# SenseNova-U1-8B-MoT-Infographic 📊

**SenseNova-U1-8B-MoT-Infographic** 是在 U1-8B-MoT 模型基础上延长了 MT 阶段训练，并在 MT 与 SFT 阶段调整了理解和生成任务中的数据配比产生的模型。在 RL 阶段，我们进一步优化了 reward recipe，以减少生成信息图中非预期黑色背景的出现。

- **模型性能：** 相比基础版 **SenseNova-U1-8B-MoT** 模型，BizGenEval hard/easy 从 **39.8 / 61.1** 提升至 **46.6 / 65.4**（**+6.8 / +4.3 points**），IGenBench Q-ACC/I-ACC 从 **51.3 / 4.2** 提升至 **69.5 / 17.0**（**+18.2 / +12.8 points**），同时保持稳健的视觉理解能力，无明显退化。
- **生成质量：** 模型能够生成涵盖 100+ 种风格与布局的复杂信息图，具备更优的视觉美观度与文字渲染能力 —— 甚至能够渲染如 arXiv 风格页面等高密度小字。

## Benchmark Highlights

| Model | BizGenEval Avg. (hard / easy) ↑ | IGenBench Q-ACC  ↑ | IGenBench I-ACC ↑ | OneIG(EN) ↑ | OneIG(ZH) ↑ |
| --- | ---: | ---: | ---: | ---: | ---: |
| ***Commercial Models*** ||
| Nano-Banana-Pro | 76.7 / 93.7 | 90.6 | 48.8 | 58.1 | 56.8 |
| Nano-Banana-2.0 | 68.5 / 92.5 | 85.6 | 34.4 | 54.0 | 54.9 |
| GPT-Image-1.5 | 35.9 / 81.6 | 55.0 | 12.0 | - | - |
| Qwen-Image-2.0 | 45.5 / 65.8 | 50.0 | 3.0 | 54.1 | 50.9 |
| Seedream-4.5 | 30.1 / 66.2 | 61.0 | 6.0 | 56.4 | 55.0 |
| ***Open-source Models*** ||
| **SenseNova-U1-8B-MoT-Infographic** | **46.6 / 65.4** | **69.5** | **17.0** | **55.6** | **53.3** |
| **SenseNova-U1-8B-MoT** | 39.8 / 61.1 | 51.3 | 4.2 | 54.5 | 53.8 |
| Z-Image | 8.2 / 43.8 | 30.0 | 1.0 | 54.6 | 53.5 |
| Qwen-Image-2512 | 6.3 / 41.0 | 32.2 | 1.0 | 53.0 | 51.5 |
| Qwen-Image | 2.8 / 23.8 | 36.0 | 0.0 | 53.9 | 54.8 |
| Bagel | 2.0 / 3.7 | 4.9 | 0.0 | 36.1 | 37.0 |

<sub>IGenBench 分数以百分制展示。Commercial 与 open-source 组内模型按照 BizGenEval hard、BizGenEval easy、IGenBench Q-ACC、IGenBench I-ACC 四项算术平均值排序。OneIG 作为通用生成能力参考。完整分项结果建议放在 Hugging Face model card 中。</sub>



## 生成质量对比

我们在以下五个关键维度上，对基础模型 **SenseNova-U1-8B-MoT** 与微调后的 **SenseNova-U1-8B-MoT-Infographic** 模型进行了定性对比：背景稳定性、图表准确性、文字渲染准确性、论文渲染质量，以及综合美观度。

### 背景稳定性 (Background Stability)

<table width="100%" style="table-layout: fixed; text-align: center;">
<tr>
<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
</tr>
<tr>
<td valign="top"><img src="https://github.com/user-attachments/assets/b2e4a010-921b-4d34-9ded-2f224a18ad16" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/318743df-6ee8-4ac8-960c-a4f75d2c51f3" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/1804c1c1-6a5d-4608-b39d-2eea01c5fa6b" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/746f3604-c81a-4e36-8f81-a58606575ac5" width="100%"></td>
</tr>
<tr>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为“版权视觉概览”，整体采用横向分栏布局，分为上下两个主要部分。上半部分为视觉化概览区，由四个彩色矩形区块并列组成，每个区块通过图标和简短标题传达一个核心概念；下半部分为“【版权基础常识】”详细解释区，包含四个编号条目，对应上半部分的四个主题，提供更详尽的文字说明。<br><br>**上半部分：版权视觉概览**<br><br>此区域由四个水平排列的彩色方块构成，从左至右依次为浅蓝色、浅黄色、浅绿色和浅紫色，每个方块内含一组图标和下方的中文标题。<br><br>1.  **第一块（浅蓝色）：创作即产生**<br>    *   **图标**：左侧是一个发光的灯泡，中间是一个带有笔的文档图标，右侧是一个锁头图标，三者之间用箭头连接，表示“创意 → 创作 → 保护”的流程。<br>    *   **文字**：<br>        *   图标下方有小字“自动保护”。<br>        *   方块底部有大字标题“创作即产生”。<br><br>2.  **第二块（浅黄色）：核心权利**<br>    *   **图标**：中心是一只手掌向上托举，上方有多个元素围绕：一个带©符号的圆圈、一个喇叭、一堆金币和美元符号、以及多个指向不同方向的箭头，象征权利的多种表现形式和收益。<br>    *   **文字**：<br>        *   图标下方无额外小字。<br>        *   方块底部有大字标题“核心权利”。<br><br>3.  **第三块（浅绿色）：特定条件平衡**<br>    *   **图标**：一个天平，左侧托盘上有打开的书本和标有“NEWS”的麦克风，代表“合理使用”；右侧托盘上有一个带锁的文件夹，代表“受控作品”。天平向右侧倾斜。<br>    *   **文字**：<br>        *   左侧托盘下方标注“合理使用”。<br>        *   右侧托盘下方标注“受控作品”。<br>        *   方块底部有大字标题“特定条件平衡”。<br><br>4.  **第四块（浅紫色）：保护期限**<br>    *   **图标**：左侧是一个沙漏，中间是一个向右的粗箭头，右侧是一个墓碑（顶部有十字架）。沙漏下方还有一个时钟图标。<br>    *   **文字**：<br>        *   墓碑旁标注“作者有生之年 + X年”。<br>        *   方块底部有大字标题“保护期限”。<br><br>**下半部分：【版权基础常识】**<br><br>此区域位于上半部分下方，背景为白色，包含四个独立的文本框，每个文本框都有一个彩色标题栏和下方的详细说明文字，颜色与上半部分对应。<br><br>1.  **1. 自动获得保护**<br>    *   **标题栏**：蓝色背景，白色文字“1. 自动获得保护”。<br>    *   **正文**：“作品创作完成之时起，即自动享有版权，无需登记（登记主要是举证）。”<br><br>2.  **2. 核心权利**<br>    *   **标题栏**：橙黄色背景，白色文字“2. 核心权利”。<br>    *   **正文**：“包括人身权（如署名权、修改权）和财产权（如复制权、发行权、信息网络传播权，可许可或转让获利）。”<br><br>3.  **3. 合理使用**<br>    *   **标题栏**：绿色背景，白色文字“3. 合理使用”。<br>    *   **正文**：“在特定条件下（如教学、新闻报道、个人学习等），可以不经许可、不支付报酬使用，但需指明作者和出处，且不得侵犯其他权利。”<br><br>4.  **4. 保护期限**<br>    *   **标题栏**：紫色背景，白色文字“4. 保护期限”。<br>    *   **正文**：“一般为作者有生之年加死后50年（中国大陆等多数地区），期限届满后进入公有领域。”<br><br>**整体风格与数据编码**：<br>该信息图采用扁平化设计风格，色彩鲜明且分区清晰。通过颜色编码（蓝、黄、绿、紫）将四个主题进行视觉区分，并在上下两部分保持一致。图标作为主要的数据可视化手段，直观地表达了抽象概念。所有文字均为简体中文，内容结构严谨，逻辑清晰，旨在以图文结合的方式普及版权基础知识。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以中文为主要语言，采用横向四格布局，清晰呈现一个品牌从衰落到复兴的四个关键阶段。整体风格为手绘卡通插画，色彩柔和，线条简洁，具有亲和力和叙事性。每个阶段由上方的标题、中间的插图和下方的文字说明三部分构成，通过虚线分隔，结构分明。<br><br>第一阶段标题为“1. 曾经的辉煌与没落”，插图描绘了一座破败的城堡，城堡上挂着悲伤的表情，周围散落着皇冠，象征昔日荣耀的消逝；旁边立有标牌“OLD BRAND”，背景中可见大本钟，暗示传统或历史品牌。下方文字说明：“曾经是市场领导者，但未能跟上时代步伐，逐渐被遗忘，面临生存危机。”<br><br>第二阶段标题为“2. 创新与重塑”，插图展示四人团队围坐讨论，其中一人指向白板上的绿色叶子标志设计，周围环绕齿轮、灯泡（代表创意）和标牌“NEW IDEAS”。下方文字说明：“进行深度市场调研，重新定位品牌，引入创新设计和数字化策略，重塑核心价值。”<br><br>第三阶段标题为“3. 成功翻盘”，插图包含一只浴火重生的凤凰，象征涅槃；右侧是上升趋势的柱状图，下方是一个带有爱心的包裹，代表产品交付；一群欢呼的人群表达喜悦。下方文字说明：“凭借新产品和新形象重获消费者信任，业绩逆势上扬，重新赢得市场份额。”<br><br>第四阶段标题为“4. 未来展望”，插图描绘一枚火箭从地球轨道发射升空，周围有星星、云朵和一片绿叶，象征可持续发展；下方横幅写着“FUTURE READY”。下方文字说明：“持续创新，关注可持续发展和用户连接，立志成为更具影响力的未来品牌。”<br><br>整个信息图通过视觉隐喻（如城堡、凤凰、火箭）和数据图表（柱状图）结合，生动讲述了一个品牌从危机到复兴的完整故事，强调创新、用户信任和可持续发展的重要性。所有文本均为简体中文，无英文以外的其他语言。</div></details></td>
</tr>
<tr>
<td valign="top"><img src="https://github.com/user-attachments/assets/c07fe790-3d5f-4842-8515-e791504b86ca" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/7b243a49-5c4f-44af-9c10-8f0402bbadf9" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/ee66732e-7b2d-4e5e-b4f7-7ab3b326cff1" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/d672d362-709c-47aa-b12e-f32a79c07434" width="100%"></td>
</tr>
<tr>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">The infographic titled "College Entrance Pathway Reforce Comparison" presents a structured comparison of key aspects for prospective students in Guangdong, China, aiming to enter college through a specialized entrance examination. The layout is organized as a multi-column table with four main columns: "Content Item / Evaluation Criteria", "Statistics", "Quotes", and "Key Terms". Each row corresponds to a distinct evaluation criterion or step in the preparation process, with visual icons, text, and data points enhancing clarity.<br><br>The infographic uses a clean, minimalist design with black line art icons on a light beige background. Text is primarily in bold sans-serif font, with headings emphasized for readability. Data is encoded using icons (e.g., graduation cap, calendar, books, target, rocket) to visually represent concepts, while numerical values are explicitly labeled for precision.<br><br>The first row addresses **Eligibility Criteria**:<br>- In the "Statistics" column, it features an icon of a person checking a map of Guangdong with the text: "Official Eligibility Requirements Confirm if you qualify to register".