CoFFT: Chain of Foresight-Focus Thought for Visual Language Models
Authors: Xinyu Zhang
Yuxuan Dong
Lingling Zhang
Chengyou Jia
Zhuohang Dang
Basura Fernando
Jun Liu
Mike Zheng Shou
Venue: NeurIPS 2025 poster ()
Keywords: visual reasoning; visual language model; traning-free; foresight
Primary area: applications

Abstract: Despite significant advances in Vision Language Models (VLMs), they remain constrained by the complexity and redundancy of visual input.
When images contain large amounts of irrelevant information, VLMs are susceptible to interference, thus generating excessive task-irrelevant reasoning processes or even hallucinations.
This limitation stems from their inability to discover and process the required regions during reasoning precisely.
To address this limitation, we present the Chain of Foresight-Focus Thought (CoFFT), a novel training-free approach that enhances VLMs' visual reasoning by emulating human visual cognition.
Each Foresight-Focus Thought consists of three stages:
(1) Diverse Sample Generation: generates diverse reasoning samples to explore potential reasoning paths, where each sample contains several reasoning steps;
(2) Dual Foresight Decoding: rigorously evaluates these samples based on both visual focus and reasoning progression, adding the first step of optimal sample to the reasoning process; 
(3) Visual Focus Adjustment: precisely adjust visual focus toward regions most beneficial for future reasoning, before returning to stage (1) to generate subsequent reasoning samples until reaching the final answer.
These stages function iteratively, creating an interdependent cycle where reasoning guides visual focus and visual focus informs subsequent reasoning.
Empirical results across multiple benchmarks using Qwen2.5-VL, InternVL-2.5, and Llava-Next demonstrate consistent performance improvements of 3.1-5.8\% with controllable increasing computational overhead.

CoFFT: Chain of Foresight-Focus Thought for Visual Language Models
Xinyu Zhang": 1.2 Yuxuan Dong!. Lingling Zhang Chengyou Jia 1,2 ZhuoHang Dang Basura Fernando 4.6 Jun Liu Mike Zheng Shou "School of Computer Science and Technology, Xi'an Jiaotong University Ministry Education Key Laboratory of Intelligent Networks and Network Security, China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, China
IIHPC Agency for Science, Technology and Research, Singapore "Show Lab, National University of Singapore
"College of Computing and Data Science, Nanyang Technological University, Singapore Shang1393869716sstu xjtu. edu...du.coo cn, bhangllingexji tu. edu. cn, mikeshou@nus eew.oouuuuuuuduu..a...e..ee
Abstract
Despite significant advances in Vision Language Models (VLMs), they remain con- strained by the complexity and redundancy of visual input. When images contain large amounts of irrelevant information, VLMs are susceptible to interference, thus generating excessive task-irrelevant reasoning processes or even hallucinations. This limitation stems from their inability to discover and process the required regions during reasoning precisely. To address this limitation, we present the Chain of Foresight-Focus Thought (CoFFT), novel training- free approach that enhances VLMs' visual reasoning by emulating human visual cognition. Each Foresight- Focus Thought consists of three stages: (1) Diverse Sample Generation: generates diverse reasoning samples to explore potential reasoning paths, where each sample contains several reasoning steps; (2) Dual Foresight Decoding: rigorously evaluates these samples based on both visual focus and reasoning progression, adding the first step of optimal sample to the reasoning process; (3) Visual Foeus Adjustment: precisely adjust visual focus toward regions most beneficial for future reasoning, be- fore returning stage (I) generate subsequent reasoning samples until reaching the final answer. These stages function iteratively, creating an interdependent cycle where reasoning guides visual focus and visual focus informs subsequent reasoning. Empirical results across multiple benchmarks using Qwen2.5-VE Intern and Llava-Next demonstrate consistent performance improvements of 3.1-5.8% with controllable increasing computational overhead.
Introduction
Vision Language Models (VLMs) have demonstrated remarkable progress across numerous domains 3 particularly in visual reasoning 3 6 77. However, their performance remains significantly constrained by the inherent complexity and redundancy of visual inputs g I0) Visual Language Models demonstrate high sensitivity to large, salient elements in images, often struggling to effectively mitigate the impact of visually dominant but semantically irrelevant information. which leads to deteriorated performance and flawed reasoning [II). These limitations are especially pronounced in complex reasoning tasks that require fine-grained image understanding such as mathematics problem-solving 12 13), chart understanding [14 15 and geolocation inference [16]
"Coresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a)
Q Quesstin
Image
Ressoning & Image
Reasoning & Image
A Asswer
In which country and within which first level administrative region of that country this picture taken?
China (Betjing
Seoul Grand Park
The seats are survouded semiccrcull benches, and there is pond view under the comidor; which is very similar to Inokashirs Park. A nswer: Tokyo, J apan.
Zoo or Shanghai
Zoo with many
Zoo's 's or Jepan
pigeon
(Tokyo), based on E A slan style....
perforrances...
maybe Roman style
(b)
Questiinn
Image
Ressoning
Processed Image
Answer
In which country and within which first level administrative
The image shows a secune natural landscape with lush green foliage and clear blue sky, feoturing numerous people engaged activities and historical structures, with on Fast Asian aesthetic that's reminiscenl of China or Jaaa..
The image shows a structure resembling Nanjing is Sun Yal-see: Mausoleun, with its distinctive architecture. nawwer J Hangsau China.
regiion that country was this picture laken?
Figure 1: An example from the Seek World [16]. (a) is the reasoning process of o3, and (b) is the reasoning process of o3 after human visual cogmition. The correct answer is Jiangsu, China.
To address this limitation, researchers have proposed Multi-modal Chain of Thought approaches 18 19], which explore the integration of visual operations during reasoning and leverage image information across multiple stages for understanding the image comprehensively. Taking the recently prominent OpenAI-O3 [20] as an example, we illustrate its reasoning process as shown in Figure []] (a). Despite multiple attempts, O3 ultimately arrives at an incorrect result. Based on the redundant and interference-ladee reasoning process, we attribute this failure to its tendency to continuously explore different image regions without evaluating their contribution to the question, thus introducing substantial interference from irrelevant visual information.
However, when humans view this image, they evaluate different regions based on their potential contribution to question-solving. This leads them to reduce attention to the commonplace elements such as crowds, pigeons, and trees while prioritizing attention toward distinctive historical buildings. We send the region of historical buildings to guide O3, as shown in Figure (b), which illustrates the necessity of learning from human visual cognition. This performance stems from two human visual cognition capabilities when analyzing complex visual scenes: (1) Foresight the foresight evaluation of which visual regions will be most valuable for future reasoning [21], and (2) dynamic visual focus- precisely shift attention toward the most future reasoning-relevant regions 22 23
Inspired by these observations, we propose the Chain of Foresight- Focus Thought (CoFFT), training-free free approach that enhances VLMs' visual reasoning capabilities, as illustrated in Figure Each Foresight-Focus Thought iteration consists of three stages: (1) Diverse Samples Generation (DSG): Based on the current reasoning process, the VI.M generates diverse reasoning samples under different temperature parameters, where each sample contains multiple subsequent reasoning steps. (2) Dual Foresight Decoding (DFD): Evaluates samples by considering both visual focus and reasoning progression to select the optimal reasoning sample, incorporating its first step into the reasoning process.
(3) Visual Focus Adjustment (VFA): First, the image is evaluated based on two criteria: relevance to the question and correlation with future reasoning steps in the optimal sample. Then, sliding window is used to select, crop, and magnify the best region the next visual focus image. Finally, this image obtained from VFA serves as visual input for the next iteration, cycling back to stage (1) to generate new reasoning samples with the updated reasoning from stage (2) until reaching the final answer. This creates a cycle where reasoning guides visual focus, and visual focus informs subsequent reasoning steps, enhancing VLMs* visual reasoning capabilities.
We conduct extensive experiments across several benchmarks using different VLMs, including Qwen2.5-VE 5L []. InterVV--2.5 [2], and Llava-Next (24). CoFFT demonstrates consistent perfor- mance gains of 3.155.8% on average across these benchmarks. Furthermore, analysis across models with varying parameter counts reveals that CoFFT's effectiveness scales positively with model size, yielding greater improvements for larger VLMs. Our computational overhead analysis demonstrates that while CoFFT requires more computation than direct VLM inference, it remains more efficient than Monte Carlo Tree Search approaches, highlighting the practical efficiency of CoFFT.
1. We propose CoFFT, novel training- free approach that improves VLM's performance on complex visual reasoning tasks without requiring model modifications or retraining.
2. We propose Dual-Foresigh Decoding, which evaluates reasoning samples by optimizing visual focus and reasoning progression jointly, along with Visual Focus Adjustment that directs visual focus to regions relevant to the question and essential for subsequent reasoning.

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Input
Stage 1: DSG
Stage 2: DFD
Stage 3: VFA
O Output
Reasoning
Step
Step
Optimal Sample
Process
Visual Focus
step
Sample
Optimal Sample
Image
Visual Focus Image
Step
Step
Reasoning Progression
Sliding Window
Question
Figure The overall approach of CoFFT, where Dual Foresight Decoding and Visual Focus Adjustment will be introduced in detail later.
3. We demonstrate through extensive experiments across multiple benchmarks that CoFFT significantly improves performance on tasks requiring fine-grained visual understanding and complex reasoning, without excessively increasing computational overhead.
2 Related work
2.1 Visual Language Model
Large Language Models' (LLMs) success has fundamentally transformed Visual-Language Mod- els (VLMs) development, advancing from basic dispatcher architectures (Visual ChatGPT [25], HuggingGPT [26]. MM-REACT (27) to more sophisticated Dision-language approaches. Key ar-
chitectural innovations include LLa VA's [28 learned image-token projectors, BLIP-2's 2 30
question transformers, and MoVA's BI innovative task-specific adaptive routing. Recent advances
feature Qwen2.5-VL's (1] novel window attention optimizations and Intern VI.2.5's [ enhanced data
processing combining Random JPEG Compression with Square Averaging techniques.
2.2
Visual Search
Early research emulated human visual search through Bayesian models with saliency maps 32 33 deep similarity mapping networks and inverse reinforcement learnin((33). These approaches primarily replicated gaze samples but lacked precise target localization capabilities and employed fixed-size attention windows unsuitable for complex scenarios. Recent methods such as SEAL[36 incorporate localization modules and visual memory systems to connect visual search with large multimodal models, though requiring additional training. DyFo (7 achieves training-fres dynamic focus through visual expertise integration, based on language gemment-anything modal, Both remain limited by their one-time image processing, lacking iterative analytical capabilities during reasoning. Our approach, by contrast, enables dynamic image manipulation throughout the reasoning process while maintaining training-fic ffringg-fre methodology, offering more robust visual understanding approach.
2.3 Multi-modal Chain-of-Thun -hooughht
Research on multi-modal chain-of-thought reasoning integration follows two main approaches: (1) specialized visual expert models [8]. such as Sketchpad and VoT/MVoT, which equip VLMs with drawing tools and enhanced spatial reasoning, and (2) improved training processes [8 [9 featuring autonomous region selection and built-in visual operations for preserving reasoning evidence. Both approaches face limitations expert models lack generalizability and require adaptation costs, while enhanced training demands extensive resources. Additionally, Multimodal -hhougt Prompting is an alternative strategy to enhance VL.Ms visual reasoning capabilities through prompt engineering [39 40 However, this method generates only text-based rationales for answers, unlike the aforementioned approaches which can produce interlcaved visual-textual rationales.

