VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Authors: Ziang Yan
Yinan He
Xinhao Li
Zhengrong Yue
Xiangyu Zeng
Yali Wang
Yu Qiao
Limin Wang
Yi Wang
Venue: NeurIPS 2025 poster ()
Keywords: MLLM
Test Time Scaling
Primary area: deep_learning

Abstract: Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-level perception and understanding. Existing methods mainly leverage LLM reasoning to analyze parsed visuals, often limited by static perception stages. This paper introduces Visual Test-Time Scaling (VTTS), a novel approach to enhance MLLMs' reasoning via iterative perception during inference. VTTS mimics humans' hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by updated textual predictions. Specifically, VTTS employs an Iterative Perception (ITP) mechanism, incorporating reinforcement learning with spatio-temporal supervision to optimize reasoning. To support this paradigm, we also present VTTS-80K, a dataset tailored for iterative perception.
These designs allows a MLLM to enhance its performance by increasing its perceptual compute.  Extensive experiments validate VTTS's effectiveness and generalization across diverse tasks and benchmarks. Our newly introduced Videochat-R1.5 model has achieved remarkable improvements, with an average increase of over 5\%, compared to robust baselines such as Qwen2.5VL-3B and -7B, across more than 15 benchmarks that encompass video conversation, video reasoning, and spatio-temporal perception.

VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception
Ziang Yan 1.2,, Xinhao Li Yinan He"; Zhengrong Yue?, Xiangyu Zeng^2, A.2 Yali Wang+.2, Yu Qiao², Limin Wang 3,2, Yi Wang
'Zhejiang University Shanghai Al Laboratory *Nanjing University 4 'Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Abstract
Inducing reasoning in multimodal large language models (MLLMs) is critical for achieving human-leve perception and understanding. Existing methods mainly leverage LLM reasoning to analyze parsed visuals, often limited by static per- ception stages. This paper introduces Visual Test-Time Scaling (VTTS), a novel approach to enhance MILMMs' reasoning via iterative perception during inference. VTTS mimics humans' hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by updated textual predictions. Specifically, VTTS employs an Iterative Perception (ITP) mechanism, incorporat- ing reinforcement learning with spatio-temporal supervision to optimize reasoning. To support this paradigm, we also present VTTS-SOK a dataset tailored for iterative perception. These designs allows a MLLM to enhance its performance by increas- ing its perceptual compute. Extensive experiments validate VTTS's effectiveness and generalization across diverse tasks and benchmarks. Our newly introduced Videochat-R R1.5 ..5 model has achieved remarkable improvements, with an average increase of over 5%, compared robust baselines such Qwen2.5VL-33 and -7B, across more than 15 benchmarks that encompass video conversation, video reasoning, and spatio-temporal perception.
1
Introduction
How to induce reasoning in multimodal large language models (MLLMs) becomes increasingly erucial in foundation models and multimodal understanding for general intelligence. Many believe
[62] 47 64 [50] it is one the indispensable prerequisites to enable MLLMs with human-level perception and understanding,
people usually perceive surroundings with reflection, more than only parsing. Existing advances in MLLMs mostly concentrate on exploiting LLMs' reasoning to process leveraging vision rules to elicit multimodal reasoning [47] [1]. Either of them explores rich causal dependencies in languages space to dig given evidences for more persuasive analysis and sound decisions. In this paper, inspired by humans' hierarchical attention with refinement [48 43 221, we study causal relations in vision for reasoning, delivering a learnable diagram to scale test-time compute of MLLMs by iterative perception.
parsed visuals deeply 55
70 67
To explicitly improve multimodal reasoning, existing approaches usually extend or enhance search space of LLMs and score them for the most reasonable one. Examples include Best-of-N (BoN) sampling 200 which selects the optimal output from multiple candidates via scoring; guided beam
Equul contributions. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Gemini11.5 Pro
5-
VideeoChat-81
VVdeeeChat-81.55
07.4519
70.6
72.3
68.7
70.9
75
60.5
62.6
Eb.1g8.6
50 60.6
51.4
57.9
46
430
MVBench
Perception
LongVideeBench
VideoMME
Chardes-STA
MMde
Short Video QA
Long
Temporal Grounding
Video Reasoning
Figure
Performance comparison with other models on multiple video benchmarks
search [65] employing learned heuristics to refine decoding paths; and Monte Carlo Tree Search (MCTS) 14 using rree-based simulations for decision refinement. Meanwhile, some employ reinforcement learning (e.g. GRPO (211) to improve LLM search space using visual rewards. In this regard, these methods only perceive once for subsequent analysis. Contrarily, V* [62 and Visual-CoT [47] show directly simulating humans' top-down perception also benefits reasoning by more accurate spatio-temporal understanding with fewer hallucinations. Note whether these aforementioned means perceive or in several times, scaling their reasoning (or estimation parts) cannot remedy the perceived evidences in cases of bad luck.
In this work, we give visual test-timing scaling (VTTS) approach for MLLMs, designed to enhance MLLMs' reasoning capabilities through iterative visual perception during inference. Inspired by the human strategy of progressively narrowing attention to relevant spatio-tempont regions, VTTS enables MILMs to dynamically allocate computational focus to high-confidence visual regions across multiple iterations. This process is guided not only by the visual input but also by the evolving textual predictions, explicitly modeling dependencies between language and vision over time.
At the core of VTTS lies an Iterative Perception (ITP) mechanism that refines the model's understand- ing of multimodal inputs in stages. Each iteration involves predicting regions of interest (ROIs) based on prior reasoning, followed by reprocessing these regions to gather more detailed context. To support this multistage visual reasoning, we formulate new objective that extends traditional autoregressive MLLM training to include visual dependency modeling. Recognizing current limitations in accurate modeling spatio-temporal locations only with autoregression, we further develop reinforcement learning (RL) method based on Generalized Reward Policy Optimization (GRPO), enabling the model learn spatio-temporal focus policies from existing spatio-temporal annotations.
To facilitate the training and evaluation of VTTS, we also introduce VTTS-8OK, dataset with fine- fine-grained annotations for visual reasoning. VTTS-80P includes QA pairs enriched with annotated spatio-temporal cues and corresponding chains of thought, enabling models to learn how to identify and reason over critical visual segments.
Our experiments demonstrate that VTTS significantly improves MLLM performance from funda- mental spatio-temporis perception, commonsense video question-answering to complex multimodal reasoning by enabling targeted visual refinement at test time. On average, it enables Qwen2.5-VLL77 to increases in numeric results in aforementioned benchmarks by 5.4% and -3B by 6.3%. VTTS represents a step toward more human-like, adaptive perception multimodal Al systems, bridging the gap between static visual processing and dynamic, context-driven attention.
We propose a new test-time scaling method (VTTS) for MLLMs. We explicitly build visual dependencies with iterative perception (ITP) in MLLMs, simulating humans' progressive attention for ROIs. For ITP, we use reinforcement learning with a spatio-temporal verification to enhance the model's perception, leading to notable gains over strong baselines (e.g. TTS in LLMs).
We propose the VTTS-8OK dataset, which enables models to achieve iterative perception and reasoning capabilities through small-scale reinforcement fine-tuning.
We verify the generalization of VTTS with different MILMs in spectrum of tasks over 15 benchmarks, including video QA, video reasoning, and spatio-temporal perception. We show consistent increases in these benchmarks for MLLMs with VTTS.

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Rewarder
ST Clue
ALA
Input
Answer
Input
Answer
Input
Answer
(a) Best-of-N (BON)
(b) Vision-aided CoT
(c) Perception Test-Time Scaling
Figure 2: Schemes of Test time scaling methods. BoN refers to generating N candidate items and selecting the best one, Vision-aided CoT involves incorporating visual information once into the reasoning process, and ITP entails iteratively generating spatiotemporal clues and selectively adding visual information to the reasoning based on these clues.
2
Related work
Multimodal Large Language Model. Large language models (LLMs) have been extended into the visual domain, leading multimodal large language models (MLLMs). These models combine text and visual information for broader understanding capabilities. Early influential works like BLIP-2 [30] LLaVA (36), and mPLUG-Owl [71] successfully solved tasks such as image captioning and visual question answering. More recently, focus has been shifted towards extending MLLMs to handle video understanding. with methods such as VideoChat
32 Intern Video 57 59 and
Video-LLaMA 77 10 75). These methods allow LLMS to interpret video by processing frame sequences and using rideo-focused instruction data. Additionally, some works [29 have developed unified frameworks for both image and video understanding, aiming to better handle diverse visual inputs by connecting static and dynamic information.
Though MLLMs unified perceptual tasks by treating them visual- dialogues, making im- pressive strides in both open- [51 and closed- 49 models, they still lag behind traditional methods, which achieve superior performance through domain expertise and task-speciff optimiza- tions. Approaches such as TimeChat (46) and AllSeeing [53 54 achieve high accuracy on specific visual task. However, these strategies often compromise generalization capabilities, restricting their applicability to diverse scenarios. Frameworks like Videochat- TPO (88 achieved strong performance on both visual tasks and conversation by integrating task-specific modules. However, this approach introduces additional complexity and requires specialized components. Enhancing both task-specific performance and robust generalization capabilities remains pivotal challenge in advancing the spatio-temporal reasoning capabilities MLLMs.
Test Time Scaling. Scaling compute at inference has proven to be an effective strategy for enhancing reasoning without increasing model or training scale. Techniques such as Best-of-N (20). guided beam search [65]. and Monte Carlo Tree Search [14] have achieved significant success LLMs.. Concerning MILMs, test-time scaling has yet to be thoroughly explored. Previous work has primarily focused on emulating the approaches used LLMs, such as extending the output length of MLLMs [47] employing search-based methods during testing [60]. Note that these methods usually perform perception only once, barely leveraging the visual input dynamically during the extended reasoning process.
Reinforcement Learning. Reinforcement learning (RL) has proven to transformative approach for enhancing the reasoning capabilities of LLMs. Recent advancements, exemplified by OpenAl- ol (25) and DeepSeek-RI (2I]. have showcased remarkable progress in handling complex tasks, particularly in domains like mathematics and code generation, through the use of verifiable reward mechanisms. Building upon these advancements, researchers have extended RL techniques to MLLMs to enhance their visual reasoning performance. For instance, frameworks such as [80] 39 78 56 79
leverage task-specific reward mechanisms tackle challenges fine-grained perception. However, the application of RL to video understanding remains relatively underexplored. Recent studies, such as VideoChat-Ri 344 and Video-RI [13,, have started to explore RI.-based approaches for spatio-temporal reasoning, paving the way for advancements in this challenging domain.

