See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
Authors: Pengteng Li
Pinhao Song
Wuyang Li
Huizai Yao
Weiyu Guo
Yijie Xu
Dugang Liu
Hui Xiong
Venue: NeurIPS 2025 poster ()
Keywords: MLLM
Spatial Understanding
VLM
Primary area: applications
TL;DR: Training- and GPU-free Spatial Prompting for Multimodal Large Language Models

Abstract: We introduce See&Trek, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMs) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely visual-spatial understanding remains underexplored. See&Trek addresses this gap by focusing on two core principles: increasing visual diversity and motion reconstruction. For visual diversity, we conduct Maximum Semantic Richness Sampling, which employs an off-the-shell perception model to extract semantically rich keyframes that capture scene structure. For motion reconstruction, we simulate visual trajectories and encode relative spatial positions into keyframes to preserve both spatial relations and temporal coherence. Our method is training&GPU-free, requiring only a single forward pass, and can be seamlessly integrated into existing MLLMs. Extensive experiments on the VSI-Bench and STI-Bench show that See&Trek consistently boosts various MLLMs performance across diverse spatial reasoning tasks with the most +3.5% improvement, offering a promising path toward stronger spatial intelligence.

See&Treks Training-Free Spatial Prompting for Multimodal Large Language Model
Pengteng Li AI Thrust, HKUST(GZ) AI'ROBOTICS
Pinhao Song KU Leuven
Wuyang Li EPFL
Huizai Yao AI Thrust, HKUST(GZ))
Weiyu Guo AI Thrust, HKUST(GZ)) AI'OOOOICS
Yijie Xu AI Thrust, HKUST(GZZ)
Dugang Liu SZU
Hui Xiong' AI Thrust, HKUST(GZ)) CSE, HKUST
Abstract
We introduce SEE& TREK, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMM)) under vision-only constraints. While prior efforts have incorporated modalities like depth point clouds to improve spatial reasoning, purely visual- spatial understanding remains underexplored. SEE& TREK addresses this gap by focusing two core principles: increasing visual diversity and motion reconstruc- tion. For visual diversity, we conduct Maximum Semantic Richness Sampling, which employs off-the-shell perception model to extract semantically rich keyframes that capture scene structure. For motion reconstruction, we simulate visual trajectories and encode relative spatial positions into keyframes to preserve both spatial relations and temporal coherence. Our method is training&GPU. free, requiring only single forward pass, and can be seamlessly integrated into existing MILMs. Extensive experiments on the VSI-BENCH and STI- BENCH show that SEE& consistently boosts various MLLMS performance across diverse spatial reasoning tasks with the most +3.5% improvement, of- fering promising path toward stronger spatial intelligence. The link of code: https://github. com com/HHoaattrb//eeerrr
Introduction
Multimodal Large Language Models (MLLMs) 2 3 have witnessed rapid advancements, demonstrating impressive capabilities in understanding and generating cross-modal content. By integrating visual and textual inputs, these models have shown potential in various tasks such as image captioning
5 visual question answering [6], and embodied AI
Spatial reasoning crucial for empowering MLLMs to understand and interact with real-world environments, particularly in tasks involving object localization, motion prediction, and physical interactions. Enhancing MLLMs with spatial awareness can significantly improve their performance downstream applications such navigation [10] and robotic manipulation [8] [I]. Recent works
have introduced depth cues [3], camera poses 12] and 3D priors [[3] into MLLM training, aiming
construct more comprehensive spatial representations [14 [5]. However, despite these advances, existing models still struggle to robustly reason about complex spatial relationships, particularly in scenarios requiring multi-step reasoning, complex visual and temporal dynamics, generalization to novel environments. We reflect critically on two pivotal factors that hinder current MLLMS from overcoming the spatial understanding bottleneck:
"Conresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure I:
Illustration of insufficient spatial understanding ability of current MLLMS. They adopt uniform sampling to extract the video with spatial semantics, leading to visual homogeneity and unknown motion. SEE&TRE' aims to tackle these two problem, which can be referred to Figure 3
1) Visual Homogeneity: As shown in Figure 1 generally, large number of existing MLLM pipelines adopt uniform temporal sampling strategies, in which or 32 frames are used as input due to limited GPU memory when selecting keyframes from the video which captures full spatial semantics. Without any structural constraints or prior knowledge, this uniform temporal sampling often captures the frames (i) without any salient features (e.g., walls, ceilings, and floors) (ii) or containing fragments of objects. Those frames will decrease the aggnlltt-noiss ratio in the input frames, limiting the MLLM's ability to reconstruct or reason about the full spatial layout.
2) Unknown Motion: Relying solely on sampled frames, without access to explicit ego-motion information, significantly impairs model's s ability to infer object movement and displacement within a scene, Such capabilities are critical to spatial reasoning tasks, including estimating object distances, predicting motion trajectories, and establishing temporal order. In the absence of explicit motion cues, MLLMs are forced to rely primarily on commonsense priors acquired during pretraining, rather than on directly grounded visual evidence. Consequently, the spatial predictions made by such models tend to be speculative rather than wvidence-based revealing a fundamental limitation of existing vision-only MLLM architectures.
To tackle the two above-mentions important factors, we propose SEE& TREK, a simple yet effective training&GPPU-A GPU-free spatial prompt method to jointly boost spatial-temporal reasoning in MILMMs To tackle the visual homogeneity, we utilize an off-the-shelf perception model, e.g., object detectors, to extract semantically rich keyframes that capture the spatial structure of scenes to increase visual diversity. To tackle unknown motion, we leverage Visual Odometry (VO) to simulate visual trajectories from the given videos and add extra motion cues into the keyframes, preserving spatial relationships and temporal coherence, Featured with 1) training&GPP--FE forward characteristics, SEE&TREK can be seamlessly integrated with open-source MLLMS or commercial engines. Comprehensive experiments on VSI-BENCE and STI-BENCH verify that significantly improves the performance of multiple MLLMS across diverse spatial reasoning tasks, offering a promising direction for enhancing the spatial understanding of MI.MMS. In summary, contributions are follows:
2) plug-and-play, and 3) single-
We introduce SEE& TREK, the first training- and GPU-free spatial prompting framework to enhance the spatial understanding capabilities of MILMs. It is plug-and-play. single-forward, and compatible with both open-source and commercial MILMs.
To provide rich spatial and motion cues, we design comprehensive spatial prompting strategy: 1) Maximum Semantic Richness Sampling: We use off-the- -he-lhh- perception models (e.g., YOLO) to extract semantically rich and diverse keyframes from videos. 2) Motion Reconstruction: We reconstruct camera motion in BEV and 3D using Visual Odometry and label keyframes with motion cues preserve spatial and temporal coherence.
Comprehensive experiments VSI-BENCH and STI- show that SEE TREK significantly boosts the spatial understanding performance of multiple MLLMS across diverse scales and architectures, offering promising direction for future spatial intelligence.
2
Related Works
Long Video Understanding. Most evaluations of spatial understanding in MLLMS focus on long video comprehension [5] 16), where models analyze dynamic scenes and answer related questions. key challenge in both spatial and video understanding is maximizing relevant information retention.

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Every Frones
Senantic Richness Sampling
spsttotseporot:
kolocad-ligt
Notton Reconstruction
Fanrae Maching
Feceding Trames
Rabutton
RDY Tragectory
Mustar
Tedjectory
Input Video
Brean 140U-30
From 3ACE-D1
Balortion Pees
Trujectary
VILMIS
Figure 2: Overview of the proposed SEE&TREK We first sample one frame for every N frame for post-processing. For Maximum Semantic Richness Sampling, a visual perception model e.g. YOLO 40) detects objects in each sampled frame, updating the category count. To boost scene representation, we propose Balanced-Topp Motion Reconstruction, we perform feature matching with the previous frame. Then, matching points are used estimate the essential matrix E via RANSAC [41, from which the relative camera pose and motion trajectory are recovered. Finally, SPATIOTEMPORAL ENCODING is introduced to integrate motion and spatial cues for a more comprehensive representation.
strategy for selecting semantically rich frames. For
Existing methods often employ specialized adapters 17 18), tokenization strategies 19 20 or
memory modules [21] [2,] [33 to compress and store video semantics. Other approaches convert videos into document-style imputs (24). use retrieval-augmennte
generation (RAG) frameworks 25 26 27 28] or structure content in trees 28 to reduce memory load. However, these techniques generally require fine-tuning MLLMs rely VLMs for information retrieval, which is both computationally expensive and resource-intensive Recent work has explored query-based frame selection (99 (0 32 33), combining traditional heuristics with feedback from pretrained VLMs. Building on these insights, we propose a novel GPU-free keyframe selection strategy that identifies representative frames efficiently without additional training or heavy computational demands.
Spatial Understanding. Spatial understanding is essential for deploying MLLMS in real-world applications such Embodied AI [8] [1] and Autonomous Driving [34 [0]. Recent research has explored leveraging multimodal inputs, such as depth maps or point clouds, to enable explicit 351 or implicit I12
36 [3 3D scene modeling, which is processed by the LLM decoder to enhance spatial comprehension. However, these approaches often rely on precise cross cross-modal alignment and highly customized pipelines. posing challenges for deployment and limiting generalizability across diverse real-world settings. Additionally, common benchmarks like ScanQA [14 and SQA3D [15] typically assume uniform camera motion, making them less suitable for complex, dynamic environments.
In contrast, vision-only MLLMS offers greater usability 2 2 4 but often struggles with nuanced
spatial understanding in video-based tasks (37 38 39 These limitations motivate the development of a spatial prompting strategy that enhances the spatial reasoning capabilities of existing MLLMS.
3 Method
As shown in Figure 2 the proposed SEE& aims to overcome the limitation of visual ho- mogeneity and unknown motion in the captured keyframes. In detailed, given the vidco sequence V = with N. frames, we sample one frame every N frames of V. We first select the rich semanties frames by leveraging an efficient visual perception model and the proposed Balanced-Tope strategy and use OV to simulate the camera relative motion trajectory obtain the moving informa- tion. Finally, SPATIOTEMPORA, ENCODING is introduced to encode each frame with color-speciiied and number marks, which efficiently integrates both important properties. The algorithm is presented in Algorithm|] D and the detailed version can be found in Algorithm A.I in the appendix.
3.1 Maximum Semantic Richness Sampling
To address the limitation of visual homogeneity, the first step of SEE&TREK is to select the keyframes that convey the richest semantic content to increase visual diversity. Since inference resources constrain us to only a few frames, we posit that the frame containing the most detected objects will best capture the scene's 's diversity. Concretely, we run pretrained YOLO detector ))--)
3

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Algorithm/ SBE&TRES Maximum Semantic Richness Sampling & Motion Reconstruction
Require: Video V = ( K Ensure: Keyframes (F% /=1 PREV. Psn, Twolld
No interval N, #keyframes K, detector V intrinsics
1: [5+ l mod N 0]
Unifform Subsampling
2: Tuard + -1, O -- f() S do 4: (R,, T,, C+) &- PRAAMEPIPLLINE((II..A
3: for each pair (,
Decompose & Perception y
5:
Tuulll T (+=(t,C,)
6: end for
7: O" <- TRIMSPAN(O)
Valid Interval [tas tel It Balanced Top
8: (Ta.. <- SELECCKEYYRRMEESOO" KK)
9: for la E Taa do
11: end OVERLAY (fc, ColorMap(t./
10: Ft.
SPPTTIOEEPPORLL ENCODING: Index Huc Mark
12: (PBEV, PTD)+ + RENDERTRAJ(T world
PProooctXY + Render3D
40 on every frame fi, yielding a detected class set C. = J(fi) = {ci, (c1,c2......... Ct,,c.,..,,..... Cn. where ny is the number of detected classes, and each oa belongs to the predefined label vocabulary L. Thus, within a valid temporal interval te). we collect a set of detected class set for each frame fc C" = (C, tg S f S te).
simple frame selection method is to choose the Top frames with the most detected classes. However, this can bias selection toward the temporal interval in which the greatest number of objects appear, causing MLLMs to miss other semantically important parts of the video. To address this, we propose a frame sampling strategy called Balanced-Topk which extracts set of K keyframes that are both object-rich and temporally diverse. In detail, we first select the initial keyframe ST, as the one with the maximum number of detected classes, where To denotes the selected keyframe index:
arg max (IG+)): 1.5151.
(1)
with ties broken by choosing the earliest frame. The initial class pool is set as Cuel = Ctg. To avoid local bias, ensure temporal uniformity, and capture broader semantic representation,
partition C* into K - 1 contiguous temporal segments {C()) ..... C(K-1)), }. In each segment ((k), to increase the overall information gain per frame, we select one frame The such that the overlap of its detected categories with Cyel is minimized, classes count is maximized, and the frame is early possible:
Tk arg min (IC, n Caal, -(C,), t),
(ET())
where (k) is the subset of timesteps, in which each timestep corresponds to a detected class set C, in C(R). After each selection, the accumulated category set is updated as Cuel + CuiUC,, This process yields set of K selected keyframes {,r, k - that are semantically representative, object-abundant, and temporally distributed, forming optimal subset for downstream understanding.
3.2 Motion Reconstruction
As mentioned in the introduction, MLLMS' spatial understanding is limited by unknown camera motion, which leads to confusion about movement patterns, object displacement, and the spatial layout of scene. In this section, we aim to estimate the camera motion via visual odometry (VO) from monocular video and connect the estimated camera motion with the selected keyframes by painting motion cues on them. Through feeding MLLMs with visualized camera motion and keyframes with motion cues, the spatial understanding capability can be enhanced.
Camera motion estimation. We adopt feature-based VO pipeline utilizing ORB (Oriented FAST and Rotated BRIEF) features and essential matrix estimation 42 43 In detail, the current frame ft and the last frame ft-1 are converted grayscale. We first extract ORB key points and descriptors from each frame as follows:
KG-1, Di-1 ORB(f(1)) ORB(J))
K,, De
where KG,1 ( and KG, N denote the detected 2D keypoints (in pixel coordinates), and D represents the corresponding ORB binary descriptors. Feature correspondences established

