Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes
Authors: ZhiYuan Feng
Zhaolu Kang
Qijie Wang
Zhiying Du
Jiongrui Yan
Shi Shubin
Chengbo Yuan
Huizhi Liang
Yu Deng
Qixiu Li
Rushuai Yang
Ruichuan An
Leqi Zheng
Weijie Wang
Shuang Chen
Sicheng Xu
Yaobo Liang
Jiaolong Yang
Baining Guo
Venue: ICLR 2026 Poster (2026)
Keywords: spatial understanding
benchmark
multi-view
vlm
robotics
Primary area: datasets and benchmarks
TL;DR: MV-RoboBench evaluates whether vision–language models can integrate multi-view images for precise robotic perception and decision-making, revealing major gaps compared to human performance.

Abstract: Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent Vision-Language-Action (VLA) models. Yet most evaluations of VLMs focus on single-view settings, leaving their ability to integrate multi-view information underexplored. At the same time, multi-camera setups are increasingly standard in robotic platforms, as they provide complementary perspectives to mitigate occlusion and depth ambiguity. Whether VLMs can effectively leverage such multi-view inputs for robotic reasoning therefore remains an open question. To bridge this gap, we introduce \textbf{MV-RoboBench}, a benchmark specifically designed to evaluate the multi-view spatial reasoning capabilities of VLMs in robotic manipulation. MV-RoboBench consists of 1.7k manually curated QA items across eight subtasks, divided into two primary categories: spatial understanding and robotic execution. We evaluate a diverse set of existing VLMs, including both open-source and closed-source models, along with enhanced versions incorporating Chain-of-Thought (CoT)-inspired techniques. The results show that state-of-the-art models remain far below human performance, underscoring the substantial challenges VLMs face in multi-view robotic perception. Additionally, our analysis uncovers two key findings: (i) spatial intelligence and robotic task execution are positively correlated in multi-view robotic scenarios; and (ii) strong performance on existing general-purpose single-view spatial understanding benchmarks does not reliably translate to success in the robotic spatial tasks assessed by our benchmark. We release MV-RoboBench as an open resource to foster progress in spatially grounded VLMs and VLAs, providing not only data but also a standardized evaluation protocol for multi-view embodied reasoning.

Published as a conference paper at ICLR 2026
SEEING ACROSS VIEWS: BENCHMARKING SPA- TIAL REASONING OF VISION-LANGUAGE MODELS IN
ROBOTIC SCENES
Zhiyuan Feng!* -
Zhaolu Kang²* 2*
Qijie Wang!
Zhiying Du
Jiongrui Yan
Shubin Shi?
Chengbo Yuan'
Huizhi Liang" If
Yu Deng" Shawn Chen
Qixiu Li
Rushuai Yang
Aretanx An
Leqi Zheng!
Weijie Wang
Sicheng Xu Yaobo Liang*
Jiaolong Yang*:
Baining Guo
"Tsinghua University
?Peking University
FFudan University
*Microsoft Research Asia
Hong Kong University of Science and Technology
*Zhejiang University
Project Page
c Code
Dataset
ABSTRACT
Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent Vision-Language-Actinn (VLA) models. Yet most eval- uations of VLMs focus on single-view settings, leaving their ability integrate multi-view information underexplored. At the same time, multi-camera setups they provide complementary
are increasingly standard in robotic platforms, perspectives to mitigate occlusion and depth ambiguity. Whether VI.Ms can ef- fectively leverage such multi-view inputs for robotic reasoning therefore remains an open question. To bridge this gap, we introduce MV-RoboBench, a bench- mark specifically designed to evaluate the multi-view spatial reasoning capabil- ities of VLMs in robotic manipulation. MV-RoboBench consists of 1.7k manu- ally curated QA items across eight subtasks, divided into two primary categories: spatial understanding and robotic execution. We evaluate diverse set of exist- ing VLMs, including both open-source and closed-source models, along with en- hanced versions incorporating Chain-of-Thoughh results show that state-of-the-ar models remain far below human performance, underscoring the substantial challenges VLMs face in multi-view robotic percep- tion. Additionally, our analysis uncovers two key findings: (i) spatial intelligence and robotic task execution are positively correlated multi-view robotic scenar- ios; and (ii) strong performance on existing general-purpose single-view spatial understanding benchmarks does not reliably translate success in the robotic spatial tasks assessed by our benchmark. We release MV-RoboBench as an open resource to foster progress in spatially grounded VLMs and VLAs, providing not only data but also a standardized evaluation protocol for multi-view embodied reasoning.
techniques. The
INTRODUCTION
Vision-language models (VLMs) (OpenAI, 2024; Team et al., 2023; Anthropic, 2024; Zhu al., 2025; Bai et al., 2025; Liu et al., 2023b) play pivotal role in Embodied AI, enabling multimodal perception and reasoning for robots while also serving as the foundation for Vi- sion-Languag--Action (VLA) models (Zitkovich et al., 2023: O'Neill al., 2024; Kim al., 2024;
'Equal contribution.
I Work done during research internship at Microsoft Research.
1 Corresponning author.

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Published as a conference paper at ICLR 2026
Table 1: Comparison of spatial reasoning benchmarks. Prior datasets emphasize single-view rela- tions, abstract reasoning, non-embodied multi-view perception. The "Partial" in "Multi- Viett" indicates that these datasets contain only a subset of multi-view samples. mixed with single-view view puts. MV-RoboBench uniquely targets multi-view spatial reasoning within robotic manipulation scenarios, combining embodiment with multi-view perception.
Benchmark
Multi-View
Task Category
Enviroument / Scenario
Annotation
QA
EmnbSpaial-Beni Bench (Du et al., 2024)
X
Sputial
Indoor ScanNet
Template
3.6K
Visual Spatial (Liu 2023a)
Sputial
Template
RoboSpatial (Song et al., 2025aj
X
Spatial
Indoor tabletop
Template
3M
Spatial MM (Shiri et 2024)
x
Sputial
Internet
Template
2.3K
SpatialVT M (Chen al., 2024)
Spatial
Wehl
Template
546
VSI-Bench (Yang et al., 2025b)
x
Spatial
Indoor egocentric vidco
Template
5K
OrniiSpatial
al., 2025)
Spatial
Internet
Manual
ShareRobox (Eval) (Ji et al., 2025)
x
Robotic
Robot manipulation
Manual
1.2K
ERQA (Team al., 2025g)
Partial Partial
Spatial Robotic
Hunan--goeenric robotic manipulation
Manual
0.4K
MMSI Bench (Yang al., 2025c)
Sputial
Multi Domain Egucentric + Driving
Manual
1.0K
All-Angles Bench (Yeh et al., 2025)
Spatial
Multi-viw photos and videos
Template
2.1K
Fgo3D-Bench (Gholami al., 2025)
Spatial
Egocentric navigation
Template
8.6K
AlV-RoboBench (Ours)
Spatial Robotic
Robot manipulation
Manual 1.7K
Li et al., 2024; Black et al., 2024; Intelligence et al., 2025) that empower robots to operate in com- plex real-world environments. By leveraging VLMs, VLAs inherit broad multimodal competence while adding the ability to ground decisions in physical execution, positioning them as the backbone of next-generation robotic intelligence.
Unlike generic multimodal reasoning, robots operate in physical environments rather than abstract 2D tasks. Robotic execution naturally requires spatial intelligence: the capacity to interpret 3D) structure, reason about geometric relationships, and maintain consistency across viewpoints. Single- view inputs are inherently limited by challenges like occlusion, depth ambiguity, and restricted fields of view. Multi-view observations, by contrast, offer complementary perspectives that help overcome these limitations. As they become increasingly standard on robotic platforms, multi-view observations enable more robust perception and decision-making
Although many benchmarks have been proposed to assess the spatial reasoning capabilities of VLMS (Du et al., 2024; Liu et al., 2023a; Shiri et al., 2024; Chen et al., 2024; Song et al., 2025a; Yang et al., 2025b; Jia et al., 2025), they mostly focus on single-view data. ERQA (Team et al., 2025a) contains only a small portion of multi-view data, and the diversity of views remains limited, and the tasks remain relatively basic. Moreover, they often emphasize general spatial intelligence tasks while giving less attention to the embodied, action-orienteds requirements of robotic manipu- lation. ShareRobot (Ji et al., 2025) extends evaluation to embodied robotic tasks but without multi- view perception. MMSI-Bench (Yang al., 2025c) includes multi-view tasks, but its questions primarily target basic spatial perception and understanding. All-Angles Bench (Yeh et al., 2025) and Ego3D-Bende (Gholami et al., 2025) expose models multi-view inputs, yet their evaluation remains confined to photographic alignment or navigation multi-view reasoming for manipulation.
innigation-orinntie oriented perception rather than embodied
To fill this gap, we introduce MV-Robo a benchmark specifically designed to evaluate multi- view spatial reasoning in robotic manipulation scenarios. is built from real robotic demonstrations with synchronized
multi-camera views and encompasses both spatial reasoning and robotic execu- tion tasks. The benchmark includes total of 1.7K carefully-curatee QA items by humans, spanning diverse manipulation tasks and environments. It offers a systematic evaluation of whether VL.Ms can effectively integrate complementary information from multiple camera views to support decision- making for robots in the real world.
Our key contributions are as follows:
We establish the first benchmark that integrates spatial and robotic reasoning with synchro- nized multi-view inputs in robotic manipulation scenarios, enabling a thorough evaluation existing open-source and closed-source VLM models.
We show through extensive experiments that robotic multi-view scenarios remain signifi- cantly challenging. The most powerful VL.M models still fall far below human performance
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Published as a conference paper at ICLR 2026
Spatial: Cross Cross-View Matching
Robotic: Action Planning
Question: In the right-g grtp-gr comero view. the them outtinee with a bounding box Which colored bounding box encloses that some item in left sraper comerr
Question: want from the kettle the table into the water cup. iHoad cocncirate systimm
Answeri fove gripper fward, ferward, downeord grasp
Answer: The pink bounding box.
kettle, move upward, nightwardd pour the oup.
All Spatiol: Distance Judgement
Robotic: Step Execution
Questions Which cornesponds Answer: Srab the item it the blue bounding box using the right gripper.
grasping
Suppose use right grpper grab spoon sysiten
Spatial: 3D Spatial Consistency
grab. right gripper
left, forward
Robotic: Trajectory Selection
coordinate estoblished the head which following sets representation iientoaionnll bether matches the relative pasitions of the objects marked the rigun??
Question:
Question: If aust place the pink uet wipes into the plastic bag, which color Insjectory noet likely to accomplas this task? Answer: The pirk Inc.
Anower: (Red box, 2,4, 4), (Yellow box, (Green box, 3, 4, (Biue box, 2, 3), (Pikk box. 2,
Robotic: Affordance Recognition
Spatial: Viewpoint Identification
Question: image which left conero's this moment?
Question: Which colour-caded line the condidate likely siccess?
Arnenr: Yellow line.
Figure 1: Representative multi-view QA instances from the eight tasks in MV-RoboBench, with spatial tasks shown on the left and robotic tasks on the right. For clarity, only simplified versions with ground-truth answers are presented here, omitting distractors. Full examples provided in Appendix F.
and many others perform close to random. We further explore CoTTiisppreed enhancements, which yield mixed and model-dependent effects across models.
We provide a correlation analysis multi-view robotic scenarios, uncovering two key find- ings. First, there is a positive correlation between spatial reasoning and robotic execution. Second, strong performance on general-purpovt single-vier view spatial benchmarks, which as- sess reasoning from concrete to abstract settings but are devoid of robotic context, does not reliably transfer either to robotic tasks or to spatial reasoning tasks within multi-view robotic scenarios. These findings highlight the unique challenges of multi-view reasoning in robotics and the need for specialized benchmarks like MV-RoboBench.
2 MV-ROBOBENCH
2.1 OVERVIEW
We introduce MV-RoboBench, benchmark designed to evaluate the multi-view reasoning capa- bilities VLMs in robotic manipulation scenarios. It is built from the AgiWorld (Bu et al., 2025) and BridgeV2 (Walke et al., 2023) datasets, spanning both single-arm and dual-arm robotic manip- ulation settings. In total, we construct 1,708 multiple-choice questions across eight subtasks, each with exactly correct answer, enabling objective, reproducible, and easily extensible evaluation.
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Published as a conference paper at ICLR 2026
I: Data Collection
II: QAA Generation
Task Templates
Tool-based onnotation
left- Qr You are glen these
sxppe, right ginpper
AgiWorld
Sumplc
Agi World Image Pairs
Q: Zo the Vieu the then with
hie.
that dome than weez.
ligle do falkwing
oepplete the 1r00
GeT beard
fullwwng
VQAss Pool
accceplin tade the
mes It
Q1.
A1,
AR
A3
A5
Q2.
A3,
45
BridgeV2
Expert Selction
BridgeV2 Image Pairs
Q5.
A1
45,45
Q6.
A3,
Tstal of 36
III: Human-ine in- the-loop Quality Review
Checklist
A Total of 1.7k
VQAs Pool
(Image Pains, Qu A)
Match question and GT answer Difference between GT and inter ference options
Human Experts
Aligr and Cansistency
Content
Sentence structure. Consistencyd Soritty Checks Image Pair Quality
X Far Quality
Bed Emage
Refire and sentience atruchune
Lamgnge/Form
X
Option randomness balance
MV-ROBOBENCH
Discard
Figure 2: Construction pipeline of MV-RoboBench, consisting of three stages: data collection, QA generation, and human-in cuuman-in-the-llee in-the-loop quality review.
Figure illustrates representative examples from the eight subtasks in MV-RoboBench. To system- atically evaluate multi-view reasoning in robotic contexts, we divide the benchmark into two com- plementary categories: spatial understanding and robotic execution. Spatial understanding focuses on perception and reasoning across multiple camera views, assessing whether multi-view observa- tions can be integrated into a coherent 3D representation of the scene. Robotic execution, contrast, extends this spatial reasoning to embodied meciionn-making... probing whether multi-view informa- tion can be effectively leveraged to support planning, execution validation, trajectory feasibility, and affordance reasoning in manipulation tasks.
The four spatial understanding subtasks each target a distinct aspect of multi-view perception: cross- view matching requires identifying the same object across different viewpoints; distance judgement evaluates relative distances between objects; viewpoint identification tests the ability to reason about viewpoint transformations; and 31) spatial consistency probes whether models can maintain consis- tent relative positions of objects in 3D space. Most of these subtasks rely on paired images as input, emphasizing the integration of complementary viewpoints.
The four robotic execution subtasks test whether multi-view information can support embodied decision-making in manipulation. Action planning requires choosing appropriate multi-step quence to complete a task, while step execution focuses on verifying whether the next single-step movement is correct. Trajectory selection evaluates the feasibility of candidate motion paths, and affordance recognition assesses the feasibility of object-specific interactions. Together, these sub- tasks emphasize the role of multi-view observations in resolving occlusion and depth ambiguity for embodied decision- decision-making.
2.2 BENCIMARK CONSTRUCTION
We design carefully engineered, multi-stage pipeline that has been iteratively refined to ensure the construction of high-quality QA pairs at scale (Figure 2).
