Small Drafts, Big Verdict: Information-Intensive Visual Reasoning via Speculation
Authors: Yuhan Liu
Lianhui Qin
Shenji Wan
Venue: ICLR 2026 Poster (2026)
Keywords: multimodal reasoning
visual question answering
vision-language model
information-intensive images
speculative decoding
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Speculative Verdict is a training-free framework that combines lightweight draft experts with a strong verdict model to achieve accurate and efficient reasoning over information-intensive images.

Abstract: Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle when reasoning over information-intensive images that densely interleave textual annotations with fine-grained graphical elements. 
The main challenges lie in precisely localizing critical cues in dense layouts and multi-hop reasoning to integrate dispersed evidence.
We propose Speculative Verdict (SV), a training-free framework inspired by speculative decoding that combines multiple lightweight draft experts with a large verdict model. 
In the draft stage, small VLMs act as draft experts to generate reasoning paths that provide diverse localization candidates; in the verdict stage, a strong VLM synthesizes these paths to produce the final answer, minimizing computational cost while recovering correct answers.
To further improve both efficiency and accuracy, SV introduces a consensus expert selection mechanism that forwards only high-agreement reasoning paths to the verdict.
Empirically, SV achieves consistent gains on challenging information-intensive and high-resolution visual question answering benchmarks, including InfographicVQA, ChartMuseum, ChartQAPro, and HR-Bench 4K. By synthesizing correct insights from partially accurate reasoning paths, SV achieves both error correction and cost-efficiency compared to large proprietary models or training pipelines.

Published as a conference paper at ICLR 2026
SMALL DRAFTS, BIG VERDICT: INFORMATION- INTENSIVE VISUAL REASONING VIA SPECULATION
Yuhan Liu', Lianhui Shengjie Wang !New York University. Universitty of California, San Diego {y110379, sw5973)enyu. edu [16qin) (errinn@ucsd.ee.
"Equal advising
ABSTRACT
Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle when reasoning over information- intensive images that densely interleave textual annotations with fine-grained graphical elements. The main challenges lie in precisely localizing critical cues in dense layouts and multi-hop reasoning to integrate dispersed evidence. We propose Speculative Verdict (SV), a training-free framework inspired by spec- ulative decoding that combines multiple lightweight draft experts with large verdict model. In the draft stage, small VLMs act as draft experts to gen- erate reasoning paths that provide diverse localization candidates; in the ver- dict stage, a strong VLM synthesizes these paths to produce the final answer, minimizing computational cost while recovering correct answers. To further improve efficiency and accuracy, SV introduces consensus expert selection mechanism that forwards only high-agreement reasoning paths to the verdict. Empirically, SV achieves consistent gains on challenging informatiooniiteesivee and high-resolution visual question answering benchmarks, including Infograph- VQA, ChartMuseum, ChartQAPro, and HR-Bench 4K. By synthesizing correct insights from multiple partially accurate reasoning paths, SV achieves both er- ror correction and cost-efficiency compared to large proprietary models train- ing pipelines. Code is available at /tttps://githubbe com 1u01133/ speculative-verdic.
INTRODUCTION
Recent advances in large vision-language models (VLMs) have delivered impressive performance on tasks such as image captioning and general visual question answering (VQA) (Li al.] 2025c]
Fu et al. 2024). However, these models encounter challenges in intoommtioo-intensiiie images that densely interleave diverse textual annotations (legends, labels, captions) with fine- grained graphical elements (charts, diagrams, plots) across multiple scales and formats (Su et al. 2025b). Address- ing this task requires two interdependent capabilities (Figure [ Ke et al. (025): (i) comprehensive and precise localization, which involves not only pinpointing the exact positions of critical cues in densely populated layouts but also ensuring that all query-relevant regions are identified; (ii) multi-hop reasoning, which chains visual analysis-eecompasiing colors, shapes, and spatial rela- tionships-win textual evidence, thereby integrating dispersed cues into a coherent and complete answer. As each reasoning step builds on the accuracy of the previous one, any intermediate er- ror can propagate through the entire chain, making the overall process highly error-sensitive and difficult to correct retrospectively.
Existing work tackles information-intensii visual reasoning with search- zoom-in pipelines that enlarge local regions for detailed reasoning. Specifically, learning- methods train rein- forcement learning policies to guide zoom operations iteratively (Zheng al. 2025 Su et al. 2025a Fan et al. 2025 Zhang et al. 2025b). Enhancing its performance would demand costly fine- ane-grained supervision. Moreover, training free methods perform cropping based on internal attention fidence scores (Zhang et al., 2025a Shen et al. 20244 Wang et al., 2025c). Yet dense layouts, we find these signals correlate weakly with true relevance, misleading the model into visually similar but

