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<!DOCTYPE html>
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<meta charset="utf-8">
<meta name="description"
content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<title>Code-vision: Evaluating Multimodal LLMs Logic Understanding and Reasoning Capabilities Through Code Generation
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<h1 class="title is-1 publication-title">
<span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span
style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span>
<br>
<span style="font-size: 0.9em">Evaluating MLLMs Logic Understanding and
Reasoning Capabilities Through Code Generation</span>
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://wanghanbinpanda.github.io/">Hanbin Wang</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="https://august6676.github.io/about/">Xiaoxuan Zhou</a><sup>*2</sup>,</span>
<span class="author-block">
<a>Zhipeng Xu</a><sup>2</sup>,
</span>
<span class="author-block">
<a>Keyuan Cheng</a><sup>1</sup>,
</span>
<span class="author-block">
<a>Yuxin Zuo</a><sup>3</sup>,
</span>
<span class="author-block">
<a>Kai Tian</a><sup>2</sup>,
</span>
<span class="author-block">
<a>Jingwei Song</a><sup>4</sup>，
</span>
<span class="author-block">
<a>Wenhui Hu</a><sup>1</sup>，
</span>
<span class="author-block">
<a>Xueyang Liu</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<p><sup>*</sup> These authors contributed equally to this work.</p>
<p><sup>1</sup> Peking University</p>
<p><sup>2</sup> Northeastern University</p> <!-- 区分机构 -->
<span class="author-block"><sup>3</sup>Chinese Academy of Sciences</span>
<span class="author-block"><sup>4</sup>The University of Hong Kong</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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</span>
<span>Paper</span>
</a>
<span class="link-block">
<a href=""
class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
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<!-- Dataset Link. -->
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<p style="font-size:18px">🤗</p>
</span>
<span>Data</span>
</a>
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</div>
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</div>
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</section>
<section class="section">
<div class="container" style="max-width: 80%;">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Abstract</h2>
<div style="width: 100%; margin: 0 auto;padding: 2rem; ">
<div style="text-align: justify; line-height: 1.8; color: #2c3e50; font-size: 1.2rem;">
<p style="margin-bottom: 1.5rem;">
We present <b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b>, a benchmark designed to evaluate the logical understanding and reasoning capabilities of Multimodal Large Language Models (MLLMs) through code generation.
<b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b> challenges MLLMs to generate a correct program that fulfills specific functionality requirements based on a given flowchart, which visually represents the desired algorithm or process.
<b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b> comprises three subsets—HumanEval-V, Algorithm, and MATH—which evaluate MLLMs' reasoning abilities across basic programming, algorithmic, and mathematical problem-solving domains.
Our experiments evaluate 12 MLLMs on <b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b>. Experimental results demonstrate that there is a large performance difference between proprietary and open-source models. On hard problems, GPT-4o achieves a 79.3% pass@1, while the best open-source model only achieves 15%.
Further experiments reveal that <b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b> poses unique challenges compared to other multimodal reasoning benchmarks, such as MMCode and MathVista. We also investigate the reasons behind the poor performance of open-source models.
All of our source codes and data will be released via GitHub.
</p>
</div>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<section>
<!--添加标题行-->
<div style="width: 80%; margin: 0 auto;">
<h1 class="title is-2" align="center" style="margin-bottom: 20px">LeaderBoard on CodeVision</h1>
</div>
<div style="width: 80%; margin: 0 auto;">
<table border="1" style="width: 100%; border-collapse: collapse; text-align: center;">
<thead>
<tr style="background-color: #f3f3f3;">
<th rowspan="2" style="line-height: 2em; font-size: x-large; padding: 5px;vertical-align: middle;">Model</th>
<th colspan="1" rowspan="2" style="line-height: 4cap; font-size: larger; padding: 5px;vertical-align: middle;">HumanEval-V</th>
<th colspan="4" style="font-size: larger; line-height: 2em; padding: 5px;vertical-align: middle;">Algorithm</th>
<th colspan="4" style="font-size: larger; line-height: 2em; padding: 5px;vertical-align: middle;">MATH</th>
<th rowspan="2" style="font-size: larger; line-height: 2em; padding: 5px;vertical-align: middle;">Avg.</th>
