## Line Chart: Model Performance Comparison ### Overview The image is a line chart comparing the performance of different models across a range of model numbers. The y-axis represents the score in percentage, and the x-axis represents the model number. Each line represents a different model, and the chart displays how the score changes for each model as the model number increases. ### Components/Axes * **X-axis:** Model Number, ranging from 1 to 22. * **Y-axis:** Score (%), ranging from 40 to 90, with gridlines at intervals of 10. * **Legend:** Located at the top of the chart, identifying each model by color and name. The models are: * AI2D (Purple) * DocVQA (Green) * ChartQA (Red) * EgoSchema (Blue) * ActivityNet (Orange) * CharXiv-D (Pink) * VideoMMMU (Light Brown) * MMMU (Dark Yellow) * CharXiv-R (Gray) * MMMU Pro (Dark Gray) * ERQA (Teal) ### Detailed Analysis * **AI2D (Purple):** Starts at approximately 89% at model number 3 and increases to approximately 94% at model number 5. * **DocVQA (Green):** Starts at approximately 87% at model number 3 and increases to approximately 93% at model number 5. * **ChartQA (Red):** Starts at approximately 78% at model number 3, increases to approximately 85% at model number 4. * **EgoSchema (Blue):** Starts at approximately 64% at model number 3, increases to approximately 73% at model number 4, decreases to approximately 60% at model number 5, and then increases to approximately 78% at model number 6. * **ActivityNet (Orange):** Starts at approximately 60% at model number 3, decreases to approximately 59% at model number 4, increases to approximately 62% at model number 5. * **CharXiv-D (Pink):** Starts at approximately 77% at model number 4, increases to approximately 90% at model number 8, decreases to approximately 74% at model number 10, increases to approximately 88% at model number 11, decreases to approximately 87% at model number 13. * **VideoMMMU (Light Brown):** Starts at approximately 74% at model number 11, increases to approximately 83% at model number 16, and plateaus around 84% at model number 22. * **MMMU (Dark Yellow):** Starts at approximately 71% at model number 10, increases to approximately 83% at model number 16, and plateaus around 84% at model number 22. * **CharXiv-R (Gray):** Starts at approximately 37% at model number 4, increases to approximately 60% at model number 5, decreases to approximately 55% at model number 8, decreases to approximately 40% at model number 10, increases to approximately 57% at model number 11, plateaus around 56% at model number 13, increases to approximately 77% at model number 16, and plateaus around 79% at model number 22. * **MMMU Pro (Dark Gray):** Starts at approximately 64% at model number 3, decreases to approximately 36% at model number 5, increases to approximately 55% at model number 8, decreases to approximately 41% at model number 10, increases to approximately 57% at model number 12, plateaus around 56% at model number 13, increases to approximately 77% at model number 16, and plateaus around 79% at model number 22. * **ERQA (Teal):** Starts at approximately 35% at model number 5, increases to approximately 64% at model number 16, and plateaus around 66% at model number 22. ### Key Observations * AI2D and DocVQA have the highest initial scores but are only evaluated for the first few model numbers. * CharXiv-D shows high variability in performance across different model numbers. * VideoMMMU and MMMU show a steady increase and plateau in performance. * CharXiv-R and MMMU Pro show significant improvement over the model numbers. * ERQA has the lowest initial score but shows a steady increase in performance. ### Interpretation The chart illustrates the performance of various models as their model number increases. Some models, like AI2D and DocVQA, have high initial scores but are not evaluated across the entire range of model numbers. Other models, such as VideoMMMU and MMMU, demonstrate a consistent improvement and then plateau. CharXiv-R and MMMU Pro show a more volatile performance, with significant fluctuations. ERQA starts with a lower score but exhibits a steady increase, suggesting potential for further improvement. The data suggests that different models have varying strengths and weaknesses, and their performance can change significantly as the model number increases.

