## Donut Chart: KGOT Tool Usage Distribution ### Overview The image is a donut chart illustrating the distribution of usage for six different tools within the KGOT framework. The chart shows the percentage of usage for each tool, with "ask_search_agent" having the highest usage and "extract_zip" having the lowest. The chart also indicates that these tools were used for 165 GAIA questions, with a total tool usage count of 173. ### Components/Axes * **Title:** KGOT Tool Usage Distribution * **Subtitle:** 6 unique tools for 165 GAIA questions * **Center Text:** Total Tool Usage Count: 173 * **Segments (Tools and Percentages):** * ask\_search\_agent: 61.3% (Blue) * inspect\_file\_as\_text: 15.6% (Teal) * llm\_query: 11% (Light Teal) * image\_inspector: 5.78% (Light Green) * run\_python\_code: 5.2% (Light Green) * extract\_zip: 1.16% (Light Yellow) ### Detailed Analysis The donut chart is divided into six segments, each representing a different tool. The size of each segment corresponds to the percentage of times that tool was used. * **ask\_search\_agent:** This tool accounts for the largest portion of the usage, at 61.3%. The segment is colored blue and occupies a significant portion of the donut. * **inspect\_file\_as\_text:** This tool represents 15.6% of the usage. The segment is teal. * **llm\_query:** This tool accounts for 11% of the usage. The segment is light teal. * **image\_inspector:** This tool represents 5.78% of the usage. The segment is light green. * **run\_python\_code:** This tool accounts for 5.2% of the usage. The segment is light green. * **extract\_zip:** This tool has the lowest usage, at 1.16%. The segment is light yellow. The total tool usage count is 173, which is displayed in the center of the donut chart. ### Key Observations * The "ask\_search\_agent" tool is used far more frequently than any other tool, accounting for over 60% of the total usage. * The "extract\_zip" tool is used very infrequently, representing only a small fraction of the total usage. * The remaining tools ("inspect\_file\_as\_text", "llm\_query", "image\_inspector", and "run\_python\_code") have moderate usage, ranging from approximately 5% to 16%. ### Interpretation The data suggests that the "ask\_search\_agent" tool is the most valuable or frequently needed tool for answering GAIA questions within the KGOT framework. The significant difference in usage between "ask\_search\_agent" and the other tools may indicate that it is either more effective, more versatile, or more user-friendly. The low usage of "extract\_zip" could indicate that it is less relevant to the types of questions being asked, or that it is less efficient or reliable than other tools. The distribution of tool usage provides insights into the relative importance and utility of each tool within the KGOT framework for addressing GAIA questions.

\n ## Pie Chart: KGoT Tool Usage Distribution ### Overview This image presents a pie chart illustrating the distribution of tool usage within the KGoT (Knowledge Graph of Tools) system. The chart details the percentage of usage for six unique tools across 165 GAIA questions, with a total tool usage count of 173. ### Components/Axes * **Title:** "KGoT Tool Usage Distribution" * **Subtitle:** "6 unique tools for 165 GAIA questions" * **Total Tool Usage Count:** 173 (displayed in the center of the pie chart) * **Categories (Tools):** * ask_search_agent * inspect_file_as_text * llm_query * image_inspector * run_python_code * extract_zip * **Values:** Percentage of total tool usage for each tool. ### Detailed Analysis The pie chart segments represent the proportion of times each tool was used. The largest segment, colored a deep blue, represents `ask_search_agent`. The segments are arranged clockwise, starting with `ask_search_agent` at the bottom. * **ask_search_agent:** The largest segment, occupying approximately 61.3% of the pie chart. * **inspect_file_as_text:** Occupies approximately 15.6% of the pie chart, colored a lighter blue. * **llm_query:** Occupies approximately 11% of the pie chart, colored a teal. * **image_inspector:** Occupies approximately 5.78% of the pie chart, colored a light green. * **run_python_code:** Occupies approximately 5.2% of the pie chart, colored a pale yellow. * **extract_zip:** Occupies approximately 1.16% of the pie chart, colored a light orange. ### Key Observations * `ask_search_agent` is by far the most frequently used tool, accounting for over 60% of all tool usage. * `extract_zip` is the least used tool, representing a very small fraction of the total usage. * The usage of the other tools (`inspect_file_as_text`, `llm_query`, `image_inspector`, `run_python_code`) is relatively similar, though `inspect_file_as_text` is notably more used than the others. ### Interpretation The data suggests that the primary method for addressing GAIA questions within the KGoT system is through the `ask_search_agent` tool. This indicates that search-based approaches are the most common strategy for answering these questions. The low usage of `extract_zip` suggests that dealing with zipped files is a rare requirement in the context of these GAIA questions. The moderate usage of `inspect_file_as_text` and `llm_query` indicates that file inspection and large language model queries are important, but less dominant, components of the workflow. The relatively similar usage of `image_inspector` and `run_python_code` suggests that image analysis and code execution are used at similar rates. The total tool usage count (173) being slightly higher than the number of GAIA questions (165) suggests that some questions may have involved the use of multiple tools.