</tr>
<tr style="background-color: #f3f3f3;">
<th style="line-height: 2em; padding: 5px;">Easy</th>
<th style="line-height: 2em; padding: 5px;">Medium</th>
<th style="line-height: 2em; padding: 5px;">Hard</th>
<th style="line-height: 2em; padding: 5px;">Overall</th>
<th style="line-height: 2em; padding: 5px;">Easy</th>
<th style="line-height: 2em; padding: 5px;">Medium</th>
<th style="line-height: 2em; padding: 5px;">Hard</th>
<th style="line-height: 2em; padding: 5px;">Overall</th>
</tr>
</thead>
<tbody>
<!-- Proprietary Models 分组 -->
<tr style="background-color: #e0f7fa;">
<td colspan="12" class="proprietary">Proprietary Models</td>
</tr>
<tr>
<td class="row-name" style="color: #cc0f35">GPT-4o
<img src="./static/images/gold-medal.png" alt="medal" class="medal">
</td>
<td><b>94.5</b></td>
<td><b>91.1</b></td>
<td><b>89.3</b></td>
<td><b>79.3</b></td>
<td><b>87.9</b></td>
<td><b>100.0</b></td>
<td><b>97.5</b></td>
<td><b>77.5</b></td>
<td><b>92.0</b></td>
<td><b>91.5</b></td>
</tr>
<tr>
<td class="row-name" style="color: #cc0f35">Gemini 1.5 Pro (May 2024)
<img src="./static/images/silver-medal.png" alt="medal" class="medal">
</td>
<!-- <td></td>-->
<td>89.6</td>
<td>88.9</td>
<td>72.0</td>
<td>44.8</td>
<td>71.8</td>
<td>95.6</td>
<td>92.5</td>
<td>57.5</td>
<td>82.4</td>
<td>81.3</td>
</tr>
<tr>
<td class="row-name" style="color: #cc0f35">Claude 3.5 Sonnet 20240620
<img src="static/images/bronze-medal.png" alt="medal" class="medal">
</td>
<td>82.3</td>
<!-- <td></td>-->
<td>84.4</td>
<td>77.3</td>
<td>48.3</td>
<td>73.8</td>
<td>97.8</td>
<td>95.0</td>
<td>52.5</td>
<td>82.4</td>
<td>79.5</td>
</tr>
<tr>
<td class="row-name">Claude 3 Sonnet</td>
<!-- <td></td>-->
<td>53.0</td>
<td>31.1</td>
<td>14.7</td>
<td>3.4</td>
<td>17.4</td>
<td>88.9</td>
<td>72.5</td>
<td>17.5</td>
<td>60.8</td>
<td>43.7</td>
</tr>
<tr>
<td class="row-name">Claude 3 Haiku</td>
<!-- <td></td>-->
<td>48.8</td>
<td>22.2</td>
<td>4.0</td>
<td>0.0</td>
<td>8.7</td>
<td>73.3</td>
<td>62.5</td>
<td>12.5</td>
<td>50.4</td>
<td>37.0</td>
</tr>
<tr>
<td class="row-name">Gemini 1.5 Flash (April 2024)</td>
<!-- <td></td>-->
<td>72.0</td>
<td>40.0</td>
<td>32.0</td>
<td>10.3</td>
<td>30.2</td>
<td>86.7</td>
<td>72.5</td>
<td>20.0</td>
<td>60.8</td>
<td>57.1</td>
</tr>
<!-- 更多 Proprietary Models 的行 -->
<!-- Open-source Models 分组 -->
<tr style="background-color: #e0f7fa;">
<td colspan="12" class="opensource">Open-source Models</td>
</tr>
<tr>
<td class="row-name">Llama-3.2-90B-Vision-Instruct</td>
<td><b>40.9</b></td>
<td>17.8</td>
<td>8.0</td>
<td>0.0</td>
<td>9.4</td>
<td><b>80.0</b></td>
<td><b>75.5</b></td>
<td><b>15.0</b></td>
<td><b>57.6</b></td>
<td><b>36.9</b></td>
</tr>
<tr>
<td class="row-name">Llama-3.2-11B-Vision-Instruct</td>
<td>29.3</td>
<!-- <td>30.5</td>-->
<td>8.9</td>
<td>1.3</td>
<td>0.0</td>
<td>3.4</td>
<td>62.2</td>
<td>27.5</td>
<td>2.5</td>
<td>32.0</td>
<td>23.8</td>
</tr>
<tr>
<td class="row-name">Phi-3-vision-128k-instruct</td>
<td>29.3</td>
<td>15.6</td>
<td>4.0</td>
<td>0.0</td>
<td>6.7</td>
<td>35.6</td>
<td>7.5</td>
<td>2.5</td>
<td>16.0</td>
<td>19.6</td>
</tr>
<tr>
<td class="row-name">Phi-3.5-vision-instruct</td>
<td>28.0</td>
<td>8.9</td>
<td>0.0</td>
<td>0.0</td>
<td>2.7</td>
<td>33.3</td>
<td>7.5</td>
<td>0.0</td>
<td>14.4</td>
<td>17.7</td>
</tr>
<tr>
<td class="row-name">MiniCPM-V 2.6</td>
<td>40.2</td>
<td><b>22.2</b></td>
<td><b>9.3</b></td>
<td>0.0</td>
<td><b>11.4</b></td>
<td>46.7</td>
<td>15.0</td>
<td>2.5</td>
<td>22.4</td>
<td>27.8</td>
</tr>
</tr>
<tr>
<td class="row-name">Qwen-VL-Plus</td>
<td>17.1</td>
<td>11.1</td>
<td>0.0</td>
<td>0.0</td>
<td>3.4</td>
<td>13.3</td>
<td>2.5</td>
<td>0.0</td>
<td>5.6</td>
<td>11.7</td>
</tr>
<!-- 更多 Open-source Models 的行 -->
</tbody>
</table>
</div>
</section>
<section class="section"></section>
<div class="container" style="max-width: 80%;">
<!-- Dataset Overview -->
<div class="columns is-centered has-text-centered">
<div class="column is-full">
<h2 class="title is-3">Dataset Overview</h2>
<div style="width: 100%; margin: 0 auto; padding: 2rem;">
<div style="text-align: justify; line-height: 1.8; color: #2c3e50; font-size: 1.2rem;">
<p>
The <b style="color: #1a73e8;"><span style="font-size: 1.3em">C</span><span style="font-size: 0.9em">ODE-</span><span style="font-size: 1.3em">V</span><span style="font-size: 0.9em">ISION</span></b> consists of three subsets: HumanEval-V, Algorithm, and MATH, covering various domains and difficulty levels (Easy, Medium, Hard). Each subset contains a distinct number of problems,
with HumanEval-V having 164 problems, Algorithm averaging 49.67 problems across difficulties,
and MATH averaging 42 problems. The number of test cases varies across subsets, with HumanEval-V averaging 8.08 test cases per problem,
while Algorithm maintains a high average of 100 test cases. In terms of flowchart complexity, the Algorithm subset shows the highest average node and edge counts,
with MATH and HumanEval-V following.
