## Line Chart: 10x10 Maze: L-ICL Performance Across LLMs ### Overview The image is a line chart comparing the performance of four different Large Language Models (LLMs) on a 10x10 maze task. The chart plots the success rate (%) of each model against the number of training examples. The models compared are DeepSeek V3, DeepSeek V3.1, Claude Haiku 4.5, and Claude Sonnet 4.5. The chart includes shaded regions around each line, representing the uncertainty or variance in the performance. ### Components/Axes * **Title:** 10x10 Maze: L-ICL Performance Across LLMs * **X-axis:** Training Examples * Scale: 0 to 240, with markers at 0, 30, 60, 90, 120, 150, 180, 210, and 240. * **Y-axis:** Success Rate (%) * Scale: 0 to 90, with markers at 0, 10, 20, 30, 40, 50, 60, 70, 80, and 90. * **Legend:** Located at the bottom of the chart. * DeepSeek V3 (Blue) * DeepSeek V3.1 (Orange) * Claude Haiku 4.5 (Green) * Claude Sonnet 4.5 (Red) ### Detailed Analysis * **DeepSeek V3 (Blue):** * Trend: Generally increasing, but plateaus and slightly decreases towards the end. * Data Points: * 0 Training Examples: ~2% * 30 Training Examples: ~10% * 60 Training Examples: ~27% * 90 Training Examples: ~33% * 120 Training Examples: ~37% * 150 Training Examples: ~30% * 180 Training Examples: ~32% * 210 Training Examples: ~38% * 240 Training Examples: ~35% * **DeepSeek V3.1 (Orange):** * Trend: Increasing, peaks around 210 training examples, then decreases. * Data Points: * 0 Training Examples: ~3% * 30 Training Examples: ~12% * 60 Training Examples: ~30% * 90 Training Examples: ~33% * 120 Training Examples: ~35% * 150 Training Examples: ~38% * 180 Training Examples: ~43% * 210 Training Examples: ~47% * 240 Training Examples: ~38% * **Claude Haiku 4.5 (Green):** * Trend: Increasing, plateaus, and slightly decreases towards the end. * Data Points: * 0 Training Examples: ~5% * 30 Training Examples: ~15% * 60 Training Examples: ~22% * 90 Training Examples: ~18% * 120 Training Examples: ~32% * 150 Training Examples: ~35% * 180 Training Examples: ~38% * 210 Training Examples: ~35% * 240 Training Examples: ~32% * **Claude Sonnet 4.5 (Red):** * Trend: Rapidly increasing initially, plateaus, and then slightly decreases. * Data Points: * 0 Training Examples: ~7% * 30 Training Examples: ~43% * 60 Training Examples: ~61% * 90 Training Examples: ~58% * 120 Training Examples: ~74% * 150 Training Examples: ~69% * 180 Training Examples: ~68% * 210 Training Examples: ~76% * 240 Training Examples: ~69% ### Key Observations * Claude Sonnet 4.5 (Red) significantly outperforms the other models, achieving a much higher success rate with fewer training examples. * DeepSeek V3 (Blue) has the lowest overall performance. * DeepSeek V3.1 (Orange) and Claude Haiku 4.5 (Green) have similar performance trends, with DeepSeek V3.1 generally performing slightly better. * All models show diminishing returns with increased training examples, with performance plateauing or even decreasing after a certain point. * The shaded regions indicate variability in performance, with Claude Sonnet 4.5 showing the widest range of variability. ### Interpretation The chart demonstrates the effectiveness of different LLMs in solving a 10x10 maze task through In-Context Learning (ICL). Claude Sonnet 4.5 exhibits superior learning capabilities, achieving high success rates with fewer training examples, suggesting a more efficient learning algorithm or a better-suited architecture for this specific task. The plateauing or decreasing performance after a certain number of training examples suggests that the models may be overfitting to the training data or reaching the limits of what can be learned through ICL for this particular maze complexity. The variability in performance, as indicated by the shaded regions, highlights the instability or sensitivity of the models to different training sets or initial conditions. The data suggests that model selection and optimization of training examples are crucial for maximizing performance in ICL tasks.

