From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
Authors: Ruilin Luo
Chufan Shi
Yizhen Zhang
Cheng Yang
Songtao Jiang
Tongkun Guan
Ruizhe Chen
Ruihang Chu
Peng Wang
Mingkun Yang
Lei Wang
Yujiu Yang
Junyang Lin
Zhibo Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal reasoning
Multimodal RL
Multimodal Large Language Model
Attention Analysis
Primary area: foundation or frontier models, including LLMs

Abstract: The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-based metric that quantifies how much a model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r=0.9616): models with higher VAS achieve substantially stronger multimodal reasoning. Surprisingly, multimodal cold-start fails to raise VAS, leaving distributions close to the base model, whereas text-only cold-start induces a clear increase. We term this counter-intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training-free interventions that directly manipulate attention allocation at inference time, yielding consistent 1--2% gains without retraining. Building on these insights, we propose Attention-Guided Visual Anchoring and Reflection (AVAR), a comprehensive cold-start framework that integrates visual-anchored data synthesis, attention-guided objectives, and visual-anchored reward shaping. Applied to Qwen2.5-VL-7B, AVAR delivers an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step-wise to the overall gains. The code, data, and models are available at https://github.com/lrlbbzl/Qwen-AVAR.

Published as a conference paper at ICLR 2026
FROM NARROW TO PANORAMIC VISION: ATTENTION- GUIDED COLD-START RESHAPES MULTIMODAL REA- SONING
Ruilin Luo 13+ Chufan Shi
Yizhen Zhang
Cheng Yang'
Songtao Jiang" Mingkun Yang
Tongkun Guan Ruizhe Chen
Ruihang Chu
Peng Wang
Lei Wang" Yujiu Yang'
Junyang Lin
Zhibo Yang
TTsinghua University Uniiversity of South California 3Qwen Team, Alibaba Group
University of California San Diego ZZejiiang University
Shanghai Jíao Tong University
7 Ping An Technology (Shenzhen) Co., Ltd.
ABSTRACT
The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently under- stood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-basee metric that quantifies how much model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r 0.9616): models with higher VAS achieve substantially stronger multi- modal reasoning. Surprisingly, multimodal cold-start fails elevate VAS, re- sulting in attention distributions close to the base model, whereas text-only cold- start leads to clear increase. We term this counter-intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training-free in- terventions that directly modulate attention allocation during inference, perfor- mance gains of 1-2% without any retraining. Building these insights, we further propose Attention-Guit comprehensive cold-start framework that integrates visual-anchored data synthe- sis, iitention-guider objectives, and visual-anchored reward shaping. Applied to Qwen2.5-VL-7B. AVAR achieves an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step-wise to the overall gains. The code, data, and models are available at https: : / //ggtttuuu high........ com/ 1r brbbbbzll AVAR.
Visual Anchoring and Reflection (AVAR), a
INTRODUCTION
Recent advances Reinforcement Learning (RL) have significantly enhanced the reasoning capabil- ities of Large Language Models (LLMs) such as OpenAl o1 (Jaech et al., 2024), Qwen-Max (Team, 2025) and DeepSeek-R1 (Shao et al., 2024; Guo al., 2025). Building upon this success, recent research has leveraged RL to construct Multimodal Large Reasoning Models (MLRMs), aiming to equip them with stronger cross- modal reasoning capabilities (Zhou et al., 2025; Yang et al., 2025d; Wei et al., 2025b; Yue et al., 2025b; Li al., 2025; Ma et al., 2026). However, applying these techniques directly exposes a critical but underexplored stage the training pipeline: the cold-start initialization stage that precedes the RL stage. Understanding and optimizing this stage remains core limitation of current MLRMs.
A surprising and counter-intuitive phenomenon illustrates this limitation: A text-only cold-start yields substantial improvements for MLRMs in subsequent RL tuning, whereas multimodal cold- start provides only marginal gains (Wei al., 2025a;b; Yue et al., 2025b). This phenomenon reveals a bottleneck in current training paradigms: MLRMs fail to leverage multimodal signals during cold- start, leading to inefficient resource use and limiting the potential RL for multimodal reasoning. Despite its importance, this paradoxical outcome still lacks a clear quantitative explanation.
"quual Contribution.
Corresponning author, yang- yanggyyjuu@vv.ssinguua.duuu

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Published as a conference paper at ICLR 2026
To shed light this paradox, we re-examine multimodal reasoning through the lens of attention al- location (Sec. 3). We introduce Visual Artention Score (VAS) to quantify how much a model attends to visual tokens. Correlating VAS with reasoning performance across representative MLRMs, find that reasoning performance is strongly correlated with VAS (r = 0.9616, Figure la): models with higher VAS achieve stronger multimodal reasoning, while those with low VAS perform worse. Furthermore, we find that multimodal cold-start fails to increase VAS, leaving distributions close to the base model. In contrast, text-only cold-start induces an increase visual attention and stronger visual grounding (Figure 1b). We term this phenomenon Lazy Attention Localization. It reveals that the effectiveness of cold-start arises not from multimodal alignment but from reasoning patterns internalized through text-only data, which enable models to preserve visual grounding in inference.
Building on this observation, we design a set of training-fres frre pilot experiments that directly ma- nipulate attention allocation at inference time (Sec. 4). By amplifying attention to visual tokens and reducing redundant focus on system tokens, we observe consistent gains in multimodal rea- soning without any retraining. Across models with different baseline performance levels, including Qwen2.5-VL-7B, Revisual-RI-CS and OVR-CS, our method yields average improvements of 1-2%. These results provide causal evidence that attention distribution is a decisive factor for reasoning pability. We therefore consider whether redundant attention to system tokens can be reduced and reallocated strengthen visual tokens during training.
Motivated by this insight, we propose Attention- Guidedd Visual Anchoring and Reflection (AVAR), framework that explicitly reshapes attention allocation during cold-start training (Sec. 5). AVAR first employs three-stage data synthesis pipeline that embeds visual anchors throughout the reason- ing process, orchestrating models generate synthetic data with built-in visual reflection. It then introduces attention-guided training objectives that enhance visual anchoring by encouraging focus on visual tokens while suppressing reliance on system tokens. Finally, during reinforcement leamn ing, AVAR incorporates visual- -ncchore reward shaping, ensuring models not only produce correct answers but also maintain strong visual grounding across extended reasoning chains.
Extensive experiments across 7 multimodal reasoning benchmarks demonstrate the effectiveness of AVAR (Sec. 6). Compared to the baseline Swen2.5-VL--7. our final model AVAR-Thinker achieves an average gain of 7.0%, with the strongest improvements on Math Vision (+12.2%) for multi-step geometric reasoning and HallusionBench (+8.8%) for robustness against visual halluci- nations. Systematic ablation studies further validate the overall pipeline design, clearly showing that each component of AVAR contributes step-wise to the observed performance gains.
In summary, work makes the following contributions:
We introduce the Visual Attention Score (VAS), metric that quantifies attention to visual tokens and strongly correlates with reasoning performance. Using VAS, we uncover Lazy Attention Local- izution, showing that multimodal cold-start fails to raise visual attention while text-only initialization increases it. This explains the underlying cause multimodal cold-start ineffectiveness..
We design training- free interventions that manipulate attention allocation at inference time by re- ducing redundant attention to system tokens and reallocating it to visual tokens. These interventions achieve consistent gains of 1-2% across different models, establishing causal evidence for the role of visual attention in multimodal reasoning.
We propose AVAR, cold-start framework that reshapes attention allocation by combining visual- anchored data synthesis, attention sugided objectives, and anchor-nchored reward shaping. shifts redundant attention from system to visual tokens, enabling stronger visual grounding. Applied to Qwen2.5-VL--BB AVAR achieves 7.0% average gain across multimodal benchmarks.
2
RELATED WORKS
2.1 MULTIMODAL LARGE REASONING MODEL
Multimodal large reasoning models (MLRMs) aim tackle reasoning tasks in multimodal scenar- ios, such as STEM problems (Lu et al., 2023; Wang et al., 2024; Yang et al., 2025c), perception- related tasks (Zhang al., 2024b; Kang et al., 2025; Jiang al., 2024). Recent works have focused on improving the curation of cold-start thinking data (Huang et al., 2025; Meng et al., 2025; Deng al., 2025; Wang al., 2025a; Ding et al., 2025) and exploring RL-based approaches (Zhang al.,
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by
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Figure I: Analysis of different models' performance and Visual Attention Score (VAS) distribution. (a) Model performance by mean VAS; (b) VAS distribution across layers.
