## Line Graph: Final-round Accuracy vs. Noise (%) ### Overview The graph illustrates the relationship between noise percentage (0-100%) and final-round accuracy (%) for four distinct models: Gemma Original, Gemma Oracle, Gemma Bayesian, and Bayesian Assistant. All models exhibit declining accuracy as noise increases, with varying rates of degradation. ### Components/Axes - **X-axis**: Noise (%) ranging from 0 to 100 in 20% increments. - **Y-axis**: Final-round Accuracy (%) ranging from 0 to 100 in 20% increments. - **Legend**: Located in the top-right corner, associating colors with models: - **Blue**: Gemma Original - **Orange**: Gemma Oracle - **Gray**: Gemma Bayesian - **Dashed Gray**: Bayesian Assistant ### Detailed Analysis 1. **Gemma Original (Blue)**: - Maintains a flat line at ~35-40% accuracy across all noise levels. - No significant deviation observed. 2. **Gemma Oracle (Orange)**: - Starts at ~75% accuracy at 0% noise. - Declines steadily to ~20% at 100% noise. - Steepest slope among all models. 3. **Gemma Bayesian (Gray)**: - Begins at ~80% accuracy at 0% noise. - Decreases to ~30% at 100% noise. - Slightly less steep decline than Gemma Oracle. 4. **Bayesian Assistant (Dashed Gray)**: - Starts at ~80% accuracy at 0% noise. - Drops to ~25% at 100% noise. - Similar trajectory to Gemma Bayesian but ends marginally higher. ### Key Observations - **Initial Performance**: Gemma Oracle and Bayesian models (Gemma Bayesian/Bayesian Assistant) outperform Gemma Original at 0% noise. - **Noise Sensitivity**: All models degrade with noise, but Gemma Oracle shows the most pronounced decline. - **Robustness**: Bayesian approaches (Gemma Bayesian/Bayesian Assistant) retain higher accuracy than Gemma Original under noise. - **Dashed Line Distinction**: Bayesian Assistant’s dashed line suggests a variant of Bayesian methods, possibly with adaptive mechanisms. ### Interpretation The data highlights trade-offs between model complexity and noise resilience. Gemma Oracle’s high initial accuracy likely stems from oracle-guided training but lacks generalization under noise. Bayesian methods (Gemma Bayesian/Bayesian Assistant) balance initial performance with moderate noise tolerance, suggesting inherent robustness. Gemma Original’s stability at lower accuracy implies simplicity but limited adaptability. The Bayesian Assistant’s dashed line may indicate a hybrid approach, optimizing for noise resistance without sacrificing initial performance. These trends underscore the importance of model design choices in real-world noisy environments.