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Published as a conference paper at ICLR 2026
Question: What is the parcentage of NEL revenue 2009?
Question: Which news media has the least number of employees?
Image:
Image:
Step Region localization
Keyword localization
Locate the followin chart region.
Locats the legend of number of smolayees:.
Sastroos LEMe carondles
amgrooge 1980
Step 2: Pattern alignment
Assiie
1.415
Step 2: Symbal identification
KAVEP MECINNAT
entry.
Su
Distinuuh betwren playe charn (blus)
Evidence aggregation
and NIL''s share (rea) the 2000 chart.
Furract avalanie amployee rumbers from media
Stop Value extraction
2.00
Fatract the
2009
ART
Step Reasoning (Sorting)
Step Reasoning (Calculation)
Sort untrected valuoa
the NFL's parcentage complementary value:
LUSA (381)
(b)
Figure I: Examples of correct reasoning paths for information-intensie VQA tasks. They illustrate distinct paths: (a) focuses on the localization a specific chart, symbol identification, and reasoning from single percentage value; (b) focuses keyword- -baee localization, evidence aggregation from multiple entries across the entire image, and cross-entity sorting to select the minimum.
irrelevant areas. Consequently, these tool-driven designs fail to capture all evidence for multi-hop reasoning, leaving the core challenges of information-inte intensive visual reasoning unsolved.
To overcome these limitations, propose Speculative Verdict (SV), training-free framework inspired by speculative decoding that combines small draft visual experts with a large verdict model (Leviathan al.) 2023). The framework operates in two stages (Figure 2): (1) Draft stage: multiple lightweight VLMs serve as draft experts, each generating a reasoning path that offers di- verse localization candidates; (2) Verdict stage: large VLM as strong verdict, which receives the reasoning paths as contextual evidence, distinguishes the correct information, and outputs the final answer. SV directly tackles core challenges through complementary strengths: draft experts expand evidence coverage across scattered regions, while the verdict prevents eπror propagation by synthesizing these multiple perspectives. Importantly, unlike using large proprietary model to reason over every image section, SV invokes the verdict only once to yield concise final answer, thereby minimizing computational cost while effectively recovering correct answers. To further bal- ance accuracy and efficiency. SV introduces consensus expert selection mechanism in the draft stage, ensuring that only reasoning paths with strong agreement are forwarded to the verdict.
We evaluate SV on information intennsiee VQA benchmarks, including Infographic VQA (Mathew ct al. 2021), ChartMuseum (Tang et al.. 2025), and ChartQAPro (Masry et al. 2025), which demand reasoning over dense textual and visual content. As training-free framework, SV consistently out- performs strong open-source models, large proprietary models, and perception-focuser search meth- ods while remaining cost-efficient. In particular, SV yields average gains of 4% small VLMs as draft experts and 10% over GPT-4o (Hurst et al., (024) as verdict. Beyond overall gains, SV suc- cessfully corrects 47-53% of cases where majority voting the verdict model alone fails, thereby reducing vulnerability to етто propagation in cetimmattio-nnneesiie visual reasoning. Furthermore, SV surpasses all baselines on HR-Bench 4K (Wang et al. 2025b). benchmark for high- geghrreolution visual perception, underscoring its effectiveness in challenging multimodal reasoning scenarios.
2 RELATED WORK
Vision-Languare Model Reasoning with Tools. Recent research has explored enhancing VLM perception by manipulating input images with zooming operations to locate relevant regions. (1) Prompting-basee methods exploit internal signals of VL.Ms to decide where to zoom. ViCrop (Zhang
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et al. 2025a) leverages models' attention maps to highlight query-related regions,, thereby generating automatic visual crops. Other works perform tree-based search, where models evaluate candidate sub-images with confidence scores to iteratively narrow down to relevant regions (Shen et al.) [024] Wang et al. 2025c). However, such signals align poorly with the required evidence in information- intensive images, Since queries often require reasoning across multiple dispersed regions. (2) Rein- forcement learning approaches instead optimize policies that interleave visual zooming with textual reasoning (Zheng al., 2025 Su al., 2025al Fan al. 2025 Zhang et al. 2025b). By calling zooming tools within the agentic framework, these methods adaptively crop regions and concatenate them into the reasoning trajectory. enabling more active evidence gathering. Yet these methods still fall short on information-intensii images, requiring costly task-specific training to scale.
General Vision-Laanguage Model Reasoning. Recent work has also explored other paradigms that enhance VI.M reasoning. Prompt-enhanced VLMS use chain-of-thoughe prompting to articulate in- termediate observations and sub-goals, yielding more structured reasoning (Xu et al.) 2025) Mitra et al. 2024 Shao et al. 2024). RL-enhanced methods further optimize these trajectories via super- vised fine-tuning and reinforcement learning, inspired by reasoning-oriented models such as ol and DeepSeek-R1 (Huang et al., 2025 Ma et al. 2025 Jaech et al. 2024] Guo et al. 2025). Recently, agentic frameworks treat the VLM as a planner that decomposes queries and actively chooses ac- tions, either invoking explicit visual tools or performing implicit latent-space reasoning (Wu & Xie) 2024 Hu et al., 2024] Qi et al. 2024; Yang et al. 2025b; Wu et al.) 2025). However, they remain vulnerable to imprecise or weakly supervised visual operations and error propagation.
LMM-as-a-Judge. Large multimodal models (LMMs) increasingly serve as general-purpose eval- uators for vision-language tasks (Zhang et al.) 2023 Ge et al.) 2025 Li al., 2025b). Specifically, ..Mjjudees are prompted or trained to score candidates, produce rankings, or select the best answer given the task context, and instruction (Xiong al., 2025). These judges can deliver fine fine-grained evaluations for open-ended generation and reasoning tasks, and are increasingly used as scalable supervision signals for stages such as alignment, retrieval, and reasoning (Li et al.) [025a]. In our framework, the verdict model acts as an off-the-shed multimodal judge that filters informative cues from diverse drafts and synthesizes an answer on information intensive images.
Speculative Decoding. Speculative decoding is draft-then-verrffy decoding paradigm accelerate LLM inference (Xia et al. 2024). Specifically, it uses a draft model to generate future tokens, and larger target model verifies them via parallel rejection sampling. Recent work extends acceptance from loven-level equivalence to step-level semantic similarity to speed up reasoning (Yang et al.]
2025a Pan al., 2025 Fu et al. 2025b; Liao et al.) 2025). Collaborative decoding via Specula- tion (Fu et al.] 2025a) further applies speculative decoding with multiple draft I.MM by verifying proposals against combined distribution of drafts and target, yielding greater speedups. However, they mainly target speed in LLM inference and do not address visual reasoning challenges.
3 SPECULATIVE VERDICT
Speculative decoding is an inference-time optimization originally developed to mitigate the latency of autoregressive generation (Leviathan et al.) 2023). The approach employs draft-then-verts paradigm: (i) a small, fast draft model proposes one or more future tokens speculatively, and (ii) a large, accurate base model verifies these proposals in parallel, accepts or revises the proposals, and generates output that is consistent with the base model's 's distribution (Xia et al., 2024 Zhang et al.) 2024]. This token-level process speeds up inference by committing several tokens at once, while maintaining quality by discarding continuations that diverge from the base model's distribution.
The key insight is that draft models expand coverage quickly, while the verifier ensures correctness. Although this idea has been mainly applied to accelerate text generation, its high-level principle is also well-suited for information-intensse multimodal reasoning.
3.1 METHOD OVERVIEW
intematiionintessinn visual question answering (VQA) requires models to localize query-relevant regions, perceive diverse fine-grained textual and visual details, and integrate dispersed evidence into a single correct answer. These tasks are highly error-sensitic as claborated in Section || a single misread mislocalized element often leads completely wrong prediction.
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Draft Experts
Large Verdict Model
Inspiration:
Candidate
Speculative decoding
Answer
scyi)
Reasoning
0.86
Input
Base Model
Giohal ConsEnsIC Rconet
0.79
Final Answer
Lowee sjph
Draft Model
M5
K x
Draft Stage
Verdict Stage
Figure 2: Overview of Speculative Verdict (SV). Inspired by speculative decoding, SV operates two stages. In the draft stage, given an input question-image pair, l small candidate VI.Ms first generate candidate answers, from which compute global consensus score s(y,) for each answer based on pairwise NLL differences. We then select m draft experts with the strongest consensus to generate reasoning paths. In the verdict stage, the large verdict model verifies and integrates these paths to yield the final answer.
To address this challenge, adapt the draft-then--vrify paradigm of speculative decoding to mul- timodal reasoning. Unlike its original use for inference acceleration, we repurpose the paradigm to improve robustness and error correction in information-intensin visual reasoning. On high level, our Speculative Verdict (SV) framework operates in two stages (Figure
(i) Draft stage, where multiple lightweight VLMs selected draft experts to provide diverse reasoning paths (Section 3.2
(ii) Verdict stage, where large VLM acts verdict verify, refine, and synthesize these reasoning paths into the final prediction (Section 3.3)
3.2 DRAFT STAGE
Chain-of-Thought (CoT) prompting exposes models' intermediate reasoning steps in an explicit,
stepwise form (Wei et al. 2022). This is critical for information-intensivee VQA, where solving question requires a sequence of localization, evidence extraction, and analytic operations (Figure However, current VLMS often lack fine-grained perception and localization densely annotated images, and existing tool-driven zoom-in methods are ineffective as elaborated in Section [ We therefore utilize multiple VLMs produce reasoning paths rather than single direct answer, so that the subsequent verdict can verify and synthesize structured evidence. Concretely, given an image-question pair (r., q). we select m lightweight VLMs { {M....., Mn } as draft experts from a pool of k candidate VLMs via a consensus based selection mechanism (detailed in Section 3.4
Each selected expert M, is then prompted with CoT template output reasoning path r;
We observe that each reasoning path provided by draft experts typically includes: (i) global scan and localization proposals that identify query-related regions, sections, or subplots, often referenc- ing axes, titles, or captions; (ii) evidence extraction, which transforms visual or textual elements into structured cues, including reading legends, mapping colors to series, parsing axis labels, or as- sembling lists of values tokens for subsequent operations; (iii) analytic and reasoning operations, which operate over the extracted cues to derive higher-level conclusions, such as filtering or select- ing relevant entities, computing differences, sorting across panels, and cross-referencing dispersed cues. As shown in the running case (Figure 3) different experts may match legends to charts differ- ently; some correctly gather the required cues while others misread adjacent values. This diversity yields a complementary but potentially noisy pool of reasoning signals.
3.3 VERDICT STAGE
The set {r;} captures diverse cues, offering richer evidence but also introducing contradictions, which motivates the need for verdict stage to verify and integrate them. Answer-level ensem-
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Small Candidate
Draft Experts
Large Verdict Model
Reasoning
Guadtion: parcantage NP
s(yr)
Region Localication:
X 89 Billion K
Information Eutraction:
playert persenticn STS
0.64
Revenue Percensage LOON 520 45%
49%
49%
49%
0.85
Information Estraction: KFI movenu presentace for 2004 RIS
X
Information Ditraction:
PUTSTTU pecsontiegs
X 51%
Figure 3: An illustration of Speculative Verdict on InfographicVQA. Five candidate VLMs first produce candidate answers, with only two providing the correct result. Consensus scoring ranks answers by agreement, and the three with the lowest scores are selected as draft experts. Although some experts commit extraction errors (confusing player's share with NFL revenue), the verdict synthesizes their reasoning paths and successfully recovers the correct answer (49%). This illustrates SV's ability to identify reliable experts and achieve error correction.
bling (e.g., majority voting) often fails in minority-correct scenarios where many experts converge on the same incorrect decision, such as mislocalizing the query-related region or misreading fine- grained textual details, even after correct localization. This failure mode is frequently observed in information-iitensive reasoning (as illustrated in Figure B3 Rather than discarding minority opin- ions through majority voting, leverage a stronger model solve conflicts, and synthesize coherent reasoning from the draft paths.
verdict validate grounding,
Specifically, given the image-question pair (ar, q) and the drafts' reasoning paths {r:) we prompt the verdict model J with: (i) the original image x as visual input, and (ii) a textual prompt containing the question q and the concatenated reasoning paths {re as context. The verdict processes this multimodal input in single inference call and outputs the final answer:
y = (z. {rt).-...
this design, the verdict acts not as a voter but as a synthesizer. evaluates grounding consistency, identifies contradictions across reasoning paths, and integrates consistent cues into coherent predic- tion. The case in Figure 3 illustrates this intended role: when only one draft extracts the correct ev- idence, the verdict is designed recover it by contrasting against competing but inconsistent paths.
This setup enables us to leverage the reasoning capabilities of large models while keeping the in- ference cost manageable. The verdict stage reduces the expensive autoregressive decoding phase by concentrating computation in prefill: it processes thousands of tokens from multiple draft reason- ing paths prefill input and produces only several answer tokens sequentially. This design avoids invoking large models iteratively for analyzing each image section separately or generating lengthy rationales, both of which would substantially increase decoding costs.
3.4 CONSENSUS EXPERT SELECTION
To keep the verdict input both efficient and accurate, we introduce a training-free expert selection mechanism at the beginning of the draft stage (Section (.2). Since each question in information- intensive VQA has unique correct answer, consensus among model answers naturally indicates which reasoning paths are more reliable. Therefore, the key idea here is to measure agreement among candidate answers and retain only those with stronger peer consensus. This mechanism is computed efficiently by prefilling the question and answer tokens, with each draft decoded only once, making plug-and-pl ald-play with minimal overhead.
Consensus Score. We define a consensus score that measures how strongly a candidate VLM's answer is agreed by its peers. Formally, let be the input image and q = (g....., 9n) the question tokens. From the pool of & candidate VLMs {M each model produces a candidate answer y: (Ut.1....,::7 ). For peer model M, (j * i) in the pool, measure how plausible it finds y
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by computing the negative log-likelihood (NLL) of the concatenated input (æ, q, Br), i... the original image together with the question tokens followed by the candidate answer tokens:
T
(yi) Σ
log PM, (yt, a, Ign V1,<t).
To account for calibration differences, we normalize against Mj's own answer yj. thus relative consensus score from Mj's perspective is:
sy 8y(,,) NLLg(1)) (x), j
where a smaller (y:) indicates stronger agreement, as M, finds yi nearly as plausible as its own answer yj-
To capture overall agreement rather than pairwise consistency, we define the global consensus score of candidate yi by summing across all peers:
s(y))
Σ Sy(),
jfi
which quantifies the overall level of peer consensus for M2 's answer, and a lower sy;) indicates stronger agreement and thus higher reliability.
Consensus Expert Selection Strategy. We adopt a cross-all strategy that selects the m VLMs with the strongest consensus, measured by the lowest consensus scores, from the pool of k candidates. As described in Section 3.2, the m selected VLMs become the draft experts to generate detailed rea- soning paths forwarded to the verdict (Figure 3 illustrates this process). By aggregating agreement across all peers, this strategy provides holistic measure of reliability. It thus yields subset of rea- soning paths that are well-grounded and compact in size, balancing informativeness and efficiency.
EXPERIMENTS
4.1
SETUPS
Configuration Details. We set the draft pool size to k = 5 considering efficiency and select m 3 draft experts in our main experiments. Ablation studies over different m values are reported in Section 4.5 The draft pool consists of the following VLMMs for expert selection: Qwen2.5-Vi VI.
7B-Instr (Bai al. 2025), MiMo- VL---R-I. (Xiaomi 2025). Intern VL.3-8B (Zhu et al. 2025) TTeam et al. 20256), Ovis2.5-9B (Lu et al., 2025). These models are chosen as candidate VLMs based on their strong performance on multimodal benchmarks and their diverse architectural designs. For the verdict models, we employ GPT-4o (Hurst et al., 2024) and Qwen2.5-VL -L--2-5-V--77----s-s- Instruct respectively, given their superior ability in visual reasoning. In particu- image benchmarks, we preprocess images with PP-StructureV3 (Cuil ct al. 2025) to produce a layout-preser image auxiliary input the verdict model.
lar, for information-intensii
structured format, provided together with the original
Baselines. We compare SV with proprietary models GPT-4o and GPT-4o-mini, and the large open- source model Swee2.5-vLL72B-instruu
it is one of our verdicts. We also evaluate SV against draft experts mentioned above. These baselines are evaluated under the same chain-of-thought prompting template in Appendix M Additionally, we include DeepEyes (Zheng et al. 2025) and Pixel-Reasoner (Su et al. 2025a) as representative tool-driven baselines with zoom-in operations.
Benchmarks. We evaluate SV on three information-intenn intennsve benchmarks and extend the evalua- tion to representative high-resolution benchmark, providing a comprehensive assessment of fine- grained visual reasoning: InfographicVQAA (Mathew et al., (021), ChartMuseum (Tang al., 2025). ChartQAPro (Masry et al. 2025) and HR-Bench 4K (Wang et al., 2025b). Infographic VQA collects infographics with an average high resolution over 2k, designed to test reasoning over layout, graph- ical and textual content, including operations such as counting, sorting, and basic arithmetic. Chart- Museum and ChartQAPro introduce substantially greater visual reasoning complexity by covering broad spectrum of real-world chart types and question formats, revealing a large performance gap between current Large VLMs and humans. These benchmarks require models to visually ground relevant regions, extract information, and conduct reasoning to answer queries.

