InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models
Authors: Nianchen Deng
Lixin Gu
Shenglong Ye
Yinan He
Zhe Chen
Songze Li
Haomin Wang
Jinhui Yin
Qi Wei
Tianshuo Yang
Min Dou
Tong He
Wenqi Shao
Kaipeng Zhang
Yi Wang
Botian Shi
Yanting Zhang
Jifeng Dai
Yu Qiao
Wenhai Wang
Hongjie Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision Language Models
Spatial Reasoning
Visual Question Answering
Primary area: datasets and benchmarks

Abstract: Recent benchmarks and datasets have been proposed to improve spatial reasoning in vision-language models (VLMs), yet existing open resources remain constrained by limited scale, narrow visual diversity, and restricted instruction expressiveness. To address these gaps, we present InternSpatial---the largest open-source dataset for spatial reasoning in VLMs---alongside InternSpatial-Bench, a comprehensive evaluation benchmark designed to assess spatial understanding across diverse instruction formats. InternSpatial contains 12 million question-answer(QA) pairs covering both single-view and multi-view scenarios, sourced from varied visual environments and supporting 19 distinct instruction formats that mirror real-world query patterns. InternSpatial-Bench aims to single-view assessment and also extends multi-view reasoning through a novel rotation estimation task. Experimental validation demonstrates that models trained on \trainset achieve substantial performance improvement of 12.1% on InternSpatial-Bench and 10.7% on VSI-Bench, while preserving competitive performance on general-purpose benchmarks. We expect these resources can advance the development of spatially-capable VLMs for practical applications in robotics and embodied AI systems. Our codes and datasets are publicly available at https://github.com/dengnianchen/intern-spatial.

Published as a conference paper at ICLR 2026
INTERNSPATIAL: A COMPREHENSIVE DATASET FOR SPATIAL REASONING IN VISION-LANGUAGE MODELS
Nianchen Deng'*
Lixin Gu
Shenglong Ye!
Yinan He!
Zhe Chen
Songze Li
Haomin Wang
Jinhui Yin
Qi Wef
Tianshuo Yang'
Min Dou
Tong He
Wenqi Shao'
Kaipeng Zhang'
Yi Wang' Botian Shi Yanting Zhang
Jifeng Dais.
Yu Qiao'
Wenhai Wang 6 Hongjie Zhang't
Shanghai AI Laboratory
Nanjing University
3 Shaanhai Jiao Tong University
Donghua University 5 Tsinghua University
TTee Chinese University Hong Kong
{
(dengrianchen, gulixin,
org.
ABSTRACT
Recent benchmarks and datasets have been proposed to improve spatial reason- ing in vision-language models (VLMs), yet existing open resources remain con- strained by limited scale, narrow visual diversity, and restricted instruction ex- pressiveness. To address these gaps, we present InternSpatial the largest open- source dataset for spatial reasoning in VLMs-alonsside InternSpatial-Bent -ennch, a comprehensive evaluation benchmark designed to assess spatial understanding across diverse instruction formats. InternSpatial contains 12 million question- answer(QA) pairs covering both single-vies view and multi-view scenarios, sourced from varied visual environments and supporting 19 distinct instruction formats that mirror real-world query patterns. InternSpatial-Bennch aims single-view as- sessment and also extends multi-view reasoning through a novel rotation estima- tion task. Experimental validation demonstrates that models trained on InternSpa- tial achieve substantial performance improvement of 12.1% 1% on InternSpatial- Bench and 10.7% on VSI-Bench, while preserving competitive performance on general-purpose benchmarks. We expect these resources can advance the development of spatially-capable VLMS for practical applications in robotics and embodied Al systems. Our codes and datasets are publicly available at /ttps://githuie htt.tt/......eeeeeeaaaneee/eteee
INTRODUCTION
Vision-language models (VLMs) have achieved remarkable progress across multimodal tasks such as visual question-answent (VQA), image captioning, and grounding. However, they continue to struggle with spatial reasoning in both single-view settings (e.g. ideetifnnti object position or size from a static image) and multi-viww scenarios (c.g estimatiin distances or tracking appearance order across video frames). Enhancing spatial reasoning capabilities crucial for real-world applications such robotics, autonomous navigation, and augmented reality.
Recent efforts have introduced ppatially-relevam VQA datasets and benchmarks et al. 2025 Cheng et al. 2024 Chen et al., 2024a Yang al. 2024). While these works have advanced the field, several limitations still persist: (1) Limited scene diversity: existing datasets are predomi- nantly drawn from narrow sources, primarily indoor outdoor scenes; (2) Restricted instruction
formats: Spatial (Chen et al. (2024)) and SpatialQA (Cai et al. 2025) rely exclusively on nat- ural language, and OSD (Cheng al. (024) uses region masks, which fail reflect the diversity of instruction formats required for practical tasks; (3) Narrow training scope: existing datasets mainly focus on single-vies view settings and basic spatial concepts such as object position existence, lacking multi-view supervision for spatial relationships across viewpoints.
"Equal Contribution. "orresponning Author.

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Table 1: Comparison of our InteraSpatial with existing spatial reasoning datasets. W: in-the-wild, I:
indoor, D: drive, E: embodied, O: object-centric
Dataset
# of QA
Scenario
Open-source
View Type
Instruction format
SpatialVLM Chen et al. 2024a
W
X
Single-view Single-view Single-view Single-forme format
Single-fornat
SpatialQA Cai el ul. 2025
0.9M
W.E
Single-formn
OSD Cheng et al. 2024
8.7M
W
/
InternSpatial
12M
W.L.E..O
/
Single-view, Multi-view
Multiple-format
To address these challenges, we introduce InterSppaiial the largest open-source spatial reasoning dataset, and a corresponding benchmark InternSpatial-Bencce specifically designed to enhance spa- tial reasoning in VLMs. InternSpatial comprises 9.5M single-view and 2.5M multi-view QA pairs, sourced from diverse visual environments including in-the-wild scenes (Lin et al. [2014] Wang et al.
| Krishna et al. (017), structured indoor spaces (Wald et al., 2ai et al. [0117]
: Mao et al,
2022). urban streetscapes (Cordts et al. 2016), object-centric scenes (Deitke et al.) (0222. and em- bodied navigation contexts (Anderson et al. 2018). To enrich instruction formats, we incorporate diverse set of query representations: masks, bounding boxes, numerical indicators embedded in images, as well as textual coordinate- -base references and spatial cues. Overall, the dataset supports 19 distinct instruction formats, enabling broader coverage of spatial reasoning queries. We further introduce a novel cross-view rotation estimation task, with 2.46M newly collected training QA pairs, addressing gap in existing datasets. To facilitate evaluation, we construct SittenSpptiil-BBe -enich with 6,008 QA pairs, serving as a comprehensive diagnostic benchmark for single- and multi-view spatial reasoning tasks. As shown in Table InternSpatial significantly expands scene coverage, instruction diversity, and multi-view supervision compared to existing benchmarks.
Our contributions are threefold: (1) The largest open- open-source spatial reasoning dataset InternSpatial for VLMs, designed for supervised fine-tuning with diverse scenes and 19 instruction formats; (2) A comprehensive evaluation benchmark InternSpatial-Bent
for both single-view and multi-view tasks, including the novel rotation estimation task; (3) Extensive experiments demonstrating the effectiveness of InternSpatial, achieving 12.1% improvement on InternSpatial-Bee -encch and 10.7% on VSI-Bench while maintaining general multimodal performance.
2
RELATED WORK
2.1 SPATIAL REASONING VIA VISION LANGUAGE MODELS
Recent advances in large language models (L.LMs) Brown et al.
2020 Achiam et al. 2023 Touvron
ct al. 2023) and languae models (VLMs) Zhu et al. 2022 Li al. [202a| al. [2023a] Wang et al., 2023: Liu et al.) 2023: Li et al. 2023b Wang et al.
2024c| Chen et al. (024c) have
demonstrated remarkable capabilities. However growing evidence indicates that VLMs still strug- gle with spatial reasoning tasks (Cai et al. 2025; Chen et al., 2024a] Cheng et al. 2024 Yang et al.)
2024). To address these issues, recent works explicitly inject spatial awareness. Ferret-v2 /Zhan et al. (024a) improves fine-grained regional referring and grounding with any-resolution visual encoding. LLc (Ranasinghe et al. (004)) enhances spatial reasoning via coordinate-based in- struction tuning. Shikra (Chen al. (023) represents spatial coordinates purely in natural language for referential dialogue. Beyond 2D localization, several approaches incorporate additional super- vision signals such as dense 3D representations (Hong et al. 2023b a). image space masks (Cheng 2025). Despite these efforts, current
jet al., 2024), and pixel-wise depth information (Cai et al. methods still fall short of comprehensive, end-to-end spatial reasoning across diverse single-view and multi-view scenarios, which motivates the design of our InternSpatial dataset.
2.2 SPATIAL REASONING DATASETS
To evaluate and enhance the spatial reasoning capabilities VLMs, several datasets and benchmarks have been proposed. SpatialEval (Wang et al. 2024a) targets 2D spatial reasoning across relation understanding, navigation, and counting tasks. On the other hand, ViewRS (Li et al., 2024ch explores spatial reasoning from a top-down perspective, emphasizing the need to enhance VLM performance in top-view settings. For 3D spatial understanding from images, several datasets have
been introduced (Cheng al. 2024 Cai al. 2025
Li et al. 2024d), primarily tailored specific
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Damn Sources
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Figure I:
Generation pipeline for InternSpatial. The optional flows (represented by dashed lines and boxes) are only performed when the relevant annotations does exist in the data source.
models and require additional inputs like segmentation masks or depth maps. SpLtialVLM (Chen al. 2024a) proposed large-scale 3D spatial VQA dataset constructed by an automatic data gener- ation framework using Internet images (Chen et al. (2024a), demonstrating that appropriate training data can help VLMs infer spatial relationships without auxiliary inputs. Unfortunately, this dataset remains publicly unavailable. Spatial reasoning over image sequences or videos presents additional challenges. assess such capabilities, the VSI benchmark (Yang et al., (024) was proposed, evalu- ating range of open-source and proprietary VLMs. Results show that current models still struggle with multi-frame spatial reasoning tasks. Our work addresses these limitations by introducing a dataset integrating both single-view and multi-view tasks, significantly enhancing VL.M spatial rea- soning across diverse contexts and highlighting their potential for deeper spatial understanding.
DATASET
3. DATA ENGINE FOR INTERNSPATIAL
We construct InteraSpatia, large-scale dataset comprising nearly million Question- Answer(QA) pairs, to enable VL.Ms to perform 3D spatial reasoning through supervised fine-tuning. InternSpatial aggregates data from wide range of sources, including in-the-wild scenes (Lin al.,
2014 Wang et al.. 2024c| Krishna et al., 2017). structured indoor spaces (Wald et al., [019 Dai
ct al. al.
2017 Mao et al. 2022), urban streetscapes (Cordts et al. 2016), object-centric scenes (Deitke
2022). and embodied navigation contexts (Anderson et 2018).
To handle the heterogeneity of source data and support large-scale QA generation, we develop a fully automated and modular data engine that consolidates intermediate annotation extraction and QA synthesis into unified pipeline applicable across diverse data sources. As illustrated in/Figure I] the pipeline begins by generating necessary annotations the image level, followed by transforming the annotations into a canonical view space. Finally, QA pairs are constructed using template-baat approach that supports a wide variety of task types and instruction formats.
Generating Image-level Annotation. To generate 3D spatial reasoning QAs grounded in ob- jects, we first obtain the necessary image-level annotations, including 2D bounding boxes, region descriptions, segmentation masks, etc. For image datasets that already provide such annotations, directly utilize the existing labels. When annotations are missing, we employ pretrained models to generate them automatically. Specifically, we use open open-source VLMs to extract object-level 2D boxes and associated textual descriptions, and apply the SAM2 model (Ravi al. 2024) to gener- ate segmentation masks within these boxes. These masks are subsequently lifted into 3D space to facilitate the construction of 3D bounding boxes. The prompts used in this step be found in
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Language
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Figure 2: Examples of diverse instruction formats in text and image. The four images illustrate dif- ferent visual formats: original (top-left), bounding boxes (top-right), segmentation masks (bottom- left), and numbered regions (bottom-right). Surrounding the images are seven corresponding text instruction formats. The color blocks beside each image indicate whether the corresponding image- text pair is included in InternSpatial and Saattap--anecceee Best viewed in color.
