## Line Chart: Success Rate vs. Avg Inference Count ### Overview The image is a line chart comparing the success rate (%) of two methods, Wan2.1 and SVD, against the average inference count per episode. The x-axis represents the average inference count per episode, ranging from 3.0 to 11.0. The y-axis represents the success rate in percentage, ranging from 52% to 64%. ### Components/Axes * **X-axis:** Avg Inference Count per Episode (ranging from 3.0 to 11.0 in increments of 1.0) * **Y-axis:** Success Rate (%) (ranging from 52 to 64 in increments of 1) * **Legend:** Located in the bottom-right corner. * Green line with circle markers: Wan2.1† * Blue line with square markers: SVD† ### Detailed Analysis * **Wan2.1† (Green Line):** The success rate generally increases with the average inference count per episode. * At 3.0 inference count, the success rate is 56.62%. * At 6.0 inference count, the success rate is 58.26%. * At 8.0 inference count, the success rate is 59.71%. * At 11.0 inference count, the success rate is 62.61%. * **SVD† (Blue Line):** The success rate also increases with the average inference count per episode. * At 3.0 inference count, the success rate is 53.36%. * At 6.0 inference count, the success rate is 56.44%. * At 8.0 inference count, the success rate is 57.17%. * At 11.0 inference count, the success rate is 60.98%. ### Key Observations * Both Wan2.1 and SVD show an increase in success rate as the average inference count per episode increases. * Wan2.1 consistently outperforms SVD across all measured inference counts. * The SVD line shows a steeper increase in success rate between inference counts of 8.0 and 11.0 compared to the segment between 3.0 and 6.0. ### Interpretation The data suggests that increasing the average inference count per episode improves the success rate for both Wan2.1 and SVD methods. Wan2.1 appears to be a more effective method overall, achieving higher success rates than SVD at each inference count level. The steeper increase in SVD's success rate at higher inference counts may indicate that SVD benefits more from increased inference counts than Wan2.1, although it still remains below Wan2.1's performance.

## Line Chart: Success Rate vs. Inference Count ### Overview This image presents a line chart comparing the success rate of two methods, Wan2.1† and SVD†, as a function of the average inference count per episode. The chart displays a clear upward trend for both methods, indicating that success rate increases with a higher number of inferences. ### Components/Axes * **X-axis Title:** Avg Inference Count per Episode * Scale: 3.0 to 11.0, with markers at 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, and 11.0. * **Y-axis Title:** Success Rate (%) * Scale: 52% to 64%, with markers at 52%, 54%, 56%, 58%, 60%, 62%, and 64%. * **Legend:** Located in the bottom-right corner. * Wan2.1†: Represented by a grey circular marker. * SVD†: Represented by a blue square marker. ### Detailed Analysis * **Wan2.1† (Grey Line):** The line slopes upward consistently. * At 3.0 Avg Inference Count: Approximately 56.62% Success Rate. * At 4.0 Avg Inference Count: Approximately 57.44% Success Rate. * At 5.0 Avg Inference Count: Approximately 58.26% Success Rate. * At 6.0 Avg Inference Count: Approximately 58.84% Success Rate. * At 7.0 Avg Inference Count: Approximately 59.71% Success Rate. * At 8.0 Avg Inference Count: Approximately 60.28% Success Rate. * At 9.0 Avg Inference Count: Approximately 60.98% Success Rate. * At 10.0 Avg Inference Count: Approximately 61.73% Success Rate. * At 11.0 Avg Inference Count: Approximately 62.61% Success Rate. * **SVD† (Blue Line):** The line also slopes upward, but appears to have a slightly lower initial success rate compared to Wan2.1†. * At 3.0 Avg Inference Count: Approximately 53.36% Success Rate. * At 4.0 Avg Inference Count: Approximately 54.44% Success Rate. * At 5.0 Avg Inference Count: Approximately 56.44% Success Rate. * At 6.0 Avg Inference Count: Approximately 57.17% Success Rate. * At 7.0 Avg Inference Count: Approximately 57.89% Success Rate. * At 8.0 Avg Inference Count: Approximately 58.61% Success Rate. * At 9.0 Avg Inference Count: Approximately 59.33% Success Rate. * At 10.0 Avg Inference Count: Approximately 60.05% Success Rate. * At 11.0 Avg Inference Count: Approximately 60.77% Success Rate. ### Key Observations * Both methods demonstrate a positive correlation between the average inference count per episode and the success rate. * Wan2.1† consistently achieves a higher success rate than SVD† across all inference counts. * The rate of increase in success rate appears to diminish as the inference count increases for both methods. * The difference in success rate between the two methods is most pronounced at lower inference counts. ### Interpretation The data suggests that increasing the number of inferences per episode generally improves the performance of both Wan2.1† and SVD†. However, Wan2.1† is consistently more effective than SVD†, indicating a potential advantage in its underlying methodology. The diminishing returns observed at higher inference counts suggest that there is a point beyond which additional inferences provide only marginal improvements in success rate. This information could be valuable for optimizing the inference process and balancing performance gains with computational cost. The "†" symbol appended to the method names suggests a possible footnote or additional information regarding the methods themselves, but this is not explicitly provided in the image. Further investigation into the meaning of the "†" symbol would be beneficial for a complete understanding of the data.

