## Bar Chart: Number of Solved Tasks by Different Models ### Overview The image is a bar chart comparing the performance of different language models on a set of tasks. The y-axis represents the number of solved tasks, with higher values indicating better performance. The x-axis represents different language models. The chart compares four different methods: GPTSwarm, HF Agents, KGOT (Neo4j + Query), and Zero-Shot. ### Components/Axes * **Y-axis:** "Number of Solved Tasks (the higher the better)". The scale ranges from 0 to 50, with tick marks at intervals of 10. * **X-axis:** Categorical axis representing different language models: * Qwen2.5-32B * DeepSeek-R1-70B * GPT-4o mini * DeepSeek-R1-32B * QWQ-32B * DeepSeek-R1-7B * DeepSeek-R1-1.5B * Qwen2.5-72B * Qwen2.5-7B * Qwen2.5-1.5B * **Legend:** Located at the top-left of the chart. * GPTSwarm (light pink) * HF Agents (light purple) * KGOT (Neo4j + Query) (blue) * Zero-Shot (gray with diagonal lines) ### Detailed Analysis Here's a breakdown of the number of solved tasks for each model and method: * **Qwen2.5-32B:** * GPTSwarm: 29 * HF Agents: 19 * KGOT (Neo4j + Query): 26 * Zero-Shot: 15 * **DeepSeek-R1-70B:** * GPTSwarm: 10 * HF Agents: 16 * KGOT (Neo4j + Query): 22 * Zero-Shot: 0 * **GPT-4o mini:** * GPTSwarm: 26 * HF Agents: 35 * KGOT (Neo4j + Query): 40 * Zero-Shot: 17 * **DeepSeek-R1-32B:** * GPTSwarm: 6 * HF Agents: 17 * KGOT (Neo4j + Query): 21 * Zero-Shot: 14 * **QWQ-32B:** * GPTSwarm: 0 * HF Agents: 16 * KGOT (Neo4j + Query): 20 * Zero-Shot: 0 * **DeepSeek-R1-7B:** * GPTSwarm: 2 * HF Agents: 3 * KGOT (Neo4j + Query): 6 * Zero-Shot: 13 * **DeepSeek-R1-1.5B:** * GPTSwarm: 0 * HF Agents: 0 * KGOT (Neo4j + Query): 2 * Zero-Shot: 0 * **Qwen2.5-72B:** * GPTSwarm: 27 * HF Agents: 38 * KGOT (Neo4j + Query): 39 * Zero-Shot: 19 * **Qwen2.5-7B:** * GPTSwarm: 11 * HF Agents: 12 * KGOT (Neo4j + Query): 12 * Zero-Shot: 9 * **Qwen2.5-1.5B:** * GPTSwarm: 5 * HF Agents: 4 * KGOT (Neo4j + Query): 4 * Zero-Shot: 3 ### Key Observations * GPT-4o mini achieves the highest number of solved tasks using KGOT (Neo4j + Query) with a value of 40. * Zero-Shot performance is generally lower than other methods across all models. * The KGOT (Neo4j + Query) method consistently performs well across different models. * DeepSeek-R1-1.5B performs poorly across all methods, with a maximum of 2 solved tasks. ### Interpretation The chart provides a comparative analysis of different language models and methods for solving tasks. The KGOT (Neo4j + Query) method appears to be the most effective overall, as it consistently achieves high scores across different models. The Zero-Shot method generally underperforms compared to the other methods, suggesting that these models benefit from additional knowledge or prompting strategies. GPT-4o mini and Qwen2.5-72B show the best overall performance, indicating their effectiveness in solving the given tasks. The performance variations across different models and methods highlight the importance of selecting the appropriate model and strategy for specific tasks.

