GTR-Bench: Evaluating Geo-Temporal Reasoning in Vision-Language Models
Authors: Qinghongbing Xie
Zhaoyuan Xia
Feng Zhu
Lijun GONG
Ziyue Li
Rui Zhao
Long ZENG
Venue: ICLR 2026 Poster (2026)
Keywords: Spatial-Temporal Intelligence
Geo-temporal Reasoning
Visual-Language Models
Primary area: datasets and benchmarks
TL;DR: We propose  GTR-Bench to evaluate spatial-temporal intelligence of VLMs through  multi-view spatial-temporal challenges, highlighting their limitations for future research.

Abstract: Recently spatial-temporal intelligence of Visual-Language Models (VLMs) has attracted much attention due to its importance for autonomous driving, embodied AI and general AI. Existing spatial-temporal benchmarks mainly focus on egocentric (first-person) perspective reasoning using images/video contexts, or geographic  reasoning with graphical context (e.g., maps), thus fail to assess VLMs' geographic spatial-temporal intelligence that requires integrating both images/video and graphical context, which is crucial for real-world scenarios such as traffic management and emergency response. To address the gaps, we introduce Geo-Temporal Reasoning benchmark (GTR-Bench), a novel challenge for geographic temporal reasoning of moving targets in a large-scale camera network. GTR-Bench is more challenging as it requires multiple perspective switches between maps and videos, joint reasoning across multiple videos with non-overlapping fields of view, and inference over spatial-temporal regions that are unobserved by any video context. Evaluations of more than 10 popular VLMs on GTR-Bench show that even the best proprietary model, Gemini-2.5-Pro (34.9\%), significantly lags behind human performance (78.61\%) on geo-temporal reasoning. Moreover, our comprehensive analysis on GTR-Bench reveals three major deficiencies of current models for geo-temporal reasoning. (1) VLMs exhibit imbalanced utilization of spatial and temporal context during reasoning. (2) they show weak temporal forecasting ability, leading to poorer performance on temporally focused tasks. (3) they lack the capability to effectively align and integrate map data with multi-view video inputs. We believe GTR-Bench offers valuable insights and opens up new opportunities for research and applications in spatial-temporal intelligence. Benchmark and code will be released at https://github.com/X-Luffy/GTR-Bench.

Published as a conference paper at ICLR 2026
GTR-BENCH: EVALUATING GEO-TEMPORAL REA- SONING IN VISION-LANGUAGE MODELS
Qinghongbing Xiel.2*;, Zhaoyuan 2ia Feng T Lijun Gong", Ziyue Li Rui Zhao?, Long Zeng'1 f
TTsinguaa University Sense'Timm Research Peking University
"Technical University of Munich, Heilbronn Data Science Center, Munich Data Science Institute xqhb23@mails itt.emmalssssssiiiiieeei tsinghua. edu. cn, 2401210 64 edu. cn, chufeng@senset ime com, zenglongesz tsinghua, edu. en, ziyue 1i0t um. de
ABSTRACT
Recently spatial-temporal intelligence of Visual-Language Models (VLMs) has attracted much attention due to its importance for autonomous driving, embod- ied Al and general AI. Existing spatial-temporal benchmarks mainly focus on egocentric (first-person) perspective reasoning using images/video contexts, geographic reasoning with graphical context (e.g., maps), thus fail to assess VLMs geographic spatial-temporal intelligence that requires integrating both im- ages/video and graphical context, which is crucial for real-world scenarios such traffic management and emergency response. To address the gaps, we in- troduce Geo-Temporal Reasoning benchmark (GTR-Bench), a novel challenge for geographic temporal reasoning of moving targets a large-scale camera network. GTR-Bench is more challenging as it requires multiple perspective switches between maps and videos, joint reasoning across multiple videos with non-overlapping fields of view, and inference over spatial-tempord regions that are unobserved by any video context. Evaluations of more than 10 popular VI.Ms on GTR-Bench show that even the best proprietary model, Gemini-2.5-Pro (34.9%), significantly lags behind human performance (78.61%) on geo-temporal reasoning. Moreover, our comprehensive analysis on GTR-Bench reveals three major deficiencies of current models for geo-temporal reasoning. (1) VLMs ex- hibit imbalanced utilization of spatial and temporal context during reasoning. (2) they show weak temporal forecasting ability, leading to poorer performance on temporally focused tasks. (3) they lack the capability to effectively align and integrate map data with multi-view video inputs. We believe GTR-Bench of- fers valuable insights and opens up new opportunities for research and applica- tions in spatial-tempont intelligence. Benchmark and code will be released at Lowfooo////LLLL/RRtto RBBech.
INTRODUCTION
Spatial intelligence is fundamental capability that underpins interaction with the physical world (Feng et al., 2025a). This capability is pivotal for wide range of applications, including autonomous driving (Cui al., 2025; Guo al., 2024) and embodied AI (Chen et al., 2024: Jiang et al., 2025; Huang et al., 2023; Zhang et al., 2025). With recent advances in deep learning, the spatial-tempora intelligence, an extension of spatial intelligence, of Visual-Language Models (VLMs) has emerged as important research direction. It encompasses the ability to understand spatial attributes such as size and distance, temporal attributes such as time intervals and velocity, and to perform spatial- temporal reasoning about dynamic events in real-world environments.
However, existing benchmarks for the spatial-temporal intelligence of Vision-Language Models (VLMs) have inherent limitations, as shown in Table 1. Current benchmarks for geographic rea- soning only focus on static geometric tasks with graphical context such as a subway map (Feng
"Equal contribution.
'Corresponding authors.
Work done during internships SenseTime Research.

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Published as a conference paper at ICLR 2026
Benchmark
Perspective
Geometry vs. Motion
Context Type
#Cam Views
ReasunMapx<eeng el al,, 2025b)
Geographic
Geometry
Graphics
0
MuluSPA(Xu et al., 2025)
Egocentric
Geometry
Image
STI-BenchLi et al. (2025b)
Egocentric
Geometry Motion
Video
VsI-BenchYang al. (2025a)
Egocentric
Geometry Motion
Video
SennSputall-ennchll. et al. (2025a)
Egocentric & Allocentric
Geometry
Image
2+
ST-VLMKo et al. (2025)
Egocentric
Motion
Video
GTR-Bench (Ours)
Goographic
Motion
Graphics & Vidco
to
Table 1: Comparison of GTR-Bench with Previous Benchmarks. Our GTR-Bench is novel challenge which can assess VLMs' geographic spatial-temporal intelligence in a camera network with both images/video and graphics context.
et al., 2025b; Chen et al., 2025). contrast, benchmarks designed for egocentric or allocentric tasks typically involve single few distinct cameras and emphasize scale reasoning between static objects or motion state reasoning of dynamic objects in videos or images (Yang et al., 2025a; Li et al.. 2025a; Yang al., 2025b; Xu et al., 2025; Chen et al., 2025; Feng et al., 2025b; Ko et al., 2025; Li et al., 2025b). These benchmarks therefore fail to evaluate models' ability to perform ge- ographic spatial-temporal reasoning across large-scale camera networks, where both graphical context (e.g., maps) and multi-view vidco observations must be jointly considered. To address these gaps, we propose Temporal Reasoning (GTR), a novel challenge for geographic temporal reasoning of moving targets in a sare--scale camera network. For example, understanding vehicle movement patterns and predicting traffic flow dynamics across a city demands sophisticated rea- soning about vehicle appearances, their trajectories, and motion trends across over 10 camera view- points. This GTR-Bench is more challenging as it requires multiple perspective switches between maps and videos in a camera network, joint reasoning across multiple videos with non-overlapping fields of view, and inference over
regions that are unobserved by any video.
In this work, we introduce the Geo-Temporal Reasoning benchmark (GTR-Bench), shown in Figure 1. GTR-Bench features a hierarchical suite of tasks grounded in real-world multi-camera networks. A key innovation of our benchmark is to extend spatial-tempont camera networks within geographic perspective. Grounded in real-world indoor and outdoor sce- narios with multi-camera network, our benchmark utilizes the actual trajectory open-source data of pedestrians and vehicles in urban environments (Tang et al., 2019; Woo et al., 2024) for task construction, including map, object footprint, and trajectory data constructed through annotations. Furthermore, GTR-Bench features a series of tasks for unobserved scenarios that require multi- view joint reasoning across timestamp sequences with non-overlapping camera views. We design a multi-level evaluation framework that separates model capabilities into basic reasoning tasks and combinatorial reasoning tasks, comprising 420 questions derived from 364 videos. The benchmark includes three basic reasoning tasks: Geo-location, Arrival Time-Interven and Motion-State, which isolate and measure a model's core inferential strengths. To evaluate how models integrate these capabilities, we further design four combinatorial tasks: Causal Reondering, Next Spot Forecasting, Trajectory Forecasting, and Multi Target Trajectory Forecasting.
reasoning tasks to real
In our evaluation, models demonstrate markedly poor performance on GTR tasks compared to isting spatial-temporal benchmarks, revealing a critical gap in current VLMs' ability to achieve spatial-temporal intelligence. Even the peppoorformin proprietary model, Gemini-2.5-Pro achieved only 34.9% accuracy compared to the human- performance of 78.61% %, while the leading open- source model, Intern VL3-38B, reached just 30. 30.76% This poor performance is further highlighted by two key trends: (1) a significant drop in accuracy from basic to combinatorial tasks, and (2) a notable disparity of performance between outdoor and indoor scenarios. Our in-depth analysis attributes these shortcomings to three fundamental deficiencies in current models. First, VLMs' rea- soning is impaired by an imbalanced utilization of context across spatial, temporal, and motion-state dimensions. Second, VLMs are weak in temporal forecasting, which leads to worse performance on spatial-tempont prediction tasks than on spatial reasoning tasks. Third, VLMs lack the proficiency in comprehending and aligning map data with multi-view video inputs from camera network.
Our main contributions are summarized as follows:
We propose Geo- Temporal Reasoning, novel spatial-temporal challenge for geographic temporal reasoning of moving targets in a large-scale camera network. It helps as- sess VL.Ms geographic spatial-temporal intelligence with both visual (images/video) and
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graphical (map) contexts, which is critical for applications such as traffic management and emergency response, beyond conventional embodied AI.
We develop GTR-Bench, a benchmark designed to evaluate geo-temporal reasoning ca- pabilities of VLMs. GTR-Bench is more challenging due to multiple perspective switch- ing between maps and videos, joint reasoning across multiple videos with non-overlapping fields of view, and inference over spatial-temport regions that are unobserved by any video context. As result, even the best proprietary model, Gemini-2.5-PPa (34.9%), significantly lags behind human performance (78.61%).
We provide a comprehensive analysis GTR-Bench, which reveals three primary defi- ciencies of current models for geo-temporal reasoning, namely, imbalanced utilization of spatial-temporal context, weakness in temporal forecasting capabilities, and lack of pro- ficiency in comprehending and aligning the map data with multi-view video inputs. We believe analysis on GTR-Bench offers valuable insights and opens up new opportunities for research and applications in spatial-temporal intelligence.
RELATED WORK
Geographic Reasoning Benchmark, Existing benchmarks for geographic reasoning primarily as- sess models for geometry tasks with graphics context such map. ReasoningMap (Feng et al., 2025b) designed a geographic reasoning task for structured and information-rich diagrams like high- resolution transit maps. SpatialLLM (Chen al., 2025) explores using VLMs to geographic infor- mation system data in geometry tasks by transforming multi-modal urban data into structured scene descriptions to prompt pre-trained LLMs. They evaluate the understanding of spatial topology and path planning from geographic view, yet typically only incorporate evaluation tasks for static geo- we combine geographic data to reason about dynamic objects. It
metric targets. In the will bring new cognitive perspective to the existing
benchmarks.