## Line Chart: Model Performance Scores across Model Numbers ### Overview This image is a multi-series line chart displaying the performance scores (in percentages) of various models across different benchmarks or datasets. The x-axis represents a sequential "Model Number," while the y-axis represents the "Score (%)." Instead of a traditional legend box, the data series are labeled directly on the chart area, typically near the beginning or end of their respective lines. ### Components/Axes * **Y-Axis (Vertical):** * **Label:** "Score (%)" * **Scale:** Ranges from 30 to roughly 95. * **Markers:** Major tick marks and corresponding horizontal dashed light-gray gridlines are placed at 40, 50, 60, 70, 80, and 90. * **X-Axis (Horizontal):** * **Label:** "Model Number" * **Scale:** Ranges from 1 to 22. * **Markers:** Major tick marks and corresponding vertical dashed light-gray gridlines are placed at every integer from 1 to 22. * **Legend/Labels:** There is no separate legend. Labels are color-coded to match their respective lines and are placed adjacent to the data points. ### Detailed Analysis The data series can be categorized into three distinct visual patterns: short-span early models, highly volatile mid-span models, and long-span steady scaling models. #### Group 1: Short-Span Early Models (x=3 to x=5) These lines only contain two data points and represent benchmarks evaluated only on early model numbers. All show an upward trend. * **AI2D (Purple line, square markers):** * *Position:* Top-left. * *Trend:* Slopes upward. * *Data Points:* (x=3, y~89.5), (x=5, y~94.5) * **DocVQA (Green line, upward triangle markers):** * *Position:* Top-left, just below AI2D. * *Trend:* Slopes upward. * *Data Points:* (x=3, y~87), (x=5, y~92.5) * **ChartQA (Red line, diamond markers):** * *Position:* Top-left, below DocVQA. * *Trend:* Slopes upward steeply. * *Data Points:* (x=3, y~78), (x=5, y~86) * **EgoSchema (Dark Blue line, circle markers):** * *Position:* Mid-left. * *Trend:* Slopes upward. * *Data Points:* (x=3, y~64), (x=5, y~72) * **ActivityNet (Orange line, square markers with cross inside):** * *Position:* Mid-left. * *Trend:* Slopes upward slightly. * *Data Points:* (x=3, y~59.5), (x=5, y~62) #### Group 2: Volatile Mid-Span Models (x=3/4 to x=13/21) These lines exhibit significant fluctuations, notably sharing a sharp, distinct drop at Model Number 10. * **CharXiv-D (Pink line, asterisk/star markers):** * *Position:* Upper-middle. Label is at x~13. * *Trend:* Starts high, dips slightly, rises, experiences a sharp drop at x=10, recovers immediately, and plateaus high. * *Data Points:* (x=4, y~76.5), (x=5, y~85.5), (x=8, y~89), (x=10, y~74), (x=11, y~88.5), (x=12, y~88), (x=13, y~90) * **MMMU (Brown line, pentagon markers):** * *Position:* Spans from mid-left to top-right. Label is at x~21. * *Trend:* Fluctuates early, drops sharply at x=10, then climbs steadily to merge with VideoMMMU at the end. * *Data Points:* (x=3, y~63), (x=4, y~59.5), (x=8, y~78), (x=10, y~55.5), (x=11, y~72.5), (x=12, y~74.5), (x=13, y~75), (x=15, y~81.5), (x=16, y~83), (x=21, y~84.5) * **CharXiv-R (Gray line, 'x' markers):** * *Position:* Spans from bottom-left to upper-right. Label is at x~21. * *Trend:* Starts very low, spikes up, declines steadily, drops sharply at x=10, recovers to a plateau, then climbs steeply. * *Data Points:* (x=4, y~37), (x=5, y~59), (x=8, y~55), (x=10, y~40.5), (x=11, y~56.5), (x=12, y~56.5), (x=13, y~55.5), (x=15, y~72), (x=16, y~78.5), (x=21, y~81) #### Group 3: Long-Span Steady Models (x=5 to x=21) These lines show a consistent, near-linear upward trajectory with very few data points spread across a wide range. * **VideoMMMU (Olive Green line, plus '+' markers):** * *Position:* Spans mid-left to top-right. Label is at x~21. * *Trend:* Steady, smooth upward slope. * *Data Points:* (x=5, y~61.5), (x=11, y~73), (x=15, y~81.5), (x=16, y~83.5), (x=21, y~84.5) * **MMMU Pro (Teal line, circle markers):** * *Position:* Spans mid-left to mid-right. Label is at x~21. * *Trend:* Steady, smooth upward slope. * *Data Points:* (x=5, y~60), (x=16, y~76.5), (x=21, y~78.5) * **ERQA (Light Blue line, downward triangle markers):** * *Position:* Spans bottom-left to mid-right. Label is at x~21. * *Trend:* Steady, smooth upward slope. * *Data Points:* (x=5, y~35.5), (x=16, y~64), (x=21, y~65.5) ### Key Observations 1. **The "Model 10" Anomaly:** There is a severe, synchronized drop in performance at Model Number 10 for three specific benchmarks: CharXiv-D, MMMU, and CharXiv-R. 2. **General Upward Trend:** Despite the volatility in the middle section, the overarching trend for every single benchmark is positive; performance at the highest recorded model number is always greater than at the lowest recorded model number. 3. **Data Sparsity:** The chart mixes high-frequency testing (e.g., models 10, 11, 12, 13 for CharXiv and MMMU) with very low-frequency testing (e.g., ERQA and MMMU Pro only have data points at 5, 16, and 21). 4. **Convergence:** By Model 21, VideoMMMU and MMMU converge at nearly the exact same score (~84.5%). ### Interpretation This chart likely illustrates the scaling laws or iterative improvements of a specific family of AI models (e.g., a series of Large Language Models or Multimodal Models released sequentially or scaled by parameter count, represented by "Model Number"). The general upward trend demonstrates that as the "Model Number" increases, the model becomes more capable across a wide variety of tasks (document reading, chart analysis, video understanding, etc.). The most critical investigative takeaway is the anomaly at **Model 10**. Because CharXiv-D, MMMU, and CharXiv-R all crash simultaneously at this exact point, it strongly implies that Model 10 suffered from a specific architectural flaw, a bug during training, or was a specialized checkpoint that catastrophically forgot certain reasoning capabilities while perhaps optimizing for something else. The immediate recovery at Model 11 suggests the developers identified and fixed this issue. Furthermore, the grouping of the data suggests different testing regimens. The short lines on the left (AI2D, DocVQA) might represent older benchmarks that were "solved" (reaching 90%+) early on and thus abandoned for later models. Conversely, the long, straight lines (VideoMMMU, ERQA) suggest benchmarks that are computationally expensive to run, resulting in them only being tested on major milestone models (e.g., 5, 16, 21) rather than every incremental iteration.

## Line Chart: Model Performance Comparison ### Overview This line chart compares the performance scores (in percentage) of several models across a range of model numbers (1 to 22). The chart displays the performance trends of AI2D, DocVQA, ChartQA, EgoSchema, ActivityNet, CharXiv-D, VideoMMMU, MMMU, CharXiv-R, MMMU Pro, and ERQA. ### Components/Axes * **X-axis:** Model Number (ranging from 1 to 22). * **Y-axis:** Score (%) (ranging from 30% to 90%). * **Legend:** Located in the top-right corner, identifies each line with a unique color and model name. * AI2D (Purple) * DocVQA (Gray) * ChartQA (Red) * EgoSchema (Light Blue) * ActivityNet (Orange) * CharXiv-D (Dark Green) * VideoMMMU (Light Green) * MMMU (Teal) * CharXiv-R (Black) * MMMU Pro (Brown) * ERQA (Turquoise) ### Detailed Analysis Here's a breakdown of each model's performance trend and approximate data points: * **AI2D (Purple):** Starts at approximately 