\n ## Bar Chart: Performance Comparison of Different Agent Architectures ### Overview This bar chart compares the performance of four different agent architectures – GPTswarm, HF Agents, KGoT (Neo4j + Query), and Zero-Shot – across a range of language models. The performance metric is the "Number of Solved Tasks" (the higher the better). The chart displays the number of solved tasks for each agent architecture on each language model. ### Components/Axes * **X-axis:** Language Models - Owen2.5-32B, DeepSeek-R1-70B, GPT-40 mini, DeepSeek-R1-32B, QwQ-32B, DeepSeek-R1-7B, Owen2.5-72B, Owen2.5-7B, Owen2.5-1.5B * **Y-axis:** Number of Solved Tasks (the higher the better), ranging from 0 to 50. * **Legend:** * GPTswarm (Light Red) * HF Agents (Light Blue) * KGoT (Neo4j + Query) (Medium Blue) * Zero-Shot (Hatched Pattern) ### Detailed Analysis The chart consists of a series of grouped bar plots, one group for each language model. For each model, there are four bars representing the performance of each agent architecture. Here's a breakdown of the data, approximate values are provided with uncertainty due to bar height estimation: * **Owen2.5-32B:** * GPTswarm: ~19 * HF Agents: ~29 * KGoT: ~26 * Zero-Shot: ~3 * **DeepSeek-R1-70B:** * GPTswarm: ~16 * HF Agents: ~26 * KGoT: ~17 * Zero-Shot: ~0 * **GPT-40 mini:** * GPTswarm: ~22 * HF Agents: ~22 * KGoT: ~14 * Zero-Shot: ~0 * **DeepSeek-R1-32B:** * GPTswarm: ~17 * HF Agents: ~40 * KGoT: ~21 * Zero-Shot: ~0 * **QwQ-32B:** * GPTswarm: ~6 * HF Agents: ~16 * KGoT: ~14 * Zero-Shot: ~0 * **DeepSeek-R1-7B:** * GPTswarm: ~20 * HF Agents: ~39 * KGoT: ~2 * Zero-Shot: ~0 * **Owen2.5-72B:** * GPTswarm: ~27 * HF Agents: ~39 * KGoT: ~5 * Zero-Shot: ~2 * **Owen2.5-7B:** * GPTswarm: ~19 * HF Agents: ~37 * KGoT: ~12 * Zero-Shot: ~3 * **Owen2.5-1.5B:** * GPTswarm: ~12 * HF Agents: ~19 * KGoT: ~9 * Zero-Shot: ~4 **Trends:** * **HF Agents** consistently outperforms the other architectures across most language models, generally achieving the highest number of solved tasks. The HF Agents line generally slopes upward, peaking at Owen2.5-72B and DeepSeek-R1-7B. * **GPTswarm** shows moderate performance, generally falling between HF Agents and KGoT. * **KGoT** generally performs the worst, with very low scores on several models. * **Zero-Shot** consistently has the lowest performance, often scoring 0 solved tasks. ### Key Observations * HF Agents demonstrate a clear advantage over other architectures. * The performance of all architectures varies significantly depending on the language model used. * Zero-Shot consistently underperforms, suggesting it is not a viable approach for these tasks. * DeepSeek-R1-32B shows the highest performance for HF Agents, reaching 40 solved tasks. ### Interpretation The data suggests that HF Agents are the most effective architecture for solving tasks using these language models. The significant difference in performance between HF Agents and other architectures highlights the benefits of the HF Agents approach. The variation in performance across different language models indicates that the choice of language model is crucial for achieving good results. The consistently poor performance of Zero-Shot suggests that it lacks the necessary capabilities for these tasks. The chart provides valuable insights into the strengths and weaknesses of different agent architectures and can inform the selection of the most appropriate architecture for a given task and language model. The high performance of HF Agents on DeepSeek-R1-32B is a notable outlier, suggesting a particularly strong synergy between these two components.