Spatial-Temporas Reasoning Benchmark. Existing benchmarks for spatial-temporal reasoning assess models video multi-image tasks from single or a few camera views. ST-VLM (Ko et al., 2025) constructs a dataset and benchmark for kinematic instruction tuning (STKit/STKit- Bench) to propose and validate ST-VLM, which demonstrates outstanding performance in object dynamics analysis. STI-Bench (Li et al., 2025b) evaluates the capabilities of VLMs on real-world spatio-temporal understanding tasks such as pose, displacement, and motion. They focus on event sequencing and state inference within a single vidco from an egocentric view. ViewSpatial-Benn (Li et al., 2025a) addresses the core problem of VLMss inadeuuate spatial reasoning when switching from egocentric to allocentric perspectives. MMSI-Bench (Yang et al., 2025b) is built upon spatial reasoning tasks that span the positions, attributes, and motions with multi-step reasoning split that chains them into long-horizon questions. Multi-MulriSPA (Xu et al., 2025) is designed for multi- frame spatial reasoning covering depth and visual correspondence perception, camera and object movement perception, and object size perception. They focused on the challenges of multi-view tasks and attempted to evaluate models to perform spatial-temporal reasoning under few adjacent yet distinet views
egocentric allocentric observer. Although these spatial-temporal works have some similarities with GTR, GTR notably combines graphic map and video as context and proposes geographic temporal reasoning task of moving targets with multi-view joint reasoning with little view overlap for unobserved scenes.
Tro-Temporal Task in Multi-Camera Systems. Geo-temporal task represents a type of spatial- temporal tasks that covers large-scale camera networks across multiple regions/districts, necessitat- ing comprehension of geographic relationships and cross-regional connections. Multi-Target Multi- Camera Tracking (MTMCT) can be considered as a form of rco-temporal task. Existing benchmarks for MTMCT include the CityFlow dataset (Tang et al., 2019), which provides vehicle trajectories in a large urban area, and MTMMC (Woo et al., 2024), which offers pedestrian trajectories in indoor environments. Complementing these datasets, various methods have been developed for multiple camera Re-ID. These include two-step matching approaches using semantic parsing and spatial- temporal attention (He et al., 2020), the integration of language models with graph neural networks (Nguyen et al., 2024), and noise-robust trajectory recovery frameworks designed to address Re- ID clustering errors (Li et al., 2025c). However, existing researches still rely primarily on visual features of objects, which remain within the computer vision domain with limited generalizability.
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GTR-Outdoor
GTR-Indoor
CBIS
CMB
Levil
Live
CAI
Gen-Location
Arrival Intervaterral
Motion-State
Timeline Reordering
Next Spot Forecasting Traj Forecasting Multi-Tra Forecasting
Target Cam ID
Basic Reasoning Tasks
Combinatorial Tasks
Figure 1: Overview of Geo-temporal Reasoning and GTR-Bench. Given graphical map and multiple video clips from non-overlapping cameras, geo-temporal reasoning infers motion state of moving targets in large-scale camera network. GTR-Bench comprises 3 basic reasoning tasks, including Geo-location, Arrival Intee-nterval, and Motion-State, and combinatorial tasks includ- ing Causal Reordering, Next Spot Forecasting, Trajectory Forecasting, and Multi-Targer Trajectory Forecasting. GTR- -eRch covers both outdoor (vehicles) and indoor (pedestrians) scenarios.
Given the extensive exploration of VLMs in seatial-temporal intelligence applications, we extend the geo-temporal task novel challenge, Geo-temporal Reasoning(GTR), which leverages the reasoning capabilities of VLMs to fully utilize geographic and temporal context in solving more challenge.
GEO-TEMPORAL REASONING
From a cognitive science perspective, spatial-temporal intelligence can be bifurcated into two cat- egories: first-person (egocentric) and third-person (allocentric) intelligence (Burgess, 2006). How- ever, if we move away from the human cognitive perspective and perceive the real world from a broader perspective, the geographic perspective can provide VLMs with omniscient understand- ing for dynamic objects.
When extend the context of the problem geographic spatial-temporal intelligence, the issue becomes more challenging and valuable, requiring the resolution of pultiipersppective changes and transformations of coordinate systems. A typical case involves inferring target's geo-location, tem- poral sequence, and motion state in a camera network comprising more than 10 viewpoints covering an urban scene. We posit that this represents crucial challenge of spatial-temporal intelligence, which we define as Geo-Tempon Reasoning (GTR), as illustrated in Figure 1.
With the expansion of contextual inputs from the GTR challenge, novel questions have arisen for existing spatial- nential-eemporl intelligence. The introduction of a graphic map requires models to reason between the map and cameras, handling multiple perspective changes. This contrasts sharply with egocentric tasks, which rely on continuous-time sequence inferred from the sequential order of image frames (Xiong et al., 2024; Su et al., 2024; Bazaga et al., 2025). Furthermore, GTR chal- lenges VLMs to perform multi-view joint reasoning about unobserved phenomena with little view overlap, such as inferring a target's path between camera views. This demands superior inferential capabilities and context integration. In contrast, egocentric tasks typically involve continuous and unified
observations, allowing models to reason about directly observed events (Ko al., 2025; Li al., 2025b).
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Task Name
Task Definition
Metric
Basic Tasks
Given the starting and ending locations of a target, infer the intermediate locations the target
passes through.
Geo-Location (GL)
Example: Based on the provided [start video] and lend video), infer which camera the tar- get passed through between the stort point (CO16, 12:00:10-12::0:22 and end point (CD18, Answer: CO17
MCQ Acc
12:00:37-15 122:0000-24
Given the starting point, ending point, and intermediate locution, infer the time interval of the target's ss arrival at a specific intermediate location.
Arrival
Example: Based on the provided [start video] (C018, 12:00:37-12.00-PP and [end video] C019, infer when the target orrived at the intermediate camera, Answer: A. 12:00:43.279. 21t.:3..799-120-433..3 12:00:43.579
MCQ Acc
Intervat (ATT)
(C020, 12:00:43-12 (0:112200::00 knowing the target passed through
Given the starting point, ending point, and intermediate location, infer the plausible motion state target intermediate locations.
Example: Based the provided [sturt video] (CO16, 12:00:10-13 10:00000220 and [end video]
MCQ Acc
Motion-Stute (MS)
(CO18. 12:00:37-12 37-12-00:42) and the intermediate camera c017 infer the target's 's motion state during the intermediote time period. Answer: B. the target movels west at a speed of 10.0 for 11.0 seconds, covering distance of 109.6 meters.
Combinatorial Tasks
Given set of unordered video clips from different camerus and map, determine the correct chronological sequence of camerus the target passed through. Example: Based the provided [local mup] and ( C021,C020), analyze the target's 's activity trajectory. Please infer the correct order in which the target passed through these cameras. Answer: D. C019 - C020 -, CU21
Causal Reordering (CR)
MCQ Acc
Given the target's last observed appearance in single camera video and map. predict the probuble next cumera locution und the corresponding time interval of appeatunce.
Next Spot Forecasting (NSF)
Example: Based on the provided flocal map] and [video] (C16, 12:00) 10- 12:00:22), which camera from the following list will likely cupture the target next? You need to select one option 0.0003..05-12000000
STHOU
the answer and infer time range. Answer: CO20
Building upon multiple historical observations across several cameras. predict the target's com- plete future trajectory by forecasting the sequence of camerus will pass through.
Example: Based on the provided [local map! and [videos] (C017, 12:01:01-122 -22:0221: CO18,
Trajectory Forecasting (TF)
12:0037-12:000
predict the next two camerus that the target will likely pass through. You
ST-OU
need to select correct sequence of options and simultaneously infer corresponding sequence
of time ranges. Answer: C019 12:00:43.22 270-4432797-200:443555 12:00:50.500
C020 12:00:43.900 905-
Multi-Turget Trajectory
This extends single-turget prediction by requiring the model to forecust the future meeting point (location and time) two distinct targets.
Example: Based on the provided [local mup] and [videos] (C018, 12:00:37-12:00.44 C019.
ST-IoU
Forecasting (MTTF)
12:01-21-12:01:22 23) showing the movement trajectories of two [Target], predict where and
when these ['arrget] will most likely meet. Answer: B. CO18 12:00:37.755. 2000:177555112000-22.222 12:00-2-22222.555
Table 2: Detailed design and examples of tasks in GTR-Bench. and combinatorial levels, evaluated by either Multiple-Choice Question Accuracy (MCQ Acc) or Spatoal-Temporal Intersection over Union (ST-LoU).
is divided into basic
GTR-BENCH
4.1 OVERVIEW OF GTR-BENCH
In this work, we constructed the Geo-Temporal Reasoning benchmark (GTR-Bench) to systemati- cally evaluate VLMs on geo-temporal reasoning challenge, as in Table 2. (1) The first level com- prises three basic reasoning tasks designed to probe fundamental abilities. Geo-Location assesses a model's comprehension of spatial topology and path planning within multi-camera networks. Ar- rival Time-Interval evaluates the ability to model temporal sequences and predict event timing across different camera views. Motion-State examines the understanding of target's ' behavior and the influence of scene semantics on motion state. (2) The second level features four combinato-
rial tasks that demand the integration of three basic reasoning skills. Causal Reordering requires the synthesis of spatial and temporal understanding to reconstruct a coherent event sequence. Next Spot Forecasting integrates all three basic skills to predict a target's subsequent location and time of appearance in the future. Trajectory Forecasting extends this by requiring Long-horizon predic- tion, testing the model's 's ability to understand sustained motion patterns. Multi-Target Trajectory Forecasting testing joint reasoning and the capacity manage multiple opatial-temport paths.
For each task in the Bench, we have designed specific problem instances and corresponding evaluation metrics, as detailed in Table 2. Each question is multi-modal, incorporating map with
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more video clips, and features rich diversity of target objects and Overall, the benchmark comprises 420 unique questions derived from 364 distinct video clips with an average duration of 10.64 seconds. As shown in Table 3, these questions meticulously bal- anced across the seven reasoning tasks, with 60 questions per task. More examples and details are available in Appendix G.
contexts.
The benchmark is equally divided into two distinct real- world scenarios, with 210 questions for each. The first is an outdoor urban environment from the CityFlow dataset (Tang et al., 2019) focused on vehicles, while the sec- ond is an indoor, multi-level setting from the MTMMC dataset (Woo al., 2024) for pedestrians. As detailed in Table 3, these environments present vastly different scales of geo-temporal complexity. The outdoor network spans city block with 31 cameras and an average inter-camera distance of 984.77 meters. In contrast, the indoor network provides building-leve coverage with 16 cameras and average distance of only 30.59 meters. This significant disparity, with the outdoor scenario being ap- proximately 32 times larger in spatial scale, establishes testbed for evaluating the spatial-temporal intelligence of VLMs.
Total Questions
420
Unique Videos
364
Avg. Images per Question
7.0
Avg. Quesion Length
185.1
Avg. Length
Sub-Dataset
GTR-Outdoor
GTR-Indoor
Targol Otjccls Number of Questions
Vehicles
Pedestrian
7 30
7 30
Number of Cameras
31
16
Avg. Distance
984.77
30.59
2389.42
Table 3: Data Statistics
The design of GTR-Bench prioritizes diverse spatial-temporal complexity static backgrounds, grounding the evaluation of VLMs in dynamic cues rather than environmental textures. We categorize tasks into three complexity tiers of Geo-temportal complexity-Long Medium, and bhort-based on physicallygroounder thresholds for trajectory length (fracka) and duration (track,) across both outdoor and indoor settings as Fig. 2. This categorization ensures balanced and com- prehensive distribution of tasks as Fig. 3, enabling rigorous assessment of geo-temporal reasoning across varying spatial and temporal scales.
Short (Dundner)
Medium (Dundoo))
Long (Ounslor)) Long Urdisrt
short (kadbar)
Medium (indaor)
8D-
Criteria
Frequeeccy Count
Time DO
120
20
10
15-
20
Distence (trakg) [m]
Distance (trecke) [m]
temporal temporal Long temporal
Figure Complexity definition for outdoor and indoor: outdoor is generally quicker in time and longer distance due to driving then indoor.
Figure Complexity Distribution: three levels of complexity are quite balanced for both outdoor and indoor.