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3 Method
3.1 Overview
We introduce CoFFT (Chain of Foresight-Focus Thought), a training-free approach designed to address VLM's limitations in complex visual reasoning, as shown in Figure CoFFT is an iterative exccution of Foresight-Focus Thought to obtain the final result. To illustrate the workflow in Foresight-Focus Thought, we take the current + 1 iteration of Foresight-Focus Thought an example, given original image V, question Q, current visual focus image V₁, and existing reasoning process R₂ (ri,...,PP) r, introduce the following three stages, shown in Algorithm |
1. Diverse Sample Generation:
The VLM generates k candidate reasoning samples S {si,..., } based on the current reasoning process Rs, visual focus image V,, and original question Q. Different temperature parameters are used to get the samples, ensuring sample diversity, where each sample s retains a maximum of / (foresight length) steps.
2. Dual Foresight Decoding: The samples are evaluated using a combination of visual focus score Eutt and reasoning progression score Eprot to ensure a comprehensive assess- ment. Eatt evaluates the relevance between the reasoning process and image to suppress inage-irrelevant hallucination, while Eprob assesses reasoning progression by measuring probability improvements across reasoning steps. The first step of the optimal sample s; integrated into the evolving reasoning process (R++1). 1), for continuous iterations.
3. Visual Focus Adjustment: First, a scoring mechanism is used to evaluate images based on two criteria: relevance to the question Q and correlation with future reasoning steps in optimal samples 8;. Then, a sliding window selects, crops, and magnifies the highest-scoring region visual focus image. Through this stage, the approach switches between global views and local details, avoiding the oversight of critical local information.
These three stages constitute a complete Foresight-Focus Thought iteration as illustrated in Figure They create synergistic cycle where the reasoning path directs visual focus, and optimized visual focus subsequently improves reasoning quality. This gegnitive-inspree process continues iteratively until the final answer is derived. By simulating human visual cognition processes, particularly Dual Foresight Decoding and Visual Focus Adjustment stages, CoFFT reduces VLMMs hallucination tendencies while improving reasoning accuracy. Next, we will first introduce the basics of CoFFT, the relative attention mechanism, and then explain the DFD and VFA stages in detail.
3.2 Relative Attention Mechanism
Due to the influence of redundancy information in images, relying solely on the original text- image paired attention maps tends to contain noise and makes challenging to directly distinguish semantically relevant regions [42 [T]. To address this limitation, we introduce a relative attention
mechanism that normalizes any text input attention against a baseline descriptive attention distribution.
Formally, taking question Q and image V as an example, we define relative attention A (ve(())) as:
(V, A Q)= Softmar( A(V,Q)
A
A(V, + ) A "xx"
(1)
where A(V, Q) and A(V, D) represent the attention maps for the question and descriptive prompts (e.g., "Describe the image in detail"") respectively, the Sofimax function is used to normalize, with (10-10) ensuring numerical stability. The division operation is performed dement-wisse It is worth noting that the attention map A between the input text and the image is provided by VLM itself and does not require additional modification during reasoning process.
This relative attention mechanism is effective for two reasons: (1) it suppresses attention to generally salient but input text-irrelevant regions by normalizing against the descriptive attention, and (2) amplifies attention to regions that are specifically relevant to the input text, thereby encouraging the model focus on regions that are relevant to the input text rather than salient elements the image.
3.3 Dual-soresight Decoding
Our Dual-Foresight Decoding mechanism evaluates reasoning samples by jointly combining visual focus and reasoning progression. Given set of candidate samples S.+1 generated at reasoning step

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Stage 1: Diverse Sample Generation
Stage 2: Dual Foresight Decoding
Input
Next step
Foresight
Scoring
Sample St
Res
Relative A Attetion
coss
n
R
Fazz
IaU
which this picture laken?
Va
ΣΣ (P) Po)
Eprott
Stage 3: Visual Focus A Adjusiment
9t
And (V.s)
A
lareal
R:
Relative ttention
(V,)
Q
(V,
Sliding Windows
V
Figure 3: The two primary components of CoFFT: (a) Dual Foresight Decoding, which evaluates different reasoning samples and selects the best sample from both visual focus and reasoning progres- sion to enhance decision robustness, and (b) Visual Focus Adjustment, which adaptively modulates visual focus adjustment to reasoning-relevan regions for optimized information understanding.
t + 1, we evaluate each sample s E S (containing up steps) through combined score:
E = A A-Softmarr s)] Vs St-1) (1- (S0)-SSttr(((ppeee
Vs E S4+1); (2)
where E 10 (empirically set 0.3) balances visual focus and reasoning progression. The Softmor function normalizes scores across all samples to obtain comparable distributions.
For the visual focus score, design Eate that measures the focus between each sample and the image, which comprehensively integrates cosine similarity and loU metrics for robust evaluation::
(3) where (r, computes the relative attention following Equation and 300% calculates the intersection over union high-attention regions (top 30%). This dual-metric formulation captures both global attention distribution and local focus similarity, effectively reducing hallucination by enforcing visual coherence through comprehensive spatial analysis.
(st((s) 0.5 cos(A"e) Av, + 0.5 30% (Arel (V,
Inspired by 43 A we observe that scores based on dynamic improvement tend to be more reliable than those simply using confidence values. Therefore, we propose the reasoning progression score Eprob that captures the stepwise improvement in reasoning quality using new reasoning steps of different lengths compare with the current reasoning process:
1 - - -P))
Eprob(s)
1=1
where Py represents the mean log probability of tokens including R, and first j-th step of sample and denotes the number of evaluated steps. This formulation identifies samples demonstrating maximum average improvement in reasoning confidence, leading to more reliable sample selection.
3.4 Visual Focus Adjustment
This stage dynamically adjusts visual focus by evaluating and selecting informative image regions based on dual-criteria scoring mechanism: question relevance and future reasoning relevance. This enables effective switching between global views and local details throughout the reasoning process.
For question relevance, we encourage VLM focus on regions that have not been explored by the current reasoning process RG. Therefore, we define Crel (V, V.Q. Q, R₂) as following:
Crd (V, Q, Rs) maz(A (V, Q) rd (V, Rt), 0).
(5)
5

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where A (VV... Rτ) represents the relative attention from completed reasoning steps (following Equation and (0, 1] (empirically set 0.3) controls the influence of processed regions.
For future reasoning relevance, we leverage the optimal sample 8, selected by the Dual Foresight
Decoding stage obtain (V, 8:) in the same way Equation 1
Based on those, the final attention map evaluation combines both criteria as following:
Acrop 0.5 C Q,, + - - A (V,
(6)
Then, dynamic sliding window algorithm determines the optimal visual region Vi+1:
arg max [B] E(E.ieB Acrop(T, v), if pB > MV + B
Vi+1
Vo.
otherwise
where B = V.a =V0
- cos (Ctrel (V, R4), (V, s;))), and 8 is the optimal region. Here, V% denotes the standard deviation of Acrop, 52 contains regions spanning 40%-90%% (interval is 10%) of original dimensions, and μs. [ E(z,)) Acrop(z; y) and μVv [Vo E(,,yjev, Acrop (zz, y) represent the maximum region and global mean scores. The selected region is scaled maintaining an aspect ratio within the original dimensions for subsequent reasoning iterations.
The mechanism employs adaptive thresholding based on the two attention maps: requiring minimal improvement over the global mean when attention centers converge (3 = 0) and stricter thresholds when they diverge (3 = ov,). This strategy prevents fixation on misleading local information while ensuring comprehensive visual understanding through balanced global-local focus transitions.
4 Experiment
4.1
Settings
We conduct comprehensive evaluations across multiple complementary benchmarks assess various aspects of visual reasoning capabilities. For mathematical and geometric reasoning, we employ Math Vista [45] and Math Vision
Benchmarks
[46]. To evaluate cross-domain visual reasoning abilities,
47 and MMStar 48). For chart comprehension assessment, we leverage Charxiv [14. Additionally, we contribute to the geographical domain by introducing two novel datasets in SeekWorld [16]: Seek World-Global, which utilizes Google Maps panoramic imagery, and SeekWorld-Chinn which incorporates data from the Xiaohongshu App.
we utilize the multi-subject benchmarks M3CoT
Comparison methods and experimental setup Our experimental approach incorporates state- of-the-art Vision Language Models (VLMs): Qwen2.5-VLLIInttrus (7B, 32B) (1., Intern VL2.5- Instruct (8B) (2), and Llava-Next (7B) 24] selected for their architectural capabilities and superior performance in visual reasoning. We establish comprehensive baseline comparisons using search- based method (MCTS (4)),, foresight reasoning method (Predictive Decoding [43)) visual search methodology (DyFo B7), and multi-modal chain-of offthhughtt prompting method (ICoT (T)). This diverse selection enables systematic evaluation against both text-based reasoning, visual search and multi-moda chain-of thhuughht methods. All experiments are run on four NVIDIA A100 GPUs with parallel processing. To ensure sample diversity, the temperature parameter ranges from 0.4 to 1 with an interval of 0.1, and sample is randomly selected each time it is generated. To prevent repeated selections, the probability weight of each chosen parameter is reduced by half in subsequent sampling processes. The weights are reset to their initial values once all parameters have been selected, ensuring balanced exploration of different temperature values. For Predictive Decoding and CoFFT, inference is considered complete when the model outputs 'REASONING COMPLETE',
Performance Metries We adopt Pass@1 I accuracy (Acc.) as our primary performance metric across all benchmarks. To quantify the computational efficiency-pperormaan performance trade-off, we calculate floating point operations (FLOPS) following the methodology in (50), where FLOPS as 6nP (P represents model parameters, n denotes generated tokens). By computing the average number of tokens generated per example, we provide a standardized measure of computational cost across different methods based on Qwen2.5-VL-7B Insttruct to enable direct efficiency comparisons.

