\n ## Bar Charts: Agent Performance on Benchmarks ### Overview The image presents a diagram illustrating a curriculum learning framework with two agents (Curriculum Agent and Executor Agent) and four bar charts comparing the performance of different models (Owen3-8B, w/ tools, and Agento) on four benchmarks: MATH, SuperGLQA, MMLU-Pro, and BBHE. The diagram on the left (a) shows the interaction between the agents, the reasoning process, and the reward mechanisms. The bar charts on the right (b) display the performance scores. ### Components/Axes The diagram (a) includes components labeled: "Curriculum Agent", "Executor Agent", "Question" (q), "Reasoning Process", "Environment", "Tool", "Model Response", "Tool Calling", "Tool Response", "Predicted Answer" (â), "Curriculum Reward" (rC), and "Executor Reward" (rE). The bar charts (b) have the following components: * **X-axis:** Model - Owen3-8B, w/ tools, Agento * **Y-axis:** Score (ranging from approximately 47 to 84, depending on the benchmark) * **Benchmarks (separate charts):** MATH, SuperGLQA, MMLU-Pro, BBHE * **Color Coding:** Owen3-8B (grey), w/ tools (dark grey), Agento (blue) ### Detailed Analysis or Content Details **MATH:** * Owen3-8B: Approximately 78.0 * w/ tools: Approximately 79.2 * Agento: Approximately 82.4 The trend is upward, with Agento performing best, followed by w/ tools, and then Owen3-8B. **SuperGLQA:** * Owen3-8B: Approximately 28.3 * w/ tools: Approximately 29.4 * Agento: Approximately 33.0 The trend is upward, with Agento performing best, followed by w/ tools, and then Owen3-8B. **MMLU-Pro:** * Owen3-8B: Approximately 51.8 * w/ tools: Approximately 54.8 * Agento: Approximately 63.4 The trend is upward, with Agento performing best, followed by w/ tools, and then Owen3-8B. **BBHE:** * Owen3-8B: Approximately 8.6 * w/ tools: Approximately 9.4 * Agento: Approximately 13.7 The trend is upward, with Agento performing best, followed by w/ tools, and then Owen3-8B. ### Key Observations * Agento consistently outperforms both Owen3-8B and the "w/ tools" model across all four benchmarks. * Adding tools ("w/ tools") consistently improves performance compared to Owen3-8B, but Agento still surpasses it. * The largest performance gains are observed in the MMLU-Pro benchmark, where Agento achieves a score of approximately 63.4, significantly higher than the other models. * BBHE shows the lowest overall scores, indicating it is the most challenging benchmark. ### Interpretation The data suggests that the Agento model, leveraging a curriculum learning framework with an Executor Agent, demonstrates superior performance on a variety of reasoning benchmarks compared to the baseline Owen3-8B model and even an enhanced version with tools. The consistent improvement across all benchmarks indicates that the curriculum learning approach and the Agento architecture are effective in enhancing reasoning capabilities. The varying degree of improvement across benchmarks suggests that the approach is more beneficial for certain types of reasoning tasks (e.g., MMLU-Pro) than others (e.g., BBHE). The diagram (a) illustrates the iterative process of question generation, reasoning, tool usage, and reward feedback, which likely contributes to the improved performance of Agento. The Executor Agent's ability to utilize tools appears to provide a moderate performance boost, but the curriculum learning framework implemented in Agento provides a more substantial advantage. The consistent trend of Agento outperforming the other models suggests a robust and generalizable improvement in reasoning ability.