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Round-1 - Percsption
Rownd-2 Perception
Perception
Clus Thicen
Visual Scaling LLM
Thiskin
faswer Teken
Histry
visaí' "rokmn
Projector
Qponation
Projector
Ristoryy
Projector
Houory
Questicn Token
Encoder
Encoder
Encoder
Oxpartat
jget
0
Loudon State Offices
by
[98, 235, 128
Lests
[94 232,135, 135,72) PRI
vehicles?
Revuseral happens liquid
contcal Baakk
6:46 2333]
[MoC 10:8)
Figure 3: Inference iterative perception.
3 Methodology
Visual test-time scaling (VTTS) aims improve MLLMs' reasoning by introducing iterative vision computation in inference in parametric manner. Inspired by human's coarse-to-fine to-fine processing for cues across visual scales, we make MLLMs simulate this progressive attention via predicting high-confidence
regions iteratively, explicitly building perceptual dependencies between processed visuals. The framework of VTTS is given in Figure 3
Formally, the current learning of MILMs mainly maximizes the conditional likelihood of the paired visual-question-asseee pair (V, W", Wa) under the forward autoregressive factorization:
L
log Po W 2e W9),
(1)
where WT and T stand for the in token and the ground truth length of the answer W respectively. V denotes video image. For the better reasoning, existing solutions add predieted length or candidate number of the answer to cover and capture more complex word dependencies. Here, we extend the dependency modeling in reasoning from language to visuals
K
L. = log
Po i,ke V W W W9), W.E.t. V = 8(V WF),
(2)
k=
where 5(V Wa) a sampling function to generate a new visual V V4+1 with focus from V based on the model's km estimation Wa.
To apply this likelihood computation concerning iterative visual handling either MLLMs' training testing is non-trivial, as it regards accurately narrowing down spatio-temporal scope based on textual predictions. Prior studies uncover the limitation of predicting spatio- naatio-temporal location, size, and shape using classical supervised text-based autoregressive formulation [74] [54]. nevertheless typical MLLMs' training corpus (visual-question-nng anneer pairs) mostly contain no specific numeric seatio-temporal descriptions. In this regard, present reinforcement learning based method to make this learning and inference tractable, as well as an accompanying dataset for its training.
3.1 Learning Iterative Perception with Reinforcement Fine-Tuning
We treat the visual sampling process 8(VA Wa) in Eqn. 2 as standard region of interest (ROI) selection. This demands ML.LM's output to contain ROI's coordinates. To incorporate this explicit spatial prediction into MLLM's learning, employ reinforcement learning (RL) framework

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based on Generalized Reward Policy Optimization [21] (GRPO). This approach enables the model to progressively refine its understanding of spatio-temporal contexts through repeated dynamic interactions with multimodal inputs. This learning is driven by both visual and textual supervisions as well as format requirements for their compatibility as:
R())= Àu= Pelue W
Welue:
clue:
Wa clue
+
A1
Tans
W
X2 Pime W W
(3)
Spatio-Temporal Awareness
Answer Supervision
Output Format Supervision
where Ay. Å₁ and A2 are balancing coefficients, and I'lmee Tanss and Finmt are rewards for RL. Wa
clue and
clue stand for the ground truth and the predicted spatio-temporal coordinates tuples, respectively. Specifically, relue verifies the spatio-temporal alignment between two spatio-temporal segments, and we exploit intersection over union (IoU) for its implementation. Tans and Fimit check the answer accuracy and format, respectively. If the generated answer or its format matches the ground truth then the reward scores 1, otherwise O. For Time, it uses regular expressions to verify adherence.
We
We also make tries optimize Eqn. 2 with standard supervised fine-tuning, letting MLLMs generate textual answers as well as numerical regional descriptions for focus. Experiments in Sec show this hardly benefits finding regions of interest and improving subsequent reasoning.
Inference with Test-Time Scaling, Our inference leverages an Iterative Perception (ITP) strategy, which enables the model progressively refine its understanding of spatio-temporal contexts through multiple perception cycles. This approach is particularly effective for handling complex multimodal inputs, where salient information is often embedded within specific regions or time segments. By iteratively focusing on these key areas while maintaining global context, the model achieves more nuanced and accurate interpretation of the input data.
The first step in the ITP strategy involves standard processing akin to that used in conventional MLLMs. For video inputs, this entails uniform frame sampling across the entire sequence to capture a broad overview of the temporal dynamics. For image inputs, the model processes the full image to establish an initial understanding of the spatial layout. During this stage, the model generates preliminary reasoning steps, identifies relevant videos or bounding boxes in images), and outputs an initial answer. While this initial pass provides a baseline understanding. it may lack sufficient granularity to address complex queries accurately.
clues (e.g., specific time intervals in
Subsequent iterations implement differential processing strategy, which dynamically reallocates computational resources to focus on the identified spatio-temporal cues while preserving the broader context. For videos, this involves dense frame sampling within the identified temporal segments where critical events or actions are likely to occur while applying sparse sampling elsewhere to reduce redundancy and computational overhead. For images, the model concurrently processes both the full image and cropped versions of the identified spatial regions. This dual-input approach ensures that the model retains a holistic view of the scene while zooming in on specific areas of interest. Simultancously, the model leverages the think process from the preceding step as a linguistic prior to inform and refine its subsequent reasoning.
This hierarchical approach not only improves the model's ability to extract meaningful features but also enhances its capacity to reason about complex interactions between objects events.
3.2 VTTS 80k Data Generation
To enable the learning of Iterative Perception with RFT, we build VTTS-80K dataset from existing ones. This dataset aims to enhance spatio-temporal awareness, enabling models to locate relevant vi- sual clues and perform reasoning based on these observations, thereby facilitating itcrative perceptual refinement. Our annotations provide rich supervision through three parts: QA pairs, relevant spatio- temporal segments (critical regions or time intervals for the question), and the thought process to reach the answer. As detailed in Fig VTTS-80K comprises three sections targeting different skills: VTTS-QA( (VVieeQQ and Image Reasoning) and for reasoning ability, VTTS-TEMM for temporal understanding, and VTTS-SPATIAL for spatial awareness. Additional dataset statistics are provided in the appendix.
The construction of VTTS follows a systematic pipeline aimed at producing high-quality reasoning annotations. The initial step involves M-bse verification: DeepSeek assesses whether the QA pairs consistent with the corresponding image/video caption. Simultancously, it evaluates
5

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QA
Temporal Glue
Why
keep gesturing with
(A) leaching Iuly. (8) pesturing while balking cycling (D) deseribing something, ( danciin with baby.
Gluue bx [116.0-20.5] (2,.2,,,,, (23.7-27.2)
=Thioktngg:: le uses his right hand guide the buby's actions (es.. renching: toword the bobty's
Charitla
the whisl]. The contest involves a kitchen tool (weissk] and interactive suggesting he demonstrating encouraging the baby use
hand, adjusting their
VTTS
sinswers Teaching the
Data
Bercaagk ago
VOA source w/ Glae
Filaning
Captining
A
Vertty
Think
oeptae
Spatial
Image Beasoning
(a) Pipeline of Data Generation
bb) Data Distribution of VTTS RL Training
Figure 4: VTTS-80K dataset generation pipeline and data distribution.
the relevance of potential spatio-tempont cues by comparing their descriptions (e.g.. captions of specific segments) against the overall image/video context and the QA pair. This dual verification filters out inconsistent QA pairs and irrelevant cues, resulting in refined set called Filtered QA (containing validated QA pairs and associated relevant cues). Next, the Filtered QA is fed into DeepSeek for Reasoning, which generates step-by-step CoT explanations. This step breaks down the reasoning needed to arrive at the answer, explicitly linking steps to relevant multimodal information (like object interactions temporal changes) within the image/video. Finally, a VLM ranks these generated QA-CoT pairs based on their clarity, relevance, and consistency, producing the final VTTS-80K dataset.
4 Experiments
Implementation. We apply VTTS to the latest Qwen series like Qwen2.5-VL-7B and Qwen2.5- VL-3B using VTTS-80K dataset with reinforcement (unnn Training performed with learning rate of 2e-6 and batch size of 16. The reward function comprise three components: format reward, clue reward (quantified by the IoU of visual clues), and answer reward. At inference time, the default number of iterative perception (TTP) iterations is set to 3.
Benchmarks. To comprehensively evaluate the general capabilities of our models, we conduct experiments across a diverse suite of benchmarks. For video perception, we report results on MVBench (32] and PerceptionTest [44), which assesses fine-grained temporal understanding, in- cluding action types, sequences, and movement directions. General video understanding assessed using VideoMME [16), consisting of videos of short, medium, and long durations. In the long-video domain, we evaluate performance on MLVU 8I. LVBench [52], and Long VideoBench [61]. Video- based knowledge modeling is quantified using the VideoMMMU 23 benchmark. To evaluate the model's 's visual-spatial intelligence, we adopt the VSIBench (69) Furthermore, we specifically assess spatio-temporal grounding capahilities using range of detection and temporal grounding datasets.
4.1 General Understanding Evaluation
Multimodal Video Understanding. To comprehensively evaluate the video understanding capabil- ities of our model, we conduct assessments on multiple video benchmarks, comparing performance under both single-perception and multi-perception settings. The 7B and 3B models of VideoChat-RII5 achieve accuracies of 70.6% and 68. %6, respectively, on MVbench (32. outperforming the baseline by 2.2% and 1.6%. As shown in Tab on VideoMME our 7B and 3B models surpass Qwen2.5- VL by 2% and 2.7%, respectively, with iterative perception outperforming single-step perception by 1.9% and 1.2%. benchmarks focused on long video understanding, such as Long VideoBench [61), L.bench [52] and MLVU 881. our 7B model achieves gains of 6.6%, 0.7%, and 1.1%, respectively, without using additional long-vides training data. On the video knowledge modeling benchmark VidcoMMMU 23), our 7B and 3B models outperform the baseline by 2.2% and 1.5%, respectively.