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Table 1: Results on test sets of four benchmarks. InfographicVQA, ChartMuseum, and PhartQAPro information-intensivee VQA benchmarks, while HR-Bench 4K focuses high-resolution per- ception. We compare SV against closed-source, open-source VLMs, and tool-driven methods. denotes results reported in the original papers and all other results reproduced by ourselves. The best results for each benchmark are highlighted in bold and the second results are underlined.
Model
Param
InfographicVOA
ChartMuseum
ChartQAPro Ace
HR-Bench
Size
ANLS
Acc
Acc
Closed-source VLMs
GPT-4o
76.5
42.7
52.6
67.4
GPT-4o-mini
67.2
31.5
44.1
53.8
Oper-soarce source VLMs
Owen2.5-VLLIIite
7B
79.8
29.5
51.0
73.0
MiMo-VL-RL (think)
7B
83.5
29.0
57.3
72.3
InternVL3
8B
72.3
25.9
45.1
68.0
IL-M-iiV-
9B
84.8
48.0
56.2
72.3
Ovix2.5
9B
81.7
34.0
55.9
69.5
Qwen2.5 S-r.es--V--Isiiiee Instrruttt
72B
84,2
40.7
60.7
23.1
Tool-driven method
DeepEyes
7B
75.5
28.0
48.7
73.0
Pixel Reasoner
7B
84.0*
25.9
39.3
w/ GPT-4o Verdict
88.4
49.3
64.0
71.4
A (vs. GPT-4o)
+11.9
+11.4
+4.0
SV w/ Qwen2,5-V1--22--
A (vs. -1-225--Vi-22--nstreee Instruct Verdict
86.7
48.2
63.0
75.6
+2.5
+7.5
+2.3
+2.5
Table 2: Results on additional multimodal reasoning benchmarks. We evaluate on Math Vista- testmini and 1000 randomly sampled complex questions from TallyQA to assess generalization.
Param Size
TallyQA-Complex
Math Acc
Model
Acc
Closed-source VLMs
GPT-4o
75.4
65.1
Open-source VLMs
Qwen2.5. Dwen2.5-VL-Immet VL-Inntruct MiMo-VI. VL.
7B
72.4
68.2
7B
72.0
80.3
InternLL3
8B
70.1
72.7
GLM-4.IV-Thikii. Ovis2.5
9B
73.9
78.9
9B
71.9
77.3
SV GPT-4o Verdict
76.9
82.9
(vs, GPT-4o)
+1.5
+17.8
We further assess generalization high-resolution perception on HR-Bench 4K. comprises two sub-tasks: FSP (Fine-grained Single-instance Perception) and FCP (Fine-grained Cross-instance Perception), stressing small-object perception and cross-instanee instance reasoning. We also test on two ad- ditional multimodal reasoning benchmarks, TallyQA (Acharya et al. (2019) and MathVista (Lu et al. 2023), covering open-ended counting with relational reasoning and mathematical visual reasoning.
4.2 RESULTS ON INFORMATION-IITTESSIY BENCHMARKS
As shown in Table T. SV demonstrates superior performance across all benchmarks, outperforming a wide range of baselines. Based on the results, we have the following key observations:
(i) SV shows consistent gains over all strong draft experts' baselines, with improvements of 3.6% on Infographic 1.3% on ChartMuseum, and 6.7% on ChariQAPro with GPT-4o verdict. SV also achieves comparable gains with Qwen2.5-VI--222 Instruct as verdict.
(ii) Importantly, SV enables strong error correction beyond simple answer aggregation. Figure 4 analyzes SV's performance on cases where the verdict itself fails, categorized by expert correctness (minority-correct, majority-correct, zero-correct). Across benchmarks, SV recovers 47-53% of minorityy-coreed cases, where few draft experts are correct and the verdict alone also fails (case in Figure 3). Moreover, SV even recovers 2.5-4.5% of zero-correct cases, where neither the drafts nor the verdict answers correctly (case in Appendix D). In these hard cases, SV exploits complementary reasoning strengths across draft experts (e.g., extraction, localization,

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Majerity correct SV
SV wrong
Minority-corre SV correct
Proportion GPT-40
SV
30
Zero correct
SV correct
20
wnoong
Majonity Minority Zero cornect cornect correct
Kajority Minority Zero cornect correct cornect
Majority Minority Zero corecct cornect correct
InfographicvOA
ChartMuseum
ChartQAPro
Figure 4: SV's correction ability on verdict's error cases across information-intensivy benchmarks (GPT-4o verdict). We consider only cases where the verdict itself fails, isolate SV's inde- pendent correction capacity. For each benchmark, three bars denote expert correctness categories (majority-correct minority-correct, and zero-correct), defined by how many selected experts pro- vide the correct answer. Within each category, the bars are split into the proportion corrected by SV (dark) versus not corrected (light). More details can be found in Appendix D
color matching) (see detailed analysis in Appendix D. This complementarity allows the verdict synthesize partially correct evidence across reasoning paths while rejecting misleading signals. Thus, SV achieves effective correction where traditional ensemble methods fail.
(ii) SV strengthens large verdict models significantly, and using GPT-4o as verdict delivers stronger results due to its reasoning advantage information-intentt benchmarks. Specifically, when GPT-4o used as verdict, SV surpasses the GPT-4o baseline by 11.9% on Infographic VQA, 6.6% on ChartMuseum, and 11.4% on ChartQAPro. These improvements come with reduced in- ference cost for the large verdict model, demonstrating that SV can outperform much larger or proprietary LVLMs in cost-efficient manner.
(iv) SV substantially outperforms representative tool-driven pipeline DeepEyes and Pixel- Reasoner. Specifically, it improves over DeepEyes by 12.9% on InfographicVQA., 21.3% Chart- Museum, and 15.3% on ChartQAPro and also exceeds Pixel-Reasoner by clear margins. While these methods benefit from zoom-in operations, such operations directed on dense infographics (see Appendix KK. Thus, it struggles with global comparison and dispersed evidence synthesis. Yet, SV's reasoning- patt synthesis enables it integrate evidence across regions reliably without relying on predefined tool-based visual search.
often under-triggered mis-
4.3 RESULTS ON RIGH-RESOLUTON BENCHMARK
We further assess generalization to high-resolution images using HR-Bench 4K to evaluate whether follows (Table T
SV enhance fine-grained visual perception. The key observations
(i) With Qwen2.5-VLL 11-.5--V--7BB-BA Instruct as verdict, SV achieves its largest margin, surpassing the best- performing draft expert by 2.6% and outperforming the verdict itself by 2.5%. The superior performance of Qwen2.5-VL-72. as verdict on this task correlates with its stronger visual localiza- tion capabilities, indicating verdiet selection should align with task-specific requirements.
(ii) SV also exceeds DeepEyes, which is explicitly trained with zoom-in tools for iterative visual search on high-resolution perception. This highlights SV's ability to generalize high-resolution tasks, where accurate recognition of small objects is critical. Aligning perceptually strong draft experts with a verdict thus provides simpler yet effective solution for high-resolution reasoning.
4.4 RESULTS ON MULTIMODAL REASONING BENCHMARKS
To examine SV's broader generalization, further evaluate it two additional multimodal rea- soning benchmarks: TallyQA (counting) and Math Vista (mathematical reasoning). Table 2 shows that SV provides consistent gains, outperforming the strongest draft expert by 3.0% TallyQA-
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Complex and 2.6% Math Vista, and improving GPT-4 by 1.5% and 17.8%, respectively. These results demonstrate that SV generalizes to diverse visual reasoning tasks.
4.5 ABLATION STUDY
To better understand the effectiveness of SV, we conduct ablation studies on information-intent benchmarks to analyze the impact of individual components. In these experiments, the reasoning baseline refers to the best-performing draft expert our pool for each benchmark [T.
Number of Draft Experts. Our setting with m 3 draft experts yields a favorable trade-off between accuracy and efficiency, as it determines the number of reasoning paths forwarded to verdict. As shown in Figure we observe that the performance improves nearly linearly up to three draft experts and then saturates, while inference cost grows roughly linearly with m.
Consensus Expert Selection Strategy. We confirm the effectiveness of our cross-all selection strat- egy by comparing it with a best-reference strategy. In the best-reference variant, the top-performing draft expert serves reference and the most consistent experts are selected with it. While best-reference is expected to be the strongest criterion, cross-all achieves comparable gains while remaining reference-free (Figure(6).
Selection Criteria. Selecting consensus-bard based experts consistently improves performance, while divergent selection even fall below the single-draft reasoning baseline (Figure 7) These results support that, for information-intensie tasks, consensus-based selection more reliably identifies the correct reasoning path than enforced diversity.
InfographicvQA
90
58.1
81.4
Strategy Reasoning
84.8
Best referrenceee Cross-al
80
ChartMuseum
Score (%)
70
50
$3.0
45
60
50
43.0
62
ChartQAPro Figure 6: Ablations on different consensus expert se- lection strategies.
InfographicVQA
ChartMuseum
Number of Consensus Experts (m)
Figure 5: Ablations on the number of draft experts m.
Impact of Verdict Stage. The verdict stage yields higher performance than majority voting across information-inteniie benchmarks (Figure Notably, majority voting with all five candidate mod- els performs comparably as majority voting with three draft experts, consistent with our finding that consensus selection can match the performance of all drafts at lower cost (Table 5). SV further surpasses both by leveraging the verdict's 's eтrоr correction ability, successfully capturing minority- correct cases that majority voting discards (Figure 4 and Figure 3)
Beyond majority voting, SV consistently outperforms strong LMM-as-a-Judge baseline (LLaVA- Critic- 72B (Xiong al., 2025)) by 4.9-11.9% (Figure 8). In this setting, LLaVA-Critic scores or ranks the same draft experts selected by SV and outputs the single best candidate. This advantage is attributed to SV's synthesis- based verdict, which cross-checks and integrates complementary factual cues across multiple trajectories, rather than relying selecting one trajectory that may be favored by reasoning style. Detailed ablations with LLa VA--Cirrti are provided in Appendix G
Choice of Verdict Textual Input. Providing full reasoning paths to the verdict yields substantially better performance than passing only final answers (Table 3), with improvements of 15% on Info- graphic VQA, and 4.8% ChartQAPro. These results highlight that rich contextual evidence is essential for the verdict to recover correct reasoning, whereas final predictions alone are insufficient.
Choice of Verdict Scale. Using large verdict model yields stronger gains than small verdict model. We evaluate three strong small verdicts (i.c., GLM-4.1 w-9h-Thinking, MiMo-VL- VL-7B-RL. 1onn2.5-ViL--B-Inttreee and all of them underperform SV across benchmarks (Table 4). Notably,