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Table I; Main results, where Claude-3.5, Gemini-2, Pred. Dec. are Claude-3, 5 Sonnett, Gemini- 2.0-Flash and Predictive decoding. The optimal results are highlighted in bold, whereas suboptimal results are underlined. The Avg. column indicates the averaged results across the six benchmarks.
Math
Multi-subjects
Chart Charxiv
Geography
Models
Math Vista
Math Vision
MMStar
M3CoT
S.W-China
L.W-Goobl
Avg.
Closed source VLMs
GPT-4o
63.8
18.75
64.7
65.75
50.5
31.90
$6.50
50.27
Claude-3.5
65.4
26.21
65.1
66.05
60.2
29.22
52.50
52.10
Gemini-2
73.1
31.83
69.4
67.73
53.2
30.83
55.31
54.49
Qwen2.5VL-77
InsB-nnst
Baseline
68.2
18.09
63.9
59.62
42.5
21.45
25.31
42.72 22.72
MCTS
69.6
18.75
66.2
60.87
44.5
26.27
26.56
44.68
Pred. Dec.
69.9
19.73
65.7
61.34
45.3
26.54
26.86
45,05
ICoT
47.5
10.53
42.1
61.17
27.5
29.22
26.37
34.91
DyFo
68.4
16.78
64.5
61.26
43.7
32.44
27.81
44.98
CoFFT
70.4
23.36
69.4
62.47
47.2
35.12
29.37
48.19
-LaVA-NeXT-77
Baseline
34.6
9.87
34.2
38.27
13.9
10.72
15.31
22.41
MCTS
35.1
10.53
36.7
39.52
14.6
12.33
16.56
23.62
Pred. Dec.
34.8
11.36
35.1
40.16
15.3
11,80
17.19
23.67
ICoT
27.3
11.18
31.2
39.34
9.5
12.87
16.56
21.14
DyFo
34.8
8.22
39.86
15.7
13.67
17.50
23.69
CoFFT
35.6
12.17
38.3
40.68
16.8
15.55
19.69
25.54
Baseline
64.4
22.00
60.5
57.16
32.9
23.32
25.63
40.84
MCTS
65.0
24.67
62.0
58.24
34.3
25.20
26.56
42.28
Pred. Dec.
65.4
25.00
62.4
58.76
35.1
26.81
27.19
42.95
ICoT
42.9
16.12
39.4
58.50
16.4
27.35
26.88
32.51
DyFo
64.7
20.39
58.58
34.2
29.22
28.13
42.39
CoFFT
66.5
28.29
64.5
59.19
36.6
31.37
30.63
45.30
Twen2.5VL-322B-Issiie
Baseline
74.7
25.33
69.5
62.81
44.5
24.13
28.41
47.05
MCTS
76.2
27.31
70.6
64.15
47.6
28.69
29.06
49.09
Pred. Dec.
76.6
27.96
64.62
48.2
29.22
30.31
49.72
ICoT
58.7
21.05
54.6
63.93
47.3
32.17
31.56
44.19
DyFo
75.6
24.67
47.7
35.38
32.19
49.99
CoFFT
77.5
29.93
72.7
66.08
50.9
38.61
34.38
52.96
Table FLOPS denotes the calculated computational cost, with lower values indicating lower costs.
Models
Baseline
MCTS
Predictive decoding
ICoT
DyFo
CoFFT
FLOPS
8.35c+12
4.05e+14
1.85e+14
1.88c+13
1.98++13
2.38e+ 14
4.2
Results
As shown in Tables 1 and 2
we compare our method against current state-of-the-ar approaches across seven datasets and several VLMs. Our analysis reveals several significant findings:
Comprehensive Performance Improvements CoFFT consistently outperforms all approaches across VLMs and seven benchmarks, demonstrated Table[]] Performance gains are substantial across diverse model scales, from 7B to 32B parameters, highlighting CoFFT's versatility and effectiveness in enhancing both visual perception and reasoning capabilities of foundation models.
Balancing Visual Understanding and Reasoning VLMs must balance image comprehension with reasoning capabilities. Language-baa methods like MCTS and Predictive Decoding show broad improvements but struggle with fine-grand fine-grained visual analysis tasks in the Seek World datasets, indicating single-pass image understanding is often insufficient. Similarly, DyFo and ICoT fail to significantly improve performance on reasoning-intensive tasks, primarily due to the fragility of the reasoning process. Our proposed CoFFT addresses these limitations by simultaneously enhancing both visual comprehension and reasoning capabilities, achieving superior performance.

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Table 3: Ablation Studies on
w/o DFD refers to sample evaluation using reasoning progression score only. w/o VFA indicates the absence of adaptive image cropping, relying instead on original images throughout the reasoning process.
Math
Multi-subjects M3CoT
Chart Charxiv
Geography
Models
Mista
Math Vision
MMStar
S. S.W-China
S.W-Global
Our
70.4
23.36
69.4
62.47
47.2
35.12
29.37
w/o DFD
68.5
20.42
66.5
61.39
44.8
28.42
27.19
w/o VFA
69.3
21.71
67.4
61.09
44.7
27.08
26.25
Table 4: Performance comparison method combinations on Qwen2.5-VL-7B. We evaluate various combinations of existing approaches (DyFo, Predictive Decoding) and our proposed components (VFA, DFD) to demonstrate the effectiveness of our integrated CoFFT framework.
Method/Combination
Math Vista
Seek Werld-Chnl
Average
Qwen2.5- VL (Baseline)
68.2
21.45
44.83
DyFo Predictive Decoding
69.0
33.24
51.02
VFA Predictive Decoding
68.5
31.37
50.24
DyFo + Dual Foresight Decoding
69.3
34.05
51.73
CoFFT (VFA DFD)
70.4
35.12
52.76
Fine-grained Detail Extraction CoFFT excels particularly in extracting easily overlooked fine- grained details from images, evidenced by substantial improvements on Charxiv, Seek BerkkWorddhhig and Seek World-Global benchmarks. Nevertheless, on World-Global our method still underper- forms compared to advanced closed-source models like GPT--4o and Gemini-2200Flaat We attribute this gap differences knowledge capabilities across regional contexts, where these closed-source models likely benefit from more extensive pre-training on globally diverse datasets.
Scaling Properties Notably, the benefits of CoFFT increase with model size in systematic manner. The absolute performance improvements are more pronounced with larger models, suggesting that CoFFT effectively leverages the enhanced capabilities of more powerful base models. For instance, comparing improvements on Dwen2.5VL-7BB-Imen
reveals larger absolute gains across most benchmarks for the 32B model. This scaling trend suggests CoFFT could yield even greater benefits when applied to larger foundation models.
versus Swen2.5VLL322B-nstmee
Computational Efficiency Analysis As shown in Table 2 while CoFFT introduces additional computational costs compared to the baseline, it remains more computationally efficient than MCTS while delivering superior performance across all benchmarks. The computational overhead primarily stems from the iterative reasoning process. Given the significant performance improvements observed, particularly on challenging reasoning tasks, this computational trade-off is well justified.
4.3 Ablation Studies
To validate the effectiveness of our proposed components, we conduct ablation experiments across all benchmarks using 1---2-5----7---nnsteee
the backbone model. We specifically examine two critical components: w/o DFD removes the visual focus score East and relies solely on reasoning progression score Epro while w/o VFA eliminates the dynamic image update module and uses only the original image for inference during the reasoning process. Results in Table demonstrate performance degradation across all benchmarks when either component is removed, confirming the integral contribution of each mechanism to the overall effectiveness of CoFFT
Additionally, we explore combinations of existing methods such as DyFo for visual search and Predic- tive Decoding for reasoning enhancement, with results shown Table Our evaluation demonstrates that CoFFT achieves superior performance with an overall accuracy of 48.19% compared to the baseline accuracy 42.72%. Experiments reveal that simply combining existing visual search and reasoning methods often yields suboptimal results due to fundamental incompatibilities between their design principles. DyFo's static region determination conflicts with Predictive Decoding's reasoning requirements, while VEA's dynamic adjustments cannot be effectively utilized by static reasoning