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Table 1: Performance on video question-answering benchmarks. The suffix "-8" denotes single- step perception, while "-m" indicates three iterations of iterative perception.
Mudd
Size
MVReech
Perception
LangVidoRRecch
VideoMMMNI
MENU KT
VidroMME
I.Bench [2]
Averae dusttoo (500)
16
507
651
1010
4101
Proprictory Models
GPT4-V
8
43.7
39.1
40.2
59.9
64.6
66.7
64.6
71,9
30.8
Gemini
60.5
33.1
OpenSouree Mt/My
8
28
68.8
46.0
61.4
$1.9
VideoCChae Flash 13]
2B
70.0
70.5
58.3
65.7
57.0
42.9
QWen2.5-V1
$4.2
68.2
VideooChaf-R155338
68.2
67.6
58.4
42.7
66.4
45.2
Wideoohatt881.533B...
3B
6866(+1.6)
68.3(41.4)
59.6(45.4)
43.0r+1.5)
09.7(+1.3)
64.2(42.7)
46.6(43.3)
VisianOneVison 10
7B
56.7
57.1
$6.3
34.4
64.7
58.2
La2enVL2.5 8
72.0
51.5
60.0
68.9
38.4
QWen2.5
FidesChattRR1.-770
7B
70.6
713
6144
49.6
70.1
65.2
46.3
VideoCChat-BB R1.57B-MM
7B
70.6(42.7)
72:3(+1.8)
62. 62.6(-466)
51 4(447)
70.9(40.7)
67. ((2.0)))
48.4(+3.1)
Moreover, iterative perception consistently surpasses single-step perception across these evaluations. These validate the effectiveness of ITP and show the initial scaling effects.
Grounded VideoQA. Grounded video QA task requires the model to not only provide accurate answers regarding videos, but also identify the specific temporal segments that support those answers. This task highlights the need for joint reasoning between semantic understanding and temporal context, leading to accurate and interpretable predictions. We evaluate our model on two grounded video QA benchmarks: NexIGQA 663 and ReXTime 77. In NexIGQA (63. Acc@ IoP@0..5 denotes the proportion of questions where ToP is bigger than 0.5 and the model correctly answers the multiple-choice question, while ACA reflects the aceuracy on questions where both IoP is bigger than 0.5 and the QA is correct. VideoChat- R1.5 demonstrates non-trivial increase over the baseline in both QA and IoP metrics across both 3B and 7B parameter scales. These gains highlight the effectiveness of our reinforcement learning in enhancing fine-grained temporal reasoning and grounding capabilities. Furthermore, on ReXTime M7. VideoChat-R1.5-77 surpasses GPT-4o 2 across all metrics, showcasing the model's strong QA capabilities and grounding performance. The superior performance on ReXTime underscores the model's s ability to handle complex and diverse video content while maintaining high accuracy in both QA and grounding tasks.
Table 2: Performance on NextGQA 63
Table 3: Performance on RexTime 77].
Model
| mioP
3PP0
IsP80.5
Midel
Ace Acc Poodleo
mitti
1alle0.33
1atrent
VIOLETVV2
1k
$4.9
12.3
23.6
25.1
23.3
UnIVTG 35
25.2
41.4
26,9
SeViLA
72.5
16.6
29.5
34.7
22,9
CG DETK
23.9
31.3
16.7
RapgRepe
59.6
VidooClat-IPU
57.4
32.2
Chanle--pus
28.4
35.7
25.0
Vileothat-T
77.7
47.5
30.8
GP 0
36.3
45.3
OWen225 VL 3B
70.3
35.0
22.1
OWenZ.5
7.4
7.5
ValeoChatt-RL33 R1
76.5
48.
62.3
71.2
63.9
VoleoChat-RI..
21.3
30.8
1WWrr? 5-V1-78
72.7
42.9
54.0
60.6
$4.5
QWin2.5-V1..
20.9
20.6
38.8
25.2
Videot 21111-7
79.9
61.9
74.9
K2.7
79.6
VideoChate R1.5-BB
74.8
38.1
45.8
61.8
46.4
4.2 Spatial-Tempon Tasks
Table 4: Fine-tuning results of tem- poral grounding.
Table 5: Zero-shot results of temporal grounding.
OVHigghLight (8
ActivityNet (
Method
mloU
Charades-TTA [1]
kW0.3
R
Method
Videnchat TPO
68
mloU
40.7
R80.3
56.9
Re0.5
40.1
R80.7
22.0
mloU
27.6
R@0.3
42.6
R@0.5
26.3
13.0
Videe2 13
TimeSuite 74
57.2
27.0
TimeSuite
79.4
V1-
21.3
34.1
23.0
13.9
10.9
17.9
10.6
4.2
QWen2.5.V V-L.BB
50.8
38.8
65.3
74.9
38.6
20.8
30.9
VideoChat- R1.553
33.9
46.8
34.4
21.9
23.9
35.0
18.9
OWea2.5 VL-7B
43.6
76.1
42.9
26.2
QWen2.5VVL-78
30.6
42.9
32.0
20.1
19.1
25.5
13.4
6.1
VideoChat- RL.5-7B
60.6
82.8
71.6
18.3
VideoChat-RI..5
52.7
71.4
55.8
38.4
35.5
52.4
32,3
16.8
Temporal grounding refers to the task of localizing target temporal segments in video that correspond to a given natural language query. This task demands precise alignment between linguistic semantics and some clip of a video. As shown in Table 4 and 5 VideoChat- R1.5 model demonstrates compelling temporal grounding performance in both zero-shot and fine- tuned settings. In fine-tuned settings, both VideoChat-Rl.5-33 and SeaeoCChat-R..--1- model
Temporal Grounding.
7

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variants significantly outperform all baselines, achieving SOTA performance among existing MLLMs. Notably, on the &@0.5 metric. our 7B model achieves score of 71.6, surpassing specialized expert models based on Intern Video2-6B [58] which are explicitly designed for temporal grounding tasks. Furthermore, in the zero-shot setting, our model exhibits substantial improvements over previous baselines, demonstrating remarkable generalization capability without task-specific training. This zero-shot performance suggests the potential for deploying our model in real-world applications with limited labeled data. This zero-shot performance suggests that our model possesses strong temporal awareness, enabling it to accurately localize relevant time segments through iterative perception.
Table 6: Results of spatial grounding.
Table 7: Results of object tracking.
We use 8-frame inputs for both train- ing and evaluation.
RefCOCo 22]
ReCCOCO+
t2
RefCCCOz [41]
Method
val
lesetA
lestB
val
lesctA
testB
val
test
MIETR
86.8
89.6
81.4
79.5
84.1
70.6
81.6
80.9
GoT 24
Vidoochat- TPO
68
85.9
90.8
81.3
80.2
85.1
71.7
79.4
81.3
Method
AO
SR600.
SR@0075
Grounding DINO-L
93.2
82.7
75.9
96.2
87.0
SiamiC
6.8
Qwen2.5-V1.388
89.1
91.7
84.0
82.4
88.0
74.1
88,2
85.7
OWen2.5-VLL VL-3B
10.7
1.5
0
VideoChattR115-388
90.3
92.8
85.3
83.4
88.9
75.8
86.2
86.3
VideoChat-R1.5s
45.9
40.3
7.6
Qwan2.5-VIL-78
90.0
92.5
85.4
84.2
89.1
64.9
87.2
87.2
QWen2.5-V1.-2
12.6
1.1
0
VideoChatt-R15--78
91.1
93.6
86.7
84.7
90.3
77.8
87.6
87.7
VideoChut-RR.550
52.2
45,7
9.8
Spatial Grounding. To evaluate the model's fine-grained localization capability, we experiment on the spatial grounding task, where models follow textual descriptions to give the corresponding bounding boxes on RefCoCO RefCOCO+ (72). RefCOCOg[4]] As shown in Table 6 we compare our VideoChat-RI. model with both MLLMs and specialized expert models such as G-DINO 37 Remarkably, our VideoChat-R1.5 model achieves performance improvements over the strong QwenVL baseline, despite being trained on only 16k in-domain samples. Moreover, outperforms several expert models that are fine-tuned on much larger datasets. This highlights the model's 's strong spatial awareness, providing a solid foundation for iterative perception and complex visual reasoning tasks.
Tracking. In tracking, the model receives the object coordinates from the first frame of a video and is expected to output the corresponding coor- dinates for the remaining frames. Due the in- put length limitations of MILMs, we uniformly sample 8 frames from each video and gener- ate predictions for all of them. We evaluate our model on the widely used tracking bench- mark GOT-10k (24). As shown in Table 7 our model demonstrates significant improvements over the baseline in terms of its ability to track objects through continuous motion. This ad- vancement reflects a notable leap in the model's spatio-temporal localization capabilities, which is highly beneficial for fine-grained perception in video understanding.
VibooMME
LangVttaetleech
BooieoBooucaa Scoru
Ochibeech
04-
02
855
850
Porcagtion Num
Figure 5: Ablation on perception times.
4.3 Ablation
In this section, we analyze the effectiveness of iterative perception, validate the contributions of different components in our VTTS-80K dataset, and demonstrate the superiority of Progressive Reinforcement Learning over SFT for enhancing spatio- ppatio-temporal understanding and reasoning.
Scaling Law with Perception. To validate the effectiveness of the iterative perception approach, we conducted ablation experiments on long video understanding and image perception datasets, focusing on how model performance scales with the number of perception iterations. As shown in F the model's performance exhibits clear scaling trend as the number of perception steps increases. Specifically, on VideoMME [5], the score improves from 65.2 to 67.9, demonstrating steady gain in multimodal reasoning capabilities. Similarly, on Long Video the performance rises
8