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Published as a conference paper at ICLR 2026
the small reasoning verdicts generate 60-200x output tokens still yield weaker performance, indicating worse cost-efficiency trade-offs (details in Appendix C). The results validate SV's design principle of invoking a strong verdict only once to achieve robust and efficient enror correction.
95
hozsorer base me Hour by vering 15 cand dene modee)
$1.
Mouer votigg 13 exeetto
Cribc 13 expert)
Score (96
70-
Score
655
03.0
$4.0
55
A33
50
66.2
45
43 43.9
InfographicVOA
ChartMuseum
ChartQAPro
ChartQAPro Figure 7: Ablations on selection criteria.
InfographicVQA ChartMuseum
Figure Performance comparison on SV, majority voting and LLaVA-Critie with different model sets.
Table Ablations on verdict textual in- put.
Table 4: Ablations on verdict scale.
Inlographic
ChartMustum Ace
ChactQAPro
Infographic VOA. ANLS
ChartQAPro Acc
Acc
Textual input
Rensoning baseline
84.8
45.0
$7.3
Reasoning
GLM-4. 0.144.VV99B. Thinking Verdict
84.7
48.0
59.4
MiMo VL-RL-7B Verdict
85.4
46.9
60.3
Answers only
73:4
59.2
Owen2. V-V--nsstee 7B Verdier
84.1
57.2
Reasoning palls (SV)
88.4
64.0
4.6 COST-EFFICIENCY ANALYSIS
We quamtify SV's cost-efficiency by comparing against large reasoning model baselines, including GPT-4o and ol. As described in Section 3.3 SV achieves cost-efficiency through its verdict stage by concentrating computation in prefill and substantially reducing expensive autoregressive decoding.
As shown in Table and 6 SV consistently improves over GPT-4o by 6.6-12.0% across datasets while maintaining comparable cost. Compared with ol, SV attains substantially better performance on VnfographicVQA and ChartQAPro and comparable performance on ChartMuseum while requir- ing only 15-26% of ol's cost. These results confirm that SV delivers markedly superior cost- efficiency, achieving and even outperforming ol-level performance with lower computational cost.
Table 5: Performance 1000 randomly sampled test instances per benchmark.
Method
InfographicVQA
CharlMuseum
CharQAAPo
GPT-4o
76.3
42.7
$1.7
ul
77.8
50.6
58.8
SV w/ GPT-4o Verdict
88.3
49.3
63.4
Table 6: Average inference API cost per sample across benchmarks.
Method
InfographieVQA
ChartMuseum
ChariQAPro
GPT-4o
$0.0038
$0.0213 $0.0663
S0.0210
al
$0.0263
S0.0478
GPT-4o Verdict
$0.0068
$0.0109
$0.0071
CONCLUSION
This paper introduces Speculative Verdict (SV), a training-free framework to address challenges of information-intensivee visual reasoning. Inspired by speculative decoding, SV repositions large models as efficient synthesizers rather than computationally expensive step-by-step reasoners. By integrating diverse reasoning paths from lightweight experts, the verdict can distinguish informa- tive cues and recover correctness from structured errors. Experiments show that SV consistently outperforms strong proprictary, open-source, and tool-driven methods, establishing a cost-efficient paradigm for reasoning informatioonintensivve images.
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6 ACKNOWLEDGEMENTS
This work was supported part by Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai and NYU IT High Performance Computing resources and services. We also thank the anonymous reviewers for their constructive comments and suggestions, which helped improve the quality of this manuseript.
ETHICS STATEMENT
All authors have read and commit adhering to the ICLR Code Ethics. This work does not in- volve human subjects, sensitive personal data, biometrics, medical information. All datasets used are publicly available under permissible licenses and are not privacy-sensitive. We recognize that any automated reasoning system may produce incorrect misleading outputs. To ensure responsi- ble use, we emphasize that our method is intended for research and analysis rather than deployment in high-stakes settings. Users are encouraged to verify model outputs and apply human oversight when necessary. We take full responsibility for all reported results, analyses, and claims, and we welcome community scrutiny and feedback.
REPRODUCIBILITY STATEMENT
To support reproducibility, we provide comprehensive implementation details throughout our paper. Key experimental configurations, such as draft expert selection, consensus scoring computation, and verdict model specifications, documented in Section 3.4 and Section 4.1 Detailed prompt tem- plates are presented in Appendix M In addition, our released code offers concrete implementation details and enables faithful reproduction of all reported results.
REFERENCES
Manoj Acharya, Kushal Kafle, and Christopher Kanan. Tallyqa: Answering complex counting questions. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pp. 8076- 8084, 2019.
Xiang An, Yin Xie, Kaicheng Yang, Wenkang Zhang, Xiuwei Zhao, Zheng Cheng, Yirui Wang, Songcen Xu, Changrui Chen, Chunsheng Wu, al. Llava-onevision-1.55 Fully open framework for democratized multimodal training. arXiv preprint arXiv:2509.2366) 2025.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502. 13923, 2025.
Guo Chen, Zhiqi Li, Shihao Wang. Jindong Jiang, Yicheng Liu, Lidong Lu, De-An Huang, Wonmin Byeon, Matthieu Le, Tuomas Rintamaki, et al. Eagle 2.5: Boosting long-context post-training for frontier vasion-language models. arXiv preprint ww.xiv22504.52777 2025.
Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin. Sushant Prakash, Charles Sutton, Xuezhi Wang, and Denny Zhou. Universal self-consistency for large language model generation. arXiv preprint arXiv:2311.1733 2023.
Cheng Cui, Ting Sun, Manhui Lin, Tingquan Gao, Yubo Zhang, Jiaxuan Liu, Xueqing Wang, Zelun Zhang, Changda Zhou, Hongen Liu, al. Paddleoer 3.0 technical report. arXiv preprint arXiv:2507.05555. 2025.
Yue Fan, Xuehai He, Diji Yang, Kaizhi Zheng, Ching Ching-Chen Kuo, Yuting Zheng, Sravana Jyothi Narayanaraju, Xinze Guan, and Xin Erie Wang. Grit: Teaching milms to think with images. arXiv preprint 2rXiv:2505./888 2025.
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin. Bo Li, Xinyu Fang. Sirui Zhao Haodong Duan, Xing Sun, Ziwei Liu, Liang Wang, et al. Mme-survey: A comprehensive survey on evaluation of multimodal lmss. arXiv preprint arXiv:2411.15296 2024.

---

Published as a conference paper at ICLR 2026
Jiale Fu, Yuchu Jiang, Junkai Chen, Jiaming Fan, Xin Geng, and Xu Yang. Fast large language model collaborative decoding via speculation. arXiv preprint arXiv:2502.01662 2025a.
Yichao Fu, Rui Ge, Zelei Shao, Zhijie Deng, and Hao Zhang. Scaling speculative decoding with lookahead reasoning. arXiv preprint arXiv:2506. 19830, 2025b.
Wentao Ge, Shumian Chen, Hardy Chen. Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xic, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, et al. Mllm-bench: evaluating multimodal Iims with per-sample criteria. In Proceedings of the 2025 Conference of the Nations of the Amer- icas Chapter of the Association for Computational Linguisties: Human Language Technologies (Volume 1: Long Papers), pp. 4951-4974, 2025.
Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Ruoyu Zhang. Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprint arXiv:2501.12945 2025.
Yushi Hu, Weijia Shi, Xingyu Fu, Dan Roth, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, and Ranjay Krishna. Visual sketchpad: Sketching as a visual chain of thought for multimodal lan- guage models. Advances in Neural Information Processing Systems, 37:139348-13977 2024.
Wenxuan Huang, Bohan Jía, Zijie Zhai, Shaosheng Cao. Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXhv:2503.06745 2025.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276. 2024.
Aaron Jacch, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412.167200 2024.
Fucai Ke, Joy Hsu, Zhixi Cai, Zixian Ma. Xin Zheng, Xindi Wu, Sukai Huang, Weiqing Wang, Pari Delir Haghighi, Gholamreza Haffari, Ranjay Krishna, Jiajun Wu, and Hamid Rezatofighi. Explain before you answer: survey on compositional visual reasoning, 2025.
Yaniv Leviatham, Matan Kalman, and Yossi Matias, Fast inference from transformers via speculative decoding. In International Conference on Machine Learning, pp. 19274-19286. PMIR, 2023.
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408. 03326, 2024.
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi. Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, et al. From generation to judgment: Op- portunities and challenges of llm-as-a-judge. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 2757-2791, 2025a.
Lei Li, Yuancheng Wei, Zhihui Xie, Xuqing Yang. Yifan Song, Peiyi Wang, Chenxin An, Tianyu Liu, Sujian Li, Bill Yuchen Lin, et al. Vl-rewardbench: A challenging benchmark for vision- language generative reward models. In Proceedings of the Computer Vision and Pattern Recog- nition Conference, pp. 24657-24668, 2025b.
Zongxia Li, Xiyang Wu, Hongyang Du Fuxiao Liu, Huy Nghiem, and Guangyao Shi. A survey of state of the art large vision language models: Alignment, benchmark, evaluations and challenges. arXiv preprint ar 2rXiv:2501.0//89 2025c.
Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese. Doyen Sahoo, and Caiming Xiong. neward-guides speculative decoding for efficient 11m reasoning. arXiv preprint arXiv:2501. 19324, 2025.
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannanch Hajishirzi, Hao Cheng, Kai- Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint 23.00.2310.02255, 2023.
12

---

Published as a conference paper at ICLR 2026
Shiyin Lu, Yang Li, Yu Xia, Yuwei Hu, Shanshan Zhao, Yanqing Ma, Zhichao Wei, Yinglun Li, Lunhao Duan, Jianshan Zhao, et al. Ovis2. 5 technical report. arXiv preprint arXiv:2508. 2025.
Xinyu Ma, Ziyang Ding, Zhicong Luo, Chi Chen, Zonghao Guo, Derek F Wong, Xiaoyi Feng, and Maosong Sun. Deepperception: Advancing rl-like cognitive visual perception in milms for knowledge-intensee visual grounding. arXiv preprint wrXv:22503.12990 2025.
Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Rahman Laskar, Mizanur Rahman, Shadikur Rahman, Mehrad Shahmoham- madi, et al. Chartqapro: A more diverse and challenging benchmark for chart question answering. arXiv preprint arXiv:2504.08 250.0:55000000506,, 2025.
Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito, Dimosthenis Karatzas, Ernest Valveny, and V Jawahar. Infographicvga. arXiv preprint 1rXiv:2104.127555 2021.
Chancharik Mitra, Brandon Huang, Trevor Darrell, and Roei Herzig, Compositional chain-of- thought prompting for large multimodal models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 14420-14431, 2024.
Rui Pan, Yinwei Dai, Zhihao Zhang, Gabriele Oliaro, Zhihao Jia, and Ravi Netravali. Specrea- son: Fast and accurate inference-time compute via speculative reasoning. arXiv preprint arXiv:2504.0789) 078911, 2025.
Qi. Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong. Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, et al. Cogcom: A visual language model with chain-of maniuullatisss reasoning. arXiv preprint arXiv:2402.042556 04.336 2024.
Jianing Qi, Xi Ye, Hao Tang, Zhigang Zhu, and Eunsol Choi. Learning to reason across parallel samples for IIm reasoning. arXiv preprint arXiv:2506.0900 09014,, 2025.
Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong. Letian Wang, Yu Liu, and Hong- sheng Li. Visual cot: Advancing multi-modal language models with comprehensive dataset and benchmark for chain-of-thoughee reasoning. Advances Neural Information Processing Systems, 37:8612-8642, 2024.
Haozhan Shen, Kangjia Zhao, Tiancheng Zhao, Ruochen Xu, Zilun Zhang, Mingwei Zhu, and Jian- wei Yin. Zoomeye: Enhancing multimodal llms with human-like zooming capabilities through tree-based image exploration. arXiv preprint arXiv:2411. 1. 16044, 2024.
Alex Su, Haozhe Wang, Weiming Ren, Fangzhen Lin, and Wenhu Chen. Pixel reasoner: In- centivizing pixel-space reasoning with curiosity-driven reinforcement learning. arXiv preprint arXiv:2505.15966, 2025a.
Zhaochen Su, Peng Xia, Hangyu Guo, Zhenhua Liu, Yan Ma, Xiaoye Qu, Jiaqi Liu, Yanshu Li, Kaide Zeng, Zhengyuan Yang, et al. Thinking with images for multimodal reasoning: Founda- tions, methods, and future frontiers. arXiv preprint arXiv:2506.22 233918 2025b.
Liyan Tang, Grace Kim, Xinyu Zhao, Thom Lake, Wenxuan Ding, Fangcong Yin, Prasann Singhal, Manya Wadhwa, Zeyu Leo Liu, Zayne Sprague, et al. Chartmuseum: Testing visual reasoning capabilities large vision-language models. arXiv preprint 2rXiv:2555.1344 2025.
Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej. Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, al. Gemma 3 technical report. arXiv preprint
2025a.
V Team, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang. Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang. Ziyang Pan, Aohan Zeng, Baoxu Wang, Bin Chen, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin. Fan Yang, Guoqing Chen, Jiazheng Xu, Jiale Zhu, Jiali Chen. Jing Chen, Jinhao Chen, Jinghao Lin, Jinjiang Wang, Junjie Chen, Leqi Lei, Letian Gong, Leyi Pan, Mingdao Liu, Mingde Xu, Mingzhi Zhang, Qinkai Zheng. Sheng Yang. Shi Zhong, Shiyu Huang, Shuyuan Zhao, Siyan Xue, Shanggin Tu, Shengbiao Meng, Tianshu Zhang, Tianeii
13

