessary general multimodal extensions. This conceptual focus makes OmniSpatial a coherent and practically usable benchmark.\n\n2. Rigorous Manual Curation:\n- The dataset is human-annotated, multi-sourced, and cross-validated with strong inter-annotator agreement, addressing common weaknesses of synthetic or template-based datasets.", "weaknesses": "1. Lack of Deep Analysis or Failure Studies\nThe paper could benefit from qualitative examples showing why models fail (e.g., depth reasoning errors, frame-of-reference confusion, or temporal misalignment)\n\n2. Marginal Quantitative Gains\nThe improvements from PointGraph and SpatialCoT are modest (≈1–2 points per dimension), raising questions about their practical impact.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KJbR9PYVhK", "reviewer": "Reviewer_8wJb", "summary": "This paper presents OmniSpatial, a new and comprehensive benchmark aimed at evaluating higher-level spatial reasoning in VLMs beyond basic left–right or counting tasks. It provides 8.4K human-curated QA pairs across four categories—dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking—covering 50 task types. Evaluating 36 models shows that state-of-the-art VLMs achieve only 56% accuracy, far below human performance, with notable weaknesses in geometric reasoning and non-egocentric perspective shifts. The paper also introduces PointGraph and SpatialCoT as two strategies to improve spatial reasoning, both yielding modest gains.", "questions": "- To what extent can models answer correctly without looking at the images? Since most questions are binary or 4-way multiple choice, some may be solvable from textual priors alone (e.g., Fig. 3: “I am entering a highway, I will encounter a ‘Give Way’ sign”). Have the authors evaluated a text-only baseline to isolate true visual reasoning?\n\n- How is PointGraph different from existing methods like [1]?\n\n- Can the authors provide an estimated compute overhead of SpatialCoT and discuss practicality for deployment?\n\n- How do the authors assess or mitigate potential data leakage, especially for web- or exam-derived content that may exist in model training corpora? Is there any decontamination or measurement of overlap?\n\n[1] Wang et al., Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models, NeurIPS 2024", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well written and easy to understand.\n- The dataset construction is solid and carefully annotated by humans.\n- The evaluation is comprehensive.", "weaknesses": "**Training Data Leakage Concern**\n- While the dataset is manually curated, some sources (e.g., web images, exam-style questions) may overlap with model pretraining corpora. A clearer discussion on leakage mitigation, measurement, and dataset decontamination would strengthen the benchmark’s credibility.\n\n**Compute Cost of SpatialCoT**\n- The proposed SpatialCoT relies on multi-view synthesis, which appears computationally expensive. A discussion of its runtime, resource requirements, and potential lightweight or more practical alternatives would improve the clarity of its applicability.\n\n\n**Lack of Discussion on Related Works**\n-  I have seen prior works that also incorporate structured spatial information through text-based scene representations (e.g.,[1]). The PointGraph idea seems to be closely related to this one. It would be appropriate to acknowledge and discuss such related methods when introducing PointGraph in Sec. 3.3.1 to better position the contribution.\n\n**Missing Error Bars in Reporting Results**\nThe main table does not present confidence intervals, variance, or statistical testing. As this is a benchmark paper, stronger evidence of robustness and significance is needed. Reporting standard deviations or significance tests can better support the claims and ensure results are reliable.\n\nOverall, I believe the paper could be a good contribution to the community, and I would be happy to reconsider the score if the above concerns are satisfactorily addressed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WQP1l5fqKm", "reviewer": "Reviewer_QVvP", "summary": "The paper proposes a new Visual Question Answering (VQA) benchmark, OmniSpatial, designed to comprehensively evaluate spatial reasoning capabilities. The benchmark highlights a broad and in-depth coverage of spatial relation tasks. The authors carefully design four key dimensions of spatial reasoning to be evaluated: perspective-taking, spatial interaction, dynamic reasoning, and compositional understanding. These four categories present significant challenges and substantially advance the complexity of spatial evaluation for current vision-language models (VLMs).\nThe proposed benchmark provides a comprehensive evaluation across diverse conditions, supported by a large collection of curated web images. The paper conducts experiments across multiple model series and sizes, including reasoning, closed-source, and open-source VLMs, while also providing a human baseline for comparison.\nIn addition, the authors investigate two approaches aimed at improving VLM spatial understanding on this benchmark, PointGraph and SpatialCoT. Both  yield consistent improvements across different VLMs.", "questions": "- In the question generation process, is a fixed template used, or are LLM involved? If the process relies on template-based questions, would it be possible to incorporate LLMs to increase the diversity of question types? If it already use LLM, the paper should expicitly said so.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The proposed benchmark introduces a new and challenging evaluation setting that explores aspects of spatial reasoning rarely addressed in previous datasets. It is notably more complex and comprehensive than prior benchmarks.\n- The question annotations involve a human-in-the-loop process to ensure clarity, answer uniqueness, and the resolution of ambiguous spatial references.\n- The evaluation includes a wide range of VLMs—covering reasoning-focused, open-source, closed-source, and human baselines—demonstrating the benchmark’s broad coverage, thorough experimental setup, and comprehensive comparisons across models.\n- Results demonstrate the significant shortcomings of VLMs across different type of spatial relation\n- The paper also introduces two promising approaches, PointGraph (which incorporates an explicit scene graph as input) and SpatialCoT (which generates multi-view points from a given image to provide diverse spatial perspectives). These methods consistently improve model performance across different VLMs.\n- Paper shows that fine-tuning models with this dataset shows potential for transferability to other VLM benchmarks.\n- The paper is well-written and includes clear illustrations that help the audience understand the proposed spatial relation benchmark and its evaluation scope.", "weaknesses": "- There is no qualitative analysis of failure cases. Investigating these failures would strengthen the paper further. Providing a few examples and categorizing the errors could help reveal which aspects of reasoning need improvement—such as perception, logical reasoning, or consistency.\n- The paper only demonstrates the effectiveness of SpatialCoT on the perspective-taking task. How does this approach affect performance on other task types? This might raise some concern that it make model perform worth in other tasks that does not require perspetive taking.\n- Minor issue, Table 4 is never mentioned in discussion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rG10qg1J1I", "reviewer": "Reviewer_EAe1", "summary": "This paper introduces OmniSpatial, a comprehensive and challenging benchmark designed to evaluate the spatial reasoning of VLMs. Grounded in cognitive psychology, OmniSpatial features over 8.4K manually curated question-answer pairs across four key categories: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking. Experiments show that even SOTA VLMs struggle significantly, performing far below human accuracy. To bridge this gap, authors propose two novel strategies PointGraph and SpatialCoT, which leverage structured scene graphs and multi-view synthesis to improve the model’s reasoning capabilities.", "questions": "Given the performance gap between frontier models and humans, it's important to consider whether current methods can help VLMs catch up. If not, what future research or scaling approaches could bridge this gap?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper’s most significant contribution is exposing that top AI models fail at complex spatial reasoning where humans excel, clearly defining a crucial and challenging direction for future VLM research.\nIt presents the highly original concept of SpatialCoT, which enhances reasoning by simulating mental imagery. This creative fusion of 3D novel-view synthesis with chain-of-thought prompting represents a significant conceptual advance for tackling view-dependent and perspective-taking tasks.\nThe work is distinguished by its quality, evident in the meticulous manual creation of its 8.4K question-answer pairs, which achieved a high inter-annotator agreement and transparent evaluation across a wide spectrum of leading AI models.", "weaknesses": "The paper demonstrates that models fail on complex tasks but does not offer a deep analysis of the reasons. Without a breakdown of specific error types, the work provides limited actionable guidance for researchers to develop targeted architectural improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)