## Line Chart: 10x10 Maze: L-ICL Performance Across LLMs ### Overview This line chart displays the success rate (%) of several Large Language Models (LLMs) on a 10x10 maze task, as a function of the number of training examples provided. The chart compares the performance of DeepSeek V3, DeepSeek V3.1, Claude Haiku 4.5, and Claude Sonnet 4.5. The x-axis represents the number of training examples, and the y-axis represents the success rate. Shaded areas around each line indicate confidence intervals or standard deviations. ### Components/Axes * **Title:** 10x10 Maze: L-ICL Performance Across LLMs * **X-axis Label:** Training Examples (ranging from 0 to 240, with increments of 30) * **Y-axis Label:** Success Rate (%) (ranging from 0 to 90, with increments of 10) * **Legend:** Located at the bottom of the chart, identifying each line by LLM name and color. * DeepSeek V3 (Blue) * DeepSeek V3.1 (Orange) * Claude Haiku 4.5 (Green) * Claude Sonnet 4.5 (Red) ### Detailed Analysis The chart shows the success rate of each LLM as a function of training examples. * **DeepSeek V3 (Blue):** The line starts at approximately 10% at 0 training examples, rises sharply to around 30% at 60 training examples, plateaus around 30-35% between 60 and 180 training examples, and then declines slightly to approximately 30% at 240 training examples. * **DeepSeek V3.1 (Orange):** The line begins at approximately 10% at 0 training examples, increases steadily to around 40% at 90 training examples, fluctuates between 35% and 45% from 90 to 210 training examples, and then decreases to approximately 35% at 240 training examples. The shaded area around this line is quite large, indicating high variability. * **Claude Haiku 4.5 (Green):** The line starts at approximately 10% at 0 training examples, rises to around 25% at 60 training examples, plateaus around 30-35% between 60 and 180 training examples, and then increases to approximately 40% at 240 training examples. * **Claude Sonnet 4.5 (Red):** The line begins at approximately 10% at 0 training examples, increases rapidly to around 70% at 90 training examples, reaches a peak of approximately 75% at 120 training examples, and then declines gradually to approximately 65% at 240 training examples. ### Key Observations * Claude Sonnet 4.5 consistently outperforms the other LLMs, especially in the range of 0-120 training examples. * DeepSeek V3 and Claude Haiku 4.5 show similar performance, with relatively stable success rates after an initial increase. * DeepSeek V3.1 exhibits the highest variability in performance, as indicated by the large shaded area around its line. * All LLMs show an initial increase in success rate with more training examples, but the rate of improvement diminishes as the number of examples increases. * After a certain point, increasing the number of training examples does not necessarily lead to a higher success rate, and in some cases, can even lead to a decrease. ### Interpretation The data suggests that the choice of LLM significantly impacts performance on the 10x10 maze task. Claude Sonnet 4.5 demonstrates superior learning capabilities, achieving a high success rate with a relatively small number of training examples. The diminishing returns observed with increasing training examples indicate that the LLMs may reach a point of saturation, where additional examples do not provide significant improvements. The variability in DeepSeek V3.1's performance suggests that its learning process may be more sensitive to the specific training data or initialization conditions. The chart highlights the importance of selecting an appropriate LLM and optimizing the training data to maximize performance on a given task. The initial rapid increase in success rate for all models suggests that even a small amount of task-specific training can significantly improve performance. The subsequent plateau and decline in some cases suggest that overfitting or the limitations of the model architecture may be factors.