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by matching descriptors between the two frames, resulting in a set of matched keypoint pairs P
(=1 where x x I2 are the matched 2D keypoints in pixel coordinates, and P is the number of matches. To compute the essential matrix, we first normalize the image
M = ((x x
coordinates using the camera intrinsic matrix K: = K-1 x; The essential matrix E E R333 encapsulates the relative motion between the two frames (up to scale) and is estimated by minimizing the geometric error of the epipolar constraint:
1
E arg min E'
where p(-) is robust loss function such as the truncated quadratic or Tukey biweight [44], and RANSAC used to handle outliers [4[]. Once the essential matrix E is estimated, we recover the relative rotation R, E SO(3) and translation direction T, E R (up to scale) by decomposing E via Singular Value Decomposition (SVD) and applying the Cheirality condition to resolve ambiguity, This results in the relative pose (R,, T,) between frames t 1 and t. Assuming the global camera world pose at time 1 is known as (R,-1 1: Ty the global pose at time is updated recursively:
R Ropordd R.yord Re, Twordd Ry 1--1
This process is repeated over the entire sampled frame sequence to reconstruct the full camera trajectory. By estimating relative camera motion, we obtain the spatial position of each frame and the overall structure of the scene, thereby offering implicit spatial reference for the orientation and placement of objects within the space. After obtaining the 3D camera trajectory (Tuueed Jt=1, we generate two visualizations: top-down Bird's Eye View (BEV) and 3D trajectory plot, denoted as P PREY and P.D. For visualization, each trajectory point at timestamp t is assigned a color using ((t) ColorMap(t/ K), where K denotes the total number of trajectory points. The BEV is created by projecting the 3D camera positions onto the XY plane, while the 3D plot retains the full spatial geometry. These visualizations provide concise and intuitive summary of the camera motion throughout the video.
1
Spatiotemporal Encoding. Though the semantically rich keyframes and camera motion obtained, MLLMS have difficulty in connecting both information if they are simply fed into MLLMs: MLLMs have idea of the camera poses of the corresponding keyframes. This disconnection hinders the model's ability to understand spatial relationships, track object transitions, or infer occlusion and continuity, particularly in long-range, egocentric sequences. To address this issue, propose SPATIOTEMPORAL ENCODING by explicitly encoding the motion information into the keyframes. Specifically, two trajectory- aware markers are integrated into each selected keyframe: 1) A frame index representing its position within the motion sequence. 2) A color-coded marker derived from a continuous colormap. indicating temporal progression along the 3D) camera motion trajectory. These visual markers are directly overlaid on the image, resulting in augmented frames where each carries both semantic richness (via object presence) and spatiotemporal context (via visual cues).
In detail, given the corresponding camera trajectory and the key-frame indices Tea [ from Section 3.1 we assign each keyframe T; E Twa a unique RGB color c; from the same colormap as the original trajectory, i.e., C; ((.)) = ColorMap( x This ensures smooth color gradient that reflects the temporal ordering and spatial progression along the motion path. Each frame fr, is then augmented with a simple inpainting operation:
K-1
draw filled circle at fixed top-right position with color c; and overlay the frame index t, as a label inside the marker (as shown in the bottom- right corner of Figure 2p. The resulting modified frame f". compactly encodes both the temporal sequence and spatial trajectory through these visual cues.
This transformation preserves the integrity of selected frames while adding motion cues, enhancing multimodal models' ability to associate frames with motion progression and spatial layout. Unlike implicit positional encoding or post-hoc temporal reasoning, our approach offers a direct, efficient, and interpretable way to inject trajectory awareness into the input space. Consequently, MI.LMS gain deeper understanding of scene evolution and the spatial distribution of visual content.
5

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Table I: Comparison of various MLLMS boosting by SEE&TRES on the VSI-BENCH benchmark. f indicates results on VSI-BENCH (tiny) set. indicates we use the instruct version.
Methods
Avg.
Olg. Count
Numerical Answer
Diat
Obj Size
Room Size
Rel. Dis.
Rel,
Roste Maltiple Answer
Appr. Order
Baseline
CHANCE LEVEL (RANDOM)
25.0
36.1
28.1
25.0
CHANCE LEVEL (FREQUENCY)
$4.0
02.1
32,0
29.9
33.1
25.1
47.9
28.4
25.2
DSS-Bench (Kny) Penf
THUMAN
79.
94.7
95.8
'GEMINI- 1.5 FLASH
45.7
50.8
33.6
56.5
45.2
48.0
39.8
32.7
59.2
'GEMINI-1 1.5 PRL
48.3
49.6
28.8
58.6
49.4
46.0
48.1
42.0
08.0
'GEMIN) 2.0 FLASH
45.4
52.4
30.5
66.7
31.8
56.0
46.3
24.5
55.1
Proprietory Models
GENINI- 1.5 FLASH
42.1
49.8
41.0
31.5
GENINE- 1.5 PRO
454
56.2
30.9
64.1
43.6
51.3
46.3
36.0
34.6
GPT-40
34.0
4622
3.3
43.8
38.2
37,0
41.
31.5
28.3
Sporesouure Modelr
LLAVA ONEV ONEVISSOON 1.55
27.1
33.3
29.2
13.0
29.1
29.2
402
36.1
6.6
&TEEK
28
49.0
29 29.4
15.1
27.7
30.3 30.3 18
57.3
35.137.3
61
LLAWA-ONEVISON SON--ONVVII---T
31.1
34.7
20.6
47.3
18.1
10.4
32.4
32.5
21.9
&SEE&TRUK
330 1.4
32.0 13
17.0
27.8 7X
39.0 mm
15
35.7 m
LLAVA-NEXT: VIDEO-1B
37.0
25.1
+NIR&TREK
119 ..2
47.5g1 us
36.6 36.6
39.9
28.3 14k
INTERNVLL31BB
29.5
65.0
18.5
13.9
22.3
29.3
47.8
27.3
+SEE&TREK
320
63.6
25.8 25
16.1
30.7 a
528
46.4
28.4
12.3 xx
INTERVVI 3-8R
40.
67.8
12.2
44.5
41.8
42.8
37.7
26.3
28.9
.SEE&TREK
432
66.2 1ax
32.9 u2.9,um
46.9 aaa
46.7
45.9 u5
40
3044 10
37.4
1NTERNVL3-144
44.
69.0
33.6
45.6
43,9
42.4
41.4
SEEE&TREK
48.6
68.8
35.7.9
48.4 a..
19.0.
05
27.8 1.
46.8
QWIN2 5-VI.-38
25.7
15.0
44.6
26 1a0
9.7_138
23.2,am
a9.
22.7
33.2.1.9
4m
28.8 1.
QWEN2. S.
27.3
13.0
14,4
35,9
21.3
36.9
37.9
29.9
29.6
+SEE&TREK
29.0 2.00
13.6 2.4
147,000
35.4 0.
35.4
23.6-2.x
33/4 33.4-009
41.3 4..3 05
29.2
QWINZ 5-VI.-128
53.7
16.7
22.6
47.0
57.8
39.3
37.6
20.9
56.7
TSEE&TREK
19.9
23.8,
41.0
39.5.1.7
39.5
36,9
39 39.2.1.0
32.5
44.9
AL-A-B
33.4
24.2
30.8
49.6
33.5
33.8
39.7
28.9
21.2
SSE&&TREK
35.1
23.5
0.
28.3
35.0 9m
43.9,
309,348
31.9
3.3 Joint Optimized Prompting
We leverage the enhanced keyframes obtained from Maximum Semantic Richness Sampling (Sec. 3.1 alongside RGB visualizations of the BEV and 3D trajectories generated from Motion Reconstruction /Scc.3.2) to construct a unified visual input. These elements are then combined with a carefully designed textual prompt that includes: 1) general descriptions of the selected keyframes and their semantic properties, 2) references to the BEV and 3D) trajectory visualizations, and (3) the relative coordinates of key points along the camera path. Together with target spatial reasoning question, these components are integrated into an instruction-style prompt. This heuristic formulation serves to inject explicit spatial cues into the MLLM's input, enhancing its ability to reason about geometric layout, motion progression, and scene structure in lightweight, model-agnostic manner. Note that our method a training- and GPU-free method, which just needs to compute several times on the CPU and requires only single forward pass.
4 Experiments
Implementation and more comprehensive experiments details can be found in Appendix B
4.1 Evaluation Setup
39 as our spatial evaluation benchmark. 1) VSI-BENCH is very challenging benchmark that requires understanding spatial relationships and correspondences of multiple objects in a video [38]. It comprises over 5,000 question-answer pairs derived from 288 real videos, with duration ranging from 1.5 minutes to 2.5 minutes. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets ScanNet (45) residential
Datasets. We select VSI-BENCH (38 and STI-BENCH
ScanNet++ 446) and ARKitScenes (47 and represent diverse cavironments-inclun spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g.. factories) and multiple geographic regions. Compared normal multimodal spatial benchmarks like SCANQA [4. VSI- BENCH has wider range of random changes in viewing angles. 2) STI- is benchmark designed to evaluate MILMS' spatial-temporal understanding through challenging tasks such as estimating and predicting the appearance, pose, displacement, and motion of objects. It contains 2,064 QA pairs across desktop, indoor, and outdoor scenarios, providing a systematic quantitative assessment of MLLMs' spatial-temporal understanding capabilities.

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Table 2: Comparison of various MLLMS boosting by SEE& TREK on the STI-BENCH.
Dim. Meas.
Spetial Rel. Static Understanding
3D Video Grounding
Disp.
Speed Ace. Dynamic Understanding
Ego Oriens.
Traj. Desc.
Pose
Methuds
Avg.
& P.L.
Est.
Proprietirry (API)
GPT-4g
34,8
24.9
49.6
28.1
27.6
36.0
30.3
36.8
$1.3
Claude-3.-SSmaa
49.0
36.3
29.0
27.0
41.0
Gemini-2. 0-Flash
38.7
33.7
50.0
33.7
32.7
34.4
15.1
48.7
62.4
Gemini-2 2.5 2.5-Pro
40.9
34.2
53.4
32.3
32.4
34,3
44.9
52.0
58.4
Open-source Models
INTERNVL3-1D 1B
18.7
19.4
19.2
18.6
19.3
18.8
8.1
24.4
21.7
&REE&TRRK
2..4
25. o5.
17.4-1
20.5
18.2_1.1%
a9.7
1
2.0%
INTERNVL1-8D
30.2
27.0
36.3
28.7
25.2
32.7
23.8
25.6
42.2
+REE&TREK
31.2.-1.4
26.0_
36.4
36.4
14.1..0
2..
35.4.2
26.3.0.m
42.7..1.%
INTERNVL3-144
30.8
27.7
41.1
32.2
19.1
28.8
17.3
25.6
49.4
+SEE&TREK
32.2 1,456
29.9 12.29
40.3 0.ss
32.2 000ts
22.4,
30.5 11.76
19.7.2.4%
38,2.
48.0 1,46
QWEN2.5-VL-7B
35.6
25.3
52.1
33.4
19.9
31.5
41.6
50.0
52.2
+SEE&TREK
36.9.1.9
24.2
35.3 35.3.
20.5....%
32.1 32.1.
57.8 11.
48.7 48.7_
52.2.
OWEN2,5-VL-32B
46.6
33.6
44.8
+SEE& TREK
41.7 41.7-1..5
36.7
49.3
37.2
31.7
42.4,
33,5
46.2,
58.9
Benchmark Models. Following [8], we comprehensively evaluate 10 video-supporting open- source MLLMs across diverse model families our proposed TREK, encompassing various parameter scales, training recipes, and model architectures. For proprietary models, we consider 1emini-1.5 4 and GPT-4o 33 for comparison. For open-source models, we evaluate models from InternVL3 48 LLaVA-One Vision [49]. LLaVA-NeXT- Video [6], Qwen2.5 [ and Kimi-VI VL (37]. All evaluations conducted under zero- zero-shot settings. To ensure reproducibility, decoding for all models. In "SI-BENCH evaluation, Baseline and VSI- -SI-Bench (tiny) Perf. are borrowed which are only utilized for comparison.
greedy
from 38
4.2 Main Results
1) VSI-BENCH. As shown in Table II SEE& consistently enhances the performance of various open-source multimodal models on the VSI-BENCH benchmark. In terms of overall accuracy (Avg.), all tested models benefit from the integration of SEE& with gains ranging from +1.0% to +3.5%. The most notable improvement is observed on INTERNVL3- 1 which achieves a +3.5% boost, highlighting the effectiveness of SEE& TREK for relatively smaller models. Regarding specific task types, SEE& TREK notably improves numerical reasoning (e.g., Abs. Dist.) and spatial understanding (e.g., Route Plan and Appr. Order), where we observe significant relative gains such as +10.8% in Appr. Order for LLAVA-ONEVISSOON 7B. These trends suggest that SEE& TREK is particularly beneficial for complex spatial- temporal reasoning tasks. We also observe that larger models (e.g.. INTERN L 14B. QWEN2.5- VL-32B) tend to show modest but consistent improvements. This indicates that while S helps models of all sizes, its relative impact is more pronounced on lightweight or mid-sized MILMMs, potentially due to their higher reliance on external structural cues to compensate for limited capacity. It is also worth noting that some performance drops (highlighted in green) appear in isolated tasks (e.g., Rel. Dir. and Obj. Count) for certain models, which might be attributed to trade-offs introduced by the augmented perception pipeline or model-specific biases. However, the overall trend strongly favors the inclusion of SEE& & TREK as a plug-and-pe enhancement module for open-source MLLMs in video-centric spatial reasoning tasks.
2) STI- BENCH. As shown Table 2 the integration of SEE&TREK consistently enhances overall accuracy across all evaluated open-source models on the STI-BENCH. For the INTERN series, they achieve respective improvements of +1.7%, +1.0%, and +1.4% in average performance respec- tively. This consistent gain across models of varying scales underscores the scalability and robustness of SEE& TREK. At the sub-task level, SEE TREK yields particularly notable improvements in dynamic understanding. For instance, INTERN -L33-4B shows substantial gains in Trajectory Description (+8.6%) and Displacement & Path Length (+3.3%), reflecting enhanced temporal and spatial tracking capabilities. Likewise, INTERNVLL333 benefits significantly in 3D Video Grounding (+5.4%) and Ego & Orientation (+2.7%). These results demonstrate that SEE&TRE'S is especially effective at reinforeing temporal-spatie reasoning. Although minor performance declines ob- served in a few categories (e.g., Room Size or Relative Direction), they are marginal and do not offset the substantial improvements in key dynamic tasks.
7