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Table 5: Analysis of the effects of Foresight parameter 7 and Sampling number K on experimental outcomes. During k parameter studies, I is maintained constant while parameter studies are conducted with & fixed at 4.
Foresight
MathVista
S.E-China
FLOPS
Sampling
Math Vista
S.E-China
FLOPS
3
68.8
33.51
1.4l++14
= 2
68.8
34.32
1.19++14
= 4
69.3
34.58
1.91c+14
= 4
70.4
35.12
2.38c+14
5
70.4
35.12
2.38c+14
6
70.8
35.65
3,56c+14
6 70.5
35.65
2.86e+14
71.4
36.19
4.75e+14
70.7
35.92
3.33c+14
10
72.2
37.27
5,93e+14
the rightmost plot, which treatment increases the most accuracy value from B-0 B-1?
The figures illustrate different treatments (T1-T4)
Across treatments
T1-T4 with varying P
affect accuracy across ß values from 1, with vertical dashed lines, and distinct
values (0 to 1), T4
uniquely showed 25% accuracy
colored lines representing each
improvement while others declined.
treatment method.
In which country
Captured China's historical district likely traditional street. Featuring rooflines, decorative signage with Chinese characters, calligraphy banners, lanterns
Likely Sichuan, featuring traditional architecture with Chinese characters, wrought- iron signs, bamboo lantems, and roof tile, but not sure.
In this scene of traditional Chinese architecture, a decoratively woven lantem hangs near a sign bearing the characters "QianX ian ToFu",, which represents tofu establishment named after QianXian town in China's Shaanxi province.
within which first level administrative region of that country was this picture taken?
(c)
As shown in the figure, the straight lines AB and CD intersect at point 0, OD bisects A0E, /BOC = 50.0, then / BOB =0
We know that <BOC 50.0 degrees... Since <BOC and ZAOD are vertical angles...AADD 50.0 degrees.
Since OD bisects
Since ZAOE and are supplementary angles (they form straight line), we have ZAOE
ZDOE ZAOE...ZDOE 50.0 degrees.... ZA0E =
/OOD + DOE = 50.0 + 50.00 = 100.0 degrees.
+ +BOE 180.0 degrees. Therefore,
<BOE = 180.0
100.0 =8000 degrees.
The final answer is
((0oxee((80)).
Figure 4: Illustrative cases demonstrating the reasoning process of CoFFT. (a), (b), and (c) show an example from Charxiv, SeekWorld-Ching and Math Vista benchmarks, respectively.
approaches. CoFFT's breakthrough lies in its synergistic design where DFD evaluates reasoning paths based on both logical progress and visual relevance to guide VFA's region selection, while VFA provides high-quality visual inputs that enable DFD make more reliable judgments. This mutual reinforcement between focus and reasoning represents CoFFT's core contribution and explains its significant performance improvements over alternative combinations.
4.4 Parameter Analysis
Our CoFFT approach incorporates two critical hyperparameters: the number of foresight statements (I) and the number of samples generated per inference step (k). For our primary experiments, we select 1= 5 and & to balance computational efficiency and performance. To investigate parametric sensitivity, systematically varied / from to (interval is 1) and k from to 10 (interval is 2) using Owen2.5-vVL--Insttun as our backbone model. Experiments on Math Vista and Seek World-Chin,, which represent different visual reasoning aspects, show consistent performance gains with increasing