Appendix C In the case of 3D datasets, which typically include global 3D annotations and per-view camera parameters, we project the 3D information onto the image plane to obtain the corresponding 2D) annotations. Although this projection is not strictly required for generating QAs, the under- lying 3D annotations are already available, it is necessary for supporting visual reference forms in prompts, such as bounding boxes and segmentation masks. In order reduce potential ambiguity in questions, We further apply several filtering strategies. For in-the-wild images, we filtered out objects without clear boundaries by object category, such as the sky and grass. For indoor scenes with 3D annotations, we detected the occlusions and objects beyond the image boundary through the projection of 3D models and bounding boxes on the image plane, and excluded QA pairs that were ambiguous due to these situations.
Aligning to View Space. To determine spatial relationships between objects, essential to obtain their positions and dimensions within well-defined 3D coordinate system. We adopt canonical view space as the reference frame, defined as a 3D Cartesian coordinate system cen- tered the camera's optical center. In this space, the y-axis aligns with the viewing direction, and the z-axis is perpendicular to the scene's horizontal plane, pointing upward. For 3D datasets, which provide global annotations and per-view camera parameters, transforming annotations into the canonical view space is straightforward. In contrast, image-only datasets contain only 2D visual information, requiring estimation of both camera parameters and depth maps. To address this, we follow the pipeline of SpatialRGPT (Cheng et al. 2024)) leveraging WildCamera (Zhu et al. (023b) for intrinsic parameter estimation, PerspectiveFields (Jin et al. (023) for extrinsic parameter infer- ence, and Metric3Dvv (Hu et al.) (2044) to predict dense depth maps. By combining the outputs of these models, we lift 2D annotations into the canonical 3D space, enabling accurate reasoning over object-level spatial relationships.
Template-based QA Generation. While prompting a large language model (LLM) to generate QA pairs directly for each image can produce diverse instructions, this approach prohibitively expensive at scale in terms of computation and time. Instead, we adopt a template-basee generation strategy that avoids invoking the L.MM during QA construction. This approach not only improves ef- ficiency but also facilitates flexible expansion to multiple prompt styles, such as object references via bounding boxes or segmentation masks. To ensure sufficient instruction diversity, we first prompt an LLM to generate several QA templates for each task type and answer format. These templates con- tain placeholders for object references and other variable content. During generation, we randomly select subset of tasks and object instances (or pairs) for each image, derive the corresponding swers using the previously constructed annotations, and instantiate the templates accordingly. We then filter out low-quality QA pairs, such those involving ambiguous spatial relationships caused
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by occlusion, and balance the number of positive and negative examples to produce well weel-structured dataset. We generate templates for single-view tasks, covering the position/size relationship of two objects. as well as ollationsip--constaiin count and existence tasks. The list of templates are shown in Appendix
Extending Instruction Formats. To enhance dataset diversity and better reflect real-world usage scenarios, we extend each QA pair into multiple instruction formats. Specifically, we generate up to five textual formats and up to four image formats per QA pair. The im- age formats include: (1) the original image, (2) the image annotated with bounding boxes, (3) the image with segmentation masks, and (4) the image annotated with numbers over key objects. The textual formats include: (I) natural language descriptions, (2) text with <ref> (caption)</rerrrtcccccccc text with <ref> (caption] < //r<f>/bboxx (bbox)+ < </oo>> and (5) text automatically gener- ated based on image content. Representative examples of these visual and textual formats are shown in Figure 2 As result, each QA pair can produce up to 19 training samples, from which only suitable ones are retained. Additionally, certain prompt types, such as images with numbers on key objects, may not directly indicate the correct object. Therefore, in these cases, we utilize the posi- tion information from the segmentation mask to correctly identify and reference the target object. During training, we uniformly sample across all instruction formats.
(3) text with <ref>regi weeffrregoon//ree rene/ref><bbxx (bbox) [obox)</box> </box> (4)
Generating Multi-view QA Pairs. To develop a comprehensive multi-view dataset for spatial understanding, we systematically collected and integrated view data derived from the training splits of the ScanNet (Dai et al. 2017), MultiScan (Mao et al., 2022). R2R (Anderson al.) 2018). and Objaverse (Deitke et al. 2022). subsequently formulating temporally-agnostic training samples that encapsulate inter-object relational attributes such relative properties, scale variations, and spatial distances, and cross-view relationships of objects such as rotation. Scene-level geometric priors were established by estimating room dimensions via the Alpha Shape algorithm (Akkiraju et al. 1995) applied to the point clouds, with the room centroid defined as the geometric center of the minimal axis-aligned bounding box enclosing the scene. We meticulously cataloged instance counts for each object semantic category. For unambiguous objects within the point clouds exhibiting principal dimension exceeding 15cm, annotations were standardized to the OrientedRoundinggBoe format using Open3D (Zhou al.) 2018). For remaining objects those with initial ambiguities, leveraged existing annotations to reduce the risk of shortcut learning by language models. Plausible alternative options were constructed by extracting distractors from other items within the dataset, thereby forming a corresponding multiple-choice question training set.
Human validation. Due the huge number of QAs in our dataset, it's almost impossible to check all generated QAs by human. Instead, we conducted manual verification on a randomly sampled subset with 500 items, including both final QAs and intermediate steps. With this validation, we ensure the accuracy of QAs in our dataset was over 95%.
3.2 SEARRNSPATAA--BENCE
To evaluate the performance of VLMs on 3D spatial reasoning tasks, particularly under diverse instruction formats, we propose InternSpatial-Benchh, a novel multi-task benchmark that features broad range of input types. Existing benchmarks such as SpatialRGPT-Bench (Cheng et al.) 2024) and SpatialBench (Cai et al. 2025) present several limitations. First, the question formats are overly simplistic and do not reflect real world application scenarios. Second, these benchmarks are tailored to specialized models and require auxiliary inputs such as region masks or depth maps. As a result, many tasks are incompatible with general-purpose VLMs that operate solely on images and text. Furthermore, SpatialBench suffers from limited number of QA pairs, reducing its effectiveness as comprehensive evaluation suite.
Intermppatial-Bench expands and refines both SpatialRGPT-Bend and SpatialBench to overcome these limitations. Specifically, we enrich instruction formats and introduce 3,000 carefully curated QA pairs, resulting in a total of 5,300 high-quality examples that span diverse task types and input modalities. Certain tasks from the original benchmarks, such as reachability prediction and quan- titative estimation of spatial extent, are excluded because they are unsuitable for general-purposs VLMS when only single-view image is provided. In the absence of additional information, such as
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depth or camera parameters, these tasks become severely under-constrained and often ambiguous, even for human annotators.
Refining and Expanding SpatialRGPT-Bench and SpatialBench. Bench (Cheng al. 2024) already provides sufficient number of QA pairs, our focus is on expand- ing the diversity of question formats rather than increasing the dataset size. Specifically, we augment the instruction styles of the original questions that do not involve numerical reasoning, following the format extension strategy described in (subsection 3.1 However, to avoid ambiguity caused by du- plicate object labels, we exclude formats that rely on natural language references or textual content containing <re f ftcaptionn//////ooo..o...cc....t ref>. For each selected question, we randomly sample three dif- ferent formats and leverage both object mask and bounding box annotations to construct the final benchmark entries. SpatialBench (Cai al, al. form. To diversify its instruction formats, we first manually extract reference phrases correspond- ing to the mentioned objects and convert the questions into templates with placcholders. Next, prompt the VLM to ground the objects based on these phrases and apply SAM2 to segment the corresponding regions. Using the resulting question templates, along with object bounding boxes and masks, we apply the format extension method described in subsection 3.1 generate diverse instruction variants for each QA. Finally, all generated QA pairs are manually verified ensure quality, with erroneous answers corrected and ambiguous or ill-formed questions removed
Since Spatial
2025 contains QA pairs exclusively in natural language
Extending the Benchmark with Curated QA Pairs. Unlike the large-scale training dataset, the benchmark is relatively small but demands higher annotation quality. To this end, we implement dedicated pipeline for generating high-quality QAs used in the benchmark. This pipeline oper- ates without relying on any pre-annotated information, making it applicable to any image-only data source. To encourage diversity and expressiveness in question formulation, we prompt the VLM to generate questions directly. Finally, we introduce a manual verification step to review all au- tomatically constructed questions and answers, ensuring the overall quality and correctness of the benchmark data. Details of the construction process are provided in [Appendix D).
3.3 DATASET STATISTICS
Statistics of InternSpatial. Our proposed dataset, InternSpatial, encompasses diverse set of tasks and instruction formats to comprehensively enhance spatial reasoning capabilities. It con- sists of a total of 12,035,415 QA pairs, covering both single-view and multi-view spatial reasoning tasks. Specifically, the single-view tasks include Position Comparison, Size Comparison, Existence Estimation, and Object Counting, while the multi-view tasks include Rotation Estimation, Object Counting. Room Size Estimation, Object Size Estimation, Route Planning, and Appearance Order. Detailed task descriptions and corresponding statistics are provided in Appendix[A] and visual exam- ples are shown in Appendix [F] In addition, InternSpatial incorporates images from various sources to enhance the robustness of the model. As illustrated in Figure 3 the dataset includes COCO (Lin) et al. 2014), AS- 1B (Wang et al. (202cc), and Visual Genome (VG) (Krishna et al., (017) for in ih-the- wild imagery: 3RScan (Wald et al.] 2019),, ScanNet (Dai et al., 2017), and MultiScan (Mao et al., 2022 for indoor scenes; Cityscapes (Cordts et al.) (016) for street scenes; Objaverse (Deitke et al. 2022 for single-object scenarios; and R2R (Anderson et al. 2018) for embodied navigation tasks. Moreover, InternSpatial emphasizes diversity in instruction formats. As shown in Figure 3] the number of samples across different formats is carefully balanced to avoid bias and ensure uniform coverage during training. In summary, InternSpatial provides a large-scale, diverse resource span- ning task types, visual domains, and instruction formats, making it well-suited for training VLMs to handle real-world spatial reasoning tasks effectively.
Statisties of InternSpatiil-Bench Following Spatial-Bench and Spatial-RGPT, our proposed benchmark, InternSpatial-Bench includes five tion Estimation, Existence Estimation, and Object Counting-designee to systematically evaluate the spatial reasoning capabilities of VLMs. In total, InternSpatial- consists of 6,008 QA pairs. Detailed task statistics are provided in Appendix A and visual examples are shown in Appendix G Bench
Estimation, Size Estimation, Rota-
To ensure robustness and diversity, InternSpatial- Bench incorporates images from a broad range of domains. As shown in 4 in addition to the sources used in Spatial- -enchh and Spatial RGPT, we include samples from the test sets of COCO, Flickr30K, Objaverse, ScanNet, and Cityscapes. This

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with thenser 24%
Objavense, 2.46M
RZR, 5K
COCO, 0.62M
Ouihh haue
AS, 2.69M
Ciryscapes
0.14M
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Image widd Masks
Image with Nansan 21%
ScanNet, 1.29M
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Figure 3: Distribution of instruction formats (Left) and data sources (Right) in InternSpatial.
diverse image collection spans wide range of real-world contexts, from indoor and outdoor envi- ronments to single-object scenarios and in-the wild imagery. We apply the same instruction format expansion strategy as used InternSpatial, with exception: for the Rotation Estimation task, since each image contains only a single object, we only use the original image format and natural language instructions. Consequently, these formats have higher proportion in this task compared to others. By combining diversity in task types, visual domains, and instruction formats, InternSpatial- Bench offers comprehensive and realistic benchmark for evaluating the spatial reasoning abilities of VLMs across wide range of practical scenarios.