4.2 METRIC DESIGN
We employ two primary metrics for evaluation: standard accuracy for multiple-choice questions (MCQs) and a novel Opatial-Tempont Intersection over Union (ST-loU) for predictive tasks, as out- lined in Table 2. For the basic and CR tasks, which are formatted as MCQs, we report standard accuracy. In contrast, the forcasting tasks (NSF, TF, and MTTF) require the model to generate both Camera ID (e.g., C.C0/7) and a corresponding time interval (e.g., 12:00:37- (2:00:42. To eval- uate spatial-temporal results, we introduce ST-IoU, a composite metric designed to assess spatial- temporal performance. Specifically, it combines spatial accuracy-veriffiin the correctness of the predicted Camera ID-with Temporal IoU, which measures the overlap between the predicted and ground-trun -tund-truth time intervals. For a given prediction i, the ST-IoU is calculated as follows:
N
[Tp, 7 TTt. [Tp. U UT gt,
ST-IOU = N
M 1=1
ICCp. = Cgt.) x
(1)

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where N is the total number of predictions, I(() is the indicator function which equals 1 if the predicted camera Cp, matches the ground truth camera Cgt, and 0 otherwise. Tp. and Tox, represent the predicted and ground-truth time intervals and the fraction calculates their temporal IoU.
4.3 BENCHMARK CONSTRUCTION PIPELINE
Data Preprocessing
Task Generation
Quality Check
Camera Sequenee
Time Interval
Motion State
Context Selection
Time Runge
MTMC Reid
Map Generation
Video Annotation
Motion Diversity
Spatial Sgin
Camera Calibration
Human Expert Filtering
Trajectory Information
Conflic & litering
Question
Prompt
Distractor
Lacatjon
Ressoning
Formation
Answer
Generation
Option
Time
Motiin
Difticulyy Eitering
Figure 4: Benchmark construction pipeline
We designed an automated benchmark construction pipeline that transforms raw video data into standardized questions across tasks to accommodate the varying temporal, geographic, and formatting requirements of different tasks, as shown in Figure 4.
Data Preprocessing. Our data preprocessing pipeline transforms raw video from two distinct datasets, CityFlow (outdoor vehicles)(Tang, et al., 2019) and MTMMC (indoor pedestrians) (Woo al., 2024), into structured format suitable for geo-temporal reasoning. The process begins by segmenting video clips for individual target instances based on bounding box annotations. Next, perform camera calibration by computing a homography matrix from manually annotated corre- spondence points, which establishes precise projective transformation from the 2D image plane to real-world map. Using this matrix, we transform target trajectories by projecting their coordinates onto the map, scaling them to real-world metrics, and algorithmically deriving key motion param- eters like velocity and direction. The resulting trajectory data then undergoes rigorous cleaning, filtering, and validation process to ensure consistency. Finally, we use a large language model to synthesize this quantitative data into qualitative Motion Summary, providing both numerical and narrative insights for each trajectory. For more details, refer to Appendix A.I.
Task Construction. We employ systematic pipeline to create standardized questions using task- specific templates. The process consists of four main steps: (1) Trajectory Selection: We randomly sample valid trajectory segments, complete with temporal and motion state data, from our prepro- cessed dataset. (2) Information Integration: The selected trajectory, along with corresponding map information and video data, is integrated into a standardized template. (3) Question and An- swer Formulation: We construct task question aligned with specific reasoning requirements and establish the ground-truth answer based on the actual trajectory and map data. (4) Distractor Gener- ation: To ensure rigorous evaluation, we generate plausible yet incorrect distractor options using sophisticated, scenario-specific strategy. This includes sourcing from architecturally distinct indoor areas, algorithmically creating synthetic outdoor cameras, and randomizing camera IDs compel models to perform genuine geo-temporal reasoning rather than relying on superficial heuristics. For more details, refer to Appendix A.3.
Quality Check. The benchmark has undergone complete two-stage manual selection process to ensure the validity of the evaluation. the first stage, we first manually select the context of each question to ensure the diversity of spatial spans and temporal durations of the questions. Meanwhile, we remove questions with large trajectory errors to bring the benchmark more in line with the laws of opatial-tempost reasoning. In the second stage, human experts select the answers of each question and select 30 questions per task covering reasonable difficulty levels. This ensures the validity of the benchmark and provides diversity in evaluation difficulty.

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5 EVALUATION ON BENCHMARK
5.1 EVALUATION SETUP
Evaluation Setup. We evaluate 13 state-of-the-art models across our benchmark. For propri- etary models (PM), we select 3.7eo-t. (Anthropic, 2025a), Claude-4-Sonnee (Anthropic, 2025b), GPT-4o (Openai, 2024), GPT-5 (Openai, 2025), and Gemini-2.5-PHr (Deepmind, 2025). For open-source models (OM), we include the
series (Zhu et al., 2025), Qwen2- series (Bai et al., 2025), and GLM- 4.1 4.hi-B-T (Hong al.. 2025). Proprictary models are accessed via their respective official APIs, while open-source models are deployed using LMDeploy on NVIDIA V100 GPUs. To ac- commodate model input constraints,
VL-2B/7B series (Wang et al., 2024), (wen2.5-VL-2BB7TI
uniformly sample the videos, ensuring the total number of frames from multiple videos remains within 20. We set the temperature to and max new token to 16,384, enabling the models to perform sufficient and stable maaii-of-thougghe reasoning. Addition- ally, traditional ReiD-based baseline is implemented to provide a performance reference, detailed in Appendix C.I.
5.2
MAIN RESULTS
Performance Level
Human Level
OS (+23.8)
CS (23.8-33.2)
PH (Avg: 33.2)
Human Lovel
78.61
Gemini-2.5-pp
34.93
GPT-5
34.05
Caude
34.03
PH Avg: 33.15
GPT-40
33:53
InternVL3-388
30.76
Model
Qwen2.5-VL-
30/44
Cleude-3-7-Sonne
29.23
GLM-4.1V-98-T1 Thiikigg
p8.ss
33:05
InterniVL--ee
2b.10
Twen2.5-vt-7B.tnenne
19.5p
3S Aug: 23.87
InternVL3-28
Qwen2-V1- -n-t--iu--tt
17.85
10
30
40
50
60
70
Figure 5: Overview Comparison of human performance and 13 VLMs on GTR-Bench. The best model (Gemini-2.5-Pro, 34.93%) remains far below human performance (78.61%). Dashed lines indicate average performance: OS Avg (23.82) < PM Avg (33.15) < Human (78.61%).
Overview of Results. A critical performance gap notable, with even the best proprietary model, Gemini-2.5-Pro, achieving score of only 34.9, compared to the human-level performance of 78.61. Table 4 presents comprehensive evaluation of 12 Visual-Language Models on our GTR--Bech, span- ning two distinct scenarios (GTR- Outtoor and GTR- Indoor) and seven reasoning tasks. The proprietary models, particularly Gemini-2.5-Pro, GPT-5 and Claude-4-Sonnet, demonstrate better performance, achieving the highest average scores 34.93, 34.05 and 34.03, respectively. This indicates that cur- rent leading proprietary models possess more robust geo-temporal reasoning capabilities. However, the performance gap is narrowing, with top-tier open- source models like VL3e33V Qwen2.5-VL- 32B, and GLM-4.1VV9BB Thinking showing compet- itive results with scores of 30.76. 30.45, and 28.51 respectively. The overall scores reveal the challenge of GTR- Bench and highlight significant room for improvement in the apatial-temporal intelligence of VLMs.
M5
ATI
NSP
MTTF
Quar2 Qwer2-Xt-22 XL-28 Instruct IntemiLS-1 Qwen2 5-VL-B Instrcti
Internii 3-88 sile Daer234t-7h-tndnm -7h-tnrut
Eluice-1=7-GGmme 1-7--ommtt Qwen? 5-0t--5--mmmtmme sill Internti I-bert/i3-3in
GPT-4e +0 Claude-6-Satree - GPT-5 Gemirk2..5ppo
Human Level
Figure 6: Task performance of GTR-Bende
8

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Published as a conference paper at ICLR 2026
GTR-Ouidsor
GTR-Indoor
Methods
Rank
ATI
MS
CR
NSC
TF
MTTU
ATI
MS
NSD
T
MITP
Proprietory Models (AP!)
Clande3-77 3-7-----
66.67
26.67
8.90
36.67
13.33
06.67
9.48
9.51
3.56
GPT 40
4
36.67
76.67
40.00
63.33
20.53
0:00
23.10
30.00
53.33
40,00
50.00
13.00
0.00
2.79
33.53
Clande Sonnet
73.33
50.00
50,00
63.33
K08
6.18
16.94
66.67
33335
03.33
2.50
4.01
0.00
34.03
GPT-5 S5mini-25-PP
40100
12.04
12:12
7.34
11.34
2.55
1.75
34,00
33.33
13.36
26.67
28.09
Open-source Models
VL-2B
13
16.67
20.00
0.00
0.24
0.00
30.00
13.33
33.53
0.00
0.21
0.30
17.65
InemVL3322
12
13.33
0.00
23.33
33.33
0.08
1.62
Quen2.5 VL Instruct
11
23.33
66.67
3.33
0.00
0.00
80.00
30.00
6.67
63.35
0.30
0.00
0.00
19.56
1ncm/VL3388
10
33.33
4.79
0.79
1.67
16.67
10.01
40.00
36.67
0.00
23.05
GLM-4, -V-iB-Thiiningggggg
$3.35
Imcem/1.3-388
10.24
Level
Roll)
Table 4: GTR-Bench Results. Our evaluation based on more than 10 popular VLMs reveals a critical performance gap. Bold best result; Underline second best result.
Outdoor vs Indoor. A comparative analysis between the GTR-Outdoor and GTR-Indoor scenarios reveals distinct performance patterns. As shown in Table 4, most models demonstrate better per- formance in outdoor settings. For instance, Claude Sruude-37-SSonet achieves outdoor scores of 25.75, 8.90, and 21.97 for NSF, TF, and MTTF tasks respectively, consistently outperforming its indoor scores of 9.48, 9,51, and 3.56. This aligns with expectations that outdoor environments provide clearer spatial cues and more regular motion patterns. However, Gemini-2.5-PPr exhibits counterin- tuitive behavior, achieving higher indoor scores of 25.11, 28.09, and 14.37 compared to its outdoor scores 19.13, 13.16, and 19.18. This pattern, demonstrated by the top-performing model, suggests that advanced models may better utilize their sophisticated reasoning capabilities when facing more complex indoor scenarios that demand deeper multi dineisioaa reasoning.
5.3 RESULT ANALYSIS
Context Utilization Anal- ysis. We designed a prompt to analyze each model's re- liance on spatial, tempo- ral, and motion state con- text, detailed in Appendix D, which instructs an LLM to assess its own reason- ing output for each ques- tion. The model assigns a binary score for its utiliza- tion of each context type: 0 for utilization and 1 for appropriate utilization. This method allows us to quantify each model's dependency on different context types and reveal its reasoning patterns. As shown in Figure 7, our analysis reveals that leading proprietary models like Gemini-2.5-PP5 demonstrate balanced utilization across all context types, which contributes their superior performance. In contrast, open-source models often exhibit imbalanced patterns. For instance, InternVL3-388 shows strong spatial and motion understanding but weaker temporal reasoning. This imbalance creates reasoning blind spots that limit performance on comprehensive geo-temporal tasks.
Spatial Leage
Temooral Usage
Nosson Laage wen Banchwork Soore
Bench WO Score
Dovent st ienruus
Sewnd sit to immecers
Comnc lentrurs
OLAnd thi Thraiin
is hatl imntroett
28
200
sstarmnt lasaes
Srane
Figure 7: Context Utilization Analysis. VLMs' reasoning is im- paired by an imbalanced utilization spatial-temporal context.