---

I and l (Table 5). This scalability suggests potential for further improvements with larger parameters,
though practical deployment requires balancing performance with computational costs.
4.5
Case Study
Figure iillsstaate the performance of CoFFT through three different cases. In case (a), CoFFT successtully identifies relevant chart correctly answer this question. In case (b), despite the presence of substantially irrelevant information in the complete image, CoFFT exhibits remarkable discrimination by precisely identifying and focusing on critical regions, thereby extracting essential information that leads to accurate results. In case (c), CoFFT successfully identifies relevant regions while demonstrating the ability dynamically return the original image when necessary for continued reasoning. These cases demonstrate the approach's ' adaptability across varying visual complexity levels and information densities, highlighting its robustness in real-world applications.
Conclusion
We have introduced CoFFT, training-free approach that enhances visual reasoning capabilities in VLMs by emulating human visual cognition through three stages: Diverse Sample Generation, Dual- Foresight Decoding, and Visual Focus Adjustment. CoFFT effectively bridges the gap between static visual processing and dynamic reasoning by implementing a sophisticated visual cognition mechanism that continuously refines visual focus based comprehensive iterative reasoning outputs. Extensive experiments demonstrate a significant 3.1 %%-58% average improvement across challenging visual reasoning tasks without requiring specialized visual expert models system modifications. However, while CoFFT successfully solves previously unsolvable problems for VL.Ms, it may introduce unexpected errors in cases where VLMs alone perform well, indicating potential interference with their original well-established capabilities. This suggests that although CoFFT represents a promising approach for VLM enhancement, its robustness requires further improvement, making the systematic optimization of the reasoning process an important direction for future research.
6 Acknowledgements
This work was supported by the National Key Research and Development Program of China (2022FF33303000), National Natural Science Foundation of China (No. 62137002, 62293550, 62293553, 62293554, 62450005, 62437002, 62477036, 62477037, 62176209, 62 62192781, 62306229), "LENOVO-XITU" Intelligent Industry Joint Laboratory Project, the Shaanxi Provincial Social Science Foundation Project (No. 2024P041), the Natural Science Basic Research Program of Shaanxi (No. 2023-JC-YB-599 the National Research Foundation, Singapore, under its NRF Fellowship (Award# NRF-NRFF14-2022 -001), the Youth Innovation Team of Shaanxi Universities "Multi-modal Data Mining and Fusion', Project of China Knowledge Center for Engineering Science and Technology, and the Youth AI Talents Fund of China Association of Automation (Grant _o._ Noo_HBRC-JKYL 2024-311). Mike Shou does receive any funding for this work.
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The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
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Reviews:
[{"review_id": "PYp2Rg4epP", "reviewer": "Reviewer_nDak", "summary": "The paper proposes a training-free method called CoFFT, inspired by human visual cognition, to address the issue of vision-language models (VLMs) failing to focus on key regions in images. By iteratively executing three stages—Diverse Sample Generation, Dual Foresight Decoding, and Visual Focus Adjustment—the method enhances the model’s reasoning capability.", "questions": "1. Mathematical problems seem to require stronger reasoning capabilities. Could the authors explain why enhancing visual focus alone leads to improved performance in mathematical reasoning tasks?\n\n2. Could the authors provide several examples from mathematical datasets to illustrate how the model’s reasoning path differs before and after applying the proposed method?\n\n3. Regarding Figure 1: The figure presents a highly knowledge-intensive visual question answering scenario. While the paper emphasizes that the method draws inspiration from human visual reasoning processes, it should be noted that even an American with exceptional visual reasoning capabilities couldn't possibly identify this as the Music Stage in Nanjing, China from just an image. The main text fails to provide any visual examples demonstrating that the proposed method can actually solve this type of problem.\n\n4. The mathematical examples shown (particularly in Figure 4) appear overly simplistic. These examples don't seem to have any clear connection to the paper's key \"visual adjustment\" methodology. This raises the question: why does the proposed method show significant improvements on datasets like MathVista when the demonstrated examples don't adequately showcase the method's capabilities?", "limitations": "See in weakness and questions.\n\nIf the authors are able to address the above questions, the reviewer will raise the score.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n\nInspired by human cognition, the paper proposes a novel training-free method to address the visual focusing issue in vision-language models (VLMs)\n\nWeakness:\n1. The figure1 presents a highly knowledge-intensive visual question answering scenario. While the paper emphasizes that the method draws inspiration from human visual reasoning processes, it should be noted that even an American with exceptional visual reasoning capabilities couldn't possibly identify this as the Music Stage in Nanjing, China from just an image. The main text fails to provide any visual examples demonstrating that the proposed method can actually solve this type of problem.\n\n2. Regarding mathematical scenarios:\nThe mathematical examples shown (particularly in Figure 4) appear overly simplistic. These examples don't seem to have any clear connection to the paper's key \"visual adjustment\" methodology. This raises the question: why does the proposed method show significant improvements on datasets like MathVista when the demonstrated examples don't adequately showcase the method's capabilities?", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: For Role of Visual Focus in Math Reasoning**\n\nWe apologize for any misunderstanding regarding our work. \n\n**(1) Benchmark composition**\n\nFirst, it should be clarified that Mathvista and Mathvision do not contain only geometric images. \nTaking MathVista as an example, this benchmark comprises 31 different datasets. We categorize its set as follows:\n\n|Category|Samples|Source Datasets|Baseline Accuracy|CoFFT Accuracy|\n|-|-|-|-|-|\n|NaturalImages|230|Super-CLEVR,A-OKVQA,CLEVR-Math,VQA-AS,VQA2.0,KVQA,VizWiz|57.82|60.43|\n|Charts|255|PlotQA, FigureQA, DVQA, FunctionQA, ChartQA|82.35|84.31|\n|Geometric Diagrams|219|UniGeo,GeoQA+,Geometry3K,GEOS,TheoremQA|67.57|68.49|\n|Documents|39|DocVQA, PaperQA, ParsVQA-Caps, TextVQA|58.97|66.67|\n|Tables|62|TabMWP|83.87|90.32|\n|Science Diagrams|85|AI2D, ScienceQA, SciBench,TQA|64.71|65.88|\n|Specialized Domains|110|VQA-RAD (Medical), MapQA (Maps), IconQA (Icons), IQTest (Logic), PMC-VQA (Medical)|55.45|56.36|\n\n\n**(2) CoFFT Framework**\n\n**Visual math reasoning requires:** (1) **Accurate Perception**: correctly identifying and extracting relevant information from complex visual inputs containing **charts, diagrams, geometric figures, and numerical tables**; (2) **Robust Reasoning**: maintaining consistent reasoning progression while integrating visual evidence throughout the reasoning process.\n\nCoFFT addresses both aspects with synergistic enhancement between the following components.\n\n**1. Visual Focus Adjustment (VFA) - Enhance Perception**\n\nVFA enables dynamic attention on step-relevant regions in images. It reduces visual noise from irrelevant content (multiple subfigures, complex statistics) and prevents interpretation errors (incorrect values/symbols, confused relationships), ensuring accurate information extraction for a solid reasoning foundation.\n\n**2. Dual Forward Decoding (DFD) - Enhance Reasoning**\n\nDFD evaluates both **reasoning progress scores** and **visual focus scores** to determine next steps. This dual verification uses current focus regions to suppress hallucinations while exploring multiple reasoning paths, maintaining consistency between visual evidence and reasoning progression.\n\nAblation studies confirm distinct contributions:\n\n- **DFD removal**: Greater impact on reasoning-type tasks\n- **VFA removal**: Greater impact on fine-grained tasks\n\n**We also conduct a detailed analysis of the performance of CoFFT on Mathvista and Mathvision in Q4.**\n\n|Models|MathVista|MathVision|S.W-China|S.W-Global|\n|-|-|-|-|-|\n|Our|70.4|23.36|35.12|29.37|\n|w/o DFD|68.5|20.42|28.42|27.19|\n|w/o VFA|69.3|21.71|27.08|26.25|\n|Baseline|68.2|18.09|21.45|25.31|\n\n**Q2: For the example from math datasets**\n\nDue to the inability to upload images, we provide a problem with its description and compare reasoning paths between VLM (Qwen2.5-VL-32B) and CoFFT. \n\n**The effectiveness of CoFFT in math problems, such as charts, multi-subfigures, and table data, is very obvious.**\n\nWe guess that you may be more interested in geometric problems, so we give **an example of this**.\n\n**Question:** Given a right triangle with sides $AB=8$, $BC=15$, and hypotenuse $CA=17$.  What is the radius $r$ of the inscribed semicircle?\n\n**Description:** The semicircle passes through the point $B$, with its diameter coinciding with side $BC$, and is tangent to hypotenuse $CA$.\n\n**VLM** \n\nVLM processes the entire image holistically but struggles with geometric constraint correlation, leading to incorrect formulations:\n\n1. **Step 1:** Identifies the right triangle with sides $AB=8$, $BC=15$, $CA=17$ and recognizes the inscribed semicircle.\n\n2. **Step 2:** The semicircle is tangent to sides $AB$ and $CA$, applying tangent properties. Sets up $\\tan(\\angle BAC) = \\frac{8}{15} = \\frac{r}{15-r}$ based on misunderstood geometric relationships. **(Error occurs)**\n\n3. **Step 3:** Solves the incorrect equation: $8(15-r) = 15r$, yielding $120 = 23r$. Concludes $r = \\frac{120}{23} \\approx 5.22$.\n\nThe global perspective prevents the proper establishment of precise geometric relationships.\n\n**VLM with CoFFT**\n\nCoFFT deconstructs the problem through iterative visual focus adjustment, enabling progressive and robust reasoning:\n\n1. **Step 1 (Global View):** Extract given parameters: right triangle with $AB=8$, $BC=15$, $CA=17$.\n\n2. **Step 2 (Remove some blank areas, Focus on triangle):** Determine that the diameter of the semicircle is at side $BC$. Therefore, $\\cos(\\angle BAC)=\\sin(\\angle ACB)=\\frac{8}{17}$, $\\tan(\\angle BAC)=\\frac{8}{15}$\n\n3. **Step 3 (Focus on Hypotenuse and Tangency):** VLM focuses on the tangency between the semicircle and the triangle. The region including $\\angle ACB$ and the semicircle is cropped out. Based on the tangency, $\\sin\\angle ACB=\\frac{r}{15-r}$ is obtained. \n\n4. **Step 4 (Solution with Global View):** Get $\\sin(\\angle ACB)=\\frac{r}{15-r}=\\frac{8}{17}$, solving $17r = 120 - 8r$ to yield $r = 4.8$.\n\nThis example shows how CoFFT's precise targeting enables correct reasoning by decomposing problems into manageable, intuitive steps, while the baseline fails due to incorrect geometric constraint interpretation.\n\n**Q3: For the Example in Figure 1:**\n\nWe apologize for any confusion this example may have caused.\n\n**(1) CoFFT does not introduce new knowledge, but can make more effective use of current knowledge.**\n\nCoFFT is a training-free method that cannot modify the model's internal parameters or knowledge when evaluated on VLMs. To ensure fair comparison, all training-free methods are evaluated on the same VLM (e.g., Qwen2.5VL-7B) while maintaining unchanged foundational knowledge. CoFFT demonstrates that by reducing interference from irrelevant visual information, models can utilize their existing knowledge more effectively rather than requiring new knowledge acquisition.\n\n**(2) Poor performance in the Figure 1 example is not attributed to background knowledge deficiency.**\n\nWe validate this across multiple models (GPT-4o, o3, Qwen2.5VL-72B, Qwen2.5-VL-7B):\n\n- **Original complete image:** Models identified locations like **Tokyo, Paris, Shanghai, or New York** based on visual elements like pigeons, crowds, trees, and amphitheater structures.\n\n- **CoFFT-generated cropped image (focusing on the semicircular buildings and curved pools):** Focusing on semicircular buildings and curved pools, models provided more accurate answers including **Nanjing, Rome, and Washington D.C** by highlighting distinctive architectural features that trigger their latent knowledge.