Data Collection. We first apply rale-based filtering to synchronized multi-view image pairs to ensure sufficient temporal separation, scene diversity, and visual clarity. GPT-4.1 then serves as an auxiliary filter by checking whether pairs satisfy at least one of the eight task definitions, after which
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Published as a conference paper at ICLR 2026
AgiWolnd (Urique Scanes: 597)
QA4s
Gross Motching 11.7%
Affordonce Recognition 12.2%
Viewe Matching
stanoo J'udgomont
120
Vioupoint Idertification
266
3D Spotial Consistency
Afferdance Recognition
120
Distance Judgement 11.8%
Trajectory Selection 11.7%
Trejectury Selection
Step Execution
154
Action Pla Planning
124
Spatial 50,
Multi-View View Robobench
Robotic
Bridge (Unique Scena: 383)
Epieodes
Viewpoitt Ide-tifi-
Matching
Shep Execution 13.7%
atance Tudgement
81
Catien
30 Spotial Coruistercy
Affordancs Recagnition
Trejectory Solection
60
3b Spaticl Consistency 11.9%
Exccution
Plarning 11.9%
Action Planning
80
Totol Ep Episode 980
Total QA 1708
Figure 3: Data distribution of MV-RoboBench, showing QA counts per subtask and dataset source (AgiWorld and BridgeV2), and the overall balance between spatial and robotic domains.
human annotators verify clarity and appropriateness. Importantly, GPT-4. is never used generate QA content but only to assist in candidate triage, and all retained items are manually validated to ensure that genuine multi-view reasoning required rather than pattern completion.
QA Generation. For each subtask, task-specific templates were designed, and trained annotators constructed corresponding five-choice QA pairs from the curated image pairs. During annotation, we explicitly avoided designing overly ambiguous or artificially tricky questions, while ensuring that distractors remain plausible yet clearly distinguishable from correct option. All annotated items were collected into shared VQA pool for subsequent refinement. Further implementation details are provided in Appendices E-F.
Human-in-the-logg Quality Review. Samples from the VQA pool were iteratively reviewed by trained annotators. Items that did not align with the objectives of the benchmark were discarded, while those with minor issues were revised. Content-related issues were corrected manually to maintain consistency between images and QA, while minor grammar or structural issues were re- fined with GPT-4.1. The revised items were then returned to the VQA pool for subsequent review and balancing. Accepted items were then rebalanced to randomize answer distributions, ensuring fairness and reducing bias before inclusion in the final benchmark.
Finally, Figure 3 provides detailed breakdown of MV-RoboBenchh showing both per-subtask statis- ties and the balance between spatial and robotic domains. In addition to the 1,708 QA pairs, the benchmark is derived from 980 episodes, highlighting its grounding in diverse real-world robotic demonstrations.
2.3 EXPLORING COT-INSPIRED ENHANCEMENTS FOR MULTI- VIEW UNDERSTANDING
Recent advances language reasoning show that Shaain-of-Though (CoT) Wei (2022) prompt- ing can elicit structured intermediate reasoning. This raises the question of whether similar staged reasoning can benefit multi-view understanding in embodied robotic settings, where challenges such as cross-view correspondence, viewpoint alignment under narrow baselines, and consistent geomet- ric fusion persist. Building on this intuition, we explore three CoT-style extensions in the context of multi-view robotic reasoning. First, enriching visual inputs with additional scene descriptions serves as a textual COT, explicitly verbalizing spatial context that may otherwise remain implicit; to implement this, adopt GPT-4.1 for generating descriptions. Second, generating additional synthesized viewpoints through novel view synthesis provides visual CoT, supplying extra visual evidence to support cross-view alignment; implement this, we adopt VGGT (Wang al., 2025a)! as a representative synthesis baseline. Third, introducing depth priors supplies a structural CoT, adding geometric constraints that reduce ambiguity in 3D reasoning; to implement this, we adopt
We also tested several recent novel view-synthesis methods, but they performed poorly in robotic multi- view settings, especially under narrow haselines, cluttered tabletops, and gripper-centric viewpoints.
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Published as a conference paper at ICLR 2026
MoGe-2 (Wang et al., 2025b) for depth estimation. Further implementation details provided in Appendix C.
2.4 FROM PERCEPTION TO ACTION: CORRELATION ANALYSIS
spatial and robotic reasoning were decoupled, improving view-based perception would not neces- sarily yield action competence; correlation analysis directly tests this assumption by leveraging the spatial-robotic task split in MV-RoboBench. We refer to this relationship as the internal cor- relation axis, probing whether stronger spatial perception leads to more reliable robotic execution. Beyond this internal relationship, we define an external generalization axis that examines whether spatial intelligence measured in existing single-view benchmarks transfers embodied multi-view tasks. Unlike single-view settings, which assess perception from a fixed perspective, multi-camera setups demand integrating complementary observations framing leads to two central questions: (i) how spatial and robotic reasoning relate within multi-view manipulation scenarios, and (ii) whether performance on general single-view benchmarks reliably transfers multi-viww embodied reasoning. We next provide systematic evidence these issues in Section 4.
a coherent 3D understanding. This
Table 2: Evaluation on MV-RoboBench under a unified zero-shot prompt. denotes the best score and the second-bes besttd-best within each column. Qwen2.5-v1-72B leads among open-source models, while GPT-5 ranks highest overall but still remains far below human accuracy.
Cross-Vier Match
Distance
Viewpoint
3D Spatial Consist.
Action
Step
Trajectory
Affordance
Judgo
ID
Plan.
Exee.
Sel.
Rec.
Method
Avg.
Rank
Spatial Tasks
Robotic Tasks
Blind Evaluation
Random Choice
17.80
19.40
20.00
19.07
21.54
19.81
GPT-3.5-tubo
18.52
15.50
22.39
20.31
12.25
21.57
18.38
23.00
16.75
GPT-4-turbo
22.91
19.00
13.43
19.92
7.84
41.67
31.20
20.00
27.27
Proprietary Models
GPT-4o-mini
22.52
24.00
11.76
24.51
GPT-4o
27.59
3
24.50
37.31
19.92
6.37
33.33
33.76
33.00
20.10
GPT-4.1-nane I--anno
20.85
9
17.50
25.37
18.75
14.71
22.55
22.22
20.00
17.22
GPT-.1 mini
23.98
28.50
33.83
25.00
7.84
26.47
21.79
32.00
18.18
GPT-4.1
30.90
26.00
43.28
32.03
6.37
29.90
31.62
41.50
28.23
Claudo-3.5
23.71
17.50
27.86
20.31
8.82
27.27
Claude-3.7
25.47
5
18.00
35.32
20.31
6.86
36.76
29.06
34.50
22.97
Gomini-2.0-Bast
28.94
28.00
32.84
21.48
7.35
32.84
29.91
20.57
Semini-2.5-tlas.
27.23
26.50
37.31
27.34
6.37
34.80
30.34
42.00
19.14
Proprictary Reasoning Models
04-mini
46.47
3
21.50
48.26
26.17
65.69 $5.69
74.51
63.25
44.00
25.36
GPT-5-chat
GPT SPPT-5-nam
32.75
5
21.50
33.33
17.58
$6.86
39.71
35.47
31.00
26.32
GPT-5-mini
22.00
25.78
72.55
27.75
GPT-5
56.41
29.00
55.22
44.14
82.35
79.41
68.38
54.50
39.23
3imude-3.7-thin'
31.67
6
24.40
35.04
36.00
52.45
21.50
37.81
21.08
23.05
Gcmini-2.5-pro
49.52
39.50
38.28
49.02
65.20
50.85
31.58
Open-Source Models
Gemma-3-4b
19.79
11
21.00
22.89
21.09
11.76
17.65
67
25.50
22.01
Gemma-3-12b
20.49
18.00
26.37
20.31
9,80
22.55
20.94
25.50
20.57
Gemma-3-277
20.55
21.50
23.88
20.31
9.31
20.10
23.08
29.00
17.22
InternVT.3.22
18.93
12
16.50
15..42
20.70
20.59
17.16
20.94
21.00
19.14
VL.38b
20.97
19.00
26.17
12.75
26.47
21.37
20.50
20.10
InterVVI.3 14b
21.47
5
19.50
22.39
24.61
10.78
23.53
23.50
24.00
23.44
InternVL.3-93.
22.80
24.50
23.44
6.86
27.94
25.21
21.05
IL3-7nVL3-78bb
23.25
2
19.00
28.86
23.83
11.76
29.90
29.06
26.50
21.05
Qwen2.5-11e
20.37
10
17.50
21.89
22.66
17.65
17.16
17.95
22.00
25.84
Qwen2.5.v1
20.84
7
20.50
20.40
20.70
8.82
22.55
26.07
24.50
22.49
Qwen2.5-v1-32h
22.48
20.50
25.87
25.39
10.78
24.51
19.66
30.50
22.49
Qwen2.5-v1-72b
20.50
4.90
28
27.35
29.00
24.88
Open-Source
Llama Scout
22.12
20.50
22.39
23.83
7.35
25.49
28.21
23.00
18.18
M-nveriik
26.11
14.00
42.79
17.58
5.88
37.75
37.18
36.00
20.10
Human Evaluation
Human

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Published as a conference paper at ICLR 2026
3 EVALUATION ON MV-ROBOBENCH
3.1 EVALUATION SETUP
We evaluate a broad spectrum of systems spanning five categories: Blind Evaluation, text-only LLMs without visual grounding (Random, GPT-3.5-turbo (Roumeliotis & Tselikas, 2023), GPT- 4-turbo (Achiam et al., 2023)); Proprietary Models, multimodal systems from major providers, including the GPT-4o family (Hurst et al., 2024), the GPT-4.1 series (OpenAI, 2024). Claude- 3.5/3.7 (Anthropic, 2024), and the Gemini-2.x flash family (Team et al., 2023); Proprictary Rea- soning Models, architectures optimized for multi-step reasoning such as 04-mini (OpenAI, 2025b), the GPT-5 family (chat/mini/nano/full (OpenAI, 2025a). Claude-3.7-thinke 7-thnnk (Anthropic, 2024), and Gemini-2.5-pro (Team al., 2023); Open-Source Models, community-developer VLMs including the Gemma-3 family (4B-27B) (Team et al., 2025b), the Intern VL.3 series (2B-78B) (Zhu et al., 2025), and the Qwen2.5-vl series (3B-72B) (Bai et al., 2025); and Open-Source MoE Models, namely Llama-4-Scout and Mlama-4MMvveriae (Meta AI, 2025). Since all tasks are formulated as multiple-choice questions, we adopt answer accuracy as the evaluation metric. This unified format avoids model-specific prompt engineering and ensures fair cross-model comparison on multi-view reasoning ability. Human evaluations were conducted separately with participants holding a com- puter science background to serve reference point. Further implementation details are provided in Appendix B.
3.2
MAIN RESULTS ON MV-ROBOBENCH
Table 2 reveals consistent trend from perception-oriented systems toward explicitly reasoning- optimized architectures. Proprietary multimodal models such as GPT-4.1 reach 30.90%, while open- source VLMs including Qwen2.5-v1-72B (24.29%) and MoE variants such 4laeri--Maverick (26.11%) perform moderately lower. The largest gains arise in the proprietary reasoning category: GPT-5 achieves 56.41%, with Gemini-2.55pre (49.52%) and 04-mini (46.47%) also performing strongly. Figure 4 contrasts leading representative models from each family against human per- formance, highlighting substantial remaining gap across both spatial and robotic subtasks.
Task-level analysis shows that 3D Spatial Consistency is especially challenging. Most non-reasoning models perform near or even below random-choice accuracy (19.07%), indicating that they fail to leverage multi-view infor- mation and effectively guess without spatial integration.
Centiseeery
In contrast, ceasoning-enhanced models rise approx- imately 49-82%. Robotic subtasks, including Action Planning, Step Execution, Trajectory Selection, and Af- fordance Recognition, also show substantial improve- ments under reasoning- based architectures. Planning in particular benefits from richer temporal structure in multi- step options compared to single-step execution evalua- tion. Human participants nearly solve the benchmark 91.0%. underscoring both the gains enabled by ex- plicit reasoning and the substantial remaining gap to- ward human-level multi-view robotic intelligence. Fur- thermore, we validate the necessity of multi-view inputs in Appendix G, showing that single-view baselines suffer significant performance drops (e.g., ~99% drop in Dis- tance Judgement for GPT-5) due to unresolved depth am- biguities.
Weepett Edenttfoonan
Trijetory
Mandiin
Becign
Figure 4: Best-per-group model perfor- mance across MV-RoboBench subtasks.
3.3 EVALUATION OF COT-INSPIRED ENHANCEMENTS
As shown in Table 3, CoT-style augmentations exert non-uniform and sometimes counterintuitive effects across models. For Qwen2.5-v1-7B auxiliary cues bring negligible even negative changes, with only the depth prior offering a slight gain. Gemma-3- 1-BB, by contrast, benefits substantially from CoT prompting, while textual augmentation and synthetic novel-view generation generally
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Table 3: Accuracy of CoT-style augmentations on MV-RoboBench. A, and A, indicate changes on spatial and robotic tasks relative to the origin baseline. Variants: w cot textual prompt, w text descriptive augmentation, vggt synthetic view, depth depth prior. indicates improvement, degradation.
Cross-View Match
Distance Judge Spatial Tasks
Viewpoint
Spatial Consist.
Step Exee.
Trajectory Sel.
Affordance Rec.
Avg.
A,
ID
Plan.
Method
Robotic Tasks
Qwen2.5-v1-7b
origin
20.84
20.50
20.40
20.70
8.82
0.00
22.55
26.07
24.50
22.49
0.00
w cot
20.49 (-0.35)
20.00
21.39
22.27
+0.58
22.55
25.50
22.55
-1.30
text
20.90 (+0.06)
20.00
20.40
22.27
4.41
-0.70
25.98
28.21
24.50
20.10
40.82
w 'ggt
20.02 (-0.82)
16.50
17.91
23.83
5.39
-1.40
21.08
25.64
23.50
24.40
-0.24
depth
21.14 (+0.30)
22.89
22.89
21.09
12.75
+1.04
19.12
27.35
23.50
23.44
-0.48
Gemma-3-112
origin
20.49
18.00
26.37
20.31
9.80
0.00
22.55
20.94
25.50
20.57
0.00
cot
24.19 (43.70)
18.00
22.89
17.97
11.27
+0.93
21.57
27.35
27.50
25.84
+2.96
text
18.43 (:2.06)
19.00
21.89
21.09
7.84
.0.94
20.10
21.79
18.50
20.10
-0.47
w vggt
18.31 (-2.18)
17.50
18.41
21.48
8.33
-1.47
18.14
22.
19.00
24.40
+0.11
depth
20.41 (-0.08)
18.00
26.37
21.09
7.84
23.50
21.00
23.44
+0.19
GPT-4.1
origin
29.87
26.00
43.28
32.03
6.37
0.00
29.90
31,62
41.50
28.23
0.00
w cot
29.84 (-0.03)
28.50
40.30
29.69
6.37
-1.21
28.92
30.34
46.00
22.49
-0.25
text
31.66 (+1.79)
28.00
46.50
34.38
6.86
+1.73
32,02
32.48
45,50
28.99
+1.81
w vggr
28.02 (-1.85)
29.80
38.69
31.50
4.50
-1.54
29.21
31.17
40.50
27.45
-1.58
depth
(43.25)
degrade performance. GPT-4.1 gains most noticeably from depth priors, with textual augmentation yielding marginal improvements and CoT remaining largely neutral.