## Line Chart: 10×10 Maze: L-ICL Performance Across LLMs ### Overview This is a line chart comparing the performance of four Large Language Models (LLMs) on a 10×10 maze-solving task using Learning from In-Context Examples (L-ICL). The chart plots the success rate percentage against the number of training examples provided. Each model's performance is represented by a colored line with markers, accompanied by a shaded region indicating variability or confidence intervals. ### Components/Axes * **Title:** "10×10 Maze: L-ICL Performance Across LLMs" (Top-left corner). * **Y-Axis:** Labeled "Success Rate (%)". Scale ranges from 0 to 90, with major gridlines at intervals of 10. * **X-Axis:** Labeled "Training Examples". Scale shows discrete points at 0, 30, 60, 90, 120, 150, 180, 210, and 240. * **Legend:** Located at the bottom center of the chart. It maps line colors and markers to model names: * **Blue line with circle markers:** DeepSeek V3 * **Orange line with circle markers:** DeepSeek V3.1 * **Green line with circle markers:** Claude Haiku 4.5 * **Red line with circle markers:** Claude Sonnet 4.5 * **Data Series:** Four lines, each with a shaded band of the same color representing the range of performance (likely standard deviation or confidence interval). ### Detailed Analysis **Trend Verification & Data Point Extraction (Approximate Values):** 1. **Claude Sonnet 4.5 (Red Line):** * **Trend:** Shows the steepest initial improvement and maintains the highest performance throughout. The trend is strongly upward from 0 to 120 examples, followed by a plateau with minor fluctuations. * **Data Points:** 0 examples: ~10% | 30: ~43% | 60: ~61% | 90: ~58% | 120: ~74% | 150: ~69% | 180: ~69% | 210: ~76% | 240: ~69%. 2. **DeepSeek V3.1 (Orange Line):** * **Trend:** Shows steady improvement, peaking at 210 examples before a decline. It generally performs second-best after the initial training phase. * **Data Points:** 0 examples: ~2% | 30: ~14% | 60: ~30% | 90: ~33% | 120: ~34% | 150: ~34% | 180: ~41% | 210: ~47% | 240: ~38%. 3. **DeepSeek V3 (Blue Line):** * **Trend:** Improves until 120 examples, experiences a notable dip at 150, then recovers slowly. It ends with performance similar to Claude Haiku 4.5. * **Data Points:** 0 examples: ~6% | 30: ~15% | 60: ~27% | 90: ~32% | 120: ~37% | 150: ~30% | 180: ~31% | 210: ~36% | 240: ~34%. 4. **Claude Haiku 4.5 (Green Line):** * **Trend:** Shows a general upward trend with a slight dip at 90 examples. It closely tracks DeepSeek V3 in the later stages. * **Data Points:** 0 examples: ~1% | 30: ~15% | 60: ~23% | 90: ~20% | 120: ~31% | 150: ~35% | 180: ~38% | 210: ~39% | 240: ~32%. **Spatial Grounding & Variability:** * The shaded confidence bands are widest for Claude Sonnet 4.5, indicating higher variance in its performance across different runs or samples. * The bands for the other three models are narrower and overlap significantly between 30 and 120 training examples, suggesting similar performance uncertainty in that range. * At 240 examples, the performance of all models except Claude Sonnet 4.5 converges within a ~6% range (approximately 32%-38%). ### Key Observations 1. **Dominant Performance:** Claude Sonnet 4.5 is the clear top performer, achieving a success rate over 20 percentage points higher than the next best model at its peak (120 examples). 2. **Learning Curves:** All models benefit from increased training examples, but the rate of improvement (slope) is most dramatic for Claude Sonnet 4.5 between 0 and 60 examples. 3. **Performance Plateaus/Dips:** Several models show performance dips or plateaus (e.g., Claude Sonnet at 90 & 150, DeepSeek V3 at 150, Claude Haiku at 90). This could indicate points where additional examples temporarily confuse the model or where the task complexity interacts with the model's learning capacity. 