Spatial-temporal Reasoning Analysis. To investigate the performance gap between spatial and spatial-temppral reasoning, we conducted detailed analysis as detailed in Table 5. Models achieve higher MCQ accuracy evaluating spatial location understanding compared to ST-IoU scores requir- ing joint spatial-temporal reasoning. GPT-4o achieves 53.33 MCQ accuracy outdoor NSF but only 20.53 ST-IoU, a 32.80 point gap. Gemini-2.5-Pa shows 45.45 MCQ accuracy versus 13.16 ST-IoU outdoor TF, indicating 32.29 point difference. This pattern persists across all models, with GLM-4.1VV9B. --hinkinng demonstrating 76.67 MCQ accuracy on outdoor MTTF but only 25.38 ST-HoU, 51.29 point difference. These consistent gaps highlight that while models identify spatial

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Published as a conference paper at ICLR 2026
GTR-Ouideor
GTR-ladoce
Methods
Rank
NSP
TF
MITTE
NSP
TF
MITTE
MCQ Arz
ST-bol
MCQ Acc:
ST-164
MCQ Ase
ST-Gd
MCQ Are
ST-bol
MCQ Ace
ST-M6
MCQ Aue
Proprissry Midech (AP))
Clanle-----Semme
13.00
700000
Genini-
19.13
51.72
50.00
36.67
Open-coorzs Model'
Qum2-VL-225 PL-26-ane Instrnt
13
36.67
0.00
30.00
0.28
43.33
0.50
3.53
0.00
23.35
0.21
10.00
0.00
LatemVL3322
12
53.33
6.15
18.35
0.00
27.59
9.95
6.67
0.65
8.33
0.08
3.57
1,62
Qwwn2 S-V1-18 Insernetntt
11
43.33
000
78.35
0.00
16.07
0.51
16.87
0.00
30.00
0.00
20.00
01.0
IntmVV 5-88
10
40.00
800
23.35
4.79
36.67
5.42
13.33
0.0
15.00
0.79
6.67
1.67
Qweaz VL Instuct
43.33
5.78
26.67
0.00
51.72
10.01
26.57
3.62
18.35
0.00
0.00
0.00
GLM 4.1V 98 Thinking
40.00
10.29
30.00
0.00
76.67
25. 33
10.34
2.87
0.00
0.80
6.67
1,67
Qwen2.5 5 VL-128 Instrctt
40/00
0.65
26.92
0.00
30.00
15.22
10.00
3.33
26
26:00
0.00
6.67
0/00
8.27
30.00
16.67
Table 5: Spatial-temporal Reasoning Analysis. VLMs' weakness in temporal forecasting causes worse performance on spatial-temporal prediction tasks (ST-IoU) than spatial reasoning tasks (MCQ Acc).
locations adequately, they struggle when temporal constraints are integrated into reasoning, suggest- ing VLMs lack temporal reasoning mechanisms for comprehensive geo-temporal understanding.
Failure Case Study. We conduct a comprehensive error analysis on representative models, in- cluding Gemini-2.5-Pro GPT-4o, and IntemVL3-3325 Our analysis reveals novel eгтоr patterns emerging from the complex interplay of multiple discontinuous perspectives and explicit temporal reasoning, as illustrated in Figure 8. In Example 1, model fails to understand wall obstructions and calculates routes using direct linear distance, resulting in Topology Error. Model correctly identifies video direction but encounters failures during world direction transformation, leading to Motion State Error. In Example 2, model fails to properly map camera FoV to the spatial layout, erroneously equating location with viewpoint and causing FoV Alignment Error. Model ignores distance and speed constraints, incorrectly estimating traversal time and resulting in Time Interval Error. Detailed definitions and comprehensive analysis are provided in Appendix E.
Input
Respanor Tapelogy Kirer
Respae
Aligoneent Erree
Meddi
Blvweing limm soed woter dlw
Beavellgg: lecaed die casce cat-ol poies aegut liiiinn Bhos
Thaia
nusding accias
Antmer Callo 126 201400
Amwwer Cell 17:8179
GP Di dii 12 10 10w tut 44720
GI R. als (260 coree 12.0
sepelao
lnd Couctss
Vitee
Goled in Fal and
Marion Stato Ervor
Time: Jaterval Errne
Beavedagg
comm oi wating Phey wet long
1700 becel miter lorge wenee speet
suswer Nell 26339-12911 aoo
Ameert Bett 12:1 12:00
femee comasiakendd inkeniaa who somte Koni bocyy Yeansed
CTE in 15.0 al 201 sie.
[or uns di ustee
CT vii 17:31
hetallyy by dhe sonteeed
yont nondts
as wine,, and peofiirnt oer anevel sworhh
Optime: A. cll
Optoee: C-ell
(a) Example
(b) Example 2
Figure 8: Failure Case Study. VLMs lack the proficiency to comprehend align the map data with multi-view video inputs. (a) Example 1: Topology Error and Motion State Error. (b) Example 2: FoV Alignment Error and Time Interval Error.
6 CONCLUSION
In this work, we introduced the Geo-Tempore Reasoning benchmark, novel challenge for evaluat- ing the spatial-tempont intelligence of Visual-Language Models through geographic temporal rea- soning tasks of moving targets in real-world multi-camera networks with large perspective changes. Our comprehensive evaluation reveals significant limitations in current models. The performing proprietary model, Gemini-2.5-Prre achieves only 34.9% accuracy, highlighting critical gap behind human performance (78.61%) %).. Our analysis suggests these shortcomings stem from imbalanced utilization of spatial-temporal context, weakness in temporal forecasting capabilities, and lack of proficiency in comprehending and aligning the map data with multi-view video inputs. We believe GTR-Bend' offers valuable insights and opens up opportunities for research and applications in spatial-temporal intelligence.
10

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Published as a conference paper at ICLR 2026
ANNOUNCEMENT
In an effort to clearly convey the innovative ideas presented in this paper, we utilized Large Lan- guage Model polish and optimize the manuscript's expression, thereby making the content more accessible to the reader.
ACKNOWLEDGMENT
This work is supported by Shenzhen Science and CIG7ZD2002472941100000333
Technology
Program (Grant No.
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A MORE DETAILS OF THE BENCHMARK CONSTRUCTION PIPELINE
A. DATA PREPROCESSING DETAILS
Data Collection. To obtain richer scenarios and diverse moving targets, we collected data from two completely distinct scenarios, Outdoor and Indoor, to construct the GTR- For outdoor data, we used the CityFlow dataset, which an urban Marge-Target Multi-Camera (MTMC) video dataset containing 345 target id of cars and 40 cameras. (Tang al., 2019) For indoor data, used the MTMMC dataset, an indoor MTMC video dataset that includes 3669 target id of people and 32 cameras. (Woo al., 2024) These datasets provide the Re-identification (ReID) information of each target in the video as well as the temporal information of bounding boxes (bbox). Starting with the raw video sequences and associated detection annotations, segment and crop frames based on target bounding boxes. This procedure isolates individual target instances, forming a discretized visual dataset that serves as the foundation for subsequent analysis.
Outdoor
Start
Indoor
Map
Figure 9: Camera Calibration
Figure 10: Trajectory Transformation
Camera Calibration. We perform geometric calibration for each camera to accurately map target trajectories from 2D image coordinates to real-world positions, as shown in Figure This process involves computing homography matrix that establishes precise projective transformation be- tween the camera's 's image plane and a top-down map the environment. The matrix is derived by manually annotating minimum of eight correspondence points between the image and the map. The resulting 3x3 homography matrix enables the transformation of a target's pixel coordinates into accurate real-world map coordinates, forming the basis for trajectory analysis.
Trajectory Transformation. We proceed to spatial-temporal feature derivation with the geometric mapping established, shown in Figure 10. This process begins by projecting the target's bounding box coordinates onto the map using the calibrated homography matrix, followed by applying scaling factors convert pixel measurements into real-world metries such as meters. Subsequently, key motion parameters- including distance, velocity, and direction are algorithmically computed for each timestamp. The resulting trajectory data undergoes a rigorous cleaning and filtering stage, where we remove anomalous points, apply interpolation for continuity, and implement multi-layer validation for temporal, spatial, and identity consistency. Finally, the quantitative motion data synthesized into qualitative textual descriptions using large language model, producing a "Motion Summary" that provides both numerical and narrative insights for each trajectory segment.
A.2 ANALYSIS OF CALIBRATION DATA AND QUALITY
GTR-Bench builds upon two well-established ReID benchmarks: CityFlow Tang et al. (2019) and MTMMC Woo et al. (2024). We leverage the synchronized bounding box sequences and precise temporal metadata provided by these datasets. Specifically, since the CityFlow dataset already in- cludes comprehensive ibrations directly. Our primary annotation effort was directed toward generating high-quality ho- mography matrices for the MTMMC dataset.
of ground-plan homography matrices, utilized these existing cal-
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Published as a conference paper at ICLR 2026
To validate the precision of our spatial alignment, conducted rigorous reprojection error anal-
ysis across all camera views. The quantitative results are summarized in Table 6.
Scene (Dataset)
Statistic
Error (pixels)
Error (meters)
Outdoor (CityFlow)
Average
13.25
2.18 0.20 (S04_c031)
Minimum
2.19 (S04_c039)
Maximum
21.26 (S04.c024)
14.52 (S04_c029)
Indoor (MTMMC)
Average
12.83
1,18
Minimum
1.37 (c04)
0.12 (c04)
Maximum
35.06 (c16)
3.12 (c16)
Table 6: Reprojection Error Analysis for CityFlow and MTMMC Datasets.
Error Margin Analysis. In outdoor scenarios, the mean metric error is 2.18m. Relative to the average inter-camera distance of 984.77m, the relative error is restricted approximately 0.22%. For indoor scenes, the mean error of 1.18m represents a 4% deviation relative to the 30.59m average baseline. The spatial perturbation scale of our designed distractors exceeds 5m for indoor settings and 15m for outdoor settings. Thus, the observed reprojection errors remain well below the threshold of noise.
Manual Verification. While certain cameras (e.g., 604_c022 in CityFlow and c16 in MTMMC) exhibit higher localized reprojection errors due to perspective distortion, we have implemented manual verification stage. This ensures that any potential error propagation is mitigated, maintaining the overall geometric integrity GTR-Bench.
A.3 MORE DETAILS ON TASK CONSTRUCTION
The foundation of our task construction pipeline rests on two core components: detailed environmental maps and corresponding video data. Depending on the scenario, we utilize either indoor floor plans or outdoor road network maps, each richly annotated with essential camera deployment details, including their precise coordinates. field-of-view (FOV) coverage, viewing orientation, and references for scale and direction. Complementing this spatial information, the video data consists of video clips which extract based target movement. Within these clips, targets are highlighted with red bounding box annotations, providing the critical visual, temporal, and behavioral context required for our comprehensive reasoning tasks.
Map Generation and Video Annotation.
Distractor Option Generation. To ensure a rigorous evaluation and prevent models from relying have developed sophisticated, scenario-specific strategy for gener-
on superficial heuristies, ating plausible yet incorrect distractor options. For indoor enviromments, distractors are sourced from cameras located on different floors or in functionally distinct areas, creating choices that are spatially diverse yet architecturally coherent. In outdoor scenarios, we algorithmically generate syn- thetic camera locations along the road network. These virtual cameras are endowed with realistic attributes, including strategically positioned coordinates, plausible viewing orientations, and stan- dard field-of-view (FOV) coverage. Furthermore, to mitigate biases arising from numerical patterns, we implement an ID randomization system that reassigns camera identifiers. This forces the model to reason based on fundamental spatial and temporal relationships rather than exploiting simple nu- merical sequences. These carefully designed distractor strategies crucial for ensuring that our benchmark robustly assesses genuine geo-temporal reasoning capabilities.
B DETAILS OF DATA DISTRIBUTION
As detailed in Table 7, our benchmark features two scenarios with vastly different spatial scales to create a diverse testbed. The outdoor network spans a city block with cameras and an average inter-camera distance of 984.77 meters. In contrast, the indoor network provides building-level cov- erage across three floors with 16 cameras and a much smaller average distance of only 30.59 meters. This significant disparity, with the outdoor scenario being approximately 32 times larger in spatial scale, establishes a robust and diverse challenge for evaluating the spatial-tempont intelligence of VLMS across both expansive and compact environments.