\n\nMoreover, we directly asked these models about the representative features of Nanjing's Sun Yat-sen Mausoleum. They all mentioned the corresponding areas shown in Figure 1 and could describe the semicircular buildings, semicircular stepped seating, and curved pools.\n\nThis experiment shows that the models presented in Figure 1 possess the relevant knowledge, but cannot correctly utilize it due to excessive visual noise.\n\n**(3) Impact of background knowledge**\n\nModel background knowledge significantly impacts reasoning, as shown in our analysis (lines 233-238).\nCoFFT achieves improvements on geography-based benchmarks like SeekWorld-China by helping identify fine-grained, region-specific details. \nHowever, gaps remain compared to GPT-4o in global settings due to knowledge limitations.\n\n|Model|S.W-China|S.W-Global|\n|-|-|-|\n|GPT-4o|31.90|56.50|\n|Qwen2.5-VL-7B|21.45|25.31|\n|7B with CoFFT|35.12|29.37|\n|Qwen2.5-VL-32B|24.13|25.63|\n|32B with CoFFT|38.61|30.63|\n\n\n**Q4: For the detailed Performance of CoFFT on Math Benchmarks:**\n\nWe apologize for any confusion. Our selection is based on two key considerations:\n\n**1. Show Flexibility**: Demonstrates CoFFT's visual adjustment mechanism - both \"zoom in\" for details and \"zoom out\" for global view, avoiding local optima.\n\n**2. Page Limitations**: Complex examples with intricate diagrams and longer reasoning chains are difficult to present clearly in single figures.\n\nTo more clearly demonstrate the impact of CoFFT, we conduct a detailed analysis.\n\n**Detailed Analysis:**\nCoFFT was analyzed on MathVista and MathVision benchmarks, which include geometric diagrams, charts, tables, algebra, and logic problems conveyed through images. \nResults show CoFFT significantly improves both **accurate visual perception** and **reasoning capabilities**.\n\n- On **MathVista**, Greatest gains in **Numeric Commonsense, Algebraic Reasoning, Arithmetic Reasoning, and Statistical Reasoning**. CoFFT excels at extracting helpful information from visually dense images, enabling correct numerical interpretation and reasoning.\n\n- On **MathVision**, Substantial improvements in **Arithmetic, Counting, and Statistics**. These tasks require interpreting complex visual structures where attention to key elements is crucial for building correct logical/combinatorial models. Limited improvements observed in Graph Theory, Topology, and Transformation Geometry.\n\n**Analysis on MathVista (Testmini)**\n\n|Skill Category|Baseline|CoFFT|Improvement|\n|-|-|-|-|\n|Numeric Commonsense|34.03%|41.67%|+7.64%|\n|Algebraic Reasoning|67.97%|71.89%|+3.92%|\n|Logical Reasoning|29.73%|32.43%|+2.70%|\n|Arithmetic Reasoning|61.19%|63.74%|+2.55%|\n|Statistical Reasoning|85.38%|87.38%|+2.00%|\n|Geometry Reasoning|67.36%|68.20%|+0.84%|\n|Scientific Reasoning|67.21%|68.03%|+0.82%|\n\n**Analysis on MathVision (Testmini)**\n\n|Category|Baseline|CoFFT|Improvement|\n|-|-|-|-|\n|Arithmetic|47.37%|68.42%|+21.05%|\n|Counting|10.53%|26.32%|+15.79%|\n|Statistics|21.05%|36.84%|+15.79%|\n|Combinatorics|21.05%|31.58%|+10.53%|\n|Logic|15.79%|21.05%|+5.26%|\n|Algebra|21.05%|26.32%|+5.26%|\n|Solid Geometry|5.26%|10.53%|+5.26%|\n|Metric Geometry - Length|10.53%|15.79%|+5.26%|\n|Analytic Geometry|31.58%|31.58%|0.00%|\n|Descriptive Geometry|15.79%|15.79%|0.00%|\n|Graph Theory|15.79%|15.79%|0.00%|\n|Metric Geometry - Angle|5.26%|5.26%|0.00%|\n|Metric Geometry - Area|21.05%|21.05%|0.00%|\n|Topology|15.79%|15.79%|0.00%|\n|Transformation Geometry|21.05%|21.05%|0.00%|\n|Combinatorial Geometry|15.79%|10.53%|-5.26%|"}, {"review_id": "xbqkPVej13", "reviewer": "Reviewer_UGRZ", "summary": "The paper proposes Chain of Foresight-Focus Thought (CoFFT), a novel training-free framework designed to enhance the visual reasoning capabilities of Vision-Language Models (VLMs). CoFFT operates in an iterative loop of three stages: (1) Diverse Samples Generation (DSG) produces multiple multi-step reasoning paths under varied temperature settings; (2) Dual Foresight Decoding (DFD) jointly evaluates these samples based on reasoning quality and visual relevance to select the best one-step continuation; and (3) Visual Focus Adjustment (VFA) refines the visual input by identifying and cropping the most informative image region, feeding it back into the next reasoning iteration. \nExperiments across multiple benchmarks and models (e.g., Qwen2.5-VL, InternVL-2.5, and Llava-Next) on mathvista, mathvision, M3CoT and MMStar Charxiv and seek-world. show that CoFFT achieves consistent gains of 3.1–5.8% in accuracy, with performance improvements scaling with model size.", "questions": "See the weaknesses anove.", "limitations": "Not discussed. One big issue and limitation is added computation. The author needs to discuss it and potential works to mitigate it, or put it in the future work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "strength:\n1. The idea is great and original. Relative attention is very intuitive. \n2. Extensive comparisons are conducted and the improvement is impressive, demonstrating the effectiveness of the method.\n\nweakness:\n1. Clarity of the paper and writing needs to be improved. For example, \n- What point are you trying to make in figure 1? It's not clear from the caption and only makes sense to read with the main text. Similar issue for Figure 2. The captions should be somewhat self-contained. Please add more details \n- Line 51 -- should use complete sentences\n- Figure 3 -- the caption uses (a) and (b), which does not correspond to the figure.\n- line 118 - 120 -- introduce terms and symbols not defined before, which is more confusing for the reader.\n- Writing needs to be improved. reorder the sections or maybe reduce the terminology and symbols if you have not define it yet, \n\n2. Limitation needs to be discussed further, especially run-time, as you are creating repetitive samples every iteration. This needs to be discussed further and maybe addreseed.", "was_revised": true, "final_justification": "I went over the rebuttal and the authors have addressed some of my concerns.", "rebuttal": "**Q1: On the Clarity of Figures and Captions**\n\nThanks for your feedback regarding the clarity of our figures.\n\nTo address this, we will revise the captions for Figures 1, 2, and 3. We will also remove the redundant parts in the Figure 3 caption.\n\n- **Revised Caption for Figure 1:** This caption will be expanded to explain the core comparison being illustrated—how our CoFFT-guided approach corrects the reasoning of a baseline VLM by focusing its attention on the relevant visual evidence. \n\n> **Figure 1:** A **motivational example** from the SeekWorld benchmark illustrating the difference in reasoning between OpenAI-O3 and our CoFFT-guided approach. **(a)** shows the original reasoning process of O3, which gets distracted by irrelevant information (crowds, pigeons) and arrives at an incorrect answer. **(b)** demonstrates how providing a focused visual region of the key semicircular buildings and curved pools, guided by a human-like cognitive process, helps VLM to identify the correct location as Jiangsu, China (Sun Yat-sen's Mausoleum). This comparison highlights the necessity of dynamic visual focus for next reasoning in complex reasoning tasks and motivates CoFFT's design—see Figure 2 for how CoFFT achieves this improved reasoning through its three-stage iterative framework.\n\n- **Revised Caption for Figure 2:** The new caption will clearly break down the iterative three-stage workflow of our CoFFT, allowing readers to grasp the entire process at a glance. \n\n> **Figure 2:** The overall iterative workflow of our proposed Chain of Foresight-Focus Thought (CoFFT) approach. The process begins with an image and a question. Each iteration consists of three stages: **(1) Diverse Sample Generation (DSG):** The VLM generates multiple potential reasoning paths. **(2) Dual Foresight Decoding (DFD):** These paths are evaluated based on both reasoning progression and visual focus to select the optimal path. **(3) Visual Focus Adjustment (VFA):** The visual focus is then shifted to the most relevant image region for the next iteration. This cycle of reasoning guiding focus, and focus informing reasoning, continues until the final answer is reached.\n\n- **Revised Caption for Figure 3:** We appreciate you catching the redundant  `(a)` and `(b)` labels. We will remove them and rewrite the caption to directly describe the components illustrated, making the figure easier to understand. \n\n> **Figure 3:** A detailed illustration of the three primary components of one Foresight-Focus Thought. **(1) Diverse Sample Generation (DSG):** The VLM generates multiple potential reasoning paths with different settings. **(2) Dual Foresight Decoding** evaluates different reasoning samples by combining a visual focus score  and a reasoning progression score to select the optimal path, and the first step of the optimal path is introduced to the reasoning process. **(3) Visual Focus Adjustment** uses a scoring mechanism based on question and future reasoning relevance to identify and crop the most informative image region for the next reasoning step.\n\n\n\n**Q2: On Writing, Terminology, and Structure**\n\nWe appreciate you highlighting the issues with writing clarity. We will revise these one by one and update them.\n\n- **Line 51:** Thank you for pointing this out. This was an oversight and will be corrected as follows: \n\n> (2) **Dual Foresight Decoding (DFD):** This module evaluates these samples by considering both visual focus and reasoning progression to select the optimal reasoning sample, and then incorporates its first step into the reasoning process.\n\n- **Lines 118-120 and Section Structure:** \n\n> We will not introduce $E_{att}$ and $E_{prob}$ mentioned in Section 3.1 (Overview) here, but will introduce them and provide detailed explanations in later sections. We will further review all subsequent symbols and terms to ensure they are formally defined upon first appearance.\n\n- **Writing needs to be improved:** \n\n> To address this issue, we plan to **reorganize Section 3 (Chain of Foresight-Focus Thought)**. We will first provide a more concise and comprehensive overview without complex symbols in **Section 3.1 (Overview)**.\nThen, in the subsequent Section 3.2, we will first introduce the relative attention mechanism that will be used throughout the following sections.\nIn the next three subsections (3.3, 3.4, and 3.5), we will introduce each stage in CoFFT in detail one by one, and ensure that all symbols (such as $E_{att}$, $E_{prob}$, etc.) are clearly defined and explained when first used. This will enable readers to understand our method progressively and avoid confusion.\n\n**Q3: For Addressing Computational Cost**\n\nThank you for your valuable suggestions. \nWe will first conduct a quantitative comparison to elaborate on CoFFT's computational overhead, then propose one pruning approaches to attempt optimization.\n\n**(1) Current Computational Overhead**: \n\nIn lines 245 to 249 of the original manuscript, we conduct a comparative analysis of CoFFT's computational cost against other training-free methods, as shown in the table below. \nThese data indicate that although CoFFT introduces certain computational overhead, **it is still relatively efficient and achieves good performance.**\n\n| Models      | Baseline | MCTS     | Predictive decoding | ICoT     | DyFo     | CoFFT (Ours) |\n| ----------- | -------- | -------- | ------------------- | -------- | -------- | ------------ |\n| Performance | 42.72    | 44.68    | 45.05               | 34.91    | 44.98    | 48.19        |\n| FLOPS       | 8.35e+12 | 4.05e+14 | 1.85e+14            | 1.88e+13 | 1.98e+13 | 2.38e+14     |\n\n**(2) Proposed Mitigation Pruning Strategies**: \n\nTo attempt to optimize CoFFT's time overhead, we design an intuitive pruning method to reduce computational cost while maintaining performance, mainly considering two aspects: **reasoning process similarity** and **reasoning advantage**, comprising two strategies:\n\n- **Adaptive Similarity Pruning**: This strategy eliminates semantic redundancy among candidate samples.\n  At each step, we calculate TF-IDF similarity between all candidate sample pairs in the current reasoning process. \n  If two samples exhibit high similarity (e.g., > 0.7) for **consecutive *n* steps**, we prune the reasoning process with high similarity to other reasoning processes to ensure diversity between reasoning processes.\n- **Adaptive Reasoning Pruning**: This strategy terminates unpromising reasoning paths early by tracking each candidate sample's reasoning progress score ($E_{prob}$). If a sample fails to achieve positive scores for **consecutive $n$ steps**, it is marked as \"stagnant.\"  To maintain search breadth, we prune only the single lowest-scoring stagnant sample per iteration, ensuring at least two candidates are always retained.