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content="GGBench introduces a geometric generative reasoning benchmark to evaluate unified multimodal models across language understanding and precise visual construction.">
<meta name="keywords"
content="Unified Multimodal Models, Generative reasoning, Geometric benchmark, Geometric construction, CVPR 2026">
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<h1 class="title is-1 publication-title is-bold" style="font-size: 2.8rem;">
<span class="mathvista" style="vertical-align: middle">GGBench </span>
</h1>
<h2 class="subtitle is-3 publication-subtitle">
A Geometric Generative Reasoning Benchmark for Unified Multimodal Models
</h2>
<div class="is-size-5 publication-authors">
<span class="author-block"><a href="https://openreview.net/profile?id=~Jingxuan_Wei1"
target="_blank" rel="noopener">Jingxuan Wei</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Caijun_Jia1"
target="_blank" rel="noopener">Caijun Jia</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Xi_Bai1"
target="_blank" rel="noopener">Xi Bai</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Xuxinglong1"
target="_blank" rel="noopener">Xinglong Xu</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Siyuan_Li6"
target="_blank" rel="noopener">Siyuan Li</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Linzhuang_Sun1"
target="_blank" rel="noopener">Linzhuang Sun</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Bihui_Yu1"
target="_blank" rel="noopener">Bihui Yu</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Conghui_He2"
target="_blank" rel="noopener">Conghui He</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Lijun_Wu1"
target="_blank" rel="noopener">Lijun Wu</a>,</span>
<span class="author-block"><a href="https://openreview.net/profile?id=~Cheng_Tan1"
target="_blank" rel="noopener">Cheng Tan</a></span>
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<h2 class="title is-3">TL;DR</h2>
<div class="content has-text-justified">
<p>
We introduce <b>GGBench</b>, a geometric generative reasoning benchmark purpose-built for
unified multimodal models (UMMs). Unlike prior evaluations that treat discriminative
understanding and unconstrained image generation separately, GGBench diagnoses whether a
model can fuse language comprehension with precise visual construction. Geometric
construction serves as an ideal testbed, revealing how well a system can actively reason
and synthesize structured solutions across modalities.
</p>
</div>
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-four-fifths">
<div class="content has-text-centered teaser-wrapper">
<img src="static/images/teaser.png" alt="introduction" />
</div>
</div>
</div>
<br><br>
<div class="container" style="margin-bottom: 2vh;">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Introduction</h2>
<div class="content has-text-justified">
<p>
Unified multimodal models (UMMs) herald a shift from passive perception toward proactive,
cross-modal generation. However, current benchmarks rarely stress-test whether these systems
can integrate reasoning with controlled synthesis. Most evaluations remain disjoint—either
probing language understanding or measuring image fidelity in isolation. As a result, we
still lack a principled way to measure <em>generative reasoning</em>.
<br><br>
GGBench closes this gap by framing geometric construction as a rigorous reasoning task. A
model must parse natural-language specifications, plan a construction, and render accurate
intermediate artifacts. This workflow surfaces fine-grained failure modes in alignment,
consistency, and controllability—dimensions that traditional captioning or free-form
generation overlook. By standardizing data, protocol, and diagnostics, GGBench offers
researchers a reproducible lens on how UMMs evolve from understanding to deliberate
problem solving.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-light is-small">
<div class="hero-body has-text-centered">
<h1 id="benchmark" class="title is-1 mathvista"><span class="mathvista"
style="vertical-align: middle">Benchmark</span>
</h1>
</div>
</section>
<section class="section">
<div class="container">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="content has-text-justified">
<p>
We curate <em>GGbench Benchmark</em>, a comprehensive benchmark providing a standardized
taxonomy and an evaluation protocol, enabling consistent and category-wise assessment beyond
surface-level metrics.
</p>
</div>
</div>
</div>
</div>
<br><br>
<div class="container">
<div class="benchmark-row">
<div class="benchmark-card">
<img src="static/images/timu_type3_01.png" alt="Evaluation Radar Map" />
<p>Evaluation Radar Map</p>
</div>
<div class="benchmark-card benchmark-card--wide">
<img src="static/images/data_analysis_01.png" alt="Category Distribution" />
<p>Category Distribution</p>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container">
<div class="columns is-centered">
<div class="column is-full has-text-centered content">
<h2 id="leaderboard" class="title is-3">Leaderboard</h2>
<div class="content">
<p class="mt-3">
Main results on GGBench. VLM-T and VLM-I denote step reasoning and final diagram quality,
respectively. VLM-Avg averages middle and final stages. All values are percentages.