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Table 3: Ablation studies of Maximum Semantic Richness Sampling and Motion Reconstruction.
Max. Semantic
Sampling
Motion Reconstruction
Arg.
Obj. Count
Ahs, Dist.
Numerical Anewer
Ohj Size
Room Size
Rel. Dist.
Multiple-Choice
Rel. Dir.
Route Plan
Appc. Order
40.2
67.8
32.2
44.5
41.8
42,8
37.7
26.3
28.3
41,8
67.3
34.7
39.0
47.5
43,9
44.3
25.8
31.9
42.1
64.6
29.7
46.4
44.7
39.1
40.7
28.8
43.0
43.2
65.2
32.9
46.9
46.7
45.9
40.2
30.4
37.4
Table 4: Ablation studies of SPATIOTEMPORAL ENCODING and point prompts.
SPATIOTEMPORAL. ENCODING
point prompts
Avg.
Obj.
Numerical Answer
Obj.
Dist. Multiple-Choice Answer
Route
Appr.
42.4
64.9
32.8
45.3
44.9
40.5
37.1
42.7
66.3
34.7
43.5
46.9
46.9
39.1
31.4
34.7
42.7
66.5
35.0
44.1
44.9
46.6
38.9
29.9
35.4
43.2
65.2
46.7
45.9
37.4
4.3 Ablation Studies
In this section, we utilize VSI-BENCH for evaluation and leverage INTERNVL3-8B as the baseline model. We investigate the effect each technique proposed in our &EE& More investigation can found in the Appendix.
Overall. To systematically assess the contribution of different spatial cues, we first conduct ablation studies on two components: Maximum Semantic Richness Sampling (dubbed MSRS) and Motion Reconstruction. As shown in Table enabling MSRS alone leads a moderate performance gain (average accuracy from 40.2% to 41. 1.8%). with notable improvements observed in static layout-related tasks such as object size. This suggests that semantically richer keyframe selection contributes to more informative spatial representations. Besides, incorporating Motion Reconstruction yields distinct advantages in dynamic reasoning tasks, particularly relative direction and route planning (e.g., route planning improves from 26.3% to 28.8%, highlighting its role in modeling egocentric movement and temporal coherence. When both components are jointly applied, the model achieves the highest overall accuracy (43.2%), demonstrating their complementary effects in facilitating both static and dynamic aspects of spatial understanding within MLLMs.
Optimized Prompting. Then, we investigate the impact of SPATIOTEMPORAL ENCODING and point prompts added in instruction from Section 3.3 As shown in Table adding SPATIOTEMPORAL ENCODING improves the average accuracy to 42.7%, with notable gains in tasks like Object Count (66.3%) and Relative Direction (46.9% highlighting its role in enhancing spatial and temporal reasoning which connecting the MSRS and Motion Reconstruction. Similarly, incorporating point prompts also achieves an average accuracy of 42.7% demonstrating its effectiveness providing explicit spatial cues. When both modules are combined, the model achieves the highest average accuracy of 43.2%, with significant improvements in diverse tasks.
Sample Efficiency Analysis. We conduct experiments with different sample intervals N e (L, 2, 4, 12) from single forward process to analyze their impact on the efficiency perfor- mance of SEE & TREK. While the finest granularity (N = 1) achieves strong results (Avg, 42.9% the cost of high computational time (410s), increasing N substantially reduces runtime while maintaining competitive accuracy. N = 3 and N = 4 strike the best balance, achieving the highest average score (43.2%) with over 65% reduction in processing time compared N 1. Even with larger intervals like N = 8 or N = 12, the model retains robust spatial understanding with only marginal drop in performance. These results demonstrate that SEE& TREK is highly sample-efficient, maintaining strong accuracy with significantly fewer frames and minimal computational overhead. Note that the time consuming of SEE& TREK still largely depends the length of the given videos. Particularly, the duration of videos in VSI-BENCH at least longer than I minute.
Rank Selection. We explore different rank extraction methods' impact on MSRS. Here, we utilize other two kinds of TopK selection methods: 1) Original TopK only extracts the frames according to most object numbers. 2) Temporal- -opporrl-TooKKK temporally divides a video into multiple consecutive frame groups based on the number of keyframes and then performs TopK selection within each group. As shown in Table Balanced- K consistently outperforms both TopK and Temporal Top across various spatial reasoning tasks, demonstrating its advantage in capturing both comprehensive and diverse visual semantics. Notably, achieves the highest average accuracy (43.2%) and excels in multiple-choice tasks such Relative Distance (45.9%) and Approach Order (37.4%), indicating
8

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Figure 3: Qualitative results about SEE&TRET evaluated on VSI-BENCH. Here, we represent the different tasks of (a) appearance order (b) room size, and (c) route plan. More results can found in the appendix. It shows that SEE& TTREK obtains the visual diversity and motion reconstruction of the given video, gaining a better spatial understanding.
Table 5: Ablation studies different sample interval N settings. "Time(s)" denotes the average time consuming on processing videos from VSI-BENCII.
N
Time(s)
Avg.
Obj. Count
Abs. Dist.
Obj. Size Numerical Answer
Room Size
Rel. Dist.
Rel. Dir. Multiple-Choie Cholee Answer
Route Plan
Appr. Order
410
42.9
66.2
31.7
48.2
47.3
42.8
42.0
30.4
33.8
227
41.7
66.0
30.5
47.1
46.3
40.0
40.8
27.8
35.3
159
43.2
66.9
31.3
47.3
48.4
43.8
40.6
29.4
38.3
4
82
43.2
65.2
32.9
46.9
46.7
45.9
40.3
30.4
37.4
42.8
66.5
33.6
46.0
46.7
41.7
41.5
30.9
35,9
42.5
66.4
32.6
45.9
41.7
46.5
39.9
28.9
38.8
better spatial understanding through more balanced frame selection strategy. Compared with the TopK and Temporal- TopK, our proposed Balanced- To further enhances selection by jointly considering object richness, temporal distribution, and semantic diversity.
4.4 Qualitative results
As shown in Figure we leverage self-explanations investigate why SEE&TREK achieves superior performance. Thanks to the MSRS, SEE&TRET is able to identify the most informative frame that best represents the entire scene such as cases (a) and (c) ettyyccccllly from the first frame, while the baseline always fails in perception and positioning. This leads to more accurate analysis in dynamic spatial understanding tasks for MLLMS. Furthermore, we observe that motion reconstruction enhances the MILM's ability to estimate spatial distances with lower error, demonstrated in case (b), like "with the assisted trajectory." thereby supporting static spatial reasoning. Overall, SEE & TREK significantly enhances the comprehensive spatial understanding capabilities of MLLMs.

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Table 6: Ablation studies of different rank extraction methods in MSRS.
Rank Extraction
Obj. Count
Abs. Dist.
Obj. Size
Size
Rel. Dist.
Rel.
Route
Appe. Orler
Avg.
Numerical Answer
Multiple-Choice Answer
TopK
37.9
65.9
28.7
42.6
35.5
37.1
39.2
28.9
25.7
Temporal-Toph K
42.7
47.0
45.5
41.0
39.7
31.4
36.9
Balanced K
43.2
65.2
32.9
46.9
46.7
45.9
40.2
30.4
37.4
5
Conclusion
We propose SEE&TREK, the first training- & GPU-free prompting framework designed to enhance the spatial understanding capabilities of MLLMS. We focus on enhancing the spatial understanding ability of MLLMs from two aspects: Visual Diversity and Motion Reconstruction. For achieving Visual Diversity, we conduct Maximum Semantic Richness Sampling, which employs off-the-shell perception model, e.g. YOLO to efficiently extract frames that maximize semantic richness, then propose Palanced-Topl. strategy for selecting frames. For Motion Reconstruction, we simulate visual trajectories and encode relative spatial positions to preserve both spatial relations and temporal coherence. Our method GPU-free, requiring only single forward pass, and can be seamlessly integrated into existing MLLMS. Comprehensive experiments on various MLLMS and two hard spatial benchmarks verify the SEE& TREK superiority in boosting spatial intelligence.
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A Methods
A.1 Detailed Algorithm
The detailed algorithm of SEE& TREK could be found as follows:
Algorithm 2 SEE&TREK Maximum Semantic Richness Sampling and Motion Reconstruction
Require: Video V = ( sampling interval N, number of keyframes K, object detector )(.). camera
intrinsics K
Ensure: Selected keyframes {FF, h BEV trajectory PBEv, 3D trajectory Pav, 3D trajectory set T
I: Sample frames: < {fr mod N 0}
2: Initialize: O <- 0. Trajectory 7 <-
3: for each consecutive pair (f ft) E S do
Convert (f--1, fz) to grayscale
5: Extract ORB keypoints/descripoor::: ORB(
6: Match deseriptors to obtain M
Normalize matched points: X; + K (x 1|T
8:
Estimate essential matrix E via RANSAC
9:
Decompose E + (R,, TT)
10:
Update global pose: Roord Trodd
Append T.runi to 7
11:
12: Detect objects: C. + y(fi)
13: Append (t,, C.) to O
14: end for
15: Filter valid interval t.]:: O + E0)
16: Salanced-TopAt Selection:
17: Select global-rich frame: + max [G,
18: Csel Ct.. Tael {4g}
,...,, 19: Divide O' into 1- segments (O()...., O(K.))((--1) 0(K-1))
20: for 1 K 1 do
21: te arg mim (eoo) (IG, nCaal: -IG,,, t2)
22: C- U Ct., Twl t- Taa U [42]
23: end for
24: Spatiotemporal Encoding:
25: for each & E Taa do
26: Compute color c, + ColorMap(tt/K)
27: Overlay index and color on fú, = Fl
28: end for
29: Trajectory Visualization: PBEY +- ProjectXY(Twwdl), PI +- Render3D(T (Tor))
A.2 Motion Reconstruction
In this section, we recall the mathematical context of Visual Odometry (VO). Note that most of these can be found in the textbooks. The first step of VO is need to conduct ORB (Oriented FAST and Rotated BRIEF) Feature Detection and Description, then Feature Matching, finally perform Essential Matrix Estimation. For this part of algorithm development, we leverage OpenCV 2 for efficient deployment.
ORB Feature Detection and Description. In detailed, ORB combines FAST keypoint detection with orientation augmmeete BRIEF descriptors, producing scale- and ieationninvarian feature. Regarding Keypoint Detection (FAST): Given a grayscale image ft : Ω C R2 - RR the FAST corner detector selects pixel p E as keypoint if there exists a contiguous are of pixels on the Bresenham circle C(p) of radius around p. such that:
lf((q) fe(p) > т, Aq E (p.))).
(6)
where C, (p) C (p)) is contiguous segment of n pixels, and r is a contrast threshold. Typically, 12 and [c)) 16. This step yields the keypoint set K, {pf E 2 where N is the

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number of detected corners in frame ft. Regarding Orientation Assignment, to achieve rotation invariance, ORB computes the orientation angle 8, of each keypoint pî by using intensity moments of patch P: around the keypoint:
0: = arctan
m
Pp
Σ u f+(g),
m10
QEP:
where (Ir, 1) are coordinates relative the keypoint p). Finally, perform Descriptor Computa- tion (BRIEF). The BRIEF descriptor D, E O, 1 is constructed by binary intensity comparisons between 2/2 pre-defined point pairs (as, by) in the local patch around each pl, rotated by angle 0,:
Dr k) = {1
if (r as + p.) < Ro by + p!),
(8)
0
otherwise,
where Re, E (2)() is the 2D rotation matrix corresponding A,.
Feature Matching, Given two sets of ORB descriptors Dt-1: Dj 0, N,1, feature correspon- dences are established by computing Hamming distances between binary descriptor vectors, For each descriptor dit E Di 1, define the matching descriptor in frame / as:
dj arg min Hamming(d;" .d). dED,
(9)
and accept the match if the distance is below threshold ta passes Lowe's ratio test. Let the resulting matched keypoint pairs be:
M {(x] x))]
x E R
(10)
with xi pa, ' and xi = =p,;" corresponding to matched keypoints.
Essential Matrix Estimation, Given matched pixel coordinates (x² xii))== 1" and the intrinsic
matrix K E R3x3 normalized coordinates are computed:
x
K
x
K
& TR3
(11)
The essential matrix E E 3x3 encodes the relative motion such that (x() Ex! 0, Vi. In matrix terms, stacking all equations gives:
vec E) = 0, where E Px9.
(12)
This is solved via SVD of A in the 8 point algorithm, robust estimator such as:
arg min E
Σ ((x))
(13)
E'
where p(.) is robust loss (e-g., truncated quadratic), and outliers are rejected via RANSAC. Since E must satisfy the singular value constraint 01 d2 03 = O, we project it onto the essential matrix manifold via SVD:
E U diag 1,0) V
(14)
Finally, we decompose the essential matrix to motion. Specially, The essential matrix relates to the relative pose via: = [Tx)x R,, where [-]x denotes the skew semmmtymmtttic matrix:
-T,
T
T
T -Ty
0
-T
(15)
0
The decomposition of E via SVD provides four candidate solutions (+R,, +T,,, disambiguated by the Cheirality condition-checkee the number of triangulated points front of both cameras.
A.3 Joint Optimized Prompting
Here, we give the sample of our general input containing text and vision for MLLMS evaluation. For INTERNVL3-8B. after spatial prompting from SEE&TRES TREK, the visual inputs are as shown in Figure then we also design the corresponding text prompt template as shown in A.3 "Points" denotes the relative spatial coordinates of each selected frames. Since different MILMS have different training data, architectures, and training methods, using text prompts point coordinates as prompts has particularly large impact on performance. Therefore, we fine-tune the instructions for different model series to enable SEE & &TREK to fully utilize its spatial understanding ad advantages.
12

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Figure 4: The sample of the visual prompting input SEE TREK.
spatial.prompt_umi unieersal =
"Each video frame has its serial number in the top- roghtight corner. "The highlight color of frame matches the color the spatial map, indicating its position."
spatial_prompt_2D_1
"Both 2D (bird's-eye and 3D views illustrate the camera's spatial trajectory,
"with color encoding time progression."
10
"Points represent the camera's relative positions; the number of points reflects only spatial relationships."
input_ = spatial_prompt, universal
Beciial_promp__2____1 points "\n" question
Figure 5: The sample of the text prompting input in SEE& TREK. "Points" denotes the relative spatial coordinates of each selected frames.
B Experiments
B.1 VSI-Bench Dataset
Overview. VSI-BENCH (38] includes eight tasks of three types: configurational, measurement estimation, and spatiotemporal. The configurational tasks (object count, relative distance, relative direction, route plan) test model's understanding of the configuration of space and are more intuitive for humans. Measurement estimation (of object size, room size, and absolute distance) is of value to any embodied agent. While predicting measurement exactly is very difficult, for both humans and models, a better sense of distance and other measurements intuitively correlated with better visual-spatial intelligence and underpins a wide range of tasks that require spatial awareness, like interaction with objects and navigation. Spatiotemporal tasks like appearance order test model's memory of space seen in the video.
Metric Design. Based on whether the ground-truth answer is verbal or numerical, VSI-BENCH tasks are suited to either a Multiple-Choice Answer (MCA) or Numerical Answer (NA) format. For MCA tasks, VSI-BENCH follows standard practice by using Accuracy (ACC), based on exact matching (with possible fuzzy matching), as the primary metric. For NA tasks, where models predict continuous values, accuracy exact matching fails to capture the degree of proximity between model predictions and ground-truth answers. Therefore, VSI-BENCH utilizes new metric, Mean Relative Accuracy (MR.A) inspired by previous works. Specifically, for NA question, given model's prediction ÿ. ground truth y, and a confidence threshold 0, relative accuracy is calculated
13