Overall, synthetic novel views are more likely to hurt performance, depth priors help only when the backbone has sufficient capacity to exploit geometric cues, and CoT enhancement most effective for mid-capacity open-source models rather than already over-optimized proprietary ones. These mixed outcomes highlight that multi-view robotic manipulation cannot be reliably improved through generic prompting, suggesting that future progress will require tighter coupling between explicit geometric understanding and structured reasoning rather than shallow prompt-level augmentation. Detailed settings of the three enhancement variants are provided in Appendix C.
FROM PERCEPTION TO ACTION: CORRELATION AND TRANSFER
Proprietory Models
Propristory Reasoning Modele
Open-Source Models
Open-Soarce MaE Models
200
45
Spetial Task Accuracy
Spatial Task Accuracy
60
SP
35
40
25
Qeee S-6-723
Liee Roneicc
ang Performence
16
Random Choice (20%)
30
Robotic Task Accuracy (%)
Robotic Task Accuracy (%)
Figure 5: Spatial robotic accuracy MV- Models clustered near the lower-left op- crate close to random guessing, while prasoning-enhane proprictary models show a clear upward trend across both axes.
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Having established the two analysis axes in Section 2.4 the internal correlation axis between spa- tial and robotic reasoning, and the external generalization axis from sngle-view to multi-view spa- tial intelligence-we now present empirical evidence along both dimensions.
4.1 INTERNAL CORRELATION: SPATIAL VS. ROBOTIC INTELLIGENCE
As shown in Figure 5, there exists a positive correlation between spatial and robotic accuracy in multi-view manipulation tasks, but this relationship is strongly model-dependent. Proprietary and ceasoning-optmizze systems exhibit monotonic trend, where improving spatial perception is companied by gains in robotic execution. In contrast, most open-source VLMs cluster near random- choice aceuracy, suggesting that without explicit multi-view fusion, perception does not translate into actionable understanding. These results confirm that spatial and robotic reasoning can align, but only when the model possesses sufficient capacity to integrate observations across viewpoints.
4.2 EXTERNAL TRANSFERABILITY SINGLE- VIEW TO MULTI- VIEW
To assess whether spatial intelligence measured in existing general single-view benchmarks carries over to multi-view robotic manipulation, we use OmniSpatial (Jia et al., 2025) as a reference due to its broad coverage of spatial reasoning. Our reproduced OmniSpatial results are reported in Appendix D.
Figure 6 shows that, aside from proprietary reasoning models, strong single-view accuracy does not reliably transfer to multi-view embodied reasoning. Many models that perform well on Om- niSpatial still remain close to random on MV-RoboBench. Even for the highest-performing rea- soning models, single-view competence only partially translates, with multi-view accuracy still lag- ging behind. This indicates that multi-view robotic reasoning introduces fundamentally different viewpoint integration, occlusion resolution, and spatial fusion-that not exercised by existing single-view benchmarks, underscoring the necessity of developing dedi- cated benchmarks tailored for multi-view robotic scenarios.
demands particulary
Proprietory Models
Proprietary Reasoning
Open Souree- Models
Open-Source
55-
CIT-I
OmaiSpatial Accuracy (%)
OmniSpatial Accuracy (%)
Schari 750
20 flask
Qeest 57b-72
40-
60-
40-
30
30
Rundes
Randon choice
Aug (suncace seepeimtryy reapning modsk)
Randor 20%
Randon 205
MV RoboBench Spatial Accuracy (%)
MV-RoboBench Robotic Accuracy (%)
Figure 6: Comparison of model accuracies on OmniSpatial versus MV-RoboBench, with the left plot for spatial subtasks and the right plot for robotic subtasks.
5 RALATED WORKS
5.1 SPATIAL UNDERSTANDING AND REASONING IN MULTIMODAL LLM
Recent Multimodal Large Language Models (MLLMs) (OpenAI, 2025a; Hurst et al., 2024; Ope- nAI, 2024; Anthropic, 2024; Team al., 2023; 2025b; Zhu et al., 2025; Bai al., 2025; Meta AI,

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2025) have demonstrated remarkable progress across diverse tasks, including captioning (Lin al., 2024: An et al., 2024; 2025), retrieval (Luo et al., 2024; Lin et al., 2025b), planning (Zhou et al., 2024), and even robotic tasks Zitkovich et al. (2023); O'Neill et al. (2024); Kim et al. (2024); Li et al. (2024); Black et al. (2024); Intelligence et al. (2025). However, despite their strong general visual-linguistic competence, these models remain limited in structured spatial grounding, particu- larly when required to maintain 3D consistency, infer depth relationships, or reason across multiple viewpoints (Fu et al., 2024b; Song et al., 2025b; Yang et al., 2025a; Cheng al., 2024).
To address these challenges, specialized approaches (Cheng et al., 2024; Ma et al., 2025; Zhou al., 2025; Fan et al.. 2025; Liu et al., 2025; Cai et al., 2025; Fu al., 2024a; Hong et al,, 2023: Chen et al., 2024) have attempted to incorporate geometric priors or explicit 3D features into ML..Ms.
ducing However, such interventions often disrupt pre-trained vision-language alignment, re- robustness. Moreover, even with access to depth or point cloud inputs, current models rarely demonstrate reliable multi-view consistency or explicit exploitation of geo- metric cues when answering spatial reasoning queries (Zha et al., 2025; Li et al., 2025; Chi et al., 2025). These observations suggest that spatial intelligence in current MLLMs remains predomi- nantly pattern-driven rather than derived from explicit spatial fusion across views.
5.2 BENCHMARKING SPATIAT, AND HULTI-VIEN UNDERSTANDING
A growing number of benchmarks have been introduced to evaluate the spatial reasoning abilities of VLMs, as summarized in Table 1. Early efforts such EmbSpatial-Bench Du et al. (2024). Visual Spatial Liu et al. (2023a), and RoboSpatial Song et al. (2025a) assess template-basee object rela- tion reasoning in static single-view scenes. Subsequent datasets, including Spatial-MM Shiri et al. (2024), VSI-Bench Yang et al. (2025b), and SpatialVLM Chen et al. (2024), extend evaluation to egocentric video and free-form spatial queries, but still remain limited single-view interpretation.
More recent works such All-Angles Bench Yeh et al. (2025) and Ego3D--Bench Gholami et al. (2025) explicitly evaluate multi-view reasoning, but their tasks are confined to photographic align- ment egocentric navigation perception rather than manipulation-oriente By contrast, OmniSpatial Jia et al. (2025) remains a single-view benchmark, although it broadens spatial evaluation to wider range of reasoning categories. However, all these efforts primarily target general spatial understanding and do not address embodiment or the precision requirements critical for robotic manipulation. In contrast, our MV-RoboBench is the first benchmark to couple multi-view spatial reasoning with robotic execution tasks, providing a realistic and comprehensive testbed for embodied multi-view intelligence.
embodied reasoning.
DISCUSSION AND FUTURE WORK
Our study highlights three main takeaways. First, multi-view robotic reasoning requires more than perception alone: perception-oriented VLMs yield only modest gains, and only reasoning- augmented systems begin to approach reliable robustness. Second, spatial and robotic intelligence are positively correlated in multi-view manipulation, yet both remain far below human performance, reflecting the absence of robust embodied 3D reasoning. Third, competitive performance on single- view spatial benchmarks does not reliably transfer, revealing a persistent gap between single-view reasoning and embodied multi-view understanding.
Looking forward, progress will likely depend on (i) architectures that explicitly encode geometric priors and enforce cross-view consistency, (ii) training pipelines that align perception with action grounding, and (iii) larger-scale multi-camera datasets that reflect the complexity of real-world ma- nipulation. Our results suggest that scaling perception alone is insufficient-modee require explicit reasoning mechanisms to transform multi-view observations into actionable. embodied understand- ing. By isolating failure modes in multi-view grounding rather than in isolated perception, MV. RoboBench exposes the precise bottlenecks that future embodied AI systems must overcome. We hope will serve not only as a yardstick but also as a catalyst for developing the next generation of spatially grounded VLMSs and VLAs.
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ETHICS STATEMENT
This work follows the ICLR Code of Ethics. MV-RoboBench is built entirely from publicly available robotic datasets (AgiWorld and B2idgeV2) and does not involve any personally identifiable or sen- sitive information. All annotations were created by trained annotators under controlled conditions, and we release the benchmark for research purposes only.
REPRODUCIBILITY STATEMENT
We provide detailed descriptions of dataset construction, evaluation setup, and experimental config- urations in the main text and appendix. All curated data, task templates, and evaluation code will be released in the supplementary material upon acceptance to ensure reproducibility, while maintaining anonymity during the review process.
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A APPENDIX OVERVIEW
In these supplementary materials, we provide additional details to complement the main paper:
Appendix B: Experimental setup details, including system prompts, inference configura- tions, and hyperparameter settings for all evaluated models (see Appendix B).
Appendix C: Implementation details CoT-inspired enhancements, including prompts used for textual augmentation, pipelines for visual augmentation, and configuration for depth priors (see Appendix C).
Appendix D: Complete evaluation setup and results on external benchmarks, covering both OmniSpatial and ERQA experiments (see Appendix D).
Appendix E: Preparations for building the benchmark, including dataset setup and annota- tion tool design (see Appendix E).
Appendix F: Detailed process of benchmark construction, including task formulation
methodology and annotation workflow (see Appendix F).
Appendix G: Ablation study demonstrating the necessity of multi-view inputs by compar- ing performance against single-view baselines (see Appendix G).
Appendix H: Analysis of model sensitivity to image orientation (see Appendix H).
Appendix I: Evaluation of model capability to determine "no correct choice" (see Ap-
pendix I).
Appendix J: Qualitative error analysis of representative model failures on MV-RoboBench. covering single-view bias, depth and occlusion confusion, frame-of-reference errors, and affordance misunderstandings (see Appendix
B EXPERIMENTAL SETUP
This appendix provides additional details of the experimental setup used in our evaluation.
B. MODEL ACCESS AND INFERENCE PROTOCOL
All models were evaluated in a zero-shot setting with unified protocol across tasks. Proprictary systems were accessed through their official APIs, while open-source models were via Hugging- Face implementations.
B.2 PROMPT TEMPLATES
For reproducibility, we report the exact system- and user-level instructions used across all experi- ments.
SYSTEM PROMPT
We employed the following JSON-foratteed system instruction:
"role": "system",
"content"; "You are assistant performing harmless academic
robotics benchmark evaluation. All content is for research purposes.
You are an evaluator for robotic vision benchmark.
You will be shown multiple-choice questien and a set of candidate answers, sometimes with images.
Your task is to carefully read the questien, consider the provided information, and then select the SINGLE best option (A, B, C, D, or
E)
Guidel ine::
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Always base your images.
only the question and the provided options/
11 Do not use external knowledge beyond what is shown.
Output strictly enc eption letter (A/B/C/D/E).
Do not explain your reasoning unless explicitly requested.
If multiple answers plausible, cheese the consistent with the given views.
15
Answer format: Answer: <option letter>"
USER PROMPT
Each QA item was wrapped into the following template, where quest ion denotes the natural- language question and opts_ste is the list of candidate options. The corresponding images (base64-encoded) were attached alongside the prompt.
Question:
(question)
Options:
(opts_str)
Please output single 1ine of the form: 'Answer:_x" where is one of A, B, C, D, E.
B.3 IMAGE ENCODING
All images were provided in base64-encodes format. We followed the OpenAl-style API conven- tion:
def encodc_ sacode_image_to_ber base64 (image_pathe Path) str: with open patagge_path,,hh "rb") as f;
return basc64 b64encode (f. read() ) decode ("utf-8")
Encoded images were attached to the user message under the "image" field.
B.4 EVALUATION PROTOCOL
All tasks are framed as multiple-choice QA. Accuracy was computed as the fraction of correctly predicted answers. Each model evaluated the entire benchmark without post-hoc filtering. We ensured identical question order and random seeds across runs for fair comparison.
B.5 HUMAN EVALUATION
We recruited five participants with strong backgrounds computer science, including PhD, mas- ter's, and senior undergraduate students, of whom were involved in dataset annotation. All participants completed the benchmark under the same interface without access to model outputs. To ensure a fair comparison, did impose time limits prohibit external references, since state-of-thee afr-----ttt-tttt models also leverage extensive Internet-scall data. We report the average accuracy of the participants as an approximate human upper bound.
C IMPLEMENTATION OF COT-INSPIRED ENHANCEMENTS
This appendix provides implementation details for the three CoT-inspired enhancement strategies explored in Section
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C.1 CHAIN-OF-THOUGHT (CoT) PROMPTING
We keep the system prompt unchanged and prepend a single sentence to the user prompt:
You are a careful, step-by-step reasoner. Think concisely.
The rest the user template (question, options, and answer format) remains identical the zero- shot setting in Appendix B.
C.2 TEXTUAL AUGMENTATION
To supply richer spatial context, we generated a holistic scene description from the multi-view im- ages using GPT-4.1 (OpenAI, 2024). We prompted the model as:
These images provide multiple views of the same scene.
Based on all of them, provide single, holistic paragraph describing the entire scene and the spatial relationship between the objects.
The generated paragraph was inserted verbatim into the user prompt under Context: header, immediately before the QA item.
C.3 VISUAL AUGMENTATION VIA NOVEL VIEW SYNTHESIS
To provide cross-view alignment signals, generated synthetic intermediate views between ex- isting camera perspectives. We experimented with several families of novel view synthesis (NVS) methods:
Object-centric synthesis. Methods such as InstantMesh (Xu et al., 2024) and Trellis (Xi- ang et al., 2025) are designed for reconstructing individual objects from sparse views. While effective for clean object-level inputs, they proved unsuitable for cluttered robotic scenes, as selecting accurate masks non-trivial and the outputs often failed to preserve global scene layout (see Appendix Figure 7).
Scene-level synthesis. LVSM (Jin al., 2024) attempts to interpolate between camera poses with minimal 3D inductive bias. In our robotic setup (c.g., gripper and hcan-mounted cameras), interpolated views were severely blurred and inconsistent, particularly under nar- row baselines and cluttered tabletops (Appendix Figure 8).
3D reconstruction-based synthesis. Geometry-guided methods such as VGGT (Wang et al., 2025a) and II3 (Wang et al., 2025c) leverage explicit multi-view consistency. Com- pared generative NVS pipelines, they provided more stable results in cluttered manip- ulation scenes. Among them, VGGT offered the best trade-off between robustness and efficiency, producing spatially consistent augmentations suitable for our benchmark (Ap- pendix Figure 9).
In practice, we adopted VGGT to generate one interpolated frame between each camera pair, resized to 224 224, and attached it as additional input.
C.4 STRUCTURAL AUGMENTATION VIA DEPTH PRIORS
To provide models with explicit geometric constraints, we augmented each view with pre- dicted depth maps. We considered recent monocular depth estimation approaches, including VniDepthV2 (Piccinelli et al., 2025), but ultimately adopted MoGe-2 (Wang et al., 2025b) as proved more robust in cluttered indoor manipulation scenes.