## Donut Chart: KGoT Tool Usage Distribution ### Overview The image displays a donut chart titled "KGoT Tool Usage Distribution" with the subtitle "6 unique tools for 165 GAIA questions." The chart visualizes the proportional usage of six different tools, with the total usage count displayed in the center. The overall aesthetic uses a cool color palette of blues and greens against a white background. ### Components/Axes * **Chart Type:** Donut Chart (a pie chart with a central hole). * **Title:** "KGoT Tool Usage Distribution" * **Subtitle:** "6 unique tools for 165 GAIA questions" * **Central Label:** "Total Tool Usage Count: 173" * **Data Series (Segments):** The chart is divided into six segments, each representing a tool. The segments are labeled directly with the tool name and its percentage of the total usage. There is no separate legend; labels are placed adjacent to their corresponding segments. * **Color Scheme:** The segments use a gradient from a dominant medium blue for the largest segment to progressively lighter shades of teal and green for the smaller segments. ### Detailed Analysis The chart presents the following data, ordered from the largest segment to the smallest: 1. **ask_search_agent** * **Color:** Medium blue. * **Placement:** Occupies the entire bottom half and a portion of the upper right quadrant of the donut. It is the visually dominant segment. * **Percentage:** 61.3% * **Trend:** This is the most frequently used tool by a significant margin. 2. **inspect_file_as_text** * **Color:** Teal blue. * **Placement:** Located in the upper right quadrant, adjacent to the `ask_search_agent` segment. * **Percentage:** 15.6% * **Trend:** The second most used tool. 3. **llm_query** * **Color:** Light teal. * **Placement:** Located in the upper center, adjacent to `inspect_file_as_text`. * **Percentage:** 11% * **Trend:** The third most used tool. 4. **image_inspector** * **Color:** Light green. * **Placement:** Located in the upper left quadrant, adjacent to `llm_query`. * **Percentage:** 5.78% * **Trend:** Usage drops significantly here. 5. **run_python_code** * **Color:** Lighter green. * **Placement:** Located in the upper left quadrant, adjacent to `image_inspector`. * **Percentage:** 5.2% * **Trend:** Very similar in usage to `image_inspector`. 6. **extract_zip** * **Color:** Pale green. * **Placement:** A very thin sliver in the upper left quadrant, adjacent to `run_python_code`. * **Percentage:** 1.16% * **Trend:** The least used tool by a wide margin. **Data Verification:** The sum of the percentages (61.3 + 15.6 + 11 + 5.78 + 5.2 + 1.16) equals 100.04%, which is within an acceptable rounding margin. The central label states a total count of 173 tool usages across 165 questions, indicating an average of approximately 1.05 tool uses per question. ### Key Observations * **Dominant Tool:** The `ask_search_agent` tool is overwhelmingly dominant, accounting for nearly two-thirds (61.3%) of all tool invocations. * **Usage Concentration:** The top three tools (`ask_search_agent`, `inspect_file_as_text`, `llm_query`) collectively account for 87.9% of all usage. * **Minimal Tools:** The `extract_zip` tool is used very infrequently (1.16%), suggesting it is either a specialized tool for rare cases or potentially underutilized. * **Total Count Discrepancy:** The total tool usage count (173) is slightly higher than the number of questions (165), implying that some questions required the use of more than one tool. ### Interpretation This chart provides a clear quantitative breakdown of tool utilization within the KGoT system for the GAIA benchmark. The data strongly suggests that the system's problem-solving strategy is heavily reliant on a search agent (`ask_search_agent`). This could indicate that retrieving and synthesizing information from external sources is the primary mode of operation for the tasks in the GAIA dataset. The secondary reliance on file inspection tools (`inspect_file_as_text`, `image_inspector`) and direct LLM queries (`llm_query`) points to a workflow that often involves analyzing provided documents or files and using the language model's internal knowledge. The very low usage of `run_python_code` and `extract_zip` suggests that tasks requiring computational execution or handling of compressed archives are rare in this specific evaluation set. The distribution is highly skewed, which is a notable pattern. It raises questions about the nature of the GAIA questions: are they predominantly research and retrieval tasks? It also suggests that optimizing the performance and reliability of the `ask_search_agent` would yield the greatest overall system improvement for this benchmark. The near-negligible use of `extract_zip` might warrant investigation to determine if it's a tool in search of a problem or if the benchmark simply lacks scenarios that require it.

## Pie Chart: KGoT Tool Usage Distribution ### Overview A circular pie chart visualizing the distribution of 6 unique tools used to answer 165 GAIA questions. The total tool usage count is 173, with segments representing percentages of usage for each tool. ### Components/Axes - **Title**: "KGoT Tool Usage Distribution" - **Subtitle**: "6 unique tools for 165 GAIA questions" - **Central Text**: "Total Tool Usage Count: 173" - **Segments**: - **ask_search_agent**: 61.3% (blue) - **inspect_file_as_text**: 15.6% (teal) - **llm_query**: 11% (green) - **image_inspector**: 5.78% (light green) - **run_python_code**: 5.2% (darker green) - **extract_zip**: 1.16% (very light green) - **Legend**: Embedded in the center, with colors matching segment labels. ### Detailed Analysis - **ask_search_agent** dominates usage at **61.3%** (106/173 total uses). - **inspect_file_as_text** follows at **15.6%** (27/173). - **llm_query** accounts for **11%** (19/173). - **image_inspector** and **run_python_code** are smaller at **5.78%** (10/173) and **5.2%** (9/173), respectively. - **extract_zip** is the least used at **1.16%** (2/173). ### Key Observations 1. **Dominance of ask_search_agent**: Over 60% of tool usage is concentrated in a single tool. 2. **Hierarchical Distribution**: The top three tools account for **87.9%** of total usage. 3. **Long Tail**: The remaining three tools contribute only **12.1%** combined. 4. **Minimal Usage**: **extract_zip** is used in just 2 instances (1.16%). ### Interpretation The data suggests a **highly skewed distribution** of tool usage, with **ask_search_agent** being the primary tool for solving GAIA questions. This implies either: - **ask_search_agent** is the most versatile or efficient tool for the dataset. - Other tools are underutilized, potentially due to complexity, lack of awareness, or niche applicability. - The **long tail** of tools (e.g., **extract_zip**) may indicate specialized but infrequent use cases. The chart highlights a need to investigate why certain tools are underused and whether their functionality could be better integrated or promoted.