## Bar Chart: Qwen2.5-14B-Instruct ### Overview The image is a bar chart comparing the accuracy of three models (Base Model, Base Model + Tools, and ARTIST) across four datasets (AMC, AIME, Olympiad, and Math 500). The y-axis represents accuracy, ranging from 0.0 to 0.7. The x-axis represents the datasets. ### Components/Axes * **Title:** Qwen2.5-14B-Instruct * **X-axis:** Datasets (AMC, AIME, Olympiad, Math 500) * **Y-axis:** Accuracy (ranging from 0.0 to 0.7, with increments of 0.1) * **Legend:** Located at the top-right of the chart. * Base Model (Turquoise) * Base Model + Tools (Light Turquoise) * ARTIST (Blue) ### Detailed Analysis Here's a breakdown of the accuracy for each model on each dataset: * **AMC:** * Base Model: ~0.33 * Base Model + Tools: ~0.41 * ARTIST: ~0.55 * **AIME:** * Base Model: ~0.06 * Base Model + Tools: ~0.10 * ARTIST: ~0.12 * **Olympiad:** * Base Model: ~0.24 * Base Model + Tools: ~0.37 * ARTIST: ~0.42 * **Math 500:** * Base Model: ~0.70 * Base Model + Tools: ~0.67 * ARTIST: ~0.72 ### Key Observations * The ARTIST model consistently outperforms the Base Model and Base Model + Tools across all datasets. * The Math 500 dataset shows the highest accuracy for all models. * The AIME dataset shows the lowest accuracy for all models. * The "Base Model + Tools" generally performs slightly better than the "Base Model" alone. ### Interpretation The bar chart demonstrates the performance of different models on various datasets. The ARTIST model shows a clear advantage in accuracy compared to the base models, suggesting that the ARTIST model architecture or training process is more effective for these tasks. The Math 500 dataset appears to be the easiest for these models, while the AIME dataset is the most challenging. The addition of tools to the base model provides a marginal improvement in accuracy.

\n ## Bar Chart: Qwen2.5-14B-Instruct Accuracy on Math Datasets ### Overview This bar chart compares the accuracy of three models – Base Model, Base Model + Tools, and ARTIST – on four different math datasets: AMC, AIME, Olympiad, and Math 500. Accuracy is measured on the y-axis, and the datasets are displayed on the x-axis. ### Components/Axes * **Title:** Qwen2.5-14B-Instruct (top-center) * **X-axis Label:** Datasets (bottom-center) * **Y-axis Label:** Accuracy (left-center) * **Legend:** Located in the top-left corner. * Base Model (light teal) * Base Model + Tools (light blue) * ARTIST (dark blue) * **Datasets (X-axis Markers):** AMC, AIME, Olympiad, Math 500. ### Detailed Analysis The chart consists of four groups of three bars, one for each dataset and model combination. **AMC Dataset:** * Base Model: Approximately 0.41 accuracy. * Base Model + Tools: Approximately 0.53 accuracy. * ARTIST: Approximately 0.55 accuracy. *Trend:* All three models show positive accuracy, with ARTIST and Base Model + Tools performing better than the Base Model. **AIME Dataset:** * Base Model: Approximately 0.08 accuracy. * Base Model + Tools: Approximately 0.09 accuracy. * ARTIST: Approximately 0.11 accuracy. *Trend:* Accuracy is significantly lower for all models on the AIME dataset compared to the AMC dataset. ARTIST performs best, but the difference between the models is smaller. **Olympiad Dataset:** * Base Model: Approximately 0.27 accuracy. * Base Model + Tools: Approximately 0.29 accuracy. * ARTIST: Approximately 0.32 accuracy. *Trend:* Accuracy is higher than AIME but lower than AMC. ARTIST consistently outperforms the other two models. **Math 500 Dataset:** * Base Model: Approximately 0.68 accuracy. * Base Model + Tools: Approximately 0.71 accuracy. * ARTIST: Approximately 0.73 accuracy. *Trend:* The highest accuracy scores are observed on the Math 500 dataset. ARTIST again shows the highest performance, followed closely by Base Model + Tools. ### Key Observations * ARTIST consistently outperforms both the Base Model and the Base Model + Tools across all datasets. * The Base Model + Tools generally performs better than the Base Model alone. * Accuracy varies significantly depending on the dataset, with the Math 500 dataset yielding the highest scores and the AIME dataset the lowest. * The performance gap between the models is most pronounced on the AMC and Math 500 datasets. ### Interpretation The data suggests that the ARTIST model is the most effective at solving math problems across the tested datasets. The addition of tools to the Base Model provides a moderate improvement in accuracy. The varying performance across datasets indicates that the difficulty and nature of the problems within each dataset influence the models' ability to solve them. The Math 500 dataset, with its higher accuracy scores, may contain problems that are more aligned with the models' training data or capabilities. The AIME dataset, with its lower scores, may present unique challenges. The consistent outperformance of ARTIST suggests that its architecture or training methodology is particularly well-suited for tackling these types of math problems. The data demonstrates a clear hierarchy of performance: ARTIST > Base Model + Tools > Base Model. This could be due to the ARTIST model's ability to leverage more complex reasoning or problem-solving strategies.