<br>- The "Quotes" column lists three eligible groups with corresponding icons: "Final-Year Guangdong Junior College Student", "Guangdong Resident <2 Years Post Graduation", and "Eligible Retired Military Personnel".<br>- The "Key Terms" column shows a magnifying glass over a document with the label: "Eligibility Verification".<br><br>The second row covers **Exam Structure & Scoring Breakdown**:<br>- "Statistics" displays icons representing different test types and scores: 100 pts (graduation cap), 200 pts (person at desk), 1000 pts (document with pen), 150 pts (document with pen). Below: "Total 500 points across 4 test papers".<br>- "Quotes" lists four subject components in document-shaped boxes: "Political Theory (100 pts)", "Major-Aligned Public Subject (100 pts)", "Professional Subject 1 (150 pts)", "Professional Subject 2 (150 pts)".<br>- "Key Terms" includes a balance scale icon with "Score Distribution".<br><br>The third row details the **Official Annual Exam Timeline**:<br>- "Statistics" contains a horizontal timeline with icons of a calendar and clock, labeled "Annual Key Timeline".<br>- "Quotes" provides a detailed timeline: Jan: Registration Open → Jan: Admission Open → Mid-Mar: Exam Date → Mid-Apr: Score Release → May-Jun: Admission Offers.<br>- "Key Terms" shows a calendar and clock with "Critical Dates".<br><br>The next three rows outline a three-step preparation strategy:<br><br>**Step 1 - Confirm Target Major & Institution**:<br>- "Statistics": Icon of a person holding a map with a target, text: "Confirm your target 6 months in advance".<br>- "Quotes": Two bullet points: "Download official exam syllabi and past professional subject papers from the target institution’s admission portal" and "Cross-verify that your junior college major meets the target major’s prerequisite requirements".<br>- "Key Terms": Clock and books with "Target Selection".<br><br>**Step 2 - Public Subject Foundation Building**:<br>- "Statistics": Icon of a person studying with books and a coffee cup, text: "Complete 3 months of structured public subject study".<br>- "Quotes": Two bullet points: "Complete 5+ years of past public subject exam papers to identify recurring test points" and "Political Theory allocates 30% of total score to current affairs from the past calendar year".<br>- "Key Terms": Box with lightbulb and "Core Knowledge".<br><br>**Step 3 - Professional Subject Sprint Revision**:<br>- "Statistics": Icon of a running person with a book and clock, text: "Focus on high-weight professional subjects in the final 2 months".<br>- "Quotes": Two bullet points: "Practice past professional subject papers from your target institution and review core major textbooks" and "60% of professional subject questions are repeated or adapted from past 3 years of papers for most institutions".<br>- "Key Terms": Trophy and gears with "Intensive Review".<br><br>Red horizontal lines separate the first three criteria from the three-step strategy, while a blue line separates Step 1 from Steps 2 and 3, visually grouping related content. All textual information is preserved exactly as presented, including spelling variations like "Oficial" (likely intended as "Official"). The infographic serves as a strategic roadmap combining official requirements, scoring details, timelines, and actionable preparation steps for candidates.</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">The infographic titled "12-Month Market Performance: US vs. Asia" presents a structured, puzzle-piece-based visual analysis comparing the performance of US and Asian equity markets over a 12-month period. The layout is organized into three main steps, arranged in a central vertical flow with interconnected puzzle pieces, emphasizing a modular, analytical approach to market comparison. The design uses clean black-and-white line art with light blue accents for key sections, icons for visual representation, and clear typography for readability.<br><br>**Step 1** (top center) introduces the scope of the analysis. It features an illustration of four people examining charts, symbolizing data analysis. To the right, it defines the market indices being compared:<br>- **US Markets**: S&P 500, NASDAQ<br>- **Asian Markets**: Nikkei 225, Hang Seng, KOSPI, CSI 300<br><br>It also lists the types of data analyzed:<br>- Trailing Return (represented by a rising bar chart icon)<br>- Average Daily Volume (represented by a stacked bar chart icon)<br>- Top Sector Return (represented by a pie chart icon)<br><br>**Step 2** (left side, labeled "Metrics that account for 72% of short-term S&P 500 volatility") focuses on US Market Core Driving Indicators. This section contains icons representing industry (factory), finance (bank building), money (hand holding dollar sign), and labor (worker in hard hat). Below these icons, a light blue banner reads "US Market Core Driving Indicators". Specific metrics are listed with red warning triangle icons:<br>- CPI YoY: 3.2%<br>- Federal Funds Rate: 5.25–5.5%<br>- Non-farm Payrolls: +187k July 2024<br><br>**Step 3** (right side, labeled "Metrics that predict 68% of MSCI Asia Ex-Japan 3-month forward returns") focuses on Asian Market Core Leading Indicators. This section includes icons for shipping (container), manufacturing (gears), and calculation (calculator). A light blue banner below reads "Asian Market Core Leading Indicators". Specific metrics are listed:<br>- Manufacturing PMI: 51.2 (with red warning triangle)<br>- Q2 Export Growth: +6.8% YoY (with red warning triangle)<br>- Avg Policy Rate: 3.1% (with information circle icon)<br><br>At the bottom center, a large puzzle piece titled "Policy Shifts & Market Volatility Correlation" displays a line graph with two fluctuating lines:<br>- **US VIX (navy line)** — representing US market volatility<br>- **Asian Avg Volatility (green line)** — representing average Asian market volatility<br><br>Arrows connect the two lines, indicating correlation. Below the graph, key insights are provided with red warning triangles:<br>- Rate hike impact: +27% US VIX<br>- Trade policy impact: +34% Asian VIX<br>- Cross-regional sell-off correlation: 0.68<br><br>The overall structure visually represents how US and Asian market performances are driven by distinct but interrelated economic indicators, with a central focus on their volatility dynamics and policy impacts. The use of puzzle pieces metaphorically suggests that these components fit together to form a complete picture of global market trends. The infographic employs consistent iconography, color-coding (red for warnings, blue for core sections), and clear textual labeling to convey complex financial data in an accessible format.</div></details></td>
</tr>
</table>