88% at Model 1, decreases slightly to around 85% at Model 3, then remains relatively stable around 85-90% until Model 18, then drops to approximately 80% at Model 22. * **DocVQA (Gray):** Begins at approximately 85% at Model 1, declines sharply to around 40% at Model 5, then increases to approximately 55% at Model 9, and remains relatively stable around 55-65% until Model 22. * **ChartQA (Red):** Starts at approximately 78% at Model 1, increases to a peak of around 88% at Model 4, then declines to approximately 75% at Model 9, and remains relatively stable around 75-80% until Model 22. * **EgoSchema (Light Blue):** Starts at approximately 72% at Model 1, increases to around 78% at Model 3, then declines to approximately 65% at Model 7, and increases to around 75% at Model 16, then declines to approximately 70% at Model 22. * **ActivityNet (Orange):** Begins at approximately 64% at Model 1, decreases to around 58% at Model 3, then increases to approximately 68% at Model 6, then declines to approximately 55% at Model 10, and remains relatively stable around 55-65% until Model 22. * **CharXiv-D (Dark Green):** Starts at approximately 75% at Model 1, increases to a peak of around 85% at Model 14, then declines to approximately 80% at Model 22. * **VideoMMMU (Light Green):** Starts at approximately 70% at Model 1, increases to around 80% at Model 16, then declines to approximately 75% at Model 22. * **MMMU (Teal):** Begins at approximately 75% at Model 1, declines to around 65% at Model 5, then increases to approximately 78% at Model 16, and remains relatively stable around 78-80% until Model 22. * **CharXiv-R (Black):** Starts at approximately 60% at Model 1, declines to around 35% at Model 5, then increases to approximately 55% at Model 10, and remains relatively stable around 55-60% until Model 22. * **MMMU Pro (Brown):** Begins at approximately 75% at Model 1, declines to around 65% at Model 5, then increases to approximately 78% at Model 16, and remains relatively stable around 78-80% until Model 22. * **ERQA (Turquoise):** Starts at approximately 60% at Model 1, increases to around 68% at Model 3, then declines to approximately 62% at Model 7, and remains relatively stable around 62-68% until Model 22. ### Key Observations * AI2D consistently demonstrates the highest scores throughout most of the model range. * DocVQA exhibits the most significant performance decline, particularly between Models 1 and 5. * CharXiv-D shows a notable increase in performance around Model 14. * Several models (MMMU, MMMU Pro, CharXiv-R) show similar performance trends. * ActivityNet and ERQA have relatively stable, but lower, performance scores. ### Interpretation The chart suggests that AI2D is the most robust model across the tested range, maintaining high performance regardless of the model number. DocVQA, while starting with a competitive score, suffers a substantial performance drop, indicating potential sensitivity to changes in the model. The convergence of performance scores for MMMU, MMMU Pro, and CharXiv-R suggests they may be utilizing similar underlying mechanisms or training data. The relatively stable performance of ActivityNet and ERQA indicates they may be less sensitive to model variations but also have lower overall performance ceilings. The peak in CharXiv-D's performance at Model 14 could indicate a specific optimization or architectural improvement implemented at that stage. Overall, the chart provides a comparative analysis of model performance, highlighting strengths and weaknesses of each model across a spectrum of model numbers. The data suggests that model number impacts performance, and different models respond to these changes in different ways.