For each image, MoGe-2 produced per-pixel depth map, which was then normalized to a fixed scale range [0, 1] and smoothed with Gaussian filter to reduce artifacts. During evaluation, depth information was appended as additional input alongside RGB, provided the following textual form:
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Figure 7: Failure of object-centric synthesis (Trellis). Top: original inputs; Bottom: synthesized views that fail to capture the full scene.
Figure 8: Failure of LVSM scene interpolation. Top: original inputs from left gripper, head, and right gripper cameras; Bottom: blurry synthesized view from interpolated extrinsics.
Corresponding estimated depth map. this depth map, red areas indicate objects that are closer, and blue areas indicate objects that are farther away."
"text": "Image context:
This additional channel allowed the model to incorporate depth priors when reasoning about oc- cluded or overlapping objects, thereby reducing spatial ambiguity.
D EVALUATION ON EXTERNAL SPATIAL BENCHMARKS
Our study focuses on spatial intelligence within robotic operation scenarios. To provide a broader context, include the OmniSpatial benchmark, which spans an unusually comprehensive range of spatial intelligence tasks, from abstract reasoning to concrete domain understanding. We also acknowledge recent efforts such HSSBench Kang et al. (2025), which benchmarks the capabilities of multimodal models across humanities and social sciences, highlighting the diverse dimensions of evaluation required for MILMs. Incorporating OmniSpatial allows to assess whether the
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Figure 9: Successful geometry-guided synthesis with VGGT. Top: original inputs; Bottom: interpo- lated novel view that preserves object layout and spatial relations.
Figure 10: Structural augmentation via depth priors. The shows the original RGB images; the bottom row shows the corresponding MoGe-2 depth predictions (red indicates closer, blue indi- cates farther).
spatial intelligence exhibited by models in general cognitive benchmarks is consistent with their performance in robotics-specifie tasks. Table 4 reports these results, where asterisked entries (*) indicate our reproductions, and remaining scores are taken directly from the OmniSpatial paper to maintain faimess and comparability.
D.1 ADDITIONAL. EVALUATION ON ERQA
Following the reviewers' suggestions, we also evaluated our models on the ERQA benchmark to investigate domain-specine transferability. ERQA focuses embodied reasoning in simulated en- vironments and includes categories such as Action Reasoning, State Estimation, and a subset of Multi-view Reasoning.
Table reports the detailed performance. While ERQA is domain- relevnnre our analysis suggests that it exhibits low discriminative power for comparing current SOTA models:
Compressed Performance Range: The overall accuracy gap between smaller open-source models (e.g., Qwen2.5-v1-7b at 43.11%) and SOTA proprietary models (e.g.. GPT-4o 46.00%) is marginally narrow (< 3%).
Lack of Gradient: Most models cluster tightly between 40% and 50% across most sub- tasks. This "flat" distribution makes it difficult to observe meaningful statistical correlations between model capability and downstream robotic performance.
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Table 4: Comparison of model performance on OmniSpatial, covering four categories: dynamic reasoning, spatial interaction, complex logic, and perspective taking. Results are reported as average accuracy (%), with asterisked rows (*) denoting our reproduced results.
Dynamic Reasoning
Spatial Interaction
Complex Logic
Perspective Taking
Method
Avg.
Manipulation
Motion Analysis
Traftic Analysis
Locali Zation
Goospatial Strategy
Pattern Recog.
Geom. Reasoning
Ego Centric
Allo Centric
Hypothetical
Blind Evaluation
Random Choice
GPT-3.5 turbo
30.67
38.38
29.19
38.35
28.76
36.91
0.82
24.00
42.16
33.67
35,90
GPT-4-turbo
28.95
22.27
31.37
33.99
Proprietary Models
GPT-4o-mini
GPT-4o
47.81
65.54
57.23
56.47
52.38
54.09
26.29
25.48
75.98
39.49
39.76
-mn--.-mann
40.95
GPT-4,I-mini
48.87
64,32
56,53
59.06
60.19
56.36
29.28
30.19
72.55
39.57
39.28
GPT-4.1
51.78
66.22
64.74
60.00
65.33
60.18
31.75
30.06
70.98
40.64
39.04
3.5
46.86
54.05
54.57
58.12
68,38
53.09
26.60
31.74
70.00
34.79
39.52
Claude-3.7
47.53
57.57
55.95
56.71
63.81
59.09
29.48
28.39
72.16
36.05
36.63
"Gemini-2.00 1-0-man
60.00
*Gemini 2.5-flash
47.55
67.57
52.89
63.53
55.24
57.27
29.99
23.87
79.41
36.44
44.58
Proprietary Reasoning Models
mini
52.77
72.97
59.83
60.00
73.33
61.82
34,02
36.77
73.53
40.69
40.96
*GPT-5-chat
46.51
59.46
46.82
56.47
59.05
53.64
34,02
25.16
70.59
41.49
45.78
*GPT-S-nano
49.25
63.51
58.09
51.76
65.71
50.00
32,99
26.45
70.59
42.29
42,17
*GPI---mini
SGPT55
58.51
64.86
68.79
67.06
76.19
70.00
35.05
38.06
79.41
48.94
46.99
Clande-3,-thinninn
48.62
57.21
59.73
53.73
67.94
57.27
30,24
28.17
68.63
37.94
36.95
Geminn-22 2.5 pro
55.19
67.57
71.39
62.35
75.24
64.55
43.30
34.84
74.51
38.03
37.35
Open-Source Models
Gemma-3-64
39.79
41.89
49.71
56.47
27. 62
36.36
23.71
24.52
59.80
36.17
38.55
Gemma-3-122
Gemma-3-27b
44.75
56.76
55.78
57.65
50.48
52.73
27. 84.84
29.03
64.71
33.51
32.53
37.98
50.00
40.58
43.29
40.00
40.55
21.86
28.52
55.49
35.11
33.01
VL3-88
41.60
52.43
40.87
48.94
51,05
44.77
24.95
28.63
64.20
38.62
40.96
IntemVLL3-14B
45.94
54.32
60.17
50.35
51.81
51.45
28.04
28.26
68.04
35.37
34.46
LL3-38
VieemVLL3-788
49.33
63.78
63.12
56.24
$9.24
51.45
27.63
30.19
74.51
38.46
35.90
Qwen2.5.vl-th
47.51
S-y1-7
39.18
58.38
35.09
50.12
45.33
44.00
31.13
29.42
64.51
33.19
37.35
Qwen2.5-v1-326
47.36
63.06
55.09
51.76
66.29
56.91
26.39
27.48
68.04
37.50
40.24
Qwen2.5.v1-728
47.85
58.38
60.12
50.12
59.81
53.64
26.19
33.03
71.37
36.81
36.39
Open-Source MoE Models
"LLLam---Scout
38.36
51.35
39.02
51.76
34,29
42.73
20.62
22.58
52.94
39.89
34.94
"LLama-4-Maverick
41.42
56.76
43.64
56.47
37.14
49.09
26.80
29.68
60.78
37.23
32.53
Human Evaluation
Human
96.53
97.30
92.94
97.14
95.74
Consequently, we retain OmniSpatial as the primary reference for the correlation analysis in the main text (Figure 6), as wider performance spread provides clearer signal for measuring general spatial intelligence.
E PREPARATIONS OF BENCHMARK CONSTRUCTION
E. ANNOTATION TOOL INTERFACE
To construct and annotate our dataset, we developed a custom graphical annotation tool based on the Qt library, running under the Windows environment. The interface is designed to be clear and lightweight, enabling annotators to efficiently load synchronized multi-view images, draw bounding boxes, trajectories, and affordance lines, and directly export QA items in JSON format that is fully compatible with our evaluation pipeline. Figures 11 illustrate the interfaces used for the Agi and Bridge V2 datasets.
We plan to release this tool as an open- sourcs resource, providing the community with simple yet powerful interface to facilitate further dataset construction and annotation research.
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Table 5: Evaluation results on the ERQA benchmark. Results are reported accuracy (%). Note the relatively narrow performance gap between open-source and proprietary models compared to MV-RoboBench or OmniSpatial.
Action Reasoning
Mulfi-view Reasoning
Spatial Reasoning
State Estimation
Task Reasoning
Method
Other
Pointing
Pmorrcttry Reasoning Models
GPT-5
59.34
33.33
33.33
82.35
38.33
69.81
60.53
GPT:S-mini
54,00
54.17
32.43
42.86
55.88
58.33
61.82
63.79
GPT: chat
GPT-S-nano
44.00
45.83
21.62
21.43
$2.94
50.00
49.09
50.00
Proprietury Models
GPT-4,1 49.00
56.94
40.54
21.43
$5.88
46.43
56.36
$7.89
GPT-4o
46.00
41.67
27.03
28.57
61.76
48.81
54.55
47.37
GPT-4.1-mini
46,00
37.50
40.54
28.57
50,00
45.24
56.36
60.53
G-a--.I-nano
37.50
21.62
32.35
50.91
60.53
Open Souree Models
Qwen2.5-vl-72bb
44.61
41.67
16.67
21.43
52.94
63.10
49.09
50.00
Qwen2.5-v4-326
44.75
38.89
32.43
21.43
58.82
52.38
54.55
47,37
Qwen2.5-v4-7b
Clrnkiy pow
Hando
Bonk live gevi and
Figure 11: Annotation interface of the AoiWorld label tool, implemented with Qt on Windows. The design emphasizes clarity and ease use for multi-view annotation.
E.2 PRE-GENERATION OF IMAGE PAIRS
Before QA construction, first pre-generated candidate image pairs from both datasets. For the AgiWorld dataset, we randomly sampled image pairs with the constraint that the interval between two selected frames was least ten frames. For the BridgeV2 dataset, we only considered videos with four available perspectives and similarly enforced minimum interval of ten frames between sampled images. To ensure diversity, sampling was performed as evenly as possible across videos and tasks.
After this automatic step, each image pair was manually inspected by human annotators, and only those judged suitable for QA retained. At this stage, we obtained more than 3,000 high- quality image pairs, which served as the foundation for constructing the benchmark. The perspective identification task required different setup, and its details described separately in Appendix C.
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E.3 DEFINITION OF THE COORDINATE SYSTEM
To ensure a consistent interpretation of spatial relations across different camera views, we define standardized right-handed orthogonal coordinate system tied each camera frame. The construc- tion proceeds as follows:
z-axis (vertical). Let g denote the gravity vector, pointing downward. We define
g
2
ggl
forword
so that the +- direction points upward (opposite to grav- ity) and - points downward.
left
+X right
2. y-axis (forward/backwarr)) Let denote the camera optical axis. Project onto the plane orthogonal
$9
C1 = (c 2)..
backword
Normalizing gives
-Z
T1
down
|c,l|
Figure 12: Illustration of the right- handed coordinate system defined rela- tive to each camera.
with orientation chosen so that the angle between y and is strictly less than 90% By convention, +y corresponds to forward, while y corresponds to backward.
3. r-axis (left/right). Finally, the r-axis is determined by the right-hand rule:
x = ý x 2.
This ensures +- points to the right side of the camera's 's perspective and -x to the left. Directional convention. In summary, + upward,
downward; +y forward, -y back- ward; + = right, -r left. Figure 12 provides an illustration of this definition.
E.4 TOOL FOR SPATIAL CUBE REASONING
To construct the spatial cube reasoning task, we developed an interactive visualization tool that renders standardized 5 x 5 cube grid aligned with the camera coordinate system, where the x-, y-, and z-axes correspond to the right, forward, and up directions. Annotators can place col- ored unit cubes integer grid coordinates, assign labels, and interactively edit regenerate cube configurations.
This design enables rapid prototyping of spatial arrangements and provides consistent interface for generating QA items that require reasoning about relative positions and geometric relationships in 3D space. The tool also supports keyboard-ber based coordinate input for efficient and reproducible annotation.
E.5 HUMAN ANNOTATION PROTOCOL AND QUALITY ASSURANCE
To ensure transparency in benchmark construction, we provide detailed information about the an- notators, training procedures, human effort, and the quality-control pipeline adopted throughout the multi-stage annotation process.
E.5. ANNOTATOR TRAINING AND TASK UNDERSTANDING
All annotators participating in the construction of MV-RoboBench were senior undergraduate stu- dents or Ph.D. candidates in computer science or closely related fields. Before large-scale annotation began, conducted structured multi-stage training process to ensure that annotators had rig- orous understanding of the purpose and design philosophy of each subtask. First, for each subtask, provided conceptual overview explaining why the subtask designed and what specific
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Seted Cuze Vasualior
Solect Color (Press Fniter Piat)
Dox
Doe
Figure 13: Screenshot of the spatial cube reasoning tool. Annotators can add, label, and manipulate colored cubes within a standardized 5 x 5
grid to construct 3D reasoning problems.
aspect of multi-view robotic reasoning it aims to evaluate. For example, some subtasks target cross- view correspondence, others highlight 3D spatial understanding, action feasibility, or multi-step execution consistency. This high-level motivation helped annotators internalize the intended reason- ing challenge behind each category rather than focusing solely on the mechanics of QA creation. Second, we supplied curated set of high-quality QA examples for each subtask, including both well-constructed samples and typical failure cases. These examples illustrated desirable properties such as clear problem formulation. meaningful distractor design, and unambiguous ground-truth answers. Annotators were instructed to study these examples closely to understand how robust QA item should be structured. Finally, annotators completed a trial stage in which they produced small batches of QA items. All trial results were reviewed individually by the authors, and detailed feedback was provided for every ambiguous, incorrect, or poorly structured item. Annotators re- vised their samples accordingly, and only after completing this iterative refinement stage were they allowed to contribute to the full annotation pipeline.