---

Published as a conference paper at ICLR 2026
Luo, Tianxiang Hao, Tianyu Tong, Wenkai Li, Wei Jia, Xiao Liu, Xiaohan Zhang, Xin Lyu, Xinyue Fan, Xuancheng Huang, Yanling Wang. Yadong Xue, Yanfeng Wang. Yanzi Wang, Yifan An, Yifan Du, Yiming Shi, Yiheng Huang, Yilin Niu, Yuan Wang. Yuanchang Yue, Yuchen Li, Yutao Zhang, Yuting Wang, Yu Wang, Yuxuan Zhang, Zhao Xue, Zhenyu Hou, Zhengxiao Du, Zihan Wang, Peng Zhang, Dehing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong, and Jie Tang. Glm-4.5v and Ji-hh.iv- Towards versatile multimodal reasoning with scalable reinforcement learning, 2025b. URL https: arwiv. org/ 2ad.s//500 01006
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, et al. Internv13. 5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508. /8265, 2025a.
Wenbin Wang, Liang Ding, Minyan Zeng, Xiabin Zhou, Li Shen, Yong Luo, Wei Yu, and Dacheng Tao. Divide, conquer and combine: A training-free framework for high-resolution image percep- tion in multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 7907-7915, 2025b.
Wenbin Wang, Yongcheng Jing, Liang Ding. Yingjie Wang, Li Shen, Yong Luo, Bo Du, and Dacheng Tao. Rerrieval-uggmented perception: High-resolution image perception meets visual rag. arXiv preprint arXiv:2503.01222, 2025c.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of -fain-hf prompting elicits reasoning in large language models. Advances in neural information processing systems, 0224824-24837 2022.
Penghao Wu and Saining Xie. V Guided visual search as core mechanism in multimodal Ilms. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 13084 14084-13094 13094, 2024.
Yilin Wu, Ran Tian, Gokul Swamy, and Andrea Bajesy. From foresight to forethought: VIm-in-the- loop policy steering via latent alignment. arXiv preprint arXiv:2502.01828, 2025.
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui. Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding. arXiv preprint arXiv:2401.0788
2024.
LLM--Cre--Tam Xiaomi. Mimo-vl technical report, 2025. URL httppss//aaxi...rgg///// 2506. 03569
Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, and Chunyuan Li. Llava-critic: Learning to evaluate multimodal models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 13618-13628, 2025.
Guowei Xu, Peng Jin, Ziang Wu, Hao Li, Yibing Song. Lichao Sun, and Li Yuan. Llava-cot: Let vision language models reason step-by-step. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2087-2098, 2025.
Wang Yang, Xiang Yue, Vipin Chaudhary, and Xiaotian Han. Speculative thinking: Enhanc- ing small-model reasoning with large model guidance at inference time. arXiv preprint arXiv:2504. 12329, 2025a.
Zeyuan Yang, Xueyang Yu, Delin Chen, Maohao Shen, and Chuang Gan. Machine mental imagery: Empower multimodal reasoning with latent visual tokens. arXiv preprint arXiv:2506. 17218, 2025b.
Chen Zhang, Zhuorui Liu, and Dawei Song. Beyond the speculative game: A survey of speculative execution in large language models. arXiv preprint arXiv:2404. 14897, 2024.
Jiarui Zhang, Mahyar Khayatkhoei, Prateek Chhikara, and Filip Ilievski. Milms know where to look: Training-free perception of small visual details with multimodal llms. arXiv preprint arXiv:2502. 17422, 2025a.
14