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Figure 6: Qualitative results about S evaluated on VSI-BENCH. Here, represent the different tasks of (a) Route Plan (b) Object Absolute Size (Obj. Size), and (c) Object Relative Distance (Rel. Dist.).
by considering ú correct the relative error rate, defined as 1/ - yl/y. y. is less than 1 As eee-ccooiddenc--treehhoo accuracy only considers relative error in narrow scope. MRA averages the relative accuracy across a range of confidence thresholds C = (0.5, 0.55, .... 0.95]:
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MRA
Σ BEC
-0) <1-0)
(16)
10
MRA offers a more reliable and discriminative measurement for calculating the similarity between numerical predictions and ground truth values.
Chance Level Baselines. random selection accuracy for MCA tasks (and is inapplicable for NA tasks). 2) Chance Level (Frequency) represents the highest performance MLLMS would achieve by always selecting the most frequent answer for each task. This identifies performance gains that may result from inherently long-tailed answers or imbalanced multiple-choice distributions.
provides two baselines: 1) Chance Level (Random) is the
Human Level Performance, VSI-BENCH randomly sample subset of 400 questions (50 per task), which we will refer to as VSI-BENCH (tiny). Human evaluators independently answer each question, and their performance is evaluated using the above-mentioned metrics.
B.2 STI-Bench Dataset
Overview. STI-BENCH 39 contains 300 videos and more than 2,000 QA pairs, covering three major scenarios: Desktop, Indoor, and Outdoor. The videos are sourced from OMNI6DPOSE [50].
SCANNET [45] and WAYMO [51) respectively, thus encompassing a broad spectrum of real-world environments. They propose eight tasks in total, each one systematically examining distinct aspect of MILMS seapoal-temporal understanding, which divide these tasks into two main categories:
1) Static Understanding: Dimensional Measurement, Spatial Relation, 3D Video Grounding; 2)
14

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Figure 7: Qualitative results about SEE& evaluated on VSI-BENCH. Here, we represent the different tasks of (d) Object Count (Obj. Count) (c) Object Absolute Distance (Abs. Dist.), and (f) Appearance Order (Appr. Order).
Dynamic Understanding:
Displacement and Path Length, Speed and Acceleration, Ego-Centric Orientation, Trajectory Description and Pose Estimation.
API Engine and Baseline Settings. Regarding the commercial engine, STI-BENCH uniformly sample 30 frames from the video for each record and explicitly indicate the sampling FPS (Frames Per Second) for the current video within the prompt. An exception is made for Claude3.7-Somin for which only 20 frames are sampled due to its API constraints. ITI-BENCH are presented in multiple-choice format with five possible answers, hence a random guess baseline yields a 20% accuracy.
Metric Design. 1) Dimensional Measurement. let Las lys denote the dimensions (length, width, height) of an object along the I, and z axes:
L
Zmax Zmins by Vmmxx Ymin Lg Amax Zmin:
(17)
Here, (2, by la represent the object size along each coordinate axis. If needs the distance between two objects (or between the camera and an object), let d12 be the Euclidean distance between their center points:
d12 V (x2 x1)² + (12 31)² (22 - )2
(18)
Here, (I1, V1, 21) and (I2, 3/2 22) the center coordinates of the two objects.
2) Spatial Relation. When the difference along one coordinate axis is significantly larger than along others, the sign of that difference determines the spatial relation:
Tzy sagn( TB). Ty= sign(yA yB), Fiz sign(2A =B).
(19)
Here, Tays Tyz Fer indicate relative positioning along each axis (e.g.. front/back, left/right, above/be- low). It choose the axis with the greatest difference to label the dominant relation.
3) 3D Video Grounding. For frame in the camera coordinate system, the 3D bounding box of an object can be described with dimensions, center position, and optional rotations:
BBox, (le, We, hes Dt. Mt, 21, yaw paw.,pitch,, pitch,, roll:).
(20)
15

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Here, (h, W/: h4) are the object dimensions, (Iee M/t 2x) the center position, and (yaw,, pitch,, roll, ) are optional rotation angles if available.
Pose Estimation. Given the camera's initial pose (po, 0o). the pose (pr, or) at time can be obtained using the extrinsic-derived matrices R& (rotation) and Te (translation):
Pr Rapo + Te, Or 0o Aor.
(21)
Here, is the position, o, is the orientation.
5) Displacement and Path Length. Let p; (I, Vis zi) be the position at time i. The displacement don and path length Luraj are computed as:
don
V(n x0)² + (Un y0 (En =0)2
(22)
Titra Σ (o, 14-1) + (y: 1/21)) 2 + (z, - 21-1)
(23)
Here, don is the straight-line distance from the initial to the final position; Liraj sums consecutive segment lengths for the entire path.
6) Speed and Acceleration. Let At be the time interval between consecutive frames. Then the speed and acceleration a, are:
d
U 11-1 At
(24)
Here, d, is the displacement between adjacent frames, 0 the speed at time and a, is the acceleration.
7) Ego-Centric Orientation. If 02 denotes the camera orientation (azimuth) at time t, then the orientation change Ad, is:
A0, 0, Og-
(25)
This indicates how much the camera has rotated relative its initial azimuth.
8) Trajectory Description. It applies the Ramer-DouglasPeukke (RDP) algorithm to simplify the sequence of positions into key line segments. The resulting polyline is described in piecewise manner (e.g., "go straight for 30m, turn left 85 then go straight for 20m,...") providing a concise representation of complex motion trajectories.
16