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from 61.4 to 62.9, highlighting the model's enhanced ability to handle complex, temporally extended content. Additionally, on OCRBench [ the score increases from 856 to 866, reflecting improved fine-grained visual perception. These consistent improvements across diverse tasks underscore a perception scaling law where iterative refinement of spatio-temporal understanding leads to measurable performance gains. The results suggest that, with each additional perception iteration, the model becomes increasingly adept at capturing subtle visual cues, leveraging insights from prior steps, and integrating these into more comprehensive and accurate representation of the input.
Table 8: Ablation studies on training strategy and data.
Model
Training strateg
Groundding
T-Gourding EMC V.MOQ)
Training data
Charade-STA ubol.
val
ReCOCO
tex
Nestgun AccOIGQAA
silee
OCRBeach Store
VSIBknch Ang
VideoMME
Ang
Triisiy Datt AMotioe
Qwer2.5 VL-7B
43.6
90.0
92.5
X5.4
70.4
20.9
856
29.2
66.]
Qwex2. NLB7BB
RFT
59.1
59.7
92.6
X5.1
79.1
61.0
851
39.7
06.9
Qwex255-1177 5-1--BB
2
43.1
91.0
16.0
56.5
72.4
21.3
853
39.8
05.3
Quen7.5 5-V1-78
00.0
91
86.8
75.6
58.0
40.3
64.8
Qwon7.5-4V -719
42.6
89.9
85.9
76.1
9
78.1
Stattgg
Qwen2-5-V1
Seapoaal-Spatial Data. We conducted ablation experiments on the training data to validate the effectiveness of each component of our VTTS-80K dataset. As shown in Table g] observed that the Spatial Grounding data and Temporal Grounding data significantly enhance performance on spatial fine-graine fine-grained tasks and temporal fine-grained tasks, respectively. Additionally, the chat data prove beneficial for long video understanding, image perception, and spatial reasoning tasks.
SFT vs. RL. To rigorously validate our training paradigm, we contrast Progressive Reinforcement Learning with canonical SFT. As summarized in Table g reinforcement learning yields double- digit gains temporally- and spatially- underperforms but actually erodes the backbone model's original competence. This degradation underscores well-known pathology of SFT: when the optimization objective is reduced to next- token likelihood, the model overfits to the annotation distribution and forgets useful pre-training priors. Reinforcement learning, by contrast, delivers uniform improvements and zero-shot out-of-domain splits, demonstrating that iterative reward shaping preserves and systematically enhances- -aaltimgraalllla perception capabilities. The core limitation of SFT is its reliance on cross-entropy over discretized bins. For inherently continuous durations, object counts-this this produces supervision signal that is oblivious to metric structure: any deviation from the exact ground-truth bin is penalized equally, regardless of numerical proximity. GRPO replaces this categorical loss with an LI penalty directly defined on the continuous quantity space. The resulting reward landscape is fine-grained and metric-aware: infinitesimal deviations incur infinitesimal penalties, while larger errors are penalized proportionally. Consequently, the policy learns to regress implicit numerical clues with sub-second (or sub-pixel) precision, prerequisite for downstream tasks that depend on accurate temporal or quantitative reasoning.
localization benchmarks, whereas SFT not only
both in-domain
5 Conclusions
In this paper, we present VTTS, novel test-time scaling framework for MILMS that enhances reasoning through iterative visual perception. Inspired by human-like progressive attention, VTTS dynamically refines focus on key spatio-temporal regions, improving interpretability and reasoning accuracy. By introducing the VTTS-80K dataset, we enable MLLMs to learn iterative perceptual refinement, supported by reinforcement learning techniques tailored for multimodal contexts.
Extensive experiments across more than 15 benchmarks demonstrate that VTTS notably outperforms strong baselines in video conversation, image reasoning, and spatio-tempora perception tasks, highlighting the effectiveness of iterative perception in enhancing multimodal reasoning.
Limitations. Currently, VTTS has been validated solely the domain of visual processing, and its inference process is not fully optimized to leverage visual-language priors or existing caching techniques during iterative computation. Extending VTTS to additional modalities and further optimizing its engincering implementation are left as directions for future work.

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Acknowledgements
This work is supported by the National Key R&D Program of China (Grant No. 20222D0160102, and the Jiangsu Frontier Technology Research and Development Program (Grant No. BF2024076).
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Appendix
In the Appendix, provide additional details to support the main content of the paper. First, we expand on the specifies of the VTTS-80K dataset, including its composition and annotation types. Next, we detail the training and inference parameters used for the model. In the Case Study section, we present multiple cases of model reasoning, showcasing both successful examples and am analysis of failure cases to provide comprchensive understanding of the model's performance. Finally, include a discussion of the paper's limitations, broader socictal impacts, and safeguards to address potential risks, ensuring responsible and ethical approach the development and deployment of the proposed method.
6 Trade-off Between Performance and Test Time.
Table 9; Trade-off between VTTS performance and inference time
Image (MMVet)
Video (VideoMME)
Model
Inference Type
Infer Time Performance
Infer Time Performance
LLaVA-CoT 66
CoT
1.415
60.3
VideoChat-RI RI
CoT
13.535
62.4
Qwen2.5V1.-22 (Baseline)
Direct Output
1.085
64.9
11,265
64,44
Qwen2.5VI..7h (Baseline)
CoT
1.465
63.2
14.905
61.3
VideoChat-RII -..-5
Single Infer Multi Infer
1.355
67.2
13.215
65.2
VideoChat-R1.5--1
2.975
68.3
30.095
67.1
As illustrated in Table 9 the proposed VideoChat-R1.5 model achieves a favorable trade-off between computational efficiency and performance when compared both the direct output baseline and other CoT reasoning methods. Specifically, VideoChat-R 1a.coCCha-R115775 1.5-7B (Single Infer) attains an average inference time of 1.35 seconds on the MMVet benchmark, which is only marginally higher than the 1.08 seconds of the direct output baseline. This inference latency is comparable to other CoT-based approaches, such as VL-CCT (1.41s) and MM-Eureka (1.39s), while significantly surpassing them in terms of performance (67.2%). Furthermore, although the multi-inference strategy introduces additional computational overhead, it yields a performance gain of 1.1%. On the Vidco.MM dataset, VideoChat-RI.. R1.5-7B (Single Infer) requires 13.21 seconds for inference, slightly exceeding the baseline's s 11,26 seconds, yet achieves a notably higher accuracy of 65.2%, outperforming most existing long-chain reasoning methods. These results demonstrate that VideoChat-RR R1.5 not only maintains competitive inference efficiency but also delivers superior performance, particularly in complex multimodal reasoning tasks.
7 Details of VTTS-80K
7.1 Dataset Annotation
As shown in Tab. 10 the VTTS-SOK dataset composed of four key components: VideoQA. Temporal Clue, Image Reasoning, and Spatial Clue, which are carefully curated from existing datasets to support iterative perception. The dataset includes five types of annotations: question, option, think, clue, and answer. Among these, the clue annotation plays critical role, as it encapsulates the essential information required to answer the question. This annotation further divided into two subcategories: temporal clue, which captures time-related information, and spatial clue, which focuses location or spatial relationships within the input data.
Notably, not all datasets contain every type of annotation. To address this, our training process dynamically selects the corresponding reward function based on the annotations available in each dataset. This ensures that the model can effectively leverage the diverse information provided by different subsets of the dataset while maintaining robustness across tasks with varying levels of annotation completeness. By aligning the reward mechanism with the available annotations, our approach maximizes the utility of the data and enables efficient learning even when certain types of annotations are missing.
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Table 10: VTTS-80K data distribution and examples.
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7.2 Dataset Statistics
The VTTS-80K dataset is designed to support diverse multimodal reasoning tasks, with varying annotation types across its data sources. In the dataset, all entries include Question and Think annotations, which form the foundational components for reasoning and iterative perception. the specific annotation types differ depending the source of the data. peerativeperceptioon However,
Data from three sources- GOT (24, Charades 11), and RefCOCO the grounding or tracking type. In these datasets, the task involves directly identifying the relevant clue (cither temporal or spatial) within the input, and as such, they only provide clue annotations without accompanying QA pairs. On the other hand, the remaining datasets follow a QA format, where the primary focus is on answering questions based on visual and textual inputs. Among these QA datasets, NexIGQA [63 and VisualCoT [47 provide both QA annotations and clue annotations enabling joint reasoning over questions and supporting evidence. In contrast, the rest of the datasets contain only QA annotations, lacking explicit clue information.
are primarily of
In total, the VTTS-80K dataset comprises 15K temporal clues, 30K spatial clues 80K Think annotations and 50K QA pairs. This diverse composition ensures that the dataset supports wide range of tasks, from fine-grained spatiotemporal localization to complex reasoning over multimodal inputs. By incorporating data with varying levels of annotation richness, VTTS-80K not only
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facilitates training models to handle incomplete or heterogeneous data but also reflects real-world scenarios where annotation availability may vary significantly.
8 Training Details
The VTTS RL training is configured with the following parameters. We use an AdamW optimizer with a learning rate of 2 x 10 zero weight decay, and a linear learning rate schedule without warmup. The total batch size is set to 16. For video inputs, the number of frames ranges from 4 to 768 at a fixed frame rate of 2 FPS, with video resolution constrained by a maximum pixel count of 768 x 28 28 and minimum of 128 28 28. In terms of image settings, the image factor is set to 28, with a maximum scaling ratio of 200. The image resolution follows the same pixel constraints as the video input, ranging from a minimum x 28 x 28 to a maximum of 768 x 28 x 28.
Case Study
As shown in Fig- 6 and 7 our Iterative Perception (ITP) approach demonstrates strong performance on both video QA and image reasoning tasks. In the two cases presented, the model initially fails to provide the correct answer during the first round of reasoning. However, through iterative perception, the model successfully identifies the critical clues required to answer the questions. By leveraging the multi-step "think" process, which integrates insights from multiple perception iterations, the model ultimately arrives at the correct answers. This highlights the effectiveness of iterative refinement in enhancing the model's 's ability to locate relevant information and reason more accurately over complex multimodal inputs.
and 12 our model demonstrates strong performance across a variety of visual tasks, including grounded video QA, temporal grounding, grounded image QA, spatial grounding. and tracking. In these fine-grained spatiotemporal perception tasks, the model not only accurately provides the perceived temporal and spatial results but also generates well-reasoned explanations for its decisions. This dual capability highlights the model's robust reasoning and spatiotemporal perception abilities, showcasing its proficiency in handling complex, multimodal inputs while maintaining interpretability and precision.
As shown in Fig B
Failure Cases. Fig. and 4 presents two failure cases of the model, corresponding to scenarios where the model either consistently provides incorrect answers across multiple perception steps or initially answers incorrectly but later corrects itself. Through analysis of these cases, we observe that the model's failures are primarily due inaccurate localization of critical clues during the identification process. For knowledge-based questions, the model struggles to pinpoint precise supporting evidence, which can lead to errors in reasoning. Additionally, global contextual information while focusing on localized clues, further contributing to its inability to arrive at the correct answer. These findings underscore the challenges of balancing fine-grained localization with holistic understanding in complex multimodal tasks.
model may lose some
10 Discussions
Limitations. Despite the effectiveness of our proposed method, several limitations remain that warrant further investigation. First, the iterative nature of our approach may lead to increased inference time, each additional perception step requires further computation. While this enhances performance, it could pose challenges for real-time applications or scenarios with strict latency constraints. Second, the "thinking" process inherent in iterative perception introduces potential safety concerns. Specifically, intermediate reasoning steps may generate unintended or inappropriate content, which could propagate into the final output. Such issues highlight the need for careful design and safeguards to ensure the reliability and safety of the model in practical deployments.
Broader Impacts. Our VTTS method for MILMs introduces significant advancements in mul- timodal reasoning but also raises important societal considerations, as outlined under the NeurIPS Code of Ethics.
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Video:
According the video, what the reason why the interviewed man frequently observes snow?
Question:
Option: (A) Because he lives in the mountains at an altitude of about
3000 meters.
(B). Because he lives in Florida.
(C) Because he likes snow.
(D) Not mentioned in the video.
Qwen-ITP Clue:
68.4s
197.5s
Qwen-ITP Thinking: The video does not provide specific information about the reasons for the interviewed man's frequent observation of snow. It only mentions that he is a photographer and provides footage of snowy scenes. Therefore, the correct answer cannot be determined from the given video content.
Qwen-ITP Answer: (D). mentioned the video. (x)
New
Video
Frames:
Question:
Option:
History:
Qwen-ITP Thinking: the second half the video, person appears on the left and skiing scene appears the right. It can be judged that the person is now at a high altitude.
Qwen-ITP Answer: (A) Because he lives the mountains altitude of about 3000 meters.
Figure 6: Iteration Perception For Video.
VTTS significantly enhances multimodal reasoning by improving iterative visual perception, enabling Al systems to interact with the world more accurately and in a human-like manner. This advancement translates into superior performance across diverse tasks, including video conversation, image rea- soning, and spatio-temporal perception, offering benefits in accessibility (e.g.. assistive technologics for visually impaired individuals), education (c.g., intelligent tools for complex subjects), safety (e.g., surveillance and autonomous systems), and content creation (c.g., video summarization and moderation). Furthermore, by introducing novel test-time scaling approach and the VTTS-8OK dataset, this work lays a strong foundation for future research, paving the way for the development of adaptive and efficient multimodal AI systems.
VTTS also introduces several potential societal risks, reflecting broader challenges associated with advanced AI systems. A significant concern is the heightened potential for misinformation and deepfakes, as the enhanced ability to generate and manipulate visual and textual content could be exploited to create deceptive or harmful media, such disinformation campaigns fabricated
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evidence. Additionally, the risk of hallucination-wwe where models generate plausible but incorrect or unsupported outputs-posse further challenges, potentially leading misleading conclusions in critical applications like news reporting, scientific analysis, or legal contexts. Privacy concerns also arise due the model's capacity to process detailed visual information, which could be misused in surveillance, unauthorized data collection, or intrusive monitoring scenarios. Moreover, biases inherent training data may be amplified by VTTS's improved reasoning capabilities, resulting in unfair or discriminatory outcomes in real-world applications, such as biased decision-making in hiring, law enforcement, or healthcare.
Safeguards. To address these risks, adhere to the NeurIPS Code of Ethics, emphasizing responsible research practices and acknowledging the need to consider and mitigate potential harms. Although no specific technical safeguards against deepfake generation outlined for VTTS, the broader AI community' s efforts in detection tools and responsible release strategies provide relevant mitigation pathways. Privacy concerns addressed through commitment to ethical guidelines and techniques in future deployments. To tackle bias and fairness
the integration issues, rigorous dataset auditing and bias mitigation practices are essential.
27

