## Heatmap: Llama 4 Maverick Performance by Subject and Metric
### Overview
This image displays a heatmap representing performance scores for "Llama 4 Maverick" across various mathematical subjects and four different metrics. The subjects are listed on the vertical axis, and the metrics are listed on the horizontal axis. The color intensity of each cell indicates the performance score, with warmer colors (yellow) generally representing higher scores and cooler colors (green) representing lower scores.
### Components/Axes
**Title:** "Llama 4 Maverick" (Centered at the top)
**Vertical Axis (Subjects):**
* Algebra
* Count. & Prob.
* Geometry
* Inter. Algebra
* Number Theory
* Prealgebra
* Precalculus
**Horizontal Axis (Metrics):**
* PoT
* CR
* MACM
* IIPC
**Data Cells:** Each cell contains a numerical score, representing the performance of Llama 4 Maverick for a specific subject under a specific metric. The color of the cell corresponds to the score.
### Detailed Analysis
The heatmap displays the following scores, with approximate uncertainty of +/- 0.01 due to visual reading:
* **Algebra:**
* PoT: 95.81 (Yellow)
* CR: 97.21 (Yellow)
* MACM: 98.14 (Yellow)
* IIPC: 98.60 (Yellow)
* **Count. & Prob.:**
* PoT: 91.00 (Yellow-Green)
* CR: 91.00 (Yellow-Green)
* MACM: 92.42 (Yellow-Green)
* IIPC: 91.47 (Yellow-Green)
* **Geometry:**
* PoT: 79.52 (Green)
* CR: 80.00 (Green)
* MACM: 75.24 (Green)
* IIPC: 80.48 (Green)
* **Inter. Algebra:**
* PoT: 83.72 (Green)
* CR: 80.00 (Green)
* MACM: 84.19 (Green)
* IIPC: 87.44 (Yellow-Green)
* **Number Theory:**
* PoT: 91.09 (Yellow-Green)
* CR: 94.06 (Yellow)
* MACM: 91.09 (Yellow-Green)
* IIPC: 94.06 (Yellow)
* **Prealgebra:**
* PoT: 94.42 (Yellow)
* CR: 95.35 (Yellow)
* MACM: 94.42 (Yellow)
* IIPC: 96.74 (Yellow)
* **Precalculus:**
* PoT: 86.98 (Yellow-Green)
* CR: 85.12 (Yellow-Green)
* MACM: 85.12 (Yellow-Green)
* IIPC: 89.77 (Yellow-Green)
### Key Observations
* **Highest Performance:** Llama 4 Maverick demonstrates its highest performance in "Algebra," with scores consistently above 95.80 across all metrics, peaking at 98.60 for the "IIPC" metric. "Prealgebra" also shows very strong performance, with scores generally above 94.40.
* **Lowest Performance:** The lowest performance is observed in "Geometry," with scores ranging from 75.24 to 80.48. "Inter. Algebra" also shows relatively lower scores compared to other subjects, particularly for the "CR" metric (80.00).
* **Metric Performance:** Across most subjects, the "IIPC" metric generally shows higher or comparable scores to other metrics, especially in subjects where Llama 4 Maverick performs well. The "CR" metric shows a notable dip in "Inter. Algebra" and "Precalculus."
* **Color Gradient:** The heatmap visually confirms the numerical data. Yellow cells are concentrated in "Algebra," "Prealgebra," and parts of "Number Theory" and "Count. & Prob.," indicating high scores. Green cells are prominent in "Geometry" and "Inter. Algebra," indicating lower scores.
### Interpretation
The heatmap suggests that Llama 4 Maverick has a strong aptitude for higher-level mathematics like Algebra and Prealgebra, as indicated by the consistently high scores and warm colors. Conversely, its performance in foundational or more abstract areas like Geometry and Intermediate Algebra appears to be weaker, as shown by the lower scores and cooler colors.
The "IIPC" metric seems to be a strong point for Llama 4 Maverick across many subjects, suggesting it might be a metric where the model excels or is better suited. The variation in scores across different metrics for the same subject (e.g., "Inter. Algebra" scores of 83.72 for PoT, 80.00 for CR, 84.19 for MACM, and 87.44 for IIPC) indicates that the model's performance is not uniform and is influenced by the specific evaluation criteria.
The data implies that while Llama 4 Maverick is a capable model, its strengths are concentrated in certain mathematical domains. Further investigation could explore why Geometry and Inter. Algebra present challenges, and whether specific training data or architectural features contribute to this pattern. The consistent high performance in Algebra and Prealgebra suggests these areas might be well-represented in its training data or align with its core capabilities.