## Line Chart: Model Performance Across Multiple Benchmarks ### Overview This is a multi-series line chart comparing the performance scores (in percentage) of various AI models, identified by a sequential "Model Number" on the x-axis, across 11 different evaluation benchmarks. The chart illustrates how different models perform on diverse tasks, showing trends of improvement, volatility, and relative performance. ### Components/Axes * **X-Axis:** Labeled "Model Number". It is a linear scale with major tick marks and labels for integers from 1 to 22. * **Y-Axis:** Labeled "Score (%)". It is a linear scale with major tick marks and labels at intervals of 10, from 40 to 90. * **Legend:** The legend is integrated directly into the chart area, with labels placed near the end of their respective data lines, primarily in the top-right quadrant. The labels and their associated line colors/markers are: * **AI2D** (Purple line, circle marker) * **DocVQA** (Green line, triangle-up marker) * **ChartQA** (Red line, diamond marker) * **CharXiv-D** (Pink line, no distinct marker) * **VideoMMMU** (Olive/Yellow-green line, plus marker) * **MMMU** (Brown line, circle marker) * **CharXiv-R** (Gray line, 'x' marker) * **MMMU-Pro** (Cyan line, circle marker) * **EgoSchema** (Blue line, circle marker) * **ActivityNet** (Orange line, square marker) * **ERQA** (Light blue/Teal line, triangle-down marker) ### Detailed Analysis **Data Series and Trends:** 1. **AI2D (Purple):** Shows a strong upward trend. Starts at ~89% (Model 3), rises to ~94% (Model 5). 2. **DocVQA (Green):** Shows a strong upward trend. Starts at ~87% (Model 3), rises to ~93% (Model 5). 3. **ChartQA (Red):** Shows a strong upward trend. Starts at ~78% (Model 3), rises to ~86% (Model 5). 4. **CharXiv-D (Pink):** Exhibits high volatility. Starts at ~76% (Model 4), peaks at ~89% (Model 8), drops sharply to ~74% (Model 10), rebounds to ~88% (Model 11), and ends at ~90% (Model 13). 5. **VideoMMMU (Olive):** Shows a steady, strong upward trend. Starts at ~60% (Model 3), rises consistently to ~84% (Model 21). 6. **MMMU (Brown):** Shows a volatile but generally upward trend. Starts at ~63% (Model 3), dips to ~59% (Model 5), peaks at ~78% (Model 8), drops sharply to ~55% (Model 10), then recovers and climbs steadily to ~84% (Model 21). 7. **CharXiv-R (Gray):** Shows a volatile, then strong upward trend. Starts very low at ~37% (Model 4), jumps to ~59% (Model 5), declines to ~55% (Model 8), drops to a low of ~40% (Model 10), then begins a strong, steady ascent to ~81% (Model 21). 8. **MMMU-Pro (Cyan):** Shows a steady, strong upward trend. Starts at ~60% (Model 5), rises consistently to ~78% (Model 21). 9. **EgoSchema (Blue):** Shows a strong upward trend. Starts at ~64% (Model 3), rises to ~72% (Model 5). 10. **ActivityNet (Orange):** Shows a slight upward trend. Starts at ~59% (Model 3), rises to ~62% (Model 5). 11. **ERQA (Light Blue/Teal):** Shows a steady, strong upward trend. Starts at ~35% (Model 5), rises consistently to ~66% (Model 21). **Spatial Grounding & Data Points (Approximate):** * **Top-Left Cluster (Models 3-5):** AI2D, DocVQA, and ChartQA show high initial scores and rapid improvement. * **Central Volatility (Models 8-13):** CharXiv-D and MMMU show significant dips and recoveries. CharXiv-R hits its lowest point at Model 10. * **Right-Side Convergence (Models 16-21):** VideoMMMU, MMMU, CharXiv-R, and MMMU-Pro all show strong, converging upward trends, ending in the 78%-84% range. * **Lower Bound:** ERQA starts the lowest but shows consistent improvement. ### Key Observations 1. **Performance Clustering:** Benchmarks fall into apparent performance tiers by Model 21: Top tier (~84%: VideoMMMU, MMMU), Middle tier (~78-81%: CharXiv-R, MMMU-Pro), Lower tier (~66%: ERQA). 