## Bar Chart: Qwen2.5-7B-Instruct Accuracy on Datasets ### Overview The image is a bar chart comparing the accuracy of three models (Base Model, Base Model + Tools, and ARTIST) on four different 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:** Qwen2.5-7B-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 in the top-left corner. * Base Model (Turquoise) * Base Model + Tools (Light Turquoise) * ARTIST (Blue) ### Detailed Analysis The chart displays the accuracy of each model on each dataset. * **AMC:** * Base Model: ~0.35 * Base Model + Tools: ~0.35 * ARTIST: ~0.47 * **AIME:** * Base Model: ~0.04 * Base Model + Tools: ~0.12 * ARTIST: ~0.16 * **Olympiad:** * Base Model: ~0.21 * Base Model + Tools: ~0.37 * ARTIST: ~0.38 * **Math 500:** * Base Model: ~0.62 * Base Model + Tools: ~0.63 * ARTIST: ~0.68 ### Key Observations * The ARTIST model consistently outperforms the Base Model and Base Model + Tools across all datasets. * The "Base Model + Tools" model generally performs slightly better than the "Base Model" alone, except for the AMC dataset where they have similar performance. * All models perform best on the "Math 500" dataset and worst on the "AIME" dataset. * The performance difference between ARTIST and the base models is most pronounced on the AMC dataset. ### Interpretation The bar chart demonstrates the performance of the Qwen2.5-7B-Instruct model under different configurations (Base Model, Base Model + Tools, and ARTIST) across various datasets. The ARTIST model shows a clear advantage, suggesting that the techniques used in ARTIST significantly improve accuracy. The varying performance across datasets indicates that the models have different strengths and weaknesses depending on the type of problem. The "Math 500" dataset seems to be the easiest for all models, while "AIME" is the most challenging. The addition of tools to the base model provides a marginal improvement in most cases.

\n ## Bar Chart: Qwen2.5-7B-Instruct Accuracy on Math Datasets ### Overview This bar chart compares the accuracy of three different models – Base Model, Base Model + Tools, and ARTIST – on four math datasets: AMC, AIME, Olympiad, and Math 500. The accuracy is measured on the y-axis, ranging from 0.0 to 0.7, while the datasets are listed on the x-axis. ### Components/Axes * **Title:** Qwen2.5-7B-Instruct * **X-axis Label:** Datasets * **Y-axis Label:** Accuracy * **Datasets (X-axis):** AMC, AIME, Olympiad, Math 500 * **Models (Legend):** * Base Model (Light Blue) * Base Model + Tools (Turquoise) * ARTIST (Blue) ### Detailed Analysis The chart consists of grouped bar plots for each dataset, representing the accuracy of each model. **AMC Dataset:** * Base Model: Approximately 0.34 * Base Model + Tools: Approximately 0.48 * ARTIST: Approximately 0.46 **AIME Dataset:** * Base Model: Approximately 0.08 * Base Model + Tools: Approximately 0.14 * ARTIST: Approximately 0.09 **Olympiad Dataset:** * Base Model: Approximately 0.21 * Base Model + Tools: Approximately 0.34 * ARTIST: Approximately 0.38 **Math 500 Dataset:** * Base Model: Approximately 0.61 * Base Model + Tools: Approximately 0.64 * ARTIST: Approximately 0.68 **Trends:** * For all datasets, ARTIST generally outperforms the Base Model. * Adding tools to the Base Model consistently improves performance. * The largest performance difference between models is observed on the Math 500 dataset. * The Base Model + Tools and ARTIST models show similar performance on the AMC dataset. ### Key Observations * The ARTIST model achieves the highest accuracy across all datasets. * The Base Model performs relatively poorly on the AIME and Olympiad datasets. * The Math 500 dataset shows the highest overall accuracy scores for all models. * The addition of tools significantly boosts the performance of the Base Model, particularly on the AMC and Olympiad datasets. ### Interpretation The data suggests that the ARTIST model is the most effective at solving problems from these math datasets, followed by the Base Model with added tools. The Base Model alone exhibits lower accuracy, especially on more challenging datasets like AIME and Olympiad. The consistent improvement observed when tools are added to the Base Model indicates that these tools provide valuable assistance in problem-solving. The higher accuracy scores on the Math 500 dataset may be due to the dataset's characteristics, potentially being less complex or more aligned with the models' training data. The differences in performance across datasets highlight the varying difficulty levels and the models' ability to generalize to different types of math problems. The chart demonstrates the effectiveness of model enhancement through tool integration and the potential for further improvement in AI-driven math problem-solving.