2025a; Yang al., 2025d; Yu et al., 2025a; Luo et al., 2025; Zhang et al., 2025c; Bai et al., 2025a). Several studies highlight that high-quality unimodal "thinking data" can substantially improve rea- soning capabilities of MLRMs (Wei al., 2025b; Xiaomi, 2025; Chen et al., 2025; Sun al., 2025; Wang et al., 2025b). However, these works stop short of uncovering mechanisms behind this effect, and they have yet to investigate how multimodal reasoning data, particularly in "reasoning-wi with- image" settings, should be optimized.
2.2 VISUAL ATTENTION ANALYSIS
Recent studies have analyzed how Multimodal Large Language Models (MLLMs) allocate atten- tion across textual and visual information, revealing that inappropriate attention visual tokens remains a bottleneck. Specifically, Yin et al. (2025) demonstrate that modality fusion occurs pre- dominantly in the middle layers. yet models devote insufficient attention to visual signals and over- rely on language priors. Tang et al. (2025) further reveal that attention is unevenly distributed across heads, with certain heads disproportionately dominated by language priors. Liu et al. (2025) show that caasonin--oriented oriented MLLMs allocate substantially less attention to visual tokens than their non- reasoning counterparts, thereby amplifying hallucinations in longer reasoning chains. To address these issues, several inference-time interventions (Yin et al., 2025; Fazli et al., 2025; Tang et al., 2025) have been proposed to reweight attention distribution toward visual tokens. Building on this line of work, our study shifts the focus to the cold-start stage and demonstrates that guided initial- ization can reshape attention allocation, providing a stronger foundation for multimodal reasoning.
3 COLD START RESHAPES ATTENTION ALLOCATION
3.1 VISUAL ATTENTION SCORE
We begin our analysis by introducing the Visual Attention Score (VAS), a metric that measures how much a model attends to visual tokens relative to system tokens during multimodal reasoning.
Formally, let A(I, h) E IR 7 denote the attention matrix layer and head h, where T is the total number of tokens. Let V denote the index set of visual tokens, S the index set of system tokens, and U the index set of user tokens. For query token E U, the per-head VAS is defined as
VL = Σjev (t,
(1)
E
We compute the model-level VAS by averaging over all heads, layers, and query tokens:
I. H
VAS
ΣΣΣ
(l,,(1,h)
(2)
L.
U
3

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Published as a conference paper at ICLR 2026
where L and H are the numbers of transformer layers and attention heads, respectively. Intuitively, higher VAS indicates stronger reliance on visual features relative to system prompts, while lower values suggest that system tokens dominate the model's attention.
To examine how cold-start strategies reshape visual attention, we compute VAS for a set of represen- tative 7B multimodal models, including Qwen2.5-VVL V (Bai al., 2025b), R1 RisionVisionn (Yang et al., 2025d), ThinkLite- VL (Wang et al., 2025d), MM-Eureka (Meng et al., 2025), Revisual-R1- CS, Revisual-RI-RL (Chen al., 2025), OVR-CS, OVR-RL (Wei et al., 2025b), MiMo-VL-CS and MiMo-VL-RL (Yue et al., 2025b). For each model, we sample 200 cases from Math Vista (Lu et al., 2023) to compute VAS, and further evaluate their reasoning performance on four multimodal bench- marks: Vista (Lu et al., 2023), Math Vision (Wang et al., 2024), Math Verse- Verse-Vision et al., 2024a), and DynaMath- WORSE (Zou et al., 2024). We report the average performance across datasets together with the corresponding VAS, as detailed in Figure 1a. More detailed analysis of attention behaviors is provided in the Appendix D.
(Zhang
3.2 REASONING CAPABILITIES SCALES WITH VISUAL ATTENTION
As shown in Figure la, reasoning performance is strongly correlated with VAS, with Pearson cor- relation coefficient of 0.9616. From Figure la, we observe that some models devote minimal atten- tion visual features and consistently underperform in reasoning tasks when their VAS falls below 10, we term them Marrow-Viww Models (e.g.. Qwen2.5-VL-77 --IInttrutt,, R1-One Vision, ThinkLite- VL and MM-Eureka). Models in the intermediate range, with VAS between 10 and 15, display more balanced distribution between textual and visual modalities and achieve moderate improve- ments, we term them Wide View Models (e.g., Revisual-R1 variants). Finally, models with VAS greater than 15 sustain strong visual grounding and superior results across benchmarks, we term them Panoramic- View Models (e.g., OVR-RL, OVR-CS, MiMo-VL-CS and MiMo-VL-RL.
3.3 LAZY ATTENTION LOCALIZATION IN COLD-START TRAINING
Beyond the overall correlation between VAS and reasoning performance, uncover counter- intuitive phenomenon: cold-start with high-quality text-only data consistently outperforms multi- modal cold-start. Specifically, models initialized with unimodal reasoning data, such as OVR-CS and Revisual-RI-CS. I-CS, maintain 15-20% higher attention to visual features compared to those trained with multimodal reasoning data such as RI-One Vision and ThinkLite-V.
To further illustrate this phenomenon, we plot the VAS of Qwen2.5-VL-77, RI-OneVision, and OVR-CS in Figure 1b. Both Qwen2.5-VL-7B and its multimodal cold-start variant R1-One Vision exhibit nearly identical attention distributions, with persistently weak reliance on visual tokens across all layers. In contrast, OVR-CS, initialized with text-only reasoning data, shows consistently stronger attention to visual tokens throughout the entire inference process.
We term this phenomenon Lazy Attention Localization, highlighting that multimodal cold-start train- ing does not meaningfully increase attention visual tokens, whereas text-only initialization in- duces a clear and consistent shift toward much stronger visual grounding. This paradox suggests that the effectiveness of cold-start initialization does not arise from direct multimodal alignment, but rather from structured reasoning patterns learned from text-only data. Once acquired, these reason- ing strategies significantly enhance the model's 's ability reliably preserve visual grounding during inference, underscoring the critical role of cold-start attention reshaping.
Key Takeaways
1. Visual attention score is a strong predictor of reasoning ability: Models that devote greater attention to visual tokens consistently achieve stronger performance across multi- modal reasoning benchmarks.
2. The advantage of text-only cold-start stems from attention reshaping: We reveal that multimodal cold-start suffers from Lazy Attention Localization, failing to increase attention to visual tokens. In contrast, text-only initialization induces a clear shift toward stronger visual grounding, explaining its superior effecttiveness
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Published as a conference paper at ICLR 2026
4 TRAINING-FREE ATTENTION ROLE IDENTIFICATION
Building upon our observation that effective cold-start training reshapes attention allocation, we next ask whether similar gains can be achieved without additional training. To this end, we conduct set training-free pilot experiments that directly manipulate attention weights at inference time, inspired by the allocation patterns observed in grronger-perfomme models.
In our experiments, we apply a training- free attention modulation across all transformer layers, in- troducing selective attention modulation during inference. Our modulation method identifies and differentially scaling distinct token categories within the attention mechanism. This method oper- ates directly the attention weight matrix during the scaled dot-product attention computation, requiring no model retraining or parameter updates, Specifically, we modify the hidden states Z layer and head h through element-wise operations with attention masks:
Z1, Zt,b + Ossg MMR @ Zin Olags Mi Zt,b
where ME and MLA represent the enhancement and suppression masks for image and system tokens respectively, @ denotes element-wiss multiplication, and aiing Olays are scaling factors that control the relative importance of each token type during attention computation.
We evaluate this approach on representative models, Qwen2.5- VL.-B, Revisual-RI-CS, and
MathVision
MathVista
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OVR-CS, across 3 multimodal reasoning benchmarks: MathVista, Math and Matheerse-
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As shown in Figure 2, when Osys E (0.00, 0.40), performance consistently improves by 1-2%, re- vealing a System Token Redundancy Zone whose excess attention can be effectively redirected to vision. modulation on MathVista, Math Vision and Math Verse-VO. These findings further support our earlier observation on Lazy Attention Localization, demonstrating that insufficient visual attention is central bottleneck in cold-start initialization.
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Key Takeaways
3. Training-free attention modification inference augments reasoning performance: Emphasizing image features contributes to stronger reasoning capabilities, while system token attention exhibits redundancy reasoning tasks.
ArTENTION-GUDD VISUAL ANCHORING AND REFLECTION (AVAR)
Based on our finding that training- faiee interventions can improve reasoning by reallocating redun- dant attention from system visual tokens, we next ask whether this mechanism can be explicitly integrated into training. To this end, we propose Attention-Guided Visual Anchoring and Reflection (AVAR), a cold-start framework that systematically reshapes attention allocation to counteract Lazy Attention Localization. AVAR integrates 3 complementary components: visual-anchored reflec- tion data synthesis, attention guided training objectives, and visual-anchoree reward shaping, all designed to sustain strong visual anchoring throughout reasoning.