## Line Chart: Success Rate vs. Average Inference Count per Episode ### Overview The image is a line chart comparing the performance of two methods, labeled "Wan2.1†" and "SVD†", by plotting their Success Rate (%) against the Average Inference Count per Episode. The chart demonstrates a positive correlation between the number of inferences and the success rate for both methods. ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis (Horizontal):** * **Title:** "Avg Inference Count per Episode" * **Scale:** Linear, ranging from 3.0 to 11.0. * **Major Tick Marks:** 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0. * **Y-Axis (Vertical):** * **Title:** "Success Rate (%)" * **Scale:** Linear, ranging from 52 to 64. * **Major Tick Marks:** 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64. * **Legend:** * **Placement:** Bottom-right corner of the chart area. * **Entry 1:** A green dashed line with a circle marker, labeled "Wan2.1†". * **Entry 2:** A blue dashed line with a square marker, labeled "SVD†". * **Data Series & Points:** * **Series 1 (Wan2.1† - Green line, circle markers):** * Point 1: x ≈ 3.0, y = 56.62% * Point 2: x ≈ 6.0, y = 58.26% * Point 3: x ≈ 8.0, y = 59.71% * Point 4: x ≈ 11.0, y = 62.61% * **Series 2 (SVD† - Blue line, square markers):** * Point 1: x ≈ 3.0, y = 53.36% * Point 2: x ≈ 6.0, y = 56.44% * Point 3: x ≈ 8.5, y = 57.17% * Point 4: x ≈ 11.0, y = 60.98% ### Detailed Analysis * **Trend Verification:** * **Wan2.1† (Green):** The line shows a consistent, nearly linear upward slope from left to right. The rate of increase is steady across the measured range. * **SVD† (Blue):** The line also slopes upward overall. The increase is steeper between the first two points (3.0 to 6.0), then flattens slightly between 6.0 and 8.5, before rising sharply again between 8.5 and 11.0. * **Data Point Extraction:** All data points are explicitly labeled on the chart with their percentage values. The x-axis positions for the points are approximate based on their visual placement relative to the axis ticks. ### Key Observations 1. **Performance Gap:** The Wan2.1† method consistently achieves a higher Success Rate than the SVD† method at every comparable average inference count. 2. **Converging Trend:** The performance gap between the two methods appears to narrow slightly as the average inference count increases. At x=3.0, the gap is 3.26 percentage points (56.62% vs 53.36%). At x=11.0, the gap is 1.63 percentage points (62.61% vs 60.98%). 3. **SVD†'s Non-Linear Response:** The SVD† method shows a less uniform response to increased inferences, with a notable plateau between approximately 6.0 and 8.5 inferences per episode. ### Interpretation The chart presents a clear performance comparison in a technical context, likely from a machine learning or AI research paper (suggested by terms like "Inference Count" and "Success Rate"). The data suggests that: * **Resource-Performance Trade-off:** For both methods, investing more computational resources (higher average inference count per episode) yields a higher success rate. This is a common trade-off in iterative or sampling-based algorithms. * **Method Superiority:** Wan2.1† is the more effective method under the tested conditions, providing a higher success rate for the same computational budget (inference count). * **Algorithmic Behavior:** The differing slopes and shapes of the lines hint at underlying algorithmic differences. Wan2.1†'s steady improvement suggests a more predictable scaling behavior. SVD†'s plateau could indicate a point of diminishing returns or a phase where additional inferences provide minimal gain before another performance jump, which might be an important characteristic for system optimization. The narrowing gap at higher inference counts could imply that SVD† benefits more from very large computational budgets, though it remains inferior in the measured range.

## Line Graph: Algorithm Performance Comparison (Success Rate vs. Inference Count) ### Overview The image is a line graph comparing the success rates of two algorithms, **Wan2.1†** (green line with circles) and **SVD†** (blue line with squares), across varying average inference counts per episode. The graph spans inference counts from 3.0 to 11.0 episodes, with success rates ranging from 52% to 64%. --- ### Components/Axes - **X-axis**: "Avg Inference Count per Episode" (3.0 to 11.0, increments of 1.0). - **Y-axis**: "Success Rate (%)" (52% to 64%, increments of 1%). - **Legend**: Located in the bottom-right corner, mapping: - Green line with circles → **Wan2.1†** - Blue line with squares → **SVD†** --- ### Detailed Analysis #### Wan2.1† (Green Line) - **Data Points**: - (3.0, 56.62%) - (5.5, 58.26%) - (8.0, 59.71%) - (11.0, 62.61%) - **Trend**: Steadily upward slope, with consistent gains in success rate as inference count increases. #### SVD† (Blue Line) - **Data Points**: - (3.0, 53.36%) - (6.0, 56.44%) - (8.0, 57.17%) - (11.0, 60.98%) - **Trend**: Gradual upward slope, but with slower growth compared to Wan2.1†. --- ### Key Observations 1. **Performance Gap**: Wan2.1† consistently outperforms SVD† across all inference counts. - At 3.0 episodes: Wan2.1† (56.62%) vs. SVD† (53.36%) → **+3.26% advantage**. - At 11.0 episodes: Wan2.1† (62.61%) vs. SVD† (60.98%) → **+1.63% advantage**. 2. **Efficiency**: Wan2.1† achieves higher success rates with fewer inference counts (e.g., 59.71% at 8.0 episodes vs. SVD†'s 57.17%). 3. **Scalability**: Both algorithms improve with more inference counts, but Wan2.1† scales more effectively. --- ### Interpretation The data suggests **Wan2.1† is a more efficient and effective algorithm** for the task measured, likely due to superior optimization or architecture. The steeper slope of Wan2.1† indicates it leverages inference counts more effectively to improve success rates. SVD†, while still improving, lags behind, highlighting potential limitations in its design or training. This trend could inform algorithm selection in resource-constrained environments where inference efficiency is critical.