\n\n**(3) Experimental Result**: \n\nWe set $n$ to $2$ and $3$ for experimental validation, respectively. We validate this pruning strategy's effectiveness through a comprehensive evaluation across five benchmark datasets. The table below shows our expected results, indicating that our pruning strategy significantly reduces FLOPS while maintaining high accuracy. We even achieve some performance gains on some benchmarks.\n\n| Method                  | MathVista | MathVision | Charxiv | S.W-China | S.W-Global | FLOPS (Lower is better) | Reduction Ratio |\n| ----------------------- | --------- | ---------- | ------- | --------- | ---------- | ----------------------- | --------------- |\n| Baseline                | 68.2      | 18.09      | 42.5    | 21.45     | 25.31      | 8.35e+12                | -               |\n| CoFFT                   | 70.4      | 23.36      | 47.2    | 35.12     | 29.37      | 2.38e+14                | -               |\n| CoFFT + Pruning ($n=2$) | 69.9      | 22.37      | 46.2    | 34.32     | 29.37      | 1.72e+14                | 27.7%           |\n| CoFFT + Pruning ($n=3$) | 70.4      | 23.03      | 46.9    | 36.19     | 30.31      | 2.11e+14                | 11.3%           |"}, {"review_id": "7W6ATU4ejs", "reviewer": "Reviewer_hXHh", "summary": "This paper introduces the Chain of Foresight-Focus Thought (CoFFT), a novel training-free approach that enhances VLMs’ visual reasoning by emulating human visual cognition and mitigating task-irrelevant interference or hallucinations. CoFFT iteratively performs (1) Diverse Sample Generation to explore multiple reasoning paths, (2) Dual Foresight Decoding to select the optimal sample by jointly optimizing visual focus and reasoning progression, and (3) Visual Focus Adjustment to direct attention to regions most beneficial for subsequent reasoning. These three interdependent stages cycle until the final answer is reached, all without model modifications or retraining. Empirical results on Qwen2.5-VL, InternVL-2.5, and Llava-Next demonstrate consistent performance gains with controllable computational overhead.", "questions": "See weakness", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n\n1.\tThe proposed training-free approach is valuable and its effectiveness is demonstrated across multiple VLM backbones, underscoring its general applicability.\n2.\tThe method is novel and offers valuable insights into improving visual reasoning without any model finetuning.\n3.\tThe Introduction is clearly written and effectively motivates the work.\n4.\tThe Appendix provides a thorough algorithmic summary, which aids reproducibility and clarity.\n\nWeaknesses\n\n1.\tThe Method section is burdened by extensive mathematics, much of which appears only once and could be simplified. Notation is inconsistent—for example, it is unclear whether A_crop denotes a function or a result (see equation 6 and 7). The authors should adopt distinct typographical conventions (e.g., calligraphic vs. italic) to distinguish variables, functions, and operators.\n2.\tFigure 1 conveys limited insight into the core mechanics of CoFFT; it merely shows a reduction in reasoning steps without clearly illustrating the key stages or their interplay, making the example hard to interpret.\n3.\tThe evaluation lacks experiments on off-the-shelf reasoning models. Given the inherent reasoning limitations of the chosen backbones, validating CoFFT on stronger reasoning baseline VLMs would better demonstrate its broad utility.\n4.\tIt is better to use pseudocode instead of math formulation for clarity.", "was_revised": true, "final_justification": "the author has addressed my concerns, so i keep the rating.", "rebuttal": "**Q1: Clarity of Method Section and Notation**\n\nThank you for your valuable suggestion. \n\nIn the revised version of our paper, we will implement the following improvements:\n\n1. **Streamlined Mathematical Formulation:** \n   We will revise Section 3 to make the mathematical exposition more accessible. \n   For complex formulas that appear only once and can be clearly explained in prose, we will integrate their descriptions into the main text whenever possible. \n   This will improve the flow of the narrative and reduce the cognitive load on the reader.\n\n2. **Consistent Notation:** You astutely identified the inconsistent use of $A_{crop}$ in Equations (6) and (7). We acknowledge this oversight. In the revision, \n   $A_{crop}$ will be used exclusively to denote the attention map (i.e., a matrix). When referring to the value at a specific coordinate, we will use the functional form $\\mathcal{A}_{crop}(x, y)$. This change will ensure its meaning is unambiguous throughout the paper.\n\n3. **Adoption of Typographical Conventions:** \n   We will adopt your excellent suggestion to use distinct typographical conventions. We will subsequently use **normal fonts for variables, calligraphic fonts for functions, and italic fonts for operators**, and introduce these conventions in the overview section.\n\nWe are confident that these revisions will significantly enhance the clarity, consistency, and readability of our Method section.\n\n\n**Q2: Interpretation of Figure 1**\n\nThank you for your insightful comment. \nWe would first like to clarify its intended purpose, then provide our modification plan.\n\n**Figure 1 is designed as a motivational example** to demonstrate the *necessity* of our method, rather than to explain its internal workings. \nIts goal is to contrast: (a) the lengthy, unfocused, and often erroneous reasoning process of a standard VLM, with (b) an efficient reasoning process guided by dynamic visual focus for next reasoning.  This comparison highlights the problem we aim to solve and serves as the inspiration for CoFFT's design, which emulates the human ability for foresight and focus adjustment. The core mechanics of our method are detailed in **Figure 2** (which presents the overall three-stage iterative framework).\n\nTo resolve any potential confusion in the revised manuscript, we will:\n\n1. **Revise the caption of Figure 1** to explicitly label it as a \"motivational example\" and direct readers to Figure 2 for a comprehensive understanding of how CoFFT achieves this improved reasoning.\n2. **Strengthen the in-text reference to Figure 1**, clarifying its role in problem motivation and immediately guiding the reader to Figure 2 for the technical details of our solution.\n\nModify the Caption as follows:\n\n> A **motivational example** from the SeekWorld benchmark illustrating the difference in reasoning between OpenAI-O3 and our CoFFT-guided approach.\n> **(a)** shows the original reasoning process of O3, which gets distracted by irrelevant information (crowds, pigeons) and arrives at an incorrect answer.\n> **(b)** demonstrates how providing a focused visual region of the key semicircular buildings and curved pools, guided by a human-like cognitive process, helps VLM to identify the correct location as Jiangsu, China (Sun Yat-sen's Mausoleum). This comparison highlights the necessity of dynamic visual focus for next reasoning in complex reasoning tasks and motivates CoFFT's design—see Figure 2 for how CoFFT achieves this improved reasoning through its three-stage iterative framework.\n\n\n**Q3: Evaluation on Stronger Reasoning Models**\n\nWe appreciate your valuable suggestion.\n\nFirst, it is essential to clarify why leading proprietary models such as  o3 or GPT-4o are not included in our initial experiments. CoFFT's core mechanism—Dual Foresight Decoding and Visual Focus Adjustment—critically depends on access to the model's internal **text-image attention maps**. These attention maps are essential for computing visual relevance and dynamically adjusting visual focus. Unfortunately, leading closed-source models are provided as \"black-box\" APIs that do not expose these underlying internal states, making direct application of CoFFT technically infeasible.\n\nSecond, to further demonstrate CoFFT's broad applicability, we conducted a series of new experiments on the state-of-the-art large-scale open-source model Qwen2.5-VL-72B (the original manuscript had been performed on Qwen2.5-VL-32B). Our objective is to validate whether CoFFT still delivers significant improvements when applied to backbones that already possess strong intrinsic reasoning capabilities. The results are presented below. These findings demonstrate that CoFFT not only achieves **significant average performance gains** on 72B-scale models but also validates our method's excellent **scalability**.\n\n| Method                   | MathVision | S.W-China | S.W-Global |\n| ------------------------ | ---------- | --------- | ---------- |\n| Qwen2.5VL-32B            | 25.33      | 24.13     | 28.41      |\n| Qwen2.5VL-32B with CoFFT | 29.93      | 38.61     | 34.38      |\n| Qwen2.5VL-72B            | 36.18      | 37.80     | 42.18      |\n| Qwen2.5VL-72B with CoFFT | 41.12      | 47.72     | 48.75      |\n\n\n\n**Q4: Use of Pseudocode for Clarity**\n\nThank you for your valuable suggestion. We have prepared a complete, detailed line-by-line pseudocode, but due to space limitations, we have put it in the appendix. However, as you say, such pseudocode provides value in providing a concise overview, so we will add the following more condensed pseudocode at the beginning of **Section 3 (Methods)**. This will enable readers to quickly and intuitively grasp the core workflow of CoFFT before seeing the detailed mathematical description.\n\n**Algorithm 1: Chain of Foresight-Focus Thought (CoFFT) - High-Level Overview**\n\n**Require:** Original image $V$, question $Q$, VLM model $M$\n\n**Ensure:** Final reasoning result $R$\n\n1: $V_{focus} \\leftarrow V$; $R \\leftarrow \\emptyset$  // Initialize focus with original image and empty reasoning\n\n 2: **while** not reached final answer **do**\n\n 3:     // --- Stage 1: Diverse Sample Generation ---\n\n 4:$\\quad$ $S \\leftarrow \\emptyset$\n\n 5:$\\quad$ **for** $i \\in [1, k]$ **do**\n\n 6:$\\quad$ $\\quad$ $T_i \\leftarrow$ Sample from temperature range // Ensure diversity\n\n 7:$\\quad$ $\\quad$$s_i \\leftarrow$ Generate reasoning sample using $M, V_{focus}, Q, R, T_i$\n\n 8:$\\quad$ $\\quad$$S \\leftarrow S \\cup \\{s_i\\}$\n\n 9:$\\quad$ **end for**\n\n 10:\n\n 11: // --- Stage 2: Dual Foresight Decoding ---\n\n 12: $\\quad$ **for** each sample $s \\in S$ **do**\n\n 13:$\\quad$ $\\quad$ $E_{att}(s) \\leftarrow$ Calculate visual relevance score (via relative attention, cosine, IoU)\n\n 14:$\\quad$ $\\quad$ $E_{prob}(s) \\leftarrow$ Calculate reasoning progression score (via log-prob improvement)\n\n 15:$\\quad$ **end for**\n\n 16:$\\quad$ $s^* \\leftarrow$ Select best sample by combining $E_{att}$ and $E_{prob}$ with factor $\\lambda$\n\n 17:$\\quad$ $R \\leftarrow R \\cup \\text{FirstStep}(s^*)$ // Update reasoning with the optimal first step\n\n 18:\n\n 19:// --- Stage 3: Visual Focus Adjustment ---\n\n 20:$\\quad$ $A_{crop} \\leftarrow$ Compute next focus map based on $Q$, $R$, and future step $s^*$\n\n 21:$\\quad$ $B^* \\leftarrow$ Find optimal region with highest score in $A_{crop}$\n\n 22:$\\quad$ **if** score of $B^*$ is significantly higher than global average **then**\n\n 23:$\\quad$ $\\quad$ $V_{focus} \\leftarrow$ Crop and magnify region $B^*$ from original image $V$\n\n 24:$\\quad$ **else**\n\n 25: $\\quad$ $\\quad$ $V_{focus} \\leftarrow V$ // Revert to the full image if no clear focus is found\n\n 26:$\\quad$ **end if**\n\n 27: **end while**\n\n 28: **return** $R$"}, {"review_id": "RqjJvDJgoA", "reviewer": "Reviewer_GWAM", "summary": "This paper introduces the Chain of Foresight-Focus Thought (CoFFT), a training-free approach that aims to enhance VLMs' visual reasoning. The pipeline consists of three stages: (1) Diverse Sample Generation; (2) Dual Foresight Decoding; (3) Visual Focus Adjustment. Through experiments, the authors demonstrate that the method is effective across different models.", "questions": "1.  The authors are expected to conduct experiments during rebuttal to explain why the ablation results are not good (as mentioned before). I would be happy to adjust my score based on the quality of the response.\n\n2. In table 2,3, and 4, why does the number of significant figures vary (sometimes one, sometimes two)? \n\n3. In line 176, the $\\lambda$ should have been $\\alpha$.", "limitations": "The authors mentioned the following limitation: ` However, while CoFFT successfully solves previously unsolvable problems for VLMs, it may introduce unexpected errors in cases where VLMs alone perform well, indicating potential interference with their original well-established capabilities. `\n\nThe authors should clearly state what the ` unexpected errors` are using concrete examples.