With seef-
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Figure Distribution of instruction formats (Left) and data sources (Right) InternSpatial-Bent -ennch.
EXPERIMENTS
We begin in Section 4.1 by introducing the baseline model and outlining the evaluation benchmarks used in our experiments. Section 4.2 then presents results on InternSpatial-Bende to assess the spatial reasoning capabilities of langugee models. Section 4.3 reports performance on VSI- Bench (Yang et al. 2024), which further evaluates the models' multi-view spatial reasoning abilities. In Section 4.44 we conduct an ablation study to analyze the impact of different instruction formats on model performance. Finally, Section 4.3 evaluates whether training with InternSpatial affects general reasoning ability by benchmarking against a suite of standard lision-language tasks.
4.1 EXPERIMENT SETUP
Baseline. We construct baselines based VL2.5588 (Chen et al. 2024c). representa- tive traditional VLM. Following the training settings of InternVL2.5, we fine-tune our models from InternVL..55881 using downsampled version of the general datasets employed in VL2.5. along with InternSpatial. For generality, we also utilize Interm VL2.5- and Qwen2.5-VV -L-BBB as ad- ditional baselines to demonstrate the transfcrability of our training strategy. We refer the models fine-tuned on InternSpatial as MnternnL-SSptiil-- (for InternV1.2.5-20 Intern Vi-eaat--Spatil-111 1B (for Intern 1B, and Qwen-Spatial-8B (for Qwen2.5-VIL-88
(Bai et al. 2025)). Detailed training
configurations are provided in Appendix
Evaluation. We evaluate the models trained on InternSpatial using three types benchmarks: proposed InternSpatial-Bente the multi-view spatial reasoning benchmark VSI-Bench (Yang

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et al. 2024), and several general-purpont benchmarks. including Math Vision (Wang al. 2024b) OCRBench (Liu et al. 2024), TextVQA (Singh et 2019), ChartQA (Masry et al. 2022). and MM- follow the evaluation protocols of Spatial- Bench (Cai et al. 2025) and Spatial-RGPT (Cheng et al. 2024), reporting relative error for counting tasks, accuracy for multiple-choice questions, and GPT--0--ssiinnee (OpenAl (225)) scores for quiz- style questions. For VSI-Bench, we adopt the official evaluation protocol, with the only modification being the use of 32 sampled frames per video during testing. For general benchmarks, we follow the evaluation procedures provided by OpenCompass CContributors) 2023).
Star (Chen et al. 2024b). For InternSpatiil-Bennch,
4.2 EVALUATION ON SNTTERSPPTTILL-BENCE
Table 2: Results on InternSpatial- Bench. Bold indicates the best performance among all models,
while underline denotes the second-best performance.
Posàtion
Size Comparison
Rotation Estimation
Object Counting
Existence Estimation
Comparison
Average
Humnn Level
99.7
100.0
98.9
100.0
99.3
GPT-1o-0044-11-20 OpennA
71.2
26.7
63.5
61.6
1orodee.7-Somm--20000 2 (Anhropic
2024
73.2
25.9
Gemini-2,5-Flan CallashCoooannn Comanici et
2025
64.5
67.3
30.2
67.0
67.3
59.3
Llama-4-ccon Platforms
42.2
20.8
35.5
Qwen2.5.V1 10.55V11-77- 72B (Bai al.
2026
54.6
55.3
30.6
60.5
63.3
52.9
Pixtral1228 128 Agrawal al 2024
65.6
62.9
5.8
52.5
78.3
53.0
LLaVA-Ome Vision-72B Li
2024b
77.8
77.0
25.8
64.5
77.6
64.5
Qwen2,5-VL-8B
(Bai
2025
57.1
60.8
26.9
58.0
66.7
53.9
Qwen-Spotial-BB
79.9(422.8)
78.7(+17.9)
34,4(+7.5)
68. 36+1.3)))
80.00+13.3)
68.3 (8.3(+14.4)
L Chen et
2024c
42.9
43.3
23.8
21.3
59.9
38.2
LneerVV-SSpttill 1B
65.4(+22.55
58.5(+15.2)
26.3(+2.55
59,4(428.1)
74.4(+14.5)
36.8(+18.6)
LL2.5 Chen et
2024c
62.8
$7.7
28.5
67.8
77.9
58.9
IntemVL-SSpatal-a
87.8(+25.0)
78.6(+20.9)
33.6(-5.1)
71.3(43.5
83.9++6.0)
71.0(=1.11)
To evaluate model performance in spatial reasoning, we conducted experiments on InternSpatial- Bench. The accuracy computation follows the methodology of Spatial- (Cai al.) (025) and Spatial-RPGT (Cheng et al., 2024). with a modification for the Object Counting task: since some VLMs struggle to follow instructions precisely, we extract the last number mentioned in the response as the predicted count and compute the relative error accordingly.
As shown in Table our model, InternL--Spatiil-88. outperforms the baseline Intern VL2.5- 8B (Chen et al. 2024c) by 12% in average accuracy. Notably, it achieves 25% improvement in the Position Comparison task and a 20.9% gain in the Size Comparison task. Furthermore, Intern VL- Spatial-8B surpasses advanced proprietary models such as GPT-4o (OpenAl 2025) and Claude 3.5 Sonnet (Anthropic) 2024) across all tasks, demonstrating the effectiveness InternSpatial in en- hancing the spatial reasoning capabilities of VLMs.
To demonstrate the generality and broad impact of our InternSpatial, we applied the same training paradigm to two additional models with varying sizes: LL2.511 and Qwen2.5-VL-8B. The results consistently confirm the effectiveness of our training data. Specifically, IntertV--Spatiall improved its Intern V1.2.5-1B baseline by 18.0 18.6% (from 38.2% 56.8% average accuracy), exhibit- ing a significant lift for smaller model. Similarly, the Qwen-Spatial-- 8B bascline by 14.4% (from 53.9% to 68 3% average accuracy). These substantial and consistent gains across different model families and sizes confirm that our proposed InternSpatial is highly effective and transferable for universally enhancing the spatial-aware capability of VLMs.
elevated the Qwen2.5-VI.
4.3 EVALUATION ON VSI-BENCH
To evaluate the additional multi-view spatial reasoning capabilities of Intern VL-Spatial-8D trained on InternSpatial, we conducted experiments on VSI-Bench (Yang et al. 20244). As shown in Table achieves notable improvements over the baseline Intern VLtem VL2.55-B (Chen al. 2024c) across all tasks in the benchmark. In particular, surpasses the baseline by more than 10% in Object Counting, Object Size Estimation, and Appearance Order tasks.
Intern InternV--Sppatill-
When compared against both open-source and proprietary models, SppataL-SSaaiil--8B delivers top-tier performance: it ranks first in Object Counting, Absolute Distance Estimation, Object Size
8

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Published as a conference paper at ICLR 2026
Table 3: Results VSI-Bench. Bold indicates the best performance among all models, while underline denotes the second-best performance.
Model
Obj.Count
Aist
Objasize
Room Size
Rei.
Ronte Plan
ApprOOrder
Average
GPT-4o Opennl
2025
16.2
5.3
43.8
38.2
37,0
31,5
28.5
32,9
Gemini- 1.5 Flasa
RRid
2024
19.8
30.8
53.5
54.4
37.7
31,5
37.8
42.3
Gemini- 1,5 Pro Reid al.
2024
56.2
30,9
64.1
43.6
51.3
36.0
34.6
45.3
VII.A-I.5-0BR
2024
22.4
24.8
48.7
22.7
31,5
3200
LLaVA-NeXT Video 728 Zhang 2024b
48.9
22.8
57.
353
4244
35,0
48.6
41.5
LLaVA-OneViiieo-22
43.5
23.9
57.6
37.5
32,5
40.2
Qwwn25-VL-88
36.6
Qwen Swen-Sputial--5
60.8(+19.3)
35.06+13.8)
53.(+2.7)
45.0++844
40.0(42.1)
36.0(+6.2)
34.5(+0.5)
43.6(+7.6)
IntemV112.518
al. 20246
$1.8
3.9
13.7
22.8
66.4(+14.6)
25.4(21155
42.0-17..3)
(8.5(-24.8)
34.1(4855)
34.0(+1.55
11.04434)
37.3(414.5)
Intem V.. Chen et al.
2024c
51.7
32.9
45.1
42.3
40.8
27.8
50.5
41.6
68.7(+17.0)) 40.9(+..0)
(3.1(-180) 54.3(-1200)
47.7(+6.9)
29.9(+2.1)
60.5(+10.0)
52.3(+10.7)
Estimation and Appearance Order, and second in the remaining tasks. Overall, it achieves the high- average score among all evaluated models, including GPT-4o (OpenAl (025) and Gemini-1.5 Pro (Reid et al., 2024). These results demonstrate that InternSpatial substantially enhances the spa- tial reasoning capabilities of vision-language models in multi-image scenarios.
On VSI-BENCH, our InternSpatial yields consistent gains across architectures and scales. The Intern WL-patt--paial-11B gained a remarkable 14.5% in average accuracy (22.8% - 37.3%), with dramatic improvements in Absolute Distance (+21.5%) and Room Size (+24.8%) Similarly, the Qwen-Spatial-88 model elevated its baseline by a strong 7.6% (36.0% 43.6% (3.6%), including 19.3% gain in Object Counting. These results on a challenging spatial benchmark further show that InternSpatial can improve multi-view spatial reasoning in VL.Ms model-agnostic manner, without any architecture-specifi modifications.
4.4 EFFECT OF THE VARIOUS QUESTION FORMATS
We conduct an ablation study on InternSpatial- Bench evaluate the impact of different instruction formats in both the training step and evaluation step. Since the Rotation Estimation task does not include instruction format expansion, we exclude it from this analysis. Additionally, we train a variant of (M2e5VLL..5881 using InternSpatial- -Bnchh without instruction format expansion, referred to as Intern Va-Saai---aaaa---aaan
As shown in Figure 5 the baseline model, InterVVL2.5-81 (Chen et al. 2024c), performs best original images and natural language instructions, which are prevalent in general-purpose training datasets. However, it performs significantly worse formats involving elements such ;box,, which are rare in typical datasets. In contrast, Intern IntenVL--Spatiil-.., trained on InternSpatial with diverse instruction format expansions, substantially narrows this performance gap across different instruction styles. Furthermore, comparing Intern VL2.5-8B with that even without instruction format expansion, VntemVL-SSpaiill Raw--Ba consistently outperforms the baseline across all instruction styles. This indicates that the model gains a degree of general- ization and cross-format transfer ability, even without being explicitly trained on diverse instruction forms. Finally, Intern ViternVL-Spatia--88 achieves the best performance across all instruction formats, including natural language and original image styles. This demonstrates that instruction format ex- pansion not only improves the model's robustness to diverse input styles but also enhances its overall spatial reasoning capability.
reveals
RP. 51.2
70L
Inage with number
made
757
72.8
TB.6
75
72.4
Accc siicc-iiigg mmlea
-bpo
76.7
242
70.3
71.7
GBC
193
$1.0
G36
80
Figure 5: The results of the different image (Left) and text (Right) formats in the ablation study.

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Published as a conference paper at ICLR 2026
4.5 GENERAL VQA
For fairness, we re-evaluated Intern VI.2.5-8B (Chen al. 2024c) under experimental setup instead of directly using the results reported in its technical report. As shown in Table 4 InternVL- Spatial-8B achieves comparable performance ing benchmarks. Specifically, Intern InternVL-Spatiall
the bascline IntemVL2.5-8B on general reason-
shows performance gain of +1.8% on Math- Vista (Wagg et al. (024b), -0.1% 1% on OCRBench (Liu al. 2024), +0.9% on TextVQA (Singh al.) 2019).- -1.6% on ChartQA (Masry et al., 2022). and +0.2% on MMStar (Chen et 2024b). These results indicate that training with InternSpatial does not compromise the model's general reason- ing capabilities, including mathematical reasoning, optical character recognition, VQA, and chart understanding.