E.5.2 HUMAN EFFORT ESTIMATION
The construction MV-RoboBench required substantial human effort across several stages of data preparation, annotation, and verification. We provide an estimate of the total annotation effort be- low. Collection and Filtering of Image Pairs (~2000 hours). This stage involved selecting suitable datasets, writing scripts automatically pre-filter candidate image pairs, and manually examining the automatically retrieved pairs to determine whether they exhibited clear multi-view correspon- dences appropriate for downstream QA construction. Annotators carefully removed pairs containing occlusions, poor synchronization, or ambiguous spatial relationships to ensure that only high-quality candidates entered the QA generation stage. QA Construction and Iterative Refinement (~-000 hours). This stage accounted for the largest portion of the human effort. The workload included multiple rounds of internal discussion annotators on the annotation protocol, and iterative communication between authors and annotators to refine how each subtask should be expressed. The actual annotation time- seeennnnnnn bounding- box drawing, spatial-cube configuration, distractor design, and multi-view reasoning checks was intentionally left flexible to allow annotators to focus on ensuring that each QA item was both cor- rect and diverse. Cross-Checking and Validation (~4000 hours). After QA items were generated, multiple annotators independently reviewed all samples to identify ambiguous phrasing, weak dis- tractors, incorrect reasoning chains. Items flagged during this stage were either revised through further discussion or discarded entirely. This iterative multi-annotator cross- valssvvaiio stage was critical for improving the reliability and robustness of the final benchmark.
finalize subtask definitions and question formats, training
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E.5.3 QUALITY-CONTROL PROCEDURES
Our benchmark follows the construction flow illustrated in Figure 2, but in this section we focus specifically on the quality-control mechanisms incorporated into each stage rather than the pipeline itself. The goal is to ensure that every QA item in MV-RoboBench is unambiguous, visually grounded, and aligned with the intended reasoning challenge of its corresponding subtask. Ini- tial Image-Pair Screening. Before any annotation begins, we employ two-stage filtering process to guarantee that only high-quality visual imputs enter the QA construction pipeline. First, we use GPT-based filtering to select image-pair candidates that satisfy the definition of each subtask. Sec- ond, trained annotators manually verify these candidates by checking whether the images exhibit stable multi-view correspondences, sufficient visual clarity, and the absence of severe motion blur or occlusion. Only pairs judged to be suitable for at least one subtask proceed to the annotation stage. Annotation with Structured Distractor Design. After subtask definitions are finalized, QA items following
notators who have undergone dedicated training (Section standardized guidelines. A critical aspect of our quality control is the design of distractors: each question contains one correct answer and four distractors, among which annotators deliberately create one or two hard distractors that closely resemble the correct answer, while the remaining distractors are intentionally more distinct. This structure ensures both a meaningful level of dif- ficulty and clear separation between high-level reasoning errors and trivial misunderstandings. During annotation, annotators also verify that the correct answer is uniquely supported by the vi- sual evidence and that no distractor inadvertently becomes correct under alternative interpretations. Multi-Annotalee Verification and Iterative Revision. Once QA items are created, they are added to a shared VQA pool and undergo multi-round cross-checking. Multiple annotators independently review each sample. If any reviewer finds QA item ambiguous, poorly structured, or misaligned with the intended subtask, the item is immediately flagged. Flagged items are either revised through further discussion-during which annotators and authors jointly inspect the visual evidence and rea- soning steps or discarded entirely. Revised items re-enter the VQA pool for additional rounds of validation. This iterative process continues until all items satisfy strict correctness, clarity, and reasoning requirements. Through this combination of automated pre-filtering human verification, structured annotation protocols, and degree of reliability and robustness across all subtasks.
cross-validation, MV-RoboBench achieves a high
F DETAILS OF BENCHMARK CONSTRUCTION
In this appendix, we describe the construction details of each subtask included in our benchmark. As introduced in Appendix E.2, we first obtained a large collection of high-quality image pairs from AoiWorld and B2 through automatic sampling and manual filtering. These image pairs serve as the common starting point for constructing the majority of subtasks, while the perspective identification task required a different setup and is discussed separately later in this section.
For clarity, organize this appendix by task category. We first present the four spatial subtasks, which focus on multi-view scene understanding: Cross- View Object Matching, Distance Judgement, Viewpoint Identification, and 3D Spatial Consistency. We then describe the four robotic subtasks, which extend spatial reasoning to manipulation scenarios: Action Planning, Step Execution, Trajec- tory Selection, and Affordance Recognition. Finally, we conclude with a summary that highlights the complementarity of these subtasks and provides an overview table (Table 6).
F.1 CROSS-VIRS OBJECT MATCHING
This subtask belongs to the spatial category and evaluates whether a model can recognize the same object across different camera viewpoints. In the construction process, one reference view is lected, where the target object is highlighted with a red bounding box. In the remaining synchro- nized views, candidate objects are marked with bounding boxes of different colors. The model is then asked to identify which candidate corresponds to the same object as the red box in the reference view.
To avoid trivial solutions based only on object category or color cues, distractor candidates are carefully chosen be visually plausible. These include objects of the same category, those in
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Template Cross View Matching
In the right-gripper camera view, the item is outlined with red bounding box. Which colored bounding box encloses that same item in the left-gripper camera view and the head camera view?
Rules
You replace the orange nouns with labeled objects,
you can just use item instead.
You can replace the right-gripper with left-gripper or head The blue text is template description, which can be copied directly.
Sample of Cross View Matching
left gripper view
head view
right gripper view
Question: In the right-gripper camera view, the item outlined with a red bounding box. Which colored bounding box encloses that same item the left fet-gripper camera view and the head camera view?
OptionA: The blue bounding box.
OptionB: The green
OptionC: The purple bounding box.
bounding box.
OptionD: The yellow
OptionE: The pink bounding box.
bounding box.
Figure 14: Example of Cross- View Object Matching constructed from the AgiWorld dataset. The reference view marks the target with red bounding box; other views contain color-coded candidate boxes, one of which corresponds the ground-truth object.
close proximity, or partially overlapping instances, making the task a genuine test of cross-view association.
Figures and 15 show representative examples this subtask, constructed from the AgiWorld and Bridge V2 datasets, respectively.
F.2 DISTANCE JUDGEMENT
This subtask belongs to the spatial category and evaluates a model's ability to reason about relative distances using synchronized multi-view observations. In each problem, one selected view presents several candidate objects, each marked with a colored bounding box. The model is asked to deter- mine which candidate corresponds to the shortest (or, alternatively, the longest) grasping distance relative to the specified gripper. Other synchronized views provide additional context, requiring the model integrate information across perspectives resolve depth ambiguities.
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Template for Cross-View Matching
In the view1, the item outlined with red bounding box. Which colored bounding box encloses that same bottle in other views.
Rules
You replace the viewl with other views, i.e, view2, view3, view4
The interference options need to be somewhat confusing and not Teo simple.
The blue text template description, which can be copied directly.
Sample of Cross -iiss Matching
viewl
view3
view4
In the viewl, the item outlined with red bounding box. Which colored bounding box encloses that some bottle in other views,
Question:
OptionA: The green
OptionB: The
OptionC: The pink bounding box.
OptionD: The purple
OptionE: The yellow
blue bounding box.
bounding box.
bounding box.
bounding box.
Figure 15: Example of Cross View Object Matching constructed from the Bridge dataset. The target is highlighted in viewl, and the model must select the corresponding bounding box in the other synchronized views.
To ensure non-triviality, distractor options manually verified that objects with similar 2D appearances may differ in their actual 3D distances. Accurate solutions therefore demand reasoning that goes beyond single-view perception.
Figures 16 and 17 illustrate both representative instances and the annotation templates employed for constructing the Distance Judgement subtask AgiWorld and B2.
F.3 VIEWPOINT IDENTIHICATION
This subtask belongs the spatial category and evaluates model's ability recognize and reason across different viewpoints. It is constructed exclusively from the AgiWorld dataset, where the reference image is always taken from the head camera. The question asks the model determine which candidate image corresponds to the correct left- or right-gripper view at the same time step, given the head camera observation. Solving the task requires a form of perspective transformation, testing whether the model can imagine how the scene would appear from another viewpoint core component of spatial intelligence.
To construct distractor options, we adopt multi-stage design. For each ground truth gripper image, we first include the image from the opposite gripper at the same time step. We then add distractors from the same episode but different time steps, ensuring non-trivial temporal gap in the gripper poses so that the distractor cannot be rejected trivially. Additional distractors are sampled from other episodes within the same closely related tasks, providing visually plausible but incorrect gripper views. This strategy prevents the model from exploiting majority-vote heuristics across options. All instances manually verified guarantee that a human annotator reliably identify the correct correspondence based on spatial details.
Figure 18 illustrates both the template and example instance of this subtask.
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Template for Distance Judgement
You are given three synchronized views-the left-gripper camera, the head camera, and the right-gripper camera. Only the head camera image contains colored bounding boxes. Which option corresponds to the shortest grasping distance?
Rules
The orange box is good interference option because there depth ambiguity in the head camera view.
You can replace the shortest with longest.
The blue text is template description, which can be copied directly.
Sample of Distance Judgement
left gripper view
head view
right gripper view
Question:
You are given three synchronized views-the left-gripper camera, the head camera, and the right- camera. Only the head camera image contains colored bounding box. Which option corresponds the shortest grasping distance?
OptionA:
OptionB: Grab the item in the yellow bounding box using the left gripper.
OptionC: Grab the item in the
OptionD: Grab the item in the yellow bounding box using the right gripper.
OptionE: Grab the item in the yellow bounding box using the right gripper.
Grab the item in the green bounding box using the left gripper.
yellow
bounding box using the right gripper.
Example of Distance Comparison constructed from the AgiWorld dataset. The head camera image contains candidate bounding boxes, and the model must select the one corresponding to the shortest grasping distance.
Figure 16:
F.4 3D SPATIAL CONSISTENCY
This subtask is part of the spatial category and evaluates model's ability reason about object locations within structured 3D coordinate system. The key challenge is to assess whether the model can the scene three-dimensional space rather than flat image, and correctly place the highlighted objects into the standardized coordinate grid such that their relative positions remain coherent across views.
We adopt a right-handed orthogonal coordinate system anchored to designated reference view (the head camera in AgiWorld, or any the four views in Bri the reference image, several target objects are highlighted with colored bounding boxes. The question then asks the model: "Which of the following sets of coordinate triplets best describes the positions of the highlighted objects?" Coordinates
normalized into 5 5 5 cubic grid, with integer values from I to along each axis. This abstraction allows spatial relations to expressed consistently without requiring precise metric depth.
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Template for Distance Judgement
You given four synchronized views, only the view? contains colored bounding boxes.
Which option corresponds to the shortest grasping distance?
Rules
You can replace the view2 with other views, i.e. viewl, view3, view4
You can replace the shortest with longest.
The blue text template description, which copied directly.
Sample of Distance Judgement
Question:
You are given four synchronized views, only viewl contains colored bounding boxes. Which option corresponds to the shortest grasping distance?
OptionA: Grab the item the green bounding box.
Option8: Grab the OptionC: Grob
OptionD: Grab the item in the pink bounding box.
OptionE: Grab the item blue bounding box.
item the yellow bounding box.
the item
red bounding box.
Figure 17:
Example of Distance Comparison constructed from the Bridge dataset. One view contains candidate bounding boxes, and the model must identify the option that corresponds to the shortest grasping distance when integrating evidence across all four views.
To construct the tasks, we leverage the interactive cube visualization tool described in Appendix E.4. This tool enables annotators map each object unit cube the grid, adjust placements, and generate candidate coordinate sets. Distractor options are created by perturbing object coordinates introduce plausible but incorrect spatial configurations. Accurate solutions therefore require inte- grating multi-view cues rather than relying on a single perspective.
Figures 19 and 20 show representative templates and examples constructed from the AgiWorld and Bridge V2 datasets, respectively.
F.5 ACTION PLANNING
This subtask belongs to the robotic category and evaluates whether a model can correctly identify the valid high-level action sequence from multiple candidates in order accomplish manipulation goal. Each instance provides synchronized multi-view observations together with task description natural language. The problem is defined with respect to designated reference view, within which we establish the standardized right-handed coordinate system described in Appendix E.3. Accordingly, all candidate action sequences are expressed as sequences of normalized directional terms (i.e., spatial adverbs such as leftward, forward, downward), which follow directly from the axis conventions defined in Appendix E.3. The model must then integrate information across views and select the sequence most likely to achieve the goal.
To ensure non-triviality, distractor options are carefully constructed. Only one option corresponds to valid sequence that completes the task while minimizing collisions, whereas the distractors follow plausible but incorrect paths. In addition, we enumerate and sort the directional terms within each option, ensuring that prevents ambiguity and forces the model to reason jointly about spatial relations and manipulation feasibility.
candidates share the same ordered sequence of actions. This design
Figures 21 and 22 illustrate representative templates and examples from the AgiWo and Bridge datasets, respectively.
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Template for Viewpoint Identification
Given the image captured by the head camera, which of the following images shows the right- camera's view at that exact moment?
Rules
You can replace the right-gripper with left-gripper.
The blue text is template description, which can be copied directly.
Sample for Viewpoint Identification
Question: Given the image captured by the head camera, which of the following images shows the left gripper camera's s view at that exact moment?
OptionA: Option A picture.
OptionB: Option picture.
OptionC: Option picture.
OptionD: Option D picture
OptionE: Option E picture
Figure 18: Example of Perspective Identification constructed from the AgiWorld dataset. The head camera view is used as the reference, and the model must infer the correct corresponding perspective among the candidate gripper views.
F.6
STEP EXECUTION
This subtask belongs to the robotic category and focuses on low-level action execution manipu- lation tasks. Each instance provides synchronized multi-view observations together with natural language description of the goal. Unlike the Action Planning task, which evaluates multi-step tra- jectories, Step Execution concentrates primitive actions such picking or placing, which can be described as short sequences directional terms (e.g., up. left, down). The coordinate system defined with respect designated reference view, following the conventions introduced in Ap-
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pendix E.3. All candidate options then expressed in these normalized directional terms, and the model must select the sequence that correctly achieves the task.
Distractor options are constructed appear plausible but correspond to incorrect motions that would fail the manipulation. To eliminate redundancy, we further enumerate and sort the directional terms within each option, ensuring that no two candidates reduce the same ordered sequence. This design requires the model to interpret spatial cues accurately across multiple views and to ground its decision in the standardized coordinate system. For the AgiWorld dataset, the template is based on synchronized left-gripper, head, and right-gripper views, while in BridgeV2 any of the four available views may serve as the reference.
Figures 23 and 24 show representative templates and examples from the AgiWorld and B2idge datasets, respectively.
F.7 TRAJECTORY SELECTION
This subtask belongs to the robotic category and evaluates a model's ability to reason about com- plete motion trajectories in multi-view settings. Each instance provides synchronized observations, where candidate trajectories are overlaid in different colors on one or more reference views. The model is asked to determine which trajectory is most likely to accomplish the described manipula- tion.
A key challenge is that trajectories drawn in a single view may be ambiguous due to occlusions, perspective distortion, or motion along the camera's ss optical axis. By providing multiple synchro- nized viewpoints, the task requires the model to integrate cross-view evidence to correctly identify the feasible trajectory.
All distractor trajectories are manually curated to be distinct from the ground truth yet visually plau- sible, so that they may appear confusing at first glance but remain distinguishable through careful multi-view reasoning. We ensure that exactly one candidate is feasible across views and can com- plete the task without collisions; every instance is numan-validated to confirm that the correct choice uniquely identifiable.
For the AgiWorld dataset, each problem is presented with synchronized left-gripper, head, and right- gripper views. For V2, all four camera perspectives are available, and candidate trajectories are described relative to these views. Figures 25 and 26 provide representative templates and exam- ples from both datasets.
F.8 AFFORDANCE RECOGNITION
This subtask belongs to the robotic category and evaluates model's ability recognize feasible grasp candidates in multi-view scenes. In real manipulation, a single viewpoint may be insufficient for identifying good grasp locations due to occlusions by objects or grippers, or because certain camera angles (e.g., top-down) obscure critical contact geometry. By incorporating synchronized multi-view observations, especially from gripper-mount
cameras, this task provides complemen- tary perspectives that make the final grasp point more reliably observable.
Each instance presents five candidate grasps illustrated with color-coded lines (red, yellow, green, blue, and pink). Each color appears exactly once across the available views, and the two endpoints of a line specify the intended positions of the gripper fingers. The model is asked: "Which color-coded line represents the grasp candidate most likely to succeed?"
All distractors are carefully designed: while they may appear physically plausible at first glance, they are infeasible in practice due to orientation, collision risk, instability. This ensures that success requires genuine spatial reasoning and affordance understanding rather than superficial cues. For the AgiWorld dataset, three views (left-gun seft-gripper, head, gripperr used, whereas in BridgeV2 the template extends naturally to four synchronized views. Figures 27 and 28 provide representative templates and examples from both datasets.