### 图表准确性 (Chart Accuracy)

<table width="100%" style="table-layout: fixed; text-align: center;">
<tr>
<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
</tr>
<td valign="top"><img src="https://github.com/user-attachments/assets/43b9d92d-8c06-4eb6-944f-6a822403182c" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/88db7fe6-2b8b-494f-8eb8-979d38d6edbd" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/f2528515-c414-4db5-9c63-36fd2400021f" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/269e777a-2a21-49f2-82a2-fbceedb08027" width="100%"></td>
</tr>
<tr>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create a vintage-styled ornithology infographic with a light beige background and dark tree silhouettes framing the left and right sides. At the top, center-aligned, place the main title 'Alter bringt Vorsicht' in a bold, serif font, followed by the subtitle 'Sekunden pro Minute für Suchen vs. Picken bei Staren verschiedener Altersgruppen'. The central visualization is a diverging horizontal bar chart featuring age groups vertically listed in the middle: 'Jungvogel', 'Einjähriger Jungvogel', 'Zweijähriger Altvogel', 'Ausgewachsener Altvogel', and 'Senior'. For each group, a Rust Red bar extending to the right represents 'Picken', while a Sepia Tone bar extending to the left represents 'Suchen'. Numerical labels are placed inside each bar near the outer edge. Below the chart, two x-axes ranging from 0 to 40 reflect the time in seconds for both directions. A legend in the bottom right corner displays square markers for 'Picken' (Rust Red) and 'Suchen' (Sepia Tone). The given data is : [{"age_group": "Jungvogel", "pecking": 20, "scanning": 10}, {"age_group": "Einjähriger Jungvogel", "pecking": 30, "scanning": 20}, {"age_group": "Zweijähriger Altvogel", "pecking": 35, "scanning": 25}, {"age_group": "Ausgewachsener Altvogel", "pecking": 28, "scanning": 30}, {"age_group": "Senior", "pecking": 20, "scanning": 35}]</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: right; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create an infographic with the title 'Ajustes de Velocidad del Sprint' and the subtitle 'Desviación de alcance del equipo backend: Trabajo planificado vs. Corrección de errores no planificados' centered at the top. The central visual is a vertical bar chart set against a light gray background, featuring a horizontal baseline at the zero-point mark. Positive values (planned work) are represented by green bars extending upwards, while negative values (unplanned bug fixes) are represented by yellow bars extending downwards. Each bar is topped or bottomed with multi-line text containing the module name and its value in 'pts'. Specifically, the bars are labeled 'Autenticación de usuario' (50 pts), 'API de pago' (35 pts), 'Sincronización de inventario' (22 pts), 'BD de analítica' (14 pts), 'Servicio de correo' (-10 pts), 'Webhooks' (-28 pts), 'Carga de multimedia' (-42 pts), and 'Pasarela heredada' (-70 pts). An illustration of mechanical gears is positioned in the upper-right area, and a cluster of small bug icons is placed in the lower-left area. The vertical axis shows tick labels ranging from +60 pts to -80 pts. The given data is : [{"category": "Autenticación de usuario", "unit": "pts", "value": 50}, {"category": "API de pago", "unit": "pts", "value": 35}, {"category": "Sincronización de inventario", "unit": "pts", "value": 22}, {"category": "BD de analítica", "unit": "pts", "value": 14}, {"category": "Servicio de correo", "unit": "pts", "value": -10}, {"category": "Webhooks", "unit": "pts", "value": -28}, {"category": "Carga de multimedia", "unit": "pts", "value": -42}, {"category": "Pasarela heredada", "unit": "pts", "value": -70}]</div></details></td>
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<td valign="top"><img src="https://github.com/user-attachments/assets/bec1bd39-72bf-4bed-abdc-df02ab2c0fec" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/5ac254e1-b1c4-4530-9070-4fb4774451b4" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/38ae1611-e9c8-4d97-a867-e2cc43ced2ad" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/7f0dab24-e67a-4ee9-b9c0-ee97e283ac9f" width="100%"></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create an infographic that features a title and a subtitle centered at the top, reading 'Fastest Cuisines to Prepare' and 'Average Ghost Kitchen Handover Time by Item Type (Minutes)' respectively. The main visual is a horizontal grouped bar chart combining a Fast-food neon visual style with checkerboard borders along the edges, featuring a centered legend above the chart area for 'QuickEats' (cyan neon border) and 'DashNow' (orange neon border). To the bottom right of the bar chart, there is a simple illustration of two mopeds waiting for orders. The chart's vertical axis lists four categories, each preceded by a simple icon, while the horizontal axis represents handover time in minutes with numerical labels at 0, 5, 10, 15, and 20, supplemented by dotted vertical gridlines. Each category features a pair of black bars representing the two platforms, with exact values displayed directly inside the right end of each bar. For 'Classic Tacos', QuickEats takes 10.0 minutes while DashNow takes 11.5 minutes. 'Supreme Burritos' require the longest preparation, with 17.5 minutes for QuickEats and 19.0 minutes for DashNow. 'Spicy Nachos' take 9.5 minutes on QuickEats and 10.0 minutes on DashNow. Finally, 'Mini Quesadillas' are the fastest, taking 8.0 minutes for QuickEats and 8.5 minutes for DashNow. The given data is : [{"category": "Classic Tacos", "platform": "QuickEats", "unit": "Minutes", "value": 10.0}, {"category": "Classic Tacos", "platform": "DashNow", "unit": "Minutes", "value": 11.5}, {"category": "Supreme Burritos", "platform": "QuickEats", "unit": "Minutes", "value": 17.5}, {"category": "Supreme Burritos", "platform": "DashNow", "unit": "Minutes", "value": 19.0}, {"category": "Spicy Nachos", "platform": "QuickEats", "unit": "Minutes", "value": 9.5}, {"category": "Spicy Nachos", "platform": "DashNow", "unit": "Minutes", "value": 10.0}, {"category": "Mini Quesadillas", "platform": "QuickEats", "unit": "Minutes", "value": 8.0}, {"category": "Mini Quesadillas", "platform": "DashNow", "unit": "Minutes", "value": 8.5}]</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create an infographic that presents a centered title at the top, stating "Übertaktet vs. Standard-Takt", with the subtitle "Temperaturanstieg bei langen Gaming-Sessions" directly below it. The main visual is a line chart spanning the width of the infographic on a dark background, embodying a Gamer Aesthetic with vibrant RGB neon accents. This chart has a vertical axis on the left labeled with numerical values in increments of 10 from 30°C to 100°C, and a horizontal axis at the bottom with time labels: '0m', '15m', '30m', '45m', '60m', '75m', '90m', '105m', and '120m'. Horizontal grid lines mark each 10°C increment. A horizontal legend is positioned under the subtitle, containing a cyan circular marker and line for "Standard-Takt" and a magenta circular marker and line for "Übertaktet (+150MHz)". Two data series are plotted as glowing neon lines with hollow circular markers at each data point, accompanied by gradient shading below each line. The cyan "Standard-Takt" line shows a steep rise from 38°C at 0m to 68°C at 15m, followed by a flat plateau reaching 73.5°C at 120m. The magenta "Übertaktet" line displays a similar initial spike from 42°C to 75°C, but continues with a gradual linear creep up to 93°C at 120m. Spike annotations (callout boxes) point to the final data points on the right, highlighting the peak temperatures: a magenta box reads "Peak: 93°C" and a cyan box reads "Peak: 73.5°C". A stylized thermometer line-art icon is subtly placed in the center of the chart's background. The given data is : [{"profile": "Standard-Takt", "temperature": 38, "time": "0m"}, {"profile": "Übertaktet", "temperature": 42, "time": "0m"}, {"profile": "Standard-Takt", "temperature": 68, "time": "15m"}, {"profile": "Übertaktet", "temperature": 75, "time": "15m"}, {"profile": "Standard-Takt", "temperature": 71, "time": "30m"}, {"profile": "Übertaktet", "temperature": 79, "time": "30m"}, {"profile": "Standard-Takt", "temperature": 72, "time": "45m"}, {"profile": "Übertaktet", "temperature": 82, "time": "45m"}, {"profile": "Standard-Takt", "temperature": 72.5, "time": "60m"}, {"profile": "Übertaktet", "temperature": 85, "time": "60m"}, {"profile": "Standard-Takt", "temperature": 73, "time": "75m"}, {"profile": "Übertaktet", "temperature": 87, "time": "75m"}, {"profile": "Standard-Takt", "temperature": 73, "time": "90m"}, {"profile": "Übertaktet", "temperature": 89, "time": "90m"}, {"profile": "Standard-Takt", "temperature": 73.5, "time": "105m"}, {"profile": "Übertaktet", "temperature": 91, "time": "105m"}, {"profile": "Standard-Takt", "temperature": 73.5, "time": "120m"}, {"profile": "Übertaktet", "temperature": 93, "time": "120m"}]</div></details></td>
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<td valign="top"><img src="https://github.com/user-attachments/assets/2387f67f-2a65-4001-b579-9c1264f4796f" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/5d8c92b4-4066-4f5d-af77-0f71fd86e51a" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/1b0ea5e1-b241-4902-9bc0-485229f60592" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/d50977de-d5ab-42fd-bcfe-a2001b18fbcb" width="100%"></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create an infographic that displays data in a vertical diverging bar chart format. At the top left of the visualization, there is a title: 'Anomalie de l'Atlantique Sud : Dérive magnétique', and a subtitle: 'Vecteurs de dérive vers l'est et l'ouest en kilomètres par rapport à la ligne de base historique'. In the upper left area below the text, an icon of a compass rose is placed within a magnetic field line curve. The main chart features a horizontal zero-axis line, labeled with a '0' on the far left, representing the historical coordinate baseline. The x-axis at the bottom displays the decades '1980', '1990', '2000', '2010', and '2020', each marked with a small vertical tick. For each decade, a vertical bar extends from the zero-axis, with its corresponding data label positioned directly at the end of the bar. The data shows westward drift represented by blue bars extending below the axis for '1980' with a value of '-15 km' and '1990' with a value of '-32 km'. Eastward drift is represented by red bars extending above the axis for '2000' with a value of '+10 km', '2010' with a value of '+45 km', and '2020' with a value of '+68 km'. The overall visual style mimics a geophysical science journal, utilizing compass red and blue color tones. The given data is : [{"decade": "1980", "drift_km": -15}, {"decade": "1990", "drift_km": -32}, {"decade": "2000", "drift_km": 10}, {"decade": "2010", "drift_km": 45}, {"decade": "2020", "drift_km": 68}]</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">Create an infographic in a corporate report minimalism style with muted corporate grays and blues, featuring a large title, 'Seasonal Fluctuations in 15-Year Mortgages', at the top. Directly below it is a subtitle, 'Historical prepayment velocities showing seasonal housing market trends'. Underneath the subtitle, a horizontal legend identifies two categories with small square icons: 'Spring/Summer Originations' in lighter gray-blue and 'Fall/Winter Originations' in darker gray-blue. The main visual is a multi-line chart in a wide landscape orientation. The vertical axis has numeric labels at 0.0, 5.0, 10.0, 15.0, and 20.0, with horizontal grid lines extending across the plot. The horizontal axis features labels: 'Jan 2018', 'Apr', 'Jul', 'Oct', 'Jan 2019', 'Apr', and 'Jul'. An icon depicting a sleek house silhouette is positioned in the upper left corner of the chart's plotting area. Two distinct lines represent the categories, characterized by cyclical seasonal bumps in the summer months. Both lines have square markers at each data point, with numerical values displayed near them. The lighter line for 'Spring/Summer Originations' plots a value of 8.0 in Jan 2018, rising to 12.5 in Apr, peaking at 16.0 in Jul, dipping to 11.0 in Oct, dropping further to 7.5 in Jan 2019, climbing to 13.0 in Apr, and reaching 17.5 in Jul. The darker line for 'Fall/Winter Originations' mirrors this pattern, starting at 6.5 in Jan 2018, increasing to 9.0 in Apr, hitting 14.5 in Jul, falling to 10.0 in Oct, bottoming out at 6.0 in Jan 2019, rising to 10.5 in Apr, and ending at 15.0 in Jul. The given data is : [{"category": "Spring/Summer Originations", "date": "2018-01", "value": 8.0}, {"category": "Fall/Winter Originations", "date": "2018-01", "value": 6.5}, {"category": "Spring/Summer Originations", "date": "2018-04", "value": 12.5}, {"category": "Fall/Winter Originations", "date": "2018-04", "value": 9.0}, {"category": "Spring/Summer Originations", "date": "2018-07", "value": 16.0}, {"category": "Fall/Winter Originations", "date": "2018-07", "value": 14.5}, {"category": "Spring/Summer Originations", "date": "2018-10", "value": 11.0}, {"category": "Fall/Winter Originations", "date": "2018-10", "value": 10.0}, {"category": "Spring/Summer Originations", "date": "2019-01", "value": 7.5}, {"category": "Fall/Winter Originations", "date": "2019-01", "value": 6.0}, {"category": "Spring/Summer Originations", "date": "2019-04", "value": 13.0}, {"category": "Fall/Winter Originations", "date": "2019-04", "value": 10.5}, {"category": "Spring/Summer Originations", "date": "2019-07", "value": 17.5}, {"category": "Fall/Winter Originations", "date": "2019-07", "value": 15.0}]</div></details></td>
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</table>