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Which the following is NOT true regarding the divided Germany shown on the map?
Question:
Option: (A): The capital of west Germany was located in East Germany
Image:
(B): West Germany experienced postwar economic miracle.
(c): Both East Germany and West Germany were allowed to rearm
(D): France had occupied West Germany.
Qwen-ITP Clue: [156, 209, 264, 437]
Qwen-ITP Thinking::: A. The capital of West Germany was located in East Germany This is not true. Bonn, the capital of West Germany, clearly shown on the map as being west of the division line. \nB. West Germany experienced a postwar economic miracle This is true. The economic recovery West Germany after World War II is well-documented. Anc. Both East Germany and West Germany were allowed to rearm This is not true. The map shows East Germany part of the Soviet zone, which would have been under Soviet control and not allowed to rearm independently. \nD. France had occupied west Germany This is not true. The map does show any French occupation of West Germany; it is clearly marked as West Germany.
Qwen-ITP Answer: (C) Both East Germany and West Germany were allowed to rearm(x)
Question:
Image:
WEST
CORONOALO
Option:
History:
conaxaa
Qwen-ITP Thinking: (A): The capital of West Germany was located East Germany. This is false. The capital of West Germany was Bonn (marked on the map within West Germany), while East Germany's s capital was East Berlin. (B) West Germany experienced postwar economic miracle True. West Germany's s rapid economic recovery in the 1950s (the wirtschaftswunder is well-documented. (C): Both East Germany and West Germany were allowed to rearm True. West Germany joined NATO and rearmed under U.S. oversight, while East Germany formed the National People's Army under Soviet influence. (D) France had occupied West GermanyTrue. France occupied the southwestern zone of West Germany as part of the Allied post- WWII occupation.
Qwen-ITP Answer: (A): The capital of West Germany was located in East Germany(v)
Figure Iteration Perception For Image.
28

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Video:
Question: Why did the man bend and lower his head the bed?
Option:
(A) laughing
(B) pick the girl up
(C) pick
toy at the side
(D) microphone too short
(E) put phone
Qwen-ITP Clue:
3.8s
8.2s
The man bends and lowers his head towards the bed he is interacting with the girl who is jumping on it. This action suggests that he might trying to engage with her or possibly avoid getting hit by her. The context of the video shows a playful interaction between the man and the girl.
Qwen-ITP Thinking::
Qwen-ITP Answer: (B) to pick the girl up
Figure 8: Example For Grounded VideoQA.
Video:
Question: accurately pinpoint the event "the dartboard is shown with three darts in it." in the video, determine the precise time period of the event.
Qwen-ITP Clue:
9s
19s
Qwen-ITP Thinking: The event \"The dartboard is shown with three darts in it\" " occurs after the man throws darts. The camera then focuses on dartboard, showing the three darts embedded in it. This suggests that the event happens sometime after the man's actions.
Figure 9: Example For Temporal Grounding.
29

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Question: What is the person in the image using to guide the animal?",
Option: (A):The person is using their hands to guide the animal.
Image:
(B):The person is using a wooden stick to guide the animal.
:C): person is using a long piece of rope to guide the animal. (D): person is using a short piece of string to guide the animal.
Qwen-ITP Clue: [251, 60, 449, 256]
Owen-ITPThinkiinge The person the image guiding the animal using a long piece of rope. The pig is tied with a rope around its neck, and the individual is holding the other end of the rope, which appears to be several feet long, allowing them to control the pig's movement without direct physical contact.
Qwen-ITP Answer: (C) The person is using long piece of rope guide the animal.
Figure 10: Example For Grounded ImageQA.
Question: Please detect woman left pink shirt.
Image:
Qwen-ITP Clue: [0, 259, 70, 480]
Ewen-ITPPhinkiing The individuals visible from left right include:A woman with dark hair wearing a blue jacket (over black shirt).A woman with light brown hair wearing white shirt. A bald man black shirt. A standing woman with blonde hair wearing a white shirt.
Figure 11: Example For Detection.
30

---

Video:
Question: To accurately pinpoint the event "the dartboard is shown with three darts in it." " in the video, determine the precise time period of the event.
Qwen-ITP Clue:
Qwen-ITP Thinking: The video depicts the process of four people on horseback chasing each other. The camera is shaking. The person riding at the end overtakes one of the people and swings polo stick play polo.
Figure 12: Example For Tracking.
31

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Video:
Question: According to the information presented in the video, what was the exact number of individuals present in the store the time?
Option: (A).
(B). 4
(C). 5
(D). 6
Qwen-ITP Clue: Os
48s
Qwen-ITP Thinking: The video shows three individuals: one man wearing cowboy hat and glasses, another man in a white shirt, and a third person who is not clearly visible but appears to be customer man in the cowboy hat is seen taking off his glasses and pointing a gun, while the other two individuals are seen running away. Therefore, there were three individuals present in the store at the time.
employee. The
Qwen-ITP Answer: (A). 3. (x)
New
Video
Frames:
Question:
Option:...
History:
Qwen-ITPPThinking the beginning of the video, there two individuals visible: one wearing cowboy hat and another standing behind the counter. the video progresses, more people enter the scene, including man in white shirt who appears to be involved in an altercation. The total count individuals the store at any given moment not clear from the provided frames alone, but it seems to exceed four due to the presence of multiple people moving around. However, without precise timestamps for each individual's entry and exit, it's challenging to provide exact number. Given the options, the closest estimate would be B, as it suggests number greater than four.
Qwen-ITP Answer: (B) (x)
Figure 13: Failure Case For Video Iteration Perception
32

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Question: A similar cartoon could be made about each of the following countries prior to 1890 EXCEPT?
Image:
Option: (A): Netherlands.
(B) France.
(C): : Belgium.
(D): Germany.
TThe Devilfah in Rgaptian Watars"" 1882
Qwen-ITP Clue: [207, 426, 310, 622]
Owen-ITPThinkkngg The image depicts a cartoon from 1882 that portrays England as a powerful entity with tentacles extending across various regions, symbolizing its influence and control over these areas. The question asks which country among the given options (Netherlands, France, Belgium, Germany) would not have been depicted similarly due to the historical context before 1890. Given the time frame, it is unlikely that any of these countries would have been portrayed in such manner because they were not as dominant or influential as England at that time. However, the question specifically asks for exception, implying that one of these countries might have been depicted differently.
Qwen-ITP Answer: (D) Germany (V)
Question:
Image:
Option:
History:
Qwen-ITP Thinking: The image is political cartoon depicting England (symbolized by figure with a top hat and "ENGLAND" on his belt) with tentacles extending various colonies, illustrating British imperialism. The question asks which country prior to 1890 could not have similar cartoon made about it, given their colonial activities. While Belgium established the Congo Free State in 1885 (just before 1890), its colonial presence was limited primarily to the Congo. Unlike the other options, Belgium lacked the multiple colonies required to justify cartoon with numerous "tentacles" akin to the one shown.
Qwen-ITP Answer: (C): Belgium(x)
Figure 14: Failure Case For Image Iteration Perception
33