\n ## Bar Chart: Qwen2.5-7B-Instruct Model Performance ### Overview This is a grouped bar chart comparing the accuracy of three different configurations of the "Qwen2.5-7B-Instruct" model across four distinct mathematical reasoning datasets. The chart visually demonstrates the performance impact of using tools and the ARTIST method. ### Components/Axes * **Chart Title:** "Qwen2.5-7B-Instruct" (centered at the top). * **Y-Axis:** Labeled "Accuracy". The scale runs from 0.0 to 0.7, with major tick marks at every 0.1 interval. * **X-Axis:** Labeled "Datasets". It contains four categorical groups: 1. AMC 2. AIME 3. Olympiad 4. Math 500 * **Legend:** Positioned in the top-left corner of the plot 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 For each dataset, the approximate accuracy values (read from the y-axis) for the three models are as follows: **1. AMC Dataset:** * **Base Model:** ~0.35 * **Base Model + Tools:** ~0.35 (appears equal to Base Model) * **ARTIST:** ~0.47 * *Trend:* ARTIST shows a clear improvement over the base configurations, which perform identically. **2. AIME Dataset:** * **Base Model:** ~0.04 * **Base Model + Tools:** ~0.12 * **ARTIST:** ~0.16 * *Trend:* All accuracies are significantly lower than on other datasets, indicating higher difficulty. There is a stepwise improvement from Base Model to Base Model + Tools to ARTIST. **3. Olympiad Dataset:** * **Base Model:** ~0.21 * **Base Model + Tools:** ~0.37 * **ARTIST:** ~0.38 * *Trend:* A substantial jump in performance is observed when adding tools to the base model. ARTIST provides a very slight additional improvement over "Base Model + Tools." **4. Math 500 Dataset:** * **Base Model:** ~0.62 * **Base Model + Tools:** ~0.63 * **ARTIST:** ~0.68 * *Trend:* This dataset yields the highest overall accuracies. The base model and base model with tools perform very similarly, while ARTIST shows a notable improvement. ### Key Observations 1. **Consistent Superiority of ARTIST:** The ARTIST method (blue bar) achieves the highest accuracy on every single dataset presented. 2. **Variable Impact of Tools:** The benefit of adding tools ("Base Model + Tools" vs. "Base Model") is highly dataset-dependent. It provides a large boost on AIME and Olympiad, a minimal boost on Math 500, and no discernible boost on AMC. 3. **Dataset Difficulty Hierarchy:** Based on the performance of the base model, the datasets appear to be ordered by increasing difficulty: Math 500 (easiest, ~0.62) > AMC (~0.35) > Olympiad (~0.21) > AIME (hardest, ~0.04). 4. **Performance Clustering:** On the Math 500 dataset, all three models perform relatively well and within a ~0.06 accuracy range. On the AIME dataset, all models perform poorly, within a ~0.12 range. ### Interpretation The data strongly suggests that the **ARTIST** method is a more effective technique for improving the mathematical reasoning accuracy of the Qwen2.5-7B-Instruct model than simply augmenting it with tools. The value of tool use is context-specific, proving crucial for certain types of problems (likely those in AIME and Olympiad that benefit from external calculation or verification) but less impactful for others. The stark difference in baseline performance across datasets highlights the importance of benchmark diversity; a model's capability is not a single number but a profile across problem types. The fact that ARTIST provides the largest relative gains on the hardest dataset (AIME) is particularly significant, as it indicates the method's potential for tackling the most challenging reasoning tasks where base models struggle the most. The chart ultimately makes a case for the ARTIST approach as a robust and consistent performance enhancer.

## Bar Chart: Qwen2.5-7B-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 labeled on the x-axis. ### Components/Axes - **X-axis (Datasets)**: AMC, AIME, Olympiad, Math 500 (categorical, evenly spaced) - **Y-axis (Accuracy)**: 0.0 to 0.7 (linear scale, increments of 0.1) - **Legend**: - Teal: Base Model - Light Teal: Base Model + Tools - Blue: ARTIST - **Bar Groups**: Each dataset has three adjacent bars representing the three model configurations. ### Detailed Analysis 1. **AMC Dataset**: - Base Model: ~0.35 - Base Model + Tools: ~0.35 - ARTIST: ~0.47 - *Trend*: ARTIST shows a significant (~12%) improvement over Base Model configurations. 2. **AIME Dataset**: - Base Model: ~0.05 - Base Model + Tools: ~0.12 - ARTIST: ~0.16 - *Trend*: All configurations underperform, but ARTIST achieves ~33% higher accuracy than Base Model. 3. **Olympiad Dataset**: - Base Model: ~0.21 - Base Model + Tools: ~0.37 - ARTIST: ~0.38 - *Trend*: Base Model + Tools shows a ~76% improvement over Base Model, with ARTIST adding marginal gains. 4. **Math 500 Dataset**: - Base Model: ~0.62 - Base Model + Tools: ~0.63 - ARTIST: ~0.68 - *Trend*: ARTIST achieves ~8% higher accuracy than Base Model + Tools, maintaining the highest performance. ### Key Observations - **ARTIST Consistency**: ARTIST outperforms all other configurations across all datasets, with the largest gap in AMC (~12%) and smallest in Math 500 (~8%). - **Tool Impact**: Base Model + Tools improves upon Base Model in all datasets except AMC (where they are equal). - **Dataset Difficulty**: AIME shows the lowest accuracies overall (~0.05–0.16), while Math 500 achieves the highest (~0.62–0.68). ### Interpretation The data suggests that the ARTIST configuration (likely enhanced with specialized tools or training) demonstrates superior performance across diverse tasks. The Base Model + Tools configuration shows meaningful improvements over the Base Model, particularly in Olympiad (~76% gain), indicating that tool integration significantly aids performance. The Math 500 dataset’s high baseline accuracy (~0.62) implies the model may have been optimized for mathematical reasoning, while AIME’s low performance (~0.05–0.16) highlights challenges with complex problem-solving tasks. The consistent ARTIST advantage across datasets suggests it incorporates critical enhancements (e.g., reasoning pipelines, external knowledge integration) that generalize well.