### 文字渲染准确性及大小合适性 (Text Rendering Accuracy)

<table width="100%" style="table-layout: fixed; text-align: center;">
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<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
<th width="25%" align="center">U1-8B-MoT</th>
<th width="25%" align="center">8B-MoT-Infographic</th>
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<td valign="top"><img src="https://github.com/user-attachments/assets/e293eea7-6ec3-44a0-80a8-8ab2760bf6a7" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/1ce99b25-eb09-4f4c-b8a5-76f27571e1a3" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/24a21e2e-4974-4b7a-98f7-e3ae49f750fa" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/ebe69047-e668-4e14-83b3-df6261a92ca7" width="100%"></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以手绘笔记本风格呈现，标题为“吉伊卡哇带你游：加泰罗尼亚国家艺术博物馆（MNAC）三天两夜不绕路攻略”，副标题为“行程路线与时间安排（中文清晰版）”。整体采用暖黄色调背景，搭配棕色边框和螺旋装订线设计，营造出温馨可爱的旅行手册氛围。内容分为三个主要垂直区块，分别对应DAY 1、DAY 2、DAY 3，每个区块顶部有圆形时钟图标和“DAY X”标签，结构清晰。<br><br>每个日期区块内均以时间轴形式列出具体行程，使用圆点连接时间点与活动描述，右侧配有吉伊卡哇系列的可爱卡通形象插画（如白熊、蓝猫、兔子等），增强趣味性。所有文字均为简体中文，字体清晰易读，视觉层次分明。<br><br>---<br><br>**DAY 1：抵达与初探**<br>- **10:00** 抵达巴塞罗那，酒店办理入住 (Poble Sec区) —— 配有白熊拖着行李箱的插画。<br>- **12:00** 午餐：西班牙Tapas —— 插画未显示。<br>- **14:00** 前往西班牙广场 (Plaza de España)，远眺MNAC全景 —— 配有西班牙广场建筑插画及地图箭头。<br>- **16:00** 参观MNAC外部建筑与周围花园 —— 配有蓝猫在花丛中跳跃的插画。<br>- **19:00** 欣赏魔幻喷泉表演 (Magic Fountain) —— 配有带闪光效果的白熊插画。<br>- **20:30** 晚餐：附近餐厅 —— 插画未显示。<br><br>---<br><br>**DAY 2：MNAC深度艺术之旅**<br>- **09:30** 早餐，步行至MNAC入口 —— 配有白熊吃面包的插画。<br>- **10:00** 进入MNAC (建议提前购票)，参观罗马式艺术馆藏 —— 配有古典油画插画。<br>- **12:30** 馆内简餐或附近午休 —— 插画未显示。<br>- **14:00** 参观哥特式、文艺复兴及巴洛克艺术馆藏 —— 配有蒙娜丽莎风格肖像画插画及蓝猫形象。<br>- **16:30** 探索现代艺术馆藏 (加泰罗尼亚现代主义) —— 配有抽象艺术风格插画。<br>- **18:30** 前往MNAC屋顶观景台，俯瞰城市日落 —— 配有兔子举手机拍照的插画。<br>- **20:00** 晚餐：Arenas商场附近 —— 插画未显示。<br><br>---<br><br>**DAY 3：蒙特惠奇山周边与返程**<br>- **09:00** 早餐，退房寄存行李 —— 插画未显示。<br>- **10:00** 乘坐缆车前往蒙特惠奇城堡 (Montjuïc Castle) —— 配有缆车插画，内含三只卡通动物。<br>- **12:00** 参观米罗基金会 (Joan Miró Foundation) —— 配有米罗风格抽象雕塑插画。<br>- **13:30** 午餐：奥林匹克港附近海鲜饭 —— 插画未显示。<br>- **15:00** 漫步奥林匹克公园 —— 插画未显示。<br>- **16:30** 提取行李，前往机场/车站返程 —— 配有开心挥手的白熊插画。<br><br>---<br><br>**底部交通贴士栏**：<br>配有公交车、地铁、步行鞋图标，文字为：“交通贴士：善用T-casual交通卡，步行探索更佳！”<br><br>---<br><br>整体图表类型为时间序列流程图，通过垂直分栏与水平时间轴结合的方式组织信息。数据编码方式包括时间点（精确到分钟）、地点名称、活动描述及配套插画，所有信息均按逻辑顺序排列，便于用户快速理解并执行三天行程计划。视觉元素丰富，兼具实用性和趣味性，适合旅游攻略类内容传播。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">The infographic presents a comprehensive architectural and structural analysis of the Temple of Kom Ombo, an ancient Egyptian temple located on the west bank of the Nile River. The title "TEMPLE OF KOM OMBO" is prominently displayed in a hand-drawn, white-bordered box in the lower-right corner of the image, set against a brown background that mimics sandstone or earth tones. The overall layout is divided into multiple sections: a central photographic image of the temple ruins under a clear blue sky, surrounded by illustrative technical diagrams, annotated floor plans, and textual data blocks, all rendered in white line art and text for high contrast.<br><br>The central photograph shows the main hypostyle hall and surrounding structures of the temple, with visitors walking among the columns and courtyards, providing a sense of scale. In the background, the Nile River and palm trees are visible, situating the temple in its natural environment. The ruins are constructed from light-colored sandstone blocks, consistent with the material noted in the text.<br><br>In the upper-left quadrant, a 3D axonometric diagram illustrates the overall dimensions of the temple complex: approximately 62 meters by 51 meters, labeled along the axes. Adjacent to this, a list of key structural facts is presented in bullet points:<br>- TEMPLE AXIS: DOUBLE SANCTUARY FOR SOBEK & HORUS<br>- OVERALL DIMENSIONS (APPROX. 62M x 51M)<br>- CONSTRUCTION MATERIAL: SANDSTONE BLOCKS<br>- COLUMN HEIGHTS: UP TO 12 METERS<br><br>Above the central photo, two schematic diagrams illustrate architectural details:<br>- A top-down view of the hypostyle hall showing 30 columns arranged in a grid, labeled “HYPOSTYLE HALL (30 COLUMNS)” and pointing to “TWO SANCTUARIES.”<br>- A cross-section labeled “PYLON AND HYPOSTYLE SECTION,” which includes a detailed vertical cutaway showing the roofing system supported by columns, with arrows indicating load paths down to foundations.<br><br>To the right of the central image, text notes “TWO ENTRANCES SYMBOLIZING DUALITY,” emphasizing the temple’s unique dual dedication. This concept is reinforced in the lower section of the infographic, where a detailed floor plan is overlaid on the brown ground area.<br><br>The floor plan, drawn in white lines, is annotated with various features:<br>- INNER TEMPLE (FOR SOBEK) — marked with a rectangular inner sanctum.<br>- INNER TEMPLE (FOR HAROERIS) — another distinct inner sanctum, indicating the dual religious function.<br>- NILOMETER — a structure used to measure the Nile’s water level.<br>- BIRTH HOUSE (MAMMISHI) — a smaller chamber associated with fertility rituals.<br>- MUMMIFIED CROCODILE MUSEUM SITE — indicating a location within the temple complex for sacred crocodile mummies.<br>- TWO ENTRANCES SYMBOLIZING DUALITY — shown as two separate entryways on the plan.<br><br>Surrounding the floor plan are inset images of relief carvings, each labeled:<br>- MEDICAL INSTRUMENT RELIEFS — depicting figures with tools.<br>- TWO ENTRANCES RELIEFS — showing doorways flanked by deities.<br>- CALENDAR RELIEFS — illustrating scenes related to timekeeping or agricultural cycles.<br><br>Additional annotations point to structural aspects:<br>- “STRUCTURAL LOAD PATHS FROM COLUMNS TO FOUNDATIONS” — illustrated with curved arrows tracing the force transfer from columns through the walls to the ground.<br>- The pylon and hypostyle section diagram also labels “ROOFING SYSTEM” and shows how the roof beams rest on column capitals.<br><br>All textual content is in English, using a clean, sans-serif font that enhances readability. The visual style blends real photography with technical illustrations and hand-drawn elements, creating an educational and engaging format suitable for tourists, students, or archaeologists. The infographic effectively communicates both the physical characteristics and symbolic significance of the Temple of Kom Ombo, highlighting its duality, engineering, and cultural importance.</div></details></td>