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Table 6: Overview of the eight subtasks in our benchmark. Spatial tasks focus on multi-view scene understanding, while robotic tasks extend this foundation to manipulation planning and execution.
Category
Subtask
Core Ability Assessed
Cross-Vies View Object Matching
Ideatity same object across differeat viewpoints despite distractors.
Distance Judgement
Compare relative distances specilied gripper using mulli-view
Spatial
Viewpoint ficatioicaionn
Infer the correct camera perspective given head view reference. Place highlighted objects into structured 3D coordinate system with colerent relative positions.
3D Spatial Consistency
Action Planning
Select the valid high-level action sequence normalized directional Jerms to accomplish a lask.
Rohoxic
Execution
Choose the correct primitive low-level action sequence (e-g.. pick/place) grounded the coordinate system. Distinguish feasible
Trajectory Selection
infeasible motion trajectorics by integrating evidence across views. Identify grasp candidute most likely to succeed among visually plan- sible alteraativss.
Affordance Recognition
F.9 ANSWER BALANCING AND RANDOMIZATION
After generating QA instances and completing manual verification, we apply an additional balancing step to ensure that answer distributions are statistically uniform. Specifically, correct answers are randomized across different option indices and color assignments, preventing systematic biases that could allow models exploit position- or color-based heuristics. This balancing guarantees that success on the benchmark requires genuine spatial reasoning and affordance understanding rather than relying superficial answer patterns.
F.10 SUMMARY OF BENCIMARK CONSTRUCTION
Taken together, the eight subtasks provide a comprehensive evaluation of spatial and robotic reason- ing in multi-view environments.
The four robotic subtasks (Action Planning, Step Execution, Trajectory Selection, and Affordance Recognition) extend this foundation manipulation scenarios. They examine whether models can ground spatial understanding into action decisions, ranging from high-level planning to low-level ex- ecution, and from trajectory-Ievel reasoning to grasp affordance prediction. Together, they highlight the importance of combining multi-view perception with physical feasibility in order to succeed in robotic tasks.
An overview of all subtasks, their categories, and the specific reasoning abilities they target is pro- vided Table 6.
G ABLATION STUDY ON MULTI- VIEW NECESSITY
In this section, address the fundamental question of whether multi-view inputs truly necessary for the proposed robotic reasoning tasks, or if single-view input would suffice. To investigate this, conducted systematic ablation study comparing the performance of representative models under a Single-
View View baseline versus the standard Multi- View setting.
G.1 TASK SELECTION AND EXPERIMENTAL SETUP
Our benchmark consists of cight subtasks. However, not all tasks are suitable for single-view eval- uation due to their inherent reliance on cross-view information. We applied the following selection criteria:
Excluded Tasks (Inherently Multi-View): For subtasks including Cross- -iew Object Matching, Viewpoint Identification, Trajectory Selection, and Affordance Recognition, the question semantics inherently depend on multiple synchronized views. Many answer can- didates exist only in specific views, removing views would yield ill ill-posed questions (c.g., some candidates become invisible). Therefore, these tasks were excluded from the ablation.
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Selected Tasks (Degradable to Single-Viene View): We focused our ablation on the remaining four subtasks: Distance Judgement, 3D Spatial Consistency, Action Planning, and Step Execution. These tasks typically formulated relative to reference coordinate system or a primary scene description. While multi-view information provides critical depth cues and occlusion handling, these questions remain logically valid even when restricted single input image. This allows us to rigorously measure the "performance drop" caused by the loss of multi-view context.
For the Single-Vies View setting, we retained only the most informative third feird- perspective to en- sure strong baseline:
For the AgiWorld dataset, we used the head camera view.
For the BridgeV2 V2 dataset, we used viewl (a fixed third-person camera).
G.2 RESULTS AND ANALYSIS
Table 7 presents the comparative results. We report the full multi-view accuracy and the performance gap (A) relative to the single-view baseline.
Table Comparison of Single-Vier View vs. Multi-View View performance on selected subtasks. The values represent Multi-Vis View accuracy, and values in parentheses indicate the change (A) compared to the Single-Vias View baseline. Positive A indicates that multi-view inputs improve performance. Bold indi- cates the best performance in each category.
Model
Avg.
Distance Judge.
3D Spatial Cons.
Action Plan.
Step Exec.
Proprietary Reasoning Models
GPT:5
64.65 (+6.69)
36.32 (+18.90)
78.43 (+3.92)
76.96 (+2.45)
66.24 (+2.14) 36.32 (+3.85)
GPT-5-chat
28.11 (+3.05)
29.35 (+13.44)
10.29 (-5.39)
35.29 (+1.47)
Proprietary Models
GPT-4.1
24.20 (+3.59)
32.84 (+10.44)
4.90 (+1 1.7
30,39 (-0.49)
28.21 (+3,41)
GPT-4o
26.81 (40.88)
31.84 (+5.47)
5.39 (40.98)
33.82 (-0.49)
35.04 (-1.28)
Open-Source Models
Qwen2.5-v1-72.
23.01 (+0.87)
30.85 (+3.98)
3.92 (+0.98)
30,88 (-2.45)
26.07 (+1.28)
Qwen2.5-vl-32h
20.28 (-0.07)
24.38 (+1,49) .49)
8.33 (+2.45)
25.49 (-1.98)
22.65 (-2.99)
Qwen2.5-vl-7b
17.91 (+1.55)
20.40 (0.00)
4.90 (+3.92)
21.08 (+1.47)
24.36 (+1.71)
Our analysis yields three key findings regarding the necessity of multi-view perception:
Multi-view is critical for resolving spatial ambiguity. The most significant impact is observed in the Distance Judgement task. Powerful reasoning models like GPT-5 and GPT- 4.1 achieve substantial gains (+18.909) and +10.44%, respectively) when provided with multi-view inputs. This confirms that single-view observations suffer from inherent depth ambiguity and occlusion-commont commonn issues in robotic manipulation are effectively mitigated by integrating complementary viewpoints.
2. Stronger reasoning capabilities unlock multi-view potential. We observe a positive cor- relation between model capability and the benefit derived from multi-view information. State-of-the-art models consistently improve with additional views, whereas smaller mod- els (c.g., Qwen2.5-v1-322 Qwen2.5-v1-7b) show negligible even negative changes. This suggests that effectively fusing discordant visual information requires strong spatial rea- soning capabilities; without this, smaller models may be distracted by the increased visual context rather than aided by it.
3. Performance gap remains significant. Even with the advantage of multi-view inputs, the performance on robotic planning tasks generally lags behind human proficiency. This highlights that while multi-view input is necessary condition for robust perception, it is not sufficient on its own. Future models must develop stronger embodied reasoning capabilities to fully leverage the rich 3D information provided by Be-RohoBench.
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H IMPACT OF IMAGE ORIENTATION ON SPATIAL REASONING
To evaluate the generalization capabilities of VLMs regarding image orientation, we conducted an additional stress test. In this experiment, we vertically flipped all camera views except for the gripper-mounted ones (e.g., the head camera in AgiWorld and all static third-person views in BridgeV2) upside down. This setup disrupts the canonical spatial structure (e.g.. visual "up" no longer aligns with gravity) without altering the intrinsic object relationships.
Crucially, this manipulation does not affeet the correctness of the ground truth answers. As defined in Appendix E.3, our coordinate systems are established based on the physical gravity vector (g) and the camera's s optical axis (c), rather than the 2D image pixel axes. Since the physical scene configuration and sensor properties remain unchanged, the logical spatial relationships (e.g., "left", "up", "forward") remain valid. Therefore, any performance drop can be attributed solely to the models' inability generalize non-canonical visual orientations.
Table 8 reports the detailed performance under this upside-down setting across all subtasks.
Table 8: Detailed performance under Upside-Down image orientation. "Avg" represents the average accuracy in the upside-domd setting. "A" denotes the performance drop compared to the original setting. The remaining columns show the specific accuracy for each subtask under the upside-down condition.
Spatial Tasks
Robotic Tasks
Model
Avg
Cross
Dist.
View.
3D Cons.
Plan
Step
Traj.
Afford.
Proprietary Reasoning Models
GPT-5
37.68
-18.73
33.50
30.35
25.00
26.47
47.55
40.60
53.50
44.50
GPT-5-mini
30.79
-7.49
31.50
23.88
21.88
17.16
46.57
38.89
42.50
23,92
GPT-5-nano
22.42
-10.33
17.00
18.41
22.66
11.76
28.43
25.21
30.50
25.36
GPT-5-chat
28.50
-3.13
31.00
35.32
27.34
6.37
33.33
33.76
35.50
25.36
Proprietary Models
GPT-4.11
26.72
-4.18
26.50
33.33
33.59
4,90
27.94
29.49
37.00
21.05
GPT-4o
25.10
-2.49
25.50
31.84
23.44
4,90
30.39
31.20
33.00
20.57
GPT-4.1-mint
23.94
-0.04
22.50
26.37
27.73
6.37
25.98
25.21
32.50
24.88
--a-..1-nano
20.08
-0.77
17.50
16.42
18.36
12.25
25.98
25.21
20.00
24.88
Open-Source Models
Qwcn2.5-v1-72b
23.33
-0.96
23.50
23.88
22.27
3.92
28.92
27.35
29.50
27.27
Owe32.5-v1-326
22.41
-0.07
24.50
28.86
23.05
3.92
25.00
23.93
28.50
21.53
Qwen2.5-vl-7b
19.71
-1.13
22.00
20.90
20.31
5.39
23.04
26.50
19.00
20.57
We observe distinct behaviors across model families:
1. Strong models exhibit strong orientation bias. The most capable model, GPT-5, suffers the largest performance drop (Á -18.73%% Comparing this its baseline performance in Table 2, we see dramatic decline in 3D Spatial Consistency (from 82.35% to 26.47%) and Distance Judgement (from 55.22% to 30.35%). This indicates that its superior spatial reasoning capabilities rely heavily the canonical "upright" orientation learned during pre-training. When the visual reference frame inverted, model struggles to maintain coherent 3D spatial relations.
2. Weaker models show floor effect. In contrast, smaller models (e.g., GPT-4.1-mini Qwen2.55v1-7b) show negligible performance changes (A R 0). As shown in Table 2, these models already perform poorly on spatial tasks in the standard setting (e.g., 7-99% on 3D Spatial Consistency). This confirms that their original performance was likely dominated by pattern matching or random guessing rather than genuine spatial understanding. making them insensitive to orientation flips.
3. Implications for deployment. While this result highlights limitation in zero-shot gener- alization, we note that in real-world robotic deployment, camera mounting poses are strictly controlled, and input images are typically inspected rectified ensure canonical ori- entation. Therefore, while VLMs lack rotation invariance, this sensitivity is manageable characteristic in engineered systems.
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I EVALUATION OF MODEL CAPABILITY TO REJECT INCORRECT OPTIONS
The ability to determine when no correct answer exists critical for safe rohotic deployment. As pointed out by the reviewers, evaluating this "rejection" capability is vital aspect of spatial reason- ing.
In original benchmark design, we partially incorporated this dimension. Specifically, in the Cross- Cross-View Object Matching task, approximately 25% of the questions explicitly include ground- truth option of "None of the bounding boxes is correct." This was designed prevent models from relying solely on elimination.
However, provide more comprehensive and extreme evaluation of this capability across the entire benchmark, we conducted a controlled stress test on 7 out of the 8 subtasks.
I.1 EXPERIMENTAL SETUP
We modified the dataset by replacing the original ground-truth option with "None of the above" for every question the selected subtasks. The original correct answer was removed, making "None of the above" the only valid choice. To avoid positional bias, we ensured this option appeared as option E.
Exclusion of Distance Judgement: We excluded the Distance Judgement task from this specific ablation. In this task, questions ask for the "shortest" or "longest" distance among candidates. Removing the absolute shortest candidate would logically make the second shortest candidate the new correct answer, creating ambiguity rather than clear "None of the above" scenario. All other subtasks allow for binary valid/invalid distinction, making them suitable for this test.
1.2 RESULTS AND ANALYSIS
Table 9 presents the results. We report the accuracy when the correct answer is "None of the above" (Reject. Avg.) and the performance drop compared the standard setting.
Table 9: Model performance on the "None of the above" rejection test. "Reject." denotes the accuracy on the modified dataset where the correct answer "None of the above". "Drop" indicates the performance decline relative the original benchmark accuracy. A significant drop implies high model over-compliance.
Reject.
Spatial Tasks
Robotic Tasks
Model
Drop
Cross-View Match
Viewpoint ID
3D Spatial Consist.
Action Plan.
Step Exec.
Trajectory Sel.
Affordance Rec.
Avg.
Pmorrcttry Reasoning Models
GPT-5
13.43
-43.99
29.00
6.64
6.37
23.04
20.09
6.00
2.87
GPT-5-mini
-34.71
26.00
9.80
8.33
20.09
0,50
1.91
GPT-5-chat
1.05
-28844
1,50
1.95
1.47
0.98
0.43
1.00
0.00
GPT-5-nano
8.61
-2.00
21.00
5.08
16.18
2.94
7.26
3,50
4,31
Proprictary Models
GPT-411
-27.35
0.98
0.49
0.43
0.00
0.00
GPT-4.1-mimi
3.74
-19.09
3.00
1.95
1.47
3.92
15.81
0.00
1.00
GPT-4.1-nano
0.78
-1821
0.00
0.00
0.00
0.00
2.56
0.50
2.39
GPT-4o
0.92
-23.51
3.12
0.49
0.00
0,50
0.00
Open-Source Models
Qwen2.5-vi-722
3.39
-19881
9.00
0.78
8.82
1.96
1.71
1.00
0.48
Qwen2.5-vl-70
0.49
-20.31
2.00
0.00
0.98
0.00
0.00
0.00
0.48
Owen2.5-vil 5-yl-3bb
-21.70
0.49
0.00
0.48
The results reveal severe "Over- Dovee-Complianee" phenomenon across all models:
Universal collapse in rejection capability. Even the strongest model, GPT-5, drops from 56% accuracy to 13% when forced to reject incorrect options. Most other models collapse to near 0%. indicating they almost always hallucinate a relationship or force a selection from the visual candidates rather than acknowledging the absence of correct answer.
2. Selection bias dominates validity judgement. The experiment demonstrates that current models exhibit strong tendency to select "visually approximate" option rather than
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jecting all options. Choosing "None of the above" imposes a higher cognitive requirement: the model must distinguish between "relative similarity" and "absolute correctness," " and possess internal standard of rationality to invalidate all candidates. The results con- firm that even advanced reasoning models currently struggle with this rigorous verification, preferring to conform to the prompt by picking a plausible-looking but incorrect answer.
3. Implication. This experiment highlights a critical safety gap in current VLM technology. While models can reason about what is present, they struggle significantly to verify cor- rectness by rejecting invalid options. MV-RoboBench effectively exposes this limitation, serving as testbed for future work on uncertainty estimation and refusal.
J ERROR ANALYSIS OF MODEL FAILURES
To better understand how current models fail on MV-RoboBench, we conduct a qualitative error analysis on three representative models: strong proprictary model (GPT-5), strong open model (Qwen2.5-V) (Qwen2.5-VL-72B--
and weaker open model (Qwen2.5-VL-77) -L---V-L-B--nssrruc)) For each model, we sample 10-20 erroneous cases from several core subtask families, and manually inspect their predictions and explanations. Below we summarize the main recurring failure modes and highlight representative examples.