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Environment
Location
Cameras
Avg. Dist. (m)
Distance Range (m)
Avg. FoV (m²)
FoV Range (m²)
Level-1
7
31.38
5.41-60.93
222.81
11.39-449.99
Level-2
30.81
4.71-56.49
141.93
5.29-407.52
Indoor
Level-3
2
9.35
9.35-9.35
53.83
6.44-101.21
Overall
16 30.59
4.71-60.93
166.30
5.29-449.99
Outdoor
Cityllow S0304/05
31
984.77
N/A
N/A
Indoor measurements based on pixel coordinates (0.089 m/pixel conversion).
Outdoor measurements calculated using GPS coordinates and Haversine formula.
FoV data available only for indoor cameras with detailed polygon annotations.
Zero distance indicates cameras identical GPS coordinates with different orientations.
Table 7: Detailed Camera Network Analysis by Environment and Location
C MORE DETAILS OF RESULTS
C.I DETAILS THE REID BASELINE
Implementation and Algorithm. To establish traditional CV reference, we implement ReID- based baseline using a modular pipeline. For each instance, target query image first extracted from the context video. We then employ YOLOv8 to generate object proposals (bounding boxes) within the candidate videos. Feature matching conducted using the SBS algorithm (an enhanced Bag-of-Trices (Luo et al., 2019)) via the FastReid framework (He et al., 2023).
For spatial-related tasks (e.g.. GL., ATT,, the model identifies the camera by selecting the option with the highest feature similarity within the predicted time range. For temporal tasks (e.g., NSF, TE, MTTF), timestamps exceeding class-specific similarity thresholds (0.4 for vehicles and 0.96 for persons) aggregated to form trajectory intervals. For forecasting tasks, utilize future video segments to align with the trajectory time.
Fairness and Task Scope. We implement this bascline for the GL, ATI, NSF, TF, and MTTF tasks. Tasks requiring complex semantic reasoning (i.e., MS and CR) are excluded as they are inherently infeasible for pure RelD methods. Is important to note that this comparison is inherently conservative for VLMs. Standard RelD typically requires extracting visual content from ground- truth options, whereas GTR-Bench restricts access to option-associated video content maintain zero-shot reasoning setting.
Analysis Results. As shown in Table 4, the ReID baseline achieves an average MCQ accuracy of 45.72% and a ST-IoU of 23.05%. Despite having direct access to fine-grained visual context, the ReID baseline's performance is merely comparable to the state-of-the-at Gemini-2.55 2.rPrr (44.90% Acc). This finding validates the significant geo-temporal reasoning capabilities of VLMs, demon- strating their ability leverage graphical maps and video context to effectively reason about and predict unobserved events beyond simple visual matching.
C.2 ANALYSIS OF PARTIAL SPATIAL CORRECTNESS
To further investigate the interplay between spatial localization and temporal reasoning in VLMs, we evaluate the models' performance specifically on instances where they achieve correct spatial grounding. This analysis aims disentangle whether low ST-IoU scores stem from complete failure in reasoning or are primarily penalized by initial spatial identification errors (e.g., incorrect camera ID).
For the NSF, TF, and MTTF tasks, we define ST-IOU (Soft) as a conditional metric. It evaluates the average ST-IoU by restricting the scope to instances where the model achieves a perfect Multiple- Choice Question (MCQ) accuracy as the model accurately predicts the spatial location. As shown in Table 8, most models exhibit a substantial performance gain under this metric. For instance, Intern VLB and Gemini-2.5-Prr demonstrate significant increase in ST-IoU when spatial con- straints are met, suggesting that these models possess latent temporal reasoning capabilities that are often masked by spatial misalignment.
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Outdoor
Indoor
Model Name
NSF
TF
MTTF
NSF
TF
MTTF
ST-IoU
SC"
ST-IoU
SC"
ST-IoU
ST-JoU
SC"
ST-JoU
SC"
ST-JOU
SC"
Owen2: Qwen2-VL--BB 7B Instract
5.78
13.33
0.00
0.00
10.01
19.36
3.62
13.59
0.00
0.00
0.00
0.00
Qwen2-VI Bree2-Vii-2B-lmee V1.22B-nttt
0.00
0.00
0.00
Swen2.5-VL-770 Instrnct
0.00
0.00
0.00
0.00
0.51
3.09
0.00
0.00
0.00
0.00
0.00
0.00
Qwen2.5-VL-32B
LLtemVL3-8B
0.00
0.00
4.79
0.00
5.42
14.79
0.00
0.00
0.79
0.00
1.67
25.05
VL3-388
8:27
35,46
8.20
52.45
11.10
4.37
0.00
61.45
I1.emVV1.32B
6.15
18:46
0.00
0.00
9.95
36.07
0.65
9.78
0.08
0.00
1.62
45.30
GPT-5
12.04
14.21
12.12
0.00
7.34
15.40
11.34
27.58
2.55
0.00
1.75
15.30
GPT-4o
20.53
38.49
0.00
0.00
23.10
30.13
13,00
43.32
0.00
0.00
2.79
20.94
GLM-4.IV-9B-Thinkee
10.29
27.44
0.00
2.72
25.38
33.11
2.87
27.74
0.00
0.00
1.67
25.05
Gemini-2.5-PP
19.13
49.75
13.16
8.42
19.18
37.07
25.11
57.94
28.09
46.87
14.37
39.19
Claude-3,5 Stnnnet
8.05
34.12
6.18
0.00
16.94
25.67
2.60
9.00
4.01
17.68
0.00
0.00
Claude-3 Sonnne
25.75
70.23
24.31
29.96
9.48
9.51
49.45
53.35
SC denotes the ST-HoU (Spatially Correct) metric, which evaluates temporal accuracy on subsets where the
model correctly identifies the spatial location.
Table Comparison of ST-IoU and Spatially Correct ST-IoU,
C.3 ANALYSIS OF SOFT ST-IOU
To more comprehensively evaluate the spatial-temporal reasoning capabilities of VLMs, particularly in cases where models predict cameras within a reasonable proximity to the ground truth, we intro- duce a Soft version of the ST-IoU metric. The Soft ST-IoU employs a stepped scoring mechanism based on the Euclidean distance, d, between each predicted camera Cp. and the ground-truth camera Cgt.
Distance (d)
Soft Score
d < scole scale I < 2 scale
1.0
0.5
2 scale S d < 3 scale
0.2
d > 3 scale
0.0
Table 9: Stepped scoring mechanism for Soft ST-IoU.
We define a base distance, termed scale, to account for the environmental context: 5m for indoor scenes (representing the average radius of room) and 15m for outdoor scenes (reflecting the effec- tive visual context in outdoor street buildings). The soft spatial score S(Cp.. Cgr.) is assigned based on this distance, detailed in Table 9.
The formula for the Soft ST-IoU is defined as the average weighted temporal overlap across N samples:
1 N
Tp. 0 TTt.
ST-
S(Cp., Cgt,)
Tp. To
where Tp; and Tot, represent the predicted and ground-truth time intervals, respectively.
Experimental results Table 10 indicate universal improvement in scores when using the Soft ST-OU metric, with the Temporal Findings (TF) task exhibiting the most significant gains. This suggests that while models often identify the correct temporal window, their spatial localization frequently falls within near-miss radius of the ground truth. Crucially, we compared the model rankings between ST-IoU and ST-IoU (soft) and found the overall hierarchy remained largely consis- effectively benchmarks
tent, with only minor fluctuations. This stability confirms that the Geo- Temporal Reasoning capabilities across different models and provides a robust evaluation across varying levels of spatial precision.
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GTR-Outdoor
GTR-Indoor
Average
Model
NSF
TF
MTTF
NSF
TF
MTTF
Std.
Sonf
Skd
Sinf
Std.
Soft
Std.
Sont
Std.
Son
Std.
Sonf
Sid.
Soft
Sid
Solt
Gemini-2.5-pp
25.11
78.09
50.73
14.37
20.74
19.84
Chude---7-sumee hieet
25.75
26.37
8.90
26.79
21.97
21.97
9.48
18.53
9.51
21.54
3.56
5.84
13.19
20.17
1
8.27
8.27
8.20
18.51
20.58
20.58
11.10
12.95
4.57
11.54
10.24
11.93
10.46
13.97
3
(PPT-40
13,00
16.65
16.40
9.50
GPT-5
12.04
14.97
12.12
13.55
7.34
7.92
11.34
10.62
2.55
10.22
1.75
4.50
7.86
10.36
GLM ULM-4.1VV 4.14 9B Thinking
10.29
10.83
0.00
14,83
25.38
25.38
2.87
8.45
0.00
13.44
1.67
2,00
6.70
12.49
3
Claudo-soneet4
11.46
16.41
6.30
Qwen2. 5-VL--28--nn-
0.65
12.55
0.00
20.52
15.72
19.84
3.33
7.11
0.00
7.00
0.00
2.87
3.38
11.65
Qwen2 VL Instruct
5.78
9.11
0.00
6.83
10.01
10.27
3.82
9.88
0.00
2,00
0.00
1.11
3.24
6.62
9
10
0.65
2.34
11
11lemV1.3-8B
0.00
0.00
4.79
8.10
5.42
0.00
0.00
0.79
4.50
1.67
1,67
2.81
1.29
Qwen2.5 VL Instract
0.00
9.95
0.00
10.86
0.51
2.92
0.00
2.14
0.00
1.07
0.00
0.31
0.09
4,54
12
12
Uwenz Instuuut
6.48
0.00
0.21
Table 10: Performance comparison on GTR-Bench using standard ST-IoU (Std.) and Soft ST-IoU (Soft.) metrics.
C.4 ABLATION EXPERIMENT OF MAP CONTEXT
We utilized graphic maps as prompts to maintain spatial consistency across cameras. To validate the effectiveness of this design in preserving inter-camera spatial relationships within our dataset construction pipeline, we conducted ablation study on map context.
Prompt Design of Map Context. We elaborate on how our prompt design endows the model with rich spatio-temporal context by incorporating spatial relationships through two primary mechanisms, as Figure 11 and 12:
Graphic Map Context: We utilize rendered map image based on real-world geographi- cal information from OpenStreetMap. This map precisely annotates the locations, orienta- tions, and FoV of all candidate cameras. Furthermore, we explicitly included north-arrow and a scale bar on the map to provide the model with absolute geographical orientation and scale references.
Textual Map Context: As a supplement and abstraction of the map information, we de- signed a text-only prompt to describe the spatial relationships between cameras. This ap- proach translates topological relations and relative positions (e.g., Camera A is located 50 meters northeast of Intersection B, facing southeast) into structured natural language de- scriptions.
Construction of Textual Map Context. We constructed the textual map context using distinct strategies for indoor and outdoor environments, tailored to their specific spatial characteristics:
Indoor Scenarios: Given the smaller spatial span and finite number of cameras in indoor settings, we employed a manual annotation process structured into three hierarchical levels:
Region and Connectivity: We divided the environment into distinct functional re- gions and identified key spatial connection points such as entrances, exits, and stair- wells- to define the topology.
2. Camera Geographic Auributes: We described the camera's location, orientation, and FoV. The FoV corresponds to the visual representation on the map, specifically the blue area enclosed by thin black line originating from the red camera icon.
3. Camera View Attribttes: We provided a detailed description of the camera's 's actual field view, summarizing the visual content observed in the video from camera.
Outdoor Scenarios:
Due to the extensive spatial coverage of outdoor environments, we adopted an automated approach leveraging geospatial data. Using the latitude and longitude coordinates of each camera, we queried OpenStreetMap to retrieve the surrounding road network topology. The resulting textual context integrates information regarding roads, intersections, and specific camera metadata to reconstruct the outdoor spatial environment.
Results and Analysis. We benchmarked two SOTA models, Gemini-1.5-pl and Intern VL-38B, on GTR-Bench. The quantitative results are presented in Table 11.