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<td valign="top"><img src="https://github.com/user-attachments/assets/7fef7b78-f696-40cc-bdb6-3f712103f516" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/a005796d-34be-45ba-85ad-b591e29bb664" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/f7762af7-6989-4362-8d37-1c5e5e84d061" width="100%"></td>
<td valign="top"><img src="https://github.com/user-attachments/assets/ee00a7df-3158-4331-a14c-bf53a1bc8b85" width="100%"></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为“面试成功技巧图解”，采用左右分栏对比结构，左侧为“成功”部分（白色背景，红色主题色），右侧为“失败”部分（灰色背景，黑色主题色），通过鲜明的视觉对比突出两种截然不同的面试表现。整体布局清晰，分为三个主要横向模块，每个模块对应一个核心维度，并配有图标、标题和要点列表。<br><br>标题“面试成功技巧图解”位于顶部中央，使用加粗黑体字，字号较大，居中对齐，作为整个图表的总纲。<br><br>左侧“成功”栏目上方有红色标题“成功”，下方副标题为“积极、准备、专业”，用黑色字体书写，强调成功者的特质。右侧“失败”栏目上方有黑色标题“失败”，下方副标题为“被动、随意、消极”，同样用黑色字体，形成对立。<br><br>第一模块：准备<br>- 成功侧：<br>  - 图标：红色文件夹叠加放大镜，象征研究与准备。<br>  - 标题：“充分准备”<br>  - 要点列表：<br>    • 深入研究公司<br>    • 模拟问答练习<br>    • 携带作品集<br>- 失败侧：<br>  - 图标：黑色文件夹内含问号，象征无知或未准备。<br>  - 标题：“准备不足”<br>  - 要点列表：<br>    • 对公司一无所知<br>    • 回答模糊不清<br>    • 两手空空赴约<br><br>第二模块：沟通<br>- 成功侧：<br>  - 图标：红色对话气泡（带声波）连接两人，中间有眼睛符号，象征倾听与交流。<br>  - 标题：“有效沟通”<br>  - 要点列表：<br>    • 清晰表达观点<br>    • 积极倾听反馈<br>    • 自信肢体语言<br>- 失败侧：<br>  - 图标：黑色对话气泡内含红色叉号，下方两人表情沮丧，象征沟通中断。<br>  - 标题：“沟通障碍”<br>  - 要点列表：<br>    • 表达混乱无序<br>    • 打断对方说话<br>    • 眼神闪躲回避<br><br>第三模块：态度<br>- 成功侧：<br>  - 图标：红色笑脸配向上箭头，象征积极向上的态度。<br>  - 标题：“积极态度”<br>  - 要点列表：<br>    • 展现热情兴趣<br>    • 提出深度问题<br>    • 表达真诚感谢<br>- 失败侧：<br>  - 图标：黑色哭脸配向下箭头，象征消极情绪。<br>  - 标题：“消极态度”<br>  - 要点列表：<br>    • 缺乏热情活力<br>    • 只关注薪资待遇<br>    • 抱怨前雇主<br><br>所有模块之间由细线分隔，成功侧各模块间以红色横线分隔，失败侧以黑色横线分隔。左右两栏由一条粗红黑双色竖线分隔，强化对比效果。整体设计简洁明了，通过颜色、图标和文字三重编码，直观传达面试成功与失败的关键差异。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以“相对论时间膨胀图解”为标题，采用几何抽象风格设计，整体布局清晰，色彩鲜明，主要使用红、黄、蓝、黑、白五种颜色构成蒙德里安风格的几何图形背景。图中通过视觉化元素和文字说明，系统解释了狭义相对论中的时间膨胀效应。<br><br>**标题部分**：<br>位于图像顶部中央，黑色粗体字“相对论时间膨胀图解”，字体醒目，占据显著位置，明确点出主题。<br><br>**主体图示部分**：<br>- **左侧圆形区域代表“地球”**：蓝色圆圈内嵌红色方块，方块中有一个黄色边框的白色时钟，指针指向约12:05（或12:06），时钟下方标注“速度慢，时间快”。此区域象征静止或低速参考系。<br>- **右侧三角形区域代表“太空飞船”**：黄色三角形内嵌蓝色圆圈，圆圈中有一个蓝色边框的白色时钟，指针指向约12:10，时钟下方标注“速度快，时间慢”。此区域象征高速运动参考系。<br>- **连接箭头与标签**：一条粗大的黄色箭头从地球指向太空飞船，表示运动方向；箭头中部标有“时间膨胀效应”，并配有一个向上弯曲的黄色弧形箭头，强调时间流逝差异。<br>- **背景装饰**：由多种几何形状（矩形、三角形、半圆、四分之一圆）拼接而成，形成类似蒙德里安艺术风格的抽象图案，增强视觉吸引力，但不干扰核心信息传递。<br><br>**底部三栏文本说明部分**：<br>位于图像下半部分，分为三个垂直栏目，分别用不同颜色标题区分：<br><br>1. **基本概念**（红色标题）：<br>   - 文本内容：“运动物体的速度越快，其自身的时间流逝相对于静止观察者就越慢。这是狭义相对论的推论。”<br>   - 说明时间膨胀的基本定义及其理论来源。<br><br>2. **核心原理**（黄色标题）：<br>   - 文本内容：“光速在真空中对于所有观察者都是不变的，导致空间和时间必须改变以维持这一恒定。”<br>   - 解释时间膨胀的根本原因——光速不变原理。<br><br>3. **应用实例**（蓝色标题）：<br>   - 文本内容：“GPS卫星因高速运动和地球引力，需进行时间校正以确保定位准确。粒子加速器中的高能粒子寿命延长。”<br>   - 列举两个实际应用场景，体现理论的现实意义。<br><br>**整体结构与设计特点**：<br>- 图表类型：概念性示意图 + 文本说明组合。<br>- 数据编码方式：通过时钟指针位置的视觉差异（地球时钟较慢，飞船时钟较快）直观展示时间流逝速率的不同，配合文字标签强化理解。<br>- 视觉元素：大量使用几何图形、对比色块、箭头和图标，使复杂物理概念易于理解。<br>- 信息层级：从宏观图示到微观文字，由形象到抽象，逻辑递进，层次分明。<br><br>该信息图成功将抽象的物理学概念转化为直观、易懂的视觉语言，兼具科学性和艺术性。</div></details></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以黑板风格设计，标题为“地方特色&活动微信公众号推广全指南”，整体采用手绘粉笔字效果，配以彩色图标和箭头，视觉上模拟真实黑板书写场景。内容结构清晰，分为三个主要部分，通过灰色弧形箭头连接，形成逻辑递进关系：从推广内容核心方向 → 高转化活动推广玩法 → 微信公众号生态适配推广技巧。<br><br>第一部分：“推广内容核心方向：深挖本地特色记忆点”，强调通过三类高流量本地内容吸引用户共鸣并吸引外地游客打卡：<br>- **本土美食**（黄色椭圆标签）：包含老字号小吃、季节性特色食俗、社区隐藏小店探店内容，配有热汤碗与筷子图标。<br>- **人文风物**（棕色椭圆标签）：涵盖非遗技艺传承故事、老街老巷历史、本地名人旧居探访内容，配有传统建筑与布鞋图标。<br>- **便民福利**（粉色椭圆标签）：包括本地专属消费券、景区免票政策、节庆活动预告等内容，配有优惠券与礼盒图标。<br><br>第二部分：“高转化活动推广3种实用玩法”，旨在拉满参与转化率：<br>- **节庆市集玩法**（橙色椭圆标签）：公众号预热发早鸟票+留言抽免费参与名额+现场打卡返现，配有灯笼与摊位图标。<br>- **非遗体验玩法**（绿色椭圆标签）：开放公众号专属报名通道+提前发布体验官预告内容+活动后用户投稿返现，配有陶艺与织布机图标。<br>- **消费促进玩法**（紫色椭圆标签）：联合本地商家推出公众号专属消费券包+到店核销送定制周边，配有购物袋与银行卡图标。<br><br>第三部分：“微信公众号生态适配推广技巧”，聚焦降低推广成本：<br>- **内容呈现技巧**（蓝色椭圆标签）：封面图用本地标志性建筑/美食做视觉符号，首图放置活动倒计时海报，文末加一键报名跳转链接，配有手机图标。<br>- **渠道联动技巧**（黄色椭圆标签）：视频号发布活动花絮挂载公众号链接，朋友圈广告定向推送给本地18-60岁人群，本地社群转发带专属抽奖码，配有三人社交网络图标。<br>- **私域留存技巧**（绿色椭圆标签）：活动参与者引导添加企业微信，拉入本地福利群后续持续推送活动信息，配有微信对话气泡图标。<br><br>整个信息图布局呈垂直流线型，各模块之间以曲线箭头连接，右侧点缀有简笔小人和感叹号等装饰元素，增强趣味性和可读性。文字排版层次分明，主标题白色粗体，副标题与核心概念使用黄色或彩色突出，细节说明则为白色常规字体。所有文本均为中文，无英文或其他语言内容。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为《儿童营养补充全指南：科学建议+产品选购要点》，采用漫画风格设计，色彩鲜明，以红、黄、蓝为主色调，布局清晰分为左右两大板块，每个板块又细分为多个模块，图文并茂地呈现了儿童营养补充的科学指导与实用建议。<br><br>整体结构分为“科学参考指引”和“实操应用指南”两大核心部分，通过卡通插图、图标、爆炸式对话框、标签等视觉元素增强可读性与吸引力。<br><br>---<br><br>**第一部分：科学参考指引**<br><br>1. **分龄营养补充重点清单**<br>   - 标题：“分龄营养补充重点清单”，副标题：“分龄补营养，精准更高效；对应年龄段按需补充，避免过度摄入”<br>   - 内容按年龄分三个阶段：<br>     - **0-6月龄**：每日常规补充维生素D 400IU，纯母乳喂养宝宝需额外补充维生素K。配图：婴儿头像、Vit D注射器、Vit K胶囊。<br>     - **7月龄-3岁**：重点补充铁（Fe）、锌（Zn）、DHA，每日维生素D补充量维持在400-600IU。配图：幼儿头像、放大镜观察胶囊、Fe和Zn符号。<br>     - **4-12岁**：重点补充钙（Ca）、维生素A、B族维生素（B_B），保证每日蛋白质摄入量达标。配图：男孩头像、Ca气泡、B_B气泡、鸡蛋、牛奶瓶、眼睛图标。<br><br>2. **营养补充原则&常见避坑指南**<br>   - 标题：“营养补充原则&常见避坑指南”，副标题：“科学补营养，这些坑要避开”<br>   - 包含两个核心原则：<br>     - **优先膳食摄入**（绿色对勾）：核心原则1：日常均衡饮食是营养摄入的首要来源，不可用补充剂代替正常三餐。配图：孩子用餐场景，盘中有蔬菜、水果、肉类。<br>     - **按需适量补充**（红色STOP标志）：核心原则2：营养素补充并非越多越好，过量摄入维生素A、钙等可能引发中毒或代谢负担。配图：多瓶补剂被红色叉号覆盖。<br>   - **避坑指南**（黄色标签）：<br>     - ① 不做体检评估盲目跟风补 ❌<br>     - ② 把网红补剂当零食给孩子吃 ❌<br>     - ③ 用成人补充剂减量给儿童服用 ❌<br>     - 配图：红色“避坑”爆炸框，带有闪电效果。<br><br>---<br><br>**第二部分：实操应用指南**<br><br>1. **儿童营养补充产品3步选购法**<br>   - 标题：“儿童营养补充产品3步选购法”，副标题：“儿童补剂选购3步判断法”<br>   - 三步法分别由放大镜图标引导：<br>     - **看合规标识**：优先选择带蓝帽标识的保健食品，或有婴幼儿/儿童专用备案标识的正规产品，拒绝三无产品。配图：放大镜聚焦“蓝帽”标志。<br>     - **看配料成分**：优先选择无额外添加蔗糖、香精、人工色素、防腐剂的产品，致敏原标注清晰明确。配图：文件上贴有“无添加”印章，绿色对勾。<br>     - **看适配年龄**：选择标注对应适用年龄段的儿童专用产品，不要自行将成人补充剂减量给孩子服用。配图：药瓶标签上“年龄”被红圈突出。<br><br>2. **常见儿童补剂适用场景对照表**<br>   - 标题：“常见儿童补剂适用场景对照表”<br>   - 表格形式，两列：左侧“补剂类型”，右侧“适用场景”，背景色交替为红、蓝。<br>   - 具体内容：<br>     - **维生素D滴剂** → 全年龄段儿童日常常规补充，预防佝偻病、促进钙吸收。配图：滴管瓶、骨头图标。<br>     - **铁剂** → 体检确诊缺铁性贫血，或日常红肉、动物肝脏摄入不足的儿童。配图：滴管瓶、儿童头像。<br>     - **DHA藻油** → 日常深海鱼摄入不足的儿童，辅助促进视网膜和大脑发育。配图：鱼形胶囊、大脑与眼睛图标。<br>     - **钙剂** → 日常奶量不足、身高增长偏缓，经体检确认缺钙的儿童。配图：白色药片、儿童测量身高图。<br><br>---<br><br>**视觉与排版特征：**<br>- 整体采用网格化布局，四个主要模块分布在2x2的象限中。<br>- 使用大量漫画元素：如爆炸框、对话气泡、箭头、感叹号、禁止符号等。<br>- 图标系统丰富：Vit D、Fe、Zn、Ca、B_B、蓝帽、无添加、年龄、STOP等均有专属图形标识。<br>- 字体加粗、阴影、边框强调关键信息，如标题、数字、警示语。<br>- 色彩编码明确：黄色用于提示重点，蓝色用于说明步骤，红色用于警示或禁止。<br><br>该信息图内容全面，逻辑清晰，兼具科学性和实用性，适合家长快速掌握儿童营养补充的核心知识与选购技巧。</div></details></td>
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### 论文渲染质量 (Paper Rendering Quality)