---

Published as a conference paper at ICLR 2026
Xinlu Zhang. Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, and Linda Ruth Petzold. Gpt-4v (ision) as a generalist evaluator for vision- language tasks. arXiv preprint arXhv:2311.0136,, 2023.
Xintong Zhang, Zhi Gao, Bofei Zhang, Pengxiang Li, Xiaowen Zhang, Yang Liu, Tao Yuan, Yuwei Wu, Yunde Jia, Song-Chun Zhu, et al. Chain-of-focus: Adaptive visual search and zooming for multimodal reasoning via rl. arXiv preprint arXiv:2505.15436 2025b.
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha, Asli Celikyilmaz, Jason Weston, and Ilia Ku- likov. The majority is not always right: RI training for solution aggregation. arXiv preprint Xiiv22509006870, 2025.
Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing" thinking with images" via reinforcement learning. arXiv preprint arXiv:2505. 2025.
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, et al. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint Skiv:2554.10479 2025.
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A
DATASET STATISTICS
Table 7 reports the statistics of the four major evaluation benchmarks. All benchmarks are based on real-world images rather than synthetic renderings, ensuring the authenticity and diversity of the evaluation setting. In particular, Infographic VQA, ChartMuseum, and ChartQAPro intensive benchmarks: they contain thousands of images and questions with dense textual and nu- merical content, collected from diverse sources spanning 2594, 157, and 184 distinet web domains respectively (Mathew et al.) 2021: Tang et al. 2025 Masry et al. 2025). This diversity reduces source bias and reflects practical challenges multimodal reasoning.
HR-Bench 4K is used primarily to evaluate the generalization of SV, serving as a high-resolution benchmark with average sizes exceeding 4000x3500 (Wang al. (025b). Meanwhile, one of our main benchmarks, InfographicVQA. also exhibits high-resolution characteristics. In particular, it contains many images where diagrams span large vertical layouts (see the case Figure 3 which further compounds the difficulty of grounding and multi-hop reasoning across dispersed regions.
Table 7: Statistics of the evaluation benchmarks. We report the number of images and questions, as well as the average image resolution (width W and height H).
Real
Dataset
#Images
#Questions
W
H
Synthetic
Infographic (test)
Real
3288
579
1092
2771
ChartMuseum (test)
Real
1000
818
1551
1213
CharlQAPro
Real
1948
1341
1194
986
HR-Bench 4K
Real
800
200
4024
3503
B ADDITIONAL RELATED WORK
Large Language Model Ensemble. Majority voting aggregates answers by frequency, but fails when the correct solution is produced by minority. Universal Self-Consistency (Chen et al., 2023) mitigates this failure mode by prompting the LLM to select the most consistent candidate samples. Further, learned aggregators read multiple rationales and synthesize them to recover cennority-corree information (Qi et al. 2025 Zhao et al. (025). However, these approaches focus on text-only ensembling. In vision-language reasoning, supervision ensembling not cost-effective since multimodal complexity requires costly, fine-graines annotations.
C ADDITIONAL COST-EFFICIENCY ANALYSIS
C.I AVERAGE API COST OF SV WITH GPT-40 VERDICT
Treoots the average inference cost of invoking GPT-4o as the verdict model per sample across benchmarks. Costs are estimated using the official GPT-4o pricing (version gpt-4o-2024-08 as of November 2025. The small variation across benchmarks is mainly attributed to differences in reasoning path length, more challenging tasks typically induce more complex reasoning. Overall, the inference cost of using GPT-4o as the verdict is under $0.01 per sample across all benchmarks.
Table 8: Average inference cost of GPT-4o as verdict per sample across benchmarks. Costs are computed using GPT-4o spp-44o-0024-88-06 pricing by November 2025.
Dataset
GPT-4o cost per sample
InfographicVQA
$0.0068
ChartMuseum
$0.0109
ChartQAPro
$0.0071
HR-Bench 4K
$0.0044
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ChartMuseum Published as a conference paper at ICLR 2026
C.2 AVERAGE OUTPUT TOKENS ACROSS VERDICTS
To compare the token efficiency of replacing the large verdict with smaller models, we keep the draft stage identical and substitute the verdict with different draft models. All verdict models prompted to output only the final answer, and their performances are reported in Table Sible4in in the main paper.
As shown in Table 2 the small reasoning models, GLM-4. (.114..V-9--TTT B-9B-Thinking and -L---VL-B--LL generate 60-200xx more output tokens than GPT-44, yet still underperform SV. This highlights SV's advantage: using a strong verdict once for compact verification and synthesis is substantially more token-efficient than relying on smaller verdicts that require long autoregressive reasoning to reach decision.
Table 9: Average verdict output tokens per sample across benchmarks under different verdicts.
Verdict model
Infographic
ChartMuseum
ChartQAPro
GLM-4.IV-9B LLMM4..V-9B-Thinkiin Thinking
272.1
604.5
440.5
MiMo-VL.-7B-BB
183.2
368.2
378.1
3.3
3.4
2.8
GPT-4o (SV)
2.7
2.2
D SUPPLEMENTARY RECOVERY ANALYSIS ON INFORMATION-LTTENSVY BENCHMARKS
Table 10 and Figure 9 show the detailed recovery statistics across benchmarks with GPT-4o as verdict. We break down SV's performance by expert correctness: (i) cases where the majority of draft experts are correct (majority-correct) (ii) cases where only minority are correct (minority- correct), (iii) cases where none are correct (zero-correct). While the main paper focuses on the provide the full results here for completeness.
GPT-4o's error cases to isolate SV's effectiveness, Notably, in the zero-correct setting, recovery occurs rarely (2.6-24%), but it demonstrates verdict's surprising ability infer the correct answer by synthesizing signal from entirely noisy reasoning.
Table 10: Recovery accuracy (%) with GPT-4o as verdict. Results are conditioned on whether GPT-
4o itself can produce the correct answer.
GPT-4o Correct
GPT-4o Wrong Minority- correct
Majority- correct
Minority- correct
Zero- correct
Majority- correct
Zero-
Dataset
correct
InfographieVOM
96.81
64.13
20.54
93.30
53.42
4.44
ChartMuseum
98.46
69.84
15.38
89.92
47.71
2.69
CharQAAro
94.59
68.18
24.00
85.25
48.43
2.86
Proportion of GPT. correct cases (%)
Majority SV connec
80
Minerity-comect SV correct
60
Zero-corres SV correct 5V
Majority Ninoriy
Majority Minority Zere
Majority Minerity
InfographicVQA
ChartQAPro
Figure 9: SV's correction ability on verdict's correct cases (GPT-40 as verdict), complementary to its error cases in Figure 4
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Table 11: Results on test sets of InfographicVQA with 7-9B draft experts. We compare SV against closed-source VLMMs, open-source VLMM, and tool-driven methods. f denotes results reported in the original papers; all other results are reproduced by us. The best results for each benchmark are highlighted in bold and the second-best results underlined.
Param
InfographicVQ2 QQA ANLS
Model
Size
Closed source VLMs
GPT-4o
76.5
GPT-44-mini
67.2
Open-source VLMs
Owen2.5-VL-Instrue
7B
79.8
LLaVA-One's V..
8B
70.3
Vn3 Eagle 2.5
8B
72.3
8B
74.5
Ovis2.5
9B
81.7
Tool-driven method
DeepEyes
7B
75.5
Pixel-Reasoner
7B
84.0
SV w/ GPT-4o Verdict
86.3
A (vs. GPT-4o)
+9.8
Table 12: Results on test sets on InfographicVQAA with 2-4B draft experts. Same evaluation and marking conventions Table IT
Param Size
InfographicVQA ANLS
Model
flosed-source VLMs
GPT-4o
76.5
GPT-4o-mini
67.2
Open-source VLMs
Qwen2.5-VL.-Instruc L..VA-OneVision--l. InternVL3.S
3B
64.9
4B
67.1
4B
74.4
Gemma 3
4B
36.0
Ovis2.5
2B
75.0
Tool-driven method
DeepEyes Pixel-Reasome
7B
75.5
7B
84.01
SV w/ GPT-4o Verdict
84.5
A (vs. GPT-4o)
+8.0
E ABLATION STUDY ON MODEL POOL COMPOSITIONS
Beyond the fixed model pool used the main experiments, we further examine SV's generalizability across different model pool compositions by testing on pools with varying model sizes and capa- bilities. The results show that SV successfully leverages reasoning paths from lightweight models, delivering strong performance while maintaining cost efficiency.
Evaluation with 7-9B Model Pool -onnreasonnnng.... SV maintains its effectiveness when replacing reasoning models with faster non-reasoning alternatives. Specifically, we replace the two reasoning models in our original pool (i.e., GLM-4.1 V-9B-Thinking (Team al., 2025b) and MiMo- MiMo-VL--7B RL (Xiaomi) 2025) with rea-onnngning models (i.e., LLaVA-One
(An et al. 2025). and Eagle 2.5-8B (Chen et al. 2025)), ). while keeping the remaining three models unchanged. While these substitutes sacrifice some reasoning capability, they enable faster inference. As shown in Table [T with GPT-4o as verdict, SV achieves 86.3% on Infographic under this configuration, surpassing all baselines. Notably, SV surpasses the best draft expert by 4.6% and improves GPT-4o by 9.8%. These results demonstrate that SV achieves strong performance by integrating reasoning paths from individually weaker but faster models.
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smaller model pool consist- ing of 2-4B models: Qwen2.5-VL-3B ---------B- (Bai et al. 2025). LLaVA-One Visiio-I..---4 (An et al. 2025). IntemmVL3.5-4B (Wang al. 2025a), Gemma 3-4B (Team et al. 2025a), and Ovis2.5- 2025). As shown in the Table 12 with GPT-4o as verdict, SV achieves 84.5% on Info- graphicVQA, surpassing the best draft expert by 9.5% and yielding 8% gain over GPT-4o. This demonstrates SV's ability to extract effective collective reasoning even from significantly weaker individual models, confirming the robustness of our paradigm across varying model scales.
Evaluation with 2-4B Model Pool. We also evaluate SV
2B (Lu et al.
F ANALYSIS OF CONSENSUS SELECTION MECHANISM
This section provides detailed analysis of consensus expert selection mechanism, addressing three aspects: (I) selection frequency distribution, (2) impact of normalization on consensus scores, and (3) impact of NLL estimation strategy.
F.1 SELECTION FREQUENCY DISTRIBUTION
To understand how the consensus mechanism selects draft experts, we analyze the selection fre- quency of each model across benchmarks. Table 13 reports the proportion of instances in which each model selected. All models participate with non-trivial frequencies (38.6%-84.7%), with no model dominating or being marginalized. This indicates that different drafts specialize in dif- ferent subsets of questions, and the consensus mechanism leverages complementary agreement and disagreement across experts rather than collapsing to single seleyedd model.
Table 13: Selection frequency of each draft model across benchmarks.
Model
InfographieVQA
ChartQAPro
W.vn2..-VLL-7B-- In-srutttt
84.7%
77.7%
V-M-- ThTnnnigg
38.6%
69.3%
MiMo-VL-7B-RU
54.8%
56.8%
VL3-SB
73.4%
53.1%
Ovis2.5-9B
48.5%
43.1% %
F.2 IMPACT OF NORMALIZATION ON CONSENSUS SCORES
We further ablate the effect of normalization consensus scores, which is motivated by the calibra- tion gap across draft experts mentioned in Section 3.4 Different VLMs produce perplexity scores on systematically different numerical scales due to training and tokenization differences. In our pool, for example, Qwen2.5-VE VL and GLM-4. V-Thinkig tend to output larger- maggit- NLLs. With- normalization, such scale mismatch causes nigh-mag models dominate the consensus even when they do not genuinely agree more often, thereby reducing selection diversity.
We evaluate normalization on two draft pools on Infographic (i) the main pool used in our primary experiments, mixing reasoming and non-reasoning drafts, and (ii) an additional pool con- sisting only of non-reasoning drafts. introduced in Appendix E Tables [4a and 14c show that, on the main pool, removing normalization collapses selection to near-fixed subset: Qwen and GLM are selected almost all instances. This collapse hurts performance on ChartQAPro, demonstrating that calibration artifacts actively degrade consensus quality. Although Infographic VQQA remains similar without normalization, this results from an accidental strong pairing being repeatedly se- lected rather than principled aggregation.
To verify that this trend is not pool-specific, we repeat the ablation on the additional non-reasoning pool. Tables 14b and 14d show the same selection-collaps, pattern without normalization, together with a clear performance drop. Across both pools, normalization mitigates calibration bias, prevents dominance by few drafts, and yields more stable performance.
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Table 14: Normalization ablations on the main and additional draft pools.
(a) Ablations on normalization on the main draft pool.
(b) Ablations on normalization on the additional non-reasoning pool.
Variant
InfographicVQA
ChartQAPro
Variant
InfographicVOA
SV w/o norm.
88.9
59.4
SV
88.4
64.0
SV w/o norm.
84.6
SV
86.3
(c) Selection frequency on main pool without nor- malization.
(d) Selection frequency on additional non- reasoning pool.
Model
InfographicVO%
ChartQAPro
Model
nurn.
w/o norm.
Qwen2.5-VL- ------VVL----ntttct
99.9%
100.0%
Breen.5-VL-7B-mmae
87.3%
99.9%
77.1%
99.9%
Eagle2.5-8B
65.4%
98.0%
MiMo-VL- VL--BB-II.
$7.0%
71.5%
28.6%
79.3%
VL3-88
47.0%
21.4%
7nterV13--88
74.5%
16.7%
Ovis2.5-9B
19.1%
2.2%
Ovis2,5-9B
44.2%
6.11%
F.3 IMPACT OF NLL ESTIMATION STRATEGY
We examine whether computing NLL only on final answers introduces off-policy bias, since draft experts' answers are generated together with reasoning trajectories. Table compares expert selec- tion using answer-only NLL (SV) versus full-traeectory NLL. The variants achieve very similar performance, suggesting that any off--ooliy bias from answer-only scoring is negligible for this task. Moreover, answer-only scoring more computationally efficient, as it avoids computing NLL over long reasoning traces with many extra tokens; answer-only NLL also provides cleaner signal by avoiding noise from diverse reasoning styles across models. We therefore use answer-only NLL in the main experiments.
Table 15: Ablation on NLL estimation strategy.
Scoring variant
InfographicVQA
ChartQAPro
Answertreasoning NLL
87.9
64.3
Answer-only NLL. (SV)
88.4
64.0
G COMPARISON TO LLAVA-CRITIC (LMM-AS-A-JUDGEE BASELINE)
We compare SV to a learned .MMaas-a-Judge baseline, LLaVA-Critic (Xiong et al. 2025). LLaVA- Critic trained as generalist multimodal evaluator jointly leverages visual evidence and textual reasoning to score or rank multiple candidates.
Experimental setup. We follow the official LLaVA-Critie evaluation template and consider two judging modes: (i) pointwise scoring, where the judge assigns scalar score to each candidate and selects the highest-scored one; (ii) pairwise ranking. where candidates are compared (i.c.. first two candidates are compared, and the winner is then compared against the third in our scenario). To ensure comprehensive comparison, we evaluate LLaVA-Critie under two settings: (1) judging the same three consensus experts selected by SV (directly replacing the verdict); (2) judging five candidate models. In all cases, the judge outputs a single best candidate.
For SV, we conduct experiments with two verdict models: GPT-4o and LLLVA--One Vision-7B Vissonnnn LLi al. 2024). GPT--4o serves as strong proprietary verdict model, matching our main experiments, while LLa VA-One Visionn-- shares the same backbone as LLaVA-Critie, enabling fairer compar- ison in the open-source regime.
Results. Table 16 shows that when judging the same three draft experts, SV consistently outper- forms LLaVA-Critic across all three benchmarks under both verdict choices. With GPT-4o as by 4.9-11.9%. With LLaVA-Ona Vision-7B as the
the verdiet, SV improves over LLaVA-Critic- verdict, SV still yields gains of 0.5-6.6%. which demonstrates that the advantage stems from SV's design rather than from verdict model superiority alone.
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Table 16: Performance of SV and LLaVA-Critie-7B.
Method
InfographicVQA
ChartMuseum
CharlQAPro
LLaVA-Critie-78 (pointwise)
83.5
40.1
52.4
LLaVA-Critie-7B (pairwise)
81.4
38.9
52.1
SV w/ GPT-44o verdict
88.4
49.3
64.0
SV w/ LLaVA-OneVision- verdict
84.0
44.1
59.0
Table 17: Average judge/verdict tokens per sample.
Method
InfographicVQA
ChartMuseum
ChartQAPro
LLaVA-Critie-P7 Critic-7B (pointwise)
1053.3
3723.7
1290.7
LLaVA-Critie-78 (pairwise)
1342.9
4689.4
1759.1
SV w/ GPT-4o verdict
701.7
3122.3
1441.7
SV w/ LLLaVA-OneVisionn-- verdict
702.4
3123.0
1442.4
Table 18: Performance of SV and LLaVA-Critic-715 judging five candidate models (Ik samples).
Method
InfographieVQA
ChartMuseum
ChartQAPro
LLaVA-Critic-77 (pointwise)
82.3
34.6
56,5
SV w/ GPT-40 verdict
88.3
49.3
63.4
SV w/ LLaVA-OneVisiceee verdict
83.6
44.1
57.8
Table 19: Performance of SV and LLaVA-Critic-777 judging three consensus experts (Ik samples).
Method
Infographic
ChartMuseum
ChariQAPro
MAAVA-Critie-211 (pointwise)
82.6
40.4
53.5
LLaVA-Critie--20 (pairwise)
83.1
43.0
55.8
SV w/ GPT-4o verdict
88.3
49.3
63.4
LLaVA One Vision-7B verdict
83.6
44.1
57.8
This gap is attributed to the different aggregation principles: LLaVA-Critic performs selection-based judging and can only pick one trajectory, while SV performs synthexis-based verification, cross- checking and integrating complementary factual cues across trajectories. In practice, the critic is also more sensitive to surface form (e.g., verbosity, repetitiveness, or stylistic fluency), whereas SV is guided by consensus evidence and is therefore more robust to such artifacts.