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Graphic Map Context
leveL_
W
Textual Map Context
Primary Areas & Key Connection:
The prämary
a large Warehouse occupying the northern portion of the level, extending approximaiely 60 meters csst-west and 30 north-south: south:
southoest quadratt, measuring coughty meters by maters; outdoor Parking Lot the southeast, spanning upgroximately 40 meters west and 25 meters north- south.
Lobby stuated
Key connection points
door the south end of the W Warcheuse's is west wall (approcimarely 10 meters from the southwest comer)
that directly the Stairwell; immedistely this doos, external cutmane exits to
Parking Lot:
south end the Stairwell conseets Edbby
positioned about
of
Lobby's soathenst comer:
the Lobby's castern entrance opens directly into the Parking Lot, located approximatzly 15 meters cast the
stairwell point;
the southeust corner of the W Warchouse connects to the Right Stairwell via doorway, which also links to the roughly
northeast
Parking Lot the through additional
stairell
Location and Abstract
Camera mounted on the east wall of the Warchonse, near its soutscast comer, approsinatelly meters Right Stairwell field view nurthwestwwrd across the central aisle of the Warchouse, covering a span of roughly 35 meters and capturing
north
depicted blue sector,
along the southen Camen positioned at trssitionn point between abby's is castem
Parking Lot, facing southestt Its FOV spans approximately 25 moters disgonally across the parking area, encompassing the first Camera located at the northeast corner of Parking Lot, near the entrance connecting the Right
of parking sprees.
Stirwell, onented soutbwestward, 8OV
widhh of lot's noethem section, extending adjacen walloway.
about meters southoesst to three parked vehicles
Camera FOV: This camera (ID: 1) averiuoks a iile central aisle in lage warchouse, The view spons main corridor nunning notth-south, flanked by till stongo racks both esst and west sides, Stacks of
polletized boses occapy floor space along aisie, workstations staging aceas visible
(nowth), No nomed roads or extemal areas are visible it entrely an indoor induerial storage sprice. parking lot adjaczet
FOV: Camem
mulli-level
modem building
open garage entrance marked od "3" The view spans central paved walkway between parked vehicles, extending toward street lined with orange trafii cones and bordered by trees and second building. No
named
parking anco acvess underground or covered gamge strectue. Diractionall. the camera fices southenst, copturing movement from the
visible,
includes pedestrian pathways
garage
roadside.
Camer FOV: Camen covers covered oundoor parking arcs adjacent to building entrance. captures row of parked vehicles (white SUV, black SUV, gray secan) aligned slong marked parking spaces. The view building's
ficade and extends toward open entryway driveway on the left, where people are entring/exxiing No named roads are visbll: the seene confined to privose semi-privite parking entry commercal insiiutiona building.
includes podestrian walkway
Figure 1I: Graphic Map vs Textual Map (Indoor)
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Published as a conference paper at ICLR 2026
Graphic Map Context
C035
COOUR
Textual Map Context
Map layout:
Asbury
Northwest, intersects intersects Univenity Avenue.
Ashton Place: runs Southeast Nurthwest, intersects with intersects with Decorah Street.
Docorah Street
Irving
trvvig Street: rars Nuthasd-Narthwes. intersects widi intersects with Decorah Street, University Avenue
OTTgagnn Street: rins NorthSuu South, intereets with internects with Univursity Avenue.
Univesityy
East, intersects intenects Asbury Irving O'llagen
Intersection relationsháps:
Asbury Rood & University Avenue:
moter north From interscation of University Avenue O'Taggnn Street
meters
University
Irving
meters northeast from intersection of Ashttnn Place & Decoral Street
northeast from intersection of Decorah Street
Street
Univerityy Avenue O'Hagen Street
moter south from intersection of Asbury Road & University Aveme intersection University
meters
170 meters northeast from intersection of Ashton Place & Decorab Street
120 meters northeust from intersection of Decorah Street
Street
Univesity Avenue lrving Street:
257 mater south from intersection of shury Road & University Averu
meters
University
142 meters east from interscction of Ashton Place & Decorah Sizeet
145 meters southeast frum intersection of Decorah Street Irving Stncel
260 meter southwest from interscction of Asbury Road & University Avenue
soulowest intersection University 12 meters west from intensection Univensity Avenus & lrving Street
O'Hagen
62 moters south from intemection of Decorah Sirvet Irving Street
199 meter soutlwest from interscction of Asbury Road & University Avease
soutbwest from intersetioon of University Avenue O'Hlagen 145 meters northwest from intersction Universit Avenue Irving Street
62 meters north from intersection of Ashioa Place & Decarah Street
Camera detaits:
2035:
LLocatin:: meters northesst
Asbury University
Orientation: facing southeast
Covering
nage approsimately
capturing iraflic
adjacent commercial properties iscluding Finley- Homecare. The view extends from the grassy median banier the for side of the read, encompassing maltipl lanes and parked vehicles.
c039:
Location: 26 meters east From intersection of University Avenue Irving Street
Orientation: facing
Field of View:
<091:
Location: meters east intersection University lrving
Orientution: facing southwest
mage approximately moters with visibility extending adjacent parking lots and cross screet traffic signal.
Field VfView: covering
Figure 12: Graphic Map vs Textual Map (Outdoor)
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GTR-Outdoor
GTR-Indoor
Model
Map Type
ATI
VIS
G1.
CR
NCF
TF
MITT
ATI
MS
G1.
CR
NCV
TF
MITF
Prrmini-Pro
image
60.00
46.67
33.31
56.67
19.13
13.16
19.18
63.33
13.35
26.67
70.00
25.11
28.00
14.37
Gemini-Pro
text
43.33
56.67
60.00
66.67
6.83
0.00
1.11
13.33
43.33
73.33
83.33
27.76
19.76
21.42
image
InternVI-3388
test
38.33
33.33
76.67
36.36
3.06
1.08
3.99
30.00
33.33
56.67
73.33
4.36
0.55
9.20
Table 11: Ablation Study of Map Context. The table compares the performance of models using Image-based vs. Text-based map contexts across Indoor and Outdoor subsets.
The results clearly indicate that there no single best method for encoding spatial relationships. The choice between visual (image) and textual context significantly impacts performance, and the optimal choice varies by task and environment: (1) Text-based prompts tend to excel tasks requir- ing abstract or logical spatial reasoning. For instance, both models perform significantly better the GL and CR tasks with textual context, particularly in the indoor setting. This suggests that for tasks involving topological understanding and logical inference, structured textual description is more effectively utilized by the models. (2) image-based prompts demonstrate a strong advantage in tasks that rely understanding physical motion and trajectories. In GTR-Outdoor both models show improved performance on motion-related tasks such as MS TF and MTTF when provided with graphic map.
D PROMPT FOR CONTEXT UTILIZATION ANALYSIS
Context Utilization Analysis Prompt
[Reasoning Text]
The video from camera c07 shows the target (ID 4) entering an indoor lobby area from the right side of the frame and walking towards the left. The last frame at 12:00:28. 8.00 shows the target moving towards a hallway... the location as the southern entrance of the building...
[Rule]
Please analyze [Reasoning Text] and determine whether it contains the specified key elements. For each element, mark it as 1 if it is explicitly mentioned or referenced in the text, otherwise mark it as O.
Camera }
1. Location: Whether the specific position, direction, or area of
camera {i} is mentioned
2. Time: Whether the specific time information of camera {i]
recording is mentioned
3. Motion: Whether the movement, direction, or behavior of targets in camera {i} is described Camera { i+1 }...
[Output]
Please return the result in JSON format: ["camera(i]_aame _nallsiss" ("Location": or 1, "Time": or 1, "Motion": 0 or 1}...
Figure 13: Context Utilization Analysis Prompt
We employ the Qwen-mat max sourcd-source model to analyse the reasoning content of various models across seven tasks in GTR-Bench, examining whether the reasoning process contains the three key
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elements of Location, Time, and Motion State for the cameras specified in the given questions. The objective is to evaluate the utilization rate of models for spatial, temporal and motion modal context. Notably, for Timeline Reordering tasks, since no Camera Time information is provided, Time elements are not included the statistical analysis. The specific prompt used is shown in Figure 13.
E DETAILED FAILURE CASE STUDY
E.1 DETAILED ERROR DEFINITIONS
Our detailed error analysis reveals systematic failure patterns across different task categories and model types. Based on our experimental results, categorize the error patterns into four main dimensions: spatial reasoning errors, temporal reasoning errors, motion reasoning errors, and rea- soning mechanism errors.
Spatial Reasoning Errors. We identify two primary categories of spatial reasoning failures:
Topology Error: Models frequently fail understand complex spatial constraints and lationships. This includes (1) constraint understanding errors, such as failing to account for road network constraints (one-way streets, restricted areas) and indoor architectural straints (walls, obstacles); (2) connection relationship errors, where models misunderstand the connectivity between roads or indoor floor transitions.
FoV Alignment Error: Models struggle to understand the correspondence between cam- era field-of-view (FoV) regions and their mapping to environmental maps, particularly in scenarios involving multiple cameras with overlapping coverage areas.
Temporal Reasoning Errors. Our analysis reveals one critical temporal reasoning limitation:
Time Interval Error: Models misestimate distance, speed, related quantities, leading to erroneous time interval estimates. Models demonstrate significant errors in temporal interval estimation, often making predictions that deviate substantially from actual time spans, particularly in scenarios involving variable movement speeds or complex temporal patterns.
Motion Reasoning Errors. We observe category of motion-related reasoning failure:
Motion State Error: Models fail to correctly transform motion states from video coordi- nates to world coordinates, leading errors in directional and other world motion states. This coordinate transformation failure affects the prediction of future target motion trajec- tories.
Reasoning Mechanism Errors. Our analysis identifies three fundamental issues in the reasoning process itself:
Information Bias: Models exhibit systematic preferences and over-reliance certain types of information (temporal, spatial, or motion) while neglecting others. This bias leads to imbalanced reasoning where models may prioritize spatial information while ignoring temporal constraints, or vice versa.
Step Jump Error: Models frequently skip crucial intermediate reasoning steps, jumping directly from observations to conclusions without proper logical progression. This results in incomplete reasoning chains that lack the necessary intermediate analysis.
Reasoning Inconsistency Error: Within the same reasoning task, models often generate contradictory reasoning steps, indicating a lack of coherent internal reasoning processes and logical consistency.
E.2 DETAILED ANALYSIS
Benchmark-Speeifne Error Patterns. Our analysis identifies several error categories that are uniquely characteristic of geo-temporal reasoning scenarios and rarely appear in conventional
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ppatial-temporal intelligence evaluations. These enrors stem from benchmark's distinctive fea- tures: (1) multi-camera networks spanning large geographic areas with 2-3 distinct viewpoint types (camera views and map perspective), (2) explicit temporal reasoning requirements beyond simple sequence ordering, and (3) the need to infer unobserved states between fragmented observations.
The most prevalent errors reflect these unique challenges. Topology Error (28.6%) and FoV Align- ment Error (16.8%) dominate our error distribution, representing failures in understanding com- plex spatial constraints that are absent in single-scene benchmarks. These include road network topology (one-way streets, restricted areas), architectural constraints (walls, obstacles), and camera field-of-view mapping- mapping-challeene challenges that only emerge when reasoning across multiple, discontinu- ous viewpoints. Motion State Error (20,4%) represents another benchmark-speciff failure mode, where models struggle with coordinate system transformations and precise velocity calculations based on map scale, rather than the simple directional motion understanding required in traditional benchmarks. Time Interval Error (12.4%) occurs when models fail to understand how temporal constraints (too long or too short time windows) invalidate certain path predictions- challenge that doesn't t exist implicit temporal reasoning scenarios.
Category
Topology
FoV
Time.Int
Motion
Info,Blas
Step-JJump
Reasoning_Inc
Overall Percentage
97
57
42
69
23
40
11
28.6/%
3.2%
By Task Type:
Antival Time-laterval
7
17
12
1
Metion Stute
10
11
13
13
4
Causal Reurdering
9
17
Spot Pocecasting
12
Trajectory Forzcasting
20
4
Multi- Torpet Tajectory Forecasting
13
16
By Seunario:
Outlour
50
30
20
37
10
23
By Model:
25
GPT-40
34
19
14
22
37
13
17
Table 12: Detailed eπror statistics. Statistics of 177 cases, each error type is counted independently, resulting in total error count of 339 across all categories.