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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">[typesetting]<br><br>The page is laid out with two tables at the top, followed by a two-column text layout. The tables span the full width of the text area. The text includes a section heading.<br><br>[paragraphs]<br><br>the TOPIC MODELER, the GENDER SEGMENTER, and an OTHER module (transcript length and duration). We test for a linear relationship between each pair of variables: $H_O : r = 0$, $H_A : r \neq 0$, where $H_O$ is the origi-nal hypothesis, $H_A$ is the alternate hypothesis, and $r$ is the Pearson’s correlation coefficient. We follow Reddy et al. (2021) and Yang et al. (2019) and apply a Bonferroni cor-rection to our $\alpha$ value of $0.05$, setting $\alpha = 0.05/z$, where $z = \binom{124}{2} = 7,626$ for LDA, representing the number of feature relationships we consider. Hence, we reject $H_O$ in favor of $H_A$ if $p \leq \alpha$. Given the largeness of $z$, our $\alpha$ value becomes small, making our criteria for significance strict and thus suitable for investigating our research ques-tions. Furthermore, we filter our correlations $r$, such that $\Vert r\Vert  > 0.1$ for our LDA experiments, and $\Vert r\Vert  > 0.05$ for our BERTopic experiments (due to the smaller sample size of 10,000 podcasts, and fewer samples may have higher vari-ance). Our results focus on a selection of these significant correlations; the full results are available on the project web-site: https://www.gendered-discourse.net/extended-results.<br><br>### RQ0: How Are Women and Men’s Discourse Different?<br><br>Using GDCF, our Gendered Discourse Correlation Frame-work shown in Figure 2, we then analyze significant corre-lations between between the gender features from the GEN-DER SEGMENTER module (Doukhan et al. 2018a), and the topic features from the TOPIC MODELER module (Blei, Ng, and Jordan 2003). We use the discourse topics to automati-cally form gendered discourse word lists via their significant correlations.<br><br>Starting with the first row of Table 1, we see that Topic 3’s word list returned by LDA with Non-Contextual Embed-dings (Bag-Of-Words) (via the TOPIC MODELER module) contains the words women, woman, men, baby, pregnant, girls, men, doctor, health, birth (in descending weighted or-der). Based on this word list, we manually interpret this topic as being a content topic, specifically about pregnancy, as noted in the column “Topic N Categories.” Then, we look to the gender correlations in the columns “Gender” and “$r$,” and see that $r(\text{Topic 3, Women}) = +0.15$ and $r(\text{Topic 3, Men}) = -0.14$. This indicates that the topic of pregnancy positively correlates with women (identified via the GENDER SEGMENTER module), and negatively corre-lates with men. Therefore, we associate Topic 3 (Content - Pregnancy) with Women, as noted in the “Topic N Gender” column. Similarly, we make these associations in the “Topic N Gender” column for Topics 10, 49, and 71.<br><br>Next, we focus on the Topic 54 row. This topic is inter-preted using the word list get, like, know, right, people, go-ing, podcast, make, want, one. This word list does not refer to any content, hence, we manually interpret this topic as being a discourse topic. Moving to the gender correlations, we see that $r(\text{Topic 54, Women}) = \emptyset$ and $r(\text{Topic 3, Men}) = +0.12$. The reason for $r(\text{Topic 54, Women}) = \emptyset$ is because the correlation between the features Topic 54 and Women did not come back as significant. However, due to the positive correlation of $0.12$ for Topic 3 and Men, we manually as-sociate Topic 3 with Men in the “Topic N Gender” column.<br><br>[tables]<br><br>Table 1: LDA with Non-Contextual Embeddings (Bag-Of-Words): The complete set of significant correlations between gender features and topic features – both content topics and discourse topics. Based on $r$, the Topic N Gender forms the gendered (discourse) word lists via Topics 54 and 60 (the masculine word lists) and Topic 62 (the feminine word list).<br><br>| Topic N | Gender | $r$ | Topic N Word List | Topic N Categories | Topic N Gender |<br>|---|---|---|---|---|---|<br>| Topic 3 | Women <br> Men | 0.15 <br> -0.14 | women, woman, men, baby, pregnant, girls, men, doctor, health, birth | Content - Pregnancy | Women |<br>| Topic 10 | Women <br> Men | 0.10 <br> -0.12 | energy, body, feel, mind, space, yoga, love, beautiful, feeling, meditation | Content - Yoga | Women |<br>| Topic 49 | Women <br> Men | -0.21 <br> 0.17 | game, know, think, team, going, mean, play, year, one, good | Content - Sports | Men |<br>| Topic 71 | Women <br> Men | 0.14 <br> -0.14 | christmas, sex, girl, hair, love, get, date, girls, let, wear | Content - Dating | Women |<br>| Topic 54 | Women <br> Men | – <br> 0.12 | get, like, know, right, people, going, podcast, make, want, one | Discourse | Men |<br>| Topic 60 | Women <br> Men | -0.27 <br> 0.20 | going, know, think, get, got, one, really, good, well, yeah | Discourse | Men |<br>| Topic 62 | Women <br> Men | 0.33 <br> -0.28 | like, know, really, going, people, want, think, get, things, life | Discourse | Women |<br><br>Table 2: BERTopic with Contextual Embeddings (BERT, ChatGPT, Llama): The complete set of significant correlations between gender features and topic features for discourse topics only (content topics are omitted).<br><br>| Topic N | Gender | $r$ | Topic N Word List | Topic N Categories | Topic N Gender |<br>|---|---|---|---|---|---|<br>| Topic 0 | Women <br> Men | -0.08 <br> 0.10 | like, yeah, know, oh, right, podcast, got, going, think, really | Discourse | Men |<br>| Topic 2 | Women <br> Men | 0.08 <br> -0.08 | life, know, things, really, people, feel, like, want, love, going | Discourse | Women |<br>| Topic 5 | Women <br> Men | 0.08 <br> – | like, know, think, yeah, episode, really, going, anchor, kind, right | Discourse | Women |</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">[typesetting]<br><br>The page is a standard academic paper layout with a single column. The text is justified and divided into sections and subsections, indicated by numbered headings. Important terms at the beginning of some paragraphs are bolded. A horizontal rule separates the header from the main content, and another rule separates the main content from the footnote at the bottom.<br><br>[paragraphs]<br><br>Preprint Version.<br><br>**Figure–Table Integration.** In addition to textual refinement, we extend the refinement process to include multimodal elements, to further enhance readability. For each section, the model first generates visualization requirements, such as tables with structured comparisons or figures with explanatory diagrams, together with natural language descriptions. Based on these descriptions, candidate figures and tables are synthesized. The compiled outputs are then fed back to an LLM for quality assessment, enabling automatic detection of issues such as oversized layouts or unreadable text. The LLM provides corrective suggestions, which are applied to improve the final visualizations. Finally, the text is refined again to ensure that all generated figures and tables are properly referenced within the survey.<br><br># 4 EXPERIMENTS<br><br>## 4.1 EXPERIMENTAL SETTINGS<br><br>**Implementation Details.** Following Wang et al. (2024b), we adopt **GPT-4o-mini** as our genera-tion model for its balance of responsiveness and cost. Our retrieval database contains 680K computer science papers from arXiv, with PDFs converted into structured Markdown using MinerU (Wang et al., 2024a) for consistent formatting. The details of the retrieval process are provided in App. A.1. In outline generation, the system consults 1000–1200 papers, with a maximum of 8 sections. For section drafting, each subsection retrieves up to 60 additional relevant papers, combined with those linked during outline generation. Finally, we apply two iterations of the review-and-refine loop to enhance coherence across sections and improve overall readability. Illustrative outputs compared with AutoSurvey are provided in App. A.8.<br><br>**Baselines.** We compare IterSurvey with a set of baselines, ranging from simple retrieval-augmented generation (Naive RAG), which directly drafts from retrieved documents, to more ad-vanced state-of-the-art systems. Specifically, we evaluate against AutoSurvey (Wang et al., 2024b), the first systematic framework for this task; SurveyForge (Yan et al., 2025), which combines heuris-tic outline generation based on the logical structures of human-written surveys with a memory-driven scholar navigation agent for high-quality retrieval; and SurveyGo (Wang et al., 2025), which em-ploys the LLM×MapReduce-V2 algorithm to address the long-context challenge. We also compare with SurveyX (Liang et al., 2025), which introduces an Attribute Tree-based outlining mechanism; however, due to access restrictions, we include SurveyX only in arena experiments. All methods are evaluated on the same retrieval database with generation hyperparameters aligned to their original settings for fairness.<br><br>## 4.2 AUTOMATIC EVALUATION RESULTS<br><br>**Evaluation Setup.** We employ multiple complementary protocols to evaluate the quality of gen-erated surveys. On the 20-topic suite from Wang et al. (2024b), we adopt multi-dimensional scoring with LLM-as-a-judge. Content quality is assessed along three dimensions: coverage, structure, and relevance followed from Wang et al. (2024b). Besides, citation quality is evaluated using the NLI-based protocol of Gao et al. (2023), reporting both recall and precision: _Citation Recall_ measures whether all statements in the generated text are fully supported by the cited passages, while _Citation Precision_ identifies irrelevant citations to ensure that references are pertinent and directly support the claims. To improve scoring stability and reliability, prompts are standardized and judges must pro-vide a rationale before assigning scores. For additional robustness, we aggregate outputs from three judge models: GPT-4o, Claude-3.5-Haiku, and GLM-4.5V.1 Full prompts are provided in App. A.7.<br><br>**Results.** The results on the 20 topics from Wang et al. (2024b) are reported in Tab. 1. Statistical significance was confirmed via paired t-tests, indicating that IterSurvey consistently outperforms baseline models ($p < 0.05$). We summarize the main observations below.<br><br>- **Overall superiority.** IterSurvey consistently outperforms all baselines across both content and citation quality, achieving the highest overall average score (4.75). This demonstrates that the proposed framework is effective and robust across multiple evaluation dimensions.<br><br>[page_number]<br><br>6<br><br>[footnotes]<br><br>1Specifically, we use `chatgpt-4o-latest`, `claude-3-5-haiku-20241022`, and `glm-4.5v`.</div></details></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">[typesetting]<br><br>This is a single-column page containing mostly text, structured with section headings and bold inline subheadings. URLs are formatted in a monospaced font and hyperlinked.<br><br>[paragraphs]<br><br># A Image generation models<br><br>This section details the two diffusion image generation models used in this work, namely Stable Diffusion 1.4 and 1.5.<br><br>**Stable Diffusion 1.4** The Stable Diffusion model is a text-conditioned image generator model that combines an autoencoder with a diffusion model to create a latent diffusion model. The autoencoder encodes images into latent representations with a reduced dimensionality when compared to the input image, reducing the computational needs during the training phase. Text prompts, on the other hand, are encoded using a text encoder and are then cross-attended by the UNet backbone of the latent diffusion model. Finally, the loss is computed using a reconstruction objective between the noise added to the latent representation and the prediction made by the UNet.<br>Stable Diffusion 1.4 (https://huggingface.co/CompVis/stable-diffusion-v1-4) had several rounds of training on the LAION dataset (https://laion.ai/), with each round changing the input image dimension, aesthetic score, and the probability of dropping the text-conditioning to improve classifier-free guidance.<br><br>**Stable Diffusion 1.5** SD 1.5, in turn, has the same architecture and even the same starting point as 1.4, with the difference being how long the model was fine-tuned on top of SD 1.2. The 1.4 version is fine-tuned for 225 thousand steps at resolution 512x512 on “laion-aesthetics v2 5+” with a 10% probability of dropping the text-conditioning, and version 1.5 for 595 thousand steps.<br>As demonstrated in Section D Stable Diffusion 1.4 has better performance than 1.5 in our approach, therefore, we will adopt SD 1.4 for most of the experiments in this paper.<br><br># B Large language models<br><br>Here we give additional details on the large language models that we used in our experiments.<br><br>**Gemma** (Mesnard et al., 2024), trained on a diverse 6 Trillion token dataset comprising web documents, code and mathematical texts. We resorted to the 7 Billion parameter instruction-tuned decoder-only model, named _gemma-7b-it_ (https://huggingface.co/google/gemma-7b-it). This model uses a chat template, which we employ during inference.<br><br>**Llama 2** (Touvron et al., 2023), of which we used the 7 Billion parameter, pre-trained-only model, _Llama-2-7b_ (https://huggingface.co/meta-llama/Llama-2-7b-hf). This model was trained with a mix of publicly available data totalling 2 Trillion tokens. While its chat versions employ supervised fine-tuning and reinforcement learning with human feedback for alignment with human preferences in helpfulness and safety, the pre-trained-only model does not. This results in a less constrained model, but it may also cause it to disperse from the task at hand. Since this model is a pre-trained-only no chat template is needed.<br><br>**Mistral** (Jiang et al., 2023) fine-tuned on various HuggingFace instruction datasets. We resorted to the 7 Billion _Mistral-7B-Instruct-v0.2_ model (https://huggingface.co/mistralai/ Mistral-7B-Instruct-v0.2) and used the respective chat template during inference.<br><br>**Phi-2** (Gunasekar et al., 2023) is a compact 2.7 Billion model (https://huggingface.co/microsoft/ phi-2). Despite its size, it offers a competitive performance with respect to models several times its size. It was trained on 250 Billion tokens, obtained through a combination of NLP synthetic data created by GPT-3.5 and filtered web data from Falcon RefinedWeb and SlimPajama, which was assessed by GPT-4. This model was not fine-tuned through reinforcement learning from human feedback and does not have guardrails.<br><br>**Model ranking**<br>A ranking of these models in terms of their performance can be found in the HuggingFace leaderboard (https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) which assesses several LLMs that are trained under the same criteria and tested on the same benchmarks, including reasoning</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">[typesetting]<br><br>The page is a standard academic paper layout, likely from a preprint server like arXiv. It features a title, author list with affiliations, an abstract, and the beginning of the "Introduction" section. A preprint notification ("Preprint. Under review.") is present at the bottom left. The text on the left margin ("arXiv:2502.01522v2 [cs.CV] 30 May 2025") is a vertical stamp typical of arXiv submissions.<br><br>[paragraphs]<br><br>arXiv:2502.01522v2 [cs.CV] 30 May 2025<br><br># Unpaired Deblurring via Decoupled Diffusion Model<br><br>**Junhao Cheng**$^1$, **Wei-Ting Chen**$^2$, **Xi Lu**$^1$, **Ming-Hsuan Yang**$^3$<br>$^1$Sun Yat-sen University $^2$ Microsoft $^3$ University of California, Merced<br>https://github.com/donahowe/UID-Diff<br><br>**Abstract**<br><br>Generative diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. In favor of their ability to supplement missing details and generate aesthetically pleasing contents, recent works have applied them to image deblurring via training an adapter on blurry-sharp image pairs to provide structural conditions for restoration. However, acquiring substantial amounts of realistic paired data is challenging and costly in real-world scenarios. On the other hand, relying solely on synthetic data often results in overfitting, leading to unsatisfactory performance when confronted with unseen blur patterns. To tackle this issue, we propose UID-Diff, a generative-diffusion-based model designed to enhance deblurring performance on unknown domains by decoupling structural features and blur patterns through joint training on three specially designed tasks. We employ two Q-Formers as structural features and blur patterns extractors separately. The features extracted by them will be used for the supervised deblurring task on synthetic data and the unsupervised blur-transfer task by leveraging unpaired blurred images from the target domain simultaneously. We further introduce a reconstruction task to make the structural features and blur patterns complementary. This blur-decoupled learning process enhances the generalization capabilities of UID-Diff when encountering unknown blur patterns. Experiments on real-world datasets demonstrate that UID-Diff outperforms existing state-of-the-art methods in blur removal and structural preservation in various challenging scenarios.<br><br># 1 Introduction<br><br>Dynamic blur occurs when the camera and subject move relative to each other during the exposure time, resulting in a smeared and blurred image. Deblurring, the process of removing the blur pattern while preserving the underlying structure of degraded images, is essential for restoring high-quality images for human perception and low-level computer vision applications.<br><br>With the rapid advancement of photographic technology, a wide range of imaging devices are now employed to capture images in real-world scenarios. Due to their diverse lenses and structural designs, these devices may produce distinct blur patterns [1, 2, 3]. This diversity makes it challenging to develop an all-in-one method for deblurring images from arbitrary and varied sources. Consequently, focusing on deblurring algorithms tailored to specific domains has become increasingly significant.<br><br>As deep learning has advanced in recent years, existing deblurring models predominantly build on data-driven approaches that employ neural networks trained via supervised learning on synthetic paired data. Existing works have made efforts to develop deblurring models upon CNN [4, 5], Transformer [6, 7], and GAN [8, 9]. Recently, a new wave of research [10, 11, 12] has begun to investigate the integration of pre-trained generative diffusion models [13], such as Stable Diffusion (SD) [14], with an adapter designed to provide structural guidance for deblurring. These approaches aim to harness the generative capabilities of diffusion models to supplement missing details and generate aesthetically pleasing outputs. However, since paired blurry-sharp training data is limited in<br><br>[footnotes]<br><br>Preprint. Under review.</div></details></td>
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### 综合布局及内容理解 (Overall Aesthetics)