## Grouped Bar Chart: Model Performance Comparison on Solved Tasks ### Overview This image displays a grouped bar chart comparing the performance of four different methods (GPTSwarm, HF Agents, KGoT (Neo4j + Query), and Zero-Shot) across ten different language models or model sizes. The performance metric is the "Number of Solved Tasks," where a higher value indicates better performance. ### Components/Axes * **Chart Type:** Grouped Bar Chart. * **Y-Axis:** * **Label:** "Number of Solved Tasks (the higher the better)" * **Scale:** Linear scale from 0 to 50, with major tick marks at intervals of 10 (0, 10, 20, 30, 40, 50). * **X-Axis:** * **Label:** Not explicitly labeled, but contains categorical labels for different models/model sizes. * **Categories (from left to right):** Qwen2.5-32B, DeepSeek-R1-70B, GPT4o mini, DeepSeek-R1-32B, QwQ-32B, DeepSeek-R1-7B, DeepSeek-R1-1.5B, Qwen2.5-7B, Qwen2.5-27B, Qwen2.5-1.5B. * **Legend:** * **Position:** Top center of the chart area. * **Items (with associated colors/patterns):** 1. **GPTSwarm:** Solid pink bar. 2. **HF Agents:** Solid purple bar. 3. **KGoT (Neo4j + Query):** Solid blue bar. 4. **Zero-Shot:** Bar with diagonal black hatching on a white background. ### Detailed Analysis The following table reconstructs the data presented in the chart. Values are read directly from the data labels positioned above each bar. | Model / Model Size | GPTSwarm (Pink) | HF Agents (Purple) | KGoT (Neo4j + Query) (Blue) | Zero-Shot (Hatched) | | :--- | :--- | :--- | :--- | :--- | | **Qwen2.5-32B** | 29 | 19 | 26 | 15 | | **DeepSeek-R1-70B** | 10 | 16 | 22 | 20 | | **GPT4o mini** | 26 | 6 | 40 | 17 | | **DeepSeek-R1-32B** | 0 | 17 | 35 | 14 | | **QwQ-32B** | 0 | 6 | 21 | 0 | | **DeepSeek-R1-7B** | 0 | 2 | 20 | 0 | | **DeepSeek-R1-1.5B** | 0 | 0 | 8 | 13 | | **Qwen2.5-7B** | 0 | 2 | 5 | 0 | | **Qwen2.5-27B** | 27 | 12 | 38 | 19 | | **Qwen2.5-1.5B** | 5 | 4 | 4 | 7 | **Trend Verification per Method:** * **KGoT (Blue):** This series shows the strongest overall performance. The blue bars are the tallest or tied for tallest in 8 out of 10 model categories. The trend is generally high performance, with a peak of 40 solved tasks for GPT4o mini and a low of 4 for Qwen2.5-1.5B. * **GPTSwarm (Pink):** Performance is highly variable. It performs well on larger models (29 for Qwen2.5-32B, 27 for Qwen2.5-27B) and GPT4o mini (26), but drops to 0 for five of the models, particularly the mid-range and smaller DeepSeek and Qwen variants. * **HF Agents (Purple):** Shows moderate, relatively consistent performance across most models, typically ranging between 2 and 19 solved tasks. It never achieves the highest score in any category but also rarely drops to zero (only for DeepSeek-R1-1.5B). * **Zero-Shot (Hatched):** Performance is inconsistent. It achieves moderate results on some models (20 for DeepSeek-R1-70B, 19 for Qwen2.5-27B) but scores 0 for three models (QwQ-32B, DeepSeek-R1-7B, Qwen2.5-7B). Its highest score is 20. ### Key Observations 1. **Dominant Method:** KGoT (Neo4j + Query) is the clear top performer across the broadest range of models. 2. **Model Size Sensitivity:** GPTSwarm appears highly sensitive to model size or capability, failing completely (0 tasks) on several mid-range and smaller models while performing well on the largest ones. 3. **Zero-Shot Failure Cases:** The Zero-Shot method completely fails (0 tasks) on three specific models: QwQ-32B, DeepSeek-R1-7B, and Qwen2.5-7B. 4. **Lowest Overall Performance:** The smallest models tested (DeepSeek-R1-1.5B and Qwen2.5-1.5B) show the lowest aggregate performance across all methods, with no method exceeding 13 solved tasks. 5. **Notable Outlier:** For the Qwen2.5-1.5B model, the Zero-Shot method (7 tasks) outperforms all other methods, which is an exception to the general trend. ### Interpretation The data suggests a significant advantage for the **KGoT (Neo4j + Query)** method in solving the given set of tasks. Its consistent high performance implies that integrating a structured knowledge graph (Neo4j) with a query-based approach provides a robust framework that generalizes well across different underlying language models, from large to relatively small. The **GPTSwarm** method's performance pattern indicates it may rely on capabilities that are only present in larger or more advanced models (like Qwen2.5-32B/27B and GPT4o mini), making it less reliable for a broader range of models. The **HF Agents** method offers a stable, middle-ground performance, suggesting it is a dependable but not state-of-the-art approach. The **Zero-Shot** method's inconsistency highlights the challenge of solving complex tasks without any specialized agent framework or external knowledge structure, as its success appears highly dependent on the specific model's inherent abilities. The chart effectively demonstrates that for this benchmark, the choice of agent or problem-solving framework (KGoT) can be more impactful than the raw size of the underlying language model, as seen by KGoT's strong performance even on mid-sized models like DeepSeek-R1-7B.