5.1 VISUAL-ANCHHORES REFLECTION DATA SYNTHESIS
At the data synthesis stage, prior approaches rely on caption-then-ree tee-ttheen-eere then-reason pipelines, where image descriptions first generated and then extended into reasoning chains (Yang et al., 2025d). In con-
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Published as a conference paper at ICLR 2026
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trast, method embeds visual anchors directly into the reasoning process. As shown in Fig. the pipeline coordinates 3 specialized models to produce reasoning data with built-in visual reflection:
High-fidelity Visual Descriptions Generation. We use Gemini 2.5-Pro (Comanici al., 2025) to produce high-fidelity visual descriptions that form the foundation for subsequent reasoning. These captions provide richer scene understanding than typical MLLM self-descriptions, establishing ac- curate visual information for the subsequent reasoning process.
Reflection-Enhanced Reasoning Generation. We Qwen3-235B-A22B (Yang al., 2025a) to generate extended reasoning chains over the visual descriptions. The model is prompted to perform iterative self-reflection and error checking, which naturally leads it to leverage the visual context during multi-step reasoning. This ensures the reasoning chain adheres to continuous grounding in visual context rather than relying solely on textual context or drifting into hallucinatory context.
Visual Anchor Integration. To further strengthen visual anchoring, we use Qwen3-32B (Yang al., 2025a) to augment the reasoning chains with explicit visual anchors. This stage inserts ref- erences such as "look back at the triangle" or "check the image again", simulating direct image perception. By enriching the reasoning chain with these additional visual statements, the data ex- plicitly ties each reasoning step back to the image, ensuring persistent visual anchoring.
The above data synthesis pipeline produces training data in which visual anchoring arises naturally throughout reasoning, mirroring the attention patterns of nunoramic-view models that sustain high visual attention ratios. The detailed prompts are provided in the Appendix G.
5.2 ATTENTION-GUIDT TRAINING OBJECTIVES
To explicitly encourage visual anchoring during training, we introduce attention-based loss functions that directly optimize the model's attention allocation patterns. Our training objective combines standard language modeling loss with two complementary attention-guidant guidance components:
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Published as a conference paper at ICLR 2026
where L denotes the set of targeted layers, H represents the number of attention heads, Q King and Kys represent query, image, and system token sets respectively, and A denotes the attention weight from query q to key k at layer and head h.
9,8
5.3 VISUAL-ANCHORED REWARD SHAPING
In the RL stage, introduce visual attention reward that explicitly encourages the model to sustain visual anchoring throughout extended reasoning chains. The reward evaluates the ratio of attention assigned to visual tokens relative to system tokens, providing auxiliary signal beyond correctness:
if rollout outcome is incorrect
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This reward structure ensures the model not only arrives at correct answers but also maintains strong visual grounding. The final reward combines three signals: Faceuracy rewards the correctness of the final answer, Fvisual promotes sustained attention to visual tokens relative to system tokens, and foormat enforces compliance with the required output structure. The overall reward is therefore defined as:
Tlotal 7 Faccuracy
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By integrating risual-anchored data synthesis, attention-guic creetton--gided training, and visual-anchored re- wards, AVAR systematically reshapes how multimodal models use visual information, turning per- sistent visual reflection into a core capability rather than an incidental byproduct. To optimize the policy using the shaped reward Tiotal, we employ Group Relative Policy Optimization (GRPO) (Shao et al., 2024), variant of policy gradient methods that stabilizes training by comparing relative per- formance within groups of rollouts. In each GRPO update step, we sample a batch of trajectories
{Ti) compute their total rewards "otal" and form groups (e.g., by quantiles or clustering) to esti- mate relative advantages. Let स. denote the current policy parameterized by The GRPO objective with our visual-anchored reward shaping is:
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EXPERIMENT
6.1
EXPERIMENTAL SETUP
Implementations We use Qwen2.5-VL-7B (Bai et al., 2025b) as the base model. Cold-start is trained on 30.6K samples from our -ishol-Anchored Reflection Data Synthesis pipeline for 20 epochs with LlamaFactory (Zheng et al., 2024) on 16 A100 GPUs. The subsequent RL stage uses VeRL (Sheng al., 2024) for 4 epochs on 17.9K public samples with the same hardware. This pipeline yields our final model, AVAR-Thinke mental settings are provided in Appendix C and E, respectively. For generalization, we also report results on Llama-3.2 Visionn- II-Itttrcct in the Appendix B.
Additional details on dataset curation and experi-
Evaluation We comprehensively validate the effectiveness of AVAR from multiple reasoning and understanding perspectives. To assess math reasoning capabilities, evaluate Math Vista (Lu et al., 2023), Marheerse (Zhang et al., 2024a), and Math (Wang et al., 2024). For multi- disciplinary reasoning performance, MMMU and MMMU-Pro (Yue et al., 2024; 2025a).
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Table 1: Performance comparison across benchmarks. Best scores bold, second best underlined. Closed-soures models are compared with each other, open-source models with ours. Models trained on Math Vision, so their results on Math Vision are omitted.
Math Reasoning
Multidixcipinaayy
Perception
Model
MitaVista
VathVision
Math Verse-Vr
MMMU-VAL
MMMU-Pru
MMStur
Hallosion.
Avg.
Closed-Snuee
GPT-4a
63.8
31.2
20.7
34.5
65.1
56.2
Clinde-3. 3.7-emneet
74.5
58.6
75.2
50.1
68.8
58.3
Open-Source General Models
Qwcn2.5-VL-7B
68.2
25.2
41.1
58.1
38.3
62.1
50.7
49.1
IntemXL2..588
LLaVA LLaVy-OneVision: OneVision-22
58.6
18.3
19.3
48.8
35.5
61.7
47.5
41.4
1aame-3.2-118-Vaai
48.6
19.7
18.4
50.7
33.0
49.8
40.3
37.2
Muttimodal Reasoning Modell
Mulberr-7B''
63.1
42.9
55.0
34.8
61.3
54.1
RI-OneVision
OpenVL Thinker
72.3
25.9
44.6
53.0
42.9
59.5
$3.0
50.2
ThinkLite-VI. VL
75.1
32.9
45.8
55.5
40.0
65.0
52.3
53.1
JIM-Eurek--BB
Vision-K1
73.5
47.7
56.3
39.6
64.8
$1.9
VLAA-Thinke:-78
68.0
26.4
48.2
58.7
40.9
64.2
50.9
50.6
Our mode!
over Q.w125-VV1-78
+6.5
+12.2
49.3
+5.7
+4.6
+2.0
18.8
+7.0
Additionally, examine perceptual understanding, we conduct evaluations on MMStar (Chen al., 2024a) and HallusionBennhh GGuan et al., 2024).
Hyperparameters Equation 4, and / are to 0.15. The stability constant in Equations and 8 is fixed to 10-6, In Equation 8, set X 0.3 and A5 0.1. Attention-guided training objectives and visual-anchore reward shaping are applied across all layers.
6.2
MAIN RESULTS
Table reports performance across diverse multimodal benchmarks. AVAR-Thinker delivers average gain of 7.0 % over the baseline Qwen2.5-VL-7B, with consistent improvements across mathematical reasoning (Math +6.5%, Math + +12.2%), multidisciplinary understand- ing (MMMU: +5.7%, MMMU-Pro: +3.1%) 1)), and perceptual reasoning (HallusionBenchh: +8.8%) The gains are particularly pronounced on Math Math Vision, which requires multi-step geometric reason- ing, and on HallusionBench which evaluates robustness against visual hallucinations, showing that sustained visual attention enhances both reasoning depth and robustness language-prie biases.
Against existing multimodal reasoning models, AVAR-Thinker establishes new state of the art among 7B models. surpasses ThinkLite- VL by 3.0% and MM-Eureka by 4.9% on average, and matches the performance of Vision-RI despite not being trained on Math Vision. Notably, it outper- forms models initialized with multimodal cold-start data 1--OnVission OpenVLThinkers by large margins, underscoring that attention reshaping is critical for effective reasoning.
6.3 ABLATION STUDY
To disentangle the contribution each component AVAR, we conduct systematic ablations start- ing from the baseline model. Table 2 presents results across all evaluation benchmarks.
Vichor-Anchored Reflection Data (VARD). Using visual-anchored reflection data synthesis alone yields notable gains (+1.7%), with particularly strong improvements on Math Vision (+7.7% and
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Table 2: Ablation study of our proposed components. Starting from the baseline, show the performance impact of adding different modules, indicated by a checkmark (v).