Table 4: General benchmark results for Intern VL2.5-8B vs. Intern LL-Spatial--
Math Wang al. 2024b
OCRBench
TextVOA
ChartQA
MMStar Chen 2024h
Model
Liu al. 2024
Singh al.[2019]
Masry et 2022
InternVI. 21.2.888
19.0
82.3
79.0
83.0
62.9
InternVI SS-pari--t
20.8 20.8(+1.8)
82.2(-0.1)
79.9(+0.9)
81.4(-1.6)
63. (3+1()).2)
CONCLUSIONS
We introduce InternSpatial, the largest open-source spatial reasoning dataset, and the benchmark InternSpatial-Benn which together advance spatial understanding in VLMs through diverse scene coverage, rich instruction formats, and multi-view supervision. InternSpatial provides 12M high- quality QA pairs covering both single-view and multi-view settings, with broad scene diversity and 19 instruction formats that reflect the varied ways users express spatial queries. InternSpatial- Bench complements this with diagnostic single-view benchmark and an extended multi-view evaluation rotation angle prediction, task not addressed prior work. Extensive experiments show that training on InternSpatial yields substantial improvements spatial reasoning benchmarks while maintaining strong performance on general multimodal tasks. Despite its scale and diversity, our template-based generation pipeline may underrepresent the full richness of natural language in real- world scenarios. Future work will explore more expressive QA generation and open-ended spatial reasoning in interactive environments. We anticipate that our dataset will support downstream ap- plications such as robotics, embodied AI, and AR/VR, where spatial understanding is essential.
ACKNOWLFDGEMENTTS
This work was supported by the Shanghai Artificial Intelligence Laboratory, the Shanghai Commit- tee Science and Technology (No. 22YF1461500), and the National Natural Science Foundation of China (No. 62206046).
REPRODUCIBILITY STATEMENT
All results reported in this paper are fully reproducible using the provided resources. The training configurations are detailed in Section 4 and Appendix E while the dataset pipeline is described in Section Appendix and Appendix
ETHICS STATEMENT
Our work does not involve sensitive personal data. All dataset components were collected from open-source and publicly available sources, with careful filtering exclude content that may be discriminatory or infringe copyright. We do not foresee any negative societal impacts arising from methods datasets.
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Published as a conference paper at ICLR 2026
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APPENDIX
A EXPLANATION AND STATISTICS TASKS
InternSpatial and InternSpatial-Benn of each task are shown in Table 5 We also count the number of QAs for each task InternSpatial and AnterSpptiil-Bene// which are shown in Table 6 and Table respectivvely
covers a total of 10 spatial reasoning tasks. The explanations
Table 5: Explanation of tasks
Task
Description
Position Comparison
Compare the position of two objects in an image, involving three pairs of positional relationship:
left/right, above/below, near/far.
Size Comparison
Compare the size of two objects in an image, involving three pairs size relationship: wider/thinner, taller/shorter, larger/smaller.
Existence Estimation
Determine whether there are objects in the image whose positional/size relationships with the specified object meet the constraint conditions.
Object Counting
Estimate how many objects that meet the constraint conditions there in single image or multiple images.
Rotation Estimation
Estimate the rotation angle of an object between two images. Estimate the closest distance between two objects given serial of im- ages.
Absolute Distance
Room Size
Estimate the volume of the room(s) given serial of images.
Object Size
Estimate the longest dimension of object given serial of images. Given a serial of images, choose what action should be performed be- tween a sequence of actions in order to route to from start point to target.
Route Plan
Appearance Order
Given a serial of images, determine the first-time appearance order of several objects.
Table 6: Statistics of tasks in InternSpatial
Task
Related Views
# of QAs
Position Comparison
Single
6,214,628
Size Comparison
Single
3,227, 124
Existence Estimation
Single
50,845
Object Counting
Single/Multiple
53,866
Rotation Estimation
Multiple
2,464,500
Absolute Distance
Multiple
14,596
Room Size
Multiple
1,181
Object Size
Multiple
3,709
Route Plan
Multiple
4,966
Appearance Order
Multiple
8,562
B TEMPLATES FOR GENERATING QAS IN INTERNSPATIAL
The QAs in InternSpatial are generated by template-bas based generation method. Here we provide the full list of templates. The "[...]" in templates are placcholders which will be replaced by object references in different formats, values, choices, and so on. Several candidates are provided to be randomly selected in generation process enrich the structure of sentences.
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Table 7: Statistics of tasks in InternSpatial-Bence
Position Comparison
Size Comparison
Rotation Estimation
Object Counting
Existence
Task
Estimation
# of QAs
1845
1855
409
899
1000
Listing 1: Templates for task Position Comparison
above prevee_reeiit... terplictt_ee_eeese_e_
"question "questicn_templeeee ttwpoetttepaaass::
"IAl is placed higher than [BI, isn't it?", "Can say that [A] is positicned above (BI?",
"Is it correct to assume that [A] is located at higher level than B]?",
"Is [A] placed higher than [B]?"
"positive_answer "taa:aaasssseettmmlaaaasss
"Absolutely, [A] clearly pesitioned above [3].", "Without a doubt, [A] is situated at a higher elevation than [B].", "Inceed, [A] is placed higher than [B].", "Certainly, [A] is located above [B]."
"negative wee_essswe_teeeplttteeeeeeeeeeee
"Not at al1, [A] is actually below [8].", "Definitely not, [A] is positicned lower than [3].", "Sorry, but [A] is not higher than [B].", "infortunately, [A] is not placed above [B]."
below below_prediet_templatte
"questicn_templeate "[A] is placed lower than [B], right?",
"Can we say that [A] is positicned below [B]?", "Is it correct to assume that [A] is situated Lower than [B]?", "Is [A] placed lower than [3]?"
attpitive__awwee_eeplleeeee "Absolutely, [A] is clearly positioned below [B].", "without doubt, [A] is located lower than [B].", "Inceed, [A] is situated beneath [B].",
"Certainly, [A] is found a lower level than [3]
"nogative_answer_ snnser_templatess:
"Not at all, [A] is actually higher than [B].".
"Definitely not, [A] positicned above [B].",
"In fact, [A] situated higher than [B].",
"Quite the opposite, [A] is at higher level than [B]."
pest__preacct_teppee
=
"questicn_tern tet._tttmmass:
"[A] is more to the left of [B], isn't it?",
"Can we say that [A] is positiened more to the left than [B]?",
"Is it correct to assume that [A] is situated to the left of [B]?",
"Is [A] more to the left of [B]?"
},
"positive_answar Lariivee__swer_tteee "nsser_templates"""
"Absclutely, [A] is clearly positioned to the left of [B]." "Without a doubt, [A] more to the left compared to [B].", "Inaeed, [A] is located to the left of [B].",
"Certainly, [A] is on the left side when compared to [B].*
"
"negat iieeivve__ae ve__nsser__aeeeeeeeeeeee_____e__ "nswer_templattes.
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the left of [B] left [3].", "Contrary to that, [A] is not situated to the left of [B].", "In fact, [A] is not on the left side when compared to [B]."
"Not all, is not
"Actually, [A] not positicned
cuvestion_templatee =
"IAl is more to the right of [BI, isn't it?",
"Can say that [A] is positicned further to the right than [B "is it correct to assume that [A] located the right side of 08 ]?",
"Is [A] more to the right of [B]?"
"positive answer templates":
ansswer
"Absolutely, [A] is clearly pesitiened to the right [B].", " "Inaced, [A] is noticeably more to the right compared to [B].", "Without a doubt, [A] is situated further to the right than [B] " "Certainly, [A] is distinctly to the right of [3]."
"negativee weteiiasaee__eeppatttteeeee
"Not at all, [A] is actually to the left of [B].", "Definitely not, [A] is not positiened to the right of [B).", "In fact, |A] is the left side of [B].",
"Contrazy that, |A] is not further to the right than [B]."
herrpprediottttemppteeess
squestion_templates""
"Is [A] positioned in front of [B]?", "Does [A] precede [B] in this arrangement?",
"Is [A] in front of [B]?",
"Is [A] closer to the observer than [B]?"
1,
"positive gee__asser__emplleee
"Without a doubt, [A] stands nearer to the viewer than [8]."] "" "pefinitely, [A] is more proximate to the observer than [8] ", "indeed, [A] is in front of [H].", "Absolutely, [&] before 1B1.",
"negative_anseee answer templates": "Not at all, [A] is not closer to the observer than [B].", "No, [A] is not in front
[B].", "Onfortunately, [A] is not ahead of [B].", "Definitely not, [A] is not closer to the observer than [B]."
far ses_preaict_template
"question_templates"
"Is [A] situated behind [BI?",
"Does [A] 1ie behind [B]?"]
"is [A] to the rear of [B]?",
"Is [A] farther from the observer than [B]?"
1,
"positive_ answer answwrr templates": "Indeed, [A] is behind [B].", "Yes, [A] behind [B].", "Without a doubt, [A] maintains a greater distance from the observer
than [3].",
"Certainly, [A] is positioned further away from the observer than [B 1."
},
"negative_ane Satpateeveanswereeee answer_ templatess
"No, [A] is not behind ., "Incorrect, |A] is not behind |2] ",
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"That's wrong. [A] is not positicned behind [B].", "Unfortunately, [A] is not to the rear of [B]."
prove_chhice__teplatees =
investion__emplatee
"Which one is positioned at a higher elevation, [A] or [B]?", "In terms of altitude, which comes first, [A] or [B]?", "Who stands taller, |AI or [3]?", IBI?"
"Which is placed higher,
l,
"answer_templatess
"[0] is the one that is placed higher.",
"The higher position belongs to [0].",
"[O] occupies the superior location.",
"It is [0] that is situated at a greater height."
pllow_cchiic__tempaateess
pruesticn__emplateet
"Which is positioned closer to the ground, [A] or [B]?",
"Which one is situated at lower elevation, [A] or [B]?",
"Which of these is nearer to the base level, [A] or IBI?",
"Which is placed lower, [A] 1B]?"
"answer_templates": [
"[O] is placed lower.", "The lower position belongs to [o].",
"[0] occupies the lower spot.", "Lower down, you' find [0]."
left_cholce_temmaatess-
"ruestion_templatess"
"Which is pasitioned further to the left, [A] or [B]?",
"In terms of leftward placement, which comes first, [A] or [8]?",
"when considering the ieft side, which one is closer, [A] or [8]2", "which more to the left, [A] [H]?"
"answer templates":
"[0] is located more to the left.",
"The position of [0] is further to the left.",
"In comparison, [0] stands out as being more on the left.",
"It is evident that [0] is situated more towards the left."
Squestion_tempatter
"Which is positioned further to the right, [A] or [B]?",
"In terms of horizontal alignment, which one is more to the right, (A or [8]?",
"when comparing their positicns, which one is situated more
right, [A] or [B]?",
"Which is more to the right, [A] or [B]?"
"answer_teme :emprrtssttppaats":
"[o] is clearly more the right.",
"The position of [0] is further to the right.",
"Comparing the two, [0] is definitively more to the right.",
"It is evident that [0] is pesitioned more to the right.
near wwwccchchhcccccceeeecc =
cnnestion_templattee "Which one is positioned further forward, [A] 13]?",
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"Between and [B], which object is closer to the observer?",
"Can you identify which of the two, [A] [B], in the foremost positicn?",
"of the two, [A] and [B], which is closer to the front?"
"answer_te tnisser_temmlates"""
"[O] is in front.",
"The frontmost object is [0].",
"10] is situated at the foremost position.",
"Among the options, [0] is the that is most ahead."
far_choice_templeeee -
"question_tent templates";
"Which one is further back, [A] [B]?",
"Can you tell me which is positioned more towards the back, [A] or [B l?",
"Between [A] and [B], which is more distant in the rear aspect?", "Comparing [A] and [B], which is more behind?"
1,
"answer_teen snnsser_temllates".
"[O] is definitely more behind.",
"I can confirm that [0] is situated further back.", sehind than the other.", "There no question that [0] is more behind."