</p>
<table class="leaderboard" id="results-model-dimension"
style="border:none; outline:none; box-shadow:none;">
<thead>
<tr>
<th rowspan="2"><strong>Model</strong></th>
<th colspan="1"><strong>Planning</strong></th>
<th colspan="1"><strong>Middle Process</strong></th>
<th colspan="4"><strong>Final Result</strong></th>
<th colspan="2"><strong>Overall Scores</strong></th>
</tr>
<tr>
<th><strong>VLM-T ↑</strong></th>
<th><strong>VLM-I-Mid ↑</strong></th>
<th><strong>VLM-I-Res ↑</strong></th>
<th><strong>LPIPS ×10<sup>-2</sup> ↓</strong></th>
<th><strong>PSNR ↑</strong></th>
<th><strong>SSIM ×10<sup>-2</sup> ↑</strong></th>
<th><strong>VLM-I ↑</strong></th>
<th><strong>Human ↑</strong></th>
</tr>
</thead>
<tbody>
<tr class="rv-group-row">
<td colspan="9"><strong>End-to-end UMMs</strong></td>
</tr>
<tr>
<td>Qwen-Image</td>
<td>—</td>
<td>—</td>
<td>22.75</td>
<td>56.39</td>
<td>58.23</td>
<td>48.06</td>
<td>22.75</td>
<td>25.56</td>
</tr>
<tr>
<td>Seedream 4.0</td>
<td>—</td>
<td>—</td>
<td>24.45</td>
<td>51.06</td>
<td>59.44</td>
<td>56.44</td>
<td>24.45</td>
<td>37.56</td>
</tr>
<tr>
<td>Janus</td>
<td>33.85</td>
<td>21.69</td>
<td>19.76</td>
<td>57.74</td>
<td>57.76</td>
<td>60.97</td>
<td>20.73</td>
<td>19.46</td>
</tr>
<tr>
<td>BAGEL</td>
<td>23.07</td>
<td>21.84</td>
<td>19.99</td>
<td>57.07</td>
<td>61.78</td>
<td>58.82</td>
<td>20.91</td>
<td>20.12</td>
</tr>
<tr>
<td>Nano Banana</td>
<td>58.54</td>
<td>44.83</td>
<td>22.81</td>
<td>51.85</td>
<td>64.53</td>
<td>59.51</td>
<td>33.82</td>
<td>45.75</td>
</tr>
<tr>
<td>GPT-image-1</td>
<td>—</td>
<td>—</td>
<td>39.11</td>
<td>50.67</td>
<td>16.95</td>
<td>64.37</td>
<td><span class="best-score-text">39.11</span></td>
<td><span class="best-score-text">49.85</span></td>
</tr>
<tr class="rv-group-row">
<td colspan="9"><strong>LLMs/LRMs</strong></td>
</tr>
<tr>
<td>GPT-4o</td>
<td>59.73</td>
<td>26.19</td>
<td>2.66</td>
<td>95.43</td>
<td>5.45</td>
<td>5.69</td>
<td>14.43</td>
<td>23.04</td>
</tr>
<tr>
<td>GLM-4.5V</td>
<td>53.32</td>
<td>25.63</td>
<td>5.02</td>
<td>52.91</td>
<td>12.19</td>
<td>12.94</td>
<td>15.33</td>
<td>30.14</td>
</tr>
<tr>
<td>Qwen3-14B</td>
<td>58.65</td>
<td>39.30</td>
<td>12.97</td>
<td>78.81</td>
<td>23.92</td>
<td>24.81</td>
<td>26.13</td>
<td>38.23</td>
</tr>
<tr>
<td>Gemini 2.5 Pro</td>
<td>38.50</td>
<td>37.41</td>
<td>15.80</td>
<td>68.39</td>
<td>37.17</td>
<td>39.73</td>
<td>26.61</td>
<td>44.68</td>
</tr>
<tr>
<td>DeepSeek-R1</td>
<td>61.16</td>
<td>62.42</td>
<td>20.48</td>
<td>66.06</td>
<td>37.94</td>
<td>37.59</td>
<td>41.45</td>
<td>49.55</td>
</tr>
<tr>
<td>GPT-4</td>
<td>55.66</td>
<td>50.39</td>
<td>20.30</td>
<td>67.35</td>
<td>35.26</td>
<td>38.31</td>
<td>33.04</td>
<td>55.99</td>
</tr>
<tr>
<td>Qwen3-VL</td>
<td>56.40</td>
<td>49.55</td>
<td>23.94</td>
<td>39.40</td>
<td>52.33</td>
<td>58.71</td>
<td>36.74</td>
<td>66.77</td>
</tr>
<tr>
<td>DeepSeek-V3.1</td>
<td>60.24</td>
<td>73.13</td>
<td>26.41</td>
<td>57.21</td>
<td>48.33</td>
<td>50.12</td>
<td>49.77</td>
<td>68.12</td>
</tr>
<tr>
<td>Claude Sonnet 4.5</td>
<td>61.19</td>
<td>77.92</td>
<td>30.29</td>
<td>52.22</td>
<td>51.74</td>
<td>50.52</td>
<td>54.11</td>
<td>72.12</td>
</tr>
<tr>
<td>GPT-5</td>
<td>62.01</td>
<td>76.79</td>
<td>37.36</td>
<td>49.65</td>
<td>54.80</td>
<td>59.49</td>
<td><span class="best-score-text">57.08</span></td>
<td><span class="best-score-text">83.06</span></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3">BibTeX</h2>
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@article{author2025yourproject,
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journal={Journal Name},
year={2025}
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