E.3 DETAILED FOV ALIGNMENT ERROR
To provide a more in-depth analysis of Map-Vider Alignment Errors, specifically FoV Alignment Error, we first distinguish between two forms of Field of View (FoV) defined in GTR-Bench.
FoV to Map Misalignment. This error type represents conventional failure where the model identifies object in the video but fails to locate its corresponding position on the map. As illus- trated in Figure 14, the model successfully reasons from the video content that the target enters a "stairwell" (observed in camera c07). However, it fails to align this visual feature with the map struc- ture, specifically missing that this stairwell corresponds to the location of the ground-truth camera c08. Consequently, the model provides an incorrect answer despite correct visual perception.
FoV Misinterpretation. This eгror occurs when the model struggles to interpret the geometric representations of FoVs on the map, particularly when multiple FoVs overlap. Figure depicts scenario where the model fails to understand the spatial relationship between cameras. For example, when analyzing a trajectory involving overlapping cameras (e.g.. moving from c12 through c09 to cl4), the model may incorrectly assume disjoint time intervals. This stems from failure to recognize that the FoV of the intermediate camera (c09) spatially overlaps with the destination camera (c14), leading logical errors in trajectory timeline prediction.
In summary, while current VLMs encounter challenges with both alignment types, the misinterpre- tation of FoVs is more prevalent and critical issue in geographic reasoning tasks involving complex map topologies.
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Context
Camera
level_..
Comera
Question
Based the provided [start vidco] and [end vidco), infer which camera target passed through between the start point (c07, 12:00:32.760-12:00 600) and end point
(c03, 12:00:48.00 00004888600-111 118.6000-120000 600-12:00:599920)
Options: c05 c02 C. c01
GT: D.c08
The target's path is from the main hall level up the stairs, level 2. The start video (c07) shows target entering stairwell on level at 12:00:40. end video (c03) shows her on the stairs urriving at level 2 at 12:00:48. The 8-second gup is the time spent on the stuirs, where cameras are located. Therefore, the question likely refers camera the target pussed immediately before the start point.
level map, reach stairwell location the target must have come from the main hall. Camera c02 is positioned in the main hall looking west, directly covering the path towards the corridor where c07 is. Thus, the target must have passed through c02 field of view just before reaching c07.
In this case, the model correctly understood from the video that the target entered the stairwell, and it also utilized the map to acquire the starting and ending locations (i.e., camera c07 in the first-floor hall and camera c03 in the second-flos -loooor stairwell). However, error occurred during the process of aligning the "stairwell" from the video to the map. The correct alignment should have been at c08 location, but the model crroncously selected c02. This is a typical case of incorrelllly locuting view item on the mup.
Figure 14: FoV to Map Misalignment. The model correctly identifies the semantic location ("stair- well"") from the concrete video view but fails map the geometric location the map.
F DETAILED HUMAN ANNOTATION AND EVALUATION PROTOCOL
To ensure the reliability of our human evaluation results, we implemented rigorous screening and training process for the personnel involved in data annotation and performance assessment.
F.1 COMPOSITION OF QUESTION ANNOTATORS AND BENCHMARK EVALUATORS
We established strict qualification criteria and organizational protocols to maintain high data quality and evaluation independence:
Professional Field: The dataset was constructed by researchers with profound academic backgrounds in 3D vision.
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Published as a conference paper at ICLR 2026
Context
Camera
leve
Comera
Question
Based the provided (start vidco] (c12, 1.00::44.50-12:22199.4444 and (end video) (c16, 12:02:25. 012:25.840-111 12:00:2222.32) and knowing the target passed through camera c09, infer the time period when the target appears the middle camera.
Options: A. 12:02:23.344 12:30000120200888:
12:02:24. 000-12:02:25.20 .200
12:02:
800
GT:
The target's appearance the intermediate camera (c09) must occur chronologically between the last sighting the start camera (c12)
first
sighting end camera (c16). The target is last seen by c12 12:02:19, 192200 and first seen by cl at 12:02:25.840 840. Therefore, the time window for c09 must fall
entirely between these two timestamps. Option 12:02:24.000 1000002:02..0 12:02:25,200 200, is the only interval that fits completely within this logical time gap. Options and
after the target is already seen in c16, and Option starts before the target has left the view of cl2.
O In this case, the model needed to predict the time interval for the intermediate camera(s) passed. However, the model failed successfully perform the abstract Field-of- View (FOV) alignment and did not correctly handle FOV overlapping. Consequently. y, it was unable to deduce the fact that the FOVs of c09 and c16 partially overlap, which led incorrect time window setting, ultimately resulting the inability select the correct
Figure 15: FoV Misinterpretation. The model fails to perceive the overlap between the Abstract FoVs of adjacent cameras on the map, leading to incorrect temporal or spatial reasoning.
Professional Experience: These researchers possess extensive experience in multi-view geometry, scene understanding, and spatial intelligence.
Knowledge Requirements: All were required to have deep understanding of core con- cepts such as spatial relationships, camera poses, and object motion, and be capable of complex spatial reasoning.
Distinction between Annotators and Evaluators: The personnel responsible for question annotation were a separate group from those who conducted the benchmark evaluation, ensuring the independence of the evaluation results.
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Published as a conference paper at ICLR 2026
F.2 ANNOTATION GUIDELINES
To maintain high standards of data integrity and annotation quality, we implemented the following guidelines:
Multi-reviewer Mechanism: During the dataset construction, each sample generated by our rules was cross-validated by multiple other independent annotators.
Dependence on Maps and Camera Videos: We ensured that questions could not be an- swered solely through the map, camera video, or common sense, but required the synthesis of information from all provided map and video inputs.
Clarity and Unambiguity of Context and Questions: We ensured that the content of the map images and camera videos was clear and that the question text was precise and unambiguous.
Uniqueness of Answer:
We ensured that there only correct distractors were designed to be plausible.
and that the
Opatio-Tempora Span Coverage: All seven question types the benchmark were re- quired to have diversity in their temporal and spatial spans to ensure the variety of the problems across different spatio-temporal dimensions.
Ethics and Licensing: We ensured that all videos and maps were either fully copyrighted used with the author's consent, and we maintained the same strict standards for personal privacy information as the reference datasets.
F.3 EVALUATION GUIDELINES
The final human performance score was determined by multiple independent evaluators, and their average accuracy was taken. This multi-evaluator mechanism effectively reduces the randomness caused by individual differences, making the evaluation results more statistically significant and stable.
F.4 CONSISTENCY WITH MODEL CONSTRAINTS
During the evaluation, evaluators had access only to the exact same information as the model input, namely the map, the camera videos and the question text. They were strictly forbidden from using any external knowledge or tools for assistance.
To maximize the evaluators' ability to utilize visual information, also designed flexible and professional front-end evaluation page, as shown in Figure 16. This interface was built to: (1) Display Map and Camera Information: Fully present all map and camera information necessary answer the question. (2) Enable Interaction: Allow evaluators to zoom in on and draw on the images from both contexts mark key elements during their reasoning process. This design ensured that evaluators could fully leverage the provided visual information for accurate reasoning and decision- making.
We provided human evaluators with unrestricted evaluation time, which differs from the constraints set for the model evaluation. This was primarily because our goal was to measure the optimal performance of humans under ideal conditions, thereby more clearly revealing the gap between the current model's capabilities and human abilities in complex spatial reasoning.
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Published as a conference paper at ICLR 2026
TTR-Bench Evaluation System
Carsea Irages
Question
"Thy and
Optioss
am
Fi84
Evaluation System. The interface is designed facilitate human reasoning by fully presenting necessary map and camera information. It enables interaction, allowing evaluators to zoom and draw on the images to mark key elements during the reasoning process.
Figure 16:
G MORE EXAMPLES
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Published as a conference paper at ICLR 2026
Map
Video
Level
C12
CB8
Level
C99
Geo-Location
Causul Reordering
Question: Based on provided vidcosk]
Question: Based on the provided [local map] and [videcso * *)) analyze the uaget's activity trajectory.
videoskt, inker which camura the arreet pessed through between
12:01:28 160-12:01 280) and
Please infer the gorved urder which turget pussed through these cameras.
(e03, 15:1::00.300-1202222 800).
Options:
Options: Al c0P c12
cl2
Arrival Iime-lnterval
Question: Based on the provided (start video [X] (e07,
12:01:28. 160- 000-12:0::362200 and (end video (c03,
Next Spot Forecasting
12:01:50,585 560-00 560-5600000004 and) knowing target passed through camera e08, infer when the tirget anived the intermediate camera.
Question: Based on the provided [local map] and (videopllt which from the following list will most likely saptuts option
tagget next?
Options: A. 12.01 012013355 160-116122111 800
and infor time range.
12:01:34 140111000::0000
Options:
C. 12:01: 900.1200010000 01-487200
GT: D. c0008 100335..6-11201:47.80
12201:34.900122:--1
Trajectory Forecasting
Motion-Stute
Question: Based provided [local map) and
Question: Based on the provided (start vadco ( 1e12,
(videss * * prdict Ubs next two camurns that the largel
5:0-12:02:19.200
will licely pass through. You need select correct option
12:02:25 (tt.112:::323300,.mm the internediate camera c09, infer targer's motion
sequence infer
range sequence simul
the intemediate
Options: A. c09
Cl
D. D.003
c12
period
GT: c12 12:02:14 560-12:02:19:440
Options:
360-1 2.0-1220200..44
arget
2.1 meters...
Multi- Multi-Target Trajectory Forecasting
sreed for 1.4 meters, then moves cast speed of for 1.8 finally moves hund spoel of 1.0 meters,
Question: Rased provided map]
[videss * * showing the movement tmaeecraris of two [Target], prediet where and when these [Targe]] persn will must likely must.
langet quickly at a speed of m'9
Options:
A. c14
D. distance of meters distance of0 meters
C. ctt
D.
12:02:03,480 12:02:04.800
Figure 17: Indoor Examples
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Published as a conference paper at ICLR 2026
Map
Fake Camera
SPA
Resl Camera
c011
JeTreson Park
Waliin Tap
e022
c065
Video
0016
C017
C018
C019
C920
Geo-Location
Arrival Time-Interval
Motion-State
Question: Based the provided Estart videosk
Question: Based on the provided [slant
Question: Based the iddd [start
video (e011, 00:37.755.
video x (c016, 12:00: 200-
somera the lageel passed through belween
12:00:42 855) and (end video [ (e0200
12:00:22.20 22.00022220) and (end video } (2018,
12:00 12:00:2. 200) and end point (e018,
12:00:33.0 905-12:000 16:-12200:00000 (2000:505505)am knowing urget passed through camera c019,
12:00:37 37.75--555551 71:7755-11200 1:00042 855), and the
camera cll
12:00:37.755 7::77550000::255555
infer when the target arrived at the intermediate camera.
target's motion state during the
Options:A. et21 B. v022 CL0012 D. c078
period.
Options: A. 12:00-13 23.229 12:01:0000 00:43.579
Options: A. The moved cast
Causal Reordering
Question: Based
provided
C. 12:00:43.9000 1:00012::0::88.00 740
10.0 for seconds, cover
map] and Iv steoss * *1 analyze the target's activity trajectory. Please infor conrect order which target passed through these Option::A
17:00:40.900-22200443.4.
disunce of 109 meters,
Next Spot Forecasting
C. ...t speed of 30 mix...
Question: Based
provided [map]
D... dismmne of meters.
and [vidko l. which camera from the following list will
Trajectory Forecasting
C. et220 c021 c019
nexe? You need to select one option as the and infer
Question: Based on the provided [local map) and (vádeus A *. predict the nexl two cameras that targel will likely pass throug. You need select option sequence infer ringe sequence simultanoouslly OptionsA, D. e.2020 e022
D 90100-00 <020 c021
Options: A. c0788, et20 c065 2016
Mulge-Target Trajectory Forecasting
12:00-43, 905-12:00
Question:
Bosed on the provided [local map) and (videos mu
* showingg
tnjecoories [Target].