"10l is clearly
above__elox__choice_tmmee cempllaasesssssltsssssssssssses
"question_tt ietestiionnemmlattsss"
"Is [A] positioned higher or lower than [B]?",
"Does [A] lie above or beneath [B]?", "Is [A] situated over or under [B]?",
"Is [A] above below [B]?"
1,
bhbor__anseer_tmmplaatss"""" [
"[A] is above [H].",
"[A] is positioned higher than [8].",
"[A] 1ies over [H].",
"[Al is situated above [3]."
1,
"below answer answer_template templates": [
"[A] is below [B].",
[AA] positioned lower than [B].",
[[A] lics under [B].",
"[A] is situated below [B]."
"question_teplattee
"Is [A] relatively farther to the left or right than [B]?",
"Does [A] li the left or right side of [B]?", "is [A] to the left or right of [H]?"
1,
"left answer deswer_templates"
"[A] is to the left of [B].",
"[A] occupies the left side relative [B].",
"[A] lies
left side of [B]."
1,
"right_anee brightt_anseer_tppaatesss
"[A] is to the right of [B].",
[[A] occupies the right side relative to [B].",
[[A] lies on the right side of [B]."
far choice templates
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"question templates":
"Is [A] relatively nearer or farther from the observer than [B]?", "Can you determine if [A] is cleser or farther from the observer compared to [B]?", "Is [A] in front of or behind [B]?"
1,
"[A] is closer to the observer than [B].", "[A] is more proximate to the ebserver than [B].",
"IAl comes before [B].",
"[A] is in front of [K]."
1,
TTar_answer_templttes
"[A] is farther from the observer than IB].",
"[A] is less proximate the observer than [B] .",
[[A] is behind [B]."
Listing Templates for task Size Comparison
wide wet___rrricttteeplsttee =
cnvestion_temlaates
"Is [A] broader than [8]2",
"Moes [A] have a larger width compared to [B]
"Can we say that [A] spans more horizontally than [8]?",
"Is [A] wider than [B]?"
"positive_answer answer tnsser_templates""
"Yes, [A] is noticeably broader than [B].", "Indeed, [A] has a significantly larger width than [B).", "Absolutcly, [A] spans more herizontally than [3].", "Certainly, [A] is wider than [B]." 1,
Sregativeaanswer tnssser_tmpplates":
"No, [A] is not broader than [B].", "In fact, does not have larger width compared [B].", "Sorry, but [A] does not span mere horizontally than (BI.", "Unfortunately, [A] is not wider than [B]."
harrow harro__predictt templates
"question_teri templates";
"Is [A] thinner than [B]?",
"Does [A] have smaller width compared to [B]?",
"Is the width of [A] less than that of [B]?", "Is [A] narrower than [B]?"
},
"Yes, [A] is noticeably narrewer than [B].", "Inceed, has signiticantly smaller width than [B].", "Absolutely, the width of IAI less than that [BI.", "Certainly, [A] is thinner than [B]."
},
"No, [A] is not narrower than [B]; in fact, it's wider.", "Definitely not; [A] has a larger width than [3].", "Not at all; the width of [A] exceeds that of [3].",
"No way; [A] is thicker than [B]."
tall call_prediet_temppates = iemtttittteee
"[A] is taller than [B], isn't it?",
"Can say that [A] surpasses [R] in height?",
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"Is it to that [A] is taller than [B]?", "Is [A] taller than [B]?"
1, atwpiv___nnswr_tempaatoo
"Absolutely, [A] towers over [B].",
"Without doubt, [A] significantly taller than [B].",
"Indeed, [A] outshines [B] in terms of height.",
"Inquesticnably: [A] is taller than [B]."
dreggtivv_answart templates::
"Not all, [8] is actually taller than [A].". "sorry, but [A] does not exceed [8] in height.", "In fact, [B] surpasses [A] in height.".
"Regrettably, [A] falls short when compared to [3]'s height."
pebbrtppprcddcc_ccmmlatttt =
"question_templateee
"Is [A] shorter than [B] in vertical direction?", "Does [A] have less height than [B]?",
"Is the vertical length of [A] smaller than that of [B]?", "Is [A] shorter than [8] in vertical direction?"
"positive answer templates":
"Yes, |AI indeed shorter than [BI the vertical direction.", "Absolutely, [A] has less height compared to [3].", "Certainly, the vertical length of [A] is smaller than that of [BI.", "without doubt, [A] is shorter than [B] vertically."
"negative_ answan nnsser_template""
"No, [A] is not shorter than [B] in the vertical direction.", "Definitely not, [A] does not have less height than [B].",
"Not at all, the vertical length of [A] is not smaller than that of B).",
"Certainly not, [A] is not shorter than [B] vertically."
"question_teri ten_tttepaass
"[A] is larger than [BI, isn't it?",
"Can we say that [A] has bigger size compared to [B]?",
"Is it correct to assume that [A] surpasses [B] in size?"
"Is [A] larger than [B]?"
1,
"positive_answar satpsr___ssssettttpaaaast""""" ttwppatess::
"Absolutely, [A] is noticeably larger than [B].",
"Without doubt, [A] outsizes [B] significantly."" "Inceed, (A] clearly more expansive than [B].", "Definitely, [A] dwarfs [B] in terms of size."
"
"Not all, [8] is actually larger than [A].",
"Quite the opposite, [B] surpasses [A] in size.",
"In fact, [B] is the larger one when compared to [A].". "Sorry, but [B] is bigger than [A]."
maall_preddet_ttmplatee =
[[A] is smaller than [B], isn't it?",
"Can say that [A] is smaller than [B]?", "Is it true that [A] smaller than [B]?",
"Is [A] smaller than [B]?"
"positive templates":
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"Absolutely, is noticeably smaller than [B].", "Yes, [A] indeed smaller than [B].", "Without a doubt, [A] is smaller than [B].",
"Definitely, [A] is smaller than [B]."
"negative_answe
templates": teepee_ssssrrrreelaasas
"Not at all, [A] is actually larger than [B].", "No, [A] is not smaller than [B].", "Quite the opposite, |A] is bigger than [B].", "False, |AI is not smaller than [BI."
w wide_choiic_ttepplttt
"question_t "question_templates: templates";
"Which has greater width, [A] [B]?",
"In terms width, which one larger, [A] or [3]?",
"When comparing widths, which one comes out on top, [A] or [B]?", "Which is wider, [A] or [B]?"
"answer_templates""
"[O] is wider.",
"The width of [0] is greater.",
"Comparing the two, [0] has the larger width.",
"In terms width, [0] surpasses the other."
crrro___chhce_templaae -
"question_temmlates""
"Which has smaller width, [A] [B]?", "In terms width, which one less, [A] or [3]?", "When comparing widths, which one comes out smaller, [A] or [B]?",
"Which is narrower, [A] or [B]?"
"answer_temp tempswertemmlatee".
"[O] is the narrower one.",
"The narrower object is [0].",
"[O] has the lesser width.",
"Comparing the two, [0] is clearly narrower."
call__choice
htt.__hhhic_eeeplltteeeeee templates
pquestion_templatess"
"Which has greater height, [A] or [B]?",
"In terms of height, which one is superior, [A] or [B]?",
"When comparing heights, which comes out on top, [A] or [B]?",
"Which is taller, [A] or [B]?"
},
Sanswer_templaters
"[0] is the taller one.",
"The height of [0] surpasses the other.".
"[o] stands out as the taller between the two.", "Comparatively speaking, [0] taller."
vshort ceroorrt_hhhiie_eeeeeeeee anteice_templatee =
"question templates":
"Which has shorter vertical length, [A] or [3]?", "In terms of vertical measurement, which one is shorter, [A] or [B]?"
"When comparing the vertical dimensions, which is shorter, [A] [B
]?",
"Which is shorter vertical direction, [A] or [B]?"
henswer_templater
"I0| is shorter in the vertical direction.".
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"The vertical length of [0] is less than the other.", "Comparing vertically, [0] comes out shorter.",
"In terms of height, [0] is the shorter one.
"questien ieteetttenttemelattes:
"Which has a greater size, [A] or [B]?",
"In terms of size, which one is bigger, [A] or [3]?", "When comparing sizes, which comes out top, [A] [B]?", "which is larger, [A] or [8]?"
1,
lanswer_templaees
[
"[o] is the larger one.",
"The bigger size belongs [0].",
"[0] surpasses the other size.",
"Comparatively speaking, [0] is the larger."
sma teechhhic__templlateee {
"Which has smaller size, [A] or [B]?",
"In terms of size, which one is smaller, [A] or 131?", "When comparing sizes, which comes out smaller, [A] [B]?", "Which smaller, |A] or IBI?"
1,
ainswr__eepplseee
[
"[o] is the smaller one.",
"The smaller of the two is [0].",
"[O] has the smaller size.",
"Comparing the two, [0] is the smaller."
wide goe__arrro__hhhie__emmppp.
"Is [A] relatively wider or narrower than [8]?",
"How does the width of [A] cemaare to [B]?",
"Can you tell if [A] has greater or lesser width than [8]?",
"Is [A] wider or narrower than IBI?"
1
"wide answer templates": "[A] is wider than [B].",
"The width of [A] exceeds that of [B].",
"[A] has a larger width compared to [B]
"In terms of width, [A] surpasses [B]."
"narrow
"narrow_answer__e answer_templateer
[
"[A] is narrower than [B].", "The width of [B] is greater than that of [A].",
"[A] has a smaller width compared to [B].",
"In terms of width, [B] surpasses [A]."
soll_ssortt_chiiee chaeeeeeeccceeeettteeeeese =
"question_f tetsstiionttmllatts::
"Is [A] relatively taller shorter than [B]?",
"How does the height [A] compare [B]?", "Can you determino if [A] is taller or shorter than [B]?",
"Is [A] taller or shorter than [B]?"
"tall gaal__answer_eemllttee
[[A] is taller than [B].",
"The height of [A] exceeds that of [B].",
"IAl surpasses [B] in height.". ",
"Compared to [BI, [A] is definitely t
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"short_aasser__ceppltts""
"[A] is shorter than [B]
"In terms of height, [A] falls bolow [B].",
"[B] is taller than [A].",
"[A]'s height is less than that of [B]."
large_ httpaall_chccceeeeeeeppee
=
jnvestioo_templates.
"Is [A] relatively larger smaller than [8]?",
"How does the size of [A] compare to [B]?",
"Can you determine if [A] is bigger or smaller than [B]?",
"Is [A] larger or smaller than [B]?"
"large answer www.aeeeteewaateesssss... templates":
[[A] is larger than [B].",
"The size of [A] exceeds that of [B].",
"[A] surpasses [B] in sizc.",
"Compared [8], [A] bigger."
1,
"small_aa _a__aaae_eeeplltteeee
[
"IAl is smaller than [B].",
"The size [A] is less than that of [B].", "IBI is larger than IAI.",
"In comparison to [B], [A] is smaller."
Listing 3:
Templates for task Existence Estimation
"Does [B] exist to the left of [A]?",
"Is there [B] to the left of [A]?", "Is there [B] more to the left than [&]?"
"Yes."
}, "nggtive__anserrreemplaeeee
"No." "
selistenc_right_tene
"qucstiio__tomplatee":
"Is there [B] positioned more the right than [A]?",
"Does [B] exist to the right [A]?",
"Is there [B] locating to the rightside of [A]?"
ppositive_answer. tttpaaaaeeettemeatsss
"Yes."
"No."
existence aebeeeecee_bbeeee__eeeeee snooe_templater
"Does [B] exist at higher elevation than [A]?",
"Can you find [B] above [A]?", "Is there [B] that is located above [A]?"
"Yes." "
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"No."
crissteneebbelooe seteeeepabblottteellateeeee
gnuestion_temllttss!"
"Is there [B] that is situated below [A]?", "Does [B] exist below IA]?",
"Is there IBI positioned lower than ?
1, "positive_ans" her.titv___ssse___mmlatstssss
"Yes."
"negative_ snnervee__nsser__emllteeesee "No."
"question_templlttes
"Does [B] exist near [A]?",
"Is there [B] that is in frent of IA]?", "Can you find that is closer than observer than [A]?" ?"
"Yes."
