Unleashing Perception-Time Scaling to Multimodal Reasoning Models
Authors: Yifan Li
Zhenghao Chen
Ziheng Wu
Kun Zhou
Ruipu Luo
Can Zhang
Zhentao he
Yufei Zhan
Xin Zhao
Minghui Qiu
Venue: ICLR 2026 Poster (2026)
Keywords: Large Vision-Language Models
Inference-Time Scaling
Fine-grained Perception
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of Large Vision-Language Models (LVLMs). Inspired by this success, similar strategies have been applied to multimodal reasoning, yet their impact on visual perception remains unclear. To investigate this gap, we introduce DisTANCE, a perception-centric benchmark for visual estimation tasks. Evaluation results show that LVLMs exhibit limited estimation precision, and inference-time scaling offers only marginal gains. We attribute this to the fast perception paradigm of current LVLMs, where visual understanding is treated as a one-shot output without modeling the underlying perceptual process. To address this, we propose Perception-Time Scaling (PTS), a novel paradigm that encourages token-rich perception and decomposes complex perception problems into intermediate tractable sub-problems, thereby enabling perception to align with and benefit from inference-time scaling. Combined with reinforcement learning techniques, PTS significantly improves perception accuracy, raising high-precision performance on DisTANCE from 8.0% to 64.7%, and generalizes well to out-of-domain tasks. Surprisingly, even though PTS data are purely synthetic, combining them with math reasoning data yields consistent gains in both reasoning and real-world perception benchmarks. Further analysis reveals that PTS introduces more perception-related tokens and increases the model’s attention to image tokens. Our code and data are released at https://github.com/RUCAIBox/PTS

Published as a conference paper at ICLR 2026
UNLEASHING PERCEPTION-TIME SCALING TO MULTIMODAL REASONING MODELS
Yifan Li Zhenghao Chen', Ziheng Wu", Kun Zhou', Ruipu Luo", Can Zhang',
Zhentao He', Yufei Zhan', Wayne Xin Zhao 1.2,, Minghui Qiu
Gaollng School of Artificial Intelligence, Renmin University of China
*Beijing Key Laboratory of Research on Large Models and Intelligent Governance
*ByteDance *School of Information, Renmin University of China Institute of Automation, Chinese Academy of Sciences
ABSTRACT
Recent advances in inference-time scaling, particularly those leveraging reinforce- ment learning with verifiable rewards, have substantially enhanced the reasoning capabilities of Large Vision-Language Models (LVLMs). Inspired by this success, similar strategies have been applied to multimodal reasoning, yet their impact visual perception remains unclear. To investigate this gap, we introduce Dis- TANCE, a perception-centrr benchmark for visual estimation tasks. Evaluation results show that LVLMs exhibit limited estimation precision, and inference-timg scaling offers only marginal gains. We attribute this to the fast perception paradigm of current LVLMs, where visual understanding is treated as one-shot output without modeling the underlying perceptual process. To address this, we propose Perception-TT Scaling (PTS), a novel paradigm that encourages token-rich per- ception and decomposes complex perception problems into intermediate tractable sub-pru uub-problems, thereby enabling perception align with and benefit from inference- time scaling. Combined with reinforcement learning techniques, PTS significantly improves perception accuracy, raising nigh-precision performance on DisTANCE from 8.0% to 64.7%, and generalizes well to out-of-domain tasks. Surprisingly, even though PTS data are purely synthetie, combining them with math reasoning data yields consistent gains in both reasoning and real-world perception bench- marks. Further analysis reveals that PTS introduces more perception-relate tokens and increases the model's attention to image tokens. Our code and data released at wttp:/ihubbbco/o/uAAABBB////T
INTRODUCTION
Recently, reinforcement learning with verifiable rewards (RLVR) (Guo et al. 2025) has emerged as popular paradigm, as it enables models to produce longer chains of thought and thereby improves their reasoning abilities. Such approaches are commonly referred as inference-time scaling methods (Zhao et al., 2023). Inspired by this success, recent work has extended similar strategies to Large Vision-Lang Language Models (LVLMss, yielding notable gains on challenging multimodal reasoning tasks, such as mathematical and multi dinliiidiccipiiaaryy domains (Wang et al.) [2024] Yue et al.)
2024p. These approaches encourage models to produce longer and more deliberate reasoning chains.
However, their benefits appear largely restricted to the reasoning stage, and it remains unclear whether such gains transfer to perception. Indeed, recent studies suggest that reasoning LVLMs may even be
more prone to hallucinations (Liu et al. 2025al 5a
Yao et al. 2025).
To systematically examine whether perception can also benefit from inference-time scaling, we introduce perception-centrcc benchmark. DiSTANCE (Distance reasoning Task with Analytical Numeric and Comparative Estimation). DisTANCE comprises synthetic geometric images (e.g., circles, triangles) paired with questions that require visual estimation (e.g., length, perimeter, area).
Equal contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
Inference-Timer Scaling
Which LEGO object higher in 3D space?
How large LNPO?
Slow
To determine the measure of <NPO, we use the Inseribed Angle that inseribed half the Reasoning of its interoepted arc. Step 1: Find EMPN..
M
Theorem,
How high the image
Fast Perception
LEGO-Puzzles
The height the image seems be times the radius.
Which line longer, PM NP? Geoperception
Perception- Time Scaling
units?
units?
symbolizttoon
tling
< unit.
It includes one two
How many units the height of the image the radius of the red circle unit?
Perception Elaboration
Perception Decomposition
(a) Visual Estimation Tasks
(b) Inference- vs Perception-Time Scaling
Figure 1: Overview of the visual estimation tasks and comparison between inference-and the proposed perception-tine scaling paradigms.
Evaluation results demonstrate that DisTANCE poses substantial challenges for existing LVLMs, while inference-time scaling methods yield only marginal gains over base models. We attribute this limitation to the prevailing Fast Perception paradigm of LVLMs (Figure I in which perception is treated as a one-shot judgment rather than a process involving intermediate steps. Further analysis reveals that perception-related tokens constitute only small fraction of the reasoning chain, and the perception accuracy drops noticeably as task complexity grows.
To tackle this limitation, we propose new paradigm, Perception-Tine Timme Scaling (PTS), which refor- mulates perception as a structured, step-by-step reasoning process. PTS mitigates the shortcomings of fast perception through two key components: (1) Perception Elaboration, which encourages models to produce loken-rich descriptions of perceptual outcomes by using symbolic tokens to represent abstract visual attributes (e.g., distance), thereby providing more grounded and interpretable represen- tations than raw numerical values. (2) Perception Decomposition, which trains models to decompose complex perception tasks into simpler nub-problems.
We instantiate PTS through a two-stage training pipeline. In the first stage, supervised cold-star phase teaches the model the structured perception pattern. In the second stage, we apply reinforcement learning with GRPO (Shao et al.) 2024) to further optimize the model, enabling it to explore and refine intermediate perceptual steps. Our experiments demonstrate that PTS enables inference time scaling benefit not only reasoning but also perception. Specifically, PTS yields substantial gains DisTANCE, improving high-pu eeehipreiiiio performance from 8.0% to 64.7%, while also generalizing to out-of-domain visual estimation and broader multimodal tasks. Moreover, although PTS data entirely synthetic, their integration with math reasoning data consistently improves both reasoning and real-world perception benchmarks. Further analysis reveals that PTS introduces more perception- related tokens and increases the model's 's attention to image tokens, explaining how it enhances perceptual competence. These findings demonstrate that explicitly modeling perception as structured process not only enhances interpretability, but also unlocks significant perceptual improvements from inference-time scaling, paving the way toward more perception-awes LVLMs.
DISTANCE: PROBING PERCEPTION OF LVLMS WITH VISUAL ESTIMATION
To examine whether inference-time scaling can enhance visual perception, we introduce Dis- TANCE (Distance reasoning Task with Analytical Numeric and Comparative Estimation).
2.1 OVERVIEW
Dis TANCE focuses on a conceptually simple yet diagnostically effective task: visual estimation, which requires models estimate geometric attributes such length, perimeter, and area in synthetic
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Published as a conference paper at ICLR 2026
Table 1: Evaluation results of proprictary, open-source and reasoning-orienne LVLMs on DisTANCE.
Length
Perimeter
Area
Average
Model
R.A.1
RAwg
RA
RAwg
RAp.1
RAwg
RAp.1
RAay
Proprietary Models
Manude3..-Sonnie (Claude 2024
25.0
55.0
23.0
55.0
9.0
15.6
19.0
41.9
Demini-2.5-FFih (Google
2025
36.0
64.2
20.0
57.4
14.0
34.0
23.3
51.9
Gemini-2.5 2.5-Pro GGogle
2025
36.0
67.8
32.0
69.2
21.0
50.4
29.7
62.5
4P (OpenAl 2024
22.0
47.4
13.0
37.6
11.0
22.0
15.3
35.7
GPT-4.1 OPenAA 2025a
32.0
63.2
19.0
55.4
10.0
33.2
20.3
50.6
GPT-5 OpenAT 20256
41.0
72.6
37.0
72.8
17.0
46.8
31.7
64.1
Open-sone source Models
Qwen2.5-VI.:3B Bai al.
2025
11.0
26.8
6.0
18.6
2.0
13.0
6.3
19.5
Qwen2.5-VL-7B 15m s1.
2025
11.0
25.6
11.0
27.8
2.0
11.2
8.0
21.5
Qwen2.5-VL-32B [Bar et 2025
14.0
38.6
11.0
26.8
5.0
16.4
10.0
27.3
Qwen2.5-VI--72B (Bai et
2125
16.0
42.0
8.0
34.8
6.0
13.4
10.0
30.1
IntemVI.33-BB (7hu
2025
11.0
29.2
8.0
21.2
5.0
11.8
8.0
20.7
MitmVV.3-144B7Z1 (Zhu 2025
16.0
42.0
10.0
37.0
9.0
14.4
11.7
31.1
IntemVL.3-38B (Zhu 2025
19.0
45.2
10.0
30.6
4.0
14.8
11.0
29.9
IntemVL3-78B Zhu 2025
11.0
35.6
17.0
38.8
10.0
17.2
12.7
30.5
Open-source Reasoning Models
R-4B Yang et al.