Single-view bias and weak multi-view fusion. Across all three models, a prominent failure mode an over-reliance on single camera view while largely ignoring contradictory evidence from other views. GPT-5, for instance, often selects the option that looks most natural in the head view but becomes inconsistent once the gripper views are taken into account. Qwen2.5-VL-77B shows an even stronger single-view bias in cross-view matching and distance judgment tasks, where it tends to align 2D screen positions across cameras (e.g., "bottom-right" to "bottom-right") instead of reasoning in shared 3D frame. Qwen2.5-VL. VL-7BB behaves similarly in viewpoint identification and cross-view tasks, effectively treating them as angle-image retrieval problems rather than enforcing multi-view geometric consistency.
Depth, occlusion, and 3D geometry confusion. A second common failure mode involves incor- rect reasoning about depth, occlusion, and 3D layout. In GPT-5, distance judgment and 3D spatial consistency errors show that the model frequently substitutes 2D heuristics (apparent size, 2D po- sition) for true 3D reasoning: it chooses objects that appear more salient closer in single pro- jection, even when other views clearly indicate that another object is nearer in 3D. Qwen2.5-VL-7B exhibits even stronger depth confusion, sometimes inverting near-far front-back relations, and occasionally treating objects on the tabletop and objects below the table (e.g., cabinet handles) as comparable candidates. Qwen2.5-VL-72B shows similar issues in fine- fine-grained 3D spatial consis- tency, where it misassigns grid coordinates along the depth axis or swaps two neighboring objects that are close in distance. These patterns suggest that current models lack robust internal 3D scene representations and often rely on weak perspective cues.
Frame-of-reference errors and instance-level correspondence. Even with explicitly defined camera-centric coordinate system (Appendix E.3), models often mishandle name-of-referenee de- tails and instance-level matching. For GPT-5, viewpoint identification and cross-view matching failures indicate that the model sometimes implicitly assumes that the same set of objects must be visible in all views, and treats changes in visibility due to ofeld-of-view or occlusion as evi- dence that the views are asynchronous unrelated. For Qwen2.5-VL-77 instance-level confusion in scenes with multiple objects the same category (e.g., several yellow peppers): the model correctly recognizes the category but fails to track which specific instance is highlighted across views, effectively performing category-level rather than instance-leve corre- spondence. Qwen2.5-VL-72B exhibits similar behavior in multi-object setups, where it matches "any object of the correct type" instead of the exact instance indicated by the bounding box. These errors highlight the difficulty of precise, instance-level multi-view alignment even for large models.
we frequently observe
Affordance and physical-feasiiite misunderstandings. tory selection tasks, models must reason about which grasps motion paths more likely to succeed physically. GPT-5 often prefers grasp lines or trajectories that look visually neat (c.g., cen- tered on the visible surface) but would be unstable collision-prone for parallel gripper in 3D; it
affordance recognition and trajec-
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rarely reasons explicitly about approach direction, object thickness, nearby obstacles. Qwen2.5- VL-7B sometimes mislocalizes the grasp line to the wrong object altogether and then optimizes within this incorrect frame, leading to explanations that sound plausible but grounded in the wrong spatial reference. Uwwe2.55VVI-722. exhibits related issues: it often treats "passing through the middle" universal heuristic for good paths, objects or collision-aware trajectories that avoid table edges and surrounding clutter. Overall, these failures indicate that current VLMs still lack robust modeling of robotic affordances and physical constraints, especially when such reasoning must be carried out jointly with multi-view geometric alignment.
when the task requires side grasps thin
Summary. Importantly these failure modes appear not only in weaker models but also in GPT- 5, which still exhibits systematic errors in multi-view fusion, depth reasoning. frame-of-reference handling, and affordance understanding. This suggests that MV-RoboBench does merely lower raw accuracy; exposes non-trivial limitations in current vision-language models' spatial reason- ing, and can serve diagnostic tool for guiding future architectural and training improvements. While human-centric research like EgoBrain (Lin et al., 2025a) focuses on decoding intentions, and methods exploring diffusion guidance and preference alignment (Chen al., 2025b;a) successfully enhance the quality and coherence of generative models, our findings highlight that current VLMs still lack the physical spatial reasoning required for successful robotic execution.
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Template for 3D Spatial Consistency
You are given three synchronized views-the left-gripper camera, the head camera, and the right-gripper camera, Only the head camera image contains colored bounding boxes. Then assuming the workspace divided into
cubic grid, with coordinates (x 2) running from 1 to 5. Which of the following sets of coordinate triplets best describes the locations of the objects circled by the bounding boxes in the image above? The following options are based the head camera view. <Description of coordinate system definition
Rules
The blue text a template description, which can be copied directly.
Sample of 3D Spatial Consistency
left gripper view
head view
right gripper view
Question: As shown above
OptionA: (Red box, 2,
OptionB: (Red box, 2,
OptionC: (Red box, 1,
4, 4), (Vellow box, 5,
4, 4), (Vellow box, 4,
4. 4), (Yellow box, 5.
3, 4). (Green box, 1.
4, 4), (Green box, 1,
3, 4), (Green box, 2,
4, 1). (Blue box, 4, 3,
4, 1). (Blue box, 4,
4, 1). (Blue box, 4, 3,
4), (Pink box, 4, 3, 2)
4), (Pink box, 4, 3,
4), (Pink box, 4, 3, 2)
OptionD: (Red box, 2, 4, 4), (Vellow box, 4, 4, 4), (Green box, 3, 4, 1). (Blue box, 1, 2, 3), (Pink box, 4, 3, 2)
OptionE: (Red box, 1, 4, 4). (Yellow box, 5, 3, 4). (Green box, 1), (Blue box, 4, 3, 2). (Pink box, 4, 3, 4)
Figure 19: Example of Spatial Cube Reasoning from the AgiWorld dataset. Objects are localized in 5 x cubic grid, and the model must select the correct coordinate triplets from the given options.
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Template for 3D Spatial Consistency
You are given four synchronized views, only the viewl contains colored bounding boxes. Then assuming the workspace is divided into 5 5 cubic grid, with coordinates (x, z) running from
following sets coordinate triplets best describes the locations of the objects circled by the bounding boxes in the image above? The following options are based on the view1, +Description of coordinate system definition>
Which
Rules
You can replace the viewl with other views, ie. view2, view3, view4
The blue text is template description, which can be copied directly.
Sample Spatial Consistency
view1
view2
view3
view4
Question: As shown above
OptionA: (Red bax, 1, 4, 3), (Vellow box, 5, 1, 2). (Blue box. 1, 2). (Pink box, 4, 5,
Option8: (Red box, 5, 4, 2). (Yellow 3, (Green box, 1, 3). (Blue box. 1, 4, 2).
OptionC: (Red box, 4, (Vellow box, (Green box. 4, 2), (Blue box, 3, 3.
(Green
(Pink box, 4, 5,
2). (Pink box, 5,
Optiond: (Red box, 4, 3). (Yellow (Green
OptionE: (Red box. 1, 3). (Yellow box. 1, (Green 5, 2). (Blue box,
bax, 4, (Eneen box, 5, 2). (Blue box, 3, 3, 2). (Pink box, 4, 5)
3, 3, 2), (Pink box. 1,
Figure 20: Example of Spatial Cube Reasoning from the Bridge dataset. One reference view (here, viewl) contains bounding boxes, and the model must infer the correct 3D coordinates the objects across the synchronized views.
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Template for Action Planning
<Tskk> Which of the following operations is most likely to complete the task with the least collision? The following options are based the head camera view. <Description of coordinate system definition
Rules
You can replace the head with left-gripper or right-gripper, The blue text is a template description, which can be copied directly.
Sample of Action Planning
left gripper view
head view
right gripper view
Question: If want to pour water from the kettle on the table into the water cup. <Template which shown as above>
OptionA: Move the left gripper leftward, then forward, then downward to grasp the handle of the kettle. Then lift the kettle, move it upward, and then rightward to pour water into the cup.
Move the left gripper rightward, then backward, then upward grasp the handle of the kettle. Then lower the kettle, move it downward, and then leftward pour water into cup.
OptionB:
OptionC: Move the left gripper downward, then rightward, then forward grasp the handle of the kettle. Then lift the kettle, move forward, and then leftward to pour water into the cup.
OptionD:
Move the left gripper forward, then leftward, then upward to grasp the handle of the kettle. Then lift the kettle, move it rightward, and then downward to pour water into the cup.
OptionE: Move the left gripper backward, then upward, then leftward grasp the handle of the kettle. Then lift the kettle, move downward, and then forward to pour water into the cup.
Example of Planning from the AgiWorld dataset. The model must select the correct sequence of normalized directional actions to accomplish the described task with minimal collisions.
Figure 21:
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Template for Action Planning
<Tole Which of the following operations is most likely to complete tosk with least collision? The following options based the viewZ. DDsccriptio of coordinate system definition>
Rules
You can replace the view2 with other views, i.e. viewl, view3, view4 The blue text template description, which can copied directly.
Sample of Action Planning
view1
view2
view3
view4
Question: want to put the carrot from the stove into the sink. Which of the following operations is most likely to complete the task with the least collision? The following options are based on the view2. Template which shown above>
OptionA: Mave the gripper to the left, then upword, then backward to grab, then left, then backward, then right, and finally upward put the
into the complete the operation.
Option8: Move the gripper to left, then forward, then downward to grab, then upward, then bockword, then right, and finolly downword to put the corrot into the sink to complete the operation.
OptionC: the gripper the left, then forward, then downword grab, then forword, then backward, then right, and finally upword to put the carrot into the sink to complete the operation.
OptionD: Move the gripper to the left, then forword, then downward grab, then left, then the carrot into the sink to complete the operation.
backward, then right, and finally upward
OptionE: Move the gripper to the left, then backward, then downeard to grab, then left, then to complete the operation.
bockword, then right, and finolly upward put carrot
Example of Planning from the V2 dataset. One reference view (here, view2) is used to describe the options, and the model must infer the correct high-level sequence to achieve the task while avoiding collisions.
Figure 22:
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Template for Step Execution
Task>, which of the following actions is most likely to accomplish this task? The following options are based on the head camera view. <Description of coordinate system definition
Rules
You replace the head with left-gripper right gripper. The blue text is a template description, which can be copied directly.
Sample for Step Execution
left gripper view
head view
right gripper view
Question:
Suppose I want to use the right gripper to grab the spoon in the bowl. Template which shown as above>
OptionA: Move
OptionB: Move
OptionC: Move
the right gripper down and then to the left, then forward to grab.
the right gripper the right gripper up and then to the left, then forward to grab.
forward and then to the right, then down to grab.
OptionD: Move the right gripper down and then to the right, then forward to grab.
OptionE: Move
the right gripper
down and then to the left, then backward to grab.
Figure 23: Example of Execution from the AgiWorld dataset. The model must select the correct primitive action, expressed in normalized directional terms, to complete the manipulation goal.
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Template for Step Execution
<Tasko, which of the following actions is most likely to accomplish this task? The following options are based on the view2, +Description of coordinate system definition
Rules
You can replace the view2 with other views, ie. viewl, view3, view4 text template description, which can be copied directly.
Sample for Step Execution
nx18in
hx18
viewl
view2
view3
view4
Question: If most likely to accomplish this task? The following options are based on the view3.
grab the blue cup the table, which of the following actions is
+Description of coordinate system definition>
OptionA:
Move the gripper
OptionB: Move the gripper
Optionc: Move the gripper
upward, then to the right,
upward, then the forward,
upward, then to the forward, then backward, and then upward grab the cup.
then backward, and then upward to grab the cup.
then backward, and then
downward to grab the cup.
OptionD: Move the gripper
OptionE: Move the gripper upward, then to the left, then backward, and then downward to grab the cup.
upward, then to the forward, then left, and then downward to grab the cup.
Figure 24: Example Execution from the Bridge dataset. One reference view (here, view3) is used describe the options, and the model must identify the correct low-level action sequence to accomplish the given task.
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Template for Trajectory Selection
You are given three synchronized views-the left deft-gripper camera, the head camera, and the right-gripper camera, <Task>, which color track most likely to complete the task?
Rules
The blue text is a template description, which can be copied directly.
Sample of Trajectory Selection
left gripper view
head view
right gripper view
Question:
You are given three synchronized views-the left gripper camera, the head camera, and the right gripper camera. If I want to place the pink wet wipes into the plastic bag, which color trajectory is most likely to accomplish this task?
OptionA: The blue line.
OptionB: line.
The green
OptionC: The pink line.
OptionD: The red line. OptionE:
The yellow line.
Figure 25: Example of Trajectory Evaluation from the AgiWorld dataset. The model must select the correct colored trajectory that successfully completes the described manipulation task.
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Template for Trajectory Selection
are given four synchronized views, <aslo,, which color track most likely to complete task?
Rules
The blue text template description, which copied directly.
Sample of Trajectory Selection
view1
view2
view3
view4
are given four synchronized views. wont to open cabinet, which trock is most likely complete the task?
Question:
OptionA:
The blue OptionB: The green line.
OptionC: The pink
OptionD: The red line.
OptionE: The yellow line.
line.
Figure 26: Example of Trajectory Evaluation from the Bridge dataset. The model is given four synchronized views and must select the correct colored trajectory (here, the green line) that com- pletes the described task.
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Template for Affordance Recognition
You are given three synchronized views-the left-gripper camera, the head camera, and the right-gripper camera. Across these images, five grasp candidates are illustrated with color-coded lines-red, yellow, green, blue, and pink-such that each color appears exactly once, on one view only. The two endpoints of every line mark the intended positions of the gripper fingers, Which colour-coded line represents the grasp candidate most likely to succeed?
Rules
The blue text is template description, which can be copied directly.
Sample of Affordance Recognition
Question: As shown above
OptionA: Blue line.
OptionB: Green line.
OptionC: Pink line.
OptionD: Red line.
OptionE: Yellow line.
Figure 27: Example of Affordance Recognition from the AgiWorld dataset. Five color-coded grasp candidates are illustrated, and the model must select the one most likely to succeed.
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Template for Affordance Recognition
You are given four synchronized views. Across these images, five grosp candidates are illustrated with color-coded lines-red, yellow, green, blue, and pink-such that each color appears exactly once, on one view only. The two endpoints of every line mark the intended positions of the gripper fingers. Which color-coded line represents the grasp candidate most likely to succeed?
Rules
blue text a template description, which can be copied directly.
Sample of Affordance Recognition
view1
view2
view3
view4
Question: As shown above
OptionA:
Blue
OptionB: Green
OptionC:
OptionD: Red line.
OptionE: Yellow line.
Figure 28: Example of Affordance Recognition from the Bridge V2 dataset. Grasp candidates are distributed across four synchronized views, with the model required to select the most feasible op- tion.
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Perceptual Hallucination / Grounding
"question": "You are given four synchronized views, only the view3 contains colored bounding boxes. Which option corresponds to the shortest grasping distance?
"A": "Grab the item the red bounding box." (Model answer)
"D": "Grab the item in the yellow bounding box." " (Correct)
Model response: To determine the shortest grasping distance, need to consider the relative positions of the objects the views. The view3 contains colored bounding boxes, which likely indicate the objects of interest for grasping. \n\n1, **Red bounding box** This box is around the yellow bottle. **Pink bounding box**; This box is around the purple cloth. \n3. **Green bounding box** This box is around the blue bottle. An4.