Reviews:
[{"review_id": "vhTryER1Wh", "reviewer": "Reviewer_T9BA", "summary": "This paper introduces Visual Test-Time Scaling (VTTS), a framework that enhances MLLMs through iterative perception during inference. The core innovation lies in mimicking human hierarchical attention by progressively refining focus on high-confidence spatio-temporal regions, guided by evolving textual predictions. The authors also contribute VTTS-80K, a dataset tailored for iterative perception training. Experiments on video and image understanding show significant gains on Qwen-VL baselines.", "questions": "1. What is the computational overhead of the iterative inference, compared with the direct answer baseline and other long-chain inference methods (e.g. LLaVA-CoT, Mulberry, etc.)?\n2. The proposed concept of test-time scaling is a type of reasoning method. What is the performance compared with other reasoning post-training (e.g. LLaVA-CoT, Mulberry, Insight-V, MM-Eureka, Visual-CoT, etc) and test-time scaling approaches?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n- The proposed concept of iterative perception is reasonable and effective. \n- The integration of reinforcement learning (GRPO) with spatio-temporal supervision is well-designed. \n- The proposed VTTS datasets are valuable for the research community. \n\nWeaknesses: \n- Lack of some essential comparisons and experiments. Please refer to the question period. \n- The authors include no sota reasoning approaches in the table for comparison.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. Therefore, I decided to raise my point to accept. Authors are encouraged to add the efficiency-performance test to the camera-ready version.", "rebuttal": "We sincerely appreciate the reviewer's thoughtful and constructive feedback on our work. We are grateful for the positive recognition of our key contributions, including the reasonableness and effectiveness of our iterative perception method, the well-designed integration of reinforcement learning with spatio-temporal supervision, and the value of our proposed VTTS datasets for the research community. These encouraging comments motivate us to further improve our work and address the remaining concerns raised by the reviewer. Below, we address each of your comments and questions in detail.\n\nTable 1: Trade-off Between VTTS Performance and Inference Time\n\n| Model                  | Inference Type | Average A100 Infer Time on MMVet(Image) | Performance on MMVet | Average A100 Infer Time on VideoMME(Video) | Performance on VideoMME |\n| ---------------------- | -------------- | --------------------------------------- | -------------------- | ------------------------------------------ | ----------------------- |\n| Mulberry               | CoT            | 1.26s                                   | 43.9                 | -                                          | -                       |\n| LLaVA-CoT              | CoT            | 1.41s                                   | 60.3                 | -                                          | -                       |\n| Insight-V              | CoT            | 1.32s                                   | 64.7                 | -                                          | -                       |\n| MM-Eureka              | CoT            | 1.39s                                   | 65.9                 | -                                          | -                       |\n| VideoChat-R1           | CoT            | -                                       | -                    | 13.63s                                     | 62.4                    |\n| Qwen2.5VL-7B(Baseline) | Direct Output  | 1.08s                                   | 64.9                 | 11.26s                                     | 64.4                    |\n| Qwen2.5VL-7B(Baseline) | CoT            | 1.46s                                   | 63.2                 | 14.90s                                     | 61.3                    |\n| VTTS-7B                | Single Infer   | 1.35s                                   | 67.2                 | 13.21s                                     | 65.2                    |\n| VTTS-7B                | Multi Infer    | 2.97s                                   | **68.3**             | 30.09s                                     | **67.1**                |\n\n\n\n> **Reply to Q1: Computational overhead of the iterative inference：**\n\nAs shown in Table 1, VTTS achieves a good balance between computational overhead and performance compared to the direct output baseline and other long-chain inference methods. Specifically, VTTS-7B (Single Infer) achieves an average inference time of 1.35 seconds on MMVet, slightly higher than the direct output baseline's 1.08 seconds. This is similar to other long-chain inference methods, such as LLaVA-CoT (1.41 seconds) and MM-Eureka (1.39 seconds), and its performance (67.2%) far exceeds these methods. Furthermore, while multi-inferring increases inference time, it also improves performance by 1.1%. For the video task (VideoMME), VTTS-7B (Single Infer) achieves an inference time of 13.21 seconds, higher than the baseline's 11.26 seconds, yet achieves a performance of 65.2%, significantly outperforming other methods. Therefore, VTTS's Single Infer method improves performance while maintaining similar inference time to existing CoT methods. Meanwhile, Multi-infer achieves significant performance improvements while incurring moderate computational overhead, outperforming most existing long-chain inference methods.\n\n\n\n> **Reply to Q2, W1, W2: Performance compared with other reasoning models:**\n\nAs shown in Table 1, VTTS-7B achieved a performance of 68.3% on MMVet (images) and 67.1% on VideoMME (videos) in multi infer mode, significantly outperforming other post-inference training methods (such as LLaVA-CoT, Insight-V, and MM-Eureka) and test-time expansion methods. For example, LLaVA-CoT achieved a performance of 60.3% on MMVet, while Insight-V and MM-Eureka achieved performances of 64.7 and 65.9, respectively, both lower than VTTS. Furthermore, VTTS also outperformed the VideoChat-R1 model (62.4) on video tasks. This performance gain demonstrates the effectiveness of the test-time expansion strategy, indicating that VTTS has stronger reasoning capabilities for complex visual tasks."}, {"review_id": "CKm8Q9CHfZ", "reviewer": "Reviewer_DAuW", "summary": "This paper proposes Visual Test-Time Scaling (VTTS), a method that enhances MLLM reasoning by enabling iterative visual perception during inference. Mimicking human attention, VTTS progressively refines focus on key spatio-temporal regions guided by evolving text predictions. It introduces an Iterative Perception (ITP) mechanism, trained via Generalized Reward Policy Optimization (GRPO) with spatio-temporal supervision. This paper also presents VTTS-80K, a dataset tailored for this paradigm. VTTS achieves consistent improvements across 20+ benchmarks.", "questions": "1. What would the performance look like if we directly use ground-truth clues for video or image QA? Alternatively, how does it compare to first extracting clues using a state-of-the-art model, followed by perception? How do these non-training strategies differ from trained approaches in terms of both accuracy and computational efficiency?\n2. Analyze why Supervised Fine-Tuning (SFT) may degrade performance in some cases, while GRPO can lead to improvements.\n3. The method performs poorly when using only the first perception step and requires multiple iterations to achieve good performance. Why is this the case?\n4. It takes more time to perceive temporal and spatial clues multiple times. Did the author count the time taken in different situations? How much difference is there in time efficiency between it and the SFT model (directly outputs the answer) and the inference model (single-inference text output)?\n5. Did the model undergo the Cold Start before reinforcement training? Did the author do some detailed ablation experiments to show whether there was any impact of cold start?\n6. The authors do not provide visualization of the reasoning results of specific instances of the proposed model, including output reasoning paths and visualizations, which makes it difficult to understand how the model performs video or image reasoning.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. VTTS mimics human hierarchical attention by iteratively refining focus on high-confidence spatio-temporal regions. This idea makes sense in many scenarios.\n2. Extensive and detailed experiments validate the effectiveness of the proposed method across a wide range of tasks, including Multimodal Video Understanding, Multimodal Image Understanding, Grounded Video Question Answering, Temporal Grounding, Spatial Grounding, and Object Tracking.\n\nWeaknesses:\n\nMajor Weakness\n1. The paper lacks a direct comparison with other state-of-the-art methods built upon the Qwen2.5-VL or other models.\n2. In terms of reinforcement learning, only the reward function was modified, with no significant novelty.\n3. Compared to the increased inference time of this method, the performance improvement is not significant enough.\n4. Although the idea that focuses on the temporal clue and spatial clue makes sense, the performance improvements of Table 1 (video benchmarks) and Table 2 (image benchmarks) are not satisfactory. Why is this the case? Is there any detailed analysis?\n\nMinor weakness\n1. Mistakenly wrote \"Fine\" as \"Fune\" in table 5.\n2.  Mistakenly wrote \"Clue\" as \"Glue\" in figure 3.\n3. The text in the figures is a bit blurry.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns by supplementing additional experiments, which have improved the overall quality of the paper. However, several notable limitations remain, preventing a stronger recommendation for acceptance. First, while the method achieves a modest performance improvement, its inference time is three times longer than the baseline, raising concerns about its practical efficiency. Second, the use of a state-of-the-art (SOTA) temporal grounding model unexpectedly leads to degraded performance, and the paper lacks a thorough analysis to explain this counterintuitive result. Additionally, the discussions on supervised fine-tuning (SFT) and GRPO remain somewhat superficial and could benefit from deeper analysis. Given these unresolved issues, I recommend a borderline accept score.", "rebuttal": "Thank you for your valuable feedback and constructive suggestions. We appreciate your recognition that our VTTS framework's core idea of mimicking human hierarchical attention through iterative refinement on high-confidence spatio-temporal regions is conceptually sound and applicable to various scenarios, as well as your acknowledgment of our extensive experimental validation across multiple tasks including Multimodal Video Understanding, Multimodal Image Understanding, Grounded Video Question Answering, Temporal Grounding, Spatial Grounding, and Object Tracking. Below, we address each of your comments and questions in detail.\n\nTable 1: Trade-off Between VTTS Performance and Inference Time\n\n| Model                  | Inference Type | Average A100 Infer Time on MMVet(Image) | Performance on MMVet | Average A100 Infer Time on VideoMME(Video) | Performance on VideoMME |\n| ---------------------- | -------------- | --------------------------------------- | -------------------- | ------------------------------------------ | ----------------------- |\n| Mulberry               | CoT            | 1.26s                                   | 43.9                 | -                                          | -                       |\n| LLaVA-CoT              | CoT            | 1.41s                                   | 60.3                 | -                                          | -                       |\n| Insight-V              | CoT            | 1.32s                                   | 64.7                 | -                                          | -                       |\n| MM-Eureka              | CoT            | 1.39s                                   | 65.9                 | -                                          | -                       |\n| VideoChat-R1           | CoT            | -                                       | -                    | 13.63s                                     | 62.4                    |\n| Qwen2.5VL-7B(Baseline) | Direct Output  | 1.08s                                   | 64.9                 | 11.26s                                     | 64.4                    |\n| Qwen2.5VL-7B(Baseline) | CoT            | 1.46s                                   | 63.2                 | 14.90s                                     | 61.3                    |\n| VTTS-7B                | Single Infer   | 1.35s                                   | 67.2                 | 13.21s                                     | 65.2                    |\n| VTTS-7B                | Multi Infer    | 2.97s                                   | **68.3**             | 30.09s                                     | **67.1**                |\n\n> **Questions 1:  Use SOTA expert models for clue finding**\n\nAs shown in Table 2, in the QA task, the clue finding method of VTTS is stronger than the traditional model. The reason is our method specifically targets implicit clues that are not directly detectable by standard perception models, which typically recognize explicit entities or actions but fail to reason about contextual or temporal dependencies. In contrast, our approach leverages the rich prior knowledge of VLMs to interpret these implicit clues and guide effective clue extraction, non-training strategies perform poorly in such cases.