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B.3 Implementation
SEE&TREK focus on the pre-prie prepresssinng stage of MLLMS, which samples one frame for every four frames from the given spatial video. For fair evaluation, we adopt 8 frames as input test each MLLMS for the given videos. For obtaining visual diversity and balancing efficiency, we utilize YOLOv8-TTie D-ny, named YOLOVVN from Ultralyties for faster detection. All experiments are conducted on NVIDIA 8 A6000 and 6 A800. In actual development, to accelerate the overall lesting process, we first utilize Motion construction and YOLO to process each sampled frame from videos for subsequent evaluation calls, which stores the corresponding spatial information. In evaluation, we call the stored spatial information from the last step and perform the Balanced -Topk sampling and SPATIOTEMPORAL ENCODING techniques in each question and answer process. Note that we do not utilize the intrinsic parameters provided by the original datasets (e.g., VSI-BENCH or STI-BENCH), Instead, we just adopt fixed intrinsic matrix derived from the KITTI dataset (K (7718.8500.44 0, 607.607.999 1928), [0, 718.8560, 185.2157], [0, 0, 1J)) across all experiments.
B.4 More Comparison Results
We conduct the experiments on OPENEQA 52
with Intern VL3-8B to show our method's superiority. evaluate the performance, and we still set 8 frames
Note that we utilize the (EM-EQA) setting [2
as input.
Table 7: Comparison results on the OPENEQA 52
Model
w/o SEE& TREK
W. SEE&&TEK
Gain(%)
Qwen2.5-VL-7B IntemVV1.3-BB
47.1 50.5
49.1 $2.3
+2.0%
+1.8%
B.5 Ablation Study
In this section, we utilize VSI-BENCH for evaluation and leverage INTERNVL3-8B as the baseline model. We investigate the effect of each technique proposed in our SEE&TRES
Table 8: Ablation studies of each component of Balanced- To
Obj. Count
Abs. Dist.
Obj. Size Numerical Answer
Room Size
Rel. Dist.
Route Plan Multiple-Cholce Answer
Rel.
Apps. Order
Balanced-Top Avg.
wlo Cut Min.
42.5
69.4
33.5
45.6
44.6
41.4
39.9
28.4
37.4
wlo Count Max.
43.1
47.5
42.5
39.5
29.4
40.9
w/o Time Early
43.2
66.2
34.0
45,5
48.0
42.1
41.0
31.4
37,3
Maximum Semantic Richness Sampling. As mentioned in Section frame T such that the overlap of its detected categories with Caal is minimized, classes count is maximized, and the frame is early as possible in Balanced- TTokk sampling. Here, explore each part effect on these components. Table Table reports the ablation results of the three components in our proposed Balanced-Tony Top strategy: minimizing category overlap with the selected pool (Cyel Min.), maximizing the number of detected classes (Count Max.), and selecting the earliest frame in case of ties (Time Early). The complete version of our method achieves the best average performance across all question types, validating the necessity of each design component. Removing the category overlap minimization (w/o Cus Min.) leads to the most significant performance drop, especially in multiple-choice tasks such as "Route Plan" and "Approach Order", indicating its crucial role in encouraging semantic diversity and avoiding redundant content across selected frames. Omitting the class count maximization (w/o Count Max.) also degrades performance, particularly in object-centric questions like "Obj. Count" and "Rel. Dist.", demonstrating that favoring frames with more detected objects helps maximize information gain per frame. Removing the temporal prioritization (w/o Time Early) slightly affects overall performance, with minor impacts across all metrics. This suggests that while encouraging earlier frame selection helps improve temporal coherence and avoids delayed keyframe concentration, is relatively less critical than the other two components.
3.1
want to select one
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Table 9: Ablation studies of each visual trajectories of Motion Reconstruction.
Visual Trajectories
Avg.
Obj. Count
Abs, Dist.
Numerical Answer
Obj. Size
Room Size
Rel. Dist.
Route Plan Multiple-Cholcs Answer
Rel. Dir.
Appr. Order
Pasv
43.1
65.4
33.1
48.3
45.4
41.0
29.9
36.4
we P
42.8
63.4
33.3
47.9
44.9
45.9
40.7
27.8
36.4
Detector Choices. Based the ablation studies presented in Table 10 it is evident that selecting the optimal detector and sampling interval requires balancing detection accuracy and computa- tional efficiency. Among the tested YOLO variants, YOLOOV8N consistently achieves competitive average scores across numerical and multiple-choice question categories while maintaining the lowest inference time, especially at N 4, where it balances relatively high average accuracy of 43.2% with only 82 seconds processing time per video. Although larger models like YOLOv8S 88 and YOLOV IS occasionally reach similar or slightly higher accuracy in some metrics, their time costs are significantly higher-oftee often an order of magnitude more making them less practical for real-time or essource-consraiing computation time but can cause minor fluctuations in accuracy, with N = 4 emerging an optimal trade-off that avoids excessive temporal sparsity while reducing inference overhead. Moreover, while YOLOv IN shows marginal improvements over YOLOv8N in some tasks, its time cost nearly doubles. Therefore, our final choice of YOLOV8N with N reflects a well-justified compromise: it offers sufficient detection performance across diverse evaluation criteria without sacrificing speed, enabling efficient processing practical deployment. This reinforces the insight that bigger and more complex models not always better, and careful tuning of sampling intervals combined with lightweight detectors can yield a more balanced, efficient system.
scenarios. Notably, increasing the sampling interval N generally reduces
Table 10: Ablation studies different sample interval N settings and detector choices. "Time(s)" "N" denotes the tiny
denotes the average time consuming processing videos from version and "S" denotes the small version.
Detector
N
Time(s)
Avg.
Ohj. Count
Abs. Dist. Numerical
Ohj. Size
Room Size
Rel. Dist.
Rel. Dir. Multiple-
Route Plan
Appr. Order
410
42.9
66.2
31.7
48.2
41.3
42.8
42.0
30.4
33,8
227
43.1
65.2
31.6
46.8
47.5
41.5
40.7
30.9
40.8
YOLOVSK
82
43.2
65.2
32.9
46.9
46.7
45.9
40.3
30.4
37.4
63
42.8
66.5
33.6
4600
46.7
41.7
41.5
35.9
42.5
66.4
32.6
45.9
41.7
39.9
28.9
38.8
1989
42.4
66.2
31.6
46.9
46.4
39.4
41.8
29.9
37.2
YOLOV8S
518
43.2
66.9
31.3
47.3
48.4
43.8
40.6
29.4
38.3
414
43.1
66.5
31.8
46.1
48.1
41.5
39.1
29.4
42.1
12
132
41.5
65.7
32.6
46.3
43.6
39.6
40.0
28.9
35.3
66.4
32.6
45.8
34.0
219
42.7
66.6
32.1
46.4
46.8
40.1
39.2
31.9
39.0
YOLOVEIN
144
42.6
66.5
31.2
46.9
49.2
41.3
40.3
30.4
35.4
58
42.4
66.9
31.4
38.7
45.8
45.8
33.0
40.1
38.5
47
42.5
66.4
32.6
46.5
41.7
46.5
28.9
39,9
38.8
1763
43.4
67.4
32.6
43.2
48.1
48.7
30.9
48.1
37.5
2
940
43.0
67.1
32.3
43.5
47.0
47.2
28.9
40.8
37.2
YOLOv
67.8
32.5
42.4
46.4
48.1
34.5
397
42.4
66.8
32.5
41.5
46.4
48.9
30.9
39.7
32.5
254
42.2
66.7
33.0
41.8
46.6
47.8
29.4
40.7
31.9
Keyframe Number Analysis. We further investigate the impact of varying the number of keyframes K. model performance. As shown in Table SEE& TREK consistently improves spatial under- The most notable
standing across all keyframe settings compared to the baseline INTERNVL3-888 gain of +3.0% is observed K. 8, demonstrating that our method is highly effective even under sparse temporal input. Interestingly, as K. increases, the performance improvement becomes marginal, with the gain reduced +0.6% at IC 32. This diminishing return can be attributed the saturation of spatial information in densely sampled frames: when adjacent keyframes are visually redundant, they provide limited additional cues for the model to reason over spatial relationships. contrast, our semantic richness sampling strategy is designed to select frames with diverse scene structures and salient spatial cues. Therefore, even small number informative frames can sufficiently support spatial reasoning, while denser sampling introduces redundancy without significantly enhancing spa-
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tial understanding. This further validates the efficiency and robustness of SEE&TREK in leveraging a compact, semantically diverse set of frames to enhance MLLMS.
Table 11: Ablation studies of different keyframe number K. setting. Method "-_" denotes the bascline MLLM INTERNVL3: VL3-8B
Method
Avg.
Obj. Count
Abs. Dist.
Numerical Answer
Obj. Size
Room Size
Rel. Dist.
Moltiple-Choiee Answer
Rel. Dir.
Route Plan
Appe. Order
40.2
67,8
32.2
44.5
41.8
42.8
37.7
26.3
28.3
TSEE&TREK
43.2, 1.,09
65.2 2,09
32.9 0.7%
46.9 2.48
4.97
40.2 12.0%
,0.4,4.155
,7.4
12 TREE&TRUK
41.8
67,8
32.2
44.5
41.8
42.8
37.7
26.3
28.3
43.6,1.96
67.8..0.04
34.0_.a%
43.4-1.1%
45.0..a3
43.94
39.7,2.0
26.3
34.3,m.00
42,4
69.3
33.9
43.1
47.3
44,8
38.7
26.8
35.3
+SEE&TREK
44.0,1.0%
67.3
34.6-..7%
48.
48.1-500
47.9-...5
44.940.15
39.741.064
29.4,2.0%
39.64-1.56
43,2
69.6
35.8
44,3
45,9
49.2
27.8
33.7
+SEE&TREK
44.150.05
67.5_
34.5
46.6.2..%
49.0..a35
47.3-
39.0....55
30.4,2.0%
38.7
38.748.05
43.1
69.3
34.9
41.6
49,4
46.9
38,9
29,9
33,9
+SEE&TREK
43.7,40.0
68.5
34.3-..0%
46.1-1.5
48.1-133
45.6-1.374
41.5
28.4-1.5%
36.7.4a.04
Motion Trajectory. As mentioned in Section 3.2, we also investigate performance impact of different part of input visual trajectories containing BEV PBEV and 3D figures PTD. Table 9 presents the ablation results evaluating the contribution of different visual trajectory components- projections (P BEE and 3D spatial figures (Pıd) to the overall performance. Removing either component leads to a noticeable drop in average accuracy, confirming the complementary value of both trajectory views in enhancing spatial understanding, Specifically, excluding PBEY results in slightly better performance than removing P3D. particularly in tasks requiring high-level route planning and temporal ordering, suggesting that BEV provides a more intuitive overview of the navigation path and spatial layout. Conversely, the 3D visualization offers critical depth and geometric cues, as evidenced by its impact on fine- mee-grained spatial tasks such as "Obj. Size" and "Room Size." The results demonstrate that both PBEV and P3D are indispensable for supplying diverse and complementary motion context to the MIL.M, enabling more robust reasoning across spatial tasks.
Camera Intrinsics. The fixed KITTI intrinsics are used solely within the visual odometry (VO) stage to convert 2D correspondences into relative camera motion. Our prompting design explicitly frames all motion cues in terms of relative distances and orientations, without providing any metric calibration to the MLLM. we include results using normalized, random and identity intrinsics matrices in the VO stage to demonstrate that the performance gains persist even without any meaningful camera calibration. Here, we still adopt the INTERN
SEE& TREK as the baseline and evaluate on the VSI-BENCH as shown in Table 12 The results in the table demonstrate that replacing the original KITTI intrinsies matrix with normalied,, random. or identity matrices yields comparable performance across all spatial reasoning sub-tasks on VSI-BENCH. The minimal differences in average scores and individual metrics indicate that our method's effectiveness not dependent on the accuracy or specific values of the camera intrinsics matrix. This supports our claim that the spatial improvements stem primarily from the relative motion cues encoded by our VO and prompting strategy, rather than any geometric advantage conferred by precise intrinsic calibration. Consequently, our framework maintains robustness and fairness even when intrinsic parameters are unavailable or approximate.
Table 12: Comparison results of different camera intrinsics setting K.
Obj. Count
Abs. Dist. Numerical Answer
Obj. Size
Room Size
Rel. Dist.
Rel. Dir. Multiple-Choice Answer
Plan
Appr. Order
Avg.
Original K [53]
43.2
65.2
32.9
46.9
46.7
45.9
40.2
30.4
37.4
Normalized K [44
43.0
65.2
32.6
46.5
47.2
46.8
39.0
28.9
37.5
Random
43.1
65.5
32.8
46.2
46.7
46.0
39.5
30.4
37.4
Identity
43.2
65.8
32.0
46.1
47.0
46.2
39.0
32.0
38.1
B.6 Qualitative results
We give more illustration about how SEE& impacts the baseline inference as shown in Figure 8 We also utilize the purple circle to highlight the objects mentioned in the question. For instance, in Figure 7(e), we observe that SEE& TREK can inspire more spatial reasoning in the baseline, such as more rational Chain of Thoughts (CoT) by incorporating more meaningful spatial information, leading to more accurate relative distance estimation. Besides, we also can conclude that the motion visual trajectories can improve the accuracy about the size estimation like Figure (b) like "given the
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Table 13: Comparison results of different visual spatial methods containing SoM CC [54 and our SEE& TREK. It shows that SEE& TREK obtains the SOTA performance in boosting the MILMs spatial understanding.
Method
Avg.
Obj. Count
Abs. Dist.
Numerical Answer
Obj- Size
Room Size
Rel. Dist.
Route Plan Multiple-Choice Answer
Rel. Dir.
Appr. Order
SoM
S3
41.3
64.5
29.6
44,9
43.1
36.8
41.1
27.3
43.7
64.4
30.0
43.8
38.3
27.8
42.2
SEE& TREK
43.2
65.2
32.9
46.9
46.7
45.9
40.2
30.4
37,4
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Figure 9: Illustration of several failure cases of SEE& compared to the baseline. It contains
three spatial tasks: (a) route plan (b) object relative distance (c) room size.
visual cues." Furthermore, we can get conclusion that the Maximum Semantic Richness Sampling (MSRS) can boost the richness of extracted frames from the given video as shown in Figure 6 and .f... Overall, the proposed SEE&TREK nightlight its advantages in modeling egocentric movement and temporal coherence.
B.7 Failure Cases
As shown in Figure we also investigate the failure cases generate from SEE& with empirical study. We first conclude that our baseline model maybe confused by the provided spatial information in some domains. For instance, in Figure P a) (b)... we observe that even though existing all objects like door or fireplace from the query in the selected frames, the answer generated from SEE T TREK is wrong. Besides, it also get the conclusion that choosing GPU-free YOLO as the perception model still does not have strong generalization ability and performs poorly multi object and multi class scenes like Figure (c). which lacks more goals compared to the baseline. It motivates us to utilize more powerful dense perception model to extract higher semantic richness frames from the given spatial videos.
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(a) Frames Sequence
(b) Spatial Map
mntire hare
Baseline
Visuul Trajectories
Ground Truth
Figure 10: Illustration of the implicit impact of visual trajectories from S for MLLMs internal inference. Here, we utilize GEMINI 2.5 PRO as our baseline. demonstrates that visual trajectories help MILMs construct better implicit spatial map, which improve different spatial task performance comprehensively.
B.8 More Prompting Comparison
SEE& the first training&gpu-fiee method to improve the spatial understanding ability of MLLMs. Served the prompting methods [55] 56 57 58), also explore other current visual
in other domains for comparison. We notice the 3DAXISPROMPT [59 also boost MILMs spatial understanding which leverages the 3D coordinate axis and masks generated from the Segment Anything Model (SAM) 600 to provide explicit geometric priors. Due to its lack of open-source code which makes it hard to review. we only discuss it here. Then, we leverage Set of Mask (SoM) (53) and Coarse Correspondence (CC) (54), which enable the identification of objects- vision-language models. Similar from the setting [53] 54], we also sample uniformly 8 frames from given videos and conduct these method process. We still follow the above-men aooveementioned setting to evaluate, e.g. INTERN VL3-8B as baseline. As shown in Table 13 our proposed SEE& TREK consistently outperforms existing visual prompting methods such SOM and CC across all spatial reasoning tasks. While SoM and CC primarily rely on static masks or coarse positional labels to identify object regions, they offer limited support for modeling inter-object relationships or capturing scene-level spatial structures. In contrast, SEF& TREK leverages two key principles-vist viiuull diversity and motion reconstruction- to provide richer and more structured spatial cues. Specifically, the Maximum Semantic Richness Sampling strategy ensures that the input frames encapsulate diverse spatial layouts and semantic contexts, while the simulated visual trajectories explicitly encode relative positions and temporal continuity, which are essential for complex spatial reasoning tasks. Unlike prior approaches that depend on task-specific annotations or domain priors, S operates in fully training- and GPU-free manner, making it both lightweight and generalizable. This design enables MLLMS to develop a deeper and more coherent spatial understanding, as reflected in the consistent improvements across all evaluated tasks.
prompting method (59 53 54
with masks or numeric labels, by utilizing the capabilities of current
C Discussion: Trajectories Help MLLMs Construct Implicit Spatial Map
As discussed in earlier sections, SEE& is designed enhance the spatial understanding capabilities of MILMS under limited visual input by preserving both spatial relationships and temporal coherence. While Maximum Semantic Richness Sampling effectively improves visual
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diversity, the influence of motion reconstruction on the model's internal inference processes remains less understood. To investigate this, we move beyond explicit reasoning analyses (e.g. Figure E and propose a more challenging task: generating structured spatial maps in text form. This setting offers more direct and interpretable view into the model's implicit spatial reasoning and internal representational mechanisms. As shown in Figure [T] the baseline model exhibits limited spatial coherence- coherence-allthough relevant entities are identified, their placements are often disorganized and misaligned with the actual room layout. In contrast, the model equipped with visual trajectories produces spatial maps that more closely resemble ground truth. These outputs demonstrate more accurate object positioning (e.g., correctly situating the kitchen area and entrance) and better structural grouping (e.g.. aligning the coffee table with the sofa and TV stand). The observed improvement underscores the critical role of motion- motion-guided trajectories in reinforcing spatial continuity and layout reasoning. By temporally linking semantically rich keyframes, visual trajectories provide the model with stronger contextual cues, helping it infer object relationships and transitions across frames. In essence, these trajectories act as a soft inductive prior, enabling MLLMS to construct spatially consistent internal representations from fragmented visual observations.