2. **Volatility vs. Stability:** Some benchmarks (CharXiv-D, MMMU, CharXiv-R) show high volatility with sharp drops and recoveries between models, while others (VideoMMMU, MMMU-Pro, ERQA) show smooth, monotonic improvement. 3. **Model Number Correlation:** There is a general, strong positive correlation between Model Number and Score for almost all benchmarks, suggesting later models (higher numbers) are generally more capable. 4. **Notable Outlier Event:** Model Number 10 appears to be a significant point of failure or difficulty for several benchmarks (CharXiv-D, MMMU, CharXiv-R all show sharp dips here). 5. **Early vs. Late Benchmarks:** Benchmarks like AI2D, DocVQA, and ChartQA are only plotted for early models (3-5), while others like ERQA, MMMU-Pro, and CharXiv-R are plotted for a longer range of later models. ### Interpretation This chart likely visualizes the progression of a series of AI models (perhaps different versions or sizes of a base model family) on a standardized, multi-faceted evaluation suite. The "Model Number" likely represents a sequence of increasing model scale, capability, or training iteration. The data suggests that: * **General Capability is Improving:** The dominant upward trend across nearly all tasks indicates that successive models are becoming more capable across a wide range of visual and textual reasoning tasks (document QA, chart understanding, video understanding, etc.). * **Tasks Have Different Difficulty Profiles:** The varying starting points, slopes, and volatility suggest some tasks (e.g., ERQA) are consistently harder, while others (e.g., AI2D) are mastered earlier. The volatility in tasks like CharXiv-D might indicate sensitivity to specific model changes or training data shifts. * **The "Model 10" Anomaly:** The synchronized performance drop at Model 10 for multiple benchmarks is a critical investigative point. It could indicate a problematic model version, a change in evaluation methodology, or a specific weakness introduced and later patched in the model lineage. * **Convergence on Complex Tasks:** The convergence of multiple benchmarks (VideoMMMU, MMMU, CharXiv-R) in the later models suggests that as models scale, their performance on diverse, complex reasoning tasks begins to plateau at a similar high level, potentially indicating a shared underlying capability ceiling or the effectiveness of the training approach across domains. In essence, the chart tells a story of iterative progress in AI, highlighting both consistent improvement and the non-linear, sometimes fragile nature of advancing capabilities across a broad spectrum of cognitive tasks.

# Technical Document: Line Chart Analysis ## 1. Chart Overview The image is a **line chart** visualizing performance scores (%) across 22 model numbers (x-axis) for multiple data series. The y-axis represents scores ranging from 30% to 95%. --- ## 2. Axis Labels - **X-axis**: "Model Number" (1–22) - **Y-axis**: "Score (%)" (30–95) --- ## 3. Legend The legend is located on the **right side** of the chart. Each data series is represented by a unique color, line style, and marker. Below is the legend mapping: | **Label** | **Color** | **Marker** | **Line Style** | |--------------------|-----------|------------|----------------| | AI2D | Purple | Circle | Solid | | DocVQA | Green | Triangle | Solid | | ChartQA | Pink | Diamond | Solid | | ActivityNet | Orange | Square | Solid | | EgoSchema | Brown | Circle | Solid | | CharXiv-D | Pink | Diamond | Dashed | | VideoMMMU | Yellow | Circle | Solid | | MMMU | Brown | Circle | Solid | | CharXiv-R | Gray | Circle | Solid | | MMMU Pro | Cyan | Circle | Solid | | ERQA | Cyan | Triangle | Solid | --- ## 4. Data Series Trends ### 4.1 AI2D (Purple, Circle) - **Trend**: Starts at 90% (Model 3), increases slightly to 95% (Model 5), then plateaus. - **Key Points**: - Model 3: 90% - Model 5: 95% - Model 20: 95% ### 4.2 DocVQA (Green, Triangle) - **Trend**: Starts at 85% (Model 3), rises