\n\nTable 2: Abaltion on Clue finding models\n\n| Clue Finding                        | Inference Type | VideoMME |\n| ----------------------------------- | -------------- | -------- |\n| VTTS                                | Single Infer   | 65.2     |\n| SG-DETR(sota in Temporal Grounding) | Multi Infer    | 64.0     |\n| VTTS                                | Multi Infer    | 67.1     |\n\n\n\n> **Questions 2: Why is the performance of sft worse than that of GRPO?** \n\nWhile both SFT and GRPO aim to train models to identify implicit clues, GRPO’s use of L1 loss—versus SFT’s cross-entropy loss—proves more effective for continuous or numerical clues (e.g., time stamps, durations, counts). Cross-entropy treats outputs categorically, so predictions like 20.1 and 21.0 incur similar penalties when the ground truth is 20.0, even though 20.1 is semantically much closer. In contrast, L1 loss in GRPO directly penalizes small deviations, enabling fine-grained, metric-aware learning. This leads to more accurate clue extraction, especially in temporal or quantitative reasoning, where precise estimation is critical for downstream perception. As a result, GRPO consistently outperforms SFT, which can degrade performance due to its coarse, discrete supervision on inherently continuous clues.\n\n> **Questions 3: Why is single-step perception not as good as multi-step perception?**\n\nDuring the initial step, the model samples visual information uniformly without leveraging any learned or predicted visual clues. At this stage, the model lacks sufficient guidance to focus on relevant regions, resulting in suboptimal perception. However, in subsequent iterations, the model can utilize implicit visual clues derived from its own predictions to perform non-uniform, adaptive sampling—effectively refining its focus on more informative regions. This iterative refinement allows the model to progressively improve its perception and prediction accuracy. Therefore, multiple perception steps are essential to fully exploit the learned visual patterns and achieve strong performance.\n\n> **Questions 4: Single and multiple inference time**\n\nWe give the inference time of VTTS model and baseline models in Table 1.\n\n> **Questions 5: Cold Start**\n\nIn our work, we did not employ a cold start phase before reinforcement learning, as the base Qwen model already exhibits strong instruction-following capabilities and a solid foundation in reasoning. Thanks to this strong pre-training, the model is able to generate partially correct output formats and meaningful reasoning trajectories solely through well-designed prompts, even before fine-tuning. After just a few steps of reinforcement training, the model quickly stabilizes and produces nearly perfect formatting and coherent reasoning chains. This rapid convergence suggests that a separate supervised fine-tuning (SFT) cold start phase is not necessary in our setup, and the policy initialization is sufficiently strong to enable effective reinforcement learning. \n\n> **Questions 6: Visualization** \n\nWe have included detailed case studies in the supplementary material, which contain visualizations of the model’s output reasoning paths. These examples illustrate how our model progressively perceives visual content, refines its reasoning, and arrives at the final prediction. \n\n> **Major Weakness 1: More comparisons with other models**\n\nIn Table 1, we provide more experimental results, showing that the VTTS model is ahead of other models, including the baseline (videochat-r1) based on qwen2.5vl.\n\n> **Major Weakness 2: Innovations in reinforcement learning**\n\nWhile our approach does involve modifying the reward function, it enables a novel training paradigm for multi-step perception and reasoning, which goes beyond a simple reward redesign. Specifically, we introduce a multi-reward structure—comprising answer reward, spatiotemporal reward, and format reward. This composite reward signal guides the model to learn not only what the correct output is, but also how to arrive at it through structured, step-by-step reasoning. The integration of these rewards within the reinforcement learning framework facilitates the emergence of interpretable and controllable reasoning behavior, which is not typically achieved through standard reward setups. Thus, our work contributes a meaningful and effective advancement in the application of RL to complex reasoning tasks.\n\n>  **Major Weakness 3: Relationship between performance and inference time**\n\nAs shown in Table 1, VTTS strikes a strong balance between efficiency and performance. On MMVet, VTTS-7B (Single Infer) achieves 1.35s inference time—comparable to LLaVA-CoT (1.41s) and MM-Eureka (1.39s), slightly above the baseline (1.08s)—but with significantly higher performance (67.2%). On VideoMME, VTTS-7B (Single Infer) takes 13.21s (vs. 11.26s baseline) yet achieves 65.2%, outperforming existing methods. Thus, VTTS improves performance with acceptable latency, offering a more effective trade-off than current long-chain inference approaches.\n\n> **Major Weakness 4: Ways to continue improving performance**\n\nWe show the failure case in Appendix Fig. 9. Despite the design of a reasonable reward function, the model still finds it difficult to accurately locate some spatiotemporal clues. This shows that the current training data and SFT stage are still insufficient in modeling spatiotemporal perception capabilities, and further data expansion and training strategy optimization are needed.\n\n>  **Minor weakness:**\n\nWe thank the reviewer for the careful feedback. We acknowledge the minor errors and will correct \"Fune\" to \"Fine\" in Table 5 and \"Glue\" to \"Clue\" in Figure 3 in the final version. Additionally, we will improve the figure resolution to ensure all text is clear and legible. These revisions will be made promptly in the camera-ready copy."}, {"review_id": "jsQgnnBs61", "reviewer": "Reviewer_DWnj", "summary": "This paper introduces VTTS (Visual Test-Time Scaling), a method to enhance reasoning in Multimodal Large Language Models (MLLMs) via iterative visual perception at inference time. Inspired by human-like progressive attention, VTTS refines attention to high-confidence spatio-temporal regions in multiple rounds, driven by evolving textual predictions. It proposes: 1) An Iterative Perception (ITP) mechanism using Reinforcement Learning (RL) based on GRPO. 2) A new dataset, VTTS-80K, designed for training MLLMs with spatio-temporal grounding and reasoning via RL. 3) Extensive experiments showing 5–6% performance boosts over strong MLLM baselines across more than 20 benchmarks, including video QA, image reasoning, and perception.", "questions": "* Heavy Reliance on Qwen2.5-VL: (no worries if this may take some time and cannot be completed during rebuttal, I can understand it.) All model improvements are tied to Qwen2.5; generalization to other MLLMs (e.g., LLaVA, Gemini) may need to be demonstrated. Could authors have a try on other frameworks if possible? \n\nI appreciate the completeness of this system paper, which demonstrates a well-integrated effort across dataset construction, model architecture design, and comprehensive empirical evaluation. However, I have concerns regarding the generalizability of the proposed algorithm and would appreciate further clarification on the disentanglement between visual scaling and language-level scaling effects.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\n* VTTS introduces a principled way to scale perceptual computation at test time, rather than only scaling generation or language reasoning. \n* Demonstrates consistent improvements (1–6% gains) across a wide range of tasks and benchmarks using Qwen2.5-VL models.\n* New Benchmark Dataset: VTTS-80K provides high-quality, multi-level annotations (QA, spatio-temporal regions, and CoT) for robust training.\n* Detailed Ablation Studies: Includes insightful ablations showing impact of perception steps, data components, and training methods.\n\n---\n\n### **Weaknesses**\n\n* Disentangling Visual Scaling from Language Scaling: Given that language reasoning can also benefit from more decoding steps (e.g., Best-of-N, beam search), how do the authors ensure that the performance gains from VTTS are due to visual test-time scaling rather than indirectly benefiting from repeated language generation?\n* The paper models the ROI selection process as a reinforcement learning problem using GRPO. Could the authors elaborate on the nature of the learned spatio-temporal policy? Is the policy generalizable across tasks, or is it overfit to the specific structure of VTTS-80K?\n* Engineering cost at inference is high: Iterative perception increases test-time compute, which might be impractical for real-time applications without optimization.\n* Quality of VTTS-80K may be bottlenecked by the capabilities of DeepSeek or other LLMs used in the annotation process. It is a concern that dataset generation pipeline heavily relies on LLMs.", "was_revised": true, "final_justification": "I appreciate the authors for addressing most of concerns during rebuttal. Therefore, i will keep my initial score and recommend the acceptance of the paper.", "rebuttal": "We sincerely thank the reviewers for their valuable feedback and insightful comments. We appreciate the reviewers' recognition of our novel VTTS approach for scaling perceptual computation at test time, the consistent performance improvements across diverse tasks using Qwen2.5-VL models, our high-quality VTTS-80K benchmark dataset with comprehensive multi-level annotations, and our thorough ablation studies providing meaningful insights into various components. Below, we address each of your comments and questions in detail.\n\n> **Questions 1: More Results on Other MLLM**\n\nAs suggested, we have conducted preliminary experiments applying VTTS to InternVL3-8B, using the same VTTS-80K training set to obtain InternVL3-8B-VTTS. The results are summarized in Table 1 below:\n\nTable 1: Results on InternVL3\n\n| Model             | Inference Type | VideoMME |\n| ----------------- | -------------- | -------- |\n| InternVL3-8B      | Direct Output  | 65.3     |\n| InternVL3-8B-VTTS | Single Infer   | 65.9     |\n| InternVL3-8B-VTTS | Multi Infer    | 66.7     |\n\nThese results show that VTTS consistently improves performance on InternVL3-8B under both single and multiple inference settings. This indicates that our method is not limited to Qwen2.5-VL and can generalize to other strong MLLMs with different architectures and training paradigms.\n\nAt the same time, we found during the training process that InternVL3 is worse than Qwen in terms of instruction following ability, and the preference disappears during RL training, so the performance improvement of InternVL3 is less than that of Qwen.\n\n\n\n> **Weakness 1: Gain From Visual Scaling**\n\nTable 2: Abalation Study on Visual Scaling\n\n| Model     | Inference Type          | VideoMME |\n| --------- | ----------------------- | -------- |\n| Qwen2.5VL | Direct Output           | 64.4     |\n| VTTS      | Single Infer            | 65.2     |\n| VTTS      | Multi infer + only text | 65.9     |\n| VTTS      | Multi Infer + visual    | **67.1** |\n\nTo disentangle the effect of visual scaling from repeated language generation, we ablate VTTS under different inference settings in Table 2. While multi-inference with text-only aggregation yields a modest gain (+0.7% over single infer), the full VTTS approach with visual resampling achieves a +1.9% improvement, outperforming text-only multi-inference by +1.2%. This clear performance gap demonstrates that the gains primarily stem from visual test-time scaling—i.e., leveraging diverse visual representations—rather than repeated language generation alone.