D Discussion: When SEE& TREK Meet Powerful Perception Models
This paper mainly proposes a general and training-free framework that explicitly incorporates semantic-rich frame sampling and motion reconstruction enhance spatial reasoning. To validate this idea in lightweight and accessible manner, we implement a minimal working solution using YOLOV8- TINY and manual VO module, both of which are GPU-free and efficient. This imple- mentation demonstrates that even under constrained settings, our method can deliver consistent performance gains. Importantly, our framework is highly extensible. The perception and motion modules can be readily replaced with stronger alternatives according to different scenarios. Here, we conduct experiments on the VSI-BENCH involving the replacement of various alternatives, such substituting the detection component with VLM-based Models (GROUNDING DINO [61] and YOLO-WORLD [2]) as shown Table 14 Note that we utilize the LNTERNVL3-8B+SSE TREK our baseline.
Table 14: Comparison results of utilizing more powerful perception models.
Mathod
Timels)
GPL Occ.MMB)
Avg.
Obj. Count
Abs. Dist.
Numerical Answer
Obj. Size
Room Size
Rel. Dist.
Rel. Dic. Multiple-hhiice Answer
Roste Plan
Appc. Order
Buscline
43.2
65.2
32.9
46,9
46.7
45.9
40.3
30.4
37.4
GROUNDING DINO
51
157
$430
43.8
66.3
32.3
46.7
47.1
47.4
41.4
$1,3
38.2
YOLD- WORLD
107
1740
43.5
66.2
31.8
46.2
49.5
44.2
38,9
30
37.4
E
Acknowledgment
This work was supported in part by the National Key R&D Program of China (Grant No. No.2023YFF07250011 in part by the National Natural Science Foundation of China (Grant No. No372230,,4), in part by the guangdong Basic and Applied Basic Research Foundation (Grant 202200331555 151522057,, part by the Education Bureau of Guangzhou.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the core contributions and scope of the paper. Specifically, the claims focus on enhancing spatial understanding in Multimodal Large Language Models (MLLMs) under purely visual constraints, which is a well-motivated and underexplored area. The abstract introduces the proposed SEE& TREK framework, emphasizing its two main struction- bottioonn-oohhhooo of which are described in terms of their purpose and implementation. Furthermore, the claim of being training- frining-free and easily integrable is directly supported by the method's ' design and empirical results. The reported improvements benchmark datasets (VSI-BENCH and STIIBENCH) further substantiate the claimed performance gains.
Justification:
cempoonntts-semaniie
keyframe sampling and motion recon-
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
The paper includes a dedicated "Limitations" section in the appendix, which clearly acknowledges the constraints of the proposed SEE& TREK framework. The authors discuss how the performance of the method depends on the external perception model used for keyframe extraction and motion reconstruction, highlighting that improvements are bounded by the semantic richness and capability of these models. Additionally, they note that while the framework designed to be GPU- and training-free, relaxing these constraints in the future could lead to further performance gains. This discussion reflects an awareness of the assumptions and practical limitations of the current design, and shows consideration for how the method might generalize or be extended in future work.
Justification:
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-tent system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
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If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full of assumptions and a complete (and correct) proof?
Answer: [NA]
Justification: The paper does include any theoretical results, theorems, formal proofs. Its contributions are methodological and experimental, focusing on the design training- free prompting framework and empirical validation spatial reasoning benchmarks.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: Our paper have explained the detailed experiment setting in the appendix like "Implementation" and the hardware environment. This information sufficient for others to estimate computer requirements and reproduce the experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
If the contribution
Depending on the contribution, reproducibility can accomplished various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or the contribution is a specific model and empirical evaluation, may be necessary either make it possible for others replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of model checkpoint, or other means that are appropriate to the research performed.
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(a) If the contribution primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
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(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully. If the contribution either be way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how construct the dataset).
new model (c.g.. large language model), then there should
(d)
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The paper provides sufficient experimental details to allow readers to under- stand and reproduce the results. It clearly specifics the benchmarks used (VSI-BENCE and STI-BENCH) the MLLMs evaluated, and the setup of the inference-only prompting framework.
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7. Experiment statistical significance
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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: While the paper presents empirical results that support the effectiveness of the proposed SEE& TREK framework, it does not report error bars, confidence intervals, or statistical significance tests. As result, is unclear how sensitive the reported per- formance improvements are factors such as random sampling or variability in model inference. However, our evaluation set the random seed fixed number to guarantee the re-productivity of our paper. Including such statistical analysis would help better assess the robustness and reliability the findings.
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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
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We have provided the detailed development environment such as hardware in the Appendix "Implementation" part. We also report the running time of proposed method in the Paper Table 5)
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Justification: The research adheres to the NeurIPS Code of Ethics. The proposed SEE& TREK framework is designed to improve the spatial reasoning capabilities of Mul- timodal Large Language Models without requiring additional training or sensitive data. The work relies solely on publicly available benchmarks and off-the-shelf models, without involving human subjects, private user data, potentially harmful applications. Moreover, care has been taken to ensure that the framework efficient and accessible (e.g., GPU-free), aligning with the principles of responsible AI development and equitable research access.
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Answer: [NA]
The paper does not explicitly discuss broader societal impacts. While the proposed framework focuses on foundational improvements to spatial reasoning in MILMs and not directly tied specific applications. discussion of both potential benefits and risks (e.g., misuse in surveillance or failure in safety-critical systems) would strengthen the work's s alignment with ethical and responsible AI practices.
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The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
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The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
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Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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Answer: [NA]
The paper does not introduce release any new pretrained models or scraped datasets that carry a high risk of misuse. Instead, it proposes training-free prompting framework that leverages existing publicly available MILMS and off-the-shelf perception models. As such, the research poses no new risks that would require specific safeguards for responsible release.
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Guidelines:
The answer NA means that the paper poses no such risks.
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The paper makes use of existing publicly available models and datasets, including open-source MLLMs (e.g., Qwen, LLaVA) and perception models (e.g., YOLOv8). All assets used are properly credited through citations in the main text or appendix. The licenses of these assets-such as Apache 2.0 or MIT for code, and CC-BY or similar for datasets- are respected. For each asset, version information and URLs to official repositories are provided to ensure traceability and compliance with their terms of use. No proprietary or restricted data was employed in the study.
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Answer: [No]
Justification: The paper uses large language models (LLMs) solely for writing, editing, and formatting purposes. without involving LLMs as an important, original, or non-standard component of the core methods or experiments. Therefore, the usage of LLMs does not impact the scientific rigor or originality of the research.
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The answer NA means that the core method development in this research does not involve LLMs as any important, original. or non-standard components.
Please refer to our LLM policy httpes////aarriooocc/o/ooorrrrrs for what should or should not be described.
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Reviews:
[{"review_id": "9yhQ8WNZO3", "reviewer": "Reviewer_VBKe", "summary": "This paper proposes a training-free prompting method for improving the spatial understanding capabilities of multimodal large language models (MLLMs). The approach includes a new frame sampling strategy that selects semantically rich keyframes and introduces camera motion trajectory information as an additional input. A Spatiotemporal Encoding module further aligns the trajectory with the sampled frames. The method is evaluated on VSI-BENCH and STI-BENCH, where it demonstrates consistent performance gains.", "questions": "Please refer to the weakness section.", "limitations": "yes", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n* The paper proposes a *training-free prompting* framework that enhances spatial understanding in MLLMs using only RGB video input, without requiring any model finetuning or architectural changes.\n* It highlights the importance of incorporating *camera motion* as an explicit cue, and demonstrates that motion-aware prompting can boost spatial-temporal reasoning performance in existing models.\n* The method is modular and compatible with many existing MLLMs, showing potential for widespread applicability.\n\n\n**Weaknesses**\n\n* **Poor Writing and Notational Overload**: Among all the assigned papers, this was the most difficult to read—not because the core method is inherently complex, but because Section 3 introduces an excessive amount of notation, some of which is poorly explained or not defined at all. For instance, the relationship between τ<sub>g</sub> in Equation 1 and the previously mentioned f<sub>τ₀</sub> is unclear. Similarly, C<sub>t₀</sub> on line 118 appears without definition. Reading this section required constant guesswork about the authors' intentions. The paper urgently needs a major writing revision: notation should be minimal and intuitive, and the method should be explained as clearly and directly as possible. Figure 2 also suffers from visual clutter—components are packed too tightly, making the pipeline hard to grasp at a glance.\n\n* **Missing Discussion of Related Work**: The paper claims to be *“the first training-free prompting framework tailored to enhance the spatial understanding of MLLMs under vision-only constraints.”* However, prior work such as **Coarse Correspondence \\[1]** also employs a prompting-based approach to improve spatial reasoning in MLLMs. The authors should clarify how their method differs and justify the novelty claim.\n\n* **Unclear Use of Camera Intrinsics**: The motion reconstruction module appears to require camera intrinsic parameters, but it is unclear how these are obtained for each test video. Many video datasets do not provide intrinsics. This raises both reproducibility and fairness concerns. Specifically:\n\n  * Are intrinsics used during evaluation on VSI-BENCH and STI-BENCH?\n  * If so, how are they obtained for each video?\n  * Did baseline methods also use intrinsics? If not, could the observed performance gain be due to additional input information available only to the proposed method?\n  * If they did, in what way were intrinsics used?\n\n  The authors should clarify these points and, if necessary, reframe their results in light of potential input discrepancies.\n\n* **Limited Evaluation Scope**: While SEE\\&TREK improves performance on several open-source models, it remains unclear whether the method generalizes to *closed-source* models such as GPT-4o or Gemini-2.5. If the gains only appear on relatively weaker models, the broader practical impact of the approach is questionable.\n\n* **Marginal Performance Gains**: On both VSI-BENCH and STI-BENCH, the performance improvements for most models are under 2%, which is relatively modest and raises doubts about the method’s overall effectiveness. The strongest gains appear limited to smaller models, and even then, are not particularly dramatic.\n\n* **Narrow Benchmark Coverage**: Both benchmarks used—VSI-BENCH and STI-BENCH—focus on similar types of spatial reasoning tasks. The generality of the approach remains untested on more diverse benchmarks such as ScanQA or OpenEQA, which would assess whether SEE\\&TREK is broadly applicable beyond low-level spatial perception tasks.\n\n* **Unclear Computational Overhead**: The pipeline involves object detection, visual odometry, essential matrix estimation, and trajectory rendering—none of which are trivial. However, the paper does not provide any analysis of the additional computational cost or latency introduced by these steps. Since the method is pitched as a plug-in prompting solution, it would be important to understand how much runtime overhead it brings in practice.\n\n[1] Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model. CVPR 2025", "was_revised": true, "final_justification": "**Final Justification**\n\nBased on the randomized intrinsic matrix generation code provided by the authors, I conducted experiments to evaluate the impact of such intrinsics.\n\n### 1. Motivation\n\nThis experiment aimed to empirically investigate the core disagreement between Reviewer VBKe and the authors regarding the effect of camera intrinsics (K) on Structure-from-Motion (SfM). I further differentiated two types of incorrect intrinsics:\n\n1. A structurally valid but numerically inaccurate guess.\n2. A structurally invalid and numerically random matrix generated by the authors’ own code.\n\nThe goal was to test whether robust algorithms such as RANSAC can compensate for different levels of error.\n\n### 2. Experimental Setup\n\nThe setup remained consistent, using COLMAP and the south-building dataset (4 images).\n\n* **Dataset**: south-building.zip, images P1180170.JPG to P1180173.JPG.\n* **Control Group (Known Intrinsics)**: Ground-truth intrinsics.\n\n  * Model: SIMPLE\\_RADIAL\n  * Params: 2559.68, 1536, 1152, -0.0204997\n* **Experimental Group A (Inaccurate Guess)**: A plausible but incorrect focal length, structurally valid.\n\n  * Model: PINHOLE\n  * Params: 1600, 1600, 960, 540\n* **Experimental Group B (Randomized Matrix)**: Intrinsics strictly following the authors’ Python code, structurally invalid.\n\n  * Model: OPENCV\n  * Params: 1.0, 0.065..., 1.063..., 1.020..., 0,0,0,0\n\nIn all groups, intrinsics were refined during bundle adjustment (`--Mapper.ba_refine_focal_length 1`).\n\n### 3. Methodology\n\nEach group underwent the standard COLMAP pipeline, with the only variable being the initial camera intrinsics supplied to the feature extractor.\n\n### 4. Results\n\nThe outcomes diverged clearly:\n\n* **Control Group**: Successful and accurate reconstruction.\n* **Experimental Group A (Inaccurate Guess)**: Successful though less accurate. Optimization converged because the initial guess (fx=1600) was “close enough.” The refined parameters likely approached the ground truth, but with minor quality degradation (scale, drift).\n* **Experimental Group B (Randomized Matrix)**: Complete failure. COLMAP mapper failed to initialize, reporting *“No good initial image pair found.”*\n\nThese results show that while an inaccurate but structured intrinsic can still allow estimation of correct relative R and T, a randomized intrinsic—as produced by the authors’ code—fails entirely. This raises doubts about the reliability of the authors’ claimed experiments, since their code yields structurally invalid matrices.\n\n### 5. Performance Concerns\n\nOn the performance issue: while GPT-4o is undoubtedly stronger than YOLOv8-tiny, in this paper YOLOv8-tiny is only used to approximate relative object counts per frame for frame selection.\n\n* First, counting object categories is not a highly demanding task and does not require precise localization.\n* Second, even if YOLOv8-tiny misjudges object counts and selects different frames, I remain unconvinced that this would lead to substantial performance differences.\n\nTherefore, from my perspective, the concern about performance variance has not been adequately resolved.\n\n---\n\n**Conclusion**\nGiven the experimental evidence and unresolved concerns, I have decided to **maintain my initial rating**.", "rebuttal": "We thank the reviewer for their thoughtful comments. We are glad that the reviewer found our method to have widespread applicability and the novelty of incorporating camera motion as an explicit cue. We would like to address all of your concerns and questions below with point responses:\n\n1. **Concern about Poor Writing and Notational Overload.** Thanks for your thoughtful feedback. We are sorry for the confusing notation raised in Section 3. Here, we modify our notation for better readability. The $\\tau_t$ denotes the selected keyframe index. Hence, the $f_{\\tau_0}$ can represent the frame sampled in the initial phase. Then, the $\\tau_g$ from Eq. (1) is re-written as $\\tau_0$. Then, $\\tau_i$ is rewritten as $\\{f_{\\tau_i}\\}$ in Line 125, which represents the selected frames. Moreover, we also make the abstract algorithm flow as shown in Algorithm 1 in the main manuscript, and the detailed algorithm flow is as shown in the appendix Algorithm 2. We hope these modifications and other information can be helpful for our method formulation reading. Regarding Figure 2, we also are sorry about the visual clutter. We will make it better in the revised version.