## Grouped Bar Chart: Qwen2.5-14B-Instruct Model Accuracy ### Overview This image is a grouped bar chart titled "Qwen2.5-14B-Instruct". It compares the accuracy performance of three different model configurations across four distinct mathematical problem-solving datasets. The chart visually demonstrates the relative effectiveness of each configuration on each dataset. ### Components/Axes * **Chart Title:** "Qwen2.5-14B-Instruct" (centered at the top). * **Y-Axis:** Labeled "Accuracy". The scale runs from 0.0 to 0.7, with major tick marks at 0.1 intervals (0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7). * **X-Axis:** Labeled "Datasets". It contains four categorical groups: 1. AMC 2. AIME 3. Olympiad 4. Math 500 * **Legend:** Positioned in the top-center of the chart area. It defines three data series by color: * **Base Model:** Represented by a teal/turquoise bar (approximate hex: #66c2a5). * **Base Model + Tools:** Represented by a light teal/aquamarine bar (approximate hex: #abdda4). * **ARTIST:** Represented by a medium blue bar (approximate hex: #3288bd). ### Detailed Analysis The chart presents accuracy scores for each model configuration on each dataset. Values are approximate based on visual alignment with the y-axis. **1. AMC Dataset (Leftmost group):** * **Trend:** Accuracy increases from Base Model to Base Model + Tools to ARTIST. * **Data Points:** * Base Model: ~0.33 * Base Model + Tools: ~0.41 * ARTIST: ~0.55 **2. AIME Dataset (Second group from left):** * **Trend:** Accuracy increases from Base Model to Base Model + Tools to ARTIST. This dataset shows the lowest overall scores. * **Data Points:** * Base Model: ~0.06 * Base Model + Tools: ~0.10 * ARTIST: ~0.12 **3. Olympiad Dataset (Third group from left):** * **Trend:** Accuracy increases from Base Model to Base Model + Tools to ARTIST. * **Data Points:** * Base Model: ~0.24 * Base Model + Tools: ~0.37 * ARTIST: ~0.42 **4. Math 500 Dataset (Rightmost group):** * **Trend:** Accuracy is high for all models. The Base Model and Base Model + Tools scores are very close, with ARTIST showing a slight improvement. * **Data Points:** * Base Model: ~0.70 * Base Model + Tools: ~0.67 * ARTIST: ~0.73 ### Key Observations 1. **Consistent Hierarchy:** The ARTIST model configuration achieves the highest accuracy on all four datasets. 2. **Dataset Difficulty:** The AIME dataset appears to be the most challenging, with all models scoring below 0.15. The Math 500 dataset appears to be the least challenging, with all models scoring near or above 0.67. 3. **Impact of Tools:** Adding tools to the Base Model ("Base Model + Tools") provides a clear accuracy boost on the AMC, AIME, and Olympiad datasets. However, on the Math 500 dataset, its performance is slightly *lower* than the Base Model alone. 4. **Greatest Improvement:** The most significant performance jump from the Base Model to ARTIST occurs on the AMC dataset (an increase of approximately 0.22 points). ### Interpretation This chart evaluates the mathematical reasoning capabilities of the Qwen2.5-14B-Instruct model under different conditions. The data suggests that the **ARTIST** method or framework provides a robust and consistent improvement in accuracy across a variety of mathematical problem sets, from competition-level (AMC, AIME, Olympiad) to more general benchmarks (Math 500). The fact that ARTIST outperforms the "Base Model + Tools" indicates that its advantage is not merely from tool augmentation but likely involves a more sophisticated approach to problem-solving. The anomaly on the Math 500 dataset, where "Base Model + Tools" slightly underperforms the Base Model, could suggest that for certain, possibly more straightforward problem types, the tool-use process might introduce minor overhead or error without a compensating benefit. Overall, the chart makes a strong case for the efficacy of the ARTIST approach for enhancing the mathematical problem-solving performance of this language model.

## Bar Chart: Qwen2.5-14B-Instruct Model Performance Across Datasets ### Overview The chart compares the accuracy of three model configurations (Base Model, Base Model + Tools, ARTIST) across four datasets (AMC, AIME, Olympiad, Math 500). Accuracy values range from 0.0 to 0.7 on the y-axis, with datasets listed on the x-axis. ### Components/Axes - **X-axis (Datasets)**: AMC, AIME, Olympiad, Math 500 (left to right) - **Y-axis (Accuracy)**: 0.0 to 0.7 in increments of 0.1 - **Legend**: - Dark teal: Base Model - Light teal: Base Model + Tools - Blue: ARTIST - **Title**: "Qwen2.5-14B-Instruct" (top center) ### Detailed Analysis 1. **AMC Dataset**: - Base Model: ~0.33 - Base Model + Tools: ~0.41 - ARTIST: ~0.55 2. **AIME Dataset**: - Base Model: ~0.06 - Base Model + Tools: ~0.10 - ARTIST: ~0.12 3. **Olympiad Dataset**: - Base Model: ~0.24 - Base Model + Tools: ~0.37 - ARTIST: ~0.42 4. **Math 500 Dataset**: - Base Model: ~0.70 - Base Model + Tools: ~0.67 - ARTIST: ~0.73 ### Key Observations - **ARTIST** consistently outperforms both Base Model and Base Model + Tools across all datasets. - **Math 500** shows the highest accuracy for all configurations, while **AIME** has the lowest. - The gap between Base Model and ARTIST is largest in AMC (~0.22) and smallest in Math 500 (~0.03). - Base Model + Tools improves over Base Model but remains inferior to ARTIST in all cases. ### Interpretation The data demonstrates that the ARTIST configuration significantly enhances model performance compared to the base model and tool-augmented version. This suggests ARTIST incorporates critical architectural or training improvements. Math 500's high accuracy across all configurations indicates it is the easiest dataset, while AIME's low performance highlights its complexity. The smaller performance gap in Math 500 implies that even basic models can achieve near-optimal results on simpler tasks, whereas complex datasets like AIME require advanced configurations like ARTIST to approach peak performance. The tools provide marginal benefits (~0.03-0.05 improvement over Base Model), suggesting they may not fully address the model's limitations in complex reasoning tasks.