2025a]
11.0
28.2
9.0
27.4
8.0
19.2
9.3
24.9
R1-OnieVsstonn7 7B Yang et al. 2025b
9.0
23.4
12.0
26.0
9.0
14.0
10.0
21.1
Vision-R1-7B (Huang et al. 2025
9.0
27.0
8.0
25.2
6.0
16.8
7.7
22.7
MM-Fureka-7B (Meng ct 2025
6.0
20.6
10.0
27.6
3.0
10.2
6.3
19.5
VI.AA-Thinke--75 (Chen et al. 2025a
10.0
31.8
9.0
31.8
7.0
15.0
8.7
26.2
NoisyRollout-7B (Liu et at. 2025b
12.0
34.8
9.0
30.4
6.0
15.8
9.0
27.0
VL-ReThinkker775 Wang et at. 2025a
(aang
11.0
28.0
15.0
37.8
4.0
14.0
10.0
26.6
ReVPT-7B (Zhou al. 20256
12.0
25.6
13.0
29.2
9.0
15.4
11.3
23.4
images. Since answers rely solely visual perception, DisTANCE serves focused benchmark to test whether current reasoning paradigms also enhance perception.
Task Formulation. As illustrated in Figure DisTANCE contains tasks that require precise
estimation of geometric measurements in synthetic images. Each image includes several geometric objects (e.g., circles, triangles, rectangles) with varied colors, sizes, and orientations. The model is prompted to predict quantitative relations between spatial properties in regression manner (e. g., "erurriuuuiin
"What is the height of the image if the radius of the red circle unit?"). requiring perceive and compare distances with numerical precision. To remove confounding effects from image resolution. all tasks are formulated as relative estimations.
Benchmark Details. DisTANCE consists of 300 image-question pairs, with each sub-task including 100 examples. All images are automatically synthesized using Python with dimensions ranging from 600 to 1200 pixels, and each image contains 3 to 7 colored shapes. The shapes include circles, triangles, and rectangles filled with distinct colors. Fach sub-task has multiple different question templates. More cases are presented in the Appendix K
2.2 EVALUATION SETUP
Following existing work that evaluates LVLMs using numerical answers (Ynng et al., 2024), we adopt Relative Accuracy (RA) as the evaluation metric. prediction ğ considered correct under threshold 0 if the relative emorr 9 vl/u < O. We report R RAng under various thresholds as:
Metrics.
RAav (0)
1
Σ
vl
<
(1)
|C|
y
BEG
where C = (0.5, 0.4, 0.1. We also report RAD1 for high-precision performance.
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Published as a conference paper at ICLR 2026
Table 2: Comparison the average response length and perception ratio (PR) base and inference-ting timeence-timeeeee scaled (ITS) LVLMs.
1.66
1.5
1.37
Average Relaive
1.0
Model
ITS
Avg Len.
PR
0.45
0.48
Qwen2.5-VI.-1
X
199
17.6
R1 R1-OneVision-77
/
322
12.4
0.0
(1.70
(2.3)
(1.4)
(4.5)
(5,in)
MM-Farcka-7BB
259
13.5
Ground-tnthi Distribution
Figure 2: Relative error trend across labels.
Evaluated Models.
We evaluate a range proprictary and open-source LVLMs, including GPT-
4o (OpenAT 2024). 1 (OpenAT 2025a). GPT-5 (OpenAl 2025b). Gemini-2.5 (Google 2025
Claude-3.5 CClaude 2024). Qwen2.5- L. (Bai et al. 2025), and Intern VL3 (Zhu et al. (025). as well
reasoning-orientee LVLMs such as R1-One
(Yang al. (022b).. Vision-RI (Huang et al.)
2025), MM-Eureka (Meng et al. 2025), VLAA-Thinker (Chen et al. 2025a). NoisyRollout
(Liu et al. 2025b), VisualThinker (Zhou al. 2025a), R-4B (Yang al. 2025a). VL-ReThinker
(Wag et al. 2025a) and ReVPT (Zhou et al. 2025b).
2.3
EVALUATION RESULTS
As shown in Table I most LVLMs perform poorly on DisTANCE: open-source models rarely exceed an RAug of 35%. Advanced proprietary LVLMs, including Gemini-2.5-PPr and GPT-5, are capable of utilizing tools during inference, resulting in significantly better performance. Area estimation is particularly difficult, with most models below 20%. Scaling model size has limited effect, and reasoning-enhaneee Vision-RI R1 (22.7%) and R1-One Vision (21.1%) perform on par with 2wen2.55VL-7BB (21.5%). These results suggest that current inference-tine scaling paradigms, although effective at generating longer reasoning chains, do not significantly enhance performance on perception-cent centric tasks. To further investigate this issue, conduct additional analysis and identify two key findings.
LVLMs show only marginal gains over their bases. For example,
LVLMs Prefer Fast Perception. By observing generated cases, notice that LVLMs often summarize perceptual information using short, fixed patterns (e.g.. "the radius of the circle is 2.5 units"), without further elaboration. We refer this behavior Fast Perception, tendency to express perception results in minimal tokens. To quantify this, we analyze the ratio of perception-relatee tokens (formally defined in Appendix B and the average length of model outputs. As shown in models produce longer answers but allocate only a small fraction to
Table 5 reasoning-orient perception. This highlights limitation of current inference-tine scaling: it scales reasoning but not perception, motivating paradigms that explicitly scale both.
LVLMs Fail to Decompose Complex Perception. We further investigate a key failure mode of LVLMs complex perception tasks. Specifically, we group length- estimation questions by ground- truth distance and measuring Qwen2.5-Vi VL--------B's relative error. As shown in Figure the model's relative error increases consistently with target length. This trend suggests that while inference-time scaling teaches models to reason step by step, such decomposition ability does not transfer to the perception stage. Even for complex perception cases, models still prefer fast perception, directly producing answer without any intermediate process or decomposing the task.
METHODOLOGY
Previous empirical results reveal a key limitation: The prevailing fast perception paradigm limits inference-time scaling from improving perception abilities of reasoning-enhancee LVLMs. To Scaling (PTS), which aims
address this, we propose new perception paradigm, Perception-Tinge to structurally enhance perception in way that is compatible with inference-tima scaling techniques. In this section, we define the PTS framework and describe how we apply to the visual estimation tasks from the proposed DiSTANCE. The overview of PTS is illustrated in Figure
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Published as a conference paper at ICLR 2026
Perception-Ting Time Scaling
(a) Perception Elaboration
(c) Training Strategies
Φ
2,3 unit
m1
Question: What is the length of seament B? Answer: 2.3 units
k 2
[1] Cold Start for Perception Pattern
(b) Perception Decomposition
Perception
PETS-style 5853 <thikt, chtinke CaTEARD2 unhuc/tmarn
Pwrception
unit
What the longth of segment 8?
SFT
unit
"Take segment the reference..."
Reasoriing
Reference Model
Reinforcement
Verifiable Reward
"A first segment
Perceptinn
Vertlable Reward
Intabatttal
"Second segment
Poliyy
Modelng
10-dil
"Third segment <oee....
Pollcy Gradient
r(o)
d1
Figure Overview of the proposed PTS paradigm. In the context of visual estimation tasks, PTS first employs symbolic tokens to intuitively represent distance information, followed by step-by-step decomposition of the estimation process. During training, LVLMs are initially taught the PTS pattern via SFT, then further optimized using GRPO to enhance perception accuracy.
3.1 PERCEPTION-TIME SCALING
To overcome the limitations of the Fast Perception paradigm, PTS introduces two complementary strategies. (1) Perception Elaboration. PTS encourages models to produce token-rich descriptions of perceptual outcomes. Specifically, abstract visual attributes such as distance are represented using symbolic tokens, which provide more grounded and interpretable descriptions than raw numerical values. (2) Perception Decomposition. PTS teaches models to decompose complex perception tasks into simpler and more tractable sub-problems. This stepwise approach enables more accurate visual understanding by mimicking how humans often solve challenging perception tasks through progressive comparison. We next describe the detailed implementation PTS in the context of visual quantitative estimation tasks.
Perception Elaboration. Given a target distance for estimation d,, we define a symbolic encoding function o. which maps de into a discrete token sequence o(d)). ((d) consists of several symbolic token groups rendered as "*=========>>" each representing distance of 1.0 unit. The angle brackets < and > are delimiters and each equal sign represents fixed length 5 0.1 unit. Given de 20 we decompose it ks [ token group and [0, 1.0) is the residual length. The encoded sequence is represented ((d))
de k; where k is the number of full symbolic
Irres (r).
(2)
times
(res(r) is the symbolization results for r, which defined as: Ores (r)
<
(3)
where | By doing so, PTS increases the presence of perception tokens in model outputs and creates stronger grounding between vision and language. Rather than a single numerical token, the model must construct the perception output step by step, allowing more intuitive and fine-grained representation of distance information.
Perception Decomposition. Previous experiments show that even when the task complexity in- creases, models continue to rely on one-shot perception. To mitigate this limitation, PTS introduces a decomposition strategy that breaks down complex perception tasks into step-by-step local subgoals. Specifically, given the target distance de, we first select reference segment d, from the image, whose length is defined 1.0 unit. Rather than directly predicting the value of d, we ask the model to perform step-by-step accumulation of d, until fully covering the d,, which can be formulated as:
Initialize:
L= 0, b =: 0;
(4)
While L + d, S de
L x L - dr, k t Btk k 1
5

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Published as a conference paper at ICLR 2026
This process lets models simulate the process of tiling d, along de and incrementally accumulating distance until the target is fully covered. Each step in this process is relatively simple and localized, making the overall task more tractable. This approach also mirrors how humans handle complex measuring tasks with a ruler. Moreover, by transforming perception into step-by-step process, this design opens up greater opportunities for inference-time scaling, allowing the model to optimize each perception step and ultimately achieve more accurate visual understanding.
3.2 COLD START FOR PERCEPTION PATTERN
We begin by defining the reasoning process under the PTS paradigm. Fach reasoning chain follows five stages: (1) Review: A brief description of the task; (2) Hint: Definition for the symbolic encoding and decomposition strategy; Referenc: Selection of a specific segment the image to serve as a reference; (4) Estimation: Visual comparison of other segments to the reference estimate their
lengths; (5) Calculation: Application of appropriate formulas to compute the final result.