Table 17 further reports the total judge/verdict tokens. Under comparable prefill- based budgets, SV achieves higher performance with similar lower token usage Infographie and ChartMu- seum, and still attains a much better cost-performance trade-off on ChartQAPro.
When increasing the candidate pool to five models (Table [8]. LLaVA-Critic-7E remains clearly below SV, indicating that simply adding more candidates to selection -based judge does not close Table [99 is still notably weaker than SV, even including the variant with the smaller LLaVA-One Vision-75 verdict. This confirms that SV is more effective than selection based judging for information cooomatton-intensivee visual reasoning.
the gap. Finally, even much larger judge (LLaVA-Critic
H ABLATION STUDIES ON VERDICT INPUT CONFIGURATION
H.1 IMPACT OF VISUAL INPUT TO VERDICT
We examine whether visual input is necessary for the verdict or if reasoning paths alone suffice. Ta- ble 20 presents results where the verdict receives only textual reasoning paths without image input. The results show that SV without visual input achieves modest gains over the reasoning baseline on InfographicVQA. and even underperforms on ChartMuseum and ChartQAPro. In contrast, incorpo- rating visual input for verdict yields substantial improvements across all benchmarks. These results demonstrate that visual grounding is essential for the verdict to cross-check the factual accuracy of extracted information and distinguish correct from incorrect interpretations of the image.
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Table 20: Ablations on visual input to the verdict GPT-4o.
InfographicVQA ANLS
ChartMuseum Acc
ChariQAPro
Method
Acc
Reasoning baseline
84.8
48.0
57.3
GPT-4o Verdict w/o input
85.9
47.1
53.2
GPT-4o Verdict w/ imput (SV)
88.4
49.3
64.0
Table 21: Ablations on additional structured image input to the verdict GPT-4o.
InfographicVOx ANLS
ChartMuseum Acc
ChartQAPr Acc
Method
Reasoning haseline
84.8
48.0
57.3
GPT-4o Verdict w/o input
88.3
49.5
59.4
GPT-4o Verdict input (SV)
88.4
49.3
64.0
H.2 IMPACT OF STRUCTURED IMAGE INPUT TO VERDICT
In our experimental setup in Section we preprocess each image via PP-StructureV3, document parsing model that generates Markdown representations capturing layout, textual blocks, and visual metadata (Cui al. 2025). This structured representation is then rendered as an image and provided as an additional image input for the verdict. This allows the verdict to access both the raw visual content and layout-aware text representation simultaneously. To verify whether this input critical or merely auxiliary, we conduct ablation study (Table 21)
The results show that SV achieves substantial gains over the reasoning baseline even without struc- tured input. With the structured input, performance is generally slightly improved, though the gain negligible or even marginally lower in some cases. This pattern suggests that structured OCR- derived signals not essential for SV's core performance, but may assist the verdict to distinguish among competing reasoning paths.
I QUANTITATIVE ANALYSIS OF DRAFT COMPLEMENTARITY
To characterize the complementarity of draft experts, we focus on minority-corred recovery cases on InfographicVQA where only one of the three selected experts correct and SV subsequently recovers the correct answer. These cases are most diagnostic for understanding how specific models provide unique, correct information and how others behave help the verdict distinguish cues.
We randomly sample 50 minority-corredt recovery instances and manually categorize their dominant reasoning bottlenecks into five types, summarized in Table [2 Extraction-related failures account for half of the cases, followed by color matching and global scan.
Distribution of dominant reasoning bottlenecks over 50 minority-correct recovery cases on Infographic
Table 22:
Reasoning bottleneck
Description
Frequency (%)
Extraction
Locating and reading fine-grained text/numbers
50
Color matching
Matching colors in legends/charts to labels
18
Global scan
Aggregating evidence across the full image Finding query-relevant regions Comparing numerical values
16
Localization
10
Numerical comparison
4
Table 23 reports per-model success rates within each bottleneck category over the 50 minority- correct instances. We observe a clear division of labor across experts: GLM-4. IV-Tiii--Tiiniing and MiMo-VL-RL are most reliable for fine-grained extraction (58% and 70% success, respectively), with MiMo additionally strong on global-scan cases (75%). This advantage is consistent with their orieente behavior: their step-by-step trajectories enable iterative verification of extracted values and cross-checkin checkinn across multiple regions. Ovis2.5 and GLM-4, GLM-4.1V-Thinkin V--hhnkiing perform best on color matching (57% and 60%), while Qwen2.5-VI. dominates localization (80%) and numer-
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ical comparison (100%). We find that other experts often fail in these categories due to keyword- comprehension errors, whereas Qwen2.5-VDL VL'ss correct grounding makes its answer salient for the verdict to identify even when other models provide detailed but misdirected reasoning. Overall, no single expert dominates all reasoning types, confirming that SV benefits from specialized and complementary strengths rather than redundant voting.
Table 23: Per-model success rates (%) on minority-correet recovery cases, broken down by reason- ing bottleneck.
Reasoning type
Qwen2.5- In-Isrruttt GLMii.JV-
MiMo-VEL VL-RL
Ovis2.5
Intern VL3
Extraction
1.
58
70
38
15
Color matching
0
60
33
57
25
Localization
80
0
0
33
0
Global scan Numerical comparison
75
100
0
0
0
Finally, we summarize the most frequent expert combinations all 243 minority-correet recovery cases on InfographicVQA, as shown in Table 24 Two typical complementarity patterns emerge. First, balanced visual-skill combinations (e.g., owen-Ovis-Intern diverse perceptual cues. Second, extraction-focused pairings (e.g.. -wen-MMio- Qwen-GILM-Ovis arise in extraction-intensii problems, where reasoning drafts provide accurate fine-grained values and non-reasoning drafts contribute robust localization for cross- verification. Overall, this analysis supports claim that SV does not simply average similar models: it ex- ploits complementary reasoning strengths across draft experts, and the minority- -ooreett recoveries observe closely associated with diverse and specialized reasoning trajectories across models.
appear in cases requiring
and
Table 24: Most frequent expert combinations among all minority-correct cases on InfographicVOA..
Rank
Model combination
Frequency (%)
1
Qwen Ovis InternVL
33.3
2
Qwen MiMo + Vn.ernVL.
14.0
3
Qwen + GLM + Ovis
11.1
J FAILURE MODE ANALYSIS OF SV
We analyze cases where the majority of selected draft experts are correct but SV produces incor- rect answer on InfographicVQA with GPT-4o as verdict. These instances isolate failures when the consensus mechanism verdict synthesis fails despite having correct information available. Under exact-match scoring, such cases account for 3.98% of majority-corret examples; after normalizing answer formats (e.g.. "Feb 11" vs. "February 11"",, the true failure rate is 1.34% (34 cases).
Failure modes. Two dominant patterns emerge. (i) Fine-grained extraction errors (52.9%) arise from tiny numbers small text very tall infographics. Although draft experts often extract the correct cue, GPT-4o may fail to verify it because its tile-based high-resolution pipeline downsamples long inputs, losing subtle details. (ii) Color matching errors (35.3%) require aligning colored legends or regions with labels. This reflects a shared VLM capability gap: when both drafts and verdict struggle with color discrimination, drafts provide uncertain reasoning (sometimes explicit guesses), and GPT-4o exhibits verbosity bias in 58.3% of such cases (i.c., it follows the draft with the longest trajectory despite majority consensus pointing elsewhere).
Overall, SV inherits current VLM weaknesses in precise color reasoning and fine tne-grained percep- tion, and when visual evidence is ambiguous the verdict may over-weight fluent yet incorrect drafts.
K FAILURE MODE ANALYSIS OF TOOL-DRIVEN PIPELINE
As mentioned in Section tool-driven methods represent a line of work that augments vision- language reasoning with explicit zoom-in operations. The representative pipeline DeepEyes is de- signed to iteratively ground into image regions, and integrate them into the ongoing reasoning trajec-
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Table 25: Breakdown of SV failure modes on Infographic VQA.
Failure mode
Proportion
Fine-grained extraction
52.9%
35.3% Color matching
Other
11.8%
tory under an RL framework. This mechanism has proven effective on high-resolution benchmarks, where localized inspection of fine details is crucial.
However, DeepEyes is not specifically trained on our benchmarks, which require reasoning over informationn-ineesivve images with densely interleaved textual and visual elements. Its performance on Infographic VQQ reveals the current limitations of such tool-based pipelines in this domain. We categorize the observed deficiencies into three core challenges:
(i) Tendency toward literal grounding. DeepEyes is proficient at small-scale grounding but often focuses literal text spans legends rather than reasoning-critical regions. For example, when question requires aligning numerical values with chart axis, the model frequently grounds directly onto the answer text or nearby labels instead of the relevant data regions. This shortcut strategy works for simple queries but fails on complex reasoning on information-intensieee images that require global comparison.
(ii) Inefficient tool usage. Although DeepEyes is trained to iteratively apply zoom-in tools, we observe that it invokes only one zoom step in more than half of the test cases. Among the double- zoom cases, 92.8% duplicate the same bounding box, which serves only for verification rather than exploration. In some instances, the model zooms into empty areas or irrelevant regions.
(iii) Lack of robustness on long and dense images. Information-intensivee images often contain multi- panel figures and dense annotations. DeepEyes cannot maintain a trajectory across multiple zoom steps, making it difficult to integrate dispersed evidence. As result, tasks requiring cross-region synthesis, such as counting, sorting, or comparing across multiple subplots, remain challenging.
Overall, this analysis indicates that while tool-driven pipelines promising for high-resolution inspection tasks, they face notable difficulties applying to cooomattoo-intensiii images without domain-specific supervision. In contrast, SV achieves strong performance without additional train- ing, offering a simple and effective alternative for reasoning over complex multimodal inputs.
L QUALITATIVE EXAMPLE
Figure TO illustrates a case where all three draft experts produced incorrect reasoning paths, yet the verdiet successfully corrected the answer. Specifically, the draft experts faced different types of failures: some mis-extracted information from the image. others extracted the key information correctly but failed to sort the values properly, and thus all generated wrong answers. Interestingly, the verdict itself, when asked directly, also tends to answer "Australia" incorrectly. However, when analyzing the noisy and conflicting reasoning paths together, the verdict was able recover the correct answer (Portugal).
This example complements the main results section: while Figure 3 illustrates recovery from minority- correct experts, here we present zero-correct case to show that SV can still synthesize the correct solution even when all drafts and the verdict individually fail.
M PROMPT TEMPLATES
M.1 CHAIN-OF-THOUGHT PROMPTS
employ Chain-of-Thought prompt for each consensus expert to generate reasoning paths and apply it identically when evaluating baselines. For InfographicVQA and HR-Bench 4K, we use the same CoT prompt. For ChartMuseum (Tang al. 2025), adopt its official reasoning prompt, and adapt that prompt strategy to CharlQAPro, given their similar- ity in task complexity. Since ChartQAPro requires different prompt templates tailored question
As described in Section
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types (Masry al. 2025), first follow its official template per question type, then concatenate with our reasoning prompt.
The reasoning prompts for these datasets are shown in Figure 11
M.2 PROMPTS FOR VERDICT
The user prompts used in the verdict stage are identical across datasets except for the final instruction sentence, which customized (see Figure 13). For GPT-4o verdict, the system prompt is shown in [2 For Swwn-2.5-VL-72B-innttee Instructt as verdict, we prepend its system prompt at the beginning of the user prompt.
N THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, we used LLMs solely for auxiliary tasks such as language polishing, prompt refining, and proofreading. Importantly, these interventions did not contribute any main scientific insight, experimental design, or methodological advance. All core ideas, experiments, analyses, and claims in this paper are the work of the authors.
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Question: In which country the percentage of female researcher was the highest in the duration of 2011-15?
Ground-truth: Portugal
Image:
FOCUS COMPUTER SCIENCES
ME
Researcher chers
mak meng
5
Silnest
10
11
2N LIN
In Het qie seaitie of named Jullias
To determinn ne the country with the highest percentage of female researchers the duration of 2011-15, let's analyze
image:
I The image shows pie chart each country, indicating the proportion of women and men among
Computer Sciences 2 We need compare the percentages of women researchers for each country liste..
Here the percentages for each
Canade: (women))
(wamen), Mexico: 209 Jwrmeen), (wemen)) Itrank: 20% (women)) (woment, Australla: 25% [wamen), 20% (woment, 22% [women)) Portugall [women), Japan: 11% (women)
highest
of researchers Australia 25%.
To anower the question, we need to look the seciion tkied "Basearchers" which shows the pr proportion of women and men among reseanchers Computer Sciences from 2011-11 Each country representie by ciide with sogments, one identily the country with the langest segment for women:
par comaae
Canada: 33% women, UKC 2018 women, France: 223 women, US: 22% women, Mexico: 18% womenn, Peru: 17%
Australia: 25% wamen, Portugal: 2586 women, EUZABET: 22%6 women
Chile.
Denmark:
fomalo researchers 33%
determine "Researchers" section, which Canada: 23% Denmarkc 277, France: 78% Us: 22%, Portugal 27% (women), Mestoo: 23%, Australls: 25% Brazi: 23%, 22%
highest percentage of female researdhers (2011 examin percentages (green) for
Chedking country's
Among thine, France has the highett percentage of female researchers 7856
: Portugal
Figure 10: A qualitative zero-correct case corrected by verdict. All three draft experts fail due to emrors in extracting or sorting visual information, yet the verdict synthesizes their noisy reasoning paths to recover the correct answer (i.c., Portugal).
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Infographic VQA HR- BeR-Benchhee
Question: (CUESTION) Please think step-by-step about the image to answer the question using a single word or phrase enclosed within V boxed((H).
ChartMuseum
Please answer the question using the chart image.
Questien: (QUESTION)
Please first generate your reascning process and then provide the user with the answer. Use the following fornat:
<think>
your thinking process here
</think>
<answer>
... your final answer (entity (8) number)
</answer>
ChartQAPro
(PROMPT for a specific fic question type)
Please first generate your reasoning process and then provide the with the answer. Use the following fornat:
<think>
your thinking process here
</think>
<answer>
your final answer (entity (s) or number)
</answer>
Figure 11: Prompt templates for reasoning.
All benchmarks
You are visian-aadd-language judge. Fol low the instruction ons strictly
Figure 12: System prompt template for verdict.
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Infographic VQA VQQA Cuscum
Question
[QUESTION]
Model
Reasoning:
[Reasoning path 1)
Proposed Answer: {Answer 1}
Model 2 ---
Reasoning:
[Reascning path 2)
Proposed Answer: {Answer 2}
Model 3
Reasening:
(Reasoning path 3)
Proposed Answer: [Answer 3
Given the raw image, the layout-annotated inage, the question, and the reascning from three models, please give the final answer using single word or phrase enclosed within (bbxxd(([).
Char
Question:
(QUESTION)
Model
Reasoning:
[Reasoning path 1)
Proposed Answer: (Answer
Model
Reasoning:
[Reasoning path 2)
Proposed Answer: (Answer 2
Model
Reasoning:
[Reasoning path 3)
Proposed Answer: (Answer 3)
Given the image, the Layout-annotated image, the question, and the reasening from three models, please directly give the final answer enclosed within \boxee][]] (H).
Question:
[QUESTION
Model
Reasoning:
(Reasening path 1)
Proposed Answer: (Answer 1}
Model
Reasoning:
[Reasoning path 2)
Proposed Answer: (Answer 2
Model
Reasoning:
(Reasoning path 3)
Proposed Answer: (Answer 3)
Given the image, the question, and the reason in from three models, please rirectly give the final anawer with the option letter enclosed within (bboxed[
Figure 13: User prompt templates for verdict.
28