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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以“曲尼司特”为标题，整体采用浅蓝白色调，布局清晰，分为多个模块化区域，围绕中央的透明胶囊图像展开。右上角展示曲尼司特的化学结构式及其分子式 C₁₈H₁₇NO₄。<br><br>**1. 活性成分数据（左上）**<br>- 以环形图形式展示成分比例：<br>  - 曲尼司特 >98%<br>  - 辅料 <2%<br>- 图下方标注：“纯度高，临床级标准”<br><br>**2. 适应症（右上）**<br>- 通过三个图标及文字说明：<br>  - 鼻部图标：过敏性疾病<br>  - 皮肤纹理图标：纤维化<br>  - 疤痕图标：瘢痕疙瘩<br><br>**3. 剂量矩阵（中左）**<br>- 表格形式，包含两列：“口服”和“频率”<br>  - 成人：100mg / 次；频率：1-3 次 / 天<br>  - 儿童：咨询医生；频率：遵医嘱<br><br>**4. 药代动力学时间轴（中右）**<br>- 折线图，横轴为时间（0h 至 24h），纵轴为浓度（无刻度）<br>  - 0h：吸收开始（水滴图标）<br>  - 1-2h：峰值浓度（山峰图标）<br>  - 4-6h：分布/代谢（循环箭头图标）<br>  - 24h：排泄（垃圾桶图标）<br>- 图中标注半衰期 ≈ 5-8h<br><br>**5. 警告网格（左下）**<br>- 分为四个象限，每个配有图标和文字：<br>  - 相互作用：CYP酶抑制剂/诱导剂（齿轮图标）<br>  - 副作用：胃肠道不适，皮疹（胃图标）<br>  - 肝功能：定期监测（肝脏图标）<br>  - 肾功能：慎用（肾脏图标）<br><br>**6. 患者适用性（中下）**<br>- 两个图标组合：<br>  - 成人：人物图标 + 对勾，标注“成人 适用”<br>  - 儿童：人物图标 + 问号 + 医生图标，标注“儿童 咨询医生”<br><br>**7. 储存指南（右下）**<br>- 三个图标并列：<br>  - 温度计图标：2-25℃ 室温<br>  - 密封瓶图标：密闭<br>  - 遮光图标（太阳加斜线）：避光<br><br>整体设计风格现代、专业，使用大量图标辅助理解，数据可视化清晰，适合医疗或药品宣传场景。所有文本均为中文，语言准确，无冗余描述。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">The infographic presents an augmented reality (AR) shopping experience overlaid on a real-world retail environment. The scene is set in a brightly lit cosmetics aisle of a store, with shelves stocked with beauty products visible in the background. In the foreground, a pair of hands holds a black rectangular compact labeled "ANASTASIA BEVERLY HILLS BROW POWDER DUO" with "EBONY" and "NET WT. 2.5 OZ." printed below. A gold ring is visible on the left hand’s ring finger, and a black wristband is partially seen on the left wrist.<br><br>Overlaid on the image are several semi-transparent, rounded-corner UI elements resembling AR pop-ups or digital cards, providing contextual information about the product and the user’s shopping list.<br><br>On the left side, a vertical panel titled "SHOPPING LIST" lists four items:<br>1. Face Wash — marked with an “X” (completed)<br>2. Shampoo — marked with an “X” (completed)<br>2. Eye Cream — marked with an empty checkbox (not completed; duplicated item number)<br>3. Eye Cream — marked with an empty checkbox (not completed)<br><br>This suggests a possible error or duplication in the list, with two entries for "Eye Cream".<br><br>In the center-right, a speech-bubble-shaped label displays the price: "$23.00".<br><br>To the right of the product, a larger panel titled "PRODUCT DETAILS:" provides information about the "ABH Brow Powder Duo". It features two color swatches:<br>- Left swatch: labeled "DEEP BROWN"<br>- Right swatch: labeled "BLACK"<br><br>Below the swatches, a star rating system shows four and a half filled stars, accompanied by the text "4.5 out of 5 stars".<br><br>Underneath the rating, a section titled "COMMON USES:" states: "DEFINES & FILLS BROWS".<br><br>Further down, a smaller rectangular box labeled "KEY INGREDIENTS" lists:<br>- Vitamin E<br>- Finely Milled Pigments<br><br>At the bottom right, another box titled "APPLICATION TIPS" includes a video icon (a rectangle with a play triangle) and the word "Video", indicating a multimedia tutorial is available.<br><br>The overall layout mimics an immersive AR interface, likely from a smart glasses or smartphone application, designed to enhance in-store shopping by providing instant, interactive product data directly within the user’s field of view. The visual style uses dark gray, translucent backgrounds with white text for high contrast and readability against the busy store backdrop. The design emphasizes usability, with clear categorization of information into distinct panels and intuitive icons. All textual content is in English, and no other languages are present.</div></details></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以深蓝色科技感背景为主，配以紫色和青色的电路板图案边框，营造出未来数字设备的视觉氛围。标题“谷歌最新血氧仪机型参数对比（社媒版）”位于顶部中央，使用发光白色字体，突出主题。整体布局为横向三栏式对比结构，左侧为参数类别标签列，中间及右侧分别为三款智能穿戴设备的参数详情。<br><br>左侧参数类别列以图标+文字形式垂直排列，包括：<br>- 芯片（图标为芯片符号）<br>- 电池（图标为电池符号）<br>- 功能（图标为心电波形符号）<br>- 重量（图标为秤盘符号）<br>- 价格（图标为价格标签符号）<br>- 发售时间（图标为日历符号）<br><br>中间三栏分别对应三款产品：<br>1. **高亮推荐机型：Google Pixel Pulse（最新推荐）**  <br>   - 标题上方有金色星形徽章“★ 高亮推荐机型”，并用金色边框高亮显示。<br>   - 芯片：Tensor G4定制芯片<br>   - 电池：7天续航，快充<br>   - 功能：连续血氧监测，睡眠/压力追踪，AI健康指导<br>   - 重量：28克（轻盈）<br>   - 价格：¥1999<br>   - 发售时间：2024年10月<br><br>2. **竞品A（例如：Apple Watch S9）**  <br>   - 芯片：S9 SiP芯片<br>   - 电池：18小时（正常使用）<br>   - 功能：按需血氧，心电图APP，摔倒检测<br>   - 重量：32克<br>   - 价格：¥3199<br>   - 发售时间：2023年9月<br><br>3. **竞品B（例如：Garmin Venu 3）**  <br>   - 芯片：Elevated V5传感器<br>   - 电池：14天（智能模式）<br>   - 功能：全天候血氧，身体电量，GPS运动<br>   - 重量：35克<br>   - 价格：¥2499<br>   - 发售时间：2023年8月<br><br>所有数据均采用清晰的横向分隔线组织，每项参数内容居中对齐，字体为简洁现代的无衬线体，颜色为浅蓝或白色，确保可读性。高亮推荐机型使用金色边框和更明亮的文字，形成视觉焦点。<br><br>底部有一行注释文字：“注：以上参数仅供参考，具体以官方发布为准。#科技 #健康 #谷歌新品 #血氧仪对比”，字体较小，颜色较暗，作为补充说明。<br><br>整体设计风格现代、科技感强，通过色彩对比、边框高亮和图标辅助，有效传达了各机型在关键性能指标上的差异，尤其突出了Google Pixel Pulse在续航、价格和功能集成方面的优势。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图以复古手绘风格呈现，整体布局如一本打开的泛黄书页，背景为米黄色仿旧纸张质感，边缘带有不规则撕裂效果。标题“博物馆游览扩展内容与要点”位于顶部中央，字体为深棕色艺术字，两侧饰有卷曲花纹装饰，视觉上突出主题。<br><br>全图采用六点式结构化布局，围绕中心分布六个核心模块，每个模块均配有独立插画、编号标题和说明文字，通过装饰性边框、花环、丝带等元素进行区分与美化。整体设计风格温馨、文艺，融合了音乐符号、星星、薰衣草、云朵等点缀元素，营造出轻松愉悦的文化探索氛围。<br><br>各模块内容如下：<br><br>1. **沉浸式体验**  <br>   - 标题：“1. 沉浸式体验”  <br>   - 说明文字：“参与互动展览，感受历史场景还原，身临其境。”  <br>   - 视觉元素：左侧描绘一位金发男孩手持放大镜观察一个微缩历史街景模型（包含房屋、摊位和人物），上方有齿轮与灯泡组成的思考气泡，象征探索与发现。右侧配有一个系着粉色蝴蝶结的礼物盒，标签写有“SURPRISE”。<br><br>2. **主题讲座与工作坊**  <br>   - 标题：“2. 主题讲座与工作坊”  <br>   - 说明文字：“聆听专家深度解读，亲手制作手工艺品，学习新知。”  <br>   - 视觉元素：右侧展示一张木桌，桌上摆放陶壶、陶罐、刻刀等手工工具，旁边堆叠书籍与卷轴；周围环绕橄榄枝花环，上方悬挂一串风铃（含月亮、星星与铃铛），背景点缀云朵与星光。<br><br>3. **馆藏珍品探索**  <br>   - 标题：“3. 馆藏珍品探索”  <br>   - 说明文字：“寻找镇馆之宝，了解背后的故事与文化价值，深度挖掘。”  <br>   - 视觉元素：左侧是一个打开的木质宝箱，内有青铜鼎状文物与发光卷轴；旁有绿色玉璧吊坠、散落铜钱，以及一支点燃的白色蜡烛，烛台装饰有薰衣草与小花束。<br><br>4. **特色导览路线**  <br>   - 标题：“4. 特色导览路线”（置于米色丝带横幅中）  <br>   - 说明文字：“跟随定制路线，发现隐秘角落与独特视角，别样精彩。”  <br>   - 视觉元素：下方是一张展开的复古地图，标有拱门、凉亭、佛像、雕塑等景点，以红色虚线连接，并配有指南针图标，体现路径规划概念。<br><br>5. **数字化互动**  <br>   - 标题：“5. 数字化互动”（置于圆形波点边框内）  <br>   - 说明文字：“利用AR/VR技术，打破时空限制，体验虚拟现实。”  <br>   - 视觉元素：右侧描绘一位戴VR眼镜的人正在触控空中悬浮的陶罐图像，周围有Wi-Fi信号、数据图表、声波图等科技元素，体现数字交互场景。<br><br>6. **文化衍生品**  <br>   - 标题：“6. 文化衍生品”  <br>   - 说明文字：“选购独特纪念品，将博物馆记忆带回家，延续美好。”  <br>   - 视觉元素：左下角陈列多种文创商品，包括印有博物馆建筑图案的帆布袋（标有“MUSEUM”）、笔记本、明信片、徽章；右下角则是一盘精致三明治（面包上烙有五角星图案），配蓝莓与卷饼，旁有一只戴派对帽、系蓝色蝴蝶结的白鹅，口中喷出音符，充满童趣。<br><br>整张信息图通过图文结合的方式，系统介绍了博物馆参观的六大延伸活动，既传达实用信息，又兼具美学感染力，适合用于宣传册、教育海报或线上推广材料。所有文本均为中文，无英文或数字编码，语言风格亲切自然，符合大众传播需求。</div></details></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为《国际货运代理业务流程与注意事项全指南》，旨在系统梳理国际货运代理从接单到交付的全流程操作规范，并重点提示各环节的核心注意事项与风险规避要点，帮助货运代理新人及外贸商家快速掌握业务逻辑，减少操作失误与纠纷。<br><br>整体设计采用“冰山模型”视觉隐喻：上方露出水面部分展示显性业务流程（共6个步骤），下方水下部分揭示隐性但关键的风险管理与合规要求（共3大注意事项）。背景为米白色纸张质感，边缘有撕纸效果，中央以蓝色渐变冰山贯穿上下，象征业务表层与深层的关联。顶部标题使用深棕色粗体字，配以浅橙色衬底条，副标题为黑色小号字体，简洁明了。<br><br>---<br><br>### 一、显性业务流程（冰山水上部分）——共6个步骤<br><br>每个步骤以圆角矩形卡片呈现，配有图标和箭头连接，形成线性流程图。卡片背景为浅米色，文字为深棕色，标题加粗。<br><br>**Step 1: 接单揽货**<br>- 标题：确认需求 签订委托<br>- 内容：<br>  - 对接客户确认货物明细、运输时效、目的地<br>  - 核对禁运/限运品，确认报价<br>  - 签订正式委托合同，明确权责<br>- 图标：握手图标<br><br>**Step 2: 订舱配载**<br>- 标题：预定舱位 确认出运计划<br>- 内容：<br>  - 选择承运商提交订舱申请<br>  - 确认舱位后发放入仓通知<br>  - 易碎品/危险品提前告知，申请特殊配载<br>- 图标：轮船+飞机图标<br><br>**Step 3: 报关报检**<br>- 标题：完成通关申报 确保合法出境<br>- 内容：<br>  - 整理报关资料提交申报<br>  - 法检货物提前报检获取通关单<br>  - 反复核对信息避免错报漏报<br>- 图标：文件+印章图标<br><br>**Step 4: 货物运输**<br>- 标题：跟踪动态 同步异常<br>- 内容：<br>  - 获取运输凭证发给客户<br>  - 全程跟踪轨迹，异常情况第一时间告知<br>  - 提醒客户购买运输保险<br>- 图标：卡车+地球图标<br><br>**Step 5: 到港清关**<br>- 标题：办理入境申报 完成货物放行<br>- 内容：<br>  - 通知收货人准备清关资料提交申报<br>  - 核算税费通知缴纳办理放行<br>  - 核实特定品类特殊准入要求<br>- 图标：起重机+文件图标<br><br>**Step 6: 派送签收**<br>- 标题：完成交付 确认闭环<br>- 内容：<br>  - 安排本地配送车辆送达指定<br>  - 提醒收货人当场核验签收<br>  - 货损少货第一时间留存证据启动理赔<br>- 图标：快递车图标<br><br>---<br><br>### 二、隐性注意事项（冰山水下部分）——共3大项<br><br>水下部分分为三层，由浅蓝到深蓝渐变，每层对应一个注意事项，左侧为总述，右侧为具体细则，通过虚线箭头连接。