sharply to 95% (Model 5), then plateaus. - **Key Points**: - Model 3: 85% - Model 5: 95% - Model 20: 95% ### 4.3 ChartQA (Pink, Diamond) - **Trend**: Peaks at 85% (Model 4), drops to 75% (Model 10), then recovers to 90% (Model 13). - **Key Points**: - Model 4: 85% - Model 10: 75% - Model 13: 90% ### 4.4 ActivityNet (Orange, Square) - **Trend**: Starts at 60% (Model 3), increases steadily to 85% (Model 20). - **Key Points**: - Model 3: 60% - Model 20: 85% ### 4.5 EgoSchema (Brown, Circle) - **Trend**: Fluctuates between 55% (Model 10) and 80% (Model 8), ending at 80% (Model 20). - **Key Points**: - Model 10: 55% - Model 8: 80% - Model 20: 80% ### 4.6 CharXiv-D (Pink, Diamond) - **Trend**: Peaks at 90% (Model 13), drops to 75% (Model 10), then recovers to 90% (Model 13). - **Key Points**: - Model 13: 90% - Model 10: 75% - Model 20: 90% ### 4.7 VideoMMMU (Yellow, Circle) - **Trend**: Starts at 75% (Model 11), increases steadily to 85% (Model 20). - **Key Points**: - Model 11: 75% - Model 20: 85% ### 4.8 MMMU (Brown, Circle) - **Trend**: Similar to VideoMMMU, starts at 75% (Model 11), increases to 85% (Model 20). - **Key Points**: - Model 11: 75% - Model 20: 85% ### 4.9 CharXiv-R (Gray, Circle) - **Trend**: Starts at 58% (Model 3), increases to 80% (Model 20). - **Key Points**: - Model 3: 58% - Model 20: 80% ### 4.10 MMMU Pro (Cyan, Circle) - **Trend**: Starts at 65% (Model 3), increases steadily to 80% (Model 20). - **Key Points**: - Model 3: 65% - Model 20: 80% ### 4.11 ERQA (Cyan, Triangle) - **Trend**: Starts at 35% (Model 3), increases steadily to 65% (Model 20). - **Key Points**: - Model 3: 35% - Model 20: 65% --- ## 5. Spatial Grounding - **Legend Position**: Right side of the chart. - **Color-Marker Consistency**: All data series match the legend (e.g., purple circles for AI2D, green triangles for DocVQA). --- ## 6. Trend Verification - **Upward Slopes**: AI2D, DocVQA, ActivityNet, VideoMMMU, MMMU, CharXiv-R, MMMU Pro, ERQA. - **Downward Slopes**: ChartQA (Model 4–10), CharXiv-D (Model 13–10). - **Fluctuations**: EgoSchema (Model 3–20). --- ## 7. Data Table Reconstruction | Model Number | AI2D (%) | DocVQA (%) | ChartQA (%) | ActivityNet (%) | EgoSchema (%) | CharXiv-D (%) | VideoMMMU (%) | MMMU (%) | CharXiv-R (%) | MMMU Pro (%) | ERQA (%) | |--------------|----------|------------|-------------|-----------------|---------------|---------------|---------------|----------|---------------|--------------|----------| | 3 | 90 | 85 | 78 | 60 | 63 | 58 | - | - | 58 | 65 | 35 | | 4 | 95 | 95 | 85 | 62 | 60 | 60 | - | - | 60 | 68 | 38 | | 5 | 95 | 95 | 85 | 65 | 72 | 55 | - | - | 55 | 70 | 40 | | 8 | 95 | 95 | 85 | 70 | 55 | 80 | - | - | 55 | 75 | 50 | | 10 | 95 | 95 | 75 | 72 | 55 | 55 | - | - | 55 | 78 | 55 | | 11 | 95 | 95 | 85 | 75 | 73 | 73 | 75 | 75 | 73 | 78 | 60 | | 13 | 95 | 95 | 90 | 77 | 75 | 75 | 80 | 80 | 75 | 80 | 65 | | 15 | 95 | 95 | 85 | 80 | 82 | 82 | 85 | 85 | 82 | 83 | 70 | | 16 | 95 | 95 | 85 | 82 | 80 | 80 | 85 | 85 | 80 | 85 | 75 | | 17 | 95 | 95 | 85 | 83 | 80 | 80 | 85 | 85 | 80 | 85 | 75 | | 18 | 95 | 95 | 85 | 84 | 80 | 80 | 85 | 85 | 80 | 85 | 75 | | 19 | 95 | 95 | 85 | 85 | 80 | 80 | 85 | 85 | 80 | 85 | 75 | | 20 | 95 | 95 | 85 | 85 | 80 | 80 | 85 | 85 | 80 | 85 | 75 | --- ## 8. Notes - **Missing Data**: Some series (e.g., VideoMMMU, MMMU) have no data for Models 1–10. - **Color Consistency**: All markers and lines align with the legend (e.g., pink diamonds for ChartQA, cyan triangles for ERQA). - **Final Trend**: Most series show improvement over time, with AI2D and DocVQA maintaining the highest scores. --- ## 9. Conclusion The chart illustrates performance trends across 11 data series. AI2D and DocVQA consistently achieve the highest scores, while ERQA shows the most significant improvement from Model 3 to 20. All data points align with the legend, and trends are visually verified.