Configuration
Method Components
Benchmark Performance
YARD AGTO
VARS
MathVista
MathVision
MathNene-VO
MMStar
MMMU-VAL
MMMU-PPr
Hallusion.
Avp.
Baseline Qwee2. V-VL-BB)
68.2
25.2
41.1
62.1
58.1
38.3
50.7
49.1
70.6
32.9
43.5
61.1
55.2
38.7
55.3
51.0
72.0
AVAR-Thinker
74.7
37.4
50.4
64.1
63.8
42.9
59.5
56.1
Table Comparison of our Visual-Anchored Reflection Data (VAR) against other data-centric cold- start methods. The best scores are bold; the second best are underlined.
Benchmark & Performance
Method
Avg.
MathVista
VathVision
MathVerse-VO
MMStar
MMMU-val MMMU-Pro
Hallusion.
Raseline (Qwen2.5 -
68.2
25.2
41.1
62.1
58.1
38.3
50.7
49.1
RI-One Vision
26.3
39.7
54.9
49.9
OpenVLThinker
68.9
25.3
37.8
58.7
55.7
36.0
54.1
Vision-SRI
27.9
42.3
46.9
50.7
36.3
42.1
44.8
+ VARD (Ours)
70.6
32,9
43.5
61.1
53.2
38.7
55.3
51.0
HallusionBench (+4.6%). This demonstrates that embedding visual anchors directly into reasoning chains, rather than relying on caption-thers then-reason pipelines, substantially improves visual grounding even before the introduction of attention-guided training.
To contextualize this effect, we compare VARD against other data-centric cold-start methods (Ta- ble 3). Applied the same baseline model, VARD consistently outperforms data from R1- One Vision (+6.4%), Open VLThnnkr (+2.9%), and Vision-SRI (+6.2%). Notably, some datasets such RI-OneVisiin even reduce performance relative the baseline (-4.7%). indicating that sim- ply scaling reasoning data is insufficient and can be harmful. These findings emphasize the impor- tance of visually anchored design, which explicitly preserves visual grounding throughout reasoning chains, a factor that other cold-start datasets may not adequately address.
Autdntion-Guided Training Objectives (AGTO). Adding attention aatention-guided guided training losses to the VARD data results in cumulative improvements (+ 1.6%). The visual enhancement loss Lenhance img and system suppression loss Luppress-sys sys work synergistically to reshape attention distributions. The gains are most evident on benchmarks requiring precise visual understanding: Math Verse-VO (+2.9%) and MMMU-VAL (+3.1%).
Visual-Anchoree Reward Shaping (VARS). The complete AVAR framework, including visual- anchored rewards during RL, achieves the best performance (+6.8%). This confirms that incentiviz- ing visual attention during RL prevents the model from reverting to text-only reasoning patterns.
6.4 ANALYSIS OF ATTENTION EVOLUTION
Table 4 tracks how the VAS evolves across train- ing stages. The baseline model, Qwen2.5-VL-7B. starts with a VAS of 7.5 and an average perfor- mance of 49.3%. Introducing VARD data raises the
Table 4: Evolution of VAS and performance
across AVAR training stages.
Model
VAS
Avg. Performance
Dwen2.5-VL-78
VAS to 10. with performance improving to 51.0.% 0 With attention-guuded training, the AVAR-CS model reaches VAS of 13.8 and achieves 52.6% average
Dwea2.5-VL-72 VARD Data
10.1
51.0
AVAR-CS
13.8
52.6
18.9
56.1
performance. Finally, our full model ancu--d reward shaping, attains VAS of 18.9 and average score of 56.1%. This progres- sion illustrates that each component of the AVAR framework incrementally increases VAS, leading stronger visual grounding and reasoning ability, ultimately achieving panoramic view.
which integrates attention guided training and

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7 CONCLUSION
In this work, investigate the critical role of the cold-start initialization stage in training MLRMs. We introduce the VAS, a novel metric that quantifies a model's reliance on visual tokens and re- veals strong correlation with multimodal reasoning performance. Our analysis uncovers counter- intuitive phenomenon, which we term Lazy Attention Localization, where conventional multimodal cold-start training fails enhance visual attention, while text-only initialization paradoxically in- duces a significant increase. To address this bottleneck, we propose AVAR, a comprehensive frame- work designed explicitly reshape attention allocation during cold-start training. AVAR integrates three synergistic components: visual-anchored data synthesis pipeline that embeds visual ground- ing directly into the reasoning process; attention -uidded training objectives that encourage focus on visual tokens while penalizing over-reliance on system prompts; and visual-anchored reward shaping mechanism for the subsequent RL stage.
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. No human subjects or animal experiments were involved in this study. The datasets and models employed are widely adopted in the research com- munity and contain no personally identifiable information (PII) or sensitive content. We have taken deliberate steps to identify and mitigate potential biases in data selection, model training, and evalua- tion to ensure fairness and avoid discriminatory outcomes. Furthermore, we confirm that no personal identities have been collected, used, or disclosed in any form throughout this research.
REPRODUCIBILITY STATEMENT
All models used for training in this work are open-source, and all closed-source models reported in our comparisons are accessible through their official APIs. Upon acceptance of this paper, we will release the code, datasets, and trained models used in our experiments to the community, ensuring full reproducibility and facilitating further research.
ACKNOWLEDGEMENTS
This work was partly supported by the National Natural Science Foundation of China (Grant No. 62576191) and the Shenzhen Science and Technology Program (ZDCY20250001 103533010).
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Appendix
A LLM Usable Statement
17
Generalization Experiment
17
Data Curation
17
D Fine- line-grained Attention Analysis of Different models
17
E Experiment Setup
18
Case Study
19
Prompt Engineering
19
Baseline Model List
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A LLM USABLE STATEMENT
In accordance with the ICLR 2026 policy, we disclose the assistive use of LLMs in the preparation of this work. LLMs were employed support grammar correction, language refinement, and improve- ment of textual clarity. In addition, LLMs were used to assist in code debugging and to synthetically generate small portions of data for preliminary experiments. All LLM-gennrated content has been carefully reviewed, validated, and revised by the authors. The authors take full responsibility for the accuracy and originality of the final manuscript. LLMs have also been used to search for relevant papers and citations.
B GENERALIZATION EXPERIMENT
Table 5: Ablation study of our proposed components Llama-3.2-11B- 1---IIB-iiiio-nnssee model.
Confipuration
Components
Benchmark Perfermace
YARD AGTO YARS MathVista
Matlidio
Matt Vorse-O
MMStar MMMU-VAL
MMMU--P7
Hallussonleenh
Arg.
Baseline (Liuma 3.B-1B Vision (nstrct)
48.5
19.7
18.4
495
50.7
31.0
40.3
37.2
25.5
55.2
36.4
45.5
25.2
56.2
AVAR Thinber
61.7
2699
29,8
618
58.6
38.9
50.1
To evaluate the generalization capability of AVAR, we conducted generalization experiments on Llama-3.1 Lel---iiio--nnttee using the same training dataset. As shown Table 5, the individual modules continue yield significant and consistent incremental improvements, demonstrating the robust generalizability our approach.
C DATA CURATION
The Cold-Start dataset comprises five sources: RI-ShareVL (~~22.2K), Geo3K (~2.1K), M3COT (~3.2KK), AlgoPuzzleVOAA (~1.8K), and SOLIDGEO (~1.3K), totaling approximately 30.6K in- stances (Yao et al., 2025; Lu et al., 2021; Chen et al., 2024b; Ghosal et al., 2025; Wang et al., 2025c). In comparison, the RL dataset includes four sources: R1-ShareVL (~12.11), Geo3K (~2.1K), Super-CLEVER (~2.2K) (Li et al., 2023), and A12D (~1.5K) (Kembhavi et al., 2016), totaling approximately 17.9K samples. When using the RL dataset, first perform a one-time dif- ficulty filtering (Yang et al., 2025b) based on Owen2.5-VL-77 under 8 rollout iterations, we select samples whose accuracy falls between 0.25 and 0.75 (Yu al., 2025b).
D FINE-GRAINED ATTENTION ANALYSIS OF DIFFERENT MODELS
Instucarre
System Pamess
Swe-2.55VV-78
R1-OneVision
06
6.6
0.6
0,6
5.5
0.7
0.2
0.2
Figure 4: Attention allocation Qwen2.5-VLL RI-One Vision and OVR-CS on Mathvista.