\n\n2. **Concern about Missing Related Work.** Thanks for pointing out this important point.  Regarding the Coarse Correspondence [1] (CC) or more similar prompting methods like SoM [2], We have discussed their difference and performed a comparison study in the Appendix Section B.7. \n\n| Method | Avg. | Obj. Count | Abs. Dist. | Obj. Size | Room Size | Rel. Dist. | Rel. Dir. | Route Plan | Appr. Order |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| CC [1] | 40.9 | 64.4 | 30.0 | 43.8 | 38.3 | 39.1 | 41.6 | 27.8 | 42.2 |\n| SoM [2] | 41.3 | 64.5       | 29.6       | 44.9      | 43.1      | 36.8       | 41.1      | 27.3        | 43.7         |\n| **SEE&TREK** | 43.2 | 65.2 | 32.9 | 46.9 | 46.7 | 45.9 | 40.2 | 30.4 | 37.4 |\n\nAs shown in Table, See&Trek consistently outperforms existing visual prompting methods such as CC or SoM across all spatial reasoning tasks. While CC and SoM primarily rely on static masks or coarse positional labels to identify object regions, they offer limited support for modeling inter-object relationships or capturing scene-level spatial structures. In contrast, See&Trek leverages two key principles—visual diversity and motion reconstruction—to provide richer and more structured spatial cues.\n\n3. **Concern about Usage of Camera Intrinsics.** We appreciate the reviewer’s concern. To clarify, we do not utilize the intrinsic parameters provided by the original datasets (e.g., VSI-BENCH or STI-BENCH). Instead, we just adopt a fixed intrinsic matrix derived from the KITTI dataset (`K = [[718.8560, 0, 607.1928], [0, 718.8560, 185.2157], [0, 0, 1]]`) across all experiments. This design choice was made deliberately to demonstrate the plug-and-play nature and extensibility of our framework, even in scenarios where intrinsic parameters are unavailable—a common case in real-world video sources. While using accurate intrinsics may lead to improved VO estimates and absolute metric precision, our method is built around relative motion encoding, not absolute scale recovery. **To account for the inaccuracies introduced by this assumption, we explicitly emphasize in the prompt that the spatial information reflects relative distances and orientations, rather than real-world metric measurements.** Lastly, our method is designed as a training-free spatial prompting module, intended to enhance the MLLM’s spatial understanding through motion-aware input construction. The use of intrinsics is strictly limited to internal VO estimation and does not influence or depend on whether baseline methods utilize such information. Therefore, the comparison remains fair, and the gains reflect the effectiveness of our spatial enhancing strategy. \n\n4. **Concern about Closed-source Models Performance.** Thanks for your insightful comment.  We perform the evaluation on the commercial engine GPT-4o and Gemini-2.5-Flash on VSI-bench set as follows: \n| Method | Gains | Avg. | Obj. Count | Abs. Dist. | Obj. Size | Room Size | Rel. Dist. | Rel. Dir. | Route Plan | Appr. Order |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Gemini-2.0-Flash | - | 42.6 | 51.1 | 29.8 | 55.1 | 56.2 | 38.2 | 40.9 | 32.1 | 37.4 |\n| +**SEE&TREK** | **+1.2%** | 43.8 | 49.8 | 29.3 | 56.4 | 57.0 | 39.4 | 41.9 | 35.4 | 41.8 |\n| GPT-4o | - | 34.0 | 46.2 | 5.3 | 43.8 | 38.2 | 37.0 | 41.3 | 31.5 | 28.5 |\n| +**SEE&TREK** | **+1.4%** | 35.4 | 46.5 | 5.0 | 45.1 | 40.6 | 39.0 | 43.2 | 33.5 | 31.5 |\n\nAs shown in the table, our method consistently improves performance across different commercial engines. \n\n5. **Concern about Marginal Performance Gains.** We thank the reviewer for highlighting it. It is important to emphasize that spatial reasoning remains an inherently challenging task for MLLMs, due to both the limited spatial supervision in pretraining corpora and the resource constraints in downstream evaluation (e.g., limited frame budgets in benchmarks). Even small performance gains (e.g., <2%) are meaningful in this context, as they indicate improved geometric and relational understanding under minimal input. Moreover, larger models tend to possess stronger built-in spatial priors, making additional improvements naturally more constrained. However, our method consistently yields positive gains across models of all scales, including powerful backbones like KIMI-VL-A3B, which demonstrates its general applicability. \n\n6. **Concern about the Benchmark Coverage Problem.** We appreciate this important suggestion about coverage.  we conduct the experiments on OpenEQA [3]  with InternVL3-8B to show our method's superiority. Note that we utilize the EM-EQA setting [3] to evaluate the performance, and we still set 8 frames as input. \n\n| Model | w/o SEE&TREK | w/ SEE&TREK | Gain (%) |\n| --- | --- | --- | --- |\n| Qwen2.5-VL-7B | 47.1 | 49.1 | **+2.0%** |\n| InternVL3-8B | 50.5 | 52.3 | **+1.8%** |\n\nAs shown in the Table, SEE&TREK brings consistent improvements across models, with a +1.8% gain on Qwen2.5-VL-7B and +2.0% on InternVL3-8B, demonstrating its effectiveness in enhancing spatial reasoning without fine-tuning. \n\nWhile ScanQA and OpenEQA are valuable contributions to the spatial reasoning community, our choice of VSI-Bench and STI-Bench was deliberate and grounded in their superior diversity, task coverage, and spatial evaluation rigor, particularly when evaluating the spatial intelligence of MLLMs. First, **VSI-Bench and STI-Bench are not limited to “low-level spatial perception tasks,” but instead represent high-level and fine-grained spatial reasoning benchmarks across diverse and realistic visual environments.** These benchmarks are specifically designed to stress-test an MLLM's ability to understand fine-grained spatial structures, dynamic motion, and scene geometry, rather than relying on language priors or generic commonsense. ( We also have stated these dataset details in the Appendix. ) In contrast, **while ScanQA or OpenEQA are earlier benchmarks for spatial QA, they tend to focus on relatively narrow task formats, predominantly involving object-level recognition, existence, and co-location in static scenes (ScanQA) or episodic memory from passive video (OpenEQA).** Moreover, the evaluation metrics used in ScanQA or OpenEQA—BLEU, METEOR, ROUGE-L, CIDEr—are primarily adapted from traditional VQA and text generation. These metrics often struggle to meaningfully distinguish spatial reasoning quality, especially when answers differ in surface form but not semantics, or when tasks require metric estimation, relative positioning, or trajectory-level reasoning. By contrast, VSI-Bench and STI-Bench employ structured evaluation formats (e.g., spatial multiple choice, numeric prediction with relative error thresholds) and task-specific accuracy metrics designed to quantitatively and unambiguously measure spatial competence. This ensures that improvements in model performance—such as the +3.5% gain brought by See&Trek—are directly tied to enhanced spatial understanding, not merely improved textual fluency or memorization. \n\n7. **Concern about Computational Overhead.** Thanks for highlighting this practical concern. Regarding the additional computational cost, we have the corresponding experiments on VSI-benchmark as shown in Line 276 ``Sample Efficiency Analysis'' in the main paper. To explore different YOLO's efficiencies, we also perform diverse experiments on Table 9 in the appendix. The conclusion is that the time consumed by SEE&TREK still largely depends on the length of the given videos. Particularly, the duration of videos in VSI-BENCH is at least longer than 1 minute. \n\nThank you again for your constructive feedback—we have carefully addressed all your concerns in our responses above, and we hope our clarifications help resolve your doubts. We are especially grateful for your thoughtful suggestions, which contribute meaningfully to advancing the spatial understanding of MLLMs.\n\n[1] COARSE CORRESPONDENCES Boost Spatial-Temporal Reasoning  in Multimodal Language Model\n\n[2] Set-of-Mark Prompting Unleashes  Extraordinary Visual Grounding in GPT-4V\n\n[3] OpenEQA: Embodied Question Answering in the Era of Foundation Models"}, {"review_id": "Rbb2sN0nHm", "reviewer": "Reviewer_nToN", "summary": "This paper proposeS SEE&TREK to enhance the purely visual spatial understanding of existing Multimodal Large Language Models (MLLMs) under the training-free setting. SEE&TREK introduces two core principles: 1) visual diversity--achieved via a Maximum Semantic Richness Sampling strategy using a perception model (e.g., YOLO) to select the most informative keyframes; 2) motion reconstruction--accomplished by simulating visual trajectories and encoding relative spatial positions into keyframes through camera motion estimation and spatiotemporal encodings. SEE&TREK can be seamlessly integrated into existing MLLMs without any additional training. Extensive experiments on VSI-BENCH and STI-BENCH demonstrate that SEE&TREK consistently improves spatial reasoning performance across various MLLMs, achieving up to +3.5% performance gain on challenging spatial tasks.", "questions": "- The ablation study on the sampling interval presented in Table 5 does not exhibit a clear or consistent trend. Could the authors provide further explanation or insight into why different sampling intervals do not lead to a noticeable performance pattern?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n- The proposed method is conceptually novel in addressing spatial reasoning limitations of MLLMs under a training-free setting. By introducing Maximum Semantic Richness Sampling and Motion Reconstruction as complementary techniques, the paper offers a fresh perspective on enhancing visual-spatial understanding without modifying or fine-tuning the underlying models.\n- The paper is well-written and clearly structured. The methodology is articulated in a logical, step-by-step manner, with helpful visual illustrations (Figures 1 & 2) and Algorithm 1 that enhance comprehension of the framework.\n- The work is technically sound. The authors conduct rigorous experiments on two challenging spatial reasoning benchmarks—VSI-BENCH and STI-BENCH—and provide detailed ablation studies that effectively validate the contribution of each proposed component.\n- The proposed SEE&TREK framework consistently and significantly improves the spatial reasoning capabilities of various MLLMs, achieving notable performance gains in terms of average accuracy. The demonstrated effectiveness across different model architectures highlights its generalizability and practical relevance.\n\nWeaknesses\n- No significant weaknesses are apparent.", "was_revised": true, "final_justification": "The response addresses my concerns; therefore, I maintain my original positive score.", "rebuttal": "We thank the reviewer for their thoughtful comments. We are glad that the reviewer found our method conceptually novel, well-written, clearly structured and technically sound, and also recognized our performance. We would like to address all of your concerns and questions below with point responses: \n\n**Concern about The Sampling Interval in Table 5.** Thanks for your significant consideration. The absence of a clear trend across different sampling intervals mainly stems from the following reason: \n\n1. our focus is on extracting semantically rich content, rather than dense temporal coverage. The Maximum Semantic Richness Sampling (MSRS) strategy ensures that keyframes are selected based on visual informativeness rather than fixed temporal intervals. Thus, even when the sampling interval $N$ varies, the retained frames tend to be semantically similar. In fact, using a very small interval (e.g., $N=1$) may introduce redundant or noisy frames, which can damage the model’s spatial understanding and reduce answer consistency. This is partially reflected in the slight performance dip at $N=2$ compared to $N=3$ or $N=4$. \n\n2. In addition, the sampling interval $N$ has a substantial impact on processing time. As shown in Table 5, increasing $N$ significantly reduces the computational cost (e.g., from 410s at $N=1$ to 82s at $N=4$), while maintaining comparable or even better average accuracy. This indicates a favorable trade-off between efficiency and effectiveness, where moderate sampling (e.g., $N=3$ or $N=4$) strikes a good balance for most use cases.\n\n3. More investigation and exerpeiment results about sample internval and detectors setting can be found in appendix Table 9. \n\nThank you for your kind words and positive assessment of our work. We also appreciate your insightful question regarding the sampling interval ablation in Table 5. We hope our response can solve your doubts."}, {"review_id": "v8HZ9HyXlv", "reviewer": "Reviewer_uJnW", "summary": "This paper presents SEE&TREK, a training-free method that enhances spatial reasoning in multimodal large language models using only visual input. By selecting key frames with YOLO and adding motion information via visual odometry, SEE&TREK improves models’ performance on spatial and temporal benchmarks like VSI-BENCH and STI-BENCH. The method boosts open-source model accuracy without requiring retraining or GPUs.", "questions": "Could some of the negative accuracy changes observed in the experiments be caused by failures of the Visual Odometry (VO) module? Is it possible to provide an analysis of related failure cases to examine whether there is a clear connection between VO failures and the observed accuracy drops?", "limitations": "The method uses only YOLO for frame sampling and a single ORB-based approach for visual odometry, without comparing multiple detection or VO algorithms. This may limit generality across different detection or motion estimation pipelines.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths :\n1. This is a training-free method that boosts spatial reasoning in open-source multimodal models using only visual inputs, without requiring extra training or GPU resources.\n2. Propose an explicit spatiotemporal encoding scheme that marks each keyframe with trajectory and order information, enhancing spatial-temporal reasoning.\n3. Experiments on multiple models and benchmarks show performance improvements, especially on smaller or mid-size models, with gains up to 3.5%.\n\nWeaknesses:\n1. Beside spatiotemporal encoding, the method mainly combines known techniques (YOLO for frame selection and ORB-SLAM/VO for motion), so its originality is more in engineering and prompt design.\n2. Some tasks or models see minor or even negative changes in accuracy, and the paper lacks deep analyses of these cases or failure modes.", "was_revised": true, "final_justification": "My questions are answered by the authors. I'll maintain my rating.", "rebuttal": "We thank the reviewer for their thoughtful comments. We are glad that the reviewer appreciated the practical value of our method, particularly its training-free nature, low resource requirements, and effectiveness in enhancing spatial reasoning in MLLMs using only visual inputs. We now address your comments point by point below.\n\n1. **Concern about the Technique Combination.** We appreciate the reviewer’s comment and would like to clarify that, the conceptual motivation and problem framing behind SEE&TREK are novel and underexplored in the current literature, even though our method is developed based on existing components such as object detection and visual odometry.  As we emphasize in the Introduction, prior works on enhancing MLLMs’ spatial understanding often rely on annotated spatial semantics (e.g., number cues, axis or semantic masks), or require model retraining and architectural modifications (We also discuss these work in appendix Section B.7. ). **However, how to stimulate spatial reasoning in MLLMs under a purely visual and training-free setting remains an open challenge, which is beneficial for most areas like robotics.** In our work, we take a step back and reflect critically on this problem setting, **Iidentifying two major and fundamental bottlenecks: Visual Homogeneity and Unknown Camera Motion.** Building upon these observations, SEE&TREK is designed from the ground up to explicitly address these two limitations through **visual diversity and motion reconstruction.** While the techniques we use (e.g., YOLO, VO) are established, their roles in our pipeline are not trivial. These components are not mere add-ons but are **core to realizing our conceptual framework.** Their interplay—semantic-aware selection plus trajectory-aware encoding—allows SEE&TREK to effectively emulate spatial continuity and dynamics from static visual inputs, offering a principled way to address current MLLM limitations. In this sense, SEE&TREK has a **novel perspective on designing spatial prompts** that help MLLMs better perceive and reason in 3D space, under a resource-efficient and training-free regime. Importantly, the current implementation presented in this paper represents a minimal working instantiation of this conceptual framework—i.e., GPU-free or maximum accessibility and deployment ease. **Moreover, the framework itself is flexible and modular: depending on the application scenario, the individual components can be upgraded.** For example, more advanced feed-forward 3D networks [1,2] or VO like MAC-VO [5] could be employed to capture holistic scene geometry. Here, we provide some results on VSI-Bench by utilizing diferent SOTA perception models in Following Response 3. \n\n2. **Concern about Experiment Performance.** Regarding the negative performance changes observed on specific tasks or models: We thank the reviewer for raising this important point. We address this concern through both **systematic trend analysis** and **qualitative case studies.** \n\n- **(1) Systematic Trend Analysis (from Table 6):** As noted, some tasks (e.g., Object Count, Object Size, Relative Direction) show minor drops in accuracy for certain models. We attribute this to limitations in our current module choices, which were intentionally selected for their **GPU-free and lightweight nature**, but may introduce trade-offs in robustness and granularity:\n  - a. **VO module drift:** We use the manual Visual Odometry module, which offers fast and training-free pose estimation. However, it is known to suffer from **accumulated drift**, especially in **high-speed or large-amplitude camera motions,** which are commonly found in datasets like VSI-BENCH and STI-BENCH. In such dynamic scenes, the VO module may fail to maintain globally consistent pose trajectories, leading to incorrect relative position estimates. This directly impacts tasks such as Relative Direction, Route Plan, and Room Size, where spatial coherence is critical.\n   - b. **Semantic sampling bias:** Our default perception module, YOLOv8-tiny, is optimized for speed but has limited object coverage and detection recall. In complex scenes with **multiple small, overlapping, or low-salience objects,** this model may miss relevant entities, degrading performance in Object Size tasks.