),
"negative_ http:ve__anvveeeeeppttttteteteee
"No."
Seistence__ar__temllattt
gquestion_templttes
"Does [B] exist far from [A]?", "Is there [8] that is behind [A]?",
"Does [8] exist behind [A]?"
1, "positive antpeiee_esweeeeeeepeeeeeeeeee "Yes."
hhegative_ansve_reemge "No." "
"Can you find [B] that is wider than [A]?",
"Is there [B] that is wider than [A]?",
"Is there [B] that has larger extent in horizontal than [A]
}, v iransttrre_sssser_taaaltttttt "Yes."
Seagative_nswer. :
"No." "
exisstncee httpancs___rrrottteeaeeeeeee
"guesticon_enmlltee"""
"Is there [B] that is narrower than [A]?", "Can you find [B] that is narrower than [A]?",
"Does [B] with smaller width than [A] exist?"
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"positive aanwwr__temlateeeeeeeeeeeseeeeee "Yes."
1,
"No."
teasstenne_tall_tengee
=
"Is there IBI that is taller than |A]?", "Can you find [8] that has larger height than [A]?"
"Is there [B] that is larger in vertical than [A]?"
},
iecpttiv__aswee_eeee ataprtve__ssseerttteaaaattssss
"Yes."
1, hoggttww__mmwrrr_mmalttttt""
"No."
cnvestion_templateee
"Is there [B] that is shorter than [A] in vertical?", "Does shorter than [A] exists?",
"Is there [BI that has a smaller height than [A] ?"
1,
"Yes." "
"negative_ httpa_awwweeeeeepaeeeeeeee "No."
Salisence__arge_temmn
ceuestion_templaate
"Can you find [8] that is larger than [A]?", "is there [8] that is larger than [A]?", "Does [8] exist that has larger volume than [A]?"
1,
"positive "positive_answer__ee Ballee__aswwer_eeee "Yes."
},
"No."
"quettion_templatess"
"Is there [B] that is smaller in size than [A]?",
"Does [B] with smaller size than [A] exist?", "Does [B] exist that is smaller than [A]?"
},
"Yes."
"No." "
Listing 4: Templates for task Object Counting
count_ wwweeeeteppleeeeeeeeeeeeeeeeeeee =
iquestion_templle :
"How many [R] located hi higher than [A]?",
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"How many [B] positicned higher than [A] ?",
"How many [B] are above [A]?"
}, "answer_ wtwpswerttmpaattss""
[[V]"
bouut__belo_ttemplaee =
"How many [B] lower than [A]?", "How many [8] are situated below [A]?",
"How many [B] are positicned lower than [A]?"
"anser templates"
[V]." "
count_left_templattee =
"How many [B] positicned the left of [A]?",
"How many [B] are more te the left than [A]?",
"How many [B] are on the leftside of [A]?"
"answer templates";
"VV."
count_rrigh_ttemplaess
"question_tent tempiiiteteeee.sss
"How many [B] are found to the right of [A] ?",
"How many [B] lie to the rightside of [A] ?"]",
"How many [B] are more to the right than [A]?"
ansser templates" [V]."
count_near_templasse
"How many [B] are closer to the observer than [A]?",
"How many [B] are in frent of [A]?", "How many [B] are located nearer to the observer than [A]?"
1, "answer_ tempatr_te [V].
cout__far_templates
"How many [B] are positicned farther from the observer than A ?"
"How many [B] are located behind [A]?"
"How many [B] are farther from the observer than [A]?"
1,
"answer templates"
"[]]"
count_widd_ttmpplate =
equestion_templateeet
"How many [B] have larger width compared [A]?",
"How many [B] are wider than [A]?",
"How many [B] have a larger extent in horizental than [A]?"
"answer templates":
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[V]."
count narut__aarrooctmppaaesss
"quesstion_templaats":
"Hew many [B] narrower than [A]?", "How many [B] have a less width than that of [A]?",
"How many [B] are thinner than [
Manswer templates":
"[V]." "
count taal tal___all_____eee teaplltteeltttss__ =
pruestion_templatess".
"How many [B] are taller than [A]?",
"How many [B] surpass [A] in height?",
"How many [B] have larger extent in vertical than [A]?"
"answer_tempe tempersstm [[]]"
count vshort pathorrttemplatteeee
"How many [B] have less height than [A]?",
"How many [B] are shorter than [A]?",
"How many [B] have smaller vertical length than that of [A]?"
ansser "amswer_templates" templates"
"[V]"
count Laant___arr___peplllee
"question_templatess
"How many [8] are larger than [A]?", "How many [8] have bigger size compared to [A]?",
"How many [8] surpass [A] size?"
ansser templates":
"VV."
count_small_tenplee =
[
"How many [B] have smaller size compared to [A]?"
"Hew many [B] smaller than [A]?",
"How many [B] are smaller volume than [A] ?"
"answe templates";
"[v]."
Listing 5: Templates for multi-view tasks
"question :ot_otttttttss::
"Here are two images of the same object: AnImage 1: I:Ansinnage>/nImae "incimage>\nPleasee estimate how [A] in image is rotated relative to image 1?",
"Here are two images of the same object: AnImage 1: L:No<inage>AAnIage 2: 2:\a<inage>\nIo what direction and by what angle has (A] in image 2
been retated from its position in image 12"
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"elockwise answer templates":
[[A] rotates about [D] degrees clockwise.",
"[A] turns clockwise by about [D] degrees.",
[[A] undergees approximately [D] degree clockwise rotation."
"counterclockwise wttprrsss_aase_eeeeeeeeeee sesseer_tmplates":
[A]] retates abount [D] degrees counterclockwise.""
"[A] turns counterclockwise by about [D] degrees.",
"[A] undergoes approximately [D] degree counterciockvise rotation."
" Btae___0___ssee__emgllll
"[A] rotates about [D] degrees.",
"[A] turns by about [D] degrees.".
[[A] undergoes approximately [D] degree rotation."
joute_plan_tcmmlattes =
"gucsst.on_tcmpaattss::
"Image-1: <image>\nImagee-...>>...a <image>\nImagee-> <image>\oImage-4:
>mage>\nlmage-1 <image>\nImage- <image>\nImage-77 <image>\nImage
8: <image>\nnmmge-- <image>\nnmage--10
<image>\nImage-111 <image>
n nImage-12: <image>>nnnnge-113 <image>>>nnmageeeeeeeeeee>>>>>>
<
image>lnnnage--l <inage>\aInage-11 <image>\nInage--e <image>>
nImage-19: <image>\minnge--20, <image>>aImage-2:: <image>\nInage- image>\nInage-23: <image>\aInage-24: <image>\nYor are robst beginning at the column and facing the staircase. You want to navigate to the grand staircase. You will perform the following actions (Note: for each (please fill in], choose either 'turn back,' 'turn left,' please fill in]. 3. Go forward until the steps. 4. Stop on the landing. And. Turn Right\n2, Turn LeftInC. Turn Back with the option's letter from the given choices directly."
'turn right.'): 1. forward until the columns. 2. [
"answer_ tesplatsemp,ates"; "[0]"
abs_diist_templatee
templates";
cimage>\nImager (annag>>nIImgeenn image>loInage-5: <image>\aImage-6
"Image-1:
(nmage>\IImage-7 <image>\n <image> \nImage-10:
<image>> nMeasuring from the closest point of each object, what is the distance between [A] and [3] (in meters) 7AnPlcase answer the question using a single word or phrase."
-8: Malmag>>\nImagee
"answer_templates""
"[V]"
ob Loj_count_templates
"questtion_empplttes"""
"Image-1: <image>\nImage- Limage>\nImage-11 <image>\nImag <image>\nInage-11 <image>>
Limage>>limaage3: <image>\nnmage--
image>\nInage-
-8: <image>\nImage-
nThese frames of video. AnHow many [A] (s) in this room?)
nPlcase answer question using a single word or phrase."
},
answer__emplates""
"[V]"
www.esiiieeeeeeeeeeeeeeeeeeeeeee =
"question temppttesss"""
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"Image-1: (anagee>nImage-2: Samaag>>nImage-- <image> \nImage-4:
image>\nIneg AnImage-:: (aigge>>iImage::: <image>\nInen Saaage>\nImmge72: L.mage>\nImage <image> <image>\nImage-> \nImage-10:
8: nThese are frames of a video. AnWhat is the si2c of this room (in square meters) AnIf multiple rooms are shown, estimate the size of the combined space. AnPlease or phrase."
<image>>
the question using single word
|,
"answer "neser_templates). "IVI"
rel rel__iist
"question_tempee templates":
"Image-1: <image>\nImage22: <image>\nImagee- <image> \nImage-4:
Image>>nInage-S: <image>\nImage--6
(aaaage>Annmag-77.. <image> VnImge
-8: <image>\nImase
<image> <image>\nImageen \nImage-10: <image> <image>\nThese \nThose are frames of video. \nMeasuring from the closest point of each object, which of
these objects ([BO), [B1l, (32), (B3]) is the closest to [A]A [BO] \ nB. [B1] \nC. [B2] \nD. InAnswer with the option's letter from the given choices directly.
},
snsser_templates""
"101"
abject_size_templatte
"guesstion_templates":
"Image-1: <image>\nImage-22 <image>\nImage- <image> \nImage-4: image>\nInage-23 <image>\IImage-6G <image>\nInage-1]
<image>\nInage <image>>
<image>>nn \nmage>\nIImae-1:: <image>\nInagee nThese are frames of a video. AnWhat is the length of the longest dimension (length, width, height) of [A], measured in centimeters Peaslease answer the question using a single word or phrase."
-8: <image>\nImmger>
1,
"answer_temp mnswer_templates". "[v]"
appear order snddr_templates =
"Image-1: <image>\nimage-- <image>\nImagee
-8: <image>\nImage <image>\nImagen
Lmage>>\nmage--
<image>\nImage-7 Jimago>\nIman
<image> <image>\oImage-- \nImage-1C: <image>\nInage--1 <image>>
TThese are frames of a video. \nWhat will be the first-time appearance
order of the following categories in the video: [A0], [A1], [A2],
A3)?\nA. [B0].nR. [nc] [82] [B3] (nAnswer with the option's letter from. the given choices directly."
}.
"answer "esseer_emplates"" "[O]"
C VLM-ASSISTED ANNOTATION FOR INTERNSPATIAL
As described in Dataset section, we involved open-source VLM to do the object detection, caption- ing, and grounding in the pipeline of InternSpatial generation. We QWen2.5-VL. 72B al.) 2025) as the assistant. For each process, we design corresponding prompt to make the VL.M under- stand what should do and what should output. Here we provide the prompts for these processes.
Listing 6: Prompts for detecting objects in images
sages
[("role": "system", "content"" faom
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You object detestor. Given image, you should find all objects image with grounding. The term "object" includes non-living things. For each detected object, you should assign a label, which represents what the object is. You should also describe each detected object in detail with phrase. The description can contain appearance, function, action, etc.
living and
Output format:
The response should be in json format, which contains a list of dicts. Each diet is for an detected object and has three keys "label" for the labe-, "caption" for the description and "box" for the grounding.
description should be lowercases and no period the end. The grounding should be a list of four ints [xl, yl, x2, y2
l, where (xl, yl) is the top-left coord and (x2, y2) is the bottom- right coord. Compact the zesponsed jsor in one line.
"""]]
apppndd
"user", "content": 'nn' join (query) })
Listing Prompts for captioning objects given bounding boxes
messages - [["role": "system", "centent": f"**
You language assistant. You will be given array dict. Bach dict contains a field "box" for grounding box and optional field label" for label of object in image. Your task is to generate brief descriptions with less than ten words for these objects. Output one description per line.
Here an example:
Input:
[("oo" [10, 20, 300, 400], "label": "bus"), ("box": [42, 512, 64, 8
Output:
blue bus seat with suitcase partially resting it
red car on right side
"""]]
append ((roole: "user", "content"; 'An . join (query) ))
Listing 8: Prompts for grounding objects given captions
messages = [["role"; "system", "content": ="*=
You professional image annctator.
will give you an image and phrase about one or more objects in the image. Please detect all objects matching the phrase. The response should be a JSON object, containing a field "boxes". "boxes" is list of grounding boxes [xl, yl, x2, y2].