4. **Final Convergence:** By 240 examples, the performance gap between the three lower-performing models narrows considerably, while Claude Sonnet 4.5 remains in a distinctly higher tier. ### Interpretation The data demonstrates a significant disparity in the in-context learning capabilities of the tested LLMs for a spatial reasoning task (maze solving). **Claude Sonnet 4.5 exhibits a superior ability to leverage provided examples to solve novel mazes**, suggesting a more robust internal representation or more effective learning algorithm for this type of problem. The general upward trend for all models confirms the efficacy of the L-ICL approach—more examples lead to better performance. However, the non-monotonic curves (dips and plateaus) are critical findings. They suggest that learning is not linear and that there may be "confusion points" where the model's hypothesis space becomes too complex or where it overfits to certain example patterns before generalizing better with even more data. The narrower confidence intervals for DeepSeek models and Claude Haiku might indicate more consistent, if lower-ceiling, performance. In contrast, Claude Sonnet 4.5's wider bands suggest higher potential reward but also higher variability, which could be important for reliability-critical applications. **In summary, this chart provides strong evidence that model architecture or training methodology has a profound impact on few-shot learning performance for spatial tasks, with Claude Sonnet 4.5 being the most effective learner in this specific benchmark.**

## Line Chart: 10x10 Maze: L-ICL Performance Across LLMs ### Overview The chart visualizes the success rate (%) of four large language models (LLMs) across varying numbers of training examples (0–240) in a 10x10 maze task. Success rates are plotted with shaded confidence intervals (likely ±1–2 standard deviations). The models compared are DeepSeek V3 (blue), DeepSeek V3.1 (orange), Claude Haiku 4.5 (green), and Claude Sonnet 4.5 (red). ### Components/Axes - **X-axis**: Training Examples (0, 30, 60, 90, 120, 150, 180, 210, 240) - **Y-axis**: Success Rate (%) (0–90) - **Legend**: Located at the bottom center, mapping colors to models: - Blue: DeepSeek V3 - Orange: DeepSeek V3.1 - Green: Claude Haiku 4.5 - Red: Claude Sonnet 4.5 ### Detailed Analysis 1. **Claude Sonnet 4.5 (Red Line)**: - Starts at ~10% success rate at 0 examples. - Peaks at ~75% at 210 examples, with a slight decline to ~70% at 240. - Shaded area widens significantly after 120 examples, indicating higher variability. 2. **DeepSeek V3.1 (Orange Line)**: - Begins at ~5% at 0 examples. - Rises steadily to ~45% at 210 examples, then dips to ~35% at 240. - Shaded area remains relatively narrow, suggesting consistent performance. 3. **Claude Haiku 4.5 (Green Line)**: - Starts at ~1% at 0 examples. - Peaks at ~35% at 180 examples, then declines to ~30% at 240. - Shaded area broadens after 150 examples. 4. **DeepSeek V3 (Blue Line)**: - Begins at ~5% at 0 examples. - Peaks at ~35% at 120 examples, then declines to ~30% at 240. - Shaded area is narrowest overall, indicating stable performance. ### Key Observations - **Claude Sonnet 4.5** dominates performance, achieving the highest success rates across all training scales. - **DeepSeek V3.1** shows the most dramatic improvement with training, surpassing other models after 120 examples. - **Claude Haiku 4.5** and **DeepSeek V3** exhibit diminishing returns after ~150–180 examples. - All models show initial rapid gains, followed by plateauing or slight declines at higher training scales. ### Interpretation The data suggests that **Claude Sonnet 4.5** is the most robust model for this task, maintaining high success rates even with limited training. **DeepSeek V3.1** demonstrates strong scalability, outperforming others at higher training volumes. The shaded areas highlight that **Claude Sonnet 4.5** has the highest variability, possibly due to complex decision-making in the maze. The decline in performance for some models at 240 examples may indicate overfitting or task-specific limitations. Notably, **DeepSeek V3.1**’s peak at 210 examples aligns with its narrowest confidence interval, suggesting optimal training efficiency.