## Bar Chart: AI Model Performance Comparison Across Tasks ### Overview The chart compares the performance of various AI models (e.g., Qwen2.5-32B, DeepSeek-R1-70B) across four task-solving methodologies: GPTswarm, HF Agents, KGoT (Neo4j + Query), and Zero-Shot. The y-axis represents the number of tasks solved, while the x-axis lists model names. Each model has four grouped bars corresponding to the methodologies. ### Components/Axes - **X-Axis (Categories)**: Model names (e.g., Qwen2.5-32B, DeepSeek-R1-70B, GPT-40 mini, etc.). - **Y-Axis (Scale)**: Number of solved tasks (0–50, increments of 10). - **Legend**: - Pink: GPTswarm - Purple: HF Agents - Blue: KGoT (Neo4j + Query) - Gray: Zero-Shot - **Bar Colors**: Match legend labels (e.g., pink bars for GPTswarm). ### Detailed Analysis - **Qwen2.5-32B**: - GPTswarm: 29 - HF Agents: 19 - KGoT: 26 - Zero-Shot: 15 - **DeepSeek-R1-70B**: - GPTswarm: 10 - HF Agents: 16 - KGoT: 22 - Zero-Shot: 20 - **GPT-40 mini**: - GPTswarm: 26 - HF Agents: 35 - KGoT: 40 - Zero-Shot: 17 - **DeepSeek-R1-32B**: - GPTswarm: 6 - HF Agents: 17 - KGoT: 21 - Zero-Shot: 14 - **QwQ-32B**: - GPTswarm: 0 - HF Agents: 16 - KGoT: 20 - Zero-Shot: 0 - **DeepSeek-R1-7B**: - GPTswarm: 2 - HF Agents: 3 - KGoT: 6 - Zero-Shot: 13 - **DeepSeek-R1-1.5B**: - GPTswarm: 0 - HF Agents: 0 - KGoT: 2 - Zero-Shot: 5 - **Qwen2.5-72B**: - GPTswarm: 27 - HF Agents: 38 - KGoT: 39 - Zero-Shot: 19 - **Qwen2.5-7B**: - GPTswarm: 12 - HF Agents: 11 - KGoT: 12 - Zero-Shot: 9 - **Qwen2.5-1.5B**: - GPTswarm: 5 - HF Agents: 4 - KGoT: 4 - Zero-Shot: 3 ### Key Observations 1. **KGoT (Neo4j + Query)** consistently outperforms other methods in most models (e.g., 40 for GPT-40 mini, 39 for Qwen2.5-72B). 2. **Zero-Shot** generally has the lowest performance across models (e.g., 3 for Qwen2.5-1.5B). 3. **HF Agents** show strong performance in larger models (e.g., 35 for GPT-40 mini, 38 for Qwen2.5-72B). 4. **GPTswarm** excels in mid-to-large models (e.g., 29 for Qwen2.5-32B, 27 for Qwen2.5-72B). 5. Smaller models (e.g., DeepSeek-R1-1.5B) have minimal task-solving capacity across all methods. ### Interpretation The data suggests that **KGoT (Neo4j + Query)** and **GPTswarm** are the most effective methodologies for solving tasks, particularly in larger models. **HF Agents** perform well in larger models but struggle with smaller ones. **Zero-Shot** underperforms universally, indicating its limitations without task-specific tuning. The disparity between methodologies highlights the importance of hybrid approaches (e.g., KGoT) for complex tasks. Outliers like QwQ-32B (all zeros for GPTswarm and Zero-Shot) suggest potential data anomalies or model-specific constraints.