</p>
<!-- 展示一个表格 -->
</div>
</div>
<div style="text-align: center;">
<table border="1" cellspacing="0" cellpadding="5" style="border-collapse: collapse; text-align: center; margin: 0 auto;">
<thead>
<tr style="background-color: #f3f3f3;">
<th rowspan="2" style="vertical-align: middle;"> </th>
<th rowspan="2" style="vertical-align: middle;">HumanEval-V</th>
<th colspan="4" style="vertical-align: middle;">Algorithm</th>
<th colspan="4" style="vertical-align: middle;">MATH</th>
</tr>
<tr style="background-color: #f3f3f3;">
<th>Easy</th>
<th>Medium</th>
<th>Hard</th>
<th>Total/Avg.</th>
<th>Easy</th>
<th>Medium</th>
<th>Hard</th>
<th>Total/Avg.</th>
</tr>
</thead>
<tbody>
<tr>
<td style="font-weight: bold;">Problem</td>
<td>164.00</td>
<td>45.00</td>
<td>75.00</td>
<td>29.00</td>
<td>149.00</td>
<td>45.00</td>
<td>41.00</td>
<td>40.00</td>
<td>126.00</td>
</tr>
<tr>
<td style="font-weight: bold;">Avg. Test Cases</td>
<td>8.08</td>
<td>100.00</td>
<td>100.00</td>
<td>100.00</td>
<td>100.00</td>
<td>9.04</td>
<td>8.95</td>
<td>8.78</td>
<td>8.92</td>
</tr>
<tr>
<td style="font-weight: bold;">Avg. Flowchart Nodes</td>
<td>10.90</td>
<td>12.62</td>
<td>15.76</td>
<td>18.86</td>
<td>15.75</td>
<td>10.67</td>
<td>12.38</td>
<td>19.20</td>
<td>14.08</td>
</tr>
<tr>
<td style="font-weight: bold;">Avg. Flowchart Edges</td>
<td>11.07</td>
<td>13.29</td>
<td>15.87</td>
<td>19.14</td>
<td>16.10</td>
<td>10.49</td>
<td>12.00</td>
<td>19.08</td>
<td>13.85</td>
</tr>
</tbody>
</table>
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<span style="font-weight: bold;">Table 1:Statistics of the CODE-VISION benchmark, including the number of problems, average test cases, and flowchart complexity.</span> </div>
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<p style="text-align: center; margin-top: 1rem; color: #2c3e50; font-size: 1.1rem;" ><span style="font-weight: bold;">Figure 1: The comparison of the dataset between CODE-VISION and MMCode.</span></span></p>
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<span style="font-weight: bold;">Figure 2: A sample problem from HumanEval-V: HumanEval-93.</span>
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<span style="font-weight: bold;">Figure 3: A sample problem from HumanEval-V: HumanEval-94.</span>
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<span style="font-weight: bold;">Figure 4: A sample problem from Algorithm: weekly-contest-360-maximize-value-of-function-in-a-ball-passing-game.</span>
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<span style="font-weight: bold;">Figure 5: A sample problem from Algorithm: weekly-contest-369-minimum-equal-sum-of-two-arrays-after-replacing-zeros.</span>
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<span style="font-weight: bold;">Figure 6: A sample problem from Math: 3116. Kth Smallest Amount With Single Denomination Combination.</span>
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<span style="font-weight: bold;">Figure 7: A sample problem from Math: weekly-contest-378-find-longest-special-substring-that-occurs-thrice-ii.</span>
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