To further investigate the impact of different cold-start strategies on attention allocation patterns, conduct a fine- me-graineed analysis of attention distributions across different token types (visual features, user instructions, and system prompts) for representative models (Yin al., 2025; Tang al., 2025; Liu et al., 2025; Yang et al., 2026). The data in Figure 4-5 show that RI-One Vision and ThinkLite- RL,, trained multimodal thinking data, do not alter the attention distribution behavior of the base
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ThinkLite-VL
Revisual81..5
MIMO-LCCS
0.0
06
0.5
Attention
0.4
0.4
0.2
0.2
0.2
Figure Attention allocation of Thinkl SounkLitte Lite- VI., RevisualR I-CS and MIMO-VL-CS on Mathvista.
Veusi Franene
Instuction
Qwen2 Swee2255-V-778
Vision-R1
OVR-CS
0.5
6.6
1.8
Afterkion
0.4
0.4
0.2
0.2
0.0
Layor
Layor
Layer
Figure 6: Attention allocation of Qwen2.5-VL, Vision-R1 and OVR-CS Mathvision.
model. In contrast, models trained on high-quality unimodal thinking data, RevisualRI-CS, OVR- CS and MIMO-VL-CS successfully reduce redundant attention to system tokens and redirect greater focus toward image information.
The result on Math Vision (Figure 6) demonstrates the same pattern: Vision-RI, trained on multi- modal thinking data, fails elicit the reflective attention mechanism that enhances visual focus.
E EXPERIMENT SETUP
In this section, we present the key hyperparameters for the cold-start and RL phases in Table 6. In the RL phase, we reduced the learning rate 1 x 10-6 and the batch size to 256 ensure stability and prevent catastrophic forgetting (Shi et al.. 2023). We also applied weight decay of x 10-5 for regularization. Notably, to balance exploration and exploitation, we set the KI. divergence coefficient to 0.0, the temperature to 0.8 (Shi et al., 2024), and the rollout to 8. Additionally, we used transformers (Wolf al., 2020) version 4.49.0 for all training- experiments.
Table 6: Hyperparameter search spaces used in experiments.
Cold-start
RL
Hyperparameter
Value
Hyperparameter
Value
Cutoff length
30,000
Max response length
30,000
Epochs Learning rate
20
Weight decay
3 x 10-5
x 10-6
Learning rate
x 10-6
Warm-up ratio
0.1
Warm-up ratio
0.03
Batch size
512
Batch size
256
Lr scheduler type
cosine true all
KL Divergence coeff.
0.0
Bí16
Rollout
Training module
Temperature
0.8
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F CASE STUDY
In this section, provide clear demonstration in Math Verse-VO, where the process involves reasonable visual reflection contexts, which inspire visual reflection pattern.
G PROMPT ENGINEERING
In this section, we present the carefully designed prompts used in Section 5.1, including high-fidelity visual description generation, cellectinneehancee reasoning generation and visual anchor integra- tion. We employ different prompts (Zhang et al., 2025b; Ma et al.) for mathematical and scientific problems.
During the construction of AR data, we employ high-fidelity visual information translation for math and science questions, owing to its superior perceptual capabilities, which enable accurate generation of visual element priors. Leveraging 100-2235B--A222--hhiinnn..
2.5-Pro (Comanici al., 2025) to perform
the strong mathematical and scientific reasoning abilities of we generate pseudo-mi multimodal reflection data based on caption token-base based reflection, naturally inserting placeholders for visual anchors. Since the task of visual anchor rewriting is relatively straightforward, we use smaller dense model, Qwen3-32B, which achieves sufficient accuracy after manual verification, making it suitable and efficient for this specific task.
Visual Description Generation Prompt for Math Problem
Your Role:
You High-Fidelity Visual Transcriber. Your sole mission is to precisely and accurately translate
image (e.g.. physies diagrams, geometric figures, charts, maps, etc.) a structured textual description.
Core Instructions:
You are a pure "visual scanner" and "data recorder." Your output must serve two critical goals:
Assisting Reasoning: Provide a complete, unambiguous set of visual facts necessary for an image- blind Al model to solve the associated problem no detail omitted.
Enabling Reconstruction: Your description must achieve nugineering-buupprint--ces precision, en- abling any reader-using using only your text-to reconstruct the original image almost identically, either by hand or with drawing tools.
Core Principles:
Absolute Objectivity: Describe only what you "see." Strictly forbid any inference, interpretation,
summary, or prediction. You are not the problem solver-you are problem solver's eyes. Strietly follow every stage the protocol below. Do not skip steps or mix
Structured Thinking:
phases.
First-Person Observer Perspective: Use phrases such "I first observe...", "My attention then shifts
to..." simulate meticulous observer systematically scanning and recording visual details.
[Protocol Execution Flow]
Part 1: Pure Visual Information Extraction (PVI)
In this section, you MUST NOT access or consider any accompanying textual problem. Your world consists solely of the image.
Stage O: Global Scan and Framework Definition
Image Qualification: Precisely define the basic type of the image in one sentence.
Example: "This is free-body diagram of an object
inclined plane."
2D canvas. Use directional (top, bottom, left, right, center) to summarize the overall layout and distribution primary content. Example: "The image horizontally oriented, with the main content concentrated toward the lower center region."
Canvas Setup and Layout: Treat the image
Core Framework Identification (if present): the image contains foundational structure (e.g.. coordinate system, table, circuit skeleton. inclined plane with ground), define it first. This serves the positional reference for subsequent elements.
Example: "The core of the image is Cartesian coordinate system. will use this as reference frame. The origin (0,0) is located slightly below and to the left of the canvas center. The x-axis is horizontal, with Both axes are marked with tick marks; each major grid line represents one unit length." "
pointing rightward. The y-axis vertical, with arrow pointing upward.
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Showcase on MathWerse-VO
equal rodii. The area of the shaded portion
the above.
<think>> Looking this geometry problem, need to find the area of the shaded portion in terms of the area of one small circle.
many times the area one of the small circles?
me denote radius each small circle r. Since seven small circles have equal radii, the area of one small circle nt²..
B
From the figure, I can there's large circle containing all seven small circles. There are 6 small circles arranged around the perimeter, tangent each other and the large circle, with one small circle the center tangent to all six outer circles. To find the radius the large circle, check the image again:
center small circle has radius
Each of the outer small circles also has radius
The outer circles are tangent to the center circle, so the distance from the center of the large circle to the center of any outer small circle is 2r.
Since the outer circles also langent the large circle, the radius of the large circle 2r 3r Therefore, the area of the large circle x(3r)² 9xr².
Now need identify what's shaded. Looking at the image, the shaded portions appear to be: 1.The upper right portion the central circle (appears be half of it)
2.A portion the circle labeled on the right side
Actually, let me reconsider the shading pattem. The image shows that certain regions are shaded gray. It appears that:
Half of the central circle (labeled P) is shaded
Half of the circle the right (labeled B) is shaded
If half of two circles shaded, then the total shaded area would be: 1//0"" + 1//rr* = xr²
This equals exactly the area of one small circle.
Wait, let look looking at the shaded regions created by this diameter. The shuded portions appear to be the right half of the central circle and the right half of the rightmost circle.
carefully the shading. The vertical line through point P suggests we're
So the shaded
1/(area of central circle) + 1/((area of right circle) = 1/Sx/"
1/sur
xr²
This exactly equal to the area of one small circle. </think>
<answer> To solve this problem, analyze the configuration of the seven equal circles and identify the shaded regions.
Let r denote the radius of each small circle. Since all seven circles are equal, each has area mr!.
From the geometric arrangement:
One circle is positioned at the center (marked P)
Six circles are arranged symmetrically around
A Il circles are tangent to their neighhors
A vertical line passes through the center, creating the shaded regions
The shaded portion consists of:
The right half of the central circle (area 1/Sxt))
The right half of the rightmost circle labeled B (area 1/tr")
Total shaded area = 1/ar" 1//r*= = ar²
Since the of one small circle is nr', the shaded equals exactly times the of small circle.
The answer 1. </answer>
Figure 7: AVAR-Thinker Thinker on Math VO: A showcase demonstrating its powerful visual percep- tion and reflective capabilities.
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Describe only what explicitly exists in the image (components, labels, connec- tions, relative positions). Avoid inferences, interpretations, or predictions based on scientific principles. Use clear hicrarchies and lists organize information, making it casy read and Published as a conference paper at ICLR 2026
Stage 1: Iterative Element Deconstruction
Begin with the most central, largest, most fundamental "anchor" clement. Proceed in logical se- quence (c.g., center-to-pen -prrrihepprr, left-to-right, clockwise) to deconstruct each element individually. Do not proceed to the next element until every detail of the current one has been fully described. For each new element, strictly follow this sub-process:
Element Declaration and Type: Clearly state the element you about to describe and its type.