Example Output: {
"boxes": [1192, 29, 321, 49), (19, 65, 392, 569], [59, 102, 439, 139]]
]]
messages messages.append ( ("role": "user", "content": Ain .joi (query) ))
D MORE DETAILS ABOUT INTERNSPATIAL-BENT GENERATION PIPELINE
The generation pipeline of InternSpatial-Be -Benhhhh-Bhhhhhe does not rely on existing annotations. Starting from the images, we carried out four steps including image filtering, image captioning, question design, and object grounding to obtain the necessary 2D annotations for the questions of the benchmark and the generation of answers. In this steps, design prompts respectively to enable the Visual Lan- guage Model (VLM) to automatically generate intermediate results. These prompts are presented in Listing 9
Listing 10 Listing 11 Listing 12 Listing 13 and Listing 8 Subsequently, reused
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the processes in the second and third stages of the training dataset pipeline generate answers and expand the instruction formats. After generated the QA pairs, we invited experienced human annotators to conduct manual verification of all the pairs to ensure the quality of the benchmark.
Listing 9: Prompts for filtering images
messages = [["role": "system", "content": f"""
You are helpful visual assistant. Please determine whether the fol following conditions are met:
5-or-more-objects: There are least 5 objects the image.
2. cartcon: This is cartoon image.
3. group-of-images: This is group of images.
4. screenshot: This is screenshet,
realistic-image: This is realistic image captured by camera.
For cach condition, answer true of falso. Response in JSON dict with five fields: "5-or-moro-ojjette,, "cartoon", "group-of-images screenshot", "realistic-image
"""]]
messages append append ((rrol": "user", "content": Ann join (query) ))
Listing 10: Prompts for captioning images
messages = [("role": "system", "content": f""*
You are helpful visual assistant. Please describe the image detail possible. Then detect all top-level objects and return their detailed deseriptions (top-level means it's not a part of another object).
"""]]
messages append ((rrole": "user", "content": A'n join (query) })
Listing 11: Prompts for design questions of task Position Comparison
messages [("role": "system", "content": f"**
will give you an image and a description about the image. You should design 2 questions regarding position judgments around top-level objects in the image (top-level means it's s not part of another object). The question should involve two object (anchor, target) type of relationship:
*The anchor and target object* should be randomly chosen from the top -level objects. You possibly need to add the attributions about appearance, behavior, posture, position in the image, etc. to the description the anchor and target objects so that they can be distinguished from others.
+The relationships should be randomly selected from: more to the left more to the right, closer (to the observer), farther (from the observer), higher, lower.
Only design questions about top-level objects. Ignore those not top-level objects list. Ignore environment objects such as water, sky grass, cloud, etc.
After that, generate more questions based on the designed questions by choose another relationship and keep other parts unchanged.
Flease respond in JSON format. All content should be in English. Heer an example of output:
"questions":
"question": "Is the wooden chair positioned higher than the blue table?",
"anchor"; "blue table", "wooden chair" "relations "relationship": "higher", "task"; "position"
"target";
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"question"; "Does the red bicycle locate more to the left than the man in a floral shirt?", "anchor": "man in floral shirt", "target": "red bicycle", "relationship"; "more left", "task": "position"
modi fiedd_gessiioos":
"question"; "Is the wooden chair farther from the observer than the blue table?",
"anchor": "blue table", "target": "wooden chair", "rclationship"; "farther", "task"; "position"
"question": "Is the red bicycle located at a lower elevation than the man in floral shirt?", "anchor"; "man in floral shirt", "target": "red bicycle", "relatienship"; "lower", "task": "position"
]
"""]]
dessages.appentt (("rooe""""eee".) ("role": "user", "content": Ann" join (query) }
Listing 12: Prompts for design questions of task Size Comparison
messages = [("role": "system", "centent": f"**
I will give you image and a description about the image. You should design 2 questions regarding size judgments around top-level objects tae image (top-leve_ means it's not part another object), The question should invol two object (anchor, target) and a type of relationship:
The anchor and target object* should be randomly chosen from the top -level objects. You possibly need to add the attributions about
appearance, behavior, posture, position in the image, etc. to the description of the anchor and target objects so that they can be distinguished from others.
+he relationships should be randomly selected from: larger, smaller, taller, shorter, wider, harrower.
Only design questions about tep-level objects. Ignore those not in top-level objects list. Ignore anvment objects such as water, sky grass, cloud, etc.
After that, generate 2 more questions based on the designed questions by choose another relationship and keep other parts unchanged.
Flease respond in JSON format. content should in English. Here is an example of output:
"questicns": [
"question"; "Is the green vase taller than the brown table?", "anchor"; "brown table", "target"; "green vase", "relations :nelattioshiip; "taller", "task": "size"
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"question"; "Is the plate with food on it narrower than the white box in the middle?",
"anchor": "white box in the middle", "target": "plate with feed en it", "relationship": "narrower", "task": "size"
modi questionns::
"question": "Is the green vase smaller than the brown table?", "anchor": "brown table", "target":
"green vase", "relationship": "smaller", "task"; "size"
"question": "Is the plate with food it larger than the white box the middle?",
"anchor"; "white box in the middle" "target": "plate with food en it", "relationship": "larger", "task": "size"
]
["*]]
.essagess.appedd ( ((rooee; "user", "content"; 'An join (query) }
Listing 13: Prompts for design questions of task Existence Estimation and Object Counting
messages ["rolee: "system", "ccntent"" f"**
I will give you an image and a description about the image. You should design 2 questions regarding existence judgments and 2 questions regarding counting around top-level objects in the image (top-level means it's s not a part of another object). The conditions in the question need to invol an anchor object and a type of relationship:
*The anchor object* should be randomly chosen from the top-level objects. If multiple objects in the image object, you need to add the attributions about appearance, behavior, posture, position in the image, etc. to the description of anchor object so that it can be distinguished from others.
similar anchor
The relationship+ should randemly selected from:
to the left,
more to the right, closer (to the observer), farther (to observer }, higher, lower, larger, smaller, taller, shorter, wider, narrower. Only design questions about top-level objects. Ignore those not in top-level objects list. Ignore envi anvironment objects such as water, sky grass, cloud, etc.
After that, generate more questions based on the designed questions by choose another type of relationship and keep other parts unchanged
Flease respond in JSON format. All content should be in English. Here is an example of output:
"questions":
"question"; "Are there chairs wider than the blue table?", "anchor"; "blue table",
"target"; "chair", "relationsl" innthipp" "wider",
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"task": "existence"
"question"; "Is there bicycle located more to the left than the man in floral shirt?", "anchor": "man in floral shirt", "target": "bicycle", "relationship": "more left", "task": "existence"
"question"; "How many green vases are positioned higher than the mi middle wooden table?",
"anchor"; "middle wooden table",
"target": "green vase", "relationship"; "higher", "task"; "count"
"question": "How many plates larger than the white box in the mi middle?",
"anchor"; "white box in the middle" "target": "plate",
"relationship"; "larger", "task": "count"
},
"modifiedd fiddsppioot
"question": "Are there chairs which have lower elevation than the blue table?",
"anchor"; "bluc table", "target"; "chair", "relationship"; "lower", "task": "existence"
"question" "Is there bicycle closer to the observer than the man a floral shirt?",
"anchor": "man in floral shirt", "target": "bicycle", "relationship": "eloser", "task"; "existence"
"question": "How many green vases are wider than the middle wooden table?",
"anchor"; "middle wooden table", "target": "green vase", "relationship"; "wider", "task": "count"
"question": "How many plates are shorter than the white box in the middle?",
"anchor"; "white in the middle", "target"; "plate", "rclationship": "shorter", "task": "count"
hessages.appeen "role": "user", "centent"; 'n'.jjod (query) 1)
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E TRAINING DETAILS
We train Intern VL-Spatia--8 using LoRA Hu et al. 2022) with approximately 291K general train- ing samples from InterVVL2.5 (Chen al., 2024c) and 2M samples from InternSpatial, counted with repetition. The training is conducted on 16 A GPUs for approximately 14 hours. We report the models and training hyperparameters of Intern VL-Spatial-88 in Table
Table Training settings and hyperparameters for Intern InternVI--Sattil--B models. Key config- urations for Intern VL--VLL-Sal Spattial-8B, including model architectures and training parameters.
Intern L-Spatial-88
Model
ViT
Intern ViT-300M
LLM
Internlm2 5-7b-chat
Tile Resolution
448
Lora Rank
16
Training Hy Hyperaaametees
Packed Batch Size
64
Optimizer
Adam W
Learning Rate
2.00E-05
Warmup Ratio
0.03
LR Scheduler
Cosine
Weight Decay
0.05
ViT Drop Path
0.1
Image Tile Threshold
40
Context Length
12.8K
Epochs
1
F VISUALIZATION OF INTERNSPATIAL
Inage Sourcs 00C3
Compationn
Format:
Frations
satrodann
deeess Takral
Devertio::
400 C27 5. Sprooon eee
MECE #11.t
Reaare IXCO
Instrctiio Formet
Mnstictionn Farmacc
ona Rock
Arlaver:
the putti mane.
snar
Amocing piee
cie 2118 regh....
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Tral Syrir
Grnserr rndrrcconn roam 3070, S8E
to hukking Rowr125 Ea om
(nlroa) Ta Seicc daubly
drooaate siee cur
cahiia
Souata A3-12
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(1h00 ta proaer V/d the lited vis decie
Eod Divetry onange bop?
glo sualtty urange
Bautee
Instrurtie Formet
Taxk ryper Exteeres Exieadon
Dayon ruce
Boneeeo 2006, stao,
Als 24/54 dlear dasee
Image
Thak opa Colert Co
estrccion Farmar:
Orgin Imaps Natura
Devetion:
Now aa howe seall
Areve:
Image Source Olycapes
Prrrrctio Fornst
mage 10100 Assordii image
Bureed
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G VISUALIZATION OF RESULTS ON PAANNNSSAPIALBBBNNC
Question: simage>
chot-the woman red costeirat> postiionad
<nat>the signshafs? Answer the
van choioes directl. (A) To the right side crefxthe sign-irat> (B) tront of tref-thee stgf- (C) Behind <raf-the aign-tret> (D) To the left side of Ktebrhh signdraf
GT.A
IntemVL-98 Prod: (C) Bohind the aign
InteM--Spaaia--BB Pred: (A) To the right side of the sign
Tesk: positioral
opllins crbbri brsecimb??
InterV 68 Prent No
Guestoon: cimagas coereggenchett bho.eetttttoooiiii.. 200 4720vibocs postored bether hom the
Ineemtt AB Predt No
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Gueaoon cinage cretasa reus jor the rige sate roadesrD stocnar than cameral willi
GT Yeo
Internii Spete d5 Poet Yex
Tosk
<image> Which abowe, <ref-region hhapsioor/he---cc--22000 179. 453, 565j<looxc <ref-rg sboee 0, 506, 516]<-baso-?
GT. Posto higher ls <retrregon (20- 0, 506, 516)</box>
InjernVL-88 Pred. region[210, 179, 453, 566] is above region[209, 0. 506, 515).
ImenVV-SSpetal-3B Prad:: regior[209, 506, 516) the one that above.
choice
Question: <image> picture with purple below with mask?
Actually, the picture with the purple mask above the pillow with pink mask.
IntemVLL-85 Pred: Yes. the pi picuure with the purale mask la below the pillow with the pink mask.
InterVLL-Spatall Pred: No, the picture with the purple mask not situated beneath the p pllow with the pink mask.
Question: <image> Does the counter with a cirdled 'I' drewn on il come behind the picture with circled '2' drawn on It?
GT. No, it is not.
InterVLL 85 Predi Yee, the counter circled "1" behindi the picture with ciroled 2.
MilemVL-Sppliiee
counter ciroled' picture circle:
Task: behind_predicate
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or the chalf with the mask?