<br><br>**注意事项1：单证管理要求**<br>- 总述：所有单证需真实准确、留存备查<br>- 细则：<br>  - 单单一致 单证一致（信息完全匹配）<br>  - 提单信息反复核对（确认无误后出具）<br>  - 单证留存≥2年（备查避免纠纷）<br><br>**注意事项2：风险防控要点**<br>- 总述：提前识别风险，减少业务损失<br>- 细则：<br>  - 核实客户与货物资质（避免涉嫌违规）<br>  - 提前做好特殊货物防护（温控、防震等）<br>  - 异常情况同步留证（明确权责）<br><br>**注意事项3：合规操作要求**<br>- 总述：严守法规红线，避免合规风险<br>- 细则：<br>  - 禁运品严禁承接（需有资质审批）<br>  - 如实申报不得逃税（严禁低报虚报）<br>  - 遵守制裁合规要求（不接违规业务）<br><br>---<br><br>### 三、视觉元素与数据编码<br><br>- **图表类型**：流程图 + 分层结构图（冰山模型）<br>- **数据编码方式**：<br>  - 步骤顺序：通过箭头连接表示时间或逻辑顺序。<br>  - 注意事项层级：通过垂直分层与虚线箭头体现“主-次”关系。<br>  - 信息密度：每个步骤/注意事项均包含标题、核心动作和子项列表，信息高度结构化。<br>- **图标系统**：每个步骤配有代表性的扁平化图标（如握手、轮船、文件、卡车等），增强可读性。<br>- **色彩方案**：主色调为米白、浅棕、蓝色系，营造专业、冷静、可靠的感觉。<br><br>---<br><br>该信息图结构清晰、内容详尽，既展示了国际货运代理业务的完整操作链路，又深入挖掘了隐藏在流程背后的管理、风控与合规维度，是极具实用价值的行业指导工具。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为“支持向量机（SVM）扩展内容与要点”，整体采用柔和的紫色渐变背景，搭配手绘卡通风格的白色圆角矩形模块，视觉清新且富有教育性。图中通过四个主要模块和一个底部总结栏，系统介绍了SVM的核心扩展内容，各模块之间以虚线箭头连接，形成逻辑关联。<br><br>标题位于顶部中央，使用加粗黑体字并带有白色描边，突出显示主题。<br><br>**1. 基础概念模块**  <br>位于左上角，标题为“基础概念”。图示展示蓝色圆形点与红色三角形点在二维空间中被一条实线分隔，两侧有虚线表示间隔区域。右侧有一个手持铅笔的卡通机器人，其对话框内写着“找到最佳分隔线！”。下方文字说明：“最大化间隔，分类数据。” 该部分强调SVM的基本目标是寻找能最大化分类间隔的最优超平面。<br><br>**2. 核技巧（Kernel Trick）模块**  <br>位于右上角，标题为“核技巧（Kernel Trick）”。左侧为二维平面上无法线性分离的蓝点与红三角点；中间箭头指向右侧三维曲面图，其中数据点被映射到高维空间后可被平面清晰分割。右侧配有一支魔法棒和星星，标注“核函数”，象征核技巧的“魔法”作用。下方文字说明：“低维不可分 → 高维可分！”，直观解释了核技巧的核心思想。<br><br>**3. 软间隔（Soft Margin）模块**  <br>位于左下角，标题为“软间隔（Soft Margin）”。图示与基础概念类似，但部分数据点越过了间隔边界，旁边放置一个橡皮擦图标，标注字母“C”，代表正则化参数。下方文字说明：“允许少量错误，提高泛化能力。” 该模块说明SVM在现实数据中引入松弛变量，以平衡分类准确率与模型泛化性能。<br><br>**4. 多分类（Multi-class）模块**  <br>位于右下角，标题为“多分类（Multi-class）”。左侧展示两个类别的数据点（蓝色圆点与红色三角），机器人对话框标“1 vs Rest”，表示一对多策略；右侧展示两个类别分别独立处理，机器人对话框标“1 vs 1”，表示一对一策略。图示通过箭头连接至两个圆形区域，分别包含各自类别的数据点，最终合并为分类结果。下方文字说明：“策略：一对多 或 一对一。” 该部分阐明SVM如何扩展到多类别分类问题。<br><br>**5. 应用与优势栏**  <br>位于底部横向长条，标题为“应用与优势”，左侧配有灯泡图标。右侧依次列出三个应用场景及优势，每个前均有对应图标：<br>- 📚 文本分类<br>- 🖼️ 图像识别<br>- 👍 小样本表现好<br><br>整体布局结构清晰，从基础到进阶，从二分类到多分类，从线性到非线性，层层递进。图中所有文字均为中文，使用简洁明了的术语，配合生动的插画，使复杂的机器学习概念易于理解。颜色编码用于区分不同类别（蓝色圆点、红色三角、绿色方块），图形元素如机器人、魔法棒、橡皮擦等增强了趣味性和记忆点。</div></details></td>
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<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">该信息图题为“钢琴&古筝学习参考指南”，采用类似地铁线路图的视觉设计风格，以清晰的分支结构组织内容，通过不同颜色的线条（红色、蓝色、绿色）区分主要主题模块，并使用圆形节点表示关键信息点，整体布局简洁明了，背景点缀有灰色城市轮廓和浅蓝色水波纹装饰元素，增强现代感与层次感。<br><br>图表顶部中央为标题“钢琴&古筝学习参考指南”，字体加粗醒目。从标题节点向下延伸出一条红色主干线路，代表“钢琴vs古筝核心演奏技巧差异”这一核心对比主题。该红色线路在中间分叉为两条平行支线：左侧为“钢琴（西洋键盘乐器）”，右侧为“古筝（中国民族弹拨乐器）”。<br><br>在“钢琴（西洋键盘乐器）”支线下，依次列出四个关键特征节点：<br>- 手指+手腕+手臂协同发力<br>- 标准化指法逻辑<br>- 触键力度均匀、落点准确<br>- 出厂固定音高<br><br>在“古筝（中国民族弹拨乐器）”支线下，依次列出四个对应特征节点：<br>- 佩戴义甲演奏<br>- 按、颤、滑、摇多类技法<br>- 触弦角度30-45度<br>- 左手按弦控制音高<br><br>两条支线在中间交汇处用一个带有双向箭头符号的圆形节点连接，强调两者对比关系。<br><br>从红色主干线路下方引出一条蓝色线路，标记为“两类乐器通用高效练习法”。该蓝色线路包含四个节点：<br>- 热身练习：手指拉伸、音阶爬音<br>- 分节练习：4-8小节片段拆分<br>- 慢练优先：原速50%保证清晰度<br>- 录音复盘：针对性调整问题点<br><br>再从蓝色线路下方引出一条绿色线路，标记为“演奏常见误区避坑指南”。该绿色线路呈发散状，连接四个警告/提示节点，分别用禁止符号（⛔）或警示符号（⚠️）标识：<br>- ⛔ 不要塌腕：手腕与琴键/弦面持平<br>- ⚠️ 不要追求速度优先：音准、节奏、情感是核心<br>- ⛔ 不要忽视乐理学习：帮助理解曲目逻辑<br>- ⚠️ 不要长时间连续练习：每45分钟休息10分钟<br><br>所有文字内容均为中文，字体清晰易读，层级分明，通过颜色编码（红=对比、蓝=方法、绿=避坑）和图标辅助（箭头、禁止、警示），使信息传递高效直观。此图旨在为学习者提供系统性指导，涵盖乐器特性、科学练习方法及常见错误规避，兼具实用性和教育意义。</div></details></td>
<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; overflow-y: auto; font-size: 0.85em; font-weight: normal; font-style: normal;">The infographic titled "Personal Insight & Casual Life Reflection Essay Collections: A Complete Guide" is presented in a tactile, clay-like 3D style with rounded edges and textured surfaces, giving it a handcrafted, approachable aesthetic. The background is a neutral light gray, which makes the colorful clay elements stand out. The title is displayed at the top in two lines: the first line "Personal Insight & Casual Life Reflection" is in a warm terracotta brown, while the second line "Essay Collections: A Complete Guide" uses a bright sky blue for "A Complete Guide," creating visual emphasis.<br><br>The infographic is organized as a grid with three main vertical sections (rows) and seven horizontal columns. The leftmost column contains the main categories, each labeled with a large blue clay tile with yellow text. These are:<br><br>1. **Core Traits of the Genre** – with a smaller brown clay subtitle: "Key traits of the personal insight & life reflection genre"<br>2. **Value for Readers & Writers** – with a smaller brown clay subtitle: "Why These Collections Matter"<br>3. **Actionable Engagement Steps** – with a smaller brown clay subtitle: "Simple steps for new readers and writers"<br><br>The remaining six columns form the header row, each represented by a colored clay tile with white text:<br><br>- **Infographic Multi-factor** (blue)<br>- **Statistics** (teal)<br>- **Quotes** (orange)<br>- **Key Terms** (green)<br>- **Style Preferences** (purple)<br>- **Layout Preferences** (light blue)<br>- **Other Requirements** (dark purple)<br><br>Each of the three main rows then has corresponding content under these headers. For the first two data points under each category (Statistics, Quotes, Key Terms), there is a white clay tile with a brown checkmark (√), indicating inclusion or relevance. For the last three headers (Style Preferences, Layout Preferences, Other Requirements), there is a white tile with a number "3" in the respective color of that column’s header.<br><br>Below each checkmark or number, there is a larger white clay tile with black text describing the specific point. The full structure is as follows:<br><br>---<br><br>### Row 1: Core Traits of the Genre<br><br>- **Statistics**: √ → *Rooted in real lived experience*<br>- **Quotes**: √ → *Inspired by small everyday moments*<br>- **Key Terms**: √ → *No formal structure rules*<br>- **Style Preferences**: 3 (orange) → *(no text)*<br>- **Layout Preferences**: 3 (yellow) → *(no text)*<br>- **Other Requirements**: 3 (purple) → *(no text)*<br><br>---<br><br>### Row 2: Value for Readers & Writers<br><br>- **Statistics**: √ → *Readers: Comfort & relatable validation*<br>- **Quotes**: √ → *Writers: Low-stakes creative outlet*<br>- **Key Terms**: √ → *All audiences: Record of personal growth*<br>- **Style Preferences**: 3 (orange) → *(no text)*<br>- **Layout Preferences**: 3 (yellow) → *(no text)*<br>- **Other Requirements**: 3 (purple) → *(no text)*<br><br>---<br><br>### Row 3: Actionable Engagement Steps<br><br>- **Statistics**: √ → *Step 1: Start with short essays to read*<br>- **Quotes**: √ → *Step 2: Write 100-word daily reflections*<br>- **Key Terms**: √ → *Step 3: Group by theme to build your collection*<br>- **Style Preferences**: 3 (orange) → *(no text)*<br>- **Layout Preferences**: 3 (green) → *(no text)*<br>- **Other Requirements**: 3 (purple) → *(no text)*<br><br>---<br><br>The design uses consistent visual encoding: checkmarks indicate presence or applicability, while the number "3" likely denotes a level of preference or importance (though no legend is provided). The colors are used both for column headers and for the numbers, creating a visual link between the header and its corresponding data point. All text is rendered in a clean, sans-serif font, and the overall layout is structured and easy to follow, with clear separation between rows and columns via subtle clay dividers. The infographic communicates a comprehensive yet accessible guide to engaging with personal insight and life reflection essays through a visually engaging and tactile design.</div></details></td>
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## 案例展示

> ✨ **想了解模型的实际效果？欢迎前往 👉 [ 🖼️ 信息图案例展示 ](./u1_infographic_showcases.md) 👈 浏览 100 个生成样例！**