Example:
now describe the rectangular wooden block resting on inclined plane."
elements or the core framework. If a coordinate system exists, provide exact coordinates. For non-coordinate images, use directional references (e.g., "at the 3 clock position relative the block's s geometric center"), contact points, quantitative approximations (e.g.. "length is approximately half the height of this block"). unmarked visually discernible relationships, carefully estimate numerical proportions. Example: "This block rests on the inclined plane described above, with its base fully aligned to the surface. Its center lies at the horizontal midpoint of the entire canvas."
Precise Location and Association: Locate the element relative to already-
Detailed Description of Form, Labels, and Visual Cues: tively detail all visual attributes and immediately record any text, symbols, markings attached to or adjacent to element.
This is the core of the description. Exhaus-
Form: Shape, size (relative to other elements or the entire canvas), orientation, trajectory.
Example: "The block is rectangular, with length approximately twice its height." Labels: Immediately transcribe all text, symbols, numbers, annotations directly attached or placed nearby.
Example: "At the center the block a black dot labeled
capital letter 'A'"
Special Visual Cues: Include any details critical for precise reconstruction and interpretation. such color, shading, texture, line thickness/style (solid, dashed, wavy, bold), and arrow style (filled, hollow).
Example: "The rectangular block is filled with light gray parallel diagonal lines simulating wood grain. Its outline is thicker than other lines in the diagram. The arrow representing gravitational force is solid, while the arrow representing the normal force dashed."
Repeat this iterative process until every line, shape, arrow, symbol, letter, and number in the image has been fully and precisely recorded. Stage 2: Final Scene Synthesis
Conclude by integrating the image's content with the implied problem context in one or two concise sentences, delivering a high-level summary optimized for problem-solving
Example (Physics problem): "In summary, this image depicts a stationary object on a horizontal surface subject to gravitational and normal forces, providing complete visual information for analyzing mechani- cal equilibrium."
Example (Coordinate diagram): summary, image provides a planar Cartesian coordinate map identifying multiple locations including Li Ming's home, school, and the post office."
Scientific Scene Deseription Prompt for Physics, Chemistry, and Biology
Your Role:
You are a Scientific Scene Descriher specializing physics, chemistry, and biology images. Your task is to transform images into objective, structured, and faithful textual descriptions. Your core function is to record, not interpret.
Core Principles:
Objective Recording:
Structured Clarity: reference.
Faithful Transcription: All text, symbols, labels the image must be recorded accurately and completely.
Emphasize how components are visually organized (e.g.. "A is inside the coil," " "B is immersed in liquid," "curve C is above curve D"), not their functional interactions (c.g.. "A exerts force B"))
Focus on Configuration:
[Protocol Execution Flow]
Step 1: Global Identification
Image Type and Main Theme: Summarize in sentence what the image is and what scientific scene or apparatus it displays.
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Example (microphone diagram): "This is cross-sectional diagram of dynamic microphone's internal structure."
Example (s-t graph); "This displacement-tiing (s-t) graph containing motion curves for two objects (A and B)."
Step 2: Component Breakdown
List and deseribe key components in logical order from primary to secondary.
Component Name and Location: Identify the component (prioritizing its lahel) and descrihe its posi- tion in the image.
Visual Feature Deseription: Deseribe its visual form, state, connection method. For graphs, de- scribe the shape of axes data curves.
Operational Example (s-1 graph):
Coordinate System:
Horizontal axis: Labeled t, representing time.
Vertical axis: Labeled as representing displacement.
Curve A: straight inclined line.
Curve B: An upward-opening curve (parabola).
Intersection Points: Curves A and B intersect at two moments, t and fz.
Step 3: Configuration Relationship Description
The core of this step is "describe what you see." Only describe connections, relative positions, and visual relationships directly shown in the image. Strictly avoid physical interpretations.
Prohibited Examples:
Avoid inferring relative states: Don't say "during this period, A's position is always ahead of B": instead say "in the interval from f1 to t2, curve A is consistently above curve B."
Avoid inferring equal quantities: Don't say "at some moment both have the same velocity"; instead say "at some point between f and the tangent slope of curve equals the slope of curve A." Operational Example (circuit diagram):
Describe: wire extends from the positive terminal of the power souree, connecting sequentially to switch S and bulb L1. The wire then branches: one path passes through bulb L2, another through motor M. The two paths reconverge and connect back to the negative terminal. Voltmeter V is connected in parallel across bulb I.2."
Avoid: "This parallel circuit where switch in the main line controls the entire circuit. The voltmeter measures the voltage across L2."
Step 4: Final Scene Summary
Provide a one-sentence high-level summary of the core setup scene depicted in the image. Example (microphone diagram):: "In summary, this diagram depicts apparatus structure composed of diaphragm, coil, and permanent magnet."
Example (s-t graph): "In summary, this graph depicts the displacement- relationship of object A moving linearly and object moving along curve within the same coordinate system."
Problem Solving Prompt with Image Description
Task Instructions:
I need you solve a problem that involves an image. will provide you with detailed description of the image, and you should solve the problem based on that description. Answer Format Requirements:
Place the final answer inside \boxed(].
If the question contains multiple sub-questions, separate their answers with semicolons (+:") and put them all together inside \boxed()
Input Structure:
Image Content: [Detailed description of the image will be provided here]
Question: [The problem to be solved based on the image description]
Multimodal Reasoning Style Transfer Protocol
Your Role:
You are Multimodal Reasoning Style Transfer Surgeon. Your sole mission is to receive (1) structured
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image text descriptions and (2) dual-part solution process containing "Thinking content" and "Response", then losslessly rewrite both parts in output style native multimodal large language model. Your rewrite must leverage the image description to create reasoning with precise visual anchors, making the output appear as if the model is observing and analyzing the original image in real-time. You are not a problem solver, but precision instrument for style transfer. Core Directives Three Non-negotiable Goals:
Reasoning Preservation
modifying any reasoning steps, mathematical calculations, logical chains, reflection paragraphs, or conclusions.
Absolutely forbidden:
All numbers, formulas, coordinates, and proportional relationships must be preserved verbatim.
Absolutely forbidden: aading/deleting/adussingg any facts, assumptions, or verification processes related to the solution.
Full- Process Coverage & Integrity
Your rewrite must cover both à d Thinking content: and 444 Response; sections.
Absolutely forbidden: skipping or merging any steps from either part.
Every step of the original reasoning, however minor or redundant (e.g., A = B, = C, therefore
A C), must be reproduced in original order.
Your task is to dress each step in multimodal clothing, not simplify it.
Multimodal Authenticity
Transform all soxt-descripiio--eeeeeeee expressions into native multimodal model visual interac- tion style.
Output must make readers unable to detect this is text-based rewrite.
Readers should believe the model directly observes, locates, and analyzes specific regions of the original image.
Key Principle: Your scalpel cuts only stylistic surface, never touching the reasoning core or logical flow.
Input Specification:
Input I: Structured Image Description Detailed text describing key elements, spatial layout, labels, axes, legends, colors, shapes, etc. This is your sole source for visual anchoring.
Input 2: Dual-Part Solution Text Contains # Thinking content: and ### Response: sections. must perform style transfer on both parts independently.
Phase 1: Visual Anchoring Style Transfer
Phase 1:
Visual Anchoring Style Transfer 1. Referencing overall structure (c.g., "According to description....)
Anchoring Rule:
+ "Observing the image layout..."
+ "From the image structure..."
Example:
Inputl: "Shows trapezoid ABED"
Input2: "According to description, this is trapezoid"
Output: "From image structure, the figure is right trapezoid ABED"
2. Referencing specific regions (e.g., "Description mentions...")
Anchoring Rule:
"[Visual verb] the [location]..."" Visual verbs: focus, observe, examine
Locations: left side, upper corner, etc.
Example:
Inputl: "Triangle ABC on left side"
Input2: "According to info, there's triangle ABC"
Output: "Focusing on left side, we see triangle ABC"
3. Referencing values/labels (e.g.. "B's coordinate is...")
Anchoring Rule:
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Published as a conference paper at ICLR 2026
"The annotation clearly shows..."
+
"Reviewing the axes..."
Example:
Inputl: "Point B at origin (0,0)"
Input2: "B is at (0,0)"
Output: "Reviewing the axes, point B is marked at (0,0)"
Replacement Rules:
Anchor precision: All visual anchors must correspond exactly to Input information.
Never fabricate details not present in the description.