C019
predict where and thene [Tanget] will most likely
Options:
cd65
c078
GT:AA.0l 23:00:43.279.1111 19:.22299112:033.599
-ett hhanc I...n ond
Figure 18: Outdoor Examples
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Published as a conference paper at ICLR 2026
Map
Video
C07
8
Based on the provided [local map] and [camera information], which camera from following list will most likely capture the target next? You need to select one option the answer and infer time range. Options: A. c05 c02 c06 c08
Question:
GT: c08 22:00:26.600-12:00::3
Reasoning:
Analyze Current Location and Trajectory: The video from camera shows the target (ID 4) entering an indoor lobby area from the right side of the frame and walking towards the left. The last frame 12:00:28.04 28.00 shows the target moving towards hallway. By comparing the visual elements in the video (glass doors, open hallway) with the map,
identify the location the southern entrance of the building. The target path from this entrance (covered by c07) moving north-west into the building. 2. Evaluate Potential Next Cameras:
D. c08: The map shows camera c08 is located in the hallway directly adjacent to the lobby where c07 is. Its field of view covers the path the target is taking. This makes the immediate and most probable next camera.
A. cOS: This camera located in large hall the north. be seen by cOS,
target must first pass through the area covered by c08.
outside in the parking lot. The target is inside and moving further into the building, away from c02.
B. c02: This
C. c06: This camera is also outside, near the entrance. The target would have likely building and being captured by c07, not after. 3. Time Estimation: The last known timestamp for the target in c07 view is
been view before entering
12:00:28. 040. Since the field of view of c08 is contiguous with c07 along the target path, the target will enter c08 view almost immediately. A reasonable estimated time range for the capture by c08 would start right after the last sighting, approximately from 12:00:28 to 12:00:38, allowing a few seconds to traverse the camera view.
Answer: D. c08 12:00:28-12:00.22
ST-IoU: 0.87
Figure 19: Correct Example
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Published as a conference paper at ICLR 2026
Map
Video
C02
2
Based on the provided [local map] and [eamera information]. which camera from following will most likely capture target next? You need to select one option as the answer and infer a time range. Options: A. c04 B. c06 C. c02
Question:
GT: A.c04 12:03:09.6600-12:00:18.4
Reasoning:
Initial Observation and Target Movement
The provided vidco frames are from camera c02, which is positioned on the building and overlooks the outdoor parking lot. The target (ID: 10) is observed walking from the open area of the parking lot towards the building entrance on the west side. The timestamps from 12:02:57 12:03:14 show the target getting progressively further from the camera and moving towards the left the frame, which corresponds north- westerly direction on the map.
Current Position Analysis and Next Camera Prediction
The target's last known position at 12:03:14 is at the edge of c02's field of view, right before the building entrance. According the map, camera c06 is positioned to monitor this specific entrance area. Therefore, as target continues on their path, they immediately enter the field of view of camera c06, Camera c04 is on the far eastern side of the parking lot, and the target walking away from it. Camera c01 inside the main building at the castern end, which not along the target's path. Thus, c06 the only logical next camera.
Time Estimation
Given the last timestamp of 12:03:14, the target will enter c06's view within few seconds. A time range of 12:03:15 to 12:03:30 reasonable estimate for the duration of the capture.
Answer: B. c06 12:00:15-12:03:30
ST-1oU: 0
Figure 20: Incorrect Example
31


Reviews:
[{"review_id": "6QVs5JeADm", "reviewer": "Reviewer_EAyu", "summary": "This paper proposes GTR Bench a geo-temporal benchmark that links real multi-camera videos with map context to test VLM reasoning. The paper studies the missing evaluation of VLMs on joint map plus multi camera spatial temporal reasoning unlike STI Bench or MapEval which use only videos or only maps. Experiments like Table 4 show that humans get 78.61% accuracy while popular modles like Gemini 2.5 Pro gets 34.93% showing current models fail on this benchmark.", "questions": "- In Table 4 (page 8), Gemini 2.5 Pro scores 25.11 on Indoor NSF, while in Table 5 (page 9), its Indoor NSF-MCQ accuracy is 43.33. These two metrics both test indoor navigation reasoning but show very different performance. Could you clarify if these datasets overlap or if the question format accounts for this difference?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The reviewer finds the proposed idea to extend spatial temporal VLM benchmarks such as STI Bench for driving to true geo-temporal multi camera settings to be interesting.\n- A surprising fact is that even strong proprietary models still lag humans by more than 40 points on the same tasks which clearly supports the need for this benchmark.\n- Interestingly, experiments in Table 4, Table 6, and Figure 4 report 12 models across indoor and outdoor tasks and show consistent drops when map reasoning is required.\n- Writing is mostly clear.", "weaknesses": "- The LLM based question and distractor generation in the dataset construction section does not show an ablation on how prompts preserve spatial relations across cameras and the closest related method is automatic MCQ generation in STI Bench. \n- The experiments do not compare against easy multi-target multi -camera tracking or ReID baselines that already exist for CityFlow and MTMMC even though the data source is aligned so such baselines should have been trivial to include.\n- Some typos: Line 179: multipe -> multiple; Line 180: resoning -> reasoning; L248: exmples -> examples; L146: geograohic -> geographic.", "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": "U9U0uvFZIw", "reviewer": "Reviewer_tV6o", "summary": "This paper presents GTR-Bench, a benchmark designed to evaluate models on geo-temporal reasoning, which requires understanding spatial, temporal, and motion relationships across multiple camera views and corresponding maps. It defines seven tasks spanning basic perception and higher-level reasoning, using diverse real-world indoor and outdoor video data. The benchmark contains 420 annotated examples drawn from 364 video clips and introduces a new metric, Spatio-Temporal Intersection over Union, to jointly assess spatial and temporal accuracy. Evaluations of twelve vision–language–action models show a wide gap between human and model performance, with the best system achieving only 34.9 percent accuracy compared to 78.6 percent for human participants. The results highlight persistent weaknesses in temporal reasoning, spatial alignment, and integration of map-based context. GTR-Bench establishes a standardized and challenging platform for advancing compositional reasoning and embodied intelligence in multi-view visual understanding.", "questions": "1. How sensitive are results to the frame sampling budget (e.g., 20 vs. 40 frames)?\n2. What are the quantitative calibration errors (pixel–meter, time sync) and their propagation to label uncertainty?\n3. Could a soft ST-IoU variant (e.g., adjacency-aware) better reflect partial spatial correctness?\n4. How were LLM-generated distractors validated for semantic plausibility and difficulty?\n5. What was the human evaluation protocol—annotator expertise, inter-rater agreement, and consistency with model constraints?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Defining a new reasoning paradigm—geo-temporal reasoning—that fuses spatial, temporal, and motion inference across non-overlapping camera views. The task design moves beyond egocentric video QA benchmarks.\n2. The benchmark construction is methodical and transparent, featuring rigorous trajectory calibration, LLM-assisted task generation, and human validation. The ST-IoU metric elegantly integrates spatial and temporal correctness.\n3. By quantifying the gap between human and model reasoning, GTR-Bench establishes a standardized, scalable platform likely to become a reference benchmark for spatiotemporal multimodal reasoning.", "weaknesses": "1. The 20-frame input cap may underrepresent temporal dynamics, biasing against models with differing context lengths. Frame-sensitivity analysis is absent.\n2. LLM-generated distractors may embed linguistic artifacts exploitable by text-heavy models; indoor/outdoor scene imbalance further limits transferability.\n3. Calibration relies on manual homographies and lacks quantified reprojection or timing errors, which could affect motion and interval labels.", "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": "61kYKZ1Wpm", "reviewer": "Reviewer_R6Jx", "summary": "This paper proposes the Geo-Temporal Reasoning Benchmark (GTR-Bench) to evaluate the geographic spatio-temporal reasoning capabilities of vision-language models in large-scale camera networks containing maps and multi-view videos. Experimental results show that current mainstream models are still insufficient for this complex task. Even the most powerful Gemini-2.5-Pro ​​achieves only approximately 34.9% accuracy, significantly lower than the human-level performance (78.61%). Analysis indicates that the problems arise from the uneven utilization of spatiotemporal context, weak temporal prediction, and difficulty aligning map data with multi-view video input.", "questions": "1. For the 7 different types of tasks, I am somewhat confused about whether the answers for different task types are multiple-choice questions (MCQ) or fill-in-the-blank questions.\n\n2. Based on the above question, what criteria did the authors use to divide the evaluation metrics, and why are some tasks evaluated using MCQ Acc while others use ST-IoU?\n\n3. In the main paper, the authors consistently claim \"joint reasoning across multiple videos with non-overlapping fields of view,\" but in L741-742, they mention \"especially in scenarios involving multiple cameras with overlapping coverage.\" Could the authors clarify this?\n\n4. For prediction tasks like NSF, TF, and MTTF, the performance gap between models on spatial and spatio-temporal reasoning is very obvious. Could an improved evaluation method be considered? For example, for cases where the predicted camera location is correct, could the average Time IoU be reported separately to more clearly isolate and quantify the model's performance on temporal prediction?\n\n5. Case analysis of Map-Video Alignment Errors: Topology Error and FoV Alignment Error are the main error types. Could more qualitative examples and in-depth analysis of FoV Alignment Error be provided? For example, did the model confuse the FoVs of adjacent cameras, or was it completely unable to relate elements in the image to locations on the map?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- GTR-Bench presents a cutting-edge challenge (geo-temporal reasoning) that combines maps and multi-view videos, filling a gap in existing space-time benchmarks. This capability is crucial for real-world applications such as traffic management and emergency response.\n\n- The research provides a reproducible benchmark, data generation, and evaluation pipeline, possessing the advantage of Scientific Reproducibility, which helps promote comparative studies within the field.\n\n- The experiments are relatively sufficient, utilizing real-world Outdoor/Indoor data, and testing current mainstream models, which allows for a relatively comprehensive evaluation of the model's spatio-temporal reasoning capabilities.", "weaknesses": "- The scenario coverage is not extensive enough: The scenarios are relatively single, and the camera configurations are also quite fixed.\n\n- Heavy Reliance on Forecasting Tasks: Among the 7 tasks in the benchmark, 4 combined tasks (NSF, TF, MTTF, CR) are strongly related to trajectory prediction and reordering, and the prediction tasks use strict ST-IoU evaluation. Although these tasks are important, GTR could also include a wider range of geographical reasoning, such as route planning/validation based on geographical knowledge, or counterfactual reasoning, to enhance the benchmark's comprehens.", "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": "GBA5Pgm6Mo", "reviewer": "Reviewer_f47F", "summary": "GTR-Bench introduces a geo-temporal reasoning benchmark that tests VLMs on jointly understanding maps and multi-camera videos across indoor/outdoor scenes, spanning seven tasks from basic (Geo-location, Arrival Time-Interval, Motion-State) to combinatorial (Causal Reordering, Next-Spot, Trajectory, Multi-Target Trajectory). \n\nAcross 12 models, the best proprietary system reaches only 34.9% vs. 78.61% human accuracy, revealing three weaknesses: imbalanced use of spatial/temporal context, weak temporal forecasting, and poor map–video alignment.", "questions": "The primary concern here is the adequacy of scenario coverage. Additional clarification or examples may be needed.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper proposes a comprehensive, well-balanced benchmark. Seven geo-temporal tasks, 420 questions (60 per task) across two real-world scenarios with maps plus multi-camera videos.\n* The paper proposes a standardized evaluation protocol (≤20 sampled frames, fixed decoding settings) across 12 diverse VLMs with the Spatial-Temporal IoU.", "weaknesses": "* The primary concern is coverage across scenarios. The benchmark has 420 questions total (60 per task; 30 indoor + 30 outdoor), across only two environments—useful but relatively small for drawing strong statistical conclusions or long-tail cases.\n* Videos are down-sampled to keep the total frames across clips 20 per question, which can disadvantage models designed for long-context video reasoning and alter temporal cues.", "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)