Does the mask with the yellow mask come in
No, it not.
Inter 88 Pred: Yes, the books with the yelow mask are placed The Blue mask.
the
Spetial-BB s6 Pred: No, the books with the yellow mask not maakooks frent the Chair with the blue maak.
front_ prnt_ppee
Quastien: <images Which more la the laft, the coffee maker in the red box or the box in the yellow box?
GT. From the more on the left side.
aerspertive, the box yallow box appears
IntemVL-80 Fred: The coffee maker in the red box is more to the left Ihan the box in the yellow box.
DderiV-SSpaialle Pred: the box in the yellow box posiiooned
Tesk:
drawn on [Ls positioned the right of the pillow with circled '2.
Question: <imagez Can you confirm
crawn on it not to the right of the GT No, the pillow th circled lt.
IntemVLL -86 Pred: Yes. the pillow with cirdeed '1' positioned to the right the books with airded '2.
MitemVL-Spetial--- Pred:
circee drawn right the books with dircled drawn it, actually.
Task: rwwgtttggheeeeee preedicatee
Question: <image> Does toaster with circled drawn laggr compared the counter with circled drawn it?
with circied drawn on not larger than the with airded drawnn
incorrett
IntervVL-888 88 Prod: Yes. the toaster with circled '1' drawn on is largoi in size compared to the counter with circled '2' drawn on
Intervii L Lo-tttall-a Pred: according Wit a drreee, drawn on it not largef than the counter dirlled
information thes
big predicale
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Queeion: simagee Ooes the berrier the suple oax it wree greader haight compared the
purple bax may whorler than truck the park boc. InterViL-BB Fred: Yee the banter in the purple box appeare have greater height compened pirk
Interniy Sparial Prect Sonry, but the bonner the purple brx does exceed the truck
predioate
Question: <image> Can you confimm the painting the purple box thinner then the potted plent in blue box?
GT. In fact, the painting in the purple box might be wider than the potter plant in the blue box.
InterniV Pred: Yes, the painting the purple thinner than the potted plant the blue box.
LnternV--Sppttlle Pred: the painting the purple box is narrower than the potted plant the blue
Tasic thin_predicate
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Published as a conference paper at ICLR 2026
H THE USE OF LARGE LANGUAGE MODELS (LLMS)
We used large language models (LLMs) as assistive tools during the preparation of this work. Specif- ically, LLMs were employed for language polishing, LaTeX code editing, and debugging of prompts in the dataset construction process. The authors take full responsibility for the content of the paper.
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Reviews:
[{"review_id": "G26d84Ijzf", "reviewer": "Reviewer_9QLW", "summary": "This paper presents InternSpatial, a large-scale open-source dataset (12M QA pairs) designed to improve spatial reasoning in Vision-Language Models (VLMs). It addresses key limitations in prior works, such as limited scene diversity, narrow instruction formats, and lack of multi-view supervision, by aggregating data from a wide range of sources (COCO, Visual Genome, ScanNet, Cityscapes, Objaverse, R2R, etc.) and generating question-answer pairs with 19 instruction formats spanning both textual and visual variations. The authors also propose InternSpatial-Bench, a benchmark comprising 6,008 QA pairs that evaluate single-view and multi-view spatial reasoning, including a new rotation angle prediction task.\n\nModels trained on InternSpatial (notably, InternVL-Spatial-8B) show large performance gains +12.1% on InternSpatial-Bench and +10.7% on VSI-Bench, while maintaining comparable performance on general VQA tasks, confirming that spatial reasoning gains do not come at the expense of general multimodal ability", "questions": "### Template Diversity:\nHow many distinct QA templates were used, and how was linguistic diversity ensured across 12M samples? Were any human validation steps introduced beyond filtering for ambiguity?\n\n### Instruction Format Sampling:\nGiven 19 formats, how were the subsets for each training batch sampled? Is there a weighting scheme, or are they uniformly sampled?\n\n### Multi-View Ground Truth Validation:\nFor the rotation estimation task, how were ground-truth rotation angles derived or verified in scenes where camera calibration might be uncertain?\n\n### Cross-Model Evaluation:\nHave the authors tested whether models other than InternVL2.5 (e.g., LLaVA-OneVision, Qwen-VL) benefit similarly from InternSpatial training?\n\n### Human Baseline or Difficulty Assessment:\nHas any human performance baseline been measured on InternSpatial-Bench to contextualize the difficulty of the tasks?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "### Reasonable Dataset Design\n- The dataset covers diverse visual domains (indoor, outdoor, object-centric, embodied, urban) and both single-view and multi-view reasoning setups.\n- It supports a wide variety of instruction modalities, text, bounding boxes, masks, numeric indicators, coordinate-based prompts, totaling 19 instruction types, a major advancement over prior datasets like SpatialVLM or OSD.\n### Data Generation Process\n- The data pipeline integrates multiple pretrained modules for depth, segmentation, and camera parameter estimation (SAM2, Metric3Dv2, PerspectiveFields, WildCamera) to lift 2D annotations into 3D canonical view space. The pipeline is modular and reproducible, allowing flexible annotation generation and QA synthesis without relying on expensive LLM prompting for each sample.\n\n### Novel Multi-view Reasoning Component\n- The addition of rotation angle prediction is new and well-motivated for embodied AI and robotics. Multi-view QA construction uses geometric consistency (e.g., Alpha Shape–based room estimation, OrientedBoundingBox fitting) to ensure physically grounded reasoning.\n\n### Strong Experimental Results and Benchmarking\n- The evaluation suite is broad: InternSpatial-Bench, VSI-Bench, and five standard multimodal tasks.\n- Results show large, consistent improvements in spatial reasoning, including outperforming commercial VLMs like GPT-4o and Claude 3.7 Sonnet in several spatial tasks.\n- Ablation studies isolate the effects of instruction format diversity and confirm its value for cross-format generalization.", "weaknesses": "### The limit of Template-Driven QA Generation. \n\nWhile efficient, the template-based QA generation may lead to limited linguistic diversity and potential overfitting to templated phrasing. The authors acknowledge this, but do not quantify how template rigidity affects generalization to natural human queries.\n\n### Lack of Qualitative Error Analysis\n\nThe evaluation focuses almost exclusively on quantitative metrics. There is little qualitative examination of failure modes (e.g., reasoning about occluded objects, symmetry, or ambiguous rotations).\n\n### Over-Reliance on InternVL2.5 Backbone\n\nExperiments are restricted to fine-tuning InternVL2.5-8B. The generality of the dataset across architectures (e.g., LLaVA, Qwen2.5-VL) is not tested. This limits claims of dataset generalizability.\n\n### Rotation Task Evaluation Unclear\n\nThe “rotation angle prediction” task is introduced as novel but evaluated using classification accuracy, without specifying the label granularity (e.g., 15° bins?). Clarifying this would help interpret improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uyurqltzBX", "reviewer": "Reviewer_S4PX", "summary": "This paper proposes InternSpatial, which is claimed to be the largest spatial QA dataset with 12M data. They sourced the data from 2D images as well as 3D datasets of various sources, generating single-view and multi-view QA pairs. They report the scores of various models on the InternSpatial Bench, including InternVL-8B model trained on their datasets. They also report results on VSI-Bench as well as other general benchmarks.", "questions": "Overall, my main questions of the paper are two-fold:\n\n1. Are both dataset sources (2D and 3D images) helpful in contributing to the training when evaluating on other benchmarks such as VSI-Bench? Are there potential ways to filter out results of bad prediction during 2D data preprocessing?\n\n2.Are there results of other methods finetuned on this dataset? Does that bring any further improvements?\n\nOverall, I do see this dataset being beneficial to the community, so I am leaning towards accept. The questions I believe would significantly add to the contribution.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "In general, I think a12M dataset is a quite significant improvement from previous QA datasets in terms of data quantity. The dataset comes from a wide variety of data, as shown by Figure 4. Results on InternSpatial-Bench, VSI-Bench as well as other general benchmark results show that training on this dataset brings a lot of improvements.", "weaknesses": "It would be great to understand how much the image datasets are helping with the training in general. The alignment to view space from 2D images requires depth estimation followed by camera estimation, both of which could potentially introduce significant errors. I wonder if it would be possible to see the improvements based on InternVL-Spatial-8B trained with only 3D datasets and/or only 2D datasets. Also, this would give more insights on whether there are domain gaps within the training dataset itself.\n\nThe paper could also be strengthened by showing more baselines of models specialized in 3D on InternSpatial-Bench (e.g., SpatialMLLM, SpaceR, etc). Currently, the results shown are mainly on general VLMs and not VLMs specifically trained for spatial reasoning. \n\nThe paper would also be improved by showing results of more methods finetuned with InternSpatial dataset to further show the effectiveness of the dataset on different model architectures/training methods.", "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": "CPyB5omVNR", "reviewer": "Reviewer_2hUS", "summary": "This paper introduces InternSpatial and the corresponding benchmark for spatial reasoning. The dataset contains 12 million QA pairs spanning both single-view and multi-view scenarios, covering 19 instruction formats (textual and visual). It also proposes InternSpatial-Bench, a new benchmark with a new rotation angle prediction task. Experiments are performed on various benchmarks.", "questions": "Please refer to Weakness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1.\tThe proposed dataset is large-scale especially regarding the number of QA pairs.\n2.\tThe data generation pipeline is sound.\n3.\tIt shows promising performance leveraging the curated data.", "weaknesses": "1.\tCould the author compare the proposed dataset with previous ones in terms of scenarios, question types, etc.?\n2.\tCould the authors validate the effectiveness of the proposed data on more open-source frameworks?\n3.\tCould the authors explain the performance show limited gain on rotation estimation and object counting?\n4.\tCould the generated QA pairs reflect the complexity or ambiguity of human spatial questions.\n5.\tWill data generation pipeline and data be publicly available?", "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": "Ynws0alW8g", "reviewer": "Reviewer_mDLs", "summary": "Authors introduce a 12M QA-pair dataset containing both single and multi view images. This dataset aims to improve spatial reasoning in vision-language models (VLMs). The data is sourced from diverse domains (in-the-wild, indoor, driving, object-centric, embodied) and also contains an evaluation benchmark of 6,000 QAs. \nThe data generation pipeline uses automated annotation (e.g. object masks, depth estimation) with pretrained model followed by template-based QA generation.\nThe authors finetune an InternVL2.5-8B models on their dataset and obtain clear performance gains on spatial reasoning benchmarks including their own, while maintaining performance on general VQA benchmarks.", "questions": "See weaknesses. \n\n1) In Table 1, the authors mention their dataset \"InternSpatial\" as Open-source. Is the dataset already open-source? If not, this claim is false? \n\n2) See related work sections of below papers for 2D spatial reasoning. Consider discussing prior 2D spatial reasoning works in detail?\n  - Ferretv2: https://arxiv.org/abs/2404.07973 \n  - LocVLM: https://arxiv.org/abs/2404.07449 \n  - Shikra: https://arxiv.org/pdf/2306.15195", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Clear improvement over baseline when trained on new data. \n2. Thorough analysis of dataset statistics.\n3. Large-scale open-source dataset contribution", "weaknesses": "1. Only `InternVL2.5-8B` is used as a baseline. How does this dataset help other models in general? \n2. Only an 8B scale model is used. Will this data improve smaller (e.g. 2B-3B models), large (14B-70B), and MoE models? \n3. Minimal description of data generation process. How do the automated tools (e.g. SAM for bbox / mask) perform? What are the error rates? These are not clearly discussed in the paper. \n4. QA generation: diversity? These is little analysis on the dataset. Maybe calculate diversity metrics on the text and image data. Also, given the template based generation (as opposed to human or LLM), this data diversity concern is amplified. \n5. Test data leakage: The training dataset is created using 3D information of some datasets, \"integrated multi-view data derived from the training splits of the ScanNet/MultiScan/R2R/Objaverse\". At the same time, their benchmark uses some of these same datasets' test splits to evaluate. Even other benchmark (e.g. VSI contains ScanNet data) use this common data. Could the performance improvement be due to this similar domain data being used to create the training dataset?", "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)