\n\n\n\n> **Weakness 2: The essence of the ROI selection process** \n\nOur ROI policy is designed to capture spatio-temporal implicit cues in response to questions, leveraging the rich priors of VLMs to interpret and localize such cues. While trained on VTTS-80K, the policy learns task-agnostic reasoning patterns—i.e., how to find relevant spatio-temporal evidence given a question—enabling generalization across diverse downstream tasks rather than overfitting to dataset-specific structures.\n\n> **Weakness 3: High Inference Cost**\n\nWe acknowledge that iterative perception increases inference costs. In future work, we plan to optimize engineering efficiency by caching previously computed visual tags and other inference optimizations to significantly reduce redundant computations and improve runtime performance.\n\n> **Weakness 4: Quality of VTTS-80K**\n\nWe acknowledge the potential limitations imposed by LLMs in the annotation pipeline. However, we employ careful filtering, multi-round verification to ensure high-quality annotations. Moreover, the spatiotemporal cues and QA of the dataset are partially derived from the original annotations, so the search for implicit spatiotemporal capabilities is not limited to LLMs."}, {"review_id": "HnZZ8IuuO3", "reviewer": "Reviewer_DqAN", "summary": "VTTS lets an MLLM re-perceive its visual input several times during inference: after each reasoning step the model predicts a region of interest, crops/resamples that patch, and feeds it back through the vision encoder, with a reinforcement-learned policy (trained via GRPO on the new VTTS-80K dataset) guiding which patches to attend to. Added to Qwen 2.5-VL (3 B and 7 B) this iterative perception loop yields 5–6% average accuracy gains on more than twenty image- and video-reasoning benchmarks, with three iterations giving the best compute-accuracy trade-off.", "questions": "- Could the authors elaborate, possibly with a formal discussion or empirical study, why reinforcement learning as implemented here succeeds where supervised fine-tuning does not in narrowing down regions of interest? Is there a formal intuition or guarantee about convergence or optimality?\n\n- What are the practical computation costs (e.g., latency, GPU-hours) associated with 3+ iterations of ITP at inference? Is there a diminishing returns point, and how should practitioners select the right trade-off for deployment?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- S1: The work tackles the significant challenge of scaling multimodal reasoning in MLLMs via iterative, adaptive computation, grounded in cognitive inspiration (humans' hierarchical attention).\n\n- S2: The core proposal of VTTS with ITP and reinforcement learning is a meaningful extension beyond static perception, modeling perceptual dependencies through reinforcement signals tied to spatio-temporal cues.\n\n- S3: VTTS-80K is a well-motivated resource, with multi-granular annotations (QA, spatio-temporal support, stepwise CoT), systematically constructed as seen in Figure 3, supporting both training and benchmarking of iterative visual reasoning.\n\n- S4: The evaluation covers a wide variety of tasks (video, image, spatial, temporal) and provides quantitative evidence for improvements.\n\n### Weaknesses\n\n- W1:  While the model is compared to strong open-source and some proprietary baselines, some result tables (e.g., Table 7 for spatial grounding) hint that specialized detection and grounding models (e.g., Grounding-DINO-L) are stronger in certain settings. The discussion of tradeoffs versus domain-specific approaches could be more critical—are there settings where VTTS-based models are still weaker?\n\n- W2: The computation and efficiency analysis are missing, such as inference-time FLOPs or latency. Iterative perception may be costly, especially on long videos.\n\n- W3: Limited backbone diversity. Results are only on Qwen-VL 3 B/7 B. Claims of generality would be stronger with models that use different vision encoders.\n\n- W4: No head-to-head with other vision-side TTS baselines such as Visual-CoT or V*, thus the gains cannot be cleanly attributed to the new components. Another straightforward training-free baseline (or ablation study) is sampling $k$ random spatial (or temporal) crops from the input image/video, and fusing the answers by a simple majority vote.\n\n- W5: Inconsistent tense and plurality could be improved for better clarity and professional polish.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns with additional experiments, which have improved the overall quality of the paper.", "rebuttal": "We sincerely appreciate the reviewer's thoughtful feedback and recognition of our work's significance in scaling multimodal reasoning through iterative, adaptive computation. Thank you for positively evaluating our VTTS approach with ITP and reinforcement learning, as well as acknowledging VTTS-80K as a valuable resource with comprehensive annotations. We are grateful for your appreciation of our diverse task evaluation and quantitative improvements. Your constructive feedback has strengthened our work, and we thank you for your careful consideration. Below, we address each of your comments and questions in detail.\n\n\n\nTable 1: Trade-off Between VTTS Performance and Inference Time\n\n| Model                  | Inference Type | Average A100 Infer Time on MMVet(Image) | Performance on MMVet | Average A100 Infer Time on VideoMME(Video) | Performance on VideoMME |\n| ---------------------- | -------------- | --------------------------------------- | -------------------- | ------------------------------------------ | ----------------------- |\n| Insight-V              | CoT            | 1.32s                                   | 64.7                 | -                                          | -                       |\n| MM-Eureka              | CoT            | 1.39s                                   | 65.9                 | -                                          | -                       |\n| VideoChat-R1           | CoT            | -                                       | -                    | 13.63s                                     | 62.4                    |\n| Qwen2.5VL-7B(Baseline) | Direct Output  | 1.08s                                   | 64.9                 | 11.26s                                     | 64.4                    |\n| Qwen2.5VL-7B(Baseline) | CoT            | 1.46s                                   | 63.2                 | 14.90s                                     | 61.3                    |\n| VTTS-7B                | Single Infer   | 1.35s                                   | 67.2                 | 13.21s                                     | 65.2                    |\n| VTTS-7B                | Multi Infer    | 2.97s                                   | **68.3**             | 30.09s                                     | **67.1**                |\n\n> **Questions 1: Why is the performance of sft worse than that of GRPO?**\n\nWhile both SFT and GRPO aim to train models to identify implicit clues, GRPO’s use of L1 loss—versus SFT’s cross-entropy loss—proves more effective for continuous or numerical clues (e.g., time stamps, durations, counts). Cross-entropy treats outputs categorically, so predictions like 20.1 and 21.0 incur similar penalties when the ground truth is 20.0, even though 20.1 is semantically much closer. In contrast, L1 loss in GRPO directly penalizes small deviations, enabling fine-grained, metric-aware learning. This leads to more accurate clue extraction, especially in temporal or quantitative reasoning, where precise estimation is critical for downstream perception. As a result, GRPO consistently outperforms SFT, which can degrade performance due to its coarse, discrete supervision on inherently continuous clues.\n\n> **Questions 2: The right trade-off for deployment** \n\nWe provide an ablation experiment in Figure 4 in the main text, discussing that as the number of perceptions increases, we find that the second round of multi inference has the greatest benefit, and the benefits thereafter gradually decrease, with almost no additional benefit after the third or fourth rounds.\n\n> **Weakness 1:  Use SOTA expert models for clue finding**\n\nAs shown in Table 2, in the QA task, the clue finding method of VTTS is stronger than the traditional model. The reason is our method specifically targets implicit clues that are not directly detectable by standard perception models, which typically recognize explicit entities or actions but fail to reason about contextual or temporal dependencies. In contrast, our approach leverages the rich prior knowledge of VLMs to interpret these implicit clues and guide effective clue extraction, non-training strategies perform poorly in such cases.\n\nTable 2: Abaltion on Clue finding models\n\n| Clue Finding                        | Inference Type | VideoMME |\n| ----------------------------------- | -------------- | -------- |\n| VTTS                                | Single Infer   | 65.2     |\n| Random                              | Multi infer    | 61.0     |\n| SG-DETR(sota in Temporal Grounding) | Multi Infer    | 64.0     |\n| VTTS                                | Multi Infer    | 67.1     |\n\n\n\n>  **Weakness 2: Inference Time of  VTTS.**\n\n As shown in Table 1, our model has similar inference costs as other CoT models in single infer mode, while its performance is improved and outperforms other CoT models. In multi infer mode, although the inference time is longer, the performance is further improved.\n\n> **Weakness 3: More Results on other MLLMs.**\n\nAs shown in Table 1, we have conducted preliminary experiments applying VTTS to InternVL3-8B, using the same VTTS-80K training set to obtain InternVL3-8B-VTTS. The results are summarized in Table 1 below:\n\nTable 3: Results on InternVL3\n\n| Model             | Inference Type | VideoMME |\n| ----------------- | -------------- | -------- |\n| InternVL3-8B      | Direct Output  | 65.3     |\n| InternVL3-8B-VTTS | Single Infer   | 65.9     |\n| InternVL3-8B-VTTS | Multi Infer    | 66.7     |\n\nThese results show that VTTS consistently improves performance on InternVL3-8B under both single and multiple inference settings. This indicates that our method is not limited to Qwen2.5-VL and can generalize to other strong MLLMs with different architectures and training paradigms.\n\nAt the same time, we found during the training process that InternVL3 is worse than Qwen in terms of instruction following ability, and the preference disappears during RL training, so the performance improvement of InternVL3 is less than that of Qwen.\n\n\n\n> **Weakness 4: More Results on Vision-side TTS baselines**\n\nIn Table 1, we provide more experimental results. You can see that our model has improved capabilities compared to visual reasoning models such as Insight-V and MM-Eureka. Since the base model used by V* and Visual COT is relatively weak, we did not compare them. At the same time, in Table 2, we provide a baseline for random clue selection. You can see that VTTs is more capable of selecting clues than random and expert models.\n\n> **Weakness 5: Problems in writing**\n\nWe thank the reviewer for the feedback. In the camera-ready version, we will carefully proofread the paper to correct inconsistent tense and plurality, ensuring improved clarity and professional presentation."}]

Author rebuttal:

Meta-review: This paper proposes an inference-time scaling approach to improve visual perception in MLLMs by progressively refining attention on high-confidence spatio-temporal regions, guided by evolving textual predictions. The work also contributes the VTTS-80K dataset, curated for iterative perception training. Experiments on image and video understanding tasks demonstrate improvements over the Qwen-VL baselines.

Some concerns remain, as highlighted by reviewer `DAuW`. These include the heavy compute cost (a 3x increase in inference time over the baseline), degraded performance when integrated with a state-of-the-art temporal grounding model, and the lack of discussion regarding SFT vs. GRPO.

Despite these limitations, all reviewers support accepting the paper (3 borderline accept, 1 accept). After carefully reading the reviews, the rebuttal, and all discussions, I concur with the reviewers' recommendation and suggest this paper be accepted.
Decision: Accept (poster)