**Yellow bounding box**: This box is around the yellow bottle. \n5, ** **Blue bounding box**; This box around the blue bottle. AnAnFrom the images, the yellow bottle the same object in all views. The red bounding box in view3 is the closest to the yellow bottle in view4, indicating the shortest grasping distance. \n\nAnswer:
Analyze: mistook the red soup can for "yellow bottle" and the green bell pepper for "blue bottle." " This is the most obvious "hallucination, directly proving that the small model failed at the Perception level.
Figure 29: Case Study 1: Perceptual Hallucination (Qwen2.5-VL-7B), The model fails basic
visual grounding. As shown in the response, it explicitly misidentifies the red bounding box (a soup can) as a "yellow bottle" and the green box (a pepper) as "blue bottle." This hallucination leads to a baseless conclusion, illustrating the perception bottleneck in smaller models.
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Instance-level Correspondence
"question": "In the right-gripper camera view, the item is outlined with red bounding box, Which colored bounding box encloses that same item in the left left-gripper camera view and the head camera view?"
"B": TTe yellow bounding box." (Model answer) "E" "The blue bounding box. (Correct)
Model response: To determine which colored bounding box encloses the some item in both the left-gripper camera view and the head camera view as the red bounding box in the right-gripper camera view, let's follow these steps: \neps:\n'nn.. Identify the item within the red bounding
the right-gripper camera view. It oppears to be a yellow bell pepper. An2. Locate the yellow bell pepper in the left-gripper camera view. It is enclosed by the pink bounding box. \n3. Confirm the location of the yellow bell pepper in the head camera view. It enclosed by the yellow bounding box. An An\nGiven this analysis, the item outlined by the red bounding box the right-gripper camera view corresponds pink bounding box the left-gripper camera view and the yellow bounding box in the head camera view. AnAnSince the question asks for the color of the bounding box that encloses the same item in both the left-gripper camera view and the head camera view, we need identify the comman item and its corresponding bounding box colors, The yellow bell pepper consistently identified by the yellow bounding box in the head camera view. InhTTeereoo,, the correct answer is: \n\nnnswer: B
the
The main problem here is that the model first identifies what the object is when making judgment. However, this scene, there are many identical objects. The model must correctly identify the correct object based on spatial location and multi-view consistency. This turns it into simple object recognition problem.
Analyze:
Instance-Level Correspondence Failure (Qwen2.5-VI--22B) The scene contains multiple instances of the same class (yellow peppers). The model correctly iden- tifies the category but fails to perform geometric alignment. It selects Option solely because contains a "yellow bell pepper," effectively reducing the multi-view consistency task simple object recognition problem.
Figure 30: Case Study 2:
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Frame-of-reference Single-view Bias
"question": "If want to pour water into the cup, which of the follawing operations is mast likely to complete the task with the least collision? The following options are based the left gripper camera view. <hee definition of Coordinate>
"A": "Move the left gripper forward, then left, and then downward to grasp the handle of the kettle. Then move upward, and then forward to pour water into the cup.", (Model Answer)
"E"; "Move the left gripper backward, then left, and then downward grasp the handle of the kettle. Then move upward, and then forward to pour water into the cup." (Correct)
Model response: Step 1: Reasoning\n: In- Goal: Grasp the kettle handle with the left gripper and then pour into the cup with minimal collision. An- From the head view, the kettle is the left side with its handle the far-left: the cup near the center. From the left-gripper view, the right gripper/ary
ahead and slightly to the right, so moving right increases collision risk, while moving left avoids it. \n- Safe approach to the handle:\N Move forward (+y) toward the kettle.\r An Then move left (-x) to align with the handle on the kettle's left side, steering away from the right arm. \n Then move downward (-z) to grasp the handle near the table surface. \n- After grasping, lift upward (+z) and moving forward (+y) toward the cup positions the kettle to pour. \nhn others are less suitable:\ne B: Moving right heads toward the right gripper and tissue box: "upward to grasp" is incorrect since the handle is lower. An- C: Downward then upward before grasping is unnecessary and risky near the table; locks lateral alignment with the handle. \n- D: Moving right first increases collision risk with the right gripper: the right-then-left path is inefficient. An- E: Moving backward moves away from the kettle, not toward it: adds unnecessary motion. \n\nStep 2: Best
A
Analyze: GPT-5 interprets "moving forward" as reaching for the cup (based on the intuition of the Head View), but in the Left Gripper coordinate system, "moving forward" actually means passing over the cup, and going any further down would make it impossible to grab it. This perfectly explains "reference frame confusion" and "single-view offset".
Error (GPT-S). The task requires planning in the Geiiper frame. The model's reasoning ("Move forward. toward the ketrle") reveals a reliance on the global Head View intuition. In the specific local frame of the left gripper, "Forward" actually leads to a collision with the cup, while the correct action is to move "Backward" (Option E).
Figure 31: Case Study 3:
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Reviews:
[{"review_id": "OaN3bl1QQx", "reviewer": "Reviewer_oGrs", "summary": "Paper presents a new benchmark for assessing cross-view reasoning-related functionalities of AI models, with a focus on tabletop manipulation scenarios. The benchmark is constructed with a 3-step procedure: 1) collecting multi-view frames (mostly left arm, right arm, and center view) from canonical robotic datasets (AgiBot  World, etc); 2) generating QA pairs by crowdsourcing with human raters and QA templates were adopted; 3) curating the benchmark with human raters to obtain a total of 1.7k QA entries. The dataset covers a broad range of reasoning tasks, primarily categorized into spatial reasoning and robotic task reasoning. The authors have conducted a rich collection of experiments, including general benchmarking on foundation AI models, the effectiveness of CoT-enhanced reasoning, the correlation between the performances of spatial reasoning and robotic task reasoning, and whether performances on single-view reasoning benchmarks could transfer to the proposed multi-view benchmarks.", "questions": "See \"Weaknesses\".", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "+The paper is clearly written and easy to follow. The topic (benchmarking of AI models, embodied AI, spatial AI) is relevant to a broad spectrum of ICLR community members.\n\n+The proposed benchmark is constructed and curated in a rigorous fashion, where human raters, instead of automatic pipelines, perform most annotation tasks. Compared to related benchmarks, this alleviate the difficulty of data curation and potentially help with a higher dataset quality.\n\n+I appreciate the authors for arranging such rich collection of tasks. Notably, showing the correlation between spatial reasoning and robotic task reasoning, as well as the transferabiliy from single view to multi-view reasoning, could indeed shed some light into future research.", "weaknesses": "My primary concerns lies with the validity of the \"multi-view\" setting in the current dataset format and relation to embodied AI tasks. \n\n-It seems that many of the designed tasks do not nessitate the need for multi-view input. For the tasks illustrated in Figure 1, most tasks seems doable with only one view, except for viewpoint identification and cross-view matching. If this is the case, I would like to see an ablation on using only one view (say the center view) as input and see if there is significant performance drop -- of course the two tasks mentioned above should be excluded. \n\n-Although there are tasks that indirectly evaluate the capability of robot manipulation (i.e., robot task reasoning), but it would be great to see if there is more direct connection, let's say the AI models control the robot directly. The authors claim this in the abstract \"spatial intelligence and robotic task execution are correlated in multi-view robotic scenarios\". However, the \"robotic task\" in the proposed dataset never touches controlling an actual robot but merely works on reasoning aspects of high-level planning. If the authors choose to go with the current setting, I suggest modifying their claim into \"reasoning for high-level planning\", not robot task executation in general. Otherwise, experiments on maybe fine-tuning these models into low-level control should be necessary.", "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": "GkXl0ITL07", "reviewer": "Reviewer_bybw", "summary": "The paper built a benchmark for evaluating the spacial reasoning ability of VLMs under multi-view setting. The experiments analyzed the how spatial and robotic reasoning relate within multi-view manipulation, and whether spatial intelligence measured in single-view settings can transfer to embodied multi-view tasks. The results revealed that the VLMs have some common shortcoming in certain spacial reasoning scenario, provided a potential direction of VLM developments.", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper has good structure and easy to follow.\n2. The author conducted a large scale experiment on multiple modern VLMs, which makes the experimental results and conclusions convicible.\n3. The spacial reasoning ability is critical for the development of VLMs. This paper provided a good example of how to evaluate the VLMs and thus might have broad influence to the community.", "weaknesses": "1. In abstract, should not use abbreviation when the the first time \"CoT-inspired techniques\" appears. Therefore \"CoT-inspired techniques\" -> \"Chain of Thought (CoT)-inspired techniques.\"\n2. Does the image orientation matters? For example, if the head camera view is upside down, can the system still get correct inference? This discussion is necessary to determine the VLMs' generalization on spacial reasoning.\n3. The benchmark have multiple-choice questions across eight subtasks, each with exactly one correct answer. However, another important aspect is that whether the system can determine there is no correct choice.", "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": "FabFjiWjzB", "reviewer": "Reviewer_drdc", "summary": "This work introduces MV-RoboBench, a benchmark specifically designed to evaluate the multi-view spatial reasoning capabilities of vision-language models (VLMs) in robotic manipulation. The benchmark contains 1.7k manually curated QA items covering eight subtasks. Evaluation results show that state-of-the-art models still perform far below human levels. The study also explores chain-of-thought (CoT)-inspired enhancements, which produce mixed, model-dependent effects and reveal two main insights:\n(1) spatial reasoning ability strongly correlates with robotic execution performance; and\n(2) high accuracy on general single-view spatial benchmarks does not reliably transfer to multi-view robotic tasks.", "questions": "1. The model performance on the 3D spatial consistency subset appears quite unusual. In particular, GPT-5-Chat achieves only 4.9%, whereas GPT-5-Mini reaches 72.55%. I am curious about the API identifiers of these two models. This large discrepancy also suggests that there might be an evaluation bug. It would be helpful if the authors could provide some example predictions from these models to illustrate and better understand the observed performance gap.\n\n2. I also wonder whether the authors believe there are potential methods to automatically collect such QA pairs for training purposes. If so, I am curious how fine-tuned models would perform on this benchmark. Moreover, the limited chain-of-thought (CoT) effects observed in this work might be due to the fact that the models have not been exposed to such reasoning patterns during training. Fine-tuning with CoT-style data could potentially enhance their performance further.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The proposed benchmark addresses an important and practical problem in robotics.\n2. The annotations for the benchmark are manually collected, which helps ensure their correctness and reliability.\n3. This work also investigates the effects of CoT prompting and uncovers two key correlations, between spatial reasoning and robotic execution, and between single- and multi-view understanding, offering interesting insights to the research community.\n4. The presentation is easy to follow.", "weaknesses": "1. The paper claims to be the first to integrate spatial and robotic reasoning with synchronized multi-view inputs in robotic manipulation scenarios. However, the previous ERQA benchmark also includes some multi-view spatial reasoning and manipulation questions in its test set. Although such samples are fewer and many ERQA items are single-image based, it nevertheless contains similar tasks, such as cross-view matching, as those in the proposed benchmark. Therefore, the authors should discuss ERQA in the paper.\nIn addition, another benchmark, MMSI-Bench, also focuses on multi-image spatial understanding. Although it is not primarily designed for manipulation scenarios, it contains samples from datasets like Agibots, which are relevant. The authors should be aware of and discuss this work as well, to provide readers with a more comprehensive understanding of the existing benchmark landscape in this domain.\n2. Although the answers in the proposed benchmark are manually crafted, the questions are generated using template-based designs, which may limit the diversity and richness of the dataset.\n3. While the benchmark emphasizes multi-view understanding, the paper does not provide a mechanism to ensure that questions cannot be answered by observing only a single image. For example, in Figure 1, the distance judgment and spatial consistency examples appear solvable from one view alone. I recommend conducting another round of quality review to verify each question’s validity and to filter out those that can be answered using only a single image.\n4. Regarding the experimental results in Figure 6, the authors use the OmniSpatial benchmark. However, I do not believe OmniSpatial is an appropriate choice for this analysis, as it includes many samples with multiple images, not-real scenes, and non-robotic content. This may distort the analysis of the correlation. A more suitable comparison would be to use the single-view questions from the ERQA benchmark instead.\n5. This work did not have an error analysis of the tested models.\n\nI am open to raising the score if my concerns are 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": "VUATbsNAuv", "reviewer": "Reviewer_Awkn", "summary": "This paper introduces a novel and valuable benchmark called MV-RoboBench, specifically engineered to test the multi-view spatial reasoning capabilities of Vision-Language Models (VLMs) within the context of robotic manipulation. Diverse existing VLMs, along with enhanced versions incorporating CoT-inspired techniques, are evaluated on the benchmark.", "questions": "1. Given that Action Planning and Step Execution answers rely on normalized directional terms (e.g., leftward, forward), how were these linguistic action sequences generated and validated to be reliable and unbiased? Were these descriptions manually authored by annotators based on visual observation, or were they extracted directly from existing action annotations in the source datasets? What specific measures were implemented to ensure the reliability and eliminate ambiguity or bias in the normalized directional language, especially concerning the coordinate system definition and the consistency of the distractor options?\n2. About Trajectory Selection questions, what is the origin of the ground-truth trajectories (e.g., raw robot waypoints from the AgiWorld/BridgeV2 data vs. human-drawn ideal paths)? What methodology was used to create the distractor options to ensure they are visually plausible but fail due to collision or poor geometric alignment, thus demanding multi-view reasoning?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The benchmark advances beyond traditional multi-view reasoning tasks by integrating them with explicit robotic execution tasks. The accompanying analysis establishes a strong, positive correlation between spatial intelligence and robotic task execution in these multi-view scenarios, validating the design and highlighting the necessity of integrated capability.\n2. MV-RoboBench consists of 1.7k manually curated QA items across eight carefully defined subtasks, ensuring comprehensive coverage and a balanced dataset. The construction process utilized a detailed human-in-the-loop quality review pipeline, including rigorous checks for question-answer alignment and randomization of answer options to prevent model bias.\n3. A diverse array of existing state-of-the-art models was thoroughly evaluated, including both open-source and closed-source VLMs, as well as variants incorporating CoT-inspired enhancements. This broad evaluation provides a robust baseline against which models are shown to remain far below human performance.\n4. The study includes rich analytical results that explore the relationship between perception and action. A particularly insightful finding is that strong performance on general-purpose single-view spatial benchmarks does not reliably transfer to the demanding, embodied tasks within MV-RoboBench, underscoring the unique challenge of multi-view reasoning in robotics.", "weaknesses": "1. A typo exists in Figure 3, where \"AgiWolrd\" should be corrected to \"AgiWorld\".\n2. The paper lacks transparency regarding the human effort involved in its construction. It should expose the origin of the hired annotators, the total number of human-hours dedicated to selecting image pairs and building the 1.7K QA items, and the compensation structure for the annotators.\n3. Since the benchmark is grounded in robotic manipulation (the domain of VLA models) and utilizes real robotic demonstration data, a VLA-centric evaluation would be highly beneficial, as VLAs are specifically designed for the physical execution and grounding that this benchmark demands, contrasting sharply with the training data distribution of many general-purpose VLMs.", "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)