Reviews:
[{"review_id": "7hfRzO6xAw", "reviewer": "Reviewer_65ys", "summary": "This paper presents a prompting framework for high-resolution image reasoning tasks, where small LVLMs generate draft reasoning trajectories and answers, and a large verdict model incorporates all reasoning and produces the final answers. The reasoning trajectories are selected based on the consensus score (i.e., the absolute difference between the model's own answer and the answer generated by the other model).  Experimental results show the framework achieves better results on high-resolution/dense-layout benchmarks such as InfoVQA and HRBench.", "questions": "## Questions\n\n- The reasoning trajectory analysis is interesting in Figure 1 and Section 3.2. I am wondering if there is any quantitative analysis of reasoning type distribution between draft models and their influence, e.g., which models are more complementary?\n\n- Why is the ablation study about the verdict scale only conducted on a subset of InfoVQA?\n\n- Will some of the small reasoning models produce a long reasoning trajectory, and then the final prefill stage exceeds the verdict model's length limit? \n## Format\n- Table 7 -> Figure 7.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The investigated problem of solving dense-layout image reasoning tasks using ensemble learning is of great practical value.\n- The performance is promising, surpassing tool-based methods such as DeepEyes.", "weaknesses": "- Unclear Connection to Speculative Decoding\nThe paper's framing as \"speculative decoding\" is confusing. Traditional speculative decoding aims at inference acceleration, whereas this work operates more as an LLM-as-a-Judge paradigm where candidate answers are evaluated by a verdict model. The paper lacks discussion and comparison with existing judging frameworks (e.g., [1, 2]), which weakens its positioning within the literature.\n\n- Limited Technical Contribution:\nViewing this work through the LLM-as-a-Judge lens, the technical novelty appears limited beyond modifying the aggregation process with consensus scores. Despite the ablation in Figure 7, several critical aspects remain underexplored:\n  - What is the distribution of agreement/disagreement among models?\n  - How does normalization affect the results? Could overconfident models skew the consensus?\n  - Since answers are generated with reasoning trajectories, does estimating NLL on answers alone introduce inaccuracy due to off-policy estimation?\n\n- Insufficient Motivation and Analysis:\nThe paper would benefit from deeper investigation to strengthen its claims:\n  - A detailed analysis of reasoning trajectory patterns across different models\n  - Exploration of whether using a smaller model as the verdict would yield similar performance gains\n  - Token efficiency comparison against standard LLM-as-a-Judge frameworks, since both approaches can leverage prefilling\n\n\n\n[1] LLaVA-Critic: Learning to Evaluate Multimodal Models, CVPR 2025\n[2] VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models, CVPR 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": "PlfNpOmZ5M", "reviewer": "Reviewer_Cnwz", "summary": "This paper addresses Large Vision-Language Models’ struggles with information-intensive images—difficulty localizing critical cues in dense layouts and multi-hop reasoning. It proposes Speculative Verdict (SV), a training-free framework combining lightweight draft experts and a large verdict model. Small VLMs generate diverse reasoning paths in the draft stage; a strong VLM synthesizes these for final answers in the verdict stage. SV adds a consensus expert selection to forward only high-agreement paths. Experiments show SV gains on benchmarks like InfographicVQA, achieving error correction and cost-efficiency vs. large models.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) This paper accurately pinpoints VLMs’ core flaws in information-intensive images—poor dense cue localization and error-prone multi-hop reasoning—and clarifies limitations of existing solutions, ensuring relevance.\n2) The “Draft-Verdict” two-stage structure (lightweight experts for coverage + large VLM for synthesis) and consensus selection balance accuracy and efficiency, with clear alignment to solving target challenges.\n3) Experiments on diverse benchmarks (InfographicVQA, HR-Bench 4K) and comparisons with various baselines, plus error correction data (47-53%), fully validate performance and cost-efficiency.", "weaknesses": "1) How about the comparison of the proposed method with specialized models? \n2) The inference speed is not presented in the experiments section. Does it add much computation cost to the baseline method thus slow down the inference1 speed, and if yes, could you give the speed?\n3) On HR-Bench 4K, SV w/ GPT-4o Verdict performs worse than SV w/ Qwen2.5-VL-72B-Instruct Verdict, and even worse than several Open-source VLMs, please explain why?", "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": "zEmKV9hSRV", "reviewer": "Reviewer_656R", "summary": "This paper addresses the difficulty of VLMs in reasoning over information-intensive images that combine dense text and fine-grained graphics (e.g., infographics, charts), and introduces Speculative Verdict (SV), a training-free framework for information-intensive visual reasoning tasks. Inspired by speculative decoding, SV operates in two stages. The first is a draft stage where multiple lightweight VLMs generate diverse reasoning paths; and the second is a verdict stage where a large VLM synthesizes these paths to produce the final answer. The authors introduce a consensus expert selection mechanism that forwards only high-agreement reasoning paths to the verdict model. SV is evaluated on several benchmarks (InfographicVQA, ChartMuseum, ChartQAPro, and HR-Bench 4K) and demonstrates consistent improvements over strong baselines while maintaining cost efficiency.", "questions": "1. Why SV improves more dramatically with GPT-4o (11.9/6.6/11.4/4) compared to Qwen 72B (2.5/7.5/2.3/2.5), especially for InfographicVQA and ChartQAPro? \n\n2. Could you provide more detailed metrics on the computational efficiency of SV compared to baselines? For example, inference time, FLOPs, or memory usage would help quantify the efficiency claims.\n\n3. How sensitive is SV to the choice of draft models? Have you experimented with draft models of different sizes and architectures beyond the ones reported?\n\n4. In what types of cases does SV fail? A more detailed analysis of failure modes would help understand the limitations.\n\n5. How does SV compare to more advanced ensemble methods beyond majority voting? For example, methods that learn to weight or combine the outputs of multiple models.\n\n6.How well does SV generalize to other types of visual reasoning tasks beyond the information-intensive benchmarks evaluated?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Originality: The paper presents a novel adaptation of speculative decoding for visual reasoning quality improvement rather than its original purpose of inference acceleration. \n\n- Quality: \n  1. The proposed approach is effective. \n  2. The experimental evaluation is comprehensive, covering multiple benchmarks and comparing against strong baselines. \n  3. The ablation studies provide insights into the importance of different components.\n\n- Clarity: \n  1. The paper is well-structured and clearly written. \n  2. The figures effectively illustrate key concepts and results. (BTW, I like the animal icons in Figure 2.)\n  3. The methodology is described in sufficient detail.\n\n- Significance: The paper addresses a significant challenge in multimodal AI with a cost-effective solution that outperforms more expensive alternatives, which has practical implications for deploying such systems.", "weaknesses": "1. The tables lack clarity: The tables lack direct comparison between baseline and SV, such as directly providing the increment from GPT-4o (line 332) to SV+4o (line 341), and also the increment on Qwen2.5VL-72B, so that readers can easily compare the performance gain regarding different base models, instead of refering to other places in the paper (such as refering to line 377). Althought I like the figures and plots, the tables really need improving. \n\n2. Limited analysis of computational efficiency: This paper claimis that SV is more cost-efficient, but doesn't provide detailed metrics comparing computational costs (e.g., FLOPs, inference time, memory usage), and the budget cost.\n\n3. Restricted Draft Model Pool: The evaluation is restricted to a fixed draft model pool, limiting understanding of how SV would perform with a more diverse set of draft models.\n\n4. Insufficient Analysis of Failure Cases: The paper would benefit from a more detailed analysis of cases where SV fails to understand the approach's limitations.\n\n5. Limited Comparison to Advanced Ensemble Methods: The paper compares SV to majority voting but doesn't compare to more advanced ensemble methods that could be applied to this problem.\n\n6. Potential Overfitting to Specific Benchmarks: While evaluating on multiple benchmarks, they all focus on similar types of information-intensive visual reasoning, making generalizability unclear.", "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": "Gufh6dx6ff", "reviewer": "Reviewer_E8fc", "summary": "This paper introduces SV, a training-free framework that combines multiple lightweight draft experts with a large verdict model. They use small VLMs to generate reasoning paths as candidates, and use strong VLMs to produce the final answer. They claim SV achieves consistent gains over many benchmarks.", "questions": "1. Address the issues mentioned in the weaknesses.\n\n2. The formatting on page 17 could be improved.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Enhancing visual reasoning ability is a highly important and actively studied research problem.\n\n2. The paper is easy to follow, the figures are clear, and the code is open-sourced.\n\n3. The experiments are detailed, and the ablation study in Section 4.4 is very comprehensive.", "weaknesses": "1. My main concern with this work lies in the evaluation. If this issue can be properly addressed, I would be willing to raise my score. Currently, Table 1 compares the performance of several VLMs, and SV claims to outperform all of them. However, I would like to see a cost analysis. Although Appendix B provides some numerical results, it lacks a comparison of the computational cost between SV and the baseline methods. How much additional cost does SV introduce compared to the baselines? Under the same computational cost, would using only the verdict yield a similar effect? Likewise, under the same cost, would conducting debates among multiple draft models achieve comparable results?\n\n2. The literature review is missing several important works. In the area of vision-language model reasoning, this paper is not limited to tool-related methods; therefore, it should also include more general VLM reasoning studies and cite some representative works in that domain.", "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)