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "In general, I would view this work as an attempt to construct a deep thinking process similar to o3 model. I find the proposed method in the paper self-consistent. The evaluations are mostly thorough. \n\nHowever, I have two major concerns:\n\n(1) The paper introduces quite a lot of new hyperparameters. ($\\alpha$, $\\lambda$, etc.) **How were these hyperparameters determined?** Were they found through experimentation or chosen randomly? The authors should clarify this or provide additional experiments.\n\n(2) I calculated the results from the ablation study in Table 3 and found that the average results of the two ablation experiments are 45.32 and 45.36, respectively. **In other words, each individual component performs similarly to the baseline algorithms (45.05).** This raises the question: why should we adopt the two methods proposed in the paper? Would using the baseline combined with only one of the proposed methods yield comparable performance?\n\nOverall, I find this is a technically solid paper where reasons to accept outweigh reasons to reject. Therefore, I recommend borderline accept.", "was_revised": true, "final_justification": "The author addressed the issues I raised during the rebuttal period well. The other reviewers do not appear to have any fundamental objections to the paper. Therefore, I am happy to raise my score to 5.", "rebuttal": "**Q1 For the Determination of Hyperparameters ($\\lambda$ and $\\alpha$):**\n\nWe appreciate your valuable suggestions. The hyperparameters $\\lambda$ and $\\alpha$ employed in CoFFT are selected through systematic experimentation. \n\n**Hyperparameter Definitions and Functions:**\n\n- **$\\lambda$** appears in our Dual-Foresight Decoding: $E_{t+1} = \\lambda \\cdot \\text{Softmax}([E_{att}(s)]) + (1-\\lambda) \\cdot \\text{Softmax}([E_{prob}(s)])$, where it balances the visual focus score ($E_{att}$) and reasoning progression score ($E_{prob}$) when evaluating candidate reasoning samples.\n- **$\\alpha$** is utilized in our Visual Focus Adjustment: $C^{rel}(V, Q, R_t) = \\max(A^{rel}(V, Q) - \\alpha \\cdot A^{rel}(V, R_t), 0)$, where it controls the suppression strength of previously explored regions, enabling the model to balance between maintaining global perspective and exploring new region details.\n\nWe conduct comprehensive experiments to demonstrate the determination process for these parameters and analyze their impact on performance. For thorough evaluation, we select two representative datasets: **SeekWorld-China** (requiring fine-grained visual detail comprehension) and **MathVista** (demanding reasoning capabilities in addition to visual understanding).\n\n**Hyperparameter $\\lambda$**: The hyperparameter $\\lambda$ aims to balance the visual focus score ($E_{att}$) and reasoning progress score ($E_{prob}$). As shown in the table below, lower $\\lambda$ values reduce performance on SeekWorld-Global, as they interfere with the utilization of visual information.\nConversely, excessively high $\\lambda$ may interfere with the reasoning process, resulting in marginal performance degradation on MathVista. **The $\\lambda=0.3$ achieves optimal balance between these competing requirements**, making it our choice for the experiments.\n\n|$\\lambda$|MathVista (Acc.)|SeekWorld-China (Acc.)|\n|-|-|-|\n|0.2|**70.5**|34.58|\n|0.3|70.4|35.12|\n|0.4|69.8|**35.66**|\n|0.5|69.3|**35.66**|\n\n**Hyperparameter  $\\alpha$**: \nThe hyperparameter $\\alpha$ controls the suppression strength of previously explored regions during the Visual Focus Adjustment (VFA), thereby balancing the maintenance of global perspective with exploration of new regions. \nExcessively high $\\alpha$ leads to aggressive exploration of new regions, and the VLM falls into local optima on problems requiring a global view for reasoning, thus degrading performance. \nConversely, excessively low $\\alpha$ would impair VFA's motivation to explore new regions, thereby affecting its performance on SeekWorld-China. **The $\\alpha=0.3$ represents a robust and balanced choice for this trade-off**.\n\n|$\\alpha$|MathVista (Acc.)|SeekWorld-China (Acc.)|\n|-|-|-|\n|0.2|**70.4**|33.78|\n|0.3|**70.4**|35.12|\n|0.4|69.9|35.39|\n|0.5|69.5|**35.66**|\n\n\n**Q2: For the Ablation Study and Component Synergy**\n\nWe appreciate your astute observation regarding the ablation study results and the critical question of component synergy.\n\nTo demonstrate that the **Visual Focus Adjustment (VFA)** and **Dual-Foresight Decoding (DFD)** in CoFFT are **synergistically designed rather than simply interchangeable**, we conduct a more comprehensive analysis and perform a series of experiments.\n\n**1. Distinct Functional Design**\n\nReviewing our ablation results, we observe that while DFD and VFA achieve similar overall performance, they exhibit distinct strengths across different benchmarks. DFD demonstrates more pronounced effects on reasoning-intensive mathematical datasets, while VFA shows greater impact on fine-grained perception tasks in SeekWorld-China and SeekWorld-Global. This indicates that these two components primarily operate in different aspects.\n\n\n|Method|MathVista|MathVision|MMStar|M3CoT|Charxiv|S.W-China|S.W-Global|AVG|\n|-|-|-|-|-|-|-|-|-|\n|Our|**70.4**|**23.36**|**69.4**|**62.47**|**47.2**|**35.12**|**29.37**|**48.19**|\n|w/o DFD|68.5|20.42|66.5|61.39|44.8|28.42|27.19|45.32|\n|w/o VFA|69.3|21.71|67.4|61.09|44.7|27.08|26.25|45.36|\n\n**2. Synergistic Design as a Whole**\n\nThe average accuracy rates for various methods are as follows:\n\n- Qwen2.5-VL-7B (baseline): 42.72%\n- Predictive Decoding (reasoning enhancement method): 45.05%\n- DyFo (visual-search method): 44.98%\n- CoFFT: 48.19%\n\nWe subsequently test various combinations of visual and reasoning modules, conducting experiments on MathVista and SeekWorld-China benchmarks to comprehensively evaluate their performance.\n\n|Combination|MathVista|SeekWorld-China|Average|\n|-|-|-|-|\n|Baseline|68.2|21.45|44.83|\n|DyFo + Predictive Decoding|69.0|33.24|51.02|\n|Visual Focus Adjustment (VFA) + Predictive Decoding|68.5|31.37|50.24|\n|DyFo + Dual Foresight Decoding (DFD)|69.3|34.05|51.73|\n|CoFFT (VFA + DFD)|**70.4**|35.12|52.76|\n\n**Analysis**:\n\n1. **DyFo + Predictive Decoding**: This combination has a fundamental **conflict**, where DyFo determines the image regions that Predictive Decoding can use. DyFo, as an independent visual search module, determines the area of interest directly based on the question. For mathematical problems (MathVista), DyFo may crop out critical information such as numbers or geometric relationships, thereby **interfering** with the reasoning process of Predictive Decoding and degrading overall performance. For geolocation tasks (SeekWorld-Global), its focused view provides some improvement.\n\n2. **VFA + Predictive Decoding**: VFA is **dynamic**, adjusting visual focus after each reasoning step. In contrast, Predictive Decoding is designed for **static** visual inputs and cannot assess the value of current reasoning paths from a visual information perspective, can only passively accept the image regions provided by VFA, causing the introduction of irrelevant and confusing cropped regions during the reasoning process. These regions disrupt the predictive decoding's reasoning process, leading to **comprehensive performance degradation**.\n\n3. **DyFo + Dual Foresight Decoding (DFD)**: The combination of DFD and DyFo is still not optimal because DyFo's focusing behavior is **not guided by DFD's foresight**, even if we encourage DyFo to perform one acquisition of the image region per inference step, and is only capable of dynamic adjustment based on the current path. Moreover, DyFo's scoring basis comes from the Lang-Segment-Anything expert visual model [1], and its scoring has compatibility issues with the relative attention mechanism in DFD, further constraining overall performance. Although this brings some performance improvement, it lacks the tight feedback cycle of **mutual support between focus and reasoning**, which is core to CoFFT. Consequently, performance improvement is limited.\n\n**Conclusion**:\n\nThese experiments powerfully demonstrate that simply connecting existing visual search and reasoning methods is not optimal and may even be detrimental. CoFFT's advantage lies precisely in **the synergistic design of its VFA and DFD modules**. DFD evaluates reasoning paths based on both reasoning progress and image relevance, and provides the foundation for VFA to determine subsequent reasoning-relevant regions; meanwhile, VFA's precise focusing provides DFD with high-quality, low-noise input, enabling more reliable judgments. This \"tightly-coupled\" iterative loop is the key to significant performance improvement and represents one of CoFFT's core contributions.\n\n\n**Q3: For the Number of Significant Figures in Tables**\n\nWe appreciate your meticulous observation. The specific precision is determined based on **the sample size of each dataset and established conventions in related work**. Except for MathVista,  CharXiv, and MMStar,  other benchmarks have two decimal places. \n\nFor the **MathVista and CharXiv**, each one contains 1000 samples. At this scale, the minimum measurable change in accuracy is 0.1%; therefore, results can only be meaningfully reported to one decimal place. \n\nFor the **MMStar**, we observed that the official reports from the Qwen and Intern teams, as well as the original MMStar paper, report performance to one decimal place. We follow this convention to facilitate direct and clear comparison with these authoritative benchmarks.\n\n**Q4: For the Typo in Line 176**\n\nThank you for pointing out our oversight. The parameter in the textual description of Equation (5) on line 176 should be $\\alpha$, not $\\lambda$. This is a typographical error, and we will correct it in the final version. \n\n\n**Q5: For Providing Concrete Examples for Limitations**\n\nDue to the inability to insert images, we can only provide a textual description of a concrete example illustrating the \"unexpected errors\" that CoFFT may introduce.\n\n**Concrete Example**:\nConsider a question about a **complex diagram**, such as a food chain diagram containing numerous organisms and multiple connecting lines representing predatory relationships. The question might be: \"When organism X decreases, how will organism Y change?\"\n\n**VLM**: The VLM can process the entire diagram from a global perspective, correctly tracing the predatory relationship between X and Y to determine the final result.\n\n**Potential Errors Introduced by CoFFT**:\n\n1. During the **VFA** stage, the framework may be attracted to visually dense regions—for example, areas containing X or Y—considering them \"most informative,\" leading to magnification that may introduce content from other organisms surrounding X or Y.\n2. Although CoFFT's mechanism allows the model to recover the global view in subsequent steps, this region focus may introduce **contextual interference**.\n3. Consequently, during subsequent reasoning, the model may over-focus on this intermediate information, disrupting the overall reasoning process and leading to an incorrect final answer.\n\nHowever, it should be noted that all current methods for cropping or searching images carry this risk of introducing potential interference due to the introduction of partial images. This is not a limitation unique to CoFFT.\n\n[1] Luca Medeiros. Language segment-anything, 2024."}]

Author rebuttal:

Meta-review: The paper introduces a training-free framework that integrates Dual Foresight Decoding (DFD) and Visual Focus Adjustment (VFA) in an iterative loop to enhance the visual reasoning ability of vision-language models. 

The reviewers recognized multiple strengths of the work. Reviewer GWAM praised the paper as technically sound, self-consistent, and supported by thorough evaluation. Reviewer UGRZ highlighted the originality of the framework, the intuitiveness of the proposed foresight-guided focus, and the strong empirical results across diverse benchmarks. Reviewer hXHh emphasized the broad applicability of a training-free approach, the clear motivation. Reviewer nDak acknowledged the inspiration from human cognition and the novelty of addressing visual focus in VLMs. 

The primary remaining reservation comes from Reviewer nDak questioned whether some figures (notably Figure 1) convincingly demonstrate the method’s effectiveness.  However, this concern relates mainly to presentation rather than the substance of the contribution, and the rebuttal clarified that Figure 1 should be read as a motivational example rather than proof of capability.

It is recommended that the authors revise Figure 1 and related text in the camera-ready version to avoid confusion and provide a clear discussion of the method’s limitations.
Decision: Accept (poster)