To teach LVLMs this structured perception reasoning pattern, construct training dataset in PTS style. Following the data collection pipeline of DisTANCE, we synthesize images and corresponding questions for length, perimeter, and estimation tasks. Each reasoning chain is formatted in line with (Guo et al., 2025), where the reasoning steps are enclosed within <th meiiikk///tttt c nk> tags and the final answer within <answer><> /atr>>>//////eee answer> tags. The dataset is synthesized through two-step procedure. We first manually collect a small set of reasoning examples for each task, serving as few-shot demonstrations. Next, we feed the distance information within images together with the demonstrations, to GPT-4o to generate instructions. This process yields 2,000 examples per task, resulting total of 6,000 PTS-style reasoning chains.
Importantly. all training images are freshly synthesized with random seeds to avoid overlap with evaluation benchmarks. The resulting dataset is then used for supervised fine-tuning (SFT), enabling the model to acquire an initial understanding of the PTS perception pattern. A full example of the reasoning process and the GPT-4o prompting template is provided in Appendix
3.3 REINFORCEMENT LEARNING WITH VERIFIABLE REWARD
To further enhance perception quality under the PTS paradigm, continue optimizing the model after the cold start stage using GRPO (Shao et al. 2024). reinforcement learning method with verifiable reward that supports inference-time scaling and improves reasoning capabilities.
Preliminary of GRPO. GRPO is a reinforcement learning algorithm, which adopts a verifiable reward function instead of a reward model, and calculates the relative advantages within a group of comple- tions for given sample. Specifically, GRPO first samples group of answers {01,02 02,-.. , ,oN] from the old policy model Tee.. for each question q. The policy model ma is then optimized by:
[(0) =
min
To (oilg) As, clip
me(0:14) Tona (o:19)
1,1-,,1++0 e, A:
BDD (To (Tol
(5)
where and are hyperparameters and A, is the advantage of o; in the group. A, is calculated by:
A ri mean( f1, ,,,)))
(6)
std(1 T1. ,rN))
where r; is the reward given by a rule-based reward function. r; is typically set as r Arace + (1 A)rtomat- Here, Face is the accuracy reward which is set to 1 if the model's prediction correct, Trommat is the format reward which is set to 1 if the model follows the expected response format, and is the hyperparameter controls the ratio between two rewards. explicitly embedded in the reasoning chain, allowing reward signals to be applied to intermediate perception steps, enabling the model to gradually improve its perception accuracy through step-by- step refinement. We further adapt GRPO to our task with the following modifications:
PTS, the perception process
Customized Reward for Regression. Since tasks in DiSTANCE involve continuous regression, binary accuracy rewards are insufficient capture how close prediction is to the ground-truth.

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Published as a conference paper at ICLR 2026
Table Effect of different training data paradigms and strategies on DisTANCE. Direct, CoI, PTS represent the training data in direct answer, Chain-of- Thought and PTS format, respectively. SFT and RL represent whether adopt supervised fine-tuning or GRPO for training.
Length
Perimeter
Area
Average
Model
SFT
RL
RAu.1
RAuu
RAa,1
RAwg
RAg,1
RAug
RAu.1
RAay
SpaceThinker-3B
7.0
39.0
8.0
38.0
6.0
26.0
7.0
34.3
Spatial-R1-7B
15.0
51.0
7.0
39.0
6.0
32.0
9.3
40.7
GPT-4o + DetToolChain
24.0
53.8
16.0
47.8
6.0
24.4
15.3
42.0
GPT-4o + Sketchpad
27.0
36.6
20.0
34.5
10.0
19.2
19.0
30.1
Qwen2.5-VL-3B
11.0
26.8
6.0
18.6
2.0
13.0
6.3
19.5
Direct
16.0
44.3
24.0
57.8
14.0
30.2
18.0
44.1
CoT
15.0
48.0
28.0
61.8
13.0
34.2
18.7
48.0
PTS
14.0
36.0
20.0
46.8
9.0
24.6
14.3
35.8
Direct
36.0
65.6
44.0
76.8
13.0
32.8
35.8
47.5
CoT
<
29.0
62.7
46.0
79.2
16.0
38.7
27.0
60.2
PTS
31.0
70.6
54.0
86.2
28.0
63.6
37.7
73.5
Qwen2.5-
11.0
25.6
11.0
27.8
2.0
11.2
8.0
21.5
0-shor
3.0
12.0
7.0
19.8
5.0
13.4
5.0
15.1
I-shot
5.0
17.8
8.0
23.2
3.0
16.2
5.3
19.1
4-shot
12.0
25.8
14.0
26.0
5.0
13.6
10.3
21.8
8-shot
4.0
16.6
3.0
15.8
6.0
15.6
4.3
16.0
Direct
X
20.0
49.4
28.0
63.0
16.0
33.4
21.33
48.6
CoT
<
X
17.0
40.8
10.0
52.2
14.0
29.2
13.7
40.7
PTS
V
12.0 X
44.2
25.0
62.0
12.0
30.2
16.3
45.4
Direct
46.0
77.4
51.0
82.4
25.0
52.4
40.7
70.7
CoT
38.0
74.6
48.0
80.8
27.0
58.6
37.7
71.3
PTS
70.0
92.2
74.0
94.6
50.0
78.0
64.7
88.3
Moreover, we expect the reward to explicitly encourage high-precision estimation. To address this, design continuous reward function based relative error in exponential form:
lo-del
r(o)
where is the model's prediction, de is the ground-truth value, and a is hyperparameter controlling the sensitivity of the reward. This formulation grants higher rewards to predictions with smaller relative errors. The exponential structure makes the reward particularly sensitive small errors, thus incentivizing fine-grained accuracy. We provide the visualization of the reward function and ablation studies on the choice of in the Appendix
Label Normalization. We further observe that the same relative error threshold can correspond to very different absolute error tolerances, depending on the scale the label value. For example, 10% relative error threshold threshold on a value of 50 allows a -55 deviation. Consequently, the reward function could assign the same reward to estimations with significantly different levels of precision, which may confuse the model at the beginning of training. To address this, we propose to first train the model on normalized samples with target values less than and later introduce data with random distributions.
distance of 0.02 permits absolute error of only =0.002, while the same
EXPERIMENT
4.1
EXPERIMENTAL SETTINGS
Baselines. We compare the proposed PTS against three categories of baselines: (1) Prompting
strategy. We reformulate the data collection process of PTS into system prompt for the model and provide different numbers PTS reasoning chains as demonstrations (k-shot). (2) Different data patterns. Using the same image source, construct two alternative instruction datasets: only includes the final numerical answer (Direct), and another that contains conventional chain-of- thought annotations synthesized by GPT-4o (CoT). These datasets used train the base LVLMS
that
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Table 4: Experimental results on out-of-domain distance reasoning tasks. LsC'is the LineComparison subset from Geoperception. Height is the Height subset from LEGO-Puzzles
Lengthood
Perimeter
GeOLhC
LEGOHtight
Model
SFT
RL
RA..1
RAag
RAo.1
RAwg
Accuracy
Accuracy
Qwen2.5-VLL VL-3B
6.0
12.2
10.0
23.8
38.9
28.0
+ CoT
x
8.0
29.8
16.0
45.0
64.5
27.0
+ PTS
/
x
10.0
32.6
7.0
31.8
68.4
22.0
Direct
X
12.0
38.9
23.0
52.4
70.4
+ CoT
<
12.0
37.6
24.0
53.4
71.1
30.0
+ PTS
16.0
41.4
26.0
55.0
73.5
44.0
Qwen2.5-ViL -LLBBBBBBBBBBBBBBB
7.0
22.2
12.0
23.6
59.8
30.0
CoT
/
x
14.0
33.8
21.0
52.0
65.6
29.0
PTS
/
x
15.0
36.6
26.0
56.0
67.1
28.0
Direct
x
/
19.0
40.2
30.0
63.4
69.4
30.0
CoT
V
18.0
39.6
20.0
55.6
72.2
30.0
PIS
20.0
43.2
36.0
71.4
78.7
with both SFT and GRPO. (3) Spatial-aware LVLMs. Models explicitly designed to estimate spatial information such as object sizes in real-world scenarios, including Spatial-RI (Ouyang 2025) and Space'Thinker (remyxai 2025). (4) Tool-augmented LVLMs. Approaches that integrate external tools (e.g., visual experts or Python code) to provide auxiliary visual information, such as Visual Sketchpad (Hu al. 2024) and De(ToolChain (Wu et al. 2024b). Implementation details presented in Appendix !] For backbone models, we select the state-of-the-an LVLMs Qwen2.5-VL-3B
and Owen2.5-VL-7B,, and implement our method on them.
Evaluated Benchmarks. We evaluate model's performance on DisTANCE and other out-o--domain benchmarks: (1) DiSTANCE we replace the images in DisTANCE shapes not included in the collected training data (e.g.. trapezoids and pentagons): (2) Geoperception (Zhang al., 2024): fine-graines perception benchmark. We select the LHC subset which requires the model to compare the length two segments geometric images; (3) LEGOOPuzzles (Tang et al. (025a)): : spatial understanding benchmark with LEGGO-based tasks, where we select the Height subset that asks the model to distinguish the relative heights of 3D LEGO objects.
4.2
MAIN RESULTS
We present the experimental results in Table and from which we conclude:
PTS Enables High-Precision Visual Estimation. Across both the 3B and 7B versions of Qwen2.55 VI., models trained with PTS data consistently outperform all baselines. For instance, when combined with SFT and RI,, PTS significantly boosts the average RAave of Qwen2.55VLL-77 V- from 21.5% to 88.3%. Notably, under the strict threshold (RAg.1) 1), the model achieves 70.0%. 74.0%, and 50.0% accuracy on the length, perimeter, and area subsets, respectively, demonstrating strong high-precision estimation capabilities. It is also worth noting that PTS strongly outperforms other baselines, despite not using any spatial-related data or external tools. Instead of relying on external modules for spatial perception, PTS internalizes the perception process into the reasoning chain through symbolic tokens, allowing it to be optimized end-to-end during training, which leads more accurate and robust estimation. Additional qualitative examples are provided the Appendix K and
PTS Generalizes Out-of-Domain Estimation Tasks. Table demonstrates that PTS exhibits strong generalization capabilities across various estimation tasks beyond the training domain. Specif- ically, PTS improves the model's estimation performance on geometric shapes not seen during training. It also enhances performance in entirely unseen scenarios. For complex geometric shapes in the Geoperception, PTS boosts the model's accuracy by approximately 20%. Furthermore, in 3D scenarios from the LEGO-puzzles, PTS improves the model's accuracy in judging the height of objects, highlighting the potential of our method in real-world scenarios.