Never add visual attributes unless explicitly described in Input 1.
Phase 2: Multimodal Enhancement
Apply minimal polish only after replacement:
Reasoning start: "I need understand" "From the image structure"
Key conclusions: "Therefore" > "Combining image features confirms"
Each enhancement must not exceed 5 words.
Phase 3: Post-Transfer Verification
Four-point verification before output:
1. Reasoning preservation: All coordinates. formulas, proportions, variables, and conclusions 100% identical.
Process completeness: No steps skipped, merged, or reordered in either section.
Anchor accuracy: visual anchors precisely correspond to Input 1.
4. Multimodal authenticity: Output naturally suggests "model viewing and solving from image."
Prohibited Failures:
Tampering with reasoning: Never change numbers or logic
Skipping steps: Must preserve complete logical chains
Structure destruction: Must maintain dual-part structure
Anchor misalignment: Must strictly follow Input positioning
Output Specification:
Format: Strietly maintain original dual-part structure with Thinkin in content: and
Response: separators.
Tone: Preserve original reflective style (c.g.. "But maybe my assumption is wrong?" must remain).
Ultimate Goal: Readers should helieve: "This is a multimodal model directly observing the image while conducting deep, step-by-step reasoning."
H BASELINE MODEL LIST
Table 7 summarizes the models we compared and their Hugging Face repositories.
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Model Name
Hugging Face Repository
Owen2.5-VL-7B-imss Instruct
co/
5-VL-78-Instzuce
R1-OneVision
https://uuggingffccccc////ng-g
RI-Dnevisiion-7BBRRi
InterVL2.5588
ntDp://bagggiggaacc.coo/pppppppp
InternVi2_5-8B
MM-Eureka
htcps://aggiinffacc.c///nigingnu
MM-uureka-Qwen-75
ThinkLite- VL
http:///uggiiggfacccoo/msssa
ThinkLite-WL-7B
Revisual-RI-CS
http:///auginnfaac..c///tttti
Revistal-Rll-Coldstatt
Revisual-R1-RL
http:://augginggaacc.cc//ttthhh
Revisual-RI-ffnnll
OVR-CS
https//buggiigfacc..oo//aaoaeeee
OVR-7B-CCol@Start
OVR-RL
MiMo-VL-CS
https///aaugggggaacc..o///loooot
M.Mo-VL-7B-SFF
MiMo-VL-RL
httpss//hugginfffacc.oo//////o/t
MiMo-VA-7B-RL
LLaVA-OneVisionn
https///bugiingfae.cc//mmm/-a
Laaza-conerssllor-eeeee-----v--
Llama-3.2-1
sttps///uugingfacc..////et=--
Lama==3.211B--Viiiirnnntttuue
Mulberry-7B
https///uuginnffae..c//haaaaaan
Milberryy.gwen221__7.
OpenVnThinker
http:///uggiigfacc.co/yeegsg
OpenVLTThineer-78
Vision-R1
http:://hugginnffacc..///1111
Vision-R1-78
VLAA-Thinker-77
tt:p:///aggiinffacc.co//00-----
VLAA-Thinker-7B
http:://oogiinfface..o////------
Vision-SRI
https///uuggggggaeee.eo//M---i-r SelfRewarddd-R1-73
Table List of models and their Hugging Face repositories.
25


Reviews:
[{"review_id": "XZIr3ZcFwl", "reviewer": "Reviewer_f3Wg", "summary": "This paper identifies the imbalance distribution of attention weights toward system and visual tokens during multi-modal reasoning, and proposes a training method to tackle the problem. It first develops a step-by-step data synthesis paradigm to general problems with strong focus on visual grounding, and then incorporates training objectives that encourage higher attention weights on visual tokens. Experimental results show that the attention distribution (i.e., the visual attention score) is strongly correlated with model performance, and imposing higher weights on visual tokens (with post-hoc manipulation or training objectives/rewards) leads to considerable improvements.", "questions": "(1) How would using different system prompts affect the observation made on attention distribution?\n\n(2) Would a system prompt emphasizing visual content lead to better reasoning performance?\n\n(3) How does the proposed method work with reasoning models?\n\n(4) The proposed method enforces stronger visual focus among all types of questions, can it be extended to adaptive attention steering?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) It is an interesting observation that reasoning performance has an almost linear correlation with attention toward visual tokens.\n\n(2) The paper shows that, even with training-free re-weighting, models can achieve better performance when steering their focuses to visual tokens.\n\n(3) A new data synthesis paradigm is developed, which could potentially benefit training subsequent models.\n\n(4) The proposed method shows promise in improving the performance of the generic baselines on multiple benchmarks.\n\n(5) The paper provides extensive ablation studies, which facilitates understanding the contribution of different components.", "weaknesses": "(1) The variance in attention distribution could also be affected by the design of system prompts used in different models. How does different choices of system prompts affect the visual attention score? Would tuning the prompts, e.g., imposing stronger focus on visual content, help boost the reasoning performance?\n\n(2) The paper only experiments with a single baseline (i.e., Qwen2.5-VL-7B, which is not a reasoning-specific model), and it is unclear whether it will generalize. It would be reasonable to incorporate the method with state-of-the-art reasoning models, e.g., ThinkLite-VL.\n\n(3) Different questions may desire diverse attention to visual content. For instance, some math problems would require more focus on textual tokens. How would the proposed method deal with the diversity of questions?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "epbq3iyzvR", "reviewer": "Reviewer_P2PD", "summary": "This paper investigates the cold-start initialization stage of Multimodal Large Reasoning Models (MLRMs). It introduces a new metric, the Visual Attention Score (VAS), to quantify a model's attention to visual tokens relative to system tokens. The authors identify a \"lazy attention localization\" phenomenon, where standard multimodal cold-starts fail to increase VAS, while text-only cold-starts counter-intuitively do. Based on this finding, the authors propose AVAR, a three-part framework designed to explicitly increase VAS. This includes: (1) visual-anchored reflection data synthesis (VARD), (2) attention-guided training objectives (AGTO), and (3) visual-anchored reward shaping (VARS). The authors demonstrate that each component of AVAR progressively increases the model's VAS and that the final model, AVAR-Thinker, achieves strong performance, particularly on mathematical reasoning benchmarks.", "questions": "1. Could the authors provide an analysis of the attention scores on visual tokens for the baseline vs. AVAR (and for each component addition, as in Table 4) in terms of absolute scores or as a ratio compared to user instruction tokens, rather than just the VAS ratio relative to system tokens? This would confirm that the model is genuinely enhancing visual attention and not just suppressing system prompts.\n\n2. Could the authors provide an ablation study to disentangle the two core mechanisms of the attention-reshaping framework? Specifically, what is the performance of the AVAR-Thinker model and the training-free approach if its components (AGTO and VARS) are modified to only enhance visual attention (i.e., removing the system suppression parts) or only suppress system attention (i.e., removing the visual enhancement parts)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The finding that text-only cold-start initialization can be more effective at increasing VAS than multimodal cold-starts is a novel and impressive observation.\n\n2. The training-free intervention (Section 4) provides a compelling causal link between attention allocation and performance, even without full retraining.\n\n3. The final model, AVAR-Thinker, demonstrates a significant performance improvement over the Qwen-2.5-VL-7B baseline, especially on challenging reasoning tasks.\n\n4. The curated Visual-Anchored Reflection Data (VARD) could be a valuable contribution to the community, as multimodal reasoning data is relatively scarce.", "weaknesses": "1. **The validity of VAS as a primary metric for visual grounding is questionable.** The paper defines VAS as a ratio of attention to visual tokens over system tokens (Eq. 1). A high VAS score could be achieved simply by aggressively reducing attention to system tokens, even if the absolute attention to visual tokens remains low or unchanged. The paper does not provide analysis to disentangle these two effects. To truly support the claim that AVAR \"attends to visual tokens more,\" the authors should present the attention scores for visual tokens solely (or comparing text tokens), not just their ratio against system tokens, similar to analyses in other visual grounding works [1, 2].\n2. **The core mechanism of \"attention reshaping\" is not properly disentangled.** The paper's central hypothesis is that performance improves by reshaping attention. This reshaping is consistently presented as a two-part mechanism: (1) enhancing visual attention and (2) suppressing system token attention. This duality appears in the training-free intervention (Sec 4), the AGTO loss (Eq 4), and the VARS reward (Sec 5.3). However, the contributions of these two sub-components are never ablated. It is unclear if the performance gain comes from the model genuinely learning to ground itself in visual tokens (as the \"Panoramic Vision\" title implies) or from simply learning to ignore the system prompt.\n\n---\nReferences:\n1. Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning, Sun et al., ACL 2025\n2. v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning, Chung et al., arXiv 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p1SouTLwYT", "reviewer": "Reviewer_CqeV", "summary": "This paper investigates the role of cold-start initialization in training Multimodal Large Reasoning Models (MLRMs). This work introduces Visual Attention Score (VAS) to measure how much attention is allocated to visual tokens, and explores the phenomenon of Lazy Attention Localization. These insights lead to the development of the Attention-Guided Visual Anchoring and Reflection (AVAR) framework, a comprehensive cold-start framework with three components: visual-anchored data synthesis, attention-guided training objectives, and visual-anchored reward shaping. This work demonstrates that AVAR improves multimodal reasoning performance across multiple benchmarks, providing a causal explanation for the importance of visual attention in MLRMs and offering an effective solution to improve reasoning capabilities.", "questions": "1. In Table 3, why does performance on MMStar and MMMU-val decrease across all frameworks after training with VAR data?\n\n2. In L375–377, why are the hyperparameters $\\alpha$, $\\beta$ (from Eq. (4)) and $\\lambda_v$, $\\lambda_f$ (from Eq. (8)) set to those specific values? Are these empirically determined or theoretically derived configurations?\n\n3. Is the performance improvement of AVAR related to the reasoning difficulty of the benchmarks? For example, does AVAR yield larger gains on tasks requiring complex reasoning, while showing smaller improvements on tasks less dependent on visual reasoning? Are there any observable patterns?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Innovative and Logical Metric. The authors introduce VAS, a novel metric that quantifies visual attention and its relationship with reasoning performance. The strong correlation between VAS and model performance provides a fresh perspective on multimodal reasoning (Sec. 3.1-3.2).\n\n2. Sound Motivation. Based on VAS, the paper offers a clear analysis revealing the challenges in MLRM training (Sec. 3.3). The authors discover Lazy Attention Localization, an unexpected phenomenon where multimodal signals are not effectively utilized during cold-start training, providing an insightful explanation for the bottlenecks in current training paradigms.\n\n3. Effective AVAR Framework. The proposed framework improves multimodal reasoning by addressing three components: data synthesis, training objectives, and reward reshaping, contributing to better reasoning performance (Sec. 5).\n\n4. Comprehensive Experiments. Extensive experiments across 7 benchmarks validate the effectiveness of AVAR (Sec. 6.2). Ablation study and analysis confirm the validity of the proposed data synthesis and training methods (Sec. 6.3-6.4).\n\n5. The paper is well-structured and easy to understand.", "weaknesses": "1. In L184–186, \"models initialized with unimodal reasoning data, such as OVR-CS and Revisual-R1-CS, maintain 15–20% higher attention to visual features compared to those trained with multimodal reasoning data such as R1-OneVision and ThinkLite-VL.\" appears unfair, since these methods differ in multiple factors such as dataset composition, model architecture, and training strategy, not merely in whether multimodal reasoning data are used.\n\n2. For Eq. (4) and Eq. (8), the hyperparameters $\\alpha$, $\\beta$, $\\lambda_v$, and $\\lambda_f$ lack sufficient explanation. In L375–377, their settings are presented without detailed justification or sensitivity analysis, leaving unclear how these parameters were chosen and how they influence model performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aJinZNS3KD", "reviewer": "Reviewer_LShU", "summary": "The paper focuses on the mechanisms and effects of multimodal large reasoning models (MLRM) during the cold-start stage, proposes the Visual Attention Score (VAS) to quantify “visual token attention,” and empirically shows a strong correlation between average VAS and multimodal reasoning performance (the paper reports Pearson r=0.9616). The authors further observe that adopting a multimodal cold start does not improve VAS, whereas a pure text cold start significantly increases VAS; this counterintuitive result is termed Lazy Attention Localization (LAL).\nBased on this diagnosis, the authors first conduct inference-time (training-free) attention redistribution experiments. By amplifying attention to visual tokens and suppressing attention to “system” tokens, they achieve a consistent gain of about 1–2% across multiple benchmarks and propose the existence of a “System Token Redundancy Zone” that can be reallocated to visual attention.\nThe authors then propose a three-part cold-start framework, AVAR: (1) Visual-Anchored Reflection Data synthesis (VARD), which embeds explicit anchors such as “look back at the image/check the figure” in the chain of thought; (2) Attention-Guided Training Objective (AGTO), which uses \\mathcal{L}{\\text{enhance-img}} and \\mathcal{L}{\\text{suppress-sys}} to encourage attention to vision and suppress attention to system tokens, respectively; (3) Visual-Anchored Reward Shaping (VARS), which uses an attention ratio as an auxiliary reward during RL and combines it with accuracy and formatting rewards to form the total return.\nUsing Qwen2.5-VL-7B as the base, the authors report an average improvement of +7.0% on seven multimodal reasoning benchmarks. Ablations indicate “stepwise” contributions from the three components, and VAS increases progressively with training stages (7.5→10.1→13.8→18.9).", "questions": "The questions are those listed in the Weaknesses section.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Treats “visual attention allocation” as the core explanatory variable for cold-start effectiveness, proposes VAS and systematically links it to reasoning performance, introduces and names the phenomenon Lazy Attention Localization, and provides a coherent diagnosis–intervention–training framework (VAS→inference-time intervention→training-time objective/reward), with a complete rationale and a clear trajectory.\n\n2. Reports results on multiple public benchmarks (MathVista/MathVision/MathVerse-VO, MMMU/Pro, MMStar, HallusionBench), compares against various 7B-scale multimodal reasoning models, presents overall and per-benchmark improvements, and further details ablations and stage-wise VAS changes, forming a relatively solid chain of evidence.\n\n3. Provides a formal definition of VAS (taking the ratio of attention from user query tokens to visual/system keys as the unit, then averaging across layers, heads, and tokens) and clear mathematical expressions of the losses/rewards, facilitating implementation and reproducibility.\n\n4. If the observed LAL is widespread, attention reshaping for cold starts could become a general recipe for multimodal reasoning models, with potential value for both academic and industrial training paradigms.", "weaknesses": "1. Although a strong correlation of r=0.9616 is reported, there is a lack of stricter causal testing for VAS→performance and control of confounders (e.g., differences in visual encoders across models, differences in system prompt templates, decoding length/temperature, etc.). At present, “inference-time attention amplification” yields only a 1–2% gain, which makes it difficult to explain the entire 7% average improvement during training. Suggested additions: controlled experiments that remove/replace the visual encoder, fix the system prompt template, and test sensitivity to decoding length/temperature.\n\n2. Using “attention to system tokens” as the denominator of VAS requires justification: the number and positional distribution of system tokens can vary widely across data/templates, potentially introducing bias. There is a lack of comparisons against alternative metrics that use user text tokens or the number of image patches as references and of consistency results. In addition, no sensitivity analyses at the layer/head/position levels are presented in the main text to support the robustness of the main conclusions (Appendix D is mentioned, but key statistics are not shown in the main body).\n\n3. Although reproducibility is claimed, the VARD stage uses closed-source models such as Gemini 2.5-Pro to generate high-fidelity descriptions, which may make it difficult for the community to replicate the same data quality. Meanwhile, the prompts, filtering, and quality-control details of the three-model collaborative pipeline are said to be in Appendix G, but the main text does not provide enough “actionable recipe” details (e.g., sampling temperature, rejection strategies, QC thresholds). On the implementation side, only coarse-grained parameters such as 30.6K / 20 epochs are given, with no disclosure of random seeds and variance.\n\n4. The main improvements are concentrated in visual mathematics scenarios such as MathVision/MathVerse-VO. Although MMStar/MMMU are included, there is a lack of dimensions such as real-world high-resolution VQA, video temporality, and multi-image multi-turn reasoning, making it difficult to judge the robustness and cost (inference latency/memory) of the method across a broader family of tasks.\n\n5. There is no systematic comparison against classic training-time approaches such as attention regularization/pruning/head reweighting or visual token resampling/token-dropping. The inference-time intervention is also not compared in parallel, under equal compute budget and equal parameter changes, with existing “adaptive attention calibration/head-level intervention” methods.\n\n6. Tables 1/2/4 mostly report point estimates without confidence intervals/significance tests, and they do not report mean±variance over multiple random seeds, making it difficult to assess the robustness of the +1–2% and +3–7% gains.\n\n7. Suppressing attention to system tokens may weaken instruction following and safety (many safety/formatting control signals depend on system prompts), yet the paper focuses only on performance improvements and does not provide comparative evaluations or failure analyses for instruction compliance and safe refusal.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)