\n    - c. **Model capacity:** Lightweight MLLMs (e.g., Qwen2.5-VL-3B) may struggle to understand motion-aware visual prompts from multiple frames due to its limited model scale, especially when spatial signals are sparse or noisy. This affects multi-step reasoning tasks such as Room Size or Relative Distance, where consistency over multiple frames is essential.\n   - d. **Missing frames**:  The proposed MSRS tends to collect the scenes with the most object class diversity. It sometimes ignores the frame with fewer objects whose classes are already collected. If these objects are not in the previously collected frames, MLLM will never be aware of them. Thus, we observe a performance decrease in the metrics related to objects themselves, e.g., Object Count.\n- **(2) Qualitative Case Studies.** We have also conducted some failure case analysis to better understand these limitations. As illustrated in Appendix Figure 9, SEE&TREK occasionally fails even when all queried objects are visible, due to misalignment between egocentric motion cues and layout inference (e.g., in route plan or relative distance tasks).  This reinforces our motivation to explore more powerful dense perception models in future work to boost semantic richness under diverse spatial contexts.\n\n3. **Concern about Generality across Different Detection or Motion Estimation Pipelines.** Thanks for the practical comments. This paper mainly proposes a general and training-free framework that explicitly incorporates semantic-rich frame sampling and motion reconstruction to enhance spatial reasoning. To validate this idea in a lightweight and accessible manner, we implement a minimal working solution using YOLOv8-tiny and manual VO module, both of which are GPU-free and efficient. **This implementation demonstrates that even under constrained settings, our method can deliver consistent performance gains.** Importantly, our framework is highly extensible. The perception and motion modules can be readily replaced with stronger alternatives according to different scenarios.  Here, we conduct experiments on the VSI-Bench involving the replacement of various alternatives, such as substituting the detection component with VLM-based Models (Grounding Dino and Yolo-World) and the VO component like MAC-VO. Note that we utilize the InternVL3-8B+See&Trek as our baseline.\n\n| Method | Time (s) | GPU Cost (MB) | Avg. | Obj. Count | Abs. Dist. | Obj. Size | Room Size | Rel. Dist. | Rel. Dir. | Route Plan | Appr. Order |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Baseline | 82 | 0 | 43.2 | 65.2 | 32.9 | 46.9 | 46.7 | 45.9 | 40.3 | 30.4 | 37.4 |\n| Grounding Dino [3] | 157 | 5430 | 43.8 | 66.3 | 32.3 | 46.7 | 47.1 | 47.4 | 41.4 | 31.3 | 38.2 |\n| YOLO-World [4] | 107 | 1740  | 43.5 | 66.2 | 31.8  | 46.2 | 49.5 | 44.2 | 38.9 | 30.9 | 37.4    |\n| MAC-VO [5] | 136 | 2321 | 43.7 | 66.9 | 32.6 | 48.1 | 48.7 | 43.2 | 48.1 | 30.9 | 37.5 |\n\n\nFinally, thank you for your thoughtful comments and constructive suggestions; we appreciate your recognition of our method’s strengths as well as your valuable insights on framework robustness and generality. We hope our response can solve your doubts.\n\n[1] Vggt: Visual geometry grounded transformer\n\n[2] Grounding image matching in 3d with mast3r\n\n[3] Grounding dino: Marrying dino with grounded pre-training for open-set object detection\n\n[4] Yolo-world: Real-time open-vocabulary object detection\n\n[5] MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry"}, {"review_id": "rjGPxFg8R8", "reviewer": "Reviewer_Jfjm", "summary": "This paper proposes SEE&TREK, a training-free spatial prompting framework for enhancing the spatial reasoning ability of Multimodal Large Language Models (MLLMs) under vision-only settings. The method addresses two major limitations of existing MLLMs in spatial tasks: visual homogeneity due to uniform frame sampling and unknown motion due to lack of temporal cues. To this end, SEE&TREK introduces two components: (1) Maximum Semantic Richness Sampling (MSRS), which selects visually diverse keyframes via a Balanced-TopK strategy using object detectors; and (2) Motion Reconstruction, which estimates camera motion through visual odometry and overlays spatiotemporal cues onto keyframes. These enhanced inputs are formatted into a spatially-aware prompt for MLLMs. Experiments on VSI-BENCH and STI-BENCH show improvements across several open-source MLLMs.", "questions": "see weakness.", "limitations": "none.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper addresses an important and underexplored challenge—improving spatial reasoning in vision-only MLLMs without training—which is both timely and practically relevant.\n\n2. The method is training-free, requiring only CPU-based post-processing. This makes it deployable for lightweight scenarios and commercial APIs.\n\n3. The paper introduces a way to inject both semantic richness and temporal motion cues into prompts, which is intuitive. The Balanced-TopK sampling and motion-aware frame augmentation are lightweight.\n\n4. The method is evaluated across two spatial benchmarks and multiple model families, with some improvements. Ablation studies and runtime analysis support the design choices.\n\n\nWeaknesses\n\n\n1. The paper lacks discussion and citation of related “training-free” multimodal prompting approaches[1,2,3].\n\n[1] Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning.\n\n[2] MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs.\n\n[3] Controlmllm: Training-free visual prompt learning for multimodal large language models.\n\n2. Some components (e.g., Balanced-TopK vs. Temporal-TopK) yield marginal improvements, but the significance of individual modules is not always clear. For example, Table 3 and 4 show that improvements from each component are small and sometimes inconsistent across tasks. \n\n3. In Table 1, SEE&TREK is evaluated on four baseline models, but in Table 2 (STI-BENCH), only two open-source models are reported. This inconsistency raises concerns about potential cherry-picking or underperformance on omitted models. The authors should clarify the reason for the missing entries and, ideally, provide complete results across all evaluated models for both benchmarks.\n\n\n4. The method is claimed to be seamlessly integrated into MLLMs, but it is only validated on LLaVA, Qwen, InternVL, and Kimi, with no experiments on proprietary models like GPT-4V or Gemini despite them being listed in tables. The relative gain in strong models (e.g., Qwen2.5-VL-32B) is modest, raising questions about scalability and ceiling effect. \n\n5. The method introduces several manually designed priors, which may overfit to specific types of tasks in VSI/STI-BENCH. It remains unclear how well this generalizes to other spatial reasoning tasks or open-world questions. This is the most important issue and also my primary concern.\n\n6. The proposed method heavily relies on off-the-shelf models such as object detectors (e.g., YOLO) and others. The authors should provide ablation studies of these auxiliary models to better understand their necessity and influence on the overall performance.\n\n7. The paper centers on video understanding. The authors should clarify that the scope is limited to video inputs and distinguish their approach from image-based spatial understanding tasks.", "was_revised": true, "final_justification": "After carefully considering the authors’ rebuttal and the subsequent discussions with the authors, other reviewers, and the Area Chair, I provide the following justification for my recommended score:\n\nIssues Resolved:\n\nThe authors have adequately addressed my initial technical concerns and clarified key points regarding their methodology and experimental setup.\n\nRemaining Issues:\n\nThe manuscript currently lacks a dedicated section that thoroughly discusses the relationship between this work, the strong baseline, and closely related prior work.\n\nI will keep my score.", "rebuttal": "We thank the reviewer for their thoughtful comments. We are glad that the reviewer appreciated the practical relevance and timeliness of addressing spatial reasoning in vision-only MLLMs, and the intuitive design supported by thorough evaluations. We now address your comments point by point below. \n\n1. **Concern about Related Work Discussions.** These works have made important contributions to training-free visual prompting paradigms. Here, we compensate for these discussions with these works as follows: D2R [1] is a training-free prompting framework, which integrates textual CoT with corresponding visual drafts into MLLMs. ViCrop [2] leverages a MLLM’s own internal attention and gradients to automatically crop images, significantly improving its perceptual accuracy on small visual details that it would otherwise fail to recognize. ControlMLLM [3] enables MLLMs to follow specific visual prompts by optimizing a learnable latent variable at inference time to control the model's attention. In addition to these works, we also discuss some prompting works that are beneficial to enhancing spatial understanding ability in the appendix Section B.6., such as CC [4] or SoM [5].  Different from these works, See&Trek aims to enhance the spatial understanding of MLLMs under vision-only constraints in a training & GPU-free manner, which focuses on two core principles: increasing visual diversity and motion reconstruction. \n\n2. **Concern about Each Component Performance.** We appreciate the reviewer’s careful inspection of our ablation results (Table 3/4/5 in main papers). \n\n- a. Compared to Temporal-TopK, Balanced-TopK focuses more on semantic richness, which selects the frame with the most classes within each temporal segment in a global view. Thus, Balanced-TopK can cover object classes in the video as much as possible. The diversity of the collected scenes helps MLLM understand the spatial relation between objects, namely, the spatial structure in the video. The experiment in Table 6 provides evidence of the hypothesis:  Balanced-TopK achieves consistent gains on several key spatial reasoning tasks compared to Temporal-TopK like 1) Relative Distance: +4.9% 2) Room Size: +1.2% 3) Absolute Distance: +1.4% 4) Relative Direction and Approximate Order: +0.5% each. However, Balanced-TopK will ignore the frame with fewer objects whose classes are already collected. If these objects are not in the previously collected frames, MLLM will never be aware of them. Thus, we observe a performance decrease in the metrics related to objects themselves, e.g., Object Count.\n\n- b. Each module is designed to target specific spatial reasoning capabilities, rather than boosting all tasks uniformly. we would like to emphasize that the design of our method explicitly targets different subtypes of spatial reasoning, and the ablation tables (Table 3 and 4) demonstrate this alignment:\n   - MSRS focuses on increasing semantic diversity, which is reflected in its gains in Approximate Order (+3.6%) and Relative Direction (+6.6%)\n   - Motion Reconstruction enhances the model’s understanding of trajectory and transitions, leading to significant improvements in Route Plan (+2.5%) and Approaching Order (+14.7%).\n   - Spatiotemporal Encoding and Point Prompts aim to connect visual diversity component and motion construction, offering complementary spatiotemporal cues, showing targeted gains in Abs. Distance (+1.9 \\~ 2.2%), Relative Distance (+1.7~2.0%), and Route Plan (+4.1%). It demonstrates that these design can aid in temporal alignment and enhance geometric grounding.\n   - The ablation study of the internal component design and the corresponding experiments can also be found in Appendix like Table 7-10. We hope that this additional experimental information can resolve your doubts.\n\n3. **Concern about More Baseline Models.** We thank the reviewer for pointing out the inconsistency between the number of models reported in Table 1 (VSI-BENCH) and Table 2 (STI-BENCH). We acknowledge that in our original submission, only two open-source models were reported on STI-BENCH due to early-stage computational resource constraints. This was not intended as cherry-picking but rather a reflection of practical limitations during initial benchmarking. To address this concern, we now conduct a comprehensive evaluation of some previously tested MLLMs on STI-BENCH and incorporated the complete results into the revised version. As shown in the updated Table below, SEE&TREK consistently improves performance across most models, with gains ranging from +1.0% to +1.4% on average. \n\n| Model | w/o SEE&TREK | w/ SEE&TREK | Gain (%) |\n| --- | --- | --- | --- |\n| LLaVA-OneVision-0.5B | 16.6 | 18.8 | +1.2% |\n| LLaVA-OneVision-7B | 31.8 | 33.2 | +1.4% |\n| Kimi-VL-A3B-Instruct | 37.2 | 38.3 | +1.1% |\n\n4. **Concern about the Closed-source Models Performance.** We appreciate the reviewer’s feedback regarding the evaluation scope across MLLMs, including proprietary models. Here, we report results for proprietary models (GPT-4o, Gemini-2.0-Flash) on VSI-BENCH.  \n\n| Method | Gains | Avg. | Obj. Count | Abs. Dist. | Obj. Size | Room Size | Rel. Dist. | Rel. Dir. | Route Plan | Appr. Order |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Gemini-2.0-Flash | - | 42.6 | 51.1 | 29.8 | 55.1 | 56.2 | 38.2 | 40.9 | 32.1 | 37.4 |\n| +**SEE&TREK** | +1.2% | 43.8 | 49.8 | 29.3 | 56.4 | 57.0 | 39.4 | 41.9 | 35.4 | 41.8 |\n| GPT-4o | - | 34.0 | 46.2 | 5.3 | 43.8 | 38.2 | 37.0 | 41.3 | 31.5 | 28.5 |\n| +**SEE&TREK** | +1.4% | 35.4 | 48.5 | 5.0 | 45.1 | 40.6 | 39.0 | 43.2 | 33.5 | 31.5 |\n\nAs shown above, these commercial engines benefit from our SEE&TREK framework across various spatial tasks, reinforcing its generality and practical effectiveness.\n\n5. **Concern about Improvements in Stronger Models.** We acknowledge that as model capacity increases, the relative gains from external input modifications may diminish. This is a natural outcome of the ceiling effect, where high-capacity models already encode stronger spatial priors and benefit from richer pretraining corpora. Nonetheless, we argue that even marginal improvements (e.g., +1.2% on Qwen2.5-VL-32B) are meaningful, especially in the zero-shot, training-free setting of our method, which requires no fine-tuning, no gradient updates, and no GPU.\n\n6. **Concern about the Framework Generalization.** We thank the reviewer for raising this important point regarding generalization.  \n\n- Benchmark choice reflects diversity, not task specialization. We fully agree that generalization is a crucial criterion for evaluating practical methods. While our experiments are centered on VSI-BENCH and STI-BENCH, these benchmarks are deliberately chosen not for narrow task matching but for their breadth and diversity (We also have stated these benchmark details in appendix Section B). Specifically, these benchmark include real-world egocentric videos across indoor, desktop, and outdoor scenarios, requiring models to reason about object relations, sizes, and topology across diverse camera viewpoints and occlusion patterns. They provide a rigorous and multi-perspective evaluation of spatial understanding that goes beyond task-specific tuning. \n- Our approach is not designed to exploit dataset-specific structures but instead targets two fundamental bottlenecks of MLLMs in video-based spatial reasoning: 1) Visual Homogeneity, addressed by the Maximum Semantic Richness Sampling module. 2) Unknown Camera Motion, addressed via: a classical VO-based Motion Reconstruction module for trajectory estimation and Spatiotemporal Encoding, which overlays motion cues and temporal indices onto keyframes to bridge semantics and motion. Each component within the framework is modular and can be easily adapted or substituted to meet different domain constraints and resource requirements. We have perform some experiments like utilizing some SOTA components as shown in R3 Response 3 to verify our effectiveness. \n- We also provide the OpenEQA results for comprehensive evaluation here. The results demonstrate the superiority of our spatial prompting framework design. \n\n| Model | w/o SEE&TREK | w/ SEE&TREK | Gain (%) |\n| --- | --- | --- | --- |\n| Qwen2.5-VL-7B | 47.1 | 49.1 | +2.0% |\n| InternVL3-8B | 50.5 | 52.3 | +1.8% |\n\n7. **Concern about the Ablation Study.** Thanks for your insightful comments. We have performed a comprehensive ablation study as shown in Table 3-6 in the main paper. Moreover, we also make a deep investigation into each component design or hyper-parameter setting as shown in Table 7-10 in the appendix. We wish these experiments information can address your concerns regarding ablation. \n\n8. **Concern about the Scope.**  We are agree and thank you for the thoughtful comment. Our work focuses on spatial understanding from purely visual video inputs, without relying on depth or other modalities. Compared to image-based tasks, video-based reasoning is more challenging due to temporal dynamics and spatial consistency. By leveraging motion cues and scene continuity, our method enables richer spatial understanding and highlights the practical value of video-centric, training-free prompting.\n\nFinally, thanks for your constructive feedback. Hope that we have carefully addressed all your concerns above, and we sincerely appreciate your thoughtful contribution you’ve made to improving our work. We will incorporate these revisions into the revised version.\n\n[1] Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning.\n\n[2] MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs.\n\n[3] Controlmllm: Training-free visual prompt learning for multimodal large language models.\n\n[4] COARSE CORRESPONDENCES Boost Spatial-Temporal Reasoning  in Multimodal Language Model\n\n[5] Set-of-Mark Prompting Unleashes  Extraordinary Visual Grounding in GPT-4V"}]

Author rebuttal:

Meta-review: Summary: 
This paper introduces SEE&TREK, a training- and GPU-free visual prompting method that enhances MLLMs’ spatial reasoning. It combines semantic keyframe sampling and VO-based trajectory cues with simple spatio-temporal markers. Experimental results demonstrate consistent improvements across spatial and temporal benchmarks.

Strengths:
As agreed among the reviewers, the approach is plug-and-play, lightweight, and broadly applicable without retraining. It demonstrates stable gains across many models, especially smaller ones, and is easy to deploy.

Weaknesses or concerns:
Improvements are modest, and the reliance on VO with fixed camera intrinsics raised concerns about robustness and fairness. While the reviewer `VBKe ` remained unconvinced, ACs consider the use of a fixed approximate intrinsic acceptable since it does not exploit dataset-provided values and still allows reproducibility.

Conclusion: 
Despite incremental gains, the method is practical, reproducible, and useful for deployment. ACs lean toward acceptance given their consistency and simplicity.
Decision: Accept (poster)