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Table 5: Performance comparison of Qwen2.5-VL variants on general multimodal benchmarks.
BLINK
Model
Math Vision
MMBench
MMVel
HalluBench
CV-Bench
Jigsaw
Multi
Local
Full
Qwen2.5-VL
25.3
83.28
66.93
51.2
73.36
66.7
46.6
48.4
55.0
+ Geo3K
26.8
83.82
67.11
52.2
74.30
66.2
52.6
52.5
54.3
PTS. Geo3K
27.2
85.68
67.39
53.0
75.68
68.9
53.4
53.3
55.9
Qwen2,5-VL
QwenZ,5-VL. PTS
Base
Image Atteation Ratio
0.06
Peccppiin Rullo
30.2
Higher Attention
Higher Attention
0.04
20:4
14.3
142
12,8
0.02
10
0.00
Length
Perimeter
Area
10
15
20
25
(a) Perception Ratios of Owen2.5-VL-7B Variants
(b) Image attention ratio across transformer layers
Figure 4: Influence of PTS on model's perception pattern.
4.3 INFLUENCE ON PERCEPTION CAPABILITIES
To further investigate the influence of PTS on models' perception capabilities, we validate its effectiveness on more diverse multimodal datasets from multiple domains. We also confirm that PTS data indeed enhances models' perceptual ability by examining both the explicit perception token ratio and the intrinsic image attention distribution. Ablation studies on symbolic token designs, granularity, training strategy and data efficiency analysis are presented Appendix E and
PTS Improves Both General and Perception-Centric Multimodal Tasks. PTS further enhances the general multimodal capabilities of LVLMs when combined with math reasoning data from Geometry3K (Lu et al., 2021). We apply GRPO to a cold-started Qwen2.5-VL-77B model and evaluate Math Vision (Wang al.) 2024) for math reasoning, MMBench (Liu et al. 2024 and
MMVet Yu et al.) 2024) for general multimodal VQA, HallusionBench (Guan et al. 2024 for hallucination, and CV-Bench (Tong al. 2024a) and BLINK (Fu et al. 2024) for perception-centric tasks. As shown in Table models trained only on math reasoning data improve performance on math-related tasks but yield limited even negative gains perception-
benchmarks. By contrast, incorporating PTS data not only strengthens reasoning ability but also brings consistent improvements across diverse domains-mos notably on CV-Bench and BLINK, where fine-grained perception is critical. Remarkably, despite consisting solely of synthetic images, PTS still transfers perception tasks, highlighting its strong generalization capacity.
effectively to
PTS Facilitates Effective Perception Refinement. While both PTS and CoT yield similar perfor- mance after SFT, their differences become evident during reinforcement learning. PTS sequences explicitly embed perception steps into the reasoning chain, offering richer structure for optimization. During GRPO training, models can explore and refine these intermediate perception results, leading substantial improvements in estimation accuracy. In contrast, CoT sequences contain minimal perceptual content, limiting the scope for refinement and resulting in marginal gains. As illustrated in Figure 4a PTS significantly increases the proportion of perception-related tokens, offering RL more opportunities to enhance perceptual accuracy.
PTS Strengthens Image-Focused Attention. We further investigate how PTS training reshapes the model's attention distribution over image tokens. Specifically, we compare the attention ratio of the first perception token in vanilla Qwen2.5- VL with that of the symbolic tokens introduced by PTS-trained models. The data are collected from the generation results on both DisTANCE and OOD tasks. As shown in Figure 4b, PTS-trained models consistently assign higher attention to image regions, especially in the early and final layers. This pattern suggests that PTS enhances low-level

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Direct
exporentii Dinary Dinary.
3.
Performance 40-
2.5
1.5
26
12K
100
Step
Figure 5: Impact of scaling training data.
Figure 6: Error curve with different rewards.
grounding at the input stage while also reinforcing saage-conditioned reasoning in the later decoding process. Together, these results indicate that PTS encourages the model to more effectively integrate visual evidence throughout the perception-reasoning pipeline.
4.4 ADDITIONAL ANALYSIS
Impact Training Data Scaling. We study the impact of scaling the training data on the model's performance. We conduct SFT on different amount of Direct (2k- 12k), CoT (2k-6k), and PTS (2k-6k) data. The performance curve is presented F Initially, scaling the data results in noticeable improvements, but as the dataset size increases, the performance gains gradually plateau. This indicates that simply scaling the data is insufficient for visual estimation tasks.
Ablation on Reward Function. In addition to the proposed continuous reward, we also test discrete binary reward defined r(o) 1 if lo del/de T, and r(o) otherwise, where is the threshold. We evaluate two values of r: 0.1 for a stricter threshold and 0.5 for more relaxed one. Based on Qwen2.5-VL-7B, we conduct 100 steps of GRPO with all three reward functions and compare their effects on relative error changes in the validation set, shown in Figure 6 The binary reward with r 0.1 performs the worst, exhibiting significant oscillations and minimal error reduction. This is likely due to the strict threshold, which prevents most training data from providing useful feedback. Increasing т to 0.5 results in smoother error reduction but slower convergence, as the binary reward does not differentiate between responses of varying precision. In contrast, the continuous exponential reward converges the fastest, achieving a final relative error that is less than 50% of that of the binary reward with 0.5.
5 CONCLUSION
In this paper, we investigated how inference-time scaling techniques affect the perception capabilities of LVLMs. To this end, we introduced DisTANCE, a lightweight yet diagnostic benchmark designed to evaluate LVLMs with visual estimation tasks. Our evaluation revealed that, under the prevailing fast perception paradigm in LVLMs, inference-time scaling offers limited perception improvements. To address this limitation, we proposed PTS, novel paradigm that makes the perception process more structured and explicit via perception elaboration and decomposition. After two-stage inference- time scaling with SFT and RL, PTS enables substantial perception improvements. Empirical results showed that PTS consistently enhances performance on both in-domain and out-of-domain visual estimation tasks. Further analysis confirmed the generality of the PTS in general multimodal tasks, suggesting promising direction for future research on inference-ting waaenncee-ieeeiiee enhenen perception LVLMs.
ACKNOWLEDGEMENTS
This paper partially supported by the National Natural Science Foundation of China No. 92470205 and Beijing Major Science and Technology Project under Contract No. Z251 100008425002. Xin Zhao and Minghui Qiu the corresponding authors.
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A USE OF LARGE LANGUAGE MODELS
In this paper, we use large language models only for minor editing tasks in writing, i.e. improving readability and grammar, and for occasional debugging hints in coding. The core contributions, including the proposed benchmark, algorithm design, experimental setup, analysis, and conclusions, were fully developed and verified by the authors.
B DEFINITION OF PERCEPTION RATIO
We define perception tokens as those directly describing visual content the input image, including:
Explicit geometric attributes, such as shape mentions (e.g., "circle," "line," "triangle");
Numerical values tied measurements, such as "2.5 units," "length of 3 cm,":
3. Symbolic markers introduced our framework (e.g., "<====>" tokens).
We implement this definition using keyword- based filter over tokenized model outputs. For each model, we randomly sample 50 generated outputs for calculation.
Lincar
1=0.0
a=3.
a.5.0
Table 6: Performance under varying a
Roward
0.6
DiSTANCE
0.4
Parameter
RA0.1
RAag
0.2
1.0
53.0
75.2
a 3.0
64.7
88.3
0.0
0.0
0.2
0.4
0.8
1.0
5.0
36.0
72.4
Relative Emar
Figure Curve of linear reward and exponential reward with different a.
C ANALYSIS ON REWARD FACTOR &
We illustrate the curves of the linear reward and the exponential reward under different values of in Figure 7 As shown, when is appropriately set (e.g., 3 used in experiments), the exponential reward remains low and varies smoothly for large relative errors (e.g., >0.6), while exhibiting steep increase for small relative errors (e.g.. <0.2). Compared to the linear reward, this design encourages the model to make high- tigh-precision estimations. We further conduct an ablation study the choice of a by comparing model performance under Table both smaller and larger values of a lead to degraded performance compared to our chosen setting. We suggest that when is small, the model receives overly high rewards beginning of training, reducing its motivation to improve. Conversely, when is too large, the reward curve becomes overly flat in the high-error region, where model's predictions commonly fall in the early stages of training, making it difficult for the model to determine a clear direction for improvement.
3, and 5. As shown in
the
D PERFORMANCE OF PROMPTING STRATEGY
To validate whether the PTS reasoning pattern can be learned by models via prompting strategies, we design prompting experiments. Specifically, first formulate the process of perception elaboration and decomposition into a system prompt (presented the Appendix). Then, we select data from the collected PTS instructions demonstrations. We Qwen2.5-VL--B the backbone model and
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provide it with the system prompt along with varying numbers of demonstrations (k: 0, 1, 4, to perform in-context learning. The results are presented in T In the 0-shot setting, model perform worse then the direct answer setting, indicating that the highly structured reasoning pattern of PTS can hardly learned by the model through prompting alone. As the number of demonstrations increases, the model's performance improves. However, when k 8, the performance drops again, reflecting that too many demonstrations can lead to confusion due to an overly long context.
E ABLATION STUDIES ON SYMBOLIC TOKEN
E.1 TOKEN DESIGN
We explore various designs for symbolic token styles. Initially, we replace line segment's symbolic representation with [Dist: xxx]. We also experiment with other abstract tokens, such as #, which is closer to length units, and ? and 8, which to replace the original tokens in the SFT data, and we perform the same SFT training. The results presented in Table 7 The results show that [Dist: xxx] performs relatively poorly compared to the original PTS data. This suggests that perception decomposition alone is insufficient, and that perception elaboration is also necessary for more accurate estimation. Among the other symbolic tokens, # yields the best performance, while and ? perform worse. We believe this outcome occurs because the shapes of and are more abstract, making it harder for the model to associate them with length or distance.
unrelated length. These symbolic tokens used
E.2 GRANULARITY
We further investigate the impact of token granularity on performance. To do this, we modify the granularity of each token represent 0.05, 0.1, 0.2, and 1.0 units, and conduct SFT for each case. The results are shown in Table [ Notably, when the granularity is too coarse, the model shows relatively better overall estimation accuracy but struggles with high-precision estimation. This highlights the trade-off between granularity and precision in symbolic token representation.
Table Ablation studies on symbolic token de- signs.
Table Ablation studies on symbolic token granularity.
DiSTANCE
Symbolic Token
DiSTANCE
RA
RAav
Granularity
RA
RAge
16.3
45.4
0.05
10.7
38.2
#
16.7
42.9
0.1
16.3
45,4
13.4
40.7
0.2
12.5
44.7
?
13.0
39.6
1.0
5.0
47.3
[DIST:XXX]
15.7
42.0
F ABLATION STUDIES ON TRAINING STRATEGY
We analyze the effectiveness of key components in our method. As shown in Table removing any of these components negatively affects the model's performance. Replacing the original exponential reward function with a linear one reduces the model's high-precision accuracy, likely due to its weaker discriminative ability across different relative error levels. Moreover, training without normalizing the ground-truth also results in a performance drop, highlighting the importance of label normalization in stablizing the training process. These results collectively validate the necessity of the designs in PTS. Beyond these ablations, we further experimented with increasing the granularity of symbolic tokens (e.g., using "=" to denote 0.05 units instead of 0.1 units). While this finer division also yields competitive results, performance slightly decreases compared to the 10-division strategy. We hypothesize that 10 equal partitions align more closely with common metric conventions, making them more effective for guiding the model's perception.
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Ceapthoely
Table 9: Ablation studies key designs in PTS.
94.6
164
$5.2
78.0
DisTANCE
Method
RA0.1
RAag
40-
21.8
Qwen2.5-VL-7B-PP
64.7
88.3
20
11.2
symbolic granularity
62.7
83.5
exponential reward
60.0
83.0
normalization
58.0
81.5
Figure 8: Comparison of the performance of
models trained with full/length-only PTS data.
G DATA EFFICIENCY ANALYSIS
We further investigate the data efficiency of PTS by training models using only length estimation data. As shown in Figure despite being restricted to single sub-task, the model achieves estimation precision on length, perimeter, and area tasks that highly comparable to the model trained on the full dataset. This finding suggests that equipping LVLMs with strong ability in primitive distance estimation enables them to generalize naturally to more complex compositional tasks involving distance aggregation (e.g., perimeter) multiplication (e.g., area). In particular, length-only training still yields substantial gains over the base model across all sub-tasks, highlighting the central role of length estimation foundational skill.
H COMPUTATIONAL EFFICIENCY ANALYSIS.
We design an efficiency experiment comparing the performance of Swve2.55VLL7B. Qwen2.5-V 5LL-- 7B-CoT, RL-trained Qwen2.5-VL-7B-PTT- and tool-augmented ReVPT-7B (Zoou al. 2025b) the DisTANCE dataset. As shown in the table, although the PTS method generates longer sequences, the increase in inference latency is still smaller compared to the tool-based method, highlighting the computational efficiency of our approach.
Model
Length
Latency
Owen2.5-VL-7B
314.0
2.71
Qwen2.5-VL-7B-Con
333.1
2.85
ReVPT-7B
360.5
6.23
Qwen2.5-VI-77-PPIS
571.8
4.87
I PTS DATA COLLECTION DETAILS
I.I INSTRUCTION CASE
Below an instruction example in the PTS format.
PTS Paradigm
<think>Okay, let me try figure this out.
<review>
The problem is to estimate the relative length of the orange triangle's to the image's height. To solve need to estimate the lengths of both and divide the length of the orange triangle's 's longest side by the image's height. </review>
' longest side compared
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<hint>
The task is precise measurement one. It requires accurately estimating the dimensions within the image. To solve such tasks, can pick dimension the reference and compare other dimensions to it for estimation. To represent length more accurately, I'll use text tokens to visualize the dimensions in the image. The reference line should be 1.0 unit long. I'll represent it <=mmanenes>> The < are the delimiters and each = is minimum length unit of units long. </hint>
<reference>
In this case, the dimensions I need to estimate are the orange triangle's longest side and the image's 's height. Let's use the longest side of the orange triangle the reference. define its length as 1.0 unit and represent it as <==== </reference>
<estimation>
Next, need to estimate the image's height. I visually compare the image's 's height to the reference line. The image's height seems be:
First segment <========>>> (1.0 unit)
Second segment <=======__> (0.9 units)
So the height is 1.0 + 0.9 1.9 units.
</estimation>
<calculation>
Now for the math: the length of the triangle's side (1.0) divided by the image height (1.9). 1.0 1.9 = 0.526, which rounds neatly to 0.53. </calculation>
Yeah, that checks out. the answer should be 0.53.</think>
<answers0.55</answer>
1.2 PROMPT FOR SYNTHESIZING PTS INSTRUCTIONS
For perimeter estimation, the PTS chain explicitly computes the length of each polygonal edge before summing them.
For area estimation, the PTS chain first identifies the appropriate geometric formula (e.g., rectangle, triangle, circle) and then estimates the required line segments.
Prompt for synthesizing PTS instructions
would like you to convert a json string into detailed thought process.
Your thought process should include the following parts:
1. Review the task (enclosed <review><<hevieww>)
2. Define the estimation methods (select reference line and symbolize dimensions as text
tokens) (enclosed in <hint></hint>)
3. Select side the reference line (enclosed in <referencee<</eferreeee>>
4. Visually compare other sides to the reference line and estimate their length (enclosed in <estination<<<ssimaation>>
5. Use the necessary formulas to calculate the final result (enclosed in <calcula- tion></calcuuattion>)
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The whole thought process should be formatted as follows: <think>[Your thought process]</thints <answer><Final <nnser]</answer>>>>>>>
Here are some good cases:
inputl: outputl:
Now, using the following data, please synthesize the thought process. When you mention the information in the json, you MUST pretend that you observe them from image. Avoid using words like "according to the symbolization result..." No matter how long the line is, you MUST go through every reference segment it contains, instead of using words segments".
like "There's ' a sequence of thirteen
J IMPLEMENTATION DETAILS
J.1 TRAINING DETAILS OF PTS
We select Drven2.5-VL-3B777 as the base model. Both models are first supervised fine-tuned with the 6,000 collected PTS data, then further trained by GRPO on another 6,000 answer-only distance reason- ing data. We normalize the ground-truth of the first 3,000 data and leave the other data with random distribution. The is set to 3 in practice. We utilize the implementation of GRPO in EasyR1 (Zheng and global
ct al. 2025), with rollout___ www.:ootttattthsisseeeeeeeeee
J.2 BASELINES
SpaceThinker (remyxai 2025) is trained to enhance spatial reasoning with test-time compute by fine-tuning synthetic reasoning traces real-world images with 3D annotations.
Spatial-R1 (Ouyang 2025 jimproves LVLMs' spatial reasoning capabilities by introducing the spatial reasoning dataset and task-specific GRPO fine-tuning.
DerToolChain (Wu al. 2024b) introduces tool-augmented prompting paradigm that equips LVLMs with zero-shot object detection ability. Since the work has not been open-sourced, we reproduced its Ruler Marker strategy by adding scale marks along the edges of the image and take the gpt-4o-0806 as the base model.
Visual Sketchpad (Hu al. 2024) equips LVLMs with drawing- based reasoning framework, enabling them to sketch lines, boxes, and marks (with optional vision tools) to enhance visual reasoning. We take the gpt 40-400000 0806 the base model and set the task_type-vil for evaluation.
K QUALITATIVE ANALYSIS
In this section, we compare the generation cases of GPT-4o, Qwen2.5-VL-7B and Owen2.5-VL-7B. PTS DiSTANCE. The questions and images presented in Figure TO and [1
K.1 LENGTH ESTIMATION
Length estimation case: Qwer2.5-VL-7B-PTS
<hhnn tackle this problem step by step.
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Question:
Determine the ratio of the length of the height of the image to that of the shortest side of the purple triangle.
Ground-truth:
8.21
Figure 9: Length estimation case from DisTANCE.
The objective is to determine the ratio of the height the image the shortest side of the purple triangle. To achieve this, need to ascertain the lengths of these two sides and then compute the ratio by dividing the height of the image by the shortest side of the purple triangle.
This task involves precise measurement, requiring accurate estimation of the dimensions within the image. strategic approach is to select one dimension as the reference and compare other dimensions against it for estimation. To visually depict the lengths, I'll use text tokens to represent the dimensions the image. I'll establish the reference line as 1.0 unit long and represent it <====--- symbolizes a minimum length unit of 0.1 units.
Here, the <> delimiters, and each
In this scenario, the two dimensions of interest are the shortest side of the purple triangle and the height the image. I'll choose the shortest side of the purple triangle the reference line. Thus, its length is defined as 1.0 unit, represented as <====
Now, need to estimate the height of the image. By visually comparing the image height to our reference line, it can broken down into segments as follows:
First segment
(1.0 unit).
Second segment
(1.0 unit).
Third segment
(1.0 unit).
Fourth segment
(1.0 unit),
Fifth segment
(1.0 unit).
Sixth segment
(1.0 unit).
Seventh segment
(1.0 unit),
Eighth segment
(1.0 unit),
And final segment <===>> (0.4 units).
Adding these together, the height of the image is 1.0 + 1.0 1.0 1.0 1.0 1.0+ 1.0 0.4 8.4 units.
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For the final calculation, I divide the height of the image (8.4) by the shortest side of the purple triangle (1.0). So, 8.4 x 1.0 = 8.4.
Everything checks out. The answer is 8.4.</think>
canswer284<<answer>
Length estimation case: GPT-40
find the ratio of the beight of the image to the shortest side of the purple triangle, let's estimate these measurements:
1. Height of the Image: The height of the image is the vertical dimension from the top to the bottom. By visually examining the image, this can be approximated as roughly 800 units based the pixel dimensions.
2. Shortest Side of the Purple Triangle: By inspecting the purple triangle, it seems that the shortest side is roughly 30 units in length.
Now, calculate the ratio:
Ratio Sbortest side the purple triangle
Height image
26.67
30
Therefore, the ratio is 26.67
Length estimation case: Qwen2.5-VI-77B
To determine the ratio of the height of the image to the shortest side of the purple triangle, need estimate the dimensions of the image and the triangle. Let's assume the image is rectangle and the triangle is a right triangle with the shortest side being the base.
First, let's estimate the dimensions of the image. The image appears to be rectangle with height of approximately 10 units and a width of approximately 15 units.
Next, let's estimate the dimensions of the purple triangle. The triangle appears to right triangle with the shortest side (the base) being approximately 5 units and the height being approximately 8 units.
The ratio of the height of the image to the shortest side the purple triangle is:
height image Shortest side of the triangle
10
2
5
Therefore, the ratio is
2
K.2 PERIMETER ESTIMATION
Perimeter estimation case: Qwen2.5-VL-7B-PP9
<think>Let's tackle this problem step by step.
The task is to determine the relative length of the perimeter of the image compared to the perimeter the blue rectangle. Essentially, we need to find out how many times the perimeter of the blue rectangle fits into the perimeter of the image. This will involve estimating the perimeters of both shapes and then dividing the perimeter of the image by that of the blue rectangle.
This task requires precise measurement. It involves accurately estimating the perimeters within the image. To solve such tasks, can select dimension reference and
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Question:
The perimeter of the image is to the perimeter of the blue rectangle as what is 1?
Ground-truth:
2.82
Figure 10: Perimeter estimation case from DisTANCE.
compare other dimensions to it for estimation. To represent length accurately, we'll text tokens to visualize the dimensions in the image. The reference line should be 1.0 unit long. We'll represent as <==-st--===>, The <> are delimiters, and each is minimum length unit of 0.1 units long. For the perimeter of the image, we add up the lengths of all its sides (width and height multiplied by 2 since it's rectangle-like shape). For the blue rectangle, we sum up the lengths of its sides well.
In this case, let's take the shorter side the blue rectangle as our reference. define length 1.0 unit and represent it as <==-=====ss> sides by comparing them to this reference. First, estimate the longer side of the blue rectangle. By visually comparing it to the reference line, it appears to consist of: A first segment
I'll estimate the lengths of other
(1.0 unit).
And an additional segment <=====>>>> (0.6 units)
So, the longer side of the blue rectangle is 1.0 0.6 1.6 units. Therefore, the perimeter of the blue rectangle equals 2 (longer side + shorter side) 2 (1.6 + 1.0) = 5.2 units. estimate the width of the image. By visually comparing it to the reference line, it appears to consist of:
A first segment
(1.0 unit).
A second segment
=> (1.0 unit).
third segment
(1.0 unit),
And final segment <== (0.7 units).
Adding these together, the width of the image is 1.0 1.0 1.0 0.7 3.7 units. estimate
the height of the image. By visually comparing the reference line, it appears consist of:
A first segment
(1.0 unit).
A second segment
>> (1.0 unit),
A third segment
(1.0 unit).
A fourth segment
(1.0 unit).
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Adding these together, the height of the image is 1.0 1.0 1.0 1.0 4.0 units. Therefore, the perimeter of the image equals 2 (width + height) = 2 (3.7 + + = 13.4 units.
Finally, the relative length of the perimeter of the image compared to the perimeter of the blue rectangle is / =2 2.96.
That seems correct. So the answer should be
canswers2.9<</answer
Perimeter estimation case: GPT-40
To determine the ratio, need estimate the perimeters of both the orange triangle and the blue rectangle visible in the image.
1. Estimate the perimeter of the blue rectangle: appears as standard rectangle. Let's estimate dimensions: assume the height is approximately 1.8 times the width.
If width æ, then height 1.8r. Perimeter of rectangle P 2(w h) 2(x + 1.8x) = 3.6x..
2. Estimate the perimeter of the orange triangle: Judging by the image, let's assume side lengths roughly proportionate: = x, 1.5x,
c= 1122..
Perimeter = a + +
+ 1.5x + 1.2r = 3.7x, compute the ratio of the total
perimeter to just the blue rectangle:Ratio Perimeter of the orange triangle calculating the ra 3.6 1.0278
3.7z
3.7
By
Perimeter the blue rectangle
3.6
3.6
Hence, the answer is: 1.03
Perimeter estimation case: Qwen2.5-VL-7B
To solve this problem, we need to calculate the perimeters of the orange and blue rectangles and then compare them.
First, let's determine the dimensions of the orange rectangle. The orange rectan- gle is a 3x4 rectangle, so its perimeter is:
(3 + 4) = 2 = 14, let's determine the dimensions of the blue rectangle. The blue rectangle is a 5x3 rectangle, so its perimeter is:2 x (5 + 3) = 2 x 8 = 16, we need to find the ratio of the perimeter of the orange rectangle to the perimeter of the blue rectangle. This ratio is: 16
0.875
Therefore, the final answer is: 0.875
K.3
AREA ESTIMATION
Area estimation case: Q
<hink>>Irighh, let's start working this out. The task to calculate how large the image relative to the purple circle in terms of area. To achieve this, need estimate the areas of both the image and the purple circle, and then divide the area of the image by the area of the purple circle. This task demands precise measurement, requiring accurate estimation of areas within the
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Question:
need you to calculate how large the image is relative to the purple circle in terms of area.
Ground-truth:
58.64
Figure Area estimation case from DisTANCE.
image. I can select a dimension as a reference and compare other dimensions it for estimation purposes. To represent lengths more precisely. I'll use text tokens to visualize the dimensions in the image. The reference line should be set 1.0 unit long and represented as <======
The < are delimiters, and each represents a minimum length unit 0.1 units. For the area of the image, since it has a rectangle-like shape, we calculate by multiplying its width and height. For the circle, we use the formula for the area of Ö3c0r00b2, where r the radius. So, need to estimate the radius of
circle, which the purple circle, the width, and the height of the image.
In this case, let's take the radius of the purple circle as our reference. I'll define its length as 1.0 unit and represent it making visual comparisons to this reference line.
I will estimate the lengths of other sides
First, I estimate the width of the image. When visually comparing the image width our reference line, can break it down as follows:
There's the first
segment (1.0 unit).
A second
segment (1.0 unit),
A third
segment (1.0 unit).
A fourth
segment (1.0 unit),
A fifth
segment (1.0 unit).
A sixth
segment (1.0 unit),
seventh
segment (1.0 unit),
An eighth
segment (1.0 unit),
A ninth
segment (1.0 unit).
A tenth
segment (1.0 unit),
An eleventh
segment (1.0 unit),
A twelfth
segment (1.0 unit),
And finally, a <=====> segment, which is 0.5 units long.
The total width of the image sums up 1.0 12 + 0.5 12.5 units.
I estimate the height of the image. By visually comparing the image height to our reference line, I can break it down like this:
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There's the first <= segment (1.0 unit);
A second
segment (1.0 unit),
A third
segment (1.0 unit),
A fourth
segment (1.0 unit),
A fifth
segment (1.0 unit).
A sixth
segment (1.0 (Omitt....miitted due to space limitations.)
thirteenth
segment (1.0 unit),
A fourteenth
segment (1.0 unit),
A fifteenth
segment (1.0 unit),
And finally, <==> segment, which is 0.2 units long.
The total height of the image sums up to 1.0 15 0.2 15.2 units.
Consequently, the area of the purple circle is calculated using the formula A = xr² with
1.0 and approximately 3.14. So, the area is 3.14 1.0² 3.14 square units. For the image, since we know the width is 12.5 units and the height 15.2 units, using the formula for the area of a rectangle = width height, the area of the image is 12.5 15.2
190.0 square units.
Finally, the proportion of the image's 's area relative to the purple circle's area is 190.0 / 3.14 60.7.
Yes, that seems right. So the answer should be 60.7.</think> <answerr60.7</aaswwrr
Area estimation case: GPT-40
To calculate the area of the image relative to the purple circle, we need to estimate the radius of the purple circle and the dimensions of the image first.
Let's start by estimating the radius the purple circle. Based on the image: The purple circle appears small in comparison to the image dimensions.
Let's estimate the purple circle's 's radius follows: Assume the radius the purple circle (r) consists of roughly 1/10th of the width of the image.
The image width appears to be in ratio of approximately 4:3 (width:height). Now, let's calculate the area of the image and the area of the circle: Assume the image width is 800 pixels and height is 600 pixels (which are typical dimensions for the aspect observed).
Image area = width x height 800 x 600 = 480, 000 pixels?.
Assuming the estimated radius of the purple circle: 10
pixels.
Circle area 2r² = ग x 80 = 6, 400m pixels?
Now calculate the relative area:
Relative area Image.Area
Circle.rea 6, 400m
180.000
~
180,000
20,096
~ 23.89.
The relative area of the image compared to the purple circle approximately/ 23.89
Area estimation case: Qwen2.5-VL
To estimate the size of the image relative to the purple circle in terms of area, we need to compare the areas of the two shapes. However, since the image provided, will use hypothetical scenario where the purple circle is standard circle with radius of 1 unit for simplicity.
1. Area of the Purple Circle:
The area A of a circle given by the formula:
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A = πr²
For a circle with radius r = 1:
A = x = = 3.14
2. Estimating the Area of the Image:
The image appears be a rectangle with a width and height that can be estimated, Let's assume the width and height of the rectangle are both approximately 2 units each (this is rough estimate based on the visual proportions). The area A of a rectangle is given by:
A width height
For rectangle with width and height both 2 units:
A x
3. Comparing the Areas:
The area of the image (rectangle) is approximately 4 square units, and the area of the purple circle is approximately 3.14 square units.
4. Ratio of the Areas:
The ratio the area of the image to the area of the purple cirele is:
Ratio
Image
3.14 Rs 1.27
Area of the Purple Circle
Therefore, the image approximately 1.27 times the area of the purple circle. 1.27
QUALITATIVE ANALYSIS ON GENERAL TASKS
L.1 CHART UNDERSTANDING
Generation case of PTS
The graph shows the value of bread, biscuits, wafers, cakes, and pastries exported from the United Kingdom (UK) from 2001 to 2019. To determine if 2010 the year with the most value, we need to compare the values for each of the given years.
The task is to visually assess the data and identify the year with the highest value. We can achieve this by selecting a reference line and comparing other values it. I'll use text tokens to represent the lengths visually. The reference line will be 1.0 unit long, depicted The < are delimiters, and each represents a minimum length unit
as <=--=--- of 0.1 units.
For this task. the relevant dimensions are the years and the corresponding values. I'll select the value for 2010 as the reference line, defining it as 1.0 unit long, represented as
Now, let's estimate the values for other years by comparing them the reference line:
-2005: The value appears be
which is 0.8 unit. which is 1.3 units. which is 1.5 units.
20015 The value is
2019: The value is
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Value of bread biscuits wafers cake pastries exported from the United Kingdom (UK) from 2001 2019 (in 1,000 GBP)
Export value thousand GBP
830,000
465,000
Year
Question:
From to 2019, 2010 exported the most value bread, biscuits, wafers, cakes and pastries from the UK?
Ground-truth:
No
Figure 12: Case of chart understanding.
The values for 2015 and 2019 are higher than the reference line, indicating they are the most valuable. However, the question asks for the value in 2010, which is already defined as 1.0 unit, and visually, matches the reference line.
So, the final answer that 2010 is one of the years with the most value, but is not the highest. The actual answer should be that 2019 has the most value.
L.2 FINE-GRAINED PERCEPTION
Question:
The image shows the famous Chubb illusion. Are the center of rectangular fields identical?
Ground-truth:
No
Figure 13: Case of fine-grained perception.
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Generation case of PTS
The task is to determine the center areas of two rectangular fields are identical. To solve this, I need to compare the two areas visually and assess their equivalence. The approach involves selecting reference dimension and comparing other dimensions to it for estimation purposes. I'll use text tokens to represent the lengths, with reference line The delimiters < and each represent
set to 1.0 unit long, depicted as <====== minimum length unit of 0.1 units long. For the areas, since they rectangular, calculate the area by multiplying their lengths. Therefore, need to estimate the lengths of the sides of both rectangles.
In this scenario, let's take the shorter side the first rectangle as our reference. define its will estimate the lengths of other
length as 1.0 unit and represent it sides by comparing them to this reference. First, estimate the longer side of the first rectangle. By visually comparing the reference line, it appears to consist of:
One segment <-==-======>>> (1.0 unit).
And additional segment <=====> (0.6 units).
So, the total length of longer side the first rectangle is 1.0 0.6 = 1.6 units.
Now, I estimate the longer side of the second rectangle. By visually comparing it to the reference line, it seems to be composed of: One segment
(1.0 umit). And another segment <=======> (0.8 units)
Adding these together, the total length of the longer side of the second rectangle is 1.0 0.8 1.8 units.
Next, estimate the shorter side of the second rectangle. By visually comparing it to the reference line, it appears to consist of:
One segment
(1.0 unit). And an extra segment <= (0.2 units).
So, the total length of the shorter side of the second rectangle 1.0 + 0.2 1.2 units.
For the first rectangle, using the formula for the area of rectangle A = length width, the area is 1.6 1.0 = 1.6 square units.
For the second rectangle, using the same formula, the area is 1.8 1.2 2.16 square units. Since the calculated areas are different (1.6 and 2.16), the center areas of the two rectangular fields not identical.
The answer that the center areas of the two rectangular fields are not identical.
L.3 TOP-DOWN VIEW UNDERSTANDING
Generation case of PTS
Let's tackle this step by step.
The objective is to determine which of the two, the master bedroom or the patio, is larger in terms of area. To do this, need to estimate the areas of both the master bedroom and the patio and then compare them. This task requires precise measurement. involves accurately estimating the areas within the image. To solve such tasks, can select a dimension as a reference and compare other dimensions to it for estimation purposes. To represent lengths more precisely, I'll use text tokens to visualize the dimensions in the image. The reference line should be set 1.0 unit long, represented and each = represents a minimum length unit of 0.1 units. For the area of the rectangle- shapes, we calculate it by multiplying the length and width. So. I need to estimate the lengths of the dimensions of both the master bedroom and the patio.
The are delimiters,
In this case, let's take the shorter side of the master bedroom as our reference. I'll define its length as 1.0 unit and represent it as < will estimate the lengths of other sides by comparing them to this reference. First, I estimate the longer side of the master bedroom. By visually comparing it to the reference line, it seems to consist of:
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AITCIN
DAT--OOM
unum
GIAN ROOM
SAINE ROOM
FORCH
Question:
Which room is bigger, the master bedroom the patio?
Ground-truth.
The master bedroom
Figure 14: Case of top-down view understanding.
The first segment
(1.0 unit),
And an additional segment <======ss (0.9 units).
So, the total length of the longer side of the master bedroom is 1.0 0.9 1.9 units.
Now, I estimate the longer side of the patio. By visually comparing it to the reference line, appears to consist of: The first segment <=
(1.0 unit).
A second segment <==-====> (0.8 unit),
Adding these together, the total length of the longer side of the patio is 1.0 + 0.8 = 1.8 units. Next, I estimate the shorter side of the patio. By visually comparing to our reference line, it seems to be composed of:
One segment <===--
(0.9 unit), So, the total length of the shorter side of the patio is 0.9 units.
Using the formula for the area of a rectangle A = length width, the area of the master bedroom is 1.9 1.0 1.9 square units.
Similarly, using the same formula, the area of the patio is 1.8 0.9 1.62 square units. Finally, to determine which is larger, we compare the areas. The area of the master bedroom (1.9) is greater than the area of the patio (1.62). Therefore, the master bedroom is bigger. That checks out correctly. So, the answer master bedroom.
M RELATED WORK
Multimodal Reasoning Models. Inspired by the success of inference-time scaling in large language
models (Tang et al.) 2025b: Wang et al. 2025b), recent research has delved into enhancing the
reasoning capabilities of Large Vision-Language Models (LVLMs) (Bai al.) 2025 Zhu et al. 2025 Wu et al. 2025 Zhan et al. 20255). Early methods (Xu et al. 2024 Du et al. 2025 Yao et al. 2024) focus on collecting multimodal Chain-of-Thoughhe data to conduct supervised tne-tuning on LVLMs. More recently, spurred by the achievements of reinforcement learning algorithms such as GRPO (Shao et al., 2024). some studies have explored the application of reinforcement learning to improve the long sequence reasoning ability of LVLMs. Some work (Chen et al.) [025b] Liu
ct al. 2025c Zhan al. 2025a Zhou et al., (025a) investigate the design different verifiable reward functions for various visual tasks. MM-Eureka (Meng et al. 2025) attempts to directly employ rule- based reinforcement learning to stimulate the model reasoning and reflective abilities without performing SFT. VLAA-Thinke (Chen et al. 2025a) explores the influence of cold start
to the reasoning capabilities of LVLMs. Our work focuses on adopting RL to enable LVLMs to solve precise measurement problems by including their perception process into text-formed long
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Published as a conference paper at ICLR 2026
reasoning chains. More recently, some work (Wang et al. 2025a Zhou et al. 2025b Yang al.) 2025a) introduce rethinking reasoning chains and agentic tool calling method to further enhance LVLMs performance on complex multimodal tasks.
Visual Perception in LVLMs. As LVLMs achieve impressive progress in multimodal reasoning tasks, their visual perception accuracy remains key bottleneck to their real-world applications. Some work studies the hallucination issue by evaluating whether LVLMS accurately describe image contents like object class (Li et al., 2023) and relationship (Wu et al. 2024a). HallusionBench (Guan et al. 2024) further challenges LVLMs with detailed visual illusions, while MMVP (Tong al.] 2024b) evaluates LVL.Ms on image pairs that share similar visual representations but significant semantic information. Recently. spatial intelligence (Chen et al. 2024) has attracted increasing attention. Existing works (Yang et al.) 2024 Cheng et al., 2024) have proposed benchmarks to evaluate the spatial reasoning abilities of LVLMs, including understanding relative spatial relationships and estimating absolute distances. Unlike real-world estimation benchmarks, DiSTANCE consists of 2D synthetic geometric shapes, eliminating the need for commonsense knowledge and concentrating solely on the model's s ability to perceive basic distance information.
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Reviews:
[{"review_id": "B5c80D4tG4", "reviewer": "Reviewer_J9pJ", "summary": "This paper studies the effectiveness of inference time scaling for perception-centric tasks. It first proposes a new visual estimation benchmark for testing LVLMs' perception capability and then proposes the idea of Perception Time Scaling, which encourages the model to produce richer perception-related tokens in perception-heavy tasks. Experiment results show promising performance improvements.", "questions": "1. In the experiments, why is there no \"Direct with SFT\" as a baseline?\n\nPlease see other questions in the weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. I like the idea of introducing the DisTANCE benchmark, which focuses on testing the perception of LVLMs. The idea is simple yet probes the models' capability in perception, in isolation of their textual reasoning capabilities. Experiment results show existing models struggle on this benchmark, even for the proprietary ones, and that reasoning models provide only marginal gains.\n\n2. Analysis on the ratio of perception-related tokens (in Table2 and Figure 2) provides insights and good intuitions on the proposed method on perception elaboration and perception decomposition as the main axes for inference time scaling.\n\n3. Main experiment setup ablates the effect of data (including Direct and CoT) and training objectives (SFT and RL). Evaluations cover perception related tasks, and also general multimodal benchmarks. The results are promising that PTS improves quite significantly on visual estimation tasks and maintain performance on general multimodal tasks.", "weaknesses": "1.  While the high-level idea of perception time scaling is interesting and perhaps general, the proposed method appears to be quite task-specific for visual estimation kind of tasks. For example, in perception elaboration, it is specifically using <==========> to represent length, and the decomposition part that teaches the models to use the operation of tiling both appears to be specific for visual estimation. Have the authors thought about how this perception time scaling paradigm can be used for other perception-centric tasks?\n\n2. In the experiments, it is perhaps less surprising to see improvements on the proposed DisTANCE benchmark (given that the models are trained on this specific dataset). My main concern is how general the proposed method is. For instance, we see relatively much less performance improvements when tested on OOD datasets comparing PTS and the baselines. For Geo and Lego tasks, why are the other subsets not considered?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "et6nwYESIT", "reviewer": "Reviewer_fJsx", "summary": "This paper investigates whether inference-time scaling techniques can enhance the visual perception capabilities of Large Vision-Language Models (LVLMs). The authors introduce DisTANCE, a perception-centric benchmark focused on visual estimation tasks involving geometric shapes. Their evaluation reveals that current LVLMs exhibit limited estimation precision, with inference-time scaling offering only marginal improvements. To address this limitation, the authors propose Perception-Time Scaling (PTS), a novel paradigm that encourages token-rich perception through two key components: (1) Perception Elaboration, which uses symbolic tokens (e.g., <==========>) to represent abstract visual attributes like distance, and (2) Perception Decomposition, which breaks down complex perception tasks into tractable sub-problems. The method is trained via a two-stage pipeline: supervised fine-tuning followed by GRPO-based reinforcement learning.", "questions": "1. The paper commits to a specific symbolic representation (<==========>) but doesn't explore whether other symbolic forms might work equally well or better. For instance:\n- Would using numbers with special tokens (e.g., [DIST:1.0], [DIST:0.1]) be more efficient?\n- Could using other symbols or patterns provide similar or better results?\n- How sensitive is the approach to the granularity of the symbolic representation beyond the δ=0.1 vs δ=0.05 comparison?\n\n2. While Table 5 shows some transfer to real-world perception tasks, the mechanism of this transfer is not well understood. Why would training on synthetic geometric shapes improve performance on natural images with complex visual content?\n\n3. The symbolic representation significantly increases sequence length. For a distance of 10 units, the model would generate 100+ tokens (<==========> repeated 10 times). What are the computational costs? How does this scale?\n\n4. What are the main failure modes of PTS? Are there specific types of geometric configurations or estimation tasks where the approach struggles? Understanding limitations would be valuable.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "I appreciate the core insight of this paper that the observation about \"fast perception\" in Section 2.3 is quite revealing: reasoning models produce longer chains of thought, but the perception-related content remains sparse. This is an important finding that hasn't been explicitly studied before, and the quantitative analysis of perception ratios in Table 2 makes this concrete.\n\nWhat I find most interesting is the symbolic tokenization idea. Using <==========> to represent distances feels almost toy-like at first, but it's actually quite elegant. It gives the model a way to \"think through\" visual measurements step by step, similar to how humans might count or measure incrementally. \n\nThe paper is generally well-written. Figure 1 effectively sets up the problem, and Figure 3 clearly illustrates the method. The examples in the appendix (Figures 7-9) are helpful for understanding how the approach works in practice. I can see the step-by-step decomposition and symbolic representation clearly.", "weaknesses": "1. Narrow task selection: While DisTANCE is well-designed for its purpose, the evaluation is limited to very specific geometric estimation tasks. The paper would benefit from demonstrating the method's effectiveness on a broader range of perception-centric tasks beyond length, perimeter, and area estimation of synthetic shapes.\n\n2. For out-of-domain evaluation (Table 4), the paper only reports results on specific subsets like Comparison subset from Geoperception and the Height subset from LEGO-Puzzles. Both benchmarks have multiple other subsets that test different aspects of spatial reasoning. What is the performance on these other subsets? Selective reporting raises concerns about cherry-picking results.\n\n3. In Table 5, only three subsets of BLINK (Jigsaw, Multi, Local) are reported. BLINK contains many more subsets designed to evaluate different perceptual capabilities. Why were only these three selected? A comprehensive evaluation across all BLINK subsets would strengthen the claims about perception improvement.\n\n4. Table 5 introduces a different training setup by combining PTS data with Geometry3K math reasoning data. While this shows that PTS can be combined with other data, it doesn't directly address whether the core PTS training (SFT + RL on synthetic data) improves general multimodal capabilities. It would be more convincing to see results of the DisTANCE-trained model (from Table 3) evaluated on the general benchmarks in Table 5 to understand whether the perception improvements truly generalize.\n\n5. Many related works misses several recent important works on multimodal reasoning and visual perception enhancement:\n- VL-Rethinker (https://arxiv.org/abs/2504.08837): Addresses similar issues in visual reasoning\n- ReVPT (https://arxiv.org/abs/2509.01656): Relevant work on vision-language model perception\n- R-4B (https://arxiv.org/abs/2508.21113): Related to multimodal reasoning enhancement\n- Zero-Thinker (https://arxiv.org/abs/2503.05132): Relevant to inference-time reasoning approaches", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lqjENd38Eh", "reviewer": "Reviewer_eNuB", "summary": "This paper introduces DisTANCE, a new perception-centric benchmark for visual estimation tasks. The authors show that most VLMs struggle to accurately assess visual perception capabilities. To address this limitation, they propose the Perception-Time Scaling (PTS) training paradigm, which consists of two stages: (1) a cold-start SFT phase using PTS-annotated data, and (2) a GRPO optimization phase. This approach achieves significant performance gains, improving accuracy by 8.0% to 64.7% over baseline models.", "questions": "Detailed questions in the weaknesses part.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- This paper introduces a new perception-centric benchmark and demonstrates that even state-of-the-art proprietary VLMs exhibit limited performance, highlighting the importance of enhancing not only reasoning but also perception capabilities.\n\n- The paper also presents the Perception-Time Scaling (PTS) paradigm, which enables small VLMs to learn to perceive images in a more human-like manner.", "weaknesses": "- One of the main contributions is the introduction of a new perception-centric benchmark, particularly focusing on visual estimation, which differs from prior perception tasks. To further enhance the value of this benchmark, I recommend evaluating proprietary reasoning models (e.g., Gemini-2.5-Pro). Since the benchmark contains only 300 samples, this additional experiment seems feasible. When I tested one of the examples from the paper using GPT-5, the model invoked tool-calling behavior and solved the task successfully. However, please note that this comment does not affect my rating of the paper.\n\n- The term Inference-Time Scaling usually refers to the phenomenon where allocating more inference-time compute leads to improved performance. However, the proposed Perception-Time Scaling seems somewhat different in nature. From my understanding, it adds perception-related steps into the reasoning process during training—so it appears closer to “perception-enhanced reasoning.” Moreover, the experiments do not show how performance changes when scaling up perception-related compute during inference. As a result, the naming feels slightly forced, as if it were attached to align with the popular term “inference-time scaling.”\n\n- In the perception elaboration and perception decomposition stages, symbolic tokens (e.g., <==========>) are used. I am curious how performance changes if these tokens are simply provided as prompt hints during inference. This could clarify whether the model truly lacked visual estimation capability and whether reinforcement learning genuinely boosted this ability.\n\n- If the benchmark was generated programmatically in Python, scaling up the dataset for training should be relatively straightforward. It would be interesting to see whether simply training on a larger dataset of similar style improves performance. Such a setting could help demonstrate that data scaling alone is insufficient for the proposed visual estimation task, reinforcing the task’s importance. That said, I understand that conducting this experiment within the rebuttal period may be unrealistic—so even a qualitative discussion or expected outcome would be helpful.\n\n- Intuitively, I expected tool-augmented RL-trained VLMs to perform better. However, I suspect that your proposed method offers significant efficiency advantages. Could you provide details on the actual inference speed or runtime efficiency?\n\n- Could you clarify the decoding settings (e.g., top-p, temperature) used during inference?\n\n- GRPO performance can be highly sensitive to reward design. I am curious why the reward was defined in a continuous form. For instance, what would happen if you used a binarized reward—assigning a reward of 1 when the estimated value difference is below 0.1, and 0 otherwise? A detailed ablation analysis on this design choice would be insightful, especially since recent work [1, Fig. 6] reports that binary rewards can yield better results.\n\n- While I recognize the substantial experimental effort behind this work, the methodological novelty appears limited. The proposed approach mainly adapts GRPO to the visual estimation task rather than introducing new RL algorithms. This is not a major issue, but it does make the paper feel more like an application of existing methods rather than a conceptual advance. I would have appreciated more insightful discussions, such as an analysis of the trade-off between perception and reasoning. If possible, could you elaborate on potential directions or reflections in this regard? For example, which part should we consider when we empower VLM's perception capability? should we strengthen VLM's reasoning part for better perception?\n\n\nAdditional Minor Suggestions (Not Affecting the Score)\n\n- The term “Fast Perception” feels somewhat ambiguous. Would “Fast Thinking” be a more appropriate description?\n\n- In line 187, the result shown in Figure 2 seems more like a fine-grained analysis than a decomposition of a complex perception task—so the phrase “decompose complex perception” was a bit confusing.\n\n- In the Appendix section “Prompt for synthesizing PTS instructions”, a newline character (\\n) seems to be missing between items 4 and 5.\n\n---\n\nReference\n\n[1] Unified Reinforcement and Imitation Learning for Vision-Language Models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LCml1FvZEW", "reviewer": "Reviewer_EEJH", "summary": "The paper introduces a new method for inference-time scaling for perception-heavy reasoning tasks such as visual estimation tasks (i.e. tasks that require estimating the size of geometric elements). The paper introduces a new way for inference-time scaling called perception-time-scaling (PTS). The main idea is that models are encouraged to decompose tasks by using some tokens for \"unit estimation\" (i.e.  <==========> corresponds to 1 unit). To encourage models to use this method, they introduce a task Distance reasoning Task with Analytical Numeric and Comparative Estimation (DisTANCE), which is automatically generated using programmatically generated images which includes a known ground truth, and PTS (and CoT for ablations) reasoning traces using GPT4o. Combining SFT and RL they train models and show that PTS leads to better performance than CoT. They also show that finetuning on this data, improves performance for other perception heavy tasks such as Geoperception and Lego-Puzzles.", "questions": "- In Table 4, why does the method yield larger improvements on GeoLHC for Qwen2.5VL-3B, but greater gains on LegoHeight for the 7B model? Reporting results over multiple runs with deviations would help clarify this.\n- In Figure 4b, which tasks are used to evaluate image-focused attention? Is this analysis specific to DisTANCE, or do the observed patterns generalize to out-of-domain tasks as well?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method is innovative and addresses a well-recognized challenge in VLLMs.\n- The ablation study comparing CoT and PTS (Tables 3 and 4) is convincing: SFT+RL with PTS yields notable gains over SFT+RL with CoT, with improvements exceeding 10 accuracy points on DisTANCE, though the benefits on out-of-distribution tasks are more modest.\n- The appendix provides a comprehensive set of ablations that further motivate and support the proposed approach.", "weaknesses": "While the method appears effective for perceptual measurement tasks, its usefulness in out-of-domain settings is less convincing. Beyond measurement tasks, the gains seem limited at best. For example, in Table 5 the improvements are marginal